Robust model predictive control for satellite formation keeping with eccentricity/inclination vector separation

NASA Astrophysics Data System (ADS)

Lim, Yeerang; Jung, Youeyun; Bang, Hyochoong

2018-05-01

This study presents model predictive formation control based on an eccentricity/inclination vector separation strategy. Alternative collision avoidance can be accomplished by using eccentricity/inclination vectors and adding a simple goal function term for optimization process. Real-time control is also achievable with model predictive controller based on convex formulation. Constraint-tightening approach is address as well improve robustness of the controller, and simulation results are presented to verify performance enhancement for the proposed approach.

Finite element based model predictive control for active vibration suppression of a one-link flexible manipulator.

PubMed

Dubay, Rickey; Hassan, Marwan; Li, Chunying; Charest, Meaghan

2014-09-01

This paper presents a unique approach for active vibration control of a one-link flexible manipulator. The method combines a finite element model of the manipulator and an advanced model predictive controller to suppress vibration at its tip. This hybrid methodology improves significantly over the standard application of a predictive controller for vibration control. The finite element model used in place of standard modelling in the control algorithm provides a more accurate prediction of dynamic behavior, resulting in enhanced control. Closed loop control experiments were performed using the flexible manipulator, instrumented with strain gauges and piezoelectric actuators. In all instances, experimental and simulation results demonstrate that the finite element based predictive controller provides improved active vibration suppression in comparison with using a standard predictive control strategy. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Dynamic Modeling, Controls, and Testing for Electrified Aircraft

NASA Technical Reports Server (NTRS)

Connolly, Joseph; Stalcup, Erik

2017-01-01

Electrified aircraft have the potential to provide significant benefits for efficiency and emissions reductions. To assess these potential benefits, modeling tools are needed to provide rapid evaluation of diverse concepts and to ensure safe operability and peak performance over the mission. The modeling challenge for these vehicles is the ability to show significant benefits over the current highly refined aircraft systems. The STARC-ABL (single-aisle turbo-electric aircraft with an aft boundary layer propulsor) is a new test proposal that builds upon previous N3-X team hybrid designs. This presentation describes the STARC-ABL concept, the NASA Electric Aircraft Testbed (NEAT) which will allow testing of the STARC-ABL powertrain, and the related modeling and simulation efforts to date. Modeling and simulation includes a turbofan simulation, Numeric Propulsion System Simulation (NPSS), which has been integrated with NEAT; and a power systems and control model for predicting testbed performance and evaluating control schemes. Model predictions provide good comparisons with testbed data for an NPSS-integrated test of the single-string configuration of NEAT.

Development of a Simulation Capability for the Space Station Active Rack Isolation System

NASA Technical Reports Server (NTRS)

Johnson, Terry L.; Tolson, Robert H.

1998-01-01

To realize quality microgravity science on the International Space Station, many microgravity facilities will utilize the Active Rack Isolation System (ARIS). Simulation capabilities for ARIS will be needed to predict the microgravity environment. This paper discusses the development of a simulation model for use in predicting the performance of the ARIS in attenuating disturbances with frequency content between 0.01 Hz and 10 Hz. The derivation of the model utilizes an energy-based approach. The complete simulation includes the dynamic model of the ISPR integrated with the model for the ARIS controller so that the entire closed-loop system is simulated. Preliminary performance predictions are made for the ARIS in attenuating both off-board disturbances as well as disturbances from hardware mounted onboard the microgravity facility. These predictions suggest that the ARIS does eliminate resonant behavior detrimental to microgravity experimentation. A limited comparison is made between the simulation predictions of ARIS attenuation of off-board disturbances and results from the ARIS flight test. These comparisons show promise, but further tuning of the simulation is needed.

Dynamic simulation of knee-joint loading during gait using force-feedback control and surrogate contact modelling.

PubMed

Walter, Jonathan P; Pandy, Marcus G

2017-10-01

The aim of this study was to perform multi-body, muscle-driven, forward-dynamics simulations of human gait using a 6-degree-of-freedom (6-DOF) model of the knee in tandem with a surrogate model of articular contact and force control. A forward-dynamics simulation incorporating position, velocity and contact force-feedback control (FFC) was used to track full-body motion capture data recorded for multiple trials of level walking and stair descent performed by two individuals with instrumented knee implants. Tibiofemoral contact force errors for FFC were compared against those obtained from a standard computed muscle control algorithm (CMC) with a 6-DOF knee contact model (CMC6); CMC with a 1-DOF translating hinge-knee model (CMC1); and static optimization with a 1-DOF translating hinge-knee model (SO). Tibiofemoral joint loads predicted by FFC and CMC6 were comparable for level walking, however FFC produced more accurate results for stair descent. SO yielded reasonable predictions of joint contact loading for level walking but significant differences between model and experiment were observed for stair descent. CMC1 produced the least accurate predictions of tibiofemoral contact loads for both tasks. Our findings suggest that reliable estimates of knee-joint loading may be obtained by incorporating position, velocity and force-feedback control with a multi-DOF model of joint contact in a forward-dynamics simulation of gait. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

Exploratory Studies in Generalized Predictive Control for Active Gust Load Alleviation

NASA Technical Reports Server (NTRS)

Kvaternik, Raymond G.; Eure, Kenneth W.; Juang, Jer-Nan

2006-01-01

The results of numerical simulations aimed at assessing the efficacy of Generalized Predictive Control (GPC) for active gust load alleviation using trailing- and leading-edge control surfaces are presented. The equations underlying the method are presented and discussed, including system identification, calculation of control law matrices, and calculation of commands applied to the control effectors. Both embedded and explicit feedforward paths for inclusion of disturbance effects are addressed. Results from two types of simulations are shown. The first used a 3-DOF math model of a mass-spring-dashpot system subject to user-defined external disturbances. The second used open-loop data from a wind-tunnel test in which a wing model was excited by sinusoidal vertical gusts; closed-loop behavior was simulated in post-test calculations. Results obtained from these simulations have been decidedly positive. In particular, results of closed-loop simulations for the wing model showed reductions in root moments by factors as high as 1000, depending on whether the excitation is from a constant- or variable-frequency gust and on the direction of the response.

Generalized Predictive Control of Dynamic Systems with Rigid-Body Modes

NASA Technical Reports Server (NTRS)

Kvaternik, Raymond G.

2013-01-01

Numerical simulations to assess the effectiveness of Generalized Predictive Control (GPC) for active control of dynamic systems having rigid-body modes are presented. GPC is a linear, time-invariant, multi-input/multi-output predictive control method that uses an ARX model to characterize the system and to design the controller. Although the method can accommodate both embedded (implicit) and explicit feedforward paths for incorporation of disturbance effects, only the case of embedded feedforward in which the disturbances are assumed to be unknown is considered here. Results from numerical simulations using mathematical models of both a free-free three-degree-of-freedom mass-spring-dashpot system and the XV-15 tiltrotor research aircraft are presented. In regulation mode operation, which calls for zero system response in the presence of disturbances, the simulations showed reductions of nearly 100%. In tracking mode operations, where the system is commanded to follow a specified path, the GPC controllers produced the desired responses, even in the presence of disturbances.

A predictive pilot model for STOL aircraft landing

NASA Technical Reports Server (NTRS)

Kleinman, D. L.; Killingsworth, W. R.

1974-01-01

An optimal control approach has been used to model pilot performance during STOL flare and landing. The model is used to predict pilot landing performance for three STOL configurations, each having a different level of automatic control augmentation. Model predictions are compared with flight simulator data. It is concluded that the model can be effective design tool for studying analytically the effects of display modifications, different stability augmentation systems, and proposed changes in the landing area geometry.

Uncertainty and sensitivity analysis of control strategies using the benchmark simulation model No1 (BSM1).

PubMed

Flores-Alsina, Xavier; Rodriguez-Roda, Ignasi; Sin, Gürkan; Gernaey, Krist V

2009-01-01

The objective of this paper is to perform an uncertainty and sensitivity analysis of the predictions of the Benchmark Simulation Model (BSM) No. 1, when comparing four activated sludge control strategies. The Monte Carlo simulation technique is used to evaluate the uncertainty in the BSM1 predictions, considering the ASM1 bio-kinetic parameters and influent fractions as input uncertainties while the Effluent Quality Index (EQI) and the Operating Cost Index (OCI) are focused on as model outputs. The resulting Monte Carlo simulations are presented using descriptive statistics indicating the degree of uncertainty in the predicted EQI and OCI. Next, the Standard Regression Coefficients (SRC) method is used for sensitivity analysis to identify which input parameters influence the uncertainty in the EQI predictions the most. The results show that control strategies including an ammonium (S(NH)) controller reduce uncertainty in both overall pollution removal and effluent total Kjeldahl nitrogen. Also, control strategies with an external carbon source reduce the effluent nitrate (S(NO)) uncertainty increasing both their economical cost and variability as a trade-off. Finally, the maximum specific autotrophic growth rate (micro(A)) causes most of the variance in the effluent for all the evaluated control strategies. The influence of denitrification related parameters, e.g. eta(g) (anoxic growth rate correction factor) and eta(h) (anoxic hydrolysis rate correction factor), becomes less important when a S(NO) controller manipulating an external carbon source addition is implemented.

Model predictive and reallocation problem for CubeSat fault recovery and attitude control

NASA Astrophysics Data System (ADS)

Franchi, Loris; Feruglio, Lorenzo; Mozzillo, Raffaele; Corpino, Sabrina

2018-01-01

In recent years, thanks to the increase of the know-how on machine-learning techniques and the advance of the computational capabilities of on-board processing, expensive computing algorithms, such as Model Predictive Control, have begun to spread in space applications even on small on-board processor. The paper presents an algorithm for an optimal fault recovery of a 3U CubeSat, developed in MathWorks Matlab & Simulink environment. This algorithm involves optimization techniques aiming at obtaining the optimal recovery solution, and involves a Model Predictive Control approach for the attitude control. The simulated system is a CubeSat in Low Earth Orbit: the attitude control is performed with three magnetic torquers and a single reaction wheel. The simulation neglects the errors in the attitude determination of the satellite, and focuses on the recovery approach and control method. The optimal recovery approach takes advantage of the properties of magnetic actuation, which gives the possibility of the redistribution of the control action when a fault occurs on a single magnetic torquer, even in absence of redundant actuators. In addition, the paper presents the results of the implementation of Model Predictive approach to control the attitude of the satellite.

A model for prediction of STOVL ejector dynamics

NASA Technical Reports Server (NTRS)

Drummond, Colin K.

1989-01-01

A semi-empirical control-volume approach to ejector modeling for transient performance prediction is presented. This new approach is motivated by the need for a predictive real-time ejector sub-system simulation for Short Take-Off Verticle Landing (STOVL) integrated flight and propulsion controls design applications. Emphasis is placed on discussion of the approximate characterization of the mixing process central to thrust augmenting ejector operation. The proposed ejector model suggests transient flow predictions are possible with a model based on steady-flow data. A practical test case is presented to illustrate model calibration.

Model Predictive Control of LCL Three-level Photovoltaic Grid-connected Inverter

NASA Astrophysics Data System (ADS)

Liang, Cheng; Tian, Engang; Pang, Baobing; Li, Juan; Yang, Yang

2018-05-01

In this paper, neutral point clamped three-level inverter circuit is analyzed to establish a mathematical model of the three-level inverter in the αβ coordinate system. The causes and harms of the midpoint potential imbalance problem are described. The paper use the method of model predictive control to control the entire inverter circuit[1]. The simulation model of the inverter system is built in Matlab/Simulink software. It is convenient to control the grid-connected current, suppress the unbalance of the midpoint potential and reduce the switching frequency by changing the weight coefficient in the cost function. The superiority of the model predictive control in the control method of the inverter system is verified.

Model Predictive Flight Control System with Full State Observer using H∞ Method

NASA Astrophysics Data System (ADS)

Sanwale, Jitu; Singh, Dhan Jeet

2018-03-01

This paper presents the application of the model predictive approach to design a flight control system (FCS) for longitudinal dynamics of a fixed wing aircraft. Longitudinal dynamics is derived for a conventional aircraft. Open loop aircraft response analysis is carried out. Simulation studies are illustrated to prove the efficacy of the proposed model predictive controller using H ∞ state observer. The estimation criterion used in the {H}_{∞} observer design is to minimize the worst possible effects of the modelling errors and additive noise on the parameter estimation.

Modeling and control for closed environment plant production systems

NASA Technical Reports Server (NTRS)

Fleisher, David H.; Ting, K. C.; Janes, H. W. (Principal Investigator)

2002-01-01

A computer program was developed to study multiple crop production and control in controlled environment plant production systems. The program simulates crop growth and development under nominal and off-nominal environments. Time-series crop models for wheat (Triticum aestivum), soybean (Glycine max), and white potato (Solanum tuberosum) are integrated with a model-based predictive controller. The controller evaluates and compensates for effects of environmental disturbances on crop production scheduling. The crop models consist of a set of nonlinear polynomial equations, six for each crop, developed using multivariate polynomial regression (MPR). Simulated data from DSSAT crop models, previously modified for crop production in controlled environments with hydroponics under elevated atmospheric carbon dioxide concentration, were used for the MPR fitting. The model-based predictive controller adjusts light intensity, air temperature, and carbon dioxide concentration set points in response to environmental perturbations. Control signals are determined from minimization of a cost function, which is based on the weighted control effort and squared-error between the system response and desired reference signal.

Qualitative simulation for process modeling and control

NASA Technical Reports Server (NTRS)

Dalle Molle, D. T.; Edgar, T. F.

1989-01-01

A qualitative model is developed for a first-order system with a proportional-integral controller without precise knowledge of the process or controller parameters. Simulation of the qualitative model yields all of the solutions to the system equations. In developing the qualitative model, a necessary condition for the occurrence of oscillatory behavior is identified. Initializations that cannot exhibit oscillatory behavior produce a finite set of behaviors. When the phase-space behavior of the oscillatory behavior is properly constrained, these initializations produce an infinite but comprehensible set of asymptotically stable behaviors. While the predictions include all possible behaviors of the real system, a class of spurious behaviors has been identified. When limited numerical information is included in the model, the number of predictions is significantly reduced.

Model predictive control of the solid oxide fuel cell stack temperature with models based on experimental data

NASA Astrophysics Data System (ADS)

Pohjoranta, Antti; Halinen, Matias; Pennanen, Jari; Kiviaho, Jari

2015-03-01

Generalized predictive control (GPC) is applied to control the maximum temperature in a solid oxide fuel cell (SOFC) stack and the temperature difference over the stack. GPC is a model predictive control method and the models utilized in this work are ARX-type (autoregressive with extra input), multiple input-multiple output, polynomial models that were identified from experimental data obtained from experiments with a complete SOFC system. The proposed control is evaluated by simulation with various input-output combinations, with and without constraints. A comparison with conventional proportional-integral-derivative (PID) control is also made. It is shown that if only the stack maximum temperature is controlled, a standard PID controller can be used to obtain output performance comparable to that obtained with the significantly more complex model predictive controller. However, in order to control the temperature difference over the stack, both the stack minimum and the maximum temperature need to be controlled and this cannot be done with a single PID controller. In such a case the model predictive controller provides a feasible and effective solution.

Numerical simulation of the actuation system for the ALDF's propulsion control valve. [Aircraft Landing Dynamics Facility

NASA Technical Reports Server (NTRS)

Korte, John J.

1990-01-01

A numerical simulation of the actuation system for the propulsion control valve (PCV) of the NASA Langley Aircraft Landing Dynamics Facility was developed during the preliminary design of the PCV and used throughout the entire project. The simulation is based on a predictive model of the PCV which is used to evaluate and design the actuation system. The PCV controls a 1.7 million-pound thrust water jet used in propelling a 108,000-pound test carriage. The PCV can open and close in 0.300 second and deliver over 9,000 gallons of water per sec at pressures up to 3150 psi. The numerical simulation results are used to predict transient performance and valve opening characteristics, specify the hydraulic control system, define transient loadings on components, and evaluate failure modes. The mathematical model used for numerically simulating the mechanical fluid power system is described, and numerical results are demonstrated for a typical opening and closing cycle of the PCV. A summary is then given on how the model is used in the design process.

Simulating Human Cognition in the Domain of Air Traffic Control

NASA Technical Reports Server (NTRS)

Freed, Michael; Johnston, James C.; Null, Cynthia H. (Technical Monitor)

1995-01-01

Experiments intended to assess performance in human-machine interactions are often prohibitively expensive, unethical or otherwise impractical to run. Approximations of experimental results can be obtained, in principle, by simulating the behavior of subjects using computer models of human mental behavior. Computer simulation technology has been developed for this purpose. Our goal is to produce a cognitive model suitable to guide the simulation machinery and enable it to closely approximate a human subject's performance in experimental conditions. The described model is designed to simulate a variety of cognitive behaviors involved in routine air traffic control. As the model is elaborated, our ability to predict the effects of novel circumstances on controller error rates and other performance characteristics should increase. This will enable the system to project the impact of proposed changes to air traffic control procedures and equipment on controller performance.

Dynamic evaluation of CMAQ part I: Separating the effects of ...

EPA Pesticide Factsheets

A dynamic evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.0.1 was conducted to evaluate the model's ability to predict changes in ozone levels between 2002 and 2005, a time period characterized by emission reductions associated with the EPA's Nitrogen Oxides State Implementation Plan as well as significant reductions in mobile source emissions. Model results for the summers of 2002 and 2005 were compared to simulations from a previous version of CMAQ to assess the impact of model updates on predicted pollutant response. Changes to the model treatment of emissions, meteorology and chemistry had substantial impacts on the simulated ozone concentrations. While the median bias for high summertime ozone decreased in both years compared to previous simulations, the observed decrease in ozone from 2002 to 2005 in the eastern US continued to be underestimated by the model. Additional “cross” simulations were used to decompose the model predicted change in ozone into the change due to emissions, the change due to meteorology and any remaining change not explained individually by these two components. The decomposition showed that the emission controls led to a decrease in modeled high summertime ozone close to twice as large as the decrease attributable to changes in meteorology alone. Quantifying the impact of retrospective emission controls by removing the impacts of meteorology during the control period can be a valuable approac

Missile Guidance Law Based on Robust Model Predictive Control Using Neural-Network Optimization.

PubMed

Li, Zhijun; Xia, Yuanqing; Su, Chun-Yi; Deng, Jun; Fu, Jun; He, Wei

2015-08-01

In this brief, the utilization of robust model-based predictive control is investigated for the problem of missile interception. Treating the target acceleration as a bounded disturbance, novel guidance law using model predictive control is developed by incorporating missile inside constraints. The combined model predictive approach could be transformed as a constrained quadratic programming (QP) problem, which may be solved using a linear variational inequality-based primal-dual neural network over a finite receding horizon. Online solutions to multiple parametric QP problems are used so that constrained optimal control decisions can be made in real time. Simulation studies are conducted to illustrate the effectiveness and performance of the proposed guidance control law for missile interception.

Drug disposition and modelling before and after gastric bypass: immediate and controlled-release metoprolol formulations.

PubMed

Gesquiere, Ina; Darwich, Adam S; Van der Schueren, Bart; de Hoon, Jan; Lannoo, Matthias; Matthys, Christophe; Rostami, Amin; Foulon, Veerle; Augustijns, Patrick

2015-11-01

The aim of the present study was to evaluate the disposition of metoprolol after oral administration of an immediate and controlled-release formulation before and after Roux-en-Y gastric bypass (RYGB) surgery in the same individuals and to validate a physiologically based pharmacokinetic (PBPK) model for predicting oral bioavailability following RYGB. A single-dose pharmacokinetic study of metoprolol tartrate 200 mg immediate release and controlled release was performed in 14 volunteers before and 6-8 months after RYGB. The observed data were compared with predicted results from the PBPK modelling and simulation of metoprolol tartrate immediate and controlled-release formulation before and after RYGB. After administration of metoprolol immediate and controlled release, no statistically significant difference in the observed area under the curve (AUC(0-24 h)) was shown, although a tendency towards an increased oral exposure could be observed as the AUC(0-24 h) was 32.4% [95% confidence interval (CI) 1.36, 63.5] and 55.9% (95% CI 5.73, 106) higher following RYGB for the immediate and controlled-release formulation, respectively. This could be explained by surgery-related weight loss and a reduced presystemic biotransformation in the proximal gastrointestinal tract. The PBPK values predicted by modelling and simulation were similar to the observed data, confirming its validity. The disposition of metoprolol from an immediate-release and a controlled-release formulation was not significantly altered after RYGB; there was a tendency to an increase, which was also predicted by PBPK modelling and simulation. © 2015 The British Pharmacological Society.

Drug disposition and modelling before and after gastric bypass: immediate and controlled-release metoprolol formulations

PubMed Central

Gesquiere, Ina; Darwich, Adam S; Van der Schueren, Bart; de Hoon, Jan; Lannoo, Matthias; Matthys, Christophe; Rostami, Amin; Foulon, Veerle; Augustijns, Patrick

2015-01-01

Aims The aim of the present study was to evaluate the disposition of metoprolol after oral administration of an immediate and controlled-release formulation before and after Roux-en-Y gastric bypass (RYGB) surgery in the same individuals and to validate a physiologically based pharmacokinetic (PBPK) model for predicting oral bioavailability following RYGB. Methods A single-dose pharmacokinetic study of metoprolol tartrate 200 mg immediate release and controlled release was performed in 14 volunteers before and 6–8 months after RYGB. The observed data were compared with predicted results from the PBPK modelling and simulation of metoprolol tartrate immediate and controlled-release formulation before and after RYGB. Results After administration of metoprolol immediate and controlled release, no statistically significant difference in the observed area under the curve (AUC0–24 h) was shown, although a tendency towards an increased oral exposure could be observed as the AUC0–24 h was 32.4% [95% confidence interval (CI) 1.36, 63.5] and 55.9% (95% CI 5.73, 106) higher following RYGB for the immediate and controlled-release formulation, respectively. This could be explained by surgery-related weight loss and a reduced presystemic biotransformation in the proximal gastrointestinal tract. The PBPK values predicted by modelling and simulation were similar to the observed data, confirming its validity. Conclusions The disposition of metoprolol from an immediate-release and a controlled-release formulation was not significantly altered after RYGB; there was a tendency to an increase, which was also predicted by PBPK modelling and simulation. PMID:25917170

High Fidelity, “Faster than Real-Time” Simulator for Predicting Power System Dynamic Behavior - Final Technical Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Flueck, Alex

The “High Fidelity, Faster than Real­Time Simulator for Predicting Power System Dynamic Behavior” was designed and developed by Illinois Institute of Technology with critical contributions from Electrocon International, Argonne National Laboratory, Alstom Grid and McCoy Energy. Also essential to the project were our two utility partners: Commonwealth Edison and AltaLink. The project was a success due to several major breakthroughs in the area of large­scale power system dynamics simulation, including (1) a validated faster than real­ time simulation of both stable and unstable transient dynamics in a large­scale positive sequence transmission grid model, (2) a three­phase unbalanced simulation platform formore » modeling new grid devices, such as independently controlled single­phase static var compensators (SVCs), (3) the world’s first high fidelity three­phase unbalanced dynamics and protection simulator based on Electrocon’s CAPE program, and (4) a first­of­its­ kind implementation of a single­phase induction motor model with stall capability. The simulator results will aid power grid operators in their true time of need, when there is a significant risk of cascading outages. The simulator will accelerate performance and enhance accuracy of dynamics simulations, enabling operators to maintain reliability and steer clear of blackouts. In the long­term, the simulator will form the backbone of the newly conceived hybrid real­time protection and control architecture that will coordinate local controls, wide­area measurements, wide­area controls and advanced real­time prediction capabilities. The nation’s citizens will benefit in several ways, including (1) less down time from power outages due to the faster­than­real­time simulator’s predictive capability, (2) higher levels of reliability due to the detailed dynamics plus protection simulation capability, and (3) more resiliency due to the three­ phase unbalanced simulator’s ability to model three­phase and single­ phase networks and devices.« less

A combined-slip predictive control of vehicle stability with experimental verification

NASA Astrophysics Data System (ADS)

Jalali, Milad; Hashemi, Ehsan; Khajepour, Amir; Chen, Shih-ken; Litkouhi, Bakhtiar

2018-02-01

In this paper, a model predictive vehicle stability controller is designed based on a combined-slip LuGre tyre model. Variations in the lateral tyre forces due to changes in tyre slip ratios are considered in the prediction model of the controller. It is observed that the proposed combined-slip controller takes advantage of the more accurate tyre model and can adjust tyre slip ratios based on lateral forces of the front axle. This results in an interesting closed-loop response that challenges the notion of braking only the wheels on one side of the vehicle in differential braking. The performance of the proposed controller is evaluated in software simulations and is compared to a similar pure-slip controller. Furthermore, experimental tests are conducted on a rear-wheel drive electric Chevrolet Equinox equipped with differential brakes to evaluate the closed-loop response of the model predictive control controller.

Nonlinear model predictive control of a wave energy converter based on differential flatness parameterisation

NASA Astrophysics Data System (ADS)

Li, Guang

2017-01-01

This paper presents a fast constrained optimization approach, which is tailored for nonlinear model predictive control of wave energy converters (WEC). The advantage of this approach relies on its exploitation of the differential flatness of the WEC model. This can reduce the dimension of the resulting nonlinear programming problem (NLP) derived from the continuous constrained optimal control of WEC using pseudospectral method. The alleviation of computational burden using this approach helps to promote an economic implementation of nonlinear model predictive control strategy for WEC control problems. The method is applicable to nonlinear WEC models, nonconvex objective functions and nonlinear constraints, which are commonly encountered in WEC control problems. Numerical simulations demonstrate the efficacy of this approach.

An evaluation of the predictive capabilities of CTRW and MRMT

NASA Astrophysics Data System (ADS)

Fiori, Aldo; Zarlenga, Antonio; Gotovac, Hrvoje; Jankovic, Igor; Cvetkovic, Vladimir; Dagan, Gedeon

2016-04-01

The prediction capability of two approximate models of non-Fickian transport in highly heterogeneous aquifers is checked by comparison with accurate numerical simulations, for mean uniform flow of velocity U. The two models considered are the MRMT (Multi Rate Mass Transfer) and CTRW (Continuous Time Random Walk) models. Both circumvent the need to solve the flow and transport equations by using proxy models, which provide the BTC μ(x,t) depending on a vector a of unknown 5 parameters. Although underlain by different conceptualisations, the two models have a similar mathematical structure. The proponents of the models suggest using field transport experiments at a small scale to calibrate a, toward predicting transport at larger scale. The strategy was tested with the aid of accurate numerical simulations in two and three dimensions from the literature. First, the 5 parameter values were calibrated by using the simulated μ at a control plane close to the injection one and subsequently using these same parameters for predicting μ at further 10 control planes. It is found that the two methods perform equally well, though the parameters identification is nonunique, with a large set of parameters providing similar fitting. Also, errors in the determination of the mean eulerian velocity may lead to significant shifts of the predicted BTC. It is found that the simulated BTCs satisfy Markovianity: they can be found as n-fold convolutions of a "kernel", in line with the models' main assumption.

A Two-Time Scale Decentralized Model Predictive Controller Based on Input and Output Model

PubMed Central

Niu, Jian; Zhao, Jun; Xu, Zuhua; Qian, Jixin

2009-01-01

A decentralized model predictive controller applicable for some systems which exhibit different dynamic characteristics in different channels was presented in this paper. These systems can be regarded as combinations of a fast model and a slow model, the response speeds of which are in two-time scale. Because most practical models used for control are obtained in the form of transfer function matrix by plant tests, a singular perturbation method was firstly used to separate the original transfer function matrix into two models in two-time scale. Then a decentralized model predictive controller was designed based on the two models derived from the original system. And the stability of the control method was proved. Simulations showed that the method was effective. PMID:19834542

Prediction of muscle activation for an eye movement with finite element modeling.

PubMed

Karami, Abbas; Eghtesad, Mohammad; Haghpanah, Seyyed Arash

2017-10-01

In this paper, a 3D finite element (FE) modeling is employed in order to predict extraocular muscles' activation and investigate force coordination in various motions of the eye orbit. A continuum constitutive hyperelastic model is employed for material description in dynamic modeling of the extraocular muscles (EOMs). Two significant features of this model are accurate mass modeling with FE method and stimulating EOMs for motion through muscle activation parameter. In order to validate the eye model, a forward dynamics simulation of the eye motion is carried out by variation of the muscle activation. Furthermore, to realize muscle activation prediction in various eye motions, two different tracking-based inverse controllers are proposed. The performance of these two inverse controllers is investigated according to their resulted muscle force magnitude and muscle force coordination. The simulation results are compared with the available experimental data and the well-known existing neurological laws. The comparison authenticates both the validation and the prediction results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Model predictive control based on reduced order models applied to belt conveyor system.

PubMed

Chen, Wei; Li, Xin

2016-11-01

In the paper, a model predictive controller based on reduced order model is proposed to control belt conveyor system, which is an electro-mechanics complex system with long visco-elastic body. Firstly, in order to design low-degree controller, the balanced truncation method is used for belt conveyor model reduction. Secondly, MPC algorithm based on reduced order model for belt conveyor system is presented. Because of the error bound between the full-order model and reduced order model, two Kalman state estimators are applied in the control scheme to achieve better system performance. Finally, the simulation experiments are shown that balanced truncation method can significantly reduce the model order with high-accuracy and model predictive control based on reduced-model performs well in controlling the belt conveyor system. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Evaluation of a musculoskeletal model with prosthetic knee through six experimental gait trials.

PubMed

Kia, Mohammad; Stylianou, Antonis P; Guess, Trent M

2014-03-01

Knowledge of the forces acting on musculoskeletal joint tissues during movement benefits tissue engineering, artificial joint replacement, and our understanding of ligament and cartilage injury. Computational models can be used to predict these internal forces, but musculoskeletal models that simultaneously calculate muscle force and the resulting loading on joint structures are rare. This study used publicly available gait, skeletal geometry, and instrumented prosthetic knee loading data [1] to evaluate muscle driven forward dynamics simulations of walking. Inputs to the simulation were measured kinematics and outputs included muscle, ground reaction, ligament, and joint contact forces. A full body musculoskeletal model with subject specific lower extremity geometries was developed in the multibody framework. A compliant contact was defined between the prosthetic femoral component and tibia insert geometries. Ligament structures were modeled with a nonlinear force-strain relationship. The model included 45 muscles on the right lower leg. During forward dynamics simulations a feedback control scheme calculated muscle forces using the error signal between the current muscle lengths and the lengths recorded during inverse kinematics simulations. Predicted tibio-femoral contact force, ground reaction forces, and muscle forces were compared to experimental measurements for six different gait trials using three different gait types (normal, trunk sway, and medial thrust). The mean average deviation (MAD) and root mean square deviation (RMSD) over one gait cycle are reported. The muscle driven forward dynamics simulations were computationally efficient and consistently reproduced the inverse kinematics motion. The forward simulations also predicted total knee contact forces (166N

Design and experiment of vehicular charger AC/DC system based on predictive control algorithm

NASA Astrophysics Data System (ADS)

He, Guangbi; Quan, Shuhai; Lu, Yuzhang

2018-06-01

For the car charging stage rectifier uncontrollable system, this paper proposes a predictive control algorithm of DC/DC converter based on the prediction model, established by the state space average method and its prediction model, obtained by the optimal mathematical description of mathematical calculation, to analysis prediction algorithm by Simulink simulation. The design of the structure of the car charging, at the request of the rated output power and output voltage adjustable control circuit, the first stage is the three-phase uncontrolled rectifier DC voltage Ud through the filter capacitor, after by using double-phase interleaved buck-boost circuit with wide range output voltage required value, analyzing its working principle and the the parameters for the design and selection of components. The analysis of current ripple shows that the double staggered parallel connection has the advantages of reducing the output current ripple and reducing the loss. The simulation experiment of the whole charging circuit is carried out by software, and the result is in line with the design requirements of the system. Finally combining the soft with hardware circuit to achieve charging of the system according to the requirements, experimental platform proved the feasibility and effectiveness of the proposed predictive control algorithm based on the car charging of the system, which is consistent with the simulation results.

An improved predictive functional control method with application to PMSM systems

NASA Astrophysics Data System (ADS)

Li, Shihua; Liu, Huixian; Fu, Wenshu

2017-01-01

In common design of prediction model-based control method, usually disturbances are not considered in the prediction model as well as the control design. For the control systems with large amplitude or strong disturbances, it is difficult to precisely predict the future outputs according to the conventional prediction model, and thus the desired optimal closed-loop performance will be degraded to some extent. To this end, an improved predictive functional control (PFC) method is developed in this paper by embedding disturbance information into the system model. Here, a composite prediction model is thus obtained by embedding the estimated value of disturbances, where disturbance observer (DOB) is employed to estimate the lumped disturbances. So the influence of disturbances on system is taken into account in optimisation procedure. Finally, considering the speed control problem for permanent magnet synchronous motor (PMSM) servo system, a control scheme based on the improved PFC method is designed to ensure an optimal closed-loop performance even in the presence of disturbances. Simulation and experimental results based on a hardware platform are provided to confirm the effectiveness of the proposed algorithm.

Feedback control of an electrorheological long-stroke vibration damper

NASA Astrophysics Data System (ADS)

Sims, Neil D.; Stanway, Roger; Johnson, Andrew R.; Peel, David J.; Bullough, William A.

1999-06-01

It is widely acknowledged that the inherent non-linearity of smart fluid dampers is inhibiting the development of effective control regimes, and mass-production devices. In an earlier publication, an innovative solution to this problem was presented -- using a simple feedback control strategy to linearize the response. The study used a quasi-steady model of a long-stroke Electrorheological damper, and showed how proportional feedback control could linearize the simulated response. However, this initial research did not consider the dynamics of the damper's behavior, and so the development of a more advanced model has been necessary. In this article, the authors present an extension to this earlier study, using a model of the damper's response that is capable of accurately predicting the dynamic response of the damper. To introduce the topic, the electrorheological long-stroke damper test rig is described, and an overview of the earlier study is given. The advanced model is then derived, and its predictions are compared to experimental data from the test rig. This model is then incorporated into the feedback control simulations, and it is shown how the control strategy is still able to linearize the response in simulations.

Rate-Based Model Predictive Control of Turbofan Engine Clearance

NASA Technical Reports Server (NTRS)

DeCastro, Jonathan A.

2006-01-01

An innovative model predictive control strategy is developed for control of nonlinear aircraft propulsion systems and sub-systems. At the heart of the controller is a rate-based linear parameter-varying model that propagates the state derivatives across the prediction horizon, extending prediction fidelity to transient regimes where conventional models begin to lose validity. The new control law is applied to a demanding active clearance control application, where the objectives are to tightly regulate blade tip clearances and also anticipate and avoid detrimental blade-shroud rub occurrences by optimally maintaining a predefined minimum clearance. Simulation results verify that the rate-based controller is capable of satisfying the objectives during realistic flight scenarios where both a conventional Jacobian-based model predictive control law and an unconstrained linear-quadratic optimal controller are incapable of doing so. The controller is evaluated using a variety of different actuators, illustrating the efficacy and versatility of the control approach. It is concluded that the new strategy has promise for this and other nonlinear aerospace applications that place high importance on the attainment of control objectives during transient regimes.

The numerical modelling and process simulation for the fault diagnosis of rotary kiln incinerator.

PubMed

Roh, S D; Kim, S W; Cho, W S

2001-10-01

The numerical modelling and process simulation for the fault diagnosis of rotary kiln incinerator were accomplished. In the numerical modelling, two models applied to the modelling within the kiln are the combustion chamber model including the mass and energy balance equations for two combustion chambers and 3D thermal model. The combustion chamber model predicts temperature within the kiln, flue gas composition, flux and heat of combustion. Using the combustion chamber model and 3D thermal model, the production-rules for the process simulation can be obtained through interrelation analysis between control and operation variables. The process simulation of the kiln is operated with the production-rules for automatic operation. The process simulation aims to provide fundamental solutions to the problems in incineration process by introducing an online expert control system to provide an integrity in process control and management. Knowledge-based expert control systems use symbolic logic and heuristic rules to find solutions for various types of problems. It was implemented to be a hybrid intelligent expert control system by mutually connecting with the process control systems which has the capability of process diagnosis, analysis and control.

A comparison between two simulation models for spread of foot-and-mouth disease.

PubMed

Halasa, Tariq; Boklund, Anette; Stockmarr, Anders; Enøe, Claes; Christiansen, Lasse E

2014-01-01

Two widely used simulation models of foot-and-mouth disease (FMD) were used in order to compare the models' predictions in term of disease spread, consequence, and the ranking of the applied control strategies, and to discuss the effect of the way disease spread is modeled on the predicted outcomes of each model. The DTU-DADS (version 0.100), and ISP (version 2.001.11) were used to simulate a hypothetical spread of FMD in Denmark. Actual herd type, movements, and location data in the period 1st October 2006 and 30th September 2007 was used. The models simulated the spread of FMD using 3 different control scenarios: 1) A basic scenario representing EU and Danish control strategies, 2) pre-emptive depopulation of susceptible herds within a 500 meters radius around the detected herds, and 3) suppressive vaccination of susceptible herds within a 1,000 meters radius around the detected herds. Depopulation and vaccination started 14 days following the detection of the first infected herd. Five thousand index herds were selected randomly, of which there were 1,000 cattle herds located in high density cattle areas and 1,000 in low density cattle areas, 1,000 swine herds located in high density swine areas and 1,000 in low density swine areas, and 1,000 sheep herds. Generally, DTU-DADS predicted larger, longer duration and costlier epidemics than ISP, except when epidemics started in cattle herds located in high density cattle areas. ISP supported suppressive vaccination rather than pre-emptive depopulation, while DTU-DADS was indifferent to the alternative control strategies. Nonetheless, the absolute differences between control strategies were small making the choice of control strategy during an outbreak to be most likely based on practical reasons.

Enhanced pid vs model predictive control applied to bldc motor

NASA Astrophysics Data System (ADS)

Gaya, M. S.; Muhammad, Auwal; Aliyu Abdulkadir, Rabiu; Salim, S. N. S.; Madugu, I. S.; Tijjani, Aminu; Aminu Yusuf, Lukman; Dauda Umar, Ibrahim; Khairi, M. T. M.

2018-01-01

BrushLess Direct Current (BLDC) motor is a multivariable and highly complex nonlinear system. Variation of internal parameter values with environment or reference signal increases the difficulty in controlling the BLDC effectively. Advanced control strategies (like model predictive control) often have to be integrated to satisfy the control desires. Enhancing or proper tuning of a conventional algorithm results in achieving the desired performance. This paper presents a performance comparison of Enhanced PID and Model Predictive Control (MPC) applied to brushless direct current motor. The simulation results demonstrated that the PSO-PID is slightly better than the PID and MPC in tracking the trajectory of the reference signal. The proposed scheme could be useful algorithms for the system.

Pilot/vehicle model analysis of visual and motion cue requirements in flight simulation. [helicopter hovering

NASA Technical Reports Server (NTRS)

Baron, S.; Lancraft, R.; Zacharias, G.

1980-01-01

The optimal control model (OCM) of the human operator is used to predict the effect of simulator characteristics on pilot performance and workload. The piloting task studied is helicopter hover. Among the simulator characteristics considered were (computer generated) visual display resolution, field of view and time delay.

Adaptive MPC based on MIMO ARX-Laguerre model.

PubMed

Ben Abdelwahed, Imen; Mbarek, Abdelkader; Bouzrara, Kais

2017-03-01

This paper proposes a method for synthesizing an adaptive predictive controller using a reduced complexity model. This latter is given by the projection of the ARX model on Laguerre bases. The resulting model is entitled MIMO ARX-Laguerre and it is characterized by an easy recursive representation. The adaptive predictive control law is computed based on multi-step-ahead finite-element predictors, identified directly from experimental input/output data. The model is tuned in each iteration by an online identification algorithms of both model parameters and Laguerre poles. The proposed approach avoids time consuming numerical optimization algorithms associated with most common linear predictive control strategies, which makes it suitable for real-time implementation. The method is used to synthesize and test in numerical simulations adaptive predictive controllers for the CSTR process benchmark. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Simulation Study of Flap Effects on a Commercial Transport Airplane in Upset Conditions

NASA Technical Reports Server (NTRS)

Cunningham, Kevin; Foster, John V.; Shah, Gautam H.; Stewart, Eric C.; Ventura, Robin N.; Rivers, Robert A.; Wilborn, James E.; Gato, William

2005-01-01

As part of NASA's Aviation Safety and Security Program, a simulation study of a twinjet transport airplane crew training simulation was conducted to address fidelity for upset or loss of control conditions and to study the effect of flap configuration in those regimes. Piloted and desktop simulations were used to compare the baseline crew training simulation model with an enhanced aerodynamic model that was developed for high-angle-of-attack conditions. These studies were conducted with various flap configurations and addressed the approach-to-stall, stall, and post-stall flight regimes. The enhanced simulation model showed that flap configuration had a significant effect on the character of departures that occurred during post-stall flight. Preliminary comparisons with flight test data indicate that the enhanced model is a significant improvement over the baseline. Some of the unrepresentative characteristics that are predicted by the baseline crew training simulation for flight in the post-stall regime have been identified. This paper presents preliminary results of this simulation study and discusses key issues regarding predicted flight dynamics characteristics during extreme upset and loss-of-control flight conditions with different flap configurations.

Forward and Inverse Predictive Model for the Trajectory Tracking Control of a Lower Limb Exoskeleton for Gait Rehabilitation: Simulation modelling analysis

NASA Astrophysics Data System (ADS)

Zakaria, M. A.; Majeed, A. P. P. A.; Taha, Z.; Alim, M. M.; Baarath, K.

2018-03-01

The movement of a lower limb exoskeleton requires a reasonably accurate control method to allow for an effective gait therapy session to transpire. Trajectory tracking is a nontrivial means of passive rehabilitation technique to correct the motion of the patients’ impaired limb. This paper proposes an inverse predictive model that is coupled together with the forward kinematics of the exoskeleton to estimate the behaviour of the system. A conventional PID control system is used to converge the required joint angles based on the desired input from the inverse predictive model. It was demonstrated through the present study, that the inverse predictive model is capable of meeting the trajectory demand with acceptable error tolerance. The findings further suggest the ability of the predictive model of the exoskeleton to predict a correct joint angle command to the system.

Data-Based Predictive Control with Multirate Prediction Step

NASA Technical Reports Server (NTRS)

Barlow, Jonathan S.

2010-01-01

Data-based predictive control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. One challenge of MPC is computational requirements increasing with prediction horizon length. This paper develops a closed-loop dynamic output feedback controller that minimizes a multi-step-ahead receding-horizon cost function with multirate prediction step. One result is a reduced influence of prediction horizon and the number of system outputs on the computational requirements of the controller. Another result is an emphasis on portions of the prediction window that are sampled more frequently. A third result is the ability to include more outputs in the feedback path than in the cost function.

Learning and Control Model of the Arm for Loading

NASA Astrophysics Data System (ADS)

Kim, Kyoungsik; Kambara, Hiroyuki; Shin, Duk; Koike, Yasuharu

We propose a learning and control model of the arm for a loading task in which an object is loaded onto one hand with the other hand, in the sagittal plane. Postural control during object interactions provides important points to motor control theories in terms of how humans handle dynamics changes and use the information of prediction and sensory feedback. For the learning and control model, we coupled a feedback-error-learning scheme with an Actor-Critic method used as a feedback controller. To overcome sensory delays, a feedforward dynamics model (FDM) was used in the sensory feedback path. We tested the proposed model in simulation using a two-joint arm with six muscles, each with time delays in muscle force generation. By applying the proposed model to the loading task, we showed that motor commands started increasing, before an object was loaded on, to stabilize arm posture. We also found that the FDM contributes to the stabilization by predicting how the hand changes based on contexts of the object and efferent signals. For comparison with other computational models, we present the simulation results of a minimum-variance model.

A Computational Model for Aperture Control in Reach-to-Grasp Movement Based on Predictive Variability

PubMed Central

Takemura, Naohiro; Fukui, Takao; Inui, Toshio

2015-01-01

In human reach-to-grasp movement, visual occlusion of a target object leads to a larger peak grip aperture compared to conditions where online vision is available. However, no previous computational and neural network models for reach-to-grasp movement explain the mechanism of this effect. We simulated the effect of online vision on the reach-to-grasp movement by proposing a computational control model based on the hypothesis that the grip aperture is controlled to compensate for both motor variability and sensory uncertainty. In this model, the aperture is formed to achieve a target aperture size that is sufficiently large to accommodate the actual target; it also includes a margin to ensure proper grasping despite sensory and motor variability. To this end, the model considers: (i) the variability of the grip aperture, which is predicted by the Kalman filter, and (ii) the uncertainty of the object size, which is affected by visual noise. Using this model, we simulated experiments in which the effect of the duration of visual occlusion was investigated. The simulation replicated the experimental result wherein the peak grip aperture increased when the target object was occluded, especially in the early phase of the movement. Both predicted motor variability and sensory uncertainty play important roles in the online visuomotor process responsible for grip aperture control. PMID:26696874

Health-aware Model Predictive Control of Pasteurization Plant

NASA Astrophysics Data System (ADS)

Karimi Pour, Fatemeh; Puig, Vicenç; Ocampo-Martinez, Carlos

2017-01-01

In order to optimize the trade-off between components life and energy consumption, the integration of a system health management and control modules is required. This paper proposes the integration of model predictive control (MPC) with a fatigue estimation approach that minimizes the damage of the components of a pasteurization plant. The fatigue estimation is assessed with the rainflow counting algorithm. Using data from this algorithm, a simplified model that characterizes the health of the system is developed and integrated with MPC. The MPC controller objective is modified by adding an extra criterion that takes into account the accumulated damage. But, a steady-state offset is created by adding this extra criterion. Finally, by including an integral action in the MPC controller, the steady-state error for regulation purpose is eliminated. The proposed control scheme is validated in simulation using a simulator of a utility-scale pasteurization plant.

Nonlinear Dynamic Inversion Baseline Control Law: Architecture and Performance Predictions

NASA Technical Reports Server (NTRS)

Miller, Christopher J.

2011-01-01

A model reference dynamic inversion control law has been developed to provide a baseline control law for research into adaptive elements and other advanced flight control law components. This controller has been implemented and tested in a hardware-in-the-loop simulation; the simulation results show excellent handling qualities throughout the limited flight envelope. A simple angular momentum formulation was chosen because it can be included in the stability proofs for many basic adaptive theories, such as model reference adaptive control. Many design choices and implementation details reflect the requirements placed on the system by the nonlinear flight environment and the desire to keep the system as basic as possible to simplify the addition of the adaptive elements. Those design choices are explained, along with their predicted impact on the handling qualities.

Effect of Interfacial Turbulence and Accommodation Coefficient on CFD Predictions of Pressurization and Pressure Control in Cryogenic Storage Tank

NASA Technical Reports Server (NTRS)

Kassemi, Mohammad; Kartuzova, Olga; Hylton, Sonya

2015-01-01

Laminar models agree closely with the pressure evolution and vapor phase temperature stratification but under-predict liquid temperatures. Turbulent SST k-w and k-e models under-predict the pressurization rate and extent of stratification in the vapor but represent liquid temperature distributions fairly well. These conclusions seem to equally apply to large cryogenic tank simulations as well as small scale simulant fluid pressurization cases. Appropriate turbulent models that represent both interfacial and bulk vapor phase turbulence with greater fidelity are needed. Application of LES models to the tank pressurization problem can serve as a starting point.

A Simulation Model of Carbon Cycling and Methane Emissions in Amazon Wetlands

NASA Technical Reports Server (NTRS)

Potter, Christopher; Melack, John; Hess, Laura; Forsberg, Bruce; Novo, Evlyn Moraes; Klooster, Steven

2004-01-01

An integrative carbon study is investigating the hypothesis that measured fluxes of methane from wetlands in the Amazon region can be predicted accurately using a combination of process modeling of ecosystem carbon cycles and remote sensing of regional floodplain dynamics. A new simulation model has been build using the NASA- CASA concept for predicting methane production and emission fluxes in Amazon river and floodplain ecosystems. Numerous innovations area being made to model Amazon wetland ecosystems, including: (1) prediction of wetland net primary production (NPP) as the source for plant litter decomposition and accumulation of sediment organic matter in two major vegetation classes - flooded forests (varzea or igapo) and floating macrophytes, (2) representation of controls on carbon processing and methane evasion at the diffusive boundary layer, through the lake water column, and in wetland sediments as a function of changes in floodplain water level, (3) inclusion of surface emissions controls on wetland methane fluxes, including variations in daily surface temperature and of hydrostatic pressure linked to water level fluctuations. A model design overview and early simulation results are presented.

Risk Assessment and Prediction of Flyrock Distance by Combined Multiple Regression Analysis and Monte Carlo Simulation of Quarry Blasting

NASA Astrophysics Data System (ADS)

Armaghani, Danial Jahed; Mahdiyar, Amir; Hasanipanah, Mahdi; Faradonbeh, Roohollah Shirani; Khandelwal, Manoj; Amnieh, Hassan Bakhshandeh

2016-09-01

Flyrock is considered as one of the main causes of human injury, fatalities, and structural damage among all undesirable environmental impacts of blasting. Therefore, it seems that the proper prediction/simulation of flyrock is essential, especially in order to determine blast safety area. If proper control measures are taken, then the flyrock distance can be controlled, and, in return, the risk of damage can be reduced or eliminated. The first objective of this study was to develop a predictive model for flyrock estimation based on multiple regression (MR) analyses, and after that, using the developed MR model, flyrock phenomenon was simulated by the Monte Carlo (MC) approach. In order to achieve objectives of this study, 62 blasting operations were investigated in Ulu Tiram quarry, Malaysia, and some controllable and uncontrollable factors were carefully recorded/calculated. The obtained results of MC modeling indicated that this approach is capable of simulating flyrock ranges with a good level of accuracy. The mean of simulated flyrock by MC was obtained as 236.3 m, while this value was achieved as 238.6 m for the measured one. Furthermore, a sensitivity analysis was also conducted to investigate the effects of model inputs on the output of the system. The analysis demonstrated that powder factor is the most influential parameter on fly rock among all model inputs. It is noticeable that the proposed MR and MC models should be utilized only in the studied area and the direct use of them in the other conditions is not recommended.

Incorporating neurophysiological concepts in mathematical thermoregulation models

NASA Astrophysics Data System (ADS)

Kingma, Boris R. M.; Vosselman, M. J.; Frijns, A. J. H.; van Steenhoven, A. A.; van Marken Lichtenbelt, W. D.

2014-01-01

Skin blood flow (SBF) is a key player in human thermoregulation during mild thermal challenges. Various numerical models of SBF regulation exist. However, none explicitly incorporates the neurophysiology of thermal reception. This study tested a new SBF model that is in line with experimental data on thermal reception and the neurophysiological pathways involved in thermoregulatory SBF control. Additionally, a numerical thermoregulation model was used as a platform to test the function of the neurophysiological SBF model for skin temperature simulation. The prediction-error of the SBF-model was quantified by root-mean-squared-residual (RMSR) between simulations and experimental measurement data. Measurement data consisted of SBF (abdomen, forearm, hand), core and skin temperature recordings of young males during three transient thermal challenges (1 development and 2 validation). Additionally, ThermoSEM, a thermoregulation model, was used to simulate body temperatures using the new neurophysiological SBF-model. The RMSR between simulated and measured mean skin temperature was used to validate the model. The neurophysiological model predicted SBF with an accuracy of RMSR < 0.27. Tskin simulation results were within 0.37 °C of the measured mean skin temperature. This study shows that (1) thermal reception and neurophysiological pathways involved in thermoregulatory SBF control can be captured in a mathematical model, and (2) human thermoregulation models can be equipped with SBF control functions that are based on neurophysiology without loss of performance. The neurophysiological approach in modelling thermoregulation is favourable over engineering approaches because it is more in line with the underlying physiology.

A Comparison between Two Simulation Models for Spread of Foot-and-Mouth Disease

PubMed Central

Halasa, Tariq; Boklund, Anette; Stockmarr, Anders; Enøe, Claes; Christiansen, Lasse E.

2014-01-01

Two widely used simulation models of foot-and-mouth disease (FMD) were used in order to compare the models’ predictions in term of disease spread, consequence, and the ranking of the applied control strategies, and to discuss the effect of the way disease spread is modeled on the predicted outcomes of each model. The DTU-DADS (version 0.100), and ISP (version 2.001.11) were used to simulate a hypothetical spread of FMD in Denmark. Actual herd type, movements, and location data in the period 1st October 2006 and 30th September 2007 was used. The models simulated the spread of FMD using 3 different control scenarios: 1) A basic scenario representing EU and Danish control strategies, 2) pre-emptive depopulation of susceptible herds within a 500 meters radius around the detected herds, and 3) suppressive vaccination of susceptible herds within a 1,000 meters radius around the detected herds. Depopulation and vaccination started 14 days following the detection of the first infected herd. Five thousand index herds were selected randomly, of which there were 1,000 cattle herds located in high density cattle areas and 1,000 in low density cattle areas, 1,000 swine herds located in high density swine areas and 1,000 in low density swine areas, and 1,000 sheep herds. Generally, DTU-DADS predicted larger, longer duration and costlier epidemics than ISP, except when epidemics started in cattle herds located in high density cattle areas. ISP supported suppressive vaccination rather than pre-emptive depopulation, while DTU-DADS was indifferent to the alternative control strategies. Nonetheless, the absolute differences between control strategies were small making the choice of control strategy during an outbreak to be most likely based on practical reasons. PMID:24667525

Reducing usage of the computational resources by event driven approach to model predictive control

NASA Astrophysics Data System (ADS)

Misik, Stefan; Bradac, Zdenek; Cela, Arben

2017-08-01

This paper deals with a real-time and optimal control of dynamic systems while also considers the constraints which these systems might be subject to. Main objective of this work is to propose a simple modification of the existing Model Predictive Control approach to better suit needs of computational resource-constrained real-time systems. An example using model of a mechanical system is presented and the performance of the proposed method is evaluated in a simulated environment.

Advancement in Watershed Modelling Using Dynamic Lateral and Longitudinal Sediment (Dis)connectivity Prediction

NASA Astrophysics Data System (ADS)

Mahoney, D. T.; al Aamery, N. M. H.; Fox, J.

2017-12-01

The authors find that sediment (dis)connectivity has seldom taken precedence within watershed models, and the present study advances this modeling framework and applies the modeling within a bedrock-controlled system. Sediment (dis)connectivity, defined as the detachment and transport of sediment from source to sink between geomorphic zones, is a major control on sediment transport. Given the availability of high resolution geospatial data, coupling sediment connectivity concepts within sediment prediction models offers an approach to simulate sediment sources and pathways within a watershed's sediment cascade. Bedrock controlled catchments are potentially unique due to the presence of rock outcrops causing longitudinal impedance to sediment transport pathways in turn impacting the longitudinal distribution of the energy gradient responsible for conveying sediment. Therefore, the authors were motivated by the need to formulate a sediment transport model that couples sediment (dis)connectivity knowledge to predict sediment flux for bedrock controlled catchments. A watershed-scale sediment transport model was formulated that incorporates sediment (dis)connectivity knowledge collected via field reconnaissance and predicts sediment flux through coupling with the Partheniades equation and sediment continuity model. Sediment (dis)connectivity was formulated by coupling probabilistic upland lateral connectivity prediction with instream longitudinal connectivity assessments via discretization of fluid and sediment pathways. Flux predictions from the upland lateral connectivity model served as an input to the instream longitudinal connectivity model. Disconnectivity in the instream model was simulated via the discretization of stream reaches due to barriers such as bedrock outcroppings and man-made check dams. The model was tested for a bedrock controlled catchment in Kentucky, USA for which extensive historic water and sediment flux data was available. Predicted sediment flux was validated via sediment flux measurements collected by the authors. Watershed configuration and the distribution of lateral and longitudinal impedances to sediment transport were found to have significant influence on sediment connectivity and thus sediment flux.

Status of Computational Aerodynamic Modeling Tools for Aircraft Loss-of-Control

NASA Technical Reports Server (NTRS)

Frink, Neal T.; Murphy, Patrick C.; Atkins, Harold L.; Viken, Sally A.; Petrilli, Justin L.; Gopalarathnam, Ashok; Paul, Ryan C.

2016-01-01

A concerted effort has been underway over the past several years to evolve computational capabilities for modeling aircraft loss-of-control under the NASA Aviation Safety Program. A principal goal has been to develop reliable computational tools for predicting and analyzing the non-linear stability & control characteristics of aircraft near stall boundaries affecting safe flight, and for utilizing those predictions for creating augmented flight simulation models that improve pilot training. Pursuing such an ambitious task with limited resources required the forging of close collaborative relationships with a diverse body of computational aerodynamicists and flight simulation experts to leverage their respective research efforts into the creation of NASA tools to meet this goal. Considerable progress has been made and work remains to be done. This paper summarizes the status of the NASA effort to establish computational capabilities for modeling aircraft loss-of-control and offers recommendations for future work.

Cascade generalized predictive control strategy for boiler drum level.

PubMed

Xu, Min; Li, Shaoyuan; Cai, Wenjian

2005-07-01

This paper proposes a cascade model predictive control scheme for boiler drum level control. By employing generalized predictive control structures for both inner and outer loops, measured and unmeasured disturbances can be effectively rejected, and drum level at constant load is maintained. In addition, nonminimum phase characteristic and system constraints in both loops can be handled effectively by generalized predictive control algorithms. Simulation results are provided to show that cascade generalized predictive control results in better performance than that of well tuned cascade proportional integral differential controllers. The algorithm has also been implemented to control a 75-MW boiler plant, and the results show an improvement over conventional control schemes.

A methodology for the assessment of manned flight simulator fidelity

NASA Technical Reports Server (NTRS)

Hess, Ronald A.; Malsbury, Terry N.

1989-01-01

A relatively simple analytical methodology for assessing the fidelity of manned flight simulators for specific vehicles and tasks is offered. The methodology is based upon an application of a structural model of the human pilot, including motion cue effects. In particular, predicted pilot/vehicle dynamic characteristics are obtained with and without simulator limitations. A procedure for selecting model parameters can be implemented, given a probable pilot control strategy. In analyzing a pair of piloting tasks for which flight and simulation data are available, the methodology correctly predicted the existence of simulator fidelity problems. The methodology permitted the analytical evaluation of a change in simulator characteristics and indicated that a major source of the fidelity problems was a visual time delay in the simulation.

Simulating forest productivity along a neotropical elevational transect: temperature variation and carbon use efficiency

NASA Astrophysics Data System (ADS)

Marthews, T.; Malhi, Y.; Girardin, C.; Silva-Espejo, J.; Aragão, L.; Metcalfe, D.; Rapp, J.; Mercado, L.; Fisher, R.; Galbraith, D.; Fisher, J.; Salinas-Revilla, N.; Friend, A.; Restrepo-Coupe, N.; Williams, R.

2012-04-01

A better understanding of the mechanisms controlling the magnitude and sign of carbon components in tropical forest ecosystems is important for reliable estimation of this important regional component of the global carbon cycle. We used the JULES vegetation model to simulate all components of the carbon balance at six sites along an Andes-Amazon transect across Peru and Brazil and compared the results to published field measurements. In the upper montane zone the model predicted a vegetation dieback, indicating a need for better parameterisation of cloud forest vegetation. In the lower montane and lowland zones simulated ecosystem productivity and respiration were predicted with reasonable accuracy, although not always within the error bounds of the observations. Model-predicted carbon use efficiency in this transect surprisingly did not increase with elevation, but remained close to the 'temperate' value 0.5. This may be explained by elevational changes in the balance between growth and maintenance respiration within the forest canopy, as controlled by both temperature- and pressure-mediated processes.

Two-Speed Gearbox Dynamic Simulation Predictions and Test Validation

NASA Technical Reports Server (NTRS)

Lewicki, David G.; DeSmidt, Hans; Smith, Edward C.; Bauman, Steven W.

2010-01-01

Dynamic simulations and experimental validation tests were performed on a two-stage, two-speed gearbox as part of the drive system research activities of the NASA Fundamental Aeronautics Subsonics Rotary Wing Project. The gearbox was driven by two electromagnetic motors and had two electromagnetic, multi-disk clutches to control output speed. A dynamic model of the system was created which included a direct current electric motor with proportional-integral-derivative (PID) speed control, a two-speed gearbox with dual electromagnetically actuated clutches, and an eddy current dynamometer. A six degree-of-freedom model of the gearbox accounted for the system torsional dynamics and included gear, clutch, shaft, and load inertias as well as shaft flexibilities and a dry clutch stick-slip friction model. Experimental validation tests were performed on the gearbox in the NASA Glenn gear noise test facility. Gearbox output speed and torque as well as drive motor speed and current were compared to those from the analytical predictions. The experiments correlate very well with the predictions, thus validating the dynamic simulation methodologies.

Sustained sensorimotor control as intermittent decisions about prediction errors: computational framework and application to ground vehicle steering.

PubMed

Markkula, Gustav; Boer, Erwin; Romano, Richard; Merat, Natasha

2018-06-01

A conceptual and computational framework is proposed for modelling of human sensorimotor control and is exemplified for the sensorimotor task of steering a car. The framework emphasises control intermittency and extends on existing models by suggesting that the nervous system implements intermittent control using a combination of (1) motor primitives, (2) prediction of sensory outcomes of motor actions, and (3) evidence accumulation of prediction errors. It is shown that approximate but useful sensory predictions in the intermittent control context can be constructed without detailed forward models, as a superposition of simple prediction primitives, resembling neurobiologically observed corollary discharges. The proposed mathematical framework allows straightforward extension to intermittent behaviour from existing one-dimensional continuous models in the linear control and ecological psychology traditions. Empirical data from a driving simulator are used in model-fitting analyses to test some of the framework's main theoretical predictions: it is shown that human steering control, in routine lane-keeping and in a demanding near-limit task, is better described as a sequence of discrete stepwise control adjustments, than as continuous control. Results on the possible roles of sensory prediction in control adjustment amplitudes, and of evidence accumulation mechanisms in control onset timing, show trends that match the theoretical predictions; these warrant further investigation. The results for the accumulation-based model align with other recent literature, in a possibly converging case against the type of threshold mechanisms that are often assumed in existing models of intermittent control.

Modeling and control of plasma rotation and βn for NSTX-U using Neoclassical Toroidal Viscosity and Neutral Beam Injection

NASA Astrophysics Data System (ADS)

Goumiri, Imene; Rowley, Clarence; Sabbagh, Steven; Gates, David; Gerhardt, Stefan; Boyer, Mark

2015-11-01

A model-based system is presented allowing control of the plasma rotation profile in a magnetically confined toroidal fusion device to maintain plasma stability for long pulse operation. The analysis, using NSTX data and NSTX-U TRANSP simulations, is aimed at controlling plasma rotation using momentum from six injected neutral beams and neoclassical toroidal viscosity generated by three-dimensional applied magnetic fields as actuators. Based on the momentum diffusion and torque balance model obtained, a feedback controller is designed and predictive simulations using TRANSP will be presented. Robustness of the model and the rotation controller will be discussed.

Energy-efficient container handling using hybrid model predictive control

NASA Astrophysics Data System (ADS)

Xin, Jianbin; Negenborn, Rudy R.; Lodewijks, Gabriel

2015-11-01

The performance of container terminals needs to be improved to adapt the growth of containers while maintaining sustainability. This paper provides a methodology for determining the trajectory of three key interacting machines for carrying out the so-called bay handling task, involving transporting containers between a vessel and the stacking area in an automated container terminal. The behaviours of the interacting machines are modelled as a collection of interconnected hybrid systems. Hybrid model predictive control (MPC) is proposed to achieve optimal performance, balancing the handling capacity and energy consumption. The underlying control problem is hereby formulated as a mixed-integer linear programming problem. Simulation studies illustrate that a higher penalty on energy consumption indeed leads to improved sustainability using less energy. Moreover, simulations illustrate how the proposed energy-efficient hybrid MPC controller performs under different types of uncertainties.

Simulating the evolution of glyphosate resistance in grains farming in northern Australia.

PubMed

Thornby, David F; Walker, Steve R

2009-09-01

The evolution of resistance to herbicides is a substantial problem in contemporary agriculture. Solutions to this problem generally consist of the use of practices to control the resistant population once it evolves, and/or to institute preventative measures before populations become resistant. Herbicide resistance evolves in populations over years or decades, so predicting the effectiveness of preventative strategies in particular relies on computational modelling approaches. While models of herbicide resistance already exist, none deals with the complex regional variability in the northern Australian sub-tropical grains farming region. For this reason, a new computer model was developed. The model consists of an age- and stage-structured population model of weeds, with an existing crop model used to simulate plant growth and competition, and extensions to the crop model added to simulate seed bank ecology and population genetics factors. Using awnless barnyard grass (Echinochloa colona) as a test case, the model was used to investigate the likely rate of evolution under conditions expected to produce high selection pressure. Simulating continuous summer fallows with glyphosate used as the only means of weed control resulted in predicted resistant weed populations after approx. 15 years. Validation of the model against the paddock history for the first real-world glyphosate-resistant awnless barnyard grass population shows that the model predicted resistance evolution to within a few years of the real situation. This validation work shows that empirical validation of herbicide resistance models is problematic. However, the model simulates the complexities of sub-tropical grains farming in Australia well, and can be used to investigate, generate and improve glyphosate resistance prevention strategies.

AAA gunnermodel based on observer theory. [predicting a gunner's tracking response

NASA Technical Reports Server (NTRS)

Kou, R. S.; Glass, B. C.; Day, C. N.; Vikmanis, M. M.

1978-01-01

The Luenberger observer theory is used to develop a predictive model of a gunner's tracking response in antiaircraft artillery systems. This model is composed of an observer, a feedback controller and a remnant element. An important feature of the model is that the structure is simple, hence a computer simulation requires only a short execution time. A parameter identification program based on the least squares curve fitting method and the Gauss Newton gradient algorithm is developed to determine the parameter values of the gunner model. Thus, a systematic procedure exists for identifying model parameters for a given antiaircraft tracking task. Model predictions of tracking errors are compared with human tracking data obtained from manned simulation experiments. Model predictions are in excellent agreement with the empirical data for several flyby and maneuvering target trajectories.

Reasoning about energy in qualitative simulation

NASA Technical Reports Server (NTRS)

Fouche, Pierre; Kuipers, Benjamin J.

1992-01-01

While possible behaviors of a mechanism that are consistent with an incomplete state of knowledge can be predicted through qualitative modeling and simulation, spurious behaviors corresponding to no solution of any ordinary differential equation consistent with the model may be generated. The present method for energy-related reasoning eliminates an important source of spurious behaviors, as demonstrated by its application to a nonlinear, proportional-integral controlled. It is shown that such qualitative properties of such a system as stability and zero-offset control are captured by the simulation.

Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions

PubMed Central

Meyer, Andrew J.; Eskinazi, Ilan; Jackson, Jennifer N.; Rao, Anil V.; Patten, Carolynn; Fregly, Benjamin J.

2016-01-01

Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject’s self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot–ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject’s walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject’s walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject’s walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations. PMID:27790612

Study on Noise Prediction Model and Control Schemes for Substation

PubMed Central

Gao, Yang; Liu, Songtao

2014-01-01

With the government's emphasis on environmental issues of power transmission and transformation project, noise pollution has become a prominent problem now. The noise from the working transformer, reactor, and other electrical equipment in the substation will bring negative effect to the ambient environment. This paper focuses on using acoustic software for the simulation and calculation method to control substation noise. According to the characteristics of the substation noise and the techniques of noise reduction, a substation's acoustic field model was established with the SoundPLAN software to predict the scope of substation noise. On this basis, 4 reasonable noise control schemes were advanced to provide some helpful references for noise control during the new substation's design and construction process. And the feasibility and application effect of these control schemes can be verified by using the method of simulation modeling. The simulation results show that the substation always has the problem of excessive noise at boundary under the conventional measures. The excess noise can be efficiently reduced by taking the corresponding noise reduction methods. PMID:24672356

Design and analysis of a model predictive controller for active queue management.

PubMed

Wang, Ping; Chen, Hong; Yang, Xiaoping; Ma, Yan

2012-01-01

Model predictive (MP) control as a novel active queue management (AQM) algorithm in dynamic computer networks is proposed. According to the predicted future queue length in the data buffer, early packets at the router are dropped reasonably by the MPAQM controller so that the queue length reaches the desired value with minimal tracking error. The drop probability is obtained by optimizing the network performance. Further, randomized algorithms are applied to analyze the robustness of MPAQM successfully, and also to provide the stability domain of systems with uncertain network parameters. The performances of MPAQM are evaluated through a series of simulations in NS2. The simulation results show that the MPAQM algorithm outperforms RED, PI, and REM algorithms in terms of stability, disturbance rejection, and robustness. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Multi-fluid modelling of pulsed discharges for flow control applications

NASA Astrophysics Data System (ADS)

Poggie, J.

2015-02-01

Experimental evidence suggests that short-pulse dielectric barrier discharge actuators are effective for speeds corresponding to take-off and approach of large aircraft, and thus are a fruitful direction for flow control technology development. Large-eddy simulations have reproduced some of the main fluid dynamic effects. The plasma models used in such simulations are semi-empirical, however, and need to be tuned for each flowfield under consideration. In this paper, the discharge physics is examined in more detail with multi-fluid modelling, comparing a five-moment model (continuity, momentum, and energy equations) to a two-moment model (continuity and energy equations). A steady-state, one-dimensional discharge was considered first, and the five-moment model was found to predict significantly lower ionisation rates and number densities than the two-moment model. A two-dimensional, transient discharge problem with an elliptical cathode was studied next. Relative to the two-moment model, the five-moment model predicted a slower response to the activation of the cathode, and lower electron velocities and temperatures as the simulation approached steady-state. The primary reason for the differences in the predictions of the two models can be attributed to the effects of particle inertia, particularly electron inertia in the cathode layer. The computational cost of the five-moment model is only about twice that of the simpler variant, suggesting that it may be feasible to use the more sophisticated model in practical calculations for flow control actuator design.

High-Fidelity Multi-Rotor Unmanned Aircraft System Simulation Development for Trajectory Prediction Under Off-Nominal Flight Dynamics

NASA Technical Reports Server (NTRS)

Foster, John V.; Hartman, David C.

2017-01-01

The NASA Unmanned Aircraft System (UAS) Traffic Management (UTM) project is conducting research to enable civilian low-altitude airspace and UAS operations. A goal of this project is to develop probabilistic methods to quantify risk during failures and off nominal flight conditions. An important part of this effort is the reliable prediction of feasible trajectories during off-nominal events such as control failure, atmospheric upsets, or navigation anomalies that can cause large deviations from the intended flight path or extreme vehicle upsets beyond the normal flight envelope. Few examples of high-fidelity modeling and prediction of off-nominal behavior for small UAS (sUAS) vehicles exist, and modeling requirements for accurately predicting flight dynamics for out-of-envelope or failure conditions are essentially undefined. In addition, the broad range of sUAS aircraft configurations already being fielded presents a significant modeling challenge, as these vehicles are often very different from one another and are likely to possess dramatically different flight dynamics and resultant trajectories and may require different modeling approaches to capture off-nominal behavior. NASA has undertaken an extensive research effort to define sUAS flight dynamics modeling requirements and develop preliminary high fidelity six degree-of-freedom (6-DOF) simulations capable of more closely predicting off-nominal flight dynamics and trajectories. This research has included a literature review of existing sUAS modeling and simulation work as well as development of experimental testing methods to measure and model key components of propulsion, airframe and control characteristics. The ultimate objective of these efforts is to develop tools to support UTM risk analyses and for the real-time prediction of off-nominal trajectories for use in the UTM Risk Assessment Framework (URAF). This paper focuses on modeling and simulation efforts for a generic quad-rotor configuration typical of many commercial vehicles in use today. An overview of relevant off-nominal multi-rotor behaviors will be presented to define modeling goals and to identify the prediction capability lacking in simplified models of multi-rotor performance. A description of recent NASA wind tunnel testing of multi-rotor propulsion and airframe components will be presented illustrating important experimental and data acquisition methods, and a description of preliminary propulsion and airframe models will be presented. Lastly, examples of predicted off-nominal flight dynamics and trajectories from the simulation will be presented.

Benchmarking of dynamic simulation predictions in two software platforms using an upper limb musculoskeletal model

PubMed Central

Saul, Katherine R.; Hu, Xiao; Goehler, Craig M.; Vidt, Meghan E.; Daly, Melissa; Velisar, Anca; Murray, Wendy M.

2014-01-01

Several opensource or commercially available software platforms are widely used to develop dynamic simulations of movement. While computational approaches are conceptually similar across platforms, technical differences in implementation may influence output. We present a new upper limb dynamic model as a tool to evaluate potential differences in predictive behavior between platforms. We evaluated to what extent differences in technical implementations in popular simulation software environments result in differences in kinematic predictions for single and multijoint movements using EMG- and optimization-based approaches for deriving control signals. We illustrate the benchmarking comparison using SIMM-Dynamics Pipeline-SD/Fast and OpenSim platforms. The most substantial divergence results from differences in muscle model and actuator paths. This model is a valuable resource and is available for download by other researchers. The model, data, and simulation results presented here can be used by future researchers to benchmark other software platforms and software upgrades for these two platforms. PMID:24995410

Benchmarking of dynamic simulation predictions in two software platforms using an upper limb musculoskeletal model.

PubMed

Saul, Katherine R; Hu, Xiao; Goehler, Craig M; Vidt, Meghan E; Daly, Melissa; Velisar, Anca; Murray, Wendy M

2015-01-01

Several opensource or commercially available software platforms are widely used to develop dynamic simulations of movement. While computational approaches are conceptually similar across platforms, technical differences in implementation may influence output. We present a new upper limb dynamic model as a tool to evaluate potential differences in predictive behavior between platforms. We evaluated to what extent differences in technical implementations in popular simulation software environments result in differences in kinematic predictions for single and multijoint movements using EMG- and optimization-based approaches for deriving control signals. We illustrate the benchmarking comparison using SIMM-Dynamics Pipeline-SD/Fast and OpenSim platforms. The most substantial divergence results from differences in muscle model and actuator paths. This model is a valuable resource and is available for download by other researchers. The model, data, and simulation results presented here can be used by future researchers to benchmark other software platforms and software upgrades for these two platforms.

Development of a computer-simulation model for a plant-nematode system.

PubMed

Ferris, H

1976-07-01

A computer-simulation model (MELSIM) of a Meloidogyne-grapevine system is developed. The objective is to attempt a holistic approach to the study of nematode population dynamics by using experimental data from controlled environmental conditions. A simulator with predictive ability would be useful in considering pest management alternatives and in teaching. Rates of flow and interaction between the components of the system are governed by environmental conditions. Equations for these rates are determined by fitting curves to data from controlled environment studies. Development of the model and trial simulations have revealed deficiencies in understanding of the system and identified areas where further research is necessary.

A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

NASA Astrophysics Data System (ADS)

Huang, Darong; Bai, Xing-Rong

Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.

Control of Warm Compression Stations Using Model Predictive Control: Simulation and Experimental Results

NASA Astrophysics Data System (ADS)

Bonne, F.; Alamir, M.; Bonnay, P.

2017-02-01

This paper deals with multivariable constrained model predictive control for Warm Compression Stations (WCS). WCSs are subject to numerous constraints (limits on pressures, actuators) that need to be satisfied using appropriate algorithms. The strategy is to replace all the PID loops controlling the WCS with an optimally designed model-based multivariable loop. This new strategy leads to high stability and fast disturbance rejection such as those induced by a turbine or a compressor stop, a key-aspect in the case of large scale cryogenic refrigeration. The proposed control scheme can be used to achieve precise control of pressures in normal operation or to avoid reaching stopping criteria (such as excessive pressures) under high disturbances (such as a pulsed heat load expected to take place in future fusion reactors, expected in the cryogenic cooling systems of the International Thermonuclear Experimental Reactor ITER or the Japan Torus-60 Super Advanced fusion experiment JT-60SA). The paper details the simulator used to validate this new control scheme and the associated simulation results on the SBTs WCS. This work is partially supported through the French National Research Agency (ANR), task agreement ANR-13-SEED-0005.

Equation-based languages – A new paradigm for building energy modeling, simulation and optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wetter, Michael; Bonvini, Marco; Nouidui, Thierry S.

Most of the state-of-the-art building simulation programs implement models in imperative programming languages. This complicates modeling and excludes the use of certain efficient methods for simulation and optimization. In contrast, equation-based modeling languages declare relations among variables, thereby allowing the use of computer algebra to enable much simpler schematic modeling and to generate efficient code for simulation and optimization. We contrast the two approaches in this paper. We explain how such manipulations support new use cases. In the first of two examples, we couple models of the electrical grid, multiple buildings, HVAC systems and controllers to test a controller thatmore » adjusts building room temperatures and PV inverter reactive power to maintain power quality. In the second example, we contrast the computing time for solving an optimal control problem for a room-level model predictive controller with and without symbolic manipulations. As a result, exploiting the equation-based language led to 2, 200 times faster solution« less

Equation-based languages – A new paradigm for building energy modeling, simulation and optimization

DOE PAGES

Wetter, Michael; Bonvini, Marco; Nouidui, Thierry S.

2016-04-01

Most of the state-of-the-art building simulation programs implement models in imperative programming languages. This complicates modeling and excludes the use of certain efficient methods for simulation and optimization. In contrast, equation-based modeling languages declare relations among variables, thereby allowing the use of computer algebra to enable much simpler schematic modeling and to generate efficient code for simulation and optimization. We contrast the two approaches in this paper. We explain how such manipulations support new use cases. In the first of two examples, we couple models of the electrical grid, multiple buildings, HVAC systems and controllers to test a controller thatmore » adjusts building room temperatures and PV inverter reactive power to maintain power quality. In the second example, we contrast the computing time for solving an optimal control problem for a room-level model predictive controller with and without symbolic manipulations. As a result, exploiting the equation-based language led to 2, 200 times faster solution« less

Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction

PubMed Central

Li, Zhencai; Wang, Yang; Liu, Zhen

2016-01-01

The purpose of this work is to investigate the accurate trajectory tracking control of a wheeled mobile robot (WMR) based on the slip model prediction. Generally, a nonholonomic WMR may increase the slippage risk, when traveling on outdoor unstructured terrain (such as longitudinal and lateral slippage of wheels). In order to control a WMR stably and accurately under the effect of slippage, an unscented Kalman filter and neural networks (NNs) are applied to estimate the slip model in real time. This method exploits the model approximating capabilities of nonlinear state–space NN, and the unscented Kalman filter is used to train NN’s weights online. The slip parameters can be estimated and used to predict the time series of deviation velocity, which can be used to compensate control inputs of a WMR. The results of numerical simulation show that the desired trajectory tracking control can be performed by predicting the nonlinear slip model. PMID:27467703

Self-Tuning of Design Variables for Generalized Predictive Control

NASA Technical Reports Server (NTRS)

Lin, Chaung; Juang, Jer-Nan

2000-01-01

Three techniques are introduced to determine the order and control weighting for the design of a generalized predictive controller. These techniques are based on the application of fuzzy logic, genetic algorithms, and simulated annealing to conduct an optimal search on specific performance indexes or objective functions. Fuzzy logic is found to be feasible for real-time and on-line implementation due to its smooth and quick convergence. On the other hand, genetic algorithms and simulated annealing are applicable for initial estimation of the model order and control weighting, and final fine-tuning within a small region of the solution space, Several numerical simulations for a multiple-input and multiple-output system are given to illustrate the techniques developed in this paper.

Robust current control-based generalized predictive control with sliding mode disturbance compensation for PMSM drives.

PubMed

Liu, Xudong; Zhang, Chenghui; Li, Ke; Zhang, Qi

2017-11-01

This paper addresses the current control of permanent magnet synchronous motor (PMSM) for electric drives with model uncertainties and disturbances. A generalized predictive current control method combined with sliding mode disturbance compensation is proposed to satisfy the requirement of fast response and strong robustness. Firstly, according to the generalized predictive control (GPC) theory based on the continuous time model, a predictive current control method is presented without considering the disturbance, which is convenient to be realized in the digital controller. In fact, it's difficult to derive the exact motor model and parameters in the practical system. Thus, a sliding mode disturbance compensation controller is studied to improve the adaptiveness and robustness of the control system. The designed controller attempts to combine the merits of both predictive control and sliding mode control, meanwhile, the controller parameters are easy to be adjusted. Lastly, the proposed controller is tested on an interior PMSM by simulation and experiment, and the results indicate that it has good performance in both current tracking and disturbance rejection. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Control of Systems With Slow Actuators Using Time Scale Separation

NASA Technical Reports Server (NTRS)

Stepanyan, Vehram; Nguyen, Nhan

2009-01-01

This paper addresses the problem of controlling a nonlinear plant with a slow actuator using singular perturbation method. For the known plant-actuator cascaded system the proposed scheme achieves tracking of a given reference model with considerably less control demand than would otherwise result when using conventional design techniques. This is the consequence of excluding the small parameter from the actuator dynamics via time scale separation. The resulting tracking error is within the order of this small parameter. For the unknown system the adaptive counterpart is developed based on the prediction model, which is driven towards the reference model by the control design. It is proven that the prediction model tracks the reference model with an error proportional to the small parameter, while the prediction error converges to zero. The resulting closed-loop system with all prediction models and adaptive laws remains stable. The benefits of the approach are demonstrated in simulation studies and compared to conventional control approaches.

Piloted Simulation Evaluation of a Model-Predictive Automatic Recovery System to Prevent Vehicle Loss of Control on Approach

NASA Technical Reports Server (NTRS)

Litt, Jonathan; Liu, Yuan; Sowers, T. Shane; Owen, A. Karl; Guo, Ten-Huei

2014-01-01

This paper describes a model-predictive automatic recovery system for aircraft on the verge of a loss-of-control situation. The system determines when it must intervene to prevent an imminent accident, resulting from a poor approach. It estimates the altitude loss that would result from a go-around maneuver at the current flight condition. If the loss is projected to violate a minimum altitude threshold, the maneuver is automatically triggered. The system deactivates to allow landing once several criteria are met. Piloted flight simulator evaluation showed the system to provide effective envelope protection during extremely unsafe landing attempts. The results demonstrate how flight and propulsion control can be integrated to recover control of the vehicle automatically and prevent a potential catastrophe.

Output-Feedback Model Predictive Control of a Pasteurization Pilot Plant based on an LPV model

NASA Astrophysics Data System (ADS)

Karimi Pour, Fatemeh; Ocampo-Martinez, Carlos; Puig, Vicenç

2017-01-01

This paper presents a model predictive control (MPC) of a pasteurization pilot plant based on an LPV model. Since not all the states are measured, an observer is also designed, which allows implementing an output-feedback MPC scheme. However, the model of the plant is not completely observable when augmented with the disturbance models. In order to solve this problem, the following strategies are used: (i) the whole system is decoupled into two subsystems, (ii) an inner state-feedback controller is implemented into the MPC control scheme. A real-time example based on the pasteurization pilot plant is simulated as a case study for testing the behavior of the approaches.

Study on model current predictive control method of PV grid- connected inverters systems with voltage sag

NASA Astrophysics Data System (ADS)

Jin, N.; Yang, F.; Shang, S. Y.; Tao, T.; Liu, J. S.

2016-08-01

According to the limitations of the LVRT technology of traditional photovoltaic inverter existed, this paper proposes a low voltage ride through (LVRT) control method based on model current predictive control (MCPC). This method can effectively improve the photovoltaic inverter output characteristics and response speed. The MCPC method of photovoltaic grid-connected inverter designed, the sum of the absolute value of the predictive current and the given current error is adopted as the cost function with the model predictive control method. According to the MCPC, the optimal space voltage vector is selected. Photovoltaic inverter has achieved automatically switches of priority active or reactive power control of two control modes according to the different operating states, which effectively improve the inverter capability of LVRT. The simulation and experimental results proves that the proposed method is correct and effective.

Experimental evaluation of model predictive control and inverse dynamics control for spacecraft proximity and docking maneuvers

NASA Astrophysics Data System (ADS)

Virgili-Llop, Josep; Zagaris, Costantinos; Park, Hyeongjun; Zappulla, Richard; Romano, Marcello

2018-03-01

An experimental campaign has been conducted to evaluate the performance of two different guidance and control algorithms on a multi-constrained docking maneuver. The evaluated algorithms are model predictive control (MPC) and inverse dynamics in the virtual domain (IDVD). A linear-quadratic approach with a quadratic programming solver is used for the MPC approach. A nonconvex optimization problem results from the IDVD approach, and a nonlinear programming solver is used. The docking scenario is constrained by the presence of a keep-out zone, an entry cone, and by the chaser's maximum actuation level. The performance metrics for the experiments and numerical simulations include the required control effort and time to dock. The experiments have been conducted in a ground-based air-bearing test bed, using spacecraft simulators that float over a granite table.

Low-Cloud Feedbacks from Cloud-Controlling Factors: A Review

DOE PAGES

Klein, Stephen A.; Hall, Alex; Norris, Joel R.; ...

2017-10-24

Here, the response to warming of tropical low-level clouds including both marine stratocumulus and trade cumulus is a major source of uncertainty in projections of future climate. Climate model simulations of the response vary widely, reflecting the difficulty the models have in simulating these clouds. These inadequacies have led to alternative approaches to predict low-cloud feedbacks. Here, we review an observational approach that relies on the assumption that observed relationships between low clouds and the “cloud-controlling factors” of the large-scale environment are invariant across time-scales. With this assumption, and given predictions of how the cloud-controlling factors change with climate warming,more » one can predict low-cloud feedbacks without using any model simulation of low clouds. We discuss both fundamental and implementation issues with this approach and suggest steps that could reduce uncertainty in the predicted low-cloud feedback. Recent studies using this approach predict that the tropical low-cloud feedback is positive mainly due to the observation that reflection of solar radiation by low clouds decreases as temperature increases, holding all other cloud-controlling factors fixed. The positive feedback from temperature is partially offset by a negative feedback from the tendency for the inversion strength to increase in a warming world, with other cloud-controlling factors playing a smaller role. A consensus estimate from these studies for the contribution of tropical low clouds to the global mean cloud feedback is 0.25 ± 0.18 W m –2 K –1 (90% confidence interval), suggesting it is very unlikely that tropical low clouds reduce total global cloud feedback. Because the prediction of positive tropical low-cloud feedback with this approach is consistent with independent evidence from low-cloud feedback studies using high-resolution cloud models, progress is being made in reducing this key climate uncertainty.« less

Low-Cloud Feedbacks from Cloud-Controlling Factors: A Review

DOE Office of Scientific and Technical Information (OSTI.GOV)

Klein, Stephen A.; Hall, Alex; Norris, Joel R.

Here, the response to warming of tropical low-level clouds including both marine stratocumulus and trade cumulus is a major source of uncertainty in projections of future climate. Climate model simulations of the response vary widely, reflecting the difficulty the models have in simulating these clouds. These inadequacies have led to alternative approaches to predict low-cloud feedbacks. Here, we review an observational approach that relies on the assumption that observed relationships between low clouds and the “cloud-controlling factors” of the large-scale environment are invariant across time-scales. With this assumption, and given predictions of how the cloud-controlling factors change with climate warming,more » one can predict low-cloud feedbacks without using any model simulation of low clouds. We discuss both fundamental and implementation issues with this approach and suggest steps that could reduce uncertainty in the predicted low-cloud feedback. Recent studies using this approach predict that the tropical low-cloud feedback is positive mainly due to the observation that reflection of solar radiation by low clouds decreases as temperature increases, holding all other cloud-controlling factors fixed. The positive feedback from temperature is partially offset by a negative feedback from the tendency for the inversion strength to increase in a warming world, with other cloud-controlling factors playing a smaller role. A consensus estimate from these studies for the contribution of tropical low clouds to the global mean cloud feedback is 0.25 ± 0.18 W m –2 K –1 (90% confidence interval), suggesting it is very unlikely that tropical low clouds reduce total global cloud feedback. Because the prediction of positive tropical low-cloud feedback with this approach is consistent with independent evidence from low-cloud feedback studies using high-resolution cloud models, progress is being made in reducing this key climate uncertainty.« less

Offset-Free Model Predictive Control of Open Water Channel Based on Moving Horizon Estimation

NASA Astrophysics Data System (ADS)

Ekin Aydin, Boran; Rutten, Martine

2016-04-01

Model predictive control (MPC) is a powerful control option which is increasingly used by operational water managers for managing water systems. The explicit consideration of constraints and multi-objective management are important features of MPC. However, due to the water loss in open water systems by seepage, leakage and evaporation a mismatch between the model and the real system will be created. These mismatch affects the performance of MPC and creates an offset from the reference set point of the water level. We present model predictive control based on moving horizon estimation (MHE-MPC) to achieve offset free control of water level for open water canals. MHE-MPC uses the past predictions of the model and the past measurements of the system to estimate unknown disturbances and the offset in the controlled water level is systematically removed. We numerically tested MHE-MPC on an accurate hydro-dynamic model of the laboratory canal UPC-PAC located in Barcelona. In addition, we also used well known disturbance modeling offset free control scheme for the same test case. Simulation experiments on a single canal reach show that MHE-MPC outperforms disturbance modeling offset free control scheme.

Piloted Simulation of a Model-Predictive Automated Recovery System

NASA Technical Reports Server (NTRS)

Liu, James (Yuan); Litt, Jonathan; Sowers, T. Shane; Owens, A. Karl; Guo, Ten-Huei

2014-01-01

This presentation describes a model-predictive automatic recovery system for aircraft on the verge of a loss-of-control situation. The system determines when it must intervene to prevent an imminent accident, resulting from a poor approach. It estimates the altitude loss that would result from a go-around maneuver at the current flight condition. If the loss is projected to violate a minimum altitude threshold, the maneuver is automatically triggered. The system deactivates to allow landing once several criteria are met. Piloted flight simulator evaluation showed the system to provide effective envelope protection during extremely unsafe landing attempts. The results demonstrate how flight and propulsion control can be integrated to recover control of the vehicle automatically and prevent a potential catastrophe.

Simulating the evolution of glyphosate resistance in grains farming in northern Australia

PubMed Central

Thornby, David F.; Walker, Steve R.

2009-01-01

Background and Aims The evolution of resistance to herbicides is a substantial problem in contemporary agriculture. Solutions to this problem generally consist of the use of practices to control the resistant population once it evolves, and/or to institute preventative measures before populations become resistant. Herbicide resistance evolves in populations over years or decades, so predicting the effectiveness of preventative strategies in particular relies on computational modelling approaches. While models of herbicide resistance already exist, none deals with the complex regional variability in the northern Australian sub-tropical grains farming region. For this reason, a new computer model was developed. Methods The model consists of an age- and stage-structured population model of weeds, with an existing crop model used to simulate plant growth and competition, and extensions to the crop model added to simulate seed bank ecology and population genetics factors. Using awnless barnyard grass (Echinochloa colona) as a test case, the model was used to investigate the likely rate of evolution under conditions expected to produce high selection pressure. Key Results Simulating continuous summer fallows with glyphosate used as the only means of weed control resulted in predicted resistant weed populations after approx. 15 years. Validation of the model against the paddock history for the first real-world glyphosate-resistant awnless barnyard grass population shows that the model predicted resistance evolution to within a few years of the real situation. Conclusions This validation work shows that empirical validation of herbicide resistance models is problematic. However, the model simulates the complexities of sub-tropical grains farming in Australia well, and can be used to investigate, generate and improve glyphosate resistance prevention strategies. PMID:19567415

The management submodel of the Wind Erosion Prediction System

USDA-ARS?s Scientific Manuscript database

The Wind Erosion Prediction System (WEPS) is a process-based, daily time-step, computer model that predicts soil erosion via simulation of the physical processes controlling wind erosion. WEPS is comprised of several individual modules (submodels) that reflect different sets of physical processes, ...

A model predictive current control of flux-switching permanent magnet machines for torque ripple minimization

NASA Astrophysics Data System (ADS)

Huang, Wentao; Hua, Wei; Yu, Feng

2017-05-01

Due to high airgap flux density generated by magnets and the special double salient structure, the cogging torque of the flux-switching permanent magnet (FSPM) machine is considerable, which limits the further applications. Based on the model predictive current control (MPCC) and the compensation control theory, a compensating-current MPCC (CC-MPCC) scheme is proposed and implemented to counteract the dominated components in cogging torque of an existing three-phase 12/10 FSPM prototyped machine, and thus to alleviate the influence of the cogging torque and improve the smoothness of electromagnetic torque as well as speed, where a comprehensive cost function is designed to evaluate the switching states. The simulated results indicate that the proposed CC-MPCC scheme can suppress the torque ripple significantly and offer satisfactory dynamic performances by comparisons with the conventional MPCC strategy. Finally, experimental results validate both the theoretical and simulated predictions.

Method and system for fault accommodation of machines

NASA Technical Reports Server (NTRS)

Goebel, Kai Frank (Inventor); Subbu, Rajesh Venkat (Inventor); Rausch, Randal Thomas (Inventor); Frederick, Dean Kimball (Inventor)

2011-01-01

A method for multi-objective fault accommodation using predictive modeling is disclosed. The method includes using a simulated machine that simulates a faulted actual machine, and using a simulated controller that simulates an actual controller. A multi-objective optimization process is performed, based on specified control settings for the simulated controller and specified operational scenarios for the simulated machine controlled by the simulated controller, to generate a Pareto frontier-based solution space relating performance of the simulated machine to settings of the simulated controller, including adjustment to the operational scenarios to represent a fault condition of the simulated machine. Control settings of the actual controller are adjusted, represented by the simulated controller, for controlling the actual machine, represented by the simulated machine, in response to a fault condition of the actual machine, based on the Pareto frontier-based solution space, to maximize desirable operational conditions and minimize undesirable operational conditions while operating the actual machine in a region of the solution space defined by the Pareto frontier.

Intelligent processing for thick composites

NASA Astrophysics Data System (ADS)

Shin, Daniel Dong-Ok

2000-10-01

Manufacturing thick composite parts are associated with adverse curing conditions such as large in-plane temperature gradient and exotherms. The condition is further aggravated because the manufacturer's cycle and the existing cure control systems do not adequately counter such affects. In response, the forecast-based thermal control system is developed to have better cure control for thick composites. Accurate cure kinetic model is crucial for correctly identifying the amount of heat generated for composite process simulation. A new technique for identifying cure parameters for Hercules AS4/3502 prepreg is presented by normalizing the DSC data. The cure kinetics is based on an autocatalytic model for the proposed method, which uses dynamic and isothermal DSC data to determine its parameters. Existing models are also used to determine kinetic parameters but rendered inadequate because of the material's temperature dependent final degree of cure. The model predictions determined from the new technique showed good agreement to both isothermal and dynamic DSC data. The final degree of cure was also in good agreement with experimental data. A realistic cure simulation model including bleeder ply analysis and compaction is validated with Hercules AS4/3501-6 based laminates. The nonsymmetrical temperature distribution resulting from the presence of bleeder plies agreed well to the model prediction. Some of the discrepancies in the predicted compaction behavior were attributed to inaccurate viscosity and permeability models. The temperature prediction was quite good for the 3cm laminate. The validated process simulation model along with cure kinetics model for AS4/3502 prepreg were integrated into the thermal control system. The 3cm Hercules AS4/3501-6 and AS4/3502 laminate were fabricated. The resulting cure cycles satisfied all imposed requirements by minimizing exotherms and temperature gradient. Although the duration of the cure cycles increased, such phenomena was inevitable since longer time was required to maintain acceptable temperature gradient. The derived cure cycles were slightly different than what was anticipated by the offline simulation. Nevertheless, the system adapted to unanticipated events to satisfy the cure requirements.

Predicting the effectiveness of depth-based technologies to prevent salmon lice infection using a dispersal model.

PubMed

Samsing, Francisca; Johnsen, Ingrid; Stien, Lars Helge; Oppedal, Frode; Albretsen, Jon; Asplin, Lars; Dempster, Tim

2016-07-01

Salmon lice is one of the major parasitic problems affecting wild and farmed salmonid species. The planktonic larval stages of these marine parasites can survive for extended periods without a host and are transported long distances by water masses. Salmon lice larvae have limited swimming capacity, but can influence their horizontal transport by vertical positioning. Here, we adapted a coupled biological-physical model to calculate the distribution of farm-produced salmon lice (Lepeophtheirus salmonis) during winter in the southwest coast of Norway. We tested 4 model simulations to see which best represented empirical data from two sources: (1) observed lice infection levels reported by farms; and (2) experimental data from a vertical exposure experiment where fish were forced to swim at different depths with a lice-barrier technology. Model simulations tested were different development time to the infective stage (35 or 50°-days), with or without the presence of temperature-controlled vertical behaviour of lice early planktonic stages (naupliar stages). The best model fit occurred with a 35°-day development time to the infective stage, and temperature-controlled vertical behaviour. We applied this model to predict the effectiveness of depth-based preventive lice-barrier technologies. Both simulated and experimental data revealed that hindering fish from swimming close to the surface efficiently reduced lice infection. Moreover, while our model simulation predicted that this preventive technology is widely applicable, its effectiveness will depend on environmental conditions. Low salinity surface waters reduce the effectiveness of this technology because salmon lice avoid these conditions, and can encounter the fish as they sink deeper in the water column. Correctly parameterized and validated salmon lice dispersal models can predict the impact of preventive approaches to control this parasite and become an essential tool in lice management strategies. Copyright © 2016 Elsevier B.V. All rights reserved.

Reynolds-Averaged Navier-Stokes Simulation of a 2D Circulation Control Wind Tunnel Experiment

NASA Technical Reports Server (NTRS)

Allan, Brian G.; Jones, Greg; Lin, John C.

2011-01-01

Numerical simulations are performed using a Reynolds-averaged Navier-Stokes (RANS) flow solver for a circulation control airfoil. 2D and 3D simulation results are compared to a circulation control wind tunnel test conducted at the NASA Langley Basic Aerodynamics Research Tunnel (BART). The RANS simulations are compared to a low blowing case with a jet momentum coefficient, C(sub u), of 0:047 and a higher blowing case of 0.115. Three dimensional simulations of the model and tunnel walls show wall effects on the lift and airfoil surface pressures. These wall effects include a 4% decrease of the midspan sectional lift for the C(sub u) 0.115 blowing condition. Simulations comparing the performance of the Spalart Allmaras (SA) and Shear Stress Transport (SST) turbulence models are also made, showing the SST model compares best to the experimental data. A Rotational/Curvature Correction (RCC) to the turbulence model is also evaluated demonstrating an improvement in the CFD predictions.

Visual Attention to Radar Displays

NASA Technical Reports Server (NTRS)

Moray, N.; Richards, M.; Brophy, C.

1984-01-01

A model is described which predicts the allocation of attention to the features of a radar display. It uses the growth of uncertainty and the probability of near collision to call the eye to a feature of the display. The main source of uncertainty is forgetting following a fixation, which is modelled as a two dimensional diffusion process. The model was used to predict information overload in intercept controllers, and preliminary validation obtained by recording eye movements of intercept controllers in simulated and live (practice) interception.

A System for Integrated Reliability and Safety Analyses

NASA Technical Reports Server (NTRS)

Kostiuk, Peter; Shapiro, Gerald; Hanson, Dave; Kolitz, Stephan; Leong, Frank; Rosch, Gene; Coumeri, Marc; Scheidler, Peter, Jr.; Bonesteel, Charles

1999-01-01

We present an integrated reliability and aviation safety analysis tool. The reliability models for selected infrastructure components of the air traffic control system are described. The results of this model are used to evaluate the likelihood of seeing outcomes predicted by simulations with failures injected. We discuss the design of the simulation model, and the user interface to the integrated toolset.

Optimal Control Based Stiffness Identification of an Ankle-Foot Orthosis Using a Predictive Walking Model

PubMed Central

Sreenivasa, Manish; Millard, Matthew; Felis, Martin; Mombaur, Katja; Wolf, Sebastian I.

2017-01-01

Predicting the movements, ground reaction forces and neuromuscular activity during gait can be a valuable asset to the clinical rehabilitation community, both to understand pathology, as well as to plan effective intervention. In this work we use an optimal control method to generate predictive simulations of pathological gait in the sagittal plane. We construct a patient-specific model corresponding to a 7-year old child with gait abnormalities and identify the optimal spring characteristics of an ankle-foot orthosis that minimizes muscle effort. Our simulations include the computation of foot-ground reaction forces, as well as the neuromuscular dynamics using computationally efficient muscle torque generators and excitation-activation equations. The optimal control problem (OCP) is solved with a direct multiple shooting method. The solution of this problem is physically consistent synthetic neural excitation commands, muscle activations and whole body motion. Our simulations produced similar changes to the gait characteristics as those recorded on the patient. The orthosis-equipped model was able to walk faster with more extended knees. Notably, our approach can be easily tuned to simulate weakened muscles, produces physiologically realistic ground reaction forces and smooth muscle activations and torques, and can be implemented on a standard workstation to produce results within a few hours. These results are an important contribution toward bridging the gap between research methods in computational neuromechanics and day-to-day clinical rehabilitation. PMID:28450833

Nonlinear predictive control for durability enhancement and efficiency improvement in a fuel cell power system

NASA Astrophysics Data System (ADS)

Luna, Julio; Jemei, Samir; Yousfi-Steiner, Nadia; Husar, Attila; Serra, Maria; Hissel, Daniel

2016-10-01

In this work, a nonlinear model predictive control (NMPC) strategy is proposed to improve the efficiency and enhance the durability of a proton exchange membrane fuel cell (PEMFC) power system. The PEMFC controller is based on a distributed parameters model that describes the nonlinear dynamics of the system, considering spatial variations along the gas channels. Parasitic power from different system auxiliaries is considered, including the main parasitic losses which are those of the compressor. A nonlinear observer is implemented, based on the discretised model of the PEMFC, to estimate the internal states. This information is included in the cost function of the controller to enhance the durability of the system by means of avoiding local starvation and inappropriate water vapour concentrations. Simulation results are presented to show the performance of the proposed controller over a given case study in an automotive application (New European Driving Cycle). With the aim of representing the most relevant phenomena that affects the PEMFC voltage, the simulation model includes a two-phase water model and the effects of liquid water on the catalyst active area. The control model is a simplified version that does not consider two-phase water dynamics.

Using a hybrid model to predict solute transfer from initially saturated soil into surface runoff with controlled drainage water.

PubMed

Tong, Juxiu; Hu, Bill X; Yang, Jinzhong; Zhu, Yan

2016-06-01

The mixing layer theory is not suitable for predicting solute transfer from initially saturated soil to surface runoff water under controlled drainage conditions. By coupling the mixing layer theory model with the numerical model Hydrus-1D, a hybrid solute transfer model has been proposed to predict soil solute transfer from an initially saturated soil into surface water, under controlled drainage water conditions. The model can also consider the increasing ponding water conditions on soil surface before surface runoff. The data of solute concentration in surface runoff and drainage water from a sand experiment is used as the reference experiment. The parameters for the water flow and solute transfer model and mixing layer depth under controlled drainage water condition are identified. Based on these identified parameters, the model is applied to another initially saturated sand experiment with constant and time-increasing mixing layer depth after surface runoff, under the controlled drainage water condition with lower drainage height at the bottom. The simulation results agree well with the observed data. Study results suggest that the hybrid model can accurately simulate the solute transfer from initially saturated soil into surface runoff under controlled drainage water condition. And it has been found that the prediction with increasing mixing layer depth is better than that with the constant one in the experiment with lower drainage condition. Since lower drainage condition and deeper ponded water depth result in later runoff start time, more solute sources in the mixing layer are needed for the surface water, and larger change rate results in the increasing mixing layer depth.

Numerical Study Comparing RANS and LES Approaches on a Circulation Control Airfoil

NASA Technical Reports Server (NTRS)

Rumsey, Christopher L.; Nishino, Takafumi

2011-01-01

A numerical study over a nominally two-dimensional circulation control airfoil is performed using a large-eddy simulation code and two Reynolds-averaged Navier-Stokes codes. Different Coanda jet blowing conditions are investigated. In addition to investigating the influence of grid density, a comparison is made between incompressible and compressible flow solvers. The incompressible equations are found to yield negligible differences from the compressible equations up to at least a jet exit Mach number of 0.64. The effects of different turbulence models are also studied. Models that do not account for streamline curvature effects tend to predict jet separation from the Coanda surface too late, and can produce non-physical solutions at high blowing rates. Three different turbulence models that account for streamline curvature are compared with each other and with large eddy simulation solutions. All three models are found to predict the Coanda jet separation location reasonably well, but one of the models predicts specific flow field details near the Coanda surface prior to separation much better than the other two. All Reynolds-averaged Navier-Stokes computations produce higher circulation than large eddy simulation computations, with different stagnation point location and greater flow acceleration around the nose onto the upper surface. The precise reasons for the higher circulation are not clear, although it is not solely a function of predicting the jet separation location correctly.

Deployment Simulation Methods for Ultra-Lightweight Inflatable Structures

NASA Technical Reports Server (NTRS)

Wang, John T.; Johnson, Arthur R.

2003-01-01

Two dynamic inflation simulation methods are employed for modeling the deployment of folded thin-membrane tubes. The simulations are necessary because ground tests include gravity effects and may poorly represent deployment in space. The two simulation methods are referred to as the Control Volume (CV) method and the Arbitrary Lagrangian Eulerian (ALE) method. They are available in the LS-DYNA nonlinear dynamic finite element code. Both methods are suitable for modeling the interactions between the inflation gas and the thin-membrane tube structures. The CV method only considers the pressure induced by the inflation gas in the simulation, while the ALE method models the actual flow of the inflation gas. Thus, the transient fluid properties at any location within the tube can be predicted by the ALE method. Deployment simulations of three packaged tube models; namely coiled, Z-folded, and telescopically-folded configurations, are performed. Results predicted by both methods for the telescopically-folded configuration are correlated and computational efficiency issues are discussed.

Analysis of explicit model predictive control for path-following control

PubMed Central

2018-01-01

In this paper, explicit Model Predictive Control(MPC) is employed for automated lane-keeping systems. MPC has been regarded as the key to handle such constrained systems. However, the massive computational complexity of MPC, which employs online optimization, has been a major drawback that limits the range of its target application to relatively small and/or slow problems. Explicit MPC can reduce this computational burden using a multi-parametric quadratic programming technique(mp-QP). The control objective is to derive an optimal front steering wheel angle at each sampling time so that autonomous vehicles travel along desired paths, including straight, circular, and clothoid parts, at high entry speeds. In terms of the design of the proposed controller, a method of choosing weighting matrices in an optimization problem and the range of horizons for path-following control are described through simulations. For the verification of the proposed controller, simulation results obtained using other control methods such as MPC, Linear-Quadratic Regulator(LQR), and driver model are employed, and CarSim, which reflects the features of a vehicle more realistically than MATLAB/Simulink, is used for reliable demonstration. PMID:29534080

Analysis of explicit model predictive control for path-following control.

PubMed

Lee, Junho; Chang, Hyuk-Jun

2018-01-01

In this paper, explicit Model Predictive Control(MPC) is employed for automated lane-keeping systems. MPC has been regarded as the key to handle such constrained systems. However, the massive computational complexity of MPC, which employs online optimization, has been a major drawback that limits the range of its target application to relatively small and/or slow problems. Explicit MPC can reduce this computational burden using a multi-parametric quadratic programming technique(mp-QP). The control objective is to derive an optimal front steering wheel angle at each sampling time so that autonomous vehicles travel along desired paths, including straight, circular, and clothoid parts, at high entry speeds. In terms of the design of the proposed controller, a method of choosing weighting matrices in an optimization problem and the range of horizons for path-following control are described through simulations. For the verification of the proposed controller, simulation results obtained using other control methods such as MPC, Linear-Quadratic Regulator(LQR), and driver model are employed, and CarSim, which reflects the features of a vehicle more realistically than MATLAB/Simulink, is used for reliable demonstration.

Forecasting the Water Demand in Chongqing, China Using a Grey Prediction Model and Recommendations for the Sustainable Development of Urban Water Consumption.

PubMed

Wu, Hua'an; Zeng, Bo; Zhou, Meng

2017-11-15

High accuracy in water demand predictions is an important basis for the rational allocation of city water resources and forms the basis for sustainable urban development. The shortage of water resources in Chongqing, the youngest central municipality in Southwest China, has significantly increased with the population growth and rapid economic development. In this paper, a new grey water-forecasting model (GWFM) was built based on the data characteristics of water consumption. The parameter estimation and error checking methods of the GWFM model were investigated. Then, the GWFM model was employed to simulate the water demands of Chongqing from 2009 to 2015 and forecast it in 2016. The simulation and prediction errors of the GWFM model was checked, and the results show the GWFM model exhibits better simulation and prediction precisions than those of the classical Grey Model with one variable and single order equation GM(1,1) for short and the frequently-used Discrete Grey Model with one variable and single order equation, DGM(1,1) for short. Finally, the water demand in Chongqing from 2017 to 2022 was forecasted, and some corresponding control measures and recommendations were provided based on the prediction results to ensure a viable water supply and promote the sustainable development of the Chongqing economy.

Feedback control design for non-inductively sustained scenarios in NSTX-U using TRANSP

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boyer, M. D.; Andre, R. G.; Gates, D. A.

This paper examines a method for real-time control of non-inductively sustained scenarios in NSTX-U by using TRANSP, a time-dependent integrated modeling code for prediction and interpretive analysis of tokamak experimental data, as a simulator. The actuators considered for control in this work are the six neutral beam sources and the plasma boundary shape. To understand the response of the plasma current, stored energy, and central safety factor to these actuators and to enable systematic design of control algorithms, simulations were run in which the actuators were modulated and a linearized dynamic response model was generated. A multi-variable model-based control schememore » that accounts for the coupling and slow dynamics of the system while mitigating the effect of actuator limitations was designed and simulated. Simulations show that modest changes in the outer gap and heating power can improve the response time of the system, reject perturbations, and track target values of the controlled values.« less

Feedback control design for non-inductively sustained scenarios in NSTX-U using TRANSP

DOE PAGES

Boyer, M. D.; Andre, R. G.; Gates, D. A.; ...

2017-04-24

This paper examines a method for real-time control of non-inductively sustained scenarios in NSTX-U by using TRANSP, a time-dependent integrated modeling code for prediction and interpretive analysis of tokamak experimental data, as a simulator. The actuators considered for control in this work are the six neutral beam sources and the plasma boundary shape. To understand the response of the plasma current, stored energy, and central safety factor to these actuators and to enable systematic design of control algorithms, simulations were run in which the actuators were modulated and a linearized dynamic response model was generated. A multi-variable model-based control schememore » that accounts for the coupling and slow dynamics of the system while mitigating the effect of actuator limitations was designed and simulated. Simulations show that modest changes in the outer gap and heating power can improve the response time of the system, reject perturbations, and track target values of the controlled values.« less

Feedback control design for non-inductively sustained scenarios in NSTX-U using TRANSP

NASA Astrophysics Data System (ADS)

Boyer, M. D.; Andre, R. G.; Gates, D. A.; Gerhardt, S. P.; Menard, J. E.; Poli, F. M.

2017-06-01

This paper examines a method for real-time control of non-inductively sustained scenarios in NSTX-U by using TRANSP, a time-dependent integrated modeling code for prediction and interpretive analysis of tokamak experimental data, as a simulator. The actuators considered for control in this work are the six neutral beam sources and the plasma boundary shape. To understand the response of the plasma current, stored energy, and central safety factor to these actuators and to enable systematic design of control algorithms, simulations were run in which the actuators were modulated and a linearized dynamic response model was generated. A multi-variable model-based control scheme that accounts for the coupling and slow dynamics of the system while mitigating the effect of actuator limitations was designed and simulated. Simulations show that modest changes in the outer gap and heating power can improve the response time of the system, reject perturbations, and track target values of the controlled values.

Combined Molecular Dynamics Simulation-Molecular-Thermodynamic Theory Framework for Predicting Surface Tensions.

PubMed

Sresht, Vishnu; Lewandowski, Eric P; Blankschtein, Daniel; Jusufi, Arben

2017-08-22

A molecular modeling approach is presented with a focus on quantitative predictions of the surface tension of aqueous surfactant solutions. The approach combines classical Molecular Dynamics (MD) simulations with a molecular-thermodynamic theory (MTT) [ Y. J. Nikas, S. Puvvada, D. Blankschtein, Langmuir 1992 , 8 , 2680 ]. The MD component is used to calculate thermodynamic and molecular parameters that are needed in the MTT model to determine the surface tension isotherm. The MD/MTT approach provides the important link between the surfactant bulk concentration, the experimental control parameter, and the surfactant surface concentration, the MD control parameter. We demonstrate the capability of the MD/MTT modeling approach on nonionic alkyl polyethylene glycol surfactants at the air-water interface and observe reasonable agreement of the predicted surface tensions and the experimental surface tension data over a wide range of surfactant concentrations below the critical micelle concentration. Our modeling approach can be extended to ionic surfactants and their mixtures with both ionic and nonionic surfactants at liquid-liquid interfaces.

Planning Models for Tuberculosis Control Programs

PubMed Central

Chorba, Ronald W.; Sanders, J. L.

1971-01-01

A discrete-state, discrete-time simulation model of tuberculosis is presented, with submodels of preventive interventions. The model allows prediction of the prevalence of the disease over the simulation period. Preventive and control programs and their optimal budgets may be planned by using the model for cost-benefit analysis: costs are assigned to the program components and disease outcomes to determine the ratio of program expenditures to future savings on medical and socioeconomic costs of tuberculosis. Optimization is achieved by allocating funds in successive increments to alternative program components in simulation and identifying those components that lead to the greatest reduction in prevalence for the given level of expenditure. The method is applied to four hypothetical disease prevalence situations. PMID:4999448

Predicting pedestrian flow: a methodology and a proof of concept based on real-life data.

PubMed

Davidich, Maria; Köster, Gerta

2013-01-01

Building a reliable predictive model of pedestrian motion is very challenging: Ideally, such models should be based on observations made in both controlled experiments and in real-world environments. De facto, models are rarely based on real-world observations due to the lack of available data; instead, they are largely based on intuition and, at best, literature values and laboratory experiments. Such an approach is insufficient for reliable simulations of complex real-life scenarios: For instance, our analysis of pedestrian motion under natural conditions at a major German railway station reveals that the values for free-flow velocities and the flow-density relationship differ significantly from widely used literature values. It is thus necessary to calibrate and validate the model against relevant real-life data to make it capable of reproducing and predicting real-life scenarios. In this work we aim at constructing such realistic pedestrian stream simulation. Based on the analysis of real-life data, we present a methodology that identifies key parameters and interdependencies that enable us to properly calibrate the model. The success of the approach is demonstrated for a benchmark model, a cellular automaton. We show that the proposed approach significantly improves the reliability of the simulation and hence the potential prediction accuracy. The simulation is validated by comparing the local density evolution of the measured data to that of the simulated data. We find that for our model the most sensitive parameters are: the source-target distribution of the pedestrian trajectories, the schedule of pedestrian appearances in the scenario and the mean free-flow velocity. Our results emphasize the need for real-life data extraction and analysis to enable predictive simulations.

High-Performance Integrated Control of water quality and quantity in urban water reservoirs

NASA Astrophysics Data System (ADS)

Galelli, S.; Castelletti, A.; Goedbloed, A.

2015-11-01

This paper contributes a novel High-Performance Integrated Control framework to support the real-time operation of urban water supply storages affected by water quality problems. We use a 3-D, high-fidelity simulation model to predict the main water quality dynamics and inform a real-time controller based on Model Predictive Control. The integration of the simulation model into the control scheme is performed by a model reduction process that identifies a low-order, dynamic emulator running 4 orders of magnitude faster. The model reduction, which relies on a semiautomatic procedural approach integrating time series clustering and variable selection algorithms, generates a compact and physically meaningful emulator that can be coupled with the controller. The framework is used to design the hourly operation of Marina Reservoir, a 3.2 Mm3 storm-water-fed reservoir located in the center of Singapore, operated for drinking water supply and flood control. Because of its recent formation from a former estuary, the reservoir suffers from high salinity levels, whose behavior is modeled with Delft3D-FLOW. Results show that our control framework reduces the minimum salinity levels by nearly 40% and cuts the average annual deficit of drinking water supply by about 2 times the active storage of the reservoir (about 4% of the total annual demand).

Design of Accelerator Online Simulator Server Using Structured Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shen, Guobao; /Brookhaven; Chu, Chungming

2012-07-06

Model based control plays an important role for a modern accelerator during beam commissioning, beam study, and even daily operation. With a realistic model, beam behaviour can be predicted and therefore effectively controlled. The approach used by most current high level application environments is to use a built-in simulation engine and feed a realistic model into that simulation engine. Instead of this traditional monolithic structure, a new approach using a client-server architecture is under development. An on-line simulator server is accessed via network accessible structured data. With this approach, a user can easily access multiple simulation codes. This paper describesmore » the design, implementation, and current status of PVData, which defines the structured data, and PVAccess, which provides network access to the structured data.« less

Modeling Lignin Polymerization. I. Simulation Model of Dehydrogenation Polymers1[OA

PubMed Central

van Parijs, Frederik R.D.; Morreel, Kris; Ralph, John; Boerjan, Wout; Merks, Roeland M.H.

2010-01-01

Lignin is a heteropolymer that is thought to form in the cell wall by combinatorial radical coupling of monolignols. Here, we present a simulation model of in vitro lignin polymerization, based on the combinatorial coupling theory, which allows us to predict the reaction conditions controlling the primary structure of lignin polymers. Our model predicts two controlling factors for the β-O-4 content of syringyl-guaiacyl lignins: the supply rate of monolignols and the relative amount of supplied sinapyl alcohol monomers. We have analyzed the in silico degradability of the resulting lignin polymers by cutting the resulting lignin polymers at β-O-4 bonds. These are cleaved in analytical methods used to study lignin composition, namely thioacidolysis and derivatization followed by reductive cleavage, under pulping conditions, and in some lignocellulosic biomass pretreatments. PMID:20472753

Demand response-enabled model predictive HVAC load control in buildings using real-time electricity pricing

NASA Astrophysics Data System (ADS)

Avci, Mesut

A practical cost and energy efficient model predictive control (MPC) strategy is proposed for HVAC load control under dynamic real-time electricity pricing. The MPC strategy is built based on a proposed model that jointly minimizes the total energy consumption and hence, cost of electricity for the user, and the deviation of the inside temperature from the consumer's preference. An algorithm that assigns temperature set-points (reference temperatures) to price ranges based on the consumer's discomfort tolerance index is developed. A practical parameter prediction model is also designed for mapping between the HVAC load and the inside temperature. The prediction model and the produced temperature set-points are integrated as inputs into the MPC controller, which is then used to generate signal actions for the AC unit. To investigate and demonstrate the effectiveness of the proposed approach, a simulation based experimental analysis is presented using real-life pricing data. An actual prototype for the proposed HVAC load control strategy is then built and a series of prototype experiments are conducted similar to the simulation studies. The experiments reveal that the MPC strategy can lead to significant reductions in overall energy consumption and cost savings for the consumer. Results suggest that by providing an efficient response strategy for the consumers, the proposed MPC strategy can enable the utility providers to adopt efficient demand management policies using real-time pricing. Finally, a cost-benefit analysis is performed to display the economic feasibility of implementing such a controller as part of a building energy management system, and the payback period is identified considering cost of prototype build and cost savings to help the adoption of this controller in the building HVAC control industry.

A mobile-mobile transport model for simulating reactive transport in connected heterogeneous fields

NASA Astrophysics Data System (ADS)

Lu, Chunhui; Wang, Zhiyuan; Zhao, Yue; Rathore, Saubhagya Singh; Huo, Jinge; Tang, Yuening; Liu, Ming; Gong, Rulan; Cirpka, Olaf A.; Luo, Jian

2018-05-01

Mobile-immobile transport models can be effective in reproducing heavily tailed breakthrough curves of concentration. However, such models may not adequately describe transport along multiple flow paths with intermediate velocity contrasts in connected fields. We propose using the mobile-mobile model for simulating subsurface flow and associated mixing-controlled reactive transport in connected fields. This model includes two local concentrations, one in the fast- and the other in the slow-flow domain, which predict both the concentration mean and variance. The normalized total concentration variance within the flux is found to be a non-monotonic function of the discharge ratio with a maximum concentration variance at intermediate values of the discharge ratio. We test the mobile-mobile model for mixing-controlled reactive transport with an instantaneous, irreversible bimolecular reaction in structured and connected random heterogeneous domains, and compare the performance of the mobile-mobile to the mobile-immobile model. The results indicate that the mobile-mobile model generally predicts the concentration breakthrough curves (BTCs) of the reactive compound better. Particularly, for cases of an elliptical inclusion with intermediate hydraulic-conductivity contrasts, where the travel-time distribution shows bimodal behavior, the prediction of both the BTCs and maximum product concentration is significantly improved. Our results exemplify that the conceptual model of two mobile domains with diffusive mass transfer in between is in general good for predicting mixing-controlled reactive transport, and particularly so in cases where the transfer in the low-conductivity zones is by slow advection rather than diffusion.

OISI dynamic end-to-end modeling tool

NASA Astrophysics Data System (ADS)

Kersten, Michael; Weidler, Alexander; Wilhelm, Rainer; Johann, Ulrich A.; Szerdahelyi, Laszlo

2000-07-01

The OISI Dynamic end-to-end modeling tool is tailored to end-to-end modeling and dynamic simulation of Earth- and space-based actively controlled optical instruments such as e.g. optical stellar interferometers. `End-to-end modeling' is meant to denote the feature that the overall model comprises besides optical sub-models also structural, sensor, actuator, controller and disturbance sub-models influencing the optical transmission, so that the system- level instrument performance due to disturbances and active optics can be simulated. This tool has been developed to support performance analysis and prediction as well as control loop design and fine-tuning for OISI, Germany's preparatory program for optical/infrared spaceborne interferometry initiated in 1994 by Dornier Satellitensysteme GmbH in Friedrichshafen.

Tailored high-resolution numerical weather forecasts for energy efficient predictive building control

NASA Astrophysics Data System (ADS)

Stauch, V. J.; Gwerder, M.; Gyalistras, D.; Oldewurtel, F.; Schubiger, F.; Steiner, P.

2010-09-01

The high proportion of the total primary energy consumption by buildings has increased the public interest in the optimisation of buildings' operation and is also driving the development of novel control approaches for the indoor climate. In this context, the use of weather forecasts presents an interesting and - thanks to advances in information and predictive control technologies and the continuous improvement of numerical weather prediction (NWP) models - an increasingly attractive option for improved building control. Within the research project OptiControl (www.opticontrol.ethz.ch) predictive control strategies for a wide range of buildings, heating, ventilation and air conditioning (HVAC) systems, and representative locations in Europe are being investigated with the aid of newly developed modelling and simulation tools. Grid point predictions for radiation, temperature and humidity of the high-resolution limited area NWP model COSMO-7 (see www.cosmo-model.org) and local measurements are used as disturbances and inputs into the building system. The control task considered consists in minimizing energy consumption whilst maintaining occupant comfort. In this presentation, we use the simulation-based OptiControl methodology to investigate the impact of COSMO-7 forecasts on the performance of predictive building control and the resulting energy savings. For this, we have selected building cases that were shown to benefit from a prediction horizon of up to 3 days and therefore, are particularly suitable for the use of numerical weather forecasts. We show that the controller performance is sensitive to the quality of the weather predictions, most importantly of the incident radiation on differently oriented façades. However, radiation is characterised by a high temporal and spatial variability in part caused by small scale and fast changing cloud formation and dissolution processes being only partially represented in the COSMO-7 grid point predictions. On the other hand, buildings are affected by particularly local weather conditions at the building site. To overcome this discrepancy, we make use of local measurements to statistically adapt the COSMO-7 model output to the meteorological conditions at the building. For this, we have developed a general correction algorithm that exploits systematic properties of the COSMO-7 prediction error and explicitly estimates the degree of temporal autocorrelation using online recursive estimation. The resulting corrected predictions are improved especially for the first few hours being the most crucial for the predictive controller and, ultimately for the reduction of primary energy consumption using predictive control. The use of numerical weather forecasts in predictive building automation is one example in a wide field of weather dependent advanced energy saving technologies. Our work particularly highlights the need for the development of specifically tailored weather forecast products by (statistical) postprocessing in order to meet the requirements of specific applications.

An equilibrium-point model of electromyographic patterns during single-joint movements based on experimentally reconstructed control signals.

PubMed

Latash, M L; Goodman, S R

1994-01-01

The purpose of this work has been to develop a model of electromyographic (EMG) patterns during single-joint movements based on a version of the equilibrium-point hypothesis, a method for experimental reconstruction of the joint compliant characteristics, the dual-strategy hypothesis, and a kinematic model of movement trajectory. EMG patterns are considered emergent properties of hypothetical control patterns that are equally affected by the control signals and peripheral feedback reflecting actual movement trajectory. A computer model generated the EMG patterns based on simulated movement kinematics and hypothetical control signals derived from the reconstructed joint compliant characteristics. The model predictions have been compared to published recordings of movement kinematics and EMG patterns in a variety of movement conditions, including movements over different distances, at different speeds, against different-known inertial loads, and in conditions of possible unexpected decrease in the inertial load. Changes in task parameters within the model led to simulated EMG patterns qualitatively similar to the experimentally recorded EMG patterns. The model's predictive power compares it favourably to the existing models of the EMG patterns. Copyright © 1994. Published by Elsevier Ltd.

Simulation for analysis and control of superplastic forming. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zacharia, T.; Aramayo, G.A.; Simunovic, S.

1996-08-01

A joint study was conducted by Oak Ridge National Laboratory (ORNL) and the Pacific Northwest Laboratory (PNL) for the U.S. Department of Energy-Lightweight Materials (DOE-LWM) Program. the purpose of the study was to assess and benchmark the current modeling capabilities with respect to accuracy of predictions and simulation time. Two modeling capabilities with respect to accuracy of predictions and simulation time. Two simulation platforms were considered in this study, which included the LS-DYNA3D code installed on ORNL`s high- performance computers and the finite element code MARC used at PNL. both ORNL and PNL performed superplastic forming (SPF) analysis on amore » standard butter-tray geometry, which was defined by PNL, to better understand the capabilities of the respective models. The specific geometry was selected and formed at PNL, and the experimental results, such as forming time and thickness at specific locations, were provided for comparisons with numerical predictions. Furthermore, comparisons between the ORNL simulation results, using elasto-plastic analysis, and PNL`s results, using rigid-plastic flow analysis, were performed.« less

Dynamics Modeling and Simulation of Large Transport Airplanes in Upset Conditions

NASA Technical Reports Server (NTRS)

Foster, John V.; Cunningham, Kevin; Fremaux, Charles M.; Shah, Gautam H.; Stewart, Eric C.; Rivers, Robert A.; Wilborn, James E.; Gato, William

2005-01-01

As part of NASA's Aviation Safety and Security Program, research has been in progress to develop aerodynamic modeling methods for simulations that accurately predict the flight dynamics characteristics of large transport airplanes in upset conditions. The motivation for this research stems from the recognition that simulation is a vital tool for addressing loss-of-control accidents, including applications to pilot training, accident reconstruction, and advanced control system analysis. The ultimate goal of this effort is to contribute to the reduction of the fatal accident rate due to loss-of-control. Research activities have involved accident analyses, wind tunnel testing, and piloted simulation. Results have shown that significant improvements in simulation fidelity for upset conditions, compared to current training simulations, can be achieved using state-of-the-art wind tunnel testing and aerodynamic modeling methods. This paper provides a summary of research completed to date and includes discussion on key technical results, lessons learned, and future research needs.

Data-driven modeling and predictive control for boiler-turbine unit using fuzzy clustering and subspace methods.

PubMed

Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y

2014-05-01

This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Verification of an analytic modeler for capillary pump loop thermal control systems

NASA Technical Reports Server (NTRS)

Schweickart, R. B.; Neiswanger, L.; Ku, J.

1987-01-01

A number of computer programs have been written to model two-phase heat transfer systems for space use. These programs support the design of thermal control systems and provide a method of predicting their performance in the wide range of thermal environments of space. Predicting the performance of one such system known as the capillary pump loop (CPL) is the intent of the CPL Modeler. By modeling two developed CPL systems and comparing the results with actual test data, the CPL Modeler has proven useful in simulating CPL operation. Results of the modeling effort are discussed, together with plans for refinements to the modeler.

Bond-valence methods for pKa prediction. II. Bond-valence, electrostatic, molecular geometry, and solvation effects

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bickmore, Barry R.; Rosso, Kevin M.; Tadanier, Christopher J.

2006-08-15

In a previous contribution, we outlined a method for predicting (hydr)oxy-acid and oxide surface acidity constants based on three main factors: bond valence, Me?O bond ionicity, and molecular shape. Here electrostatics calculations and ab initio molecular dynamics simulations are used to qualitatively show that Me?O bond ionicity controls the extent to which the electrostatic work of proton removal departs from ideality, bond valence controls the extent of solvation of individual functional groups, and bond valence and molecular shape controls local dielectric response. These results are consistent with our model of acidity, but completely at odds with other methods of predictingmore » acidity constants for use in multisite complexation models. In particular, our ab initio molecular dynamics simulations of solvated monomers clearly indicate that hydrogen bonding between (hydr)oxo-groups and water molecules adjusts to obey the valence sum rule, rather than maintaining a fixed valence based on the coordination of the oxygen atom as predicted by the standard MUSIC model.« less

Further validation of artificial neural network-based emissions simulation models for conventional and hybrid electric vehicles.

PubMed

Tóth-Nagy, Csaba; Conley, John J; Jarrett, Ronald P; Clark, Nigel N

2006-07-01

With the advent of hybrid electric vehicles, computer-based vehicle simulation becomes more useful to the engineer and designer trying to optimize the complex combination of control strategy, power plant, drive train, vehicle, and driving conditions. With the desire to incorporate emissions as a design criterion, researchers at West Virginia University have developed artificial neural network (ANN) models for predicting emissions from heavy-duty vehicles. The ANN models were trained on engine and exhaust emissions data collected from transient dynamometer tests of heavy-duty diesel engines then used to predict emissions based on engine speed and torque data from simulated operation of a tractor truck and hybrid electric bus. Simulated vehicle operation was performed with the ADVISOR software package. Predicted emissions (carbon dioxide [CO2] and oxides of nitrogen [NO(x)]) were then compared with actual emissions data collected from chassis dynamometer tests of similar vehicles. This paper expands on previous research to include different driving cycles for the hybrid electric bus and varying weights of the conventional truck. Results showed that different hybrid control strategies had a significant effect on engine behavior (and, thus, emissions) and may affect emissions during different driving cycles. The ANN models underpredicted emissions of CO2 and NO(x) in the case of a class-8 truck but were more accurate as the truck weight increased.

Adaptive Data-based Predictive Control for Short Take-off and Landing (STOL) Aircraft

NASA Technical Reports Server (NTRS)

Barlow, Jonathan Spencer; Acosta, Diana Michelle; Phan, Minh Q.

2010-01-01

Data-based Predictive Control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. The characteristics of adaptive data-based predictive control are particularly appropriate for the control of nonlinear and time-varying systems, such as Short Take-off and Landing (STOL) aircraft. STOL is a capability of interest to NASA because conceptual Cruise Efficient Short Take-off and Landing (CESTOL) transport aircraft offer the ability to reduce congestion in the terminal area by utilizing existing shorter runways at airports, as well as to lower community noise by flying steep approach and climb-out patterns that reduce the noise footprint of the aircraft. In this study, adaptive data-based predictive control is implemented as an integrated flight-propulsion controller for the outer-loop control of a CESTOL-type aircraft. Results show that the controller successfully tracks velocity while attempting to maintain a constant flight path angle, using longitudinal command, thrust and flap setting as the control inputs.

Optimal strategy analysis based on robust predictive control for inventory system with random demand

NASA Astrophysics Data System (ADS)

Saputra, Aditya; Widowati, Sutrisno

2017-12-01

In this paper, the optimal strategy for a single product single supplier inventory system with random demand is analyzed by using robust predictive control with additive random parameter. We formulate the dynamical system of this system as a linear state space with additive random parameter. To determine and analyze the optimal strategy for the given inventory system, we use robust predictive control approach which gives the optimal strategy i.e. the optimal product volume that should be purchased from the supplier for each time period so that the expected cost is minimal. A numerical simulation is performed with some generated random inventory data. We simulate in MATLAB software where the inventory level must be controlled as close as possible to a set point decided by us. From the results, robust predictive control model provides the optimal strategy i.e. the optimal product volume that should be purchased and the inventory level was followed the given set point.

Predictive control strategies for wind turbine system based on permanent magnet synchronous generator.

PubMed

Maaoui-Ben Hassine, Ikram; Naouar, Mohamed Wissem; Mrabet-Bellaaj, Najiba

2016-05-01

In this paper, Model Predictive Control and Dead-beat predictive control strategies are proposed for the control of a PMSG based wind energy system. The proposed MPC considers the model of the converter-based system to forecast the possible future behavior of the controlled variables. It allows selecting the voltage vector to be applied that leads to a minimum error by minimizing a predefined cost function. The main features of the MPC are low current THD and robustness against parameters variations. The Dead-beat predictive control is based on the system model to compute the optimum voltage vector that ensures zero-steady state error. The optimum voltage vector is then applied through Space Vector Modulation (SVM) technique. The main advantages of the Dead-beat predictive control are low current THD and constant switching frequency. The proposed control techniques are presented and detailed for the control of back-to-back converter in a wind turbine system based on PMSG. Simulation results (under Matlab-Simulink software environment tool) and experimental results (under developed prototyping platform) are presented in order to show the performances of the considered control strategies. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

On Application of Model Predictive Control to Power Converter with Switching

NASA Astrophysics Data System (ADS)

Zanma, Tadanao; Fukuta, Junichi; Doki, Shinji; Ishida, Muneaki; Okuma, Shigeru; Matsumoto, Takashi; Nishimori, Eiji

This paper concerns a DC-DC converter control. In DC-DC converters, there exist both continuous components such as inductance, conductance and resistance and discrete ones, IGBT and MOSFET as semiconductor switching elements. Such a system can be regarded as a hybrid dynamical system. Thus, this paper presents a dc-dc control technique based on the model predictive control. Specifically, a case in which the load of the dc-dc converter changes from active to sleep is considered. In the case, a control method which makes the output voltage follow to the reference quickly in transition, and the switching frequency be constant in steady state. In addition, in applying the model predictive control to power electronics circuits, the switching characteristic of the device and the restriction condition for protection are also considered. The effectiveness of the proposed method is illustrated by comparing a conventional method through some simulation results.

Time Sharing Between Robotics and Process Control: Validating a Model of Attention Switching.

PubMed

Wickens, Christopher Dow; Gutzwiller, Robert S; Vieane, Alex; Clegg, Benjamin A; Sebok, Angelia; Janes, Jess

2016-03-01

The aim of this study was to validate the strategic task overload management (STOM) model that predicts task switching when concurrence is impossible. The STOM model predicts that in overload, tasks will be switched to, to the extent that they are attractive on task attributes of high priority, interest, and salience and low difficulty. But more-difficult tasks are less likely to be switched away from once they are being performed. In Experiment 1, participants performed four tasks of the Multi-Attribute Task Battery and provided task-switching data to inform the role of difficulty and priority. In Experiment 2, participants concurrently performed an environmental control task and a robotic arm simulation. Workload was varied by automation of arm movement and both the phases of environmental control and existence of decision support for fault management. Attention to the two tasks was measured using a head tracker. Experiment 1 revealed the lack of influence of task priority and confirmed the differing roles of task difficulty. In Experiment 2, the percentage attention allocation across the eight conditions was predicted by the STOM model when participants rated the four attributes. Model predictions were compared against empirical data and accounted for over 95% of variance in task allocation. More-difficult tasks were performed longer than easier tasks. Task priority does not influence allocation. The multiattribute decision model provided a good fit to the data. The STOM model is useful for predicting cognitive tunneling given that human-in-the-loop simulation is time-consuming and expensive. © 2016, Human Factors and Ergonomics Society.

Correlations in state space can cause sub-optimal adaptation of optimal feedback control models.

PubMed

Aprasoff, Jonathan; Donchin, Opher

2012-04-01

Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedback control (OFC) framework of control theory. OFC is a powerful tool for describing motor control, it does not describe adaptation. Some assume that adaptation of the forward model alone could explain motor adaptation, but this is widely understood to be overly simplistic. However, an adaptive optimal controller is difficult to implement. A reasonable alternative is to allow forward model adaptation to 're-tune' the controller. Our simulations show that, as expected, forward model adaptation alone does not produce optimal trajectories during reaching movements perturbed by force fields. However, they also show that re-optimizing the controller from the forward model can be sub-optimal. This is because, in a system with state correlations or redundancies, accurate prediction requires different information than optimal control. We find that adding noise to the movements that matches noise found in human data is enough to overcome this problem. However, since the state space for control of real movements is far more complex than in our simple simulations, the effects of correlations on re-adaptation of the controller from the forward model cannot be overlooked.

A Closed-Loop Model of Operator Visual Attention, Situation Awareness, and Performance Across Automation Mode Transitions.

PubMed

Johnson, Aaron W; Duda, Kevin R; Sheridan, Thomas B; Oman, Charles M

2017-03-01

This article describes a closed-loop, integrated human-vehicle model designed to help understand the underlying cognitive processes that influenced changes in subject visual attention, mental workload, and situation awareness across control mode transitions in a simulated human-in-the-loop lunar landing experiment. Control mode transitions from autopilot to manual flight may cause total attentional demands to exceed operator capacity. Attentional resources must be reallocated and reprioritized, which can increase the average uncertainty in the operator's estimates of low-priority system states. We define this increase in uncertainty as a reduction in situation awareness. We present a model built upon the optimal control model for state estimation, the crossover model for manual control, and the SEEV (salience, effort, expectancy, value) model for visual attention. We modify the SEEV attention executive to direct visual attention based, in part, on the uncertainty in the operator's estimates of system states. The model was validated using the simulated lunar landing experimental data, demonstrating an average difference in the percentage of attention ≤3.6% for all simulator instruments. The model's predictions of mental workload and situation awareness, measured by task performance and system state uncertainty, also mimicked the experimental data. Our model supports the hypothesis that visual attention is influenced by the uncertainty in system state estimates. Conceptualizing situation awareness around the metric of system state uncertainty is a valuable way for system designers to understand and predict how reallocations in the operator's visual attention during control mode transitions can produce reallocations in situation awareness of certain states.

A systems approach to college drinking: development of a deterministic model for testing alcohol control policies.

PubMed

Scribner, Richard; Ackleh, Azmy S; Fitzpatrick, Ben G; Jacquez, Geoffrey; Thibodeaux, Jeremy J; Rommel, Robert; Simonsen, Neal

2009-09-01

The misuse and abuse of alcohol among college students remain persistent problems. Using a systems approach to understand the dynamics of student drinking behavior and thus forecasting the impact of campus policy to address the problem represents a novel approach. Toward this end, the successful development of a predictive mathematical model of college drinking would represent a significant advance for prevention efforts. A deterministic, compartmental model of college drinking was developed, incorporating three processes: (1) individual factors, (2) social interactions, and (3) social norms. The model quantifies these processes in terms of the movement of students between drinking compartments characterized by five styles of college drinking: abstainers, light drinkers, moderate drinkers, problem drinkers, and heavy episodic drinkers. Predictions from the model were first compared with actual campus-level data and then used to predict the effects of several simulated interventions to address heavy episodic drinking. First, the model provides a reasonable fit of actual drinking styles of students attending Social Norms Marketing Research Project campuses varying by "wetness" and by drinking styles of matriculating students. Second, the model predicts that a combination of simulated interventions targeting heavy episodic drinkers at a moderately "dry" campus would extinguish heavy episodic drinkers, replacing them with light and moderate drinkers. Instituting the same combination of simulated interventions at a moderately "wet" campus would result in only a moderate reduction in heavy episodic drinkers (i.e., 50% to 35%). A simple, five-state compartmental model adequately predicted the actual drinking patterns of students from a variety of campuses surveyed in the Social Norms Marketing Research Project study. The model predicted the impact on drinking patterns of several simulated interventions to address heavy episodic drinking on various types of campuses.

A Systems Approach to College Drinking: Development of a Deterministic Model for Testing Alcohol Control Policies*

PubMed Central

Scribner, Richard; Ackleh, Azmy S.; Fitzpatrick, Ben G.; Jacquez, Geoffrey; Thibodeaux, Jeremy J.; Rommel, Robert; Simonsen, Neal

2009-01-01

Objective: The misuse and abuse of alcohol among college students remain persistent problems. Using a systems approach to understand the dynamics of student drinking behavior and thus forecasting the impact of campus policy to address the problem represents a novel approach. Toward this end, the successful development of a predictive mathematical model of college drinking would represent a significant advance for prevention efforts. Method: A deterministic, compartmental model of college drinking was developed, incorporating three processes: (1) individual factors, (2) social interactions, and (3) social norms. The model quantifies these processes in terms of the movement of students between drinking compartments characterized by five styles of college drinking: abstainers, light drinkers, moderate drinkers, problem drinkers, and heavy episodic drinkers. Predictions from the model were first compared with actual campus-level data and then used to predict the effects of several simulated interventions to address heavy episodic drinking. Results: First, the model provides a reasonable fit of actual drinking styles of students attending Social Norms Marketing Research Project campuses varying by “wetness” and by drinking styles of matriculating students. Second, the model predicts that a combination of simulated interventions targeting heavy episodic drinkers at a moderately “dry” campus would extinguish heavy episodic drinkers, replacing them with light and moderate drinkers. Instituting the same combination of simulated interventions at a moderately “wet” campus would result in only a moderate reduction in heavy episodic drinkers (i.e., 50% to 35%). Conclusions: A simple, five-state compartmental model adequately predicted the actual drinking patterns of students from a variety of campuses surveyed in the Social Norms Marketing Research Project study. The model predicted the impact on drinking patterns of several simulated interventions to address heavy episodic drinking on various types of campuses. PMID:19737506

Phase Distribution Phenomena for Simulated Microgravity Conditions: Experimental Work

NASA Technical Reports Server (NTRS)

Singhal, Maneesh; Bonetto, Fabian J.; Lahey, R. T., Jr.

1996-01-01

This report summarizes the work accomplished at Rensselaer to study phase distribution phenomenon under simulated microgravity conditions. Our group at Rensselaer has been able to develop sophisticated analytical models to predict phase distribution in two-phase flows under a variety of conditions. These models are based on physics and data obtained from carefully controlled experiments that are being conducted here. These experiments also serve to verify the models developed.

Phase Distribution Phenomena for Simulated Microgravity Conditions: Experimental Work

NASA Technical Reports Server (NTRS)

Singhal, Maneesh; Bonetto, Fabian J.; Lahey, R. T., Jr.

1996-01-01

This report summarizes the work accomplished at Rensselaer to study phase distribution phenomenon under simulated microgravity conditions. Our group at Rensselaer has been able to develop sophisticated analytical models to predict phase distribution in two-phase flows under variety of conditions. These models are based on physics and data obtained from carefully controlled experiments that are being conducted here. These experiments also serve to verify the models developed.

Adaptive model-predictive controller for magnetic resonance guided focused ultrasound therapy.

PubMed

de Bever, Joshua; Todd, Nick; Payne, Allison; Christensen, Douglas A; Roemer, Robert B

2014-11-01

Minimising treatment time and protecting healthy tissues are conflicting goals that play major roles in making magnetic resonance image-guided focused ultrasound (MRgFUS) therapies clinically practical. We have developed and tested in vivo an adaptive model-predictive controller (AMPC) that reduces treatment time, ensures safety and efficacy, and provides flexibility in treatment set-up. The controller realises time savings by modelling the heated treatment cell's future temperatures and thermal dose accumulation in order to anticipate the optimal time to switch to the next cell. Selected tissues are safeguarded by a configurable temperature constraint. Simulations quantified the time savings realised by each controller feature as well as the trade-offs between competing safety and treatment time parameters. In vivo experiments in rabbit thighs established the controller's effectiveness and reliability. In all in vivo experiments the target thermal dose of at least 240 CEM43 was delivered everywhere in the treatment volume. The controller's temperature safety limit reliably activated and constrained all protected tissues to <9 CEM43. Simulations demonstrated the path independence of the controller, and that a path which successively proceeds to the hottest untreated neighbouring cell leads to significant time savings, e.g. when compared to a concentric spiral path. Use of the AMPC produced a compounding time-saving effect; reducing the treatment cells' heating times concurrently reduced heating of normal tissues, which eliminated cooling periods. Adaptive model-predictive control can automatically deliver safe, effective MRgFUS treatments while significantly reducing treatment times.

A plasma rotation control scheme for NSTX and NSTX-U

NASA Astrophysics Data System (ADS)

Goumiri, Imene

2016-10-01

Plasma rotation has been proven to play a key role in stabilizing large scale instabilities and improving plasma confinement by suppressing micro-turbulence. A model-based feedback system which controls the plasma rotation profile on the National Spherical Torus Experiment (NSTX) and its upgrade (NSTX-U) is presented. The first part of this work uses experimental measurements from NSTX as a starting point and models the control of plasma rotation using two different types of actuation: momentum from injected neutral beams and neoclassical toroidal viscosity generated by three-dimensional applied magnetic fields. Whether based on the data-driven model for NSTX or purely predictive modeling for NSTX-U, a reduced order model based feedback controller was designed. Predictive simulations using the TRANSP plasma transport code with the actuator input determined by the controller (controller-in-the-loop) show that the controller drives the plasma's rotation to the desired profiles in less than 100 ms given practical constraints on the actuators and the available real-time rotation measurements. This is the first time that TRANSP has been used as a plasma in simulator in a closed feedback loop test. Another approach to control simultaneously the toroidal rotation profile as well as βN is then shown for NSTX-U. For this case, the neutral beams (actuators) have been augmented in the modeling to match the upgrade version which spread the injection throughout the edge of the plasma. Control robustness in stability and performance has then been tested and used to predict the limits of the resulting controllers when the energy confinement time (τE) and the momentum diffusivity coefficient (χϕ) vary.

Evidence for Emergency Vaccination Having Played a Crucial Role to Control the 1965/66 Foot-and-Mouth Disease Outbreak in Switzerland

PubMed Central

Zingg, Dana; Häsler, Stephan; Schuepbach-Regula, Gertraud; Schwermer, Heinzpeter; Dürr, Salome

2015-01-01

Foot-and-mouth disease (FMD) is a highly contagious disease that caused several large outbreaks in Europe in the last century. The last important outbreak in Switzerland took place in 1965/66 and affected more than 900 premises and more than 50,000 animals were slaughtered. Large-scale emergency vaccination of the cattle and pig population has been applied to control the epidemic. In recent years, many studies have used infectious disease models to assess the impact of different disease control measures, including models developed for diseases exotic for the specific region of interest. Often, the absence of real outbreak data makes a validation of such models impossible. This study aimed to evaluate whether a spatial, stochastic simulation model (the Davis Animal Disease Simulation model) can predict the course of a Swiss FMD epidemic based on the available historic input data on population structure, contact rates, epidemiology of the virus, and quality of the vaccine. In addition, the potential outcome of the 1965/66 FMD epidemic without application of vaccination was investigated. Comparing the model outcomes to reality, only the largest 10% of the simulated outbreaks approximated the number of animals being culled. However, the simulation model highly overestimated the number of culled premises. While the outbreak duration could not be well reproduced by the model compared to the 1965/66 epidemic, it was able to accurately estimate the size of the area infected. Without application of vaccination, the model predicted a much higher mean number of culled animals than with vaccination, demonstrating that vaccination was likely crucial in disease control for the Swiss FMD outbreak in 1965/66. The study demonstrated the feasibility to analyze historical outbreak data with modern analytical tools. However, it also confirmed that predicted epidemics from a most carefully parameterized model cannot integrate all eventualities of a real epidemic. Therefore, decision makers need to be aware that infectious disease models are useful tools to support the decision-making process but their results are not equal valuable as real observations and should always be interpreted with caution. PMID:26697436

Network congestion control algorithm based on Actor-Critic reinforcement learning model

NASA Astrophysics Data System (ADS)

Xu, Tao; Gong, Lina; Zhang, Wei; Li, Xuhong; Wang, Xia; Pan, Wenwen

2018-04-01

Aiming at the network congestion control problem, a congestion control algorithm based on Actor-Critic reinforcement learning model is designed. Through the genetic algorithm in the congestion control strategy, the network congestion problems can be better found and prevented. According to Actor-Critic reinforcement learning, the simulation experiment of network congestion control algorithm is designed. The simulation experiments verify that the AQM controller can predict the dynamic characteristics of the network system. Moreover, the learning strategy is adopted to optimize the network performance, and the dropping probability of packets is adaptively adjusted so as to improve the network performance and avoid congestion. Based on the above finding, it is concluded that the network congestion control algorithm based on Actor-Critic reinforcement learning model can effectively avoid the occurrence of TCP network congestion.

Control Theory and Statistical Generalizations.

ERIC Educational Resources Information Center

Powers, William T.

1990-01-01

Contrasts modeling methods in control theory to the methods of statistical generalizations in empirical studies of human or animal behavior. Presents a computer simulation that predicts behavior based on variables (effort and rewards) determined by the invariable (desired reward). Argues that control theory methods better reflect relationships to…

Modeling the rate-controlled sorption of hexavalent chromium

USGS Publications Warehouse

Grove, D.B.; Stollenwerk, K.G.

1985-01-01

Sorption of chromium VI on the iron-oxide- and hydroxide-coated surface of alluvial material was numerically simulated with rate-controlled reactions. Reaction kinetics and diffusional processes, in the form of film, pore, and particle diffusion, were simulated and compared with experimental results. The use of empirically calculated rate coefficients for diffusion through the reacting surface was found to simulate experimental data; pore or particle diffusion is believed to be a possible rate-controlling mechanism. The use of rate equations to predict conservative transport and rate- and local-equilibrium-controlled reactions was shown to be feasible.

Forecasting the Water Demand in Chongqing, China Using a Grey Prediction Model and Recommendations for the Sustainable Development of Urban Water Consumption

PubMed Central

Wu, Hua’an; Zhou, Meng

2017-01-01

High accuracy in water demand predictions is an important basis for the rational allocation of city water resources and forms the basis for sustainable urban development. The shortage of water resources in Chongqing, the youngest central municipality in Southwest China, has significantly increased with the population growth and rapid economic development. In this paper, a new grey water-forecasting model (GWFM) was built based on the data characteristics of water consumption. The parameter estimation and error checking methods of the GWFM model were investigated. Then, the GWFM model was employed to simulate the water demands of Chongqing from 2009 to 2015 and forecast it in 2016. The simulation and prediction errors of the GWFM model was checked, and the results show the GWFM model exhibits better simulation and prediction precisions than those of the classical Grey Model with one variable and single order equation GM(1,1) for short and the frequently-used Discrete Grey Model with one variable and single order equation, DGM(1,1) for short. Finally, the water demand in Chongqing from 2017 to 2022 was forecasted, and some corresponding control measures and recommendations were provided based on the prediction results to ensure a viable water supply and promote the sustainable development of the Chongqing economy. PMID:29140266

Enhancing BEM simulations of a stalled wind turbine using a 3D correction model

NASA Astrophysics Data System (ADS)

Bangga, Galih; Hutomo, Go; Syawitri, Taurista; Kusumadewi, Tri; Oktavia, Winda; Sabila, Ahmad; Setiadi, Herlambang; Faisal, Muhamad; Hendranata, Yongki; Lastomo, Dwi; Putra, Louis; Kristiadi, Stefanus; Bumi, Ilmi

2018-03-01

Nowadays wind turbine rotors are usually employed with pitch control mechanisms to avoid deep stall conditions. Despite that, wind turbines often operate under pitch fault situation causing massive flow separation to occur. Pure Blade Element Momentum (BEM) approaches are not designed for this situation and inaccurate load predictions are already expected. In the present studies, BEM predictions are improved through the inclusion of a stall delay model for a wind turbine rotor operating under pitch fault situation of -2.3° towards stall. The accuracy of the stall delay model is assessed by comparing the results with available Computational Fluid Dynamics (CFD) simulations data.

Geochemical modeling of leaching of Ca, Mg, Al, and Pb from cementitious waste forms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martens, E., E-mail: evelien.martens@csiro.a; Jacques, D.; Van Gerven, T.

2010-08-15

Results from extraction tests on cement-waste samples were simulated with a thermodynamic equilibrium model using a consistent database, to which lead data were added. Subsequent diffusion tests were modeled by means of a 3D diffusive transport model combined with the geochemical model derived from the extraction tests. Modeling results of the leached major element concentrations for both uncarbonated and (partially) carbonated samples agreed well with the extraction test using the set of pure minerals and solid solutions present in the database. The observed decrease in Ca leaching with increasing carbonation level was qualitatively predicted. Simulations also revealed that Pb leachingmore » is not controlled by dissolution/precipitation only. The addition of the calcite-cerrusite solid solution and adsorption reactions on amorphous Fe- and Al-oxides improved the predictions and are considered to control the Pb leaching during the extractions tests. The dynamic diffusive leaching tests were appropriately modeled for Na, K, Ca and Pb.« less

Development of a model to simulate infection dynamics of Mycobacterium bovis in cattle herds in the United States

PubMed Central

Smith, Rebecca L.; Schukken, Ynte H.; Lu, Zhao; Mitchell, Rebecca M.; Grohn, Yrjo T.

2013-01-01

Objective To develop a mathematical model to simulate infection dynamics of Mycobacterium bovis in cattle herds in the United States and predict efficacy of the current national control strategy for tuberculosis in cattle. Design Stochastic simulation model. Sample Theoretical cattle herds in the United States. Procedures A model of within-herd M bovis transmission dynamics following introduction of 1 latently infected cow was developed. Frequency- and density-dependent transmission modes and 3 tuberculin-test based culling strategies (no test-based culling, constant (annual) testing with test-based culling, and the current strategy of slaughterhouse detection-based testing and culling) were investigated. Results were evaluated for 3 herd sizes over a 10-year period and validated via simulation of known outbreaks of M bovis infection. Results On the basis of 1,000 simulations (1000 herds each) at replacement rates typical for dairy cattle (0.33/y), median time to detection of M bovis infection in medium-sized herds (276 adult cattle) via slaughterhouse surveillance was 27 months after introduction, and 58% of these herds would spontaneously clear the infection prior to that time. Sixty-two percent of medium-sized herds without intervention and 99% of those managed with constant test-based culling were predicted to clear infection < 10 years after introduction. The model predicted observed outbreaks best for frequency-dependent transmission, and probability of clearance was most sensitive to replacement rate. Conclusions and Clinical Relevance Although modeling indicated the current national control strategy was sufficient for elimination of M bovis infection from dairy herds after detection, slaughterhouse surveillance was not sufficient to detect M bovis infection in all herds and resulted in subjectively delayed detection, compared with the constant testing method. Further research is required to economically optimize this strategy. PMID:23865885

A predictive control framework for optimal energy extraction of wind farms

NASA Astrophysics Data System (ADS)

Vali, M.; van Wingerden, J. W.; Boersma, S.; Petrović, V.; Kühn, M.

2016-09-01

This paper proposes an adjoint-based model predictive control for optimal energy extraction of wind farms. It employs the axial induction factor of wind turbines to influence their aerodynamic interactions through the wake. The performance index is defined here as the total power production of the wind farm over a finite prediction horizon. A medium-fidelity wind farm model is utilized to predict the inflow propagation in advance. The adjoint method is employed to solve the formulated optimization problem in a cost effective way and the first part of the optimal solution is implemented over the control horizon. This procedure is repeated at the next controller sample time providing the feedback into the optimization. The effectiveness and some key features of the proposed approach are studied for a two turbine test case through simulations.

Verification of component mode techniques for flexible multibody systems

NASA Technical Reports Server (NTRS)

Wiens, Gloria J.

1990-01-01

Investigations were conducted in the modeling aspects of flexible multibodies undergoing large angular displacements. Models were to be generated and analyzed through application of computer simulation packages employing the 'component mode synthesis' techniques. Multibody Modeling, Verification and Control Laboratory (MMVC) plan was implemented, which includes running experimental tests on flexible multibody test articles. From these tests, data was to be collected for later correlation and verification of the theoretical results predicted by the modeling and simulation process.

Model predictive control of an air suspension system with damping multi-mode switching damper based on hybrid model

NASA Astrophysics Data System (ADS)

Sun, Xiaoqiang; Yuan, Chaochun; Cai, Yingfeng; Wang, Shaohua; Chen, Long

2017-09-01

This paper presents the hybrid modeling and the model predictive control of an air suspension system with damping multi-mode switching damper. Unlike traditional damper with continuously adjustable damping, in this study, a new damper with four discrete damping modes is applied to vehicle semi-active air suspension. The new damper can achieve different damping modes by just controlling the on-off statuses of two solenoid valves, which makes its damping adjustment more efficient and more reliable. However, since the damping mode switching induces different modes of operation, the air suspension system with the new damper poses challenging hybrid control problem. To model both the continuous/discrete dynamics and the switching between different damping modes, the framework of mixed logical dynamical (MLD) systems is used to establish the system hybrid model. Based on the resulting hybrid dynamical model, the system control problem is recast as a model predictive control (MPC) problem, which allows us to optimize the switching sequences of the damping modes by taking into account the suspension performance requirements. Numerical simulations results demonstrate the efficacy of the proposed control method finally.

Representation of fine scale atmospheric variability in a nudged limited area quasi-geostrophic model: application to regional climate modelling

NASA Astrophysics Data System (ADS)

Omrani, H.; Drobinski, P.; Dubos, T.

2009-09-01

In this work, we consider the effect of indiscriminate nudging time on the large and small scales of an idealized limited area model simulation. The limited area model is a two layer quasi-geostrophic model on the beta-plane driven at its boundaries by its « global » version with periodic boundary condition. This setup mimics the configuration used for regional climate modelling. Compared to a previous study by Salameh et al. (2009) who investigated the existence of an optimal nudging time minimizing the error on both large and small scale in a linear model, we here use a fully non-linear model which allows us to represent the chaotic nature of the atmosphere: given the perfect quasi-geostrophic model, errors in the initial conditions, concentrated mainly in the smaller scales of motion, amplify and cascade into the larger scales, eventually resulting in a prediction with low skill. To quantify the predictability of our quasi-geostrophic model, we measure the rate of divergence of the system trajectories in phase space (Lyapunov exponent) from a set of simulations initiated with a perturbation of a reference initial state. Predictability of the "global", periodic model is mostly controlled by the beta effect. In the LAM, predictability decreases as the domain size increases. Then, the effect of large-scale nudging is studied by using the "perfect model” approach. Two sets of experiments were performed: (1) the effect of nudging is investigated with a « global » high resolution two layer quasi-geostrophic model driven by a low resolution two layer quasi-geostrophic model. (2) similar simulations are conducted with the two layer quasi-geostrophic LAM where the size of the LAM domain comes into play in addition to the first set of simulations. In the two sets of experiments, the best spatial correlation between the nudge simulation and the reference is observed with a nudging time close to the predictability time.

The Mixed Instrumental Controller: Using Value of Information to Combine Habitual Choice and Mental Simulation

PubMed Central

Pezzulo, Giovanni; Rigoli, Francesco; Chersi, Fabian

2013-01-01

Instrumental behavior depends on both goal-directed and habitual mechanisms of choice. Normative views cast these mechanisms in terms of model-free and model-based methods of reinforcement learning, respectively. An influential proposal hypothesizes that model-free and model-based mechanisms coexist and compete in the brain according to their relative uncertainty. In this paper we propose a novel view in which a single Mixed Instrumental Controller produces both goal-directed and habitual behavior by flexibly balancing and combining model-based and model-free computations. The Mixed Instrumental Controller performs a cost-benefits analysis to decide whether to chose an action immediately based on the available “cached” value of actions (linked to model-free mechanisms) or to improve value estimation by mentally simulating the expected outcome values (linked to model-based mechanisms). Since mental simulation entails cognitive effort and increases the reward delay, it is activated only when the associated “Value of Information” exceeds its costs. The model proposes a method to compute the Value of Information, based on the uncertainty of action values and on the distance of alternative cached action values. Overall, the model by default chooses on the basis of lighter model-free estimates, and integrates them with costly model-based predictions only when useful. Mental simulation uses a sampling method to produce reward expectancies, which are used to update the cached value of one or more actions; in turn, this updated value is used for the choice. The key predictions of the model are tested in different settings of a double T-maze scenario. Results are discussed in relation with neurobiological evidence on the hippocampus – ventral striatum circuit in rodents, which has been linked to goal-directed spatial navigation. PMID:23459512

The mixed instrumental controller: using value of information to combine habitual choice and mental simulation.

PubMed

Pezzulo, Giovanni; Rigoli, Francesco; Chersi, Fabian

2013-01-01

Instrumental behavior depends on both goal-directed and habitual mechanisms of choice. Normative views cast these mechanisms in terms of model-free and model-based methods of reinforcement learning, respectively. An influential proposal hypothesizes that model-free and model-based mechanisms coexist and compete in the brain according to their relative uncertainty. In this paper we propose a novel view in which a single Mixed Instrumental Controller produces both goal-directed and habitual behavior by flexibly balancing and combining model-based and model-free computations. The Mixed Instrumental Controller performs a cost-benefits analysis to decide whether to chose an action immediately based on the available "cached" value of actions (linked to model-free mechanisms) or to improve value estimation by mentally simulating the expected outcome values (linked to model-based mechanisms). Since mental simulation entails cognitive effort and increases the reward delay, it is activated only when the associated "Value of Information" exceeds its costs. The model proposes a method to compute the Value of Information, based on the uncertainty of action values and on the distance of alternative cached action values. Overall, the model by default chooses on the basis of lighter model-free estimates, and integrates them with costly model-based predictions only when useful. Mental simulation uses a sampling method to produce reward expectancies, which are used to update the cached value of one or more actions; in turn, this updated value is used for the choice. The key predictions of the model are tested in different settings of a double T-maze scenario. Results are discussed in relation with neurobiological evidence on the hippocampus - ventral striatum circuit in rodents, which has been linked to goal-directed spatial navigation.

Efficient operation scheduling for adsorption chillers using predictive optimization-based control methods

NASA Astrophysics Data System (ADS)

Bürger, Adrian; Sawant, Parantapa; Bohlayer, Markus; Altmann-Dieses, Angelika; Braun, Marco; Diehl, Moritz

2017-10-01

Within this work, the benefits of using predictive control methods for the operation of Adsorption Cooling Machines (ACMs) are shown on a simulation study. Since the internal control decisions of series-manufactured ACMs often cannot be influenced, the work focuses on optimized scheduling of an ACM considering its internal functioning as well as forecasts for load and driving energy occurrence. For illustration, an assumed solar thermal climate system is introduced and a system model suitable for use within gradient-based optimization methods is developed. The results of a system simulation using a conventional scheme for ACM scheduling are compared to the results of a predictive, optimization-based scheduling approach for the same exemplary scenario of load and driving energy occurrence. The benefits of the latter approach are shown and future actions for application of these methods for system control are addressed.

Initial Evaluations of LoC Prediction Algorithms Using the NASA Vertical Motion Simulator

NASA Technical Reports Server (NTRS)

Krishnakumar, Kalmanje; Stepanyan, Vahram; Barlow, Jonathan; Hardy, Gordon; Dorais, Greg; Poolla, Chaitanya; Reardon, Scott; Soloway, Donald

2014-01-01

Flying near the edge of the safe operating envelope is an inherently unsafe proposition. Edge of the envelope here implies that small changes or disturbances in system state or system dynamics can take the system out of the safe envelope in a short time and could result in loss-of-control events. This study evaluated approaches to predicting loss-of-control safety margins as the aircraft gets closer to the edge of the safe operating envelope. The goal of the approach is to provide the pilot aural, visual, and tactile cues focused on maintaining the pilot's control action within predicted loss-of-control boundaries. Our predictive architecture combines quantitative loss-of-control boundaries, an adaptive prediction method to estimate in real-time Markov model parameters and associated stability margins, and a real-time data-based predictive control margins estimation algorithm. The combined architecture is applied to a nonlinear transport class aircraft. Evaluations of various feedback cues using both test and commercial pilots in the NASA Ames Vertical Motion-base Simulator (VMS) were conducted in the summer of 2013. The paper presents results of this evaluation focused on effectiveness of these approaches and the cues in preventing the pilots from entering a loss-of-control event.

Robot trajectory tracking with self-tuning predicted control

NASA Technical Reports Server (NTRS)

Cui, Xianzhong; Shin, Kang G.

1988-01-01

A controller that combines self-tuning prediction and control is proposed for robot trajectory tracking. The controller has two feedback loops: one is used to minimize the prediction error, and the other is designed to make the system output track the set point input. Because the velocity and position along the desired trajectory are given and the future output of the system is predictable, a feedforward loop can be designed for robot trajectory tracking with self-tuning predicted control (STPC). Parameters are estimated online to account for the model uncertainty and the time-varying property of the system. The authors describe the principle of STPC, analyze the system performance, and discuss the simplification of the robot dynamic equations. To demonstrate its utility and power, the controller is simulated for a Stanford arm.

Computer simulation of multigrid body dynamics and control

NASA Technical Reports Server (NTRS)

Swaminadham, M.; Moon, Young I.; Venkayya, V. B.

1990-01-01

The objective is to set up and analyze benchmark problems on multibody dynamics and to verify the predictions of two multibody computer simulation codes. TREETOPS and DISCOS have been used to run three example problems - one degree-of-freedom spring mass dashpot system, an inverted pendulum system, and a triple pendulum. To study the dynamics and control interaction, an inverted planar pendulum with an external body force and a torsional control spring was modeled as a hinge connected two-rigid body system. TREETOPS and DISCOS affected the time history simulation of this problem. System state space variables and their time derivatives from two simulation codes were compared.

Model Predictive Control-based Power take-off Control of an Oscillating Water Column Wave Energy Conversion System

NASA Astrophysics Data System (ADS)

Rajapakse, G.; Jayasinghe, S. G.; Fleming, A.; Shahnia, F.

2017-07-01

Australia’s extended coastline asserts abundance of wave and tidal power. The predictability of these energy sources and their proximity to cities and towns make them more desirable. Several tidal current turbine and ocean wave energy conversion projects have already been planned in the coastline of southern Australia. Some of these projects use air turbine technology with air driven turbines to harvest the energy from an oscillating water column. This study focuses on the power take-off control of a single stage unidirectional oscillating water column air turbine generator system, and proposes a model predictive control-based speed controller for the generator-turbine assembly. The proposed method is verified with simulation results that show the efficacy of the controller in extracting power from the turbine while maintaining the speed at the desired level.

A model of neuro-musculo-skeletal system for human locomotion under position constraint condition.

PubMed

Ni, Jiangsheng; Hiramatsu, Seiji; Kato, Atsuo

2003-08-01

The human locomotion was studied on the basis of the interaction of the musculo-skeletal system, the neural system and the environment. A mathematical model of human locomotion under position constraint condition was established. Besides the neural rhythm generator, the posture controller and the sensory system, the environment feedback controller and the stability controller were taken into account in the model. The environment feedback controller was proposed for two purposes, obstacle avoidance and target position control of the swing foot. The stability controller was proposed to imitate the self-balancing ability of a human body and improve the stability of the model. In the stability controller, the ankle torque was used to control the velocity of the body gravity center. A prediction control algorithm was applied to calculate the torque magnitude of the stability controller. As an example, human stairs climbing movement was simulated and the results were given. The simulation result proved that the mathematical modeling of the task was successful.

Preliminary results from a four-working space, double-acting piston, Stirling engine controls model

NASA Technical Reports Server (NTRS)

Daniele, C. J.; Lorenzo, C. F.

1980-01-01

A four working space, double acting piston, Stirling engine simulation is being developed for controls studies. The development method is to construct two simulations, one for detailed fluid behavior, and a second model with simple fluid behaviour but containing the four working space aspects and engine inertias, validate these models separately, then upgrade the four working space model by incorporating the detailed fluid behaviour model for all four working spaces. The single working space (SWS) model contains the detailed fluid dynamics. It has seven control volumes in which continuity, energy, and pressure loss effects are simulated. Comparison of the SWS model with experimental data shows reasonable agreement in net power versus speed characteristics for various mean pressure levels in the working space. The four working space (FWS) model was built to observe the behaviour of the whole engine. The drive dynamics and vehicle inertia effects are simulated. To reduce calculation time, only three volumes are used in each working space and the gas temperature are fixed (no energy equation). Comparison of the FWS model predicted power with experimental data shows reasonable agreement. Since all four working spaces are simulated, the unique capabilities of the model are exercised to look at working fluid supply transients, short circuit transients, and piston ring leakage effects.

Application of a Novel Grey Self-Memory Coupling Model to Forecast the Incidence Rates of Two Notifiable Diseases in China: Dysentery and Gonorrhea

PubMed Central

Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling

2014-01-01

Objective In this study, a novel grey self-memory coupling model was developed to forecast the incidence rates of two notifiable infectious diseases (dysentery and gonorrhea); the effectiveness and applicability of this model was assessed based on its ability to predict the epidemiological trend of infectious diseases in China. Methods The linear model, the conventional GM(1,1) model and the GM(1,1) model with self-memory principle (SMGM(1,1) model) were used to predict the incidence rates of the two notifiable infectious diseases based on statistical incidence data. Both simulation accuracy and prediction accuracy were assessed to compare the predictive performances of the three models. The best-fit model was applied to predict future incidence rates. Results Simulation results show that the SMGM(1,1) model can take full advantage of the systematic multi-time historical data and possesses superior predictive performance compared with the linear model and the conventional GM(1,1) model. By applying the novel SMGM(1,1) model, we obtained the possible incidence rates of the two representative notifiable infectious diseases in China. Conclusion The disadvantages of the conventional grey prediction model, such as sensitivity to initial value, can be overcome by the self-memory principle. The novel grey self-memory coupling model can predict the incidence rates of infectious diseases more accurately than the conventional model, and may provide useful references for making decisions involving infectious disease prevention and control. PMID:25546054

Application of a novel grey self-memory coupling model to forecast the incidence rates of two notifiable diseases in China: dysentery and gonorrhea.

PubMed

Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling

2014-01-01

In this study, a novel grey self-memory coupling model was developed to forecast the incidence rates of two notifiable infectious diseases (dysentery and gonorrhea); the effectiveness and applicability of this model was assessed based on its ability to predict the epidemiological trend of infectious diseases in China. The linear model, the conventional GM(1,1) model and the GM(1,1) model with self-memory principle (SMGM(1,1) model) were used to predict the incidence rates of the two notifiable infectious diseases based on statistical incidence data. Both simulation accuracy and prediction accuracy were assessed to compare the predictive performances of the three models. The best-fit model was applied to predict future incidence rates. Simulation results show that the SMGM(1,1) model can take full advantage of the systematic multi-time historical data and possesses superior predictive performance compared with the linear model and the conventional GM(1,1) model. By applying the novel SMGM(1,1) model, we obtained the possible incidence rates of the two representative notifiable infectious diseases in China. The disadvantages of the conventional grey prediction model, such as sensitivity to initial value, can be overcome by the self-memory principle. The novel grey self-memory coupling model can predict the incidence rates of infectious diseases more accurately than the conventional model, and may provide useful references for making decisions involving infectious disease prevention and control.

New Paradigm for Translational Modeling to Predict Long‐term Tuberculosis Treatment Response

PubMed Central

Bartelink, IH; Zhang, N; Keizer, RJ; Strydom, N; Converse, PJ; Dooley, KE; Nuermberger, EL

2017-01-01

Abstract Disappointing results of recent tuberculosis chemotherapy trials suggest that knowledge gained from preclinical investigations was not utilized to maximal effect. A mouse‐to‐human translational pharmacokinetics (PKs) – pharmacodynamics (PDs) model built on a rich mouse database may improve clinical trial outcome predictions. The model included Mycobacterium tuberculosis growth function in mice, adaptive immune response effect on bacterial growth, relationships among moxifloxacin, rifapentine, and rifampin concentrations accelerating bacterial death, clinical PK data, species‐specific protein binding, drug‐drug interactions, and patient‐specific pathology. Simulations of recent trials testing 4‐month regimens predicted 65% (95% confidence interval [CI], 55–74) relapse‐free patients vs. 80% observed in the REMox‐TB trial, and 79% (95% CI, 72–87) vs. 82% observed in the Rifaquin trial. Simulation of 6‐month regimens predicted 97% (95% CI, 93–99) vs. 92% and 95% observed in 2RHZE/4RH control arms, and 100% predicted and observed in the 35 mg/kg rifampin arm of PanACEA MAMS. These results suggest that the model can inform regimen optimization and predict outcomes of ongoing trials. PMID:28561946

The development of an industrial-scale fed-batch fermentation simulation.

PubMed

Goldrick, Stephen; Ştefan, Andrei; Lovett, David; Montague, Gary; Lennox, Barry

2015-01-10

This paper describes a simulation of an industrial-scale fed-batch fermentation that can be used as a benchmark in process systems analysis and control studies. The simulation was developed using a mechanistic model and validated using historical data collected from an industrial-scale penicillin fermentation process. Each batch was carried out in a 100,000 L bioreactor that used an industrial strain of Penicillium chrysogenum. The manipulated variables recorded during each batch were used as inputs to the simulator and the predicted outputs were then compared with the on-line and off-line measurements recorded in the real process. The simulator adapted a previously published structured model to describe the penicillin fermentation and extended it to include the main environmental effects of dissolved oxygen, viscosity, temperature, pH and dissolved carbon dioxide. In addition the effects of nitrogen and phenylacetic acid concentrations on the biomass and penicillin production rates were also included. The simulated model predictions of all the on-line and off-line process measurements, including the off-gas analysis, were in good agreement with the batch records. The simulator and industrial process data are available to download at www.industrialpenicillinsimulation.com and can be used to evaluate, study and improve on the current control strategy implemented on this facility. Crown Copyright © 2014. Published by Elsevier B.V. All rights reserved.

Concurrent musculoskeletal dynamics and finite element analysis predicts altered gait patterns to reduce foot tissue loading.

PubMed

Halloran, Jason P; Ackermann, Marko; Erdemir, Ahmet; van den Bogert, Antonie J

2010-10-19

Current computational methods for simulating locomotion have primarily used muscle-driven multibody dynamics, in which neuromuscular control is optimized. Such simulations generally represent joints and soft tissue as simple kinematic or elastic elements for computational efficiency. These assumptions limit application in studies such as ligament injury or osteoarthritis, where local tissue loading must be predicted. Conversely, tissue can be simulated using the finite element method with assumed or measured boundary conditions, but this does not represent the effects of whole body dynamics and neuromuscular control. Coupling the two domains would overcome these limitations and allow prediction of movement strategies guided by tissue stresses. Here we demonstrate this concept in a gait simulation where a musculoskeletal model is coupled to a finite element representation of the foot. Predictive simulations incorporated peak plantar tissue deformation into the objective of the movement optimization, as well as terms to track normative gait data and minimize fatigue. Two optimizations were performed, first without the strain minimization term and second with the term. Convergence to realistic gait patterns was achieved with the second optimization realizing a 44% reduction in peak tissue strain energy density. The study demonstrated that it is possible to alter computationally predicted neuromuscular control to minimize tissue strain while including desired kinematic and muscular behavior. Future work should include experimental validation before application of the methodology to patient care. Copyright © 2010 Elsevier Ltd. All rights reserved.

A Combined Adaptive Neural Network and Nonlinear Model Predictive Control for Multirate Networked Industrial Process Control.

PubMed

Wang, Tong; Gao, Huijun; Qiu, Jianbin

2016-02-01

This paper investigates the multirate networked industrial process control problem in double-layer architecture. First, the output tracking problem for sampled-data nonlinear plant at device layer with sampling period T(d) is investigated using adaptive neural network (NN) control, and it is shown that the outputs of subsystems at device layer can track the decomposed setpoints. Then, the outputs and inputs of the device layer subsystems are sampled with sampling period T(u) at operation layer to form the index prediction, which is used to predict the overall performance index at lower frequency. Radial basis function NN is utilized as the prediction function due to its approximation ability. Then, considering the dynamics of the overall closed-loop system, nonlinear model predictive control method is proposed to guarantee the system stability and compensate the network-induced delays and packet dropouts. Finally, a continuous stirred tank reactor system is given in the simulation part to demonstrate the effectiveness of the proposed method.

Development and Validation of Computational Fluid Dynamics Models for Prediction of Heat Transfer and Thermal Microenvironments of Corals

PubMed Central

Ong, Robert H.; King, Andrew J. C.; Mullins, Benjamin J.; Cooper, Timothy F.; Caley, M. Julian

2012-01-01

We present Computational Fluid Dynamics (CFD) models of the coupled dynamics of water flow, heat transfer and irradiance in and around corals to predict temperatures experienced by corals. These models were validated against controlled laboratory experiments, under constant and transient irradiance, for hemispherical and branching corals. Our CFD models agree very well with experimental studies. A linear relationship between irradiance and coral surface warming was evident in both the simulation and experimental result agreeing with heat transfer theory. However, CFD models for the steady state simulation produced a better fit to the linear relationship than the experimental data, likely due to experimental error in the empirical measurements. The consistency of our modelling results with experimental observations demonstrates the applicability of CFD simulations, such as the models developed here, to coral bleaching studies. A study of the influence of coral skeletal porosity and skeletal bulk density on surface warming was also undertaken, demonstrating boundary layer behaviour, and interstitial flow magnitude and temperature profiles in coral cross sections. Our models compliment recent studies showing systematic changes in these parameters in some coral colonies and have utility in the prediction of coral bleaching. PMID:22701582

Adaptive adjustment of interval predictive control based on combined model and application in shell brand petroleum distillation tower

NASA Astrophysics Data System (ADS)

Sun, Chao; Zhang, Chunran; Gu, Xinfeng; Liu, Bin

2017-10-01

Constraints of the optimization objective are often unable to be met when predictive control is applied to industrial production process. Then, online predictive controller will not find a feasible solution or a global optimal solution. To solve this problem, based on Back Propagation-Auto Regressive with exogenous inputs (BP-ARX) combined control model, nonlinear programming method is used to discuss the feasibility of constrained predictive control, feasibility decision theorem of the optimization objective is proposed, and the solution method of soft constraint slack variables is given when the optimization objective is not feasible. Based on this, for the interval control requirements of the controlled variables, the slack variables that have been solved are introduced, the adaptive weighted interval predictive control algorithm is proposed, achieving adaptive regulation of the optimization objective and automatically adjust of the infeasible interval range, expanding the scope of the feasible region, and ensuring the feasibility of the interval optimization objective. Finally, feasibility and effectiveness of the algorithm is validated through the simulation comparative experiments.

Control of Tollmien-Schlichting instabilities by finite distributed wall actuation

NASA Astrophysics Data System (ADS)

Losse, Nikolas R.; King, Rudibert; Zengl, Marcus; Rist, Ulrich; Noack, Bernd R.

2011-06-01

Tollmien-Schlichting waves are one of the key mechanisms triggering the laminar-turbulent transition in a flat-plate boundary-layer flow. By damping these waves and thus delaying transition, skin friction drag can be significantly decreased. In this simulation study, a wall segment is actuated according to a control scheme based on a POD-Galerkin model driven extended Kalman filter for state estimation and a model predictive controller to dampen TS waves by negative superposition based on this information. The setup of the simulation is chosen to resemble actuation with a driven compliant wall, such as a membrane actuator. Most importantly, a method is proposed to integrate such a localized wall actuation into a Galerkin model.

Optimal Predictive Control for Path Following of a Full Drive-by-Wire Vehicle at Varying Speeds

NASA Astrophysics Data System (ADS)

SONG, Pan; GAO, Bolin; XIE, Shugang; FANG, Rui

2017-05-01

The current research of the global chassis control problem for the full drive-by-wire vehicle focuses on the control allocation (CA) of the four-wheel-distributed traction/braking/steering systems. However, the path following performance and the handling stability of the vehicle can be enhanced a step further by automatically adjusting the vehicle speed to the optimal value. The optimal solution for the combined longitudinal and lateral motion control (MC) problem is given. First, a new variable step-size spatial transformation method is proposed and utilized in the prediction model to derive the dynamics of the vehicle with respect to the road, such that the tracking errors can be explicitly obtained over the prediction horizon at varying speeds. Second, a nonlinear model predictive control (NMPC) algorithm is introduced to handle the nonlinear coupling between any two directions of the vehicular planar motion and computes the sequence of the optimal motion states for following the desired path. Third, a hierarchical control structure is proposed to separate the motion controller into a NMPC based path planner and a terminal sliding mode control (TSMC) based path follower. As revealed through off-line simulations, the hierarchical methodology brings nearly 1700% improvement in computational efficiency without loss of control performance. Finally, the control algorithm is verified through a hardware in-the-loop simulation system. Double-lane-change (DLC) test results show that by using the optimal predictive controller, the root-mean-square (RMS) values of the lateral deviations and the orientation errors can be reduced by 41% and 30%, respectively, comparing to those by the optimal preview acceleration (OPA) driver model with the non-preview speed-tracking method. Additionally, the average vehicle speed is increased by 0.26 km/h with the peak sideslip angle suppressed to 1.9°. This research proposes a novel motion controller, which provides the full drive-by-wire vehicle with better lane-keeping and collision-avoidance capabilities during autonomous driving.

A multi-species model to assess the effect of refugia on worm control and anthelmintic resistance in sheep grazing systems.

PubMed

Dobson, R J; Barnes, E H; Tyrrell, K L; Hosking, B C; Larsen, J W A; Besier, R B; Love, S; Rolfe, P F; Bailey, J N

2011-06-01

Develop a computer simulation model that uses daily meteorological data and farm management practices to predict populations of Trichostrongylus colubriformis, Haemonchus contortus and Teladorsagia (Ostertagia) circumcincta and the evolution of anthelmintic resistance within a sheep flock. Use the model to explore if increased refugia, provided by leaving some adult sheep untreated, would delay development of anthelmintic resistance without compromising nematode control. Compare model predictions with field observations from a breeding flock in Armidale, NSW. Simulate the impact of leaving 1-10% of adult sheep untreated in diverse sheep-grazing systems. Predicted populations of Tr. colubriformis and T. circumcincta were less than those observed in the field, attributed to nutritional stress experienced by the sheep during drought and not accounted for by the model. Observed variation in faecal egg counts explained by the model (R(2) ) for these species was 40-50%. The H. contortus populations and R(2) were both low. Leaving some sheep untreated worked best in situations where animals were already grazing or were moved onto pastures with low populations of infective larvae. In those cases, anthelmintic resistance was delayed and nematode control was maintained when 1-4% of adult stock remained untreated. In general, the model predicted that leaving more than 4% of adults untreated did not sufficiently delay the development of anthelmintic resistance to justify the increased production risk from such a strategy. The choice of a drug rotation strategy had an equal or larger effect on nematode control, and selection for resistance, than leaving 1-10% of adults untreated. © 2011 The Authors. Australian Veterinary Journal © 2011 Australian Veterinary Association.

New Predictive Filters for Compensating the Transport Delay on a Flight Simulator

NASA Technical Reports Server (NTRS)

Guo, Liwen; Cardullo, Frank M.; Houck, Jacob A.; Kelly, Lon C.; Wolters, Thomas E.

2004-01-01

The problems of transport delay in a flight simulator, such as its sources and effects, are reviewed. Then their effects on a pilot-in-the-loop control system are investigated with simulations. Three current prominent delay compensators the lead/lag filter, McFarland filter, and the Sobiski/Cardullo filter were analyzed and compared. This paper introduces two novel delay compensation techniques an adaptive predictor using the Kalman estimator and a state space predictive filter using a reference aerodynamic model. Applications of these two new compensators on recorded data from the NASA Langley Research Center Visual Motion Simulator show that they achieve better compensation over the current ones.

Simulated limnological effects of the Shasta Lake temperature control device

USGS Publications Warehouse

Bartholow, J.; Hanna, R.B.; Saito, L.; Lieberman, D.; Horn, M.

2001-01-01

We estimated the effects of a temperature control device (TCD) on a suite of thermodynamic and limnological attributes for a large storage reservoir, Shasta Lake, in northern California. Shasta Dam was constructed in 1945 with a fixed-elevation penstock. The TCD was installed in 1997 to improve downstream temperatures for endangered salmonids by releasing epilimnetic waters in the winter/spring and hypolimnetic waters in the summer/fall. We calibrated a two-dimensional hydrodynamic reservoir water quality model, CE-QUAL-W2, and applied a structured design-of-experiment simulation procedure to predict the principal limnological effects of the TCD under a variety of environmental scenarios. Calibration goodness-of-fit ranged from good to poor depending on the constituent simulated, with an R2 of 0.9 for water temperature but 0.3 for phytoplankton. Although the chemical and thermal characteristics of the discharge changed markedly, the reservoir's characteristics remained relatively unchanged. Simulations showed the TCD causing an earlier onset and shorter duration of summer stratification, but no dramatic affect on Shasta's nutrient composition. Peak in-reservoir phytoplankton production may begin earlier and be stronger in the fall with the TCD, while outfall phytoplankton concentrations may be much greater in the spring. Many model predictions differed from our a priori expectations that had been shaped by an intensive, but limited-duration, data collection effort. Hydrologic and meteorological variables, most notably reservoir carryover storage at the beginning of the calendar year, influenced model predictions much more strongly than the TCD. Model results indicate that greater control over reservoir limnology and release quality may be gained by carefully managing reservoir volume through the year than with the TCD alone.

Longitudinal train dynamics model for a rail transit simulation system

DOE PAGES

Wang, Jinghui; Rakha, Hesham A.

2018-01-01

The paper develops a longitudinal train dynamics model in support of microscopic railway transportation simulation. The model can be calibrated without any mechanical data making it ideal for implementation in transportation simulators. The calibration and validation work is based on data collected from the Portland light rail train fleet. The calibration procedure is mathematically formulated as a constrained non-linear optimization problem. The validity of the model is assessed by comparing instantaneous model predictions against field observations, and also evaluated in the domains of acceleration/deceleration versus speed and acceleration/deceleration versus distance. A test is conducted to investigate the adequacy of themore » model in simulation implementation. The results demonstrate that the proposed model can adequately capture instantaneous train dynamics, and provides good performance in the simulation test. Thus, the model provides a simple theoretical foundation for microscopic simulators and will significantly support the planning, management and control of railway transportation systems.« less

Longitudinal train dynamics model for a rail transit simulation system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jinghui; Rakha, Hesham A.

The paper develops a longitudinal train dynamics model in support of microscopic railway transportation simulation. The model can be calibrated without any mechanical data making it ideal for implementation in transportation simulators. The calibration and validation work is based on data collected from the Portland light rail train fleet. The calibration procedure is mathematically formulated as a constrained non-linear optimization problem. The validity of the model is assessed by comparing instantaneous model predictions against field observations, and also evaluated in the domains of acceleration/deceleration versus speed and acceleration/deceleration versus distance. A test is conducted to investigate the adequacy of themore » model in simulation implementation. The results demonstrate that the proposed model can adequately capture instantaneous train dynamics, and provides good performance in the simulation test. Thus, the model provides a simple theoretical foundation for microscopic simulators and will significantly support the planning, management and control of railway transportation systems.« less

Construction of multi-functional open modulized Matlab simulation toolbox for imaging ladar system

NASA Astrophysics Data System (ADS)

Wu, Long; Zhao, Yuan; Tang, Meng; He, Jiang; Zhang, Yong

2011-06-01

Ladar system simulation is to simulate the ladar models using computer simulation technology in order to predict the performance of the ladar system. This paper presents the developments of laser imaging radar simulation for domestic and overseas studies and the studies of computer simulation on ladar system with different application requests. The LadarSim and FOI-LadarSIM simulation facilities of Utah State University and Swedish Defence Research Agency are introduced in details. This paper presents the low level of simulation scale, un-unified design and applications of domestic researches in imaging ladar system simulation, which are mostly to achieve simple function simulation based on ranging equations for ladar systems. Design of laser imaging radar simulation with open and modularized structure is proposed to design unified modules for ladar system, laser emitter, atmosphere models, target models, signal receiver, parameters setting and system controller. Unified Matlab toolbox and standard control modules have been built with regulated input and output of the functions, and the communication protocols between hardware modules. A simulation based on ICCD gain-modulated imaging ladar system for a space shuttle is made based on the toolbox. The simulation result shows that the models and parameter settings of the Matlab toolbox are able to simulate the actual detection process precisely. The unified control module and pre-defined parameter settings simplify the simulation of imaging ladar detection. Its open structures enable the toolbox to be modified for specialized requests. The modulization gives simulations flexibility.

Optical Modeling Activities for NASA's James Webb Space Telescope (JWST): V. Operational Alignment Updates

NASA Technical Reports Server (NTRS)

Howard, Joseph M.; Ha, Kong Q.; Shiri, Ron; Smith, J. Scott; Mosier, Gary; Muheim, Danniella

2008-01-01

This paper is part five of a series on the ongoing optical modeling activities for the James Webb Space Telescope (JWST). The first two papers discussed modeling JWST on-orbit performance using wavefront sensitivities to predict line of sight motion induced blur, and stability during thermal transients. The third paper investigates the aberrations resulting from alignment and figure compensation of the controllable degrees of freedom (primary and secondary mirrors), which may be encountered during ground alignment and on-orbit commissioning of the observatory, and the fourth introduced the software toolkits used to perform much of the optical analysis for JWST. The work here models observatory operations by simulating line-of-sight image motion and alignment drifts over a two-week period. Alignment updates are then simulated using wavefront sensing and control processes to calculate and perform the corrections. A single model environment in Matlab is used for evaluating the predicted performance of the observatory during these operations.

Simulation of flexible appendage interactions with Mariner Venus/Mercury attitude control and science platform pointing

NASA Technical Reports Server (NTRS)

Fleischer, G. E.

1973-01-01

A new computer subroutine, which solves the attitude equations of motion for any vehicle idealized as a topological tree of hinge-connected rigid bodies, is used to simulate and analyze science instrument pointing control interaction with a flexible Mariner Venus/Mercury (MVM) spacecraft. The subroutine's user options include linearized or partially linearized hinge-connected models whose computational advantages are demonstrated for the MVM problem. Results of the pointing control/flexible vehicle interaction simulations, including imaging experiment pointing accuracy predictions and implications for MVM science sequence planning, are described in detail.

Method for Prediction of the Power Output from Photovoltaic Power Plant under Actual Operating Conditions

NASA Astrophysics Data System (ADS)

Obukhov, S. G.; Plotnikov, I. A.; Surzhikova, O. A.; Savkin, K. D.

2017-04-01

Solar photovoltaic technology is one of the most rapidly growing renewable sources of electricity that has practical application in various fields of human activity due to its high availability, huge potential and environmental compatibility. The original simulation model of the photovoltaic power plant has been developed to simulate and investigate the plant operating modes under actual operating conditions. The proposed model considers the impact of the external climatic factors on the solar panel energy characteristics that improves accuracy in the power output prediction. The data obtained through the photovoltaic power plant operation simulation enable a well-reasoned choice of the required capacity for storage devices and determination of the rational algorithms to control the energy complex.

Model predictive control of attitude maneuver of a geostationary flexible satellite based on genetic algorithm

NASA Astrophysics Data System (ADS)

TayyebTaher, M.; Esmaeilzadeh, S. Majid

2017-07-01

This article presents an application of Model Predictive Controller (MPC) to the attitude control of a geostationary flexible satellite. SIMO model has been used for the geostationary satellite, using the Lagrange equations. Flexibility is also included in the modelling equations. The state space equations are expressed in order to simplify the controller. Naturally there is no specific tuning rule to find the best parameters of an MPC controller which fits the desired controller. Being an intelligence method for optimizing problem, Genetic Algorithm has been used for optimizing the performance of MPC controller by tuning the controller parameter due to minimum rise time, settling time, overshoot of the target point of the flexible structure and its mode shape amplitudes to make large attitude maneuvers possible. The model included geosynchronous orbit environment and geostationary satellite parameters. The simulation results of the flexible satellite with attitude maneuver shows the efficiency of proposed optimization method in comparison with LQR optimal controller.

Toward Process-resolving Synthesis and Prediction of Arctic Climate Change Using the Regional Arctic System Model

NASA Astrophysics Data System (ADS)

Maslowski, W.

2017-12-01

The Regional Arctic System Model (RASM) has been developed to better understand the operation of Arctic System at process scale and to improve prediction of its change at a spectrum of time scales. RASM is a pan-Arctic, fully coupled ice-ocean-atmosphere-land model with marine biogeochemistry extension to the ocean and sea ice models. The main goal of our research is to advance a system-level understanding of critical processes and feedbacks in the Arctic and their links with the Earth System. The secondary, an equally important objective, is to identify model needs for new or additional observations to better understand such processes and to help constrain models. Finally, RASM has been used to produce sea ice forecasts for September 2016 and 2017, in contribution to the Sea Ice Outlook of the Sea Ice Prediction Network. Future RASM forecasts, are likely to include increased resolution for model components and ecosystem predictions. Such research is in direct support of the US environmental assessment and prediction needs, including those of the U.S. Navy, Department of Defense, and the recent IARPC Arctic Research Plan 2017-2021. In addition to an overview of RASM technical details, selected model results are presented from a hierarchy of climate models together with available observations in the region to better understand potential oceanic contributions to polar amplification. RASM simulations are analyzed to evaluate model skill in representing seasonal climatology as well as interannual and multi-decadal climate variability and predictions. Selected physical processes and resulting feedbacks are discussed to emphasize the need for fully coupled climate model simulations, high model resolution and sensitivity of simulated sea ice states to scale dependent model parameterizations controlling ice dynamics, thermodynamics and coupling with the atmosphere and ocean.

Robust distributed model predictive control of linear systems with structured time-varying uncertainties

NASA Astrophysics Data System (ADS)

Zhang, Langwen; Xie, Wei; Wang, Jingcheng

2017-11-01

In this work, synthesis of robust distributed model predictive control (MPC) is presented for a class of linear systems subject to structured time-varying uncertainties. By decomposing a global system into smaller dimensional subsystems, a set of distributed MPC controllers, instead of a centralised controller, are designed. To ensure the robust stability of the closed-loop system with respect to model uncertainties, distributed state feedback laws are obtained by solving a min-max optimisation problem. The design of robust distributed MPC is then transformed into solving a minimisation optimisation problem with linear matrix inequality constraints. An iterative online algorithm with adjustable maximum iteration is proposed to coordinate the distributed controllers to achieve a global performance. The simulation results show the effectiveness of the proposed robust distributed MPC algorithm.

An alternative approach based on artificial neural networks to study controlled drug release.

PubMed

Reis, Marcus A A; Sinisterra, Rubén D; Belchior, Jadson C

2004-02-01

An alternative methodology based on artificial neural networks is proposed to be a complementary tool to other conventional methods to study controlled drug release. Two systems are used to test the approach; namely, hydrocortisone in a biodegradable matrix and rhodium (II) butyrate complexes in a bioceramic matrix. Two well-established mathematical models are used to simulate different release profiles as a function of fundamental properties; namely, diffusion coefficient (D), saturation solubility (C(s)), drug loading (A), and the height of the device (h). The models were tested, and the results show that these fundamental properties can be predicted after learning the experimental or model data for controlled drug release systems. The neural network results obtained after the learning stage can be considered to quantitatively predict ideal experimental conditions. Overall, the proposed methodology was shown to be efficient for ideal experiments, with a relative average error of <1% in both tests. This approach can be useful for the experimental analysis to simulate and design efficient controlled drug-release systems. Copyright 2004 Wiley-Liss, Inc. and the American Pharmacists Association

Tobacco Policies in Louisiana: Recommendations for Future Tobacco Control Investment from SimSmoke, a Policy Simulation Model.

PubMed

Levy, David; Fergus, Cristin; Rudov, Lindsey; McCormick-Ricket, Iben; Carton, Thomas

2016-02-01

Despite the presence of tobacco control policies, Louisiana continues to experience a high smoking burden and elevated smoking-attributable deaths. The SimSmoke model provides projections of these health outcomes in the face of existing and expanded (simulated) tobacco control polices. The SimSmoke model utilizes population data, smoking rates, and various tobacco control policy measures from Louisiana to predict smoking prevalence and smoking-attributable deaths. The model begins in 1993 and estimates are projected through 2054. The model is validated against existing Louisiana smoking prevalence data. The most powerful individual policy measure for reducing smoking prevalence is cigarette excise tax. However, a comprehensive cessation treatment policy is predicted to save the most lives. A combination of tobacco control policies provides the greatest reduction in smoking prevalence and smoking-attributable deaths. The existing Louisiana excise tax ranks as one of the lowest in the country and the legislature is against further increases. Alternative policy measures aimed at lowering prevalence and attributable deaths are: cessation treatments, comprehensive smoke-free policies, and limiting youth access. These three policies have a substantial effect on smoking prevalence and attributable deaths and are likely to encounter more favor in the Louisiana legislature than increasing the state excise tax.

Critical length scale controls adhesive wear mechanisms

PubMed Central

Aghababaei, Ramin; Warner, Derek H.; Molinari, Jean-Francois

2016-01-01

The adhesive wear process remains one of the least understood areas of mechanics. While it has long been established that adhesive wear is a direct result of contacting surface asperities, an agreed upon understanding of how contacting asperities lead to wear debris particle has remained elusive. This has restricted adhesive wear prediction to empirical models with limited transferability. Here we show that discrepant observations and predictions of two distinct adhesive wear mechanisms can be reconciled into a unified framework. Using atomistic simulations with model interatomic potentials, we reveal a transition in the asperity wear mechanism when contact junctions fall below a critical length scale. A simple analytic model is formulated to predict the transition in both the simulation results and experiments. This new understanding may help expand use of computer modelling to explore adhesive wear processes and to advance physics-based wear laws without empirical coefficients. PMID:27264270

An analytical model for non-conservative pollutants mixing in the surf zone.

PubMed

Ki, Seo Jin; Hwang, Jin Hwan; Kang, Joo-Hyon; Kim, Joon Ha

2009-01-01

Accurate simulation of the surf zone is a prerequisite to improve beach management as well as to understand the fundamentals of fate and transport of contaminants. In the present study, a diagnostic model modified from a classic solute model is provided to illuminate non-conservative pollutants behavior in the surf zone. To readily understand controlling processes in the surf zone, a new dimensionless quantity is employed with index of kappa number (K, a ratio of inactivation rate to transport rate of microbial pollutant in the surf zone), which was then evaluated under different environmental frames during a week simulation period. The sensitivity analysis showed that hydrodynamics and concentration gradients in the surf zone mostly depend on n (number of rip currents), indicating that n should be carefully adjusted in the model. The simulation results reveal, furthermore, that large deviation typically occurs in the daytime, signifying inactivation of fecal indicator bacteria is the main process to control surf zone water quality during the day. Overall, the analytical model shows a good agreement between predicted and synthetic data (R(2) = 0.51 and 0.67 for FC and ENT, respectively) for the simulated period, amplifying its potential use in the surf zone modelling. It is recommended that when the dimensionless index is much larger than 0.5, the present modified model can predict better than the conventional model, but if index is smaller than 0.5, the conventional model is more efficient with respect to time and cost.

Simulation and experimental comparison of the thermo-mechanical history and 3D microstructure evolution of 304L stainless steel tubes manufactured using LENS

NASA Astrophysics Data System (ADS)

Johnson, Kyle L.; Rodgers, Theron M.; Underwood, Olivia D.; Madison, Jonathan D.; Ford, Kurtis R.; Whetten, Shaun R.; Dagel, Daryl J.; Bishop, Joseph E.

2018-05-01

Additive manufacturing enables the production of previously unachievable designs in conjunction with time and cost savings. However, spatially and temporally fluctuating thermal histories can lead to residual stress states and microstructural variations that challenge conventional assumptions used to predict part performance. Numerical simulations offer a viable way to explore the root causes of these characteristics, and can provide insight into methods of controlling them. Here, the thermal history of a 304L stainless steel cylinder produced using the Laser Engineered Net Shape process is simulated using finite element analysis (FEA). The resultant thermal history is coupled to both a solid mechanics FEA simulation to predict residual stress and a kinetic Monte Carlo model to predict the three-dimensional grain structure evolution. Experimental EBSD measurements of grain structure and in-process infrared thermal data are compared to the predictions.

Simulation and experimental comparison of the thermo-mechanical history and 3D microstructure evolution of 304L stainless steel tubes manufactured using LENS

NASA Astrophysics Data System (ADS)

Johnson, Kyle L.; Rodgers, Theron M.; Underwood, Olivia D.; Madison, Jonathan D.; Ford, Kurtis R.; Whetten, Shaun R.; Dagel, Daryl J.; Bishop, Joseph E.

2017-12-01

Additive manufacturing enables the production of previously unachievable designs in conjunction with time and cost savings. However, spatially and temporally fluctuating thermal histories can lead to residual stress states and microstructural variations that challenge conventional assumptions used to predict part performance. Numerical simulations offer a viable way to explore the root causes of these characteristics, and can provide insight into methods of controlling them. Here, the thermal history of a 304L stainless steel cylinder produced using the Laser Engineered Net Shape process is simulated using finite element analysis (FEA). The resultant thermal history is coupled to both a solid mechanics FEA simulation to predict residual stress and a kinetic Monte Carlo model to predict the three-dimensional grain structure evolution. Experimental EBSD measurements of grain structure and in-process infrared thermal data are compared to the predictions.

RANS Simulation of the Separated Flow over a Bump with Active Control

NASA Technical Reports Server (NTRS)

Iaccarino, Gianluca; Marongiu, Claudio; Catalano, Pietro; Amato, Marcello

2003-01-01

The objective of this paper is to investigate the accuracy of Reynolds-Averaged Navier- Stokes (RANS) techniques in predicting the effect of steady and unsteady flow control devices. This is part of a larger effort in applying numerical simulation tools to investigate of the performance of synthetic jets in high Reynolds number turbulent flows. RANS techniques have been successful in predicting isolated synthetic jets as reported by Kral et al. Nevertheless, due to the complex, and inherently unsteady nature of the interaction between the synthetic jet and the external boundary layer flow, it is not clear whether RANS models can represent the turbulence statistics correctly.

Human-robot interaction modeling and simulation of supervisory control and situational awareness during field experimentation with military manned and unmanned ground vehicles

NASA Astrophysics Data System (ADS)

Johnson, Tony; Metcalfe, Jason; Brewster, Benjamin; Manteuffel, Christopher; Jaswa, Matthew; Tierney, Terrance

2010-04-01

The proliferation of intelligent systems in today's military demands increased focus on the optimization of human-robot interactions. Traditional studies in this domain involve large-scale field tests that require humans to operate semiautomated systems under varying conditions within military-relevant scenarios. However, provided that adequate constraints are employed, modeling and simulation can be a cost-effective alternative and supplement. The current presentation discusses a simulation effort that was executed in parallel with a field test with Soldiers operating military vehicles in an environment that represented key elements of the true operational context. In this study, "constructive" human operators were designed to represent average Soldiers executing supervisory control over an intelligent ground system. The constructive Soldiers were simulated performing the same tasks as those performed by real Soldiers during a directly analogous field test. Exercising the models in a high-fidelity virtual environment provided predictive results that represented actual performance in certain aspects, such as situational awareness, but diverged in others. These findings largely reflected the quality of modeling assumptions used to design behaviors and the quality of information available on which to articulate principles of operation. Ultimately, predictive analyses partially supported expectations, with deficiencies explicable via Soldier surveys, experimenter observations, and previously-identified knowledge gaps.

Pesticide transport with runoff from turf: observations compared with TurfPQ model simulations.

PubMed

Kramer, Kirsten E; Rice, Pamela J; Horgan, Brian P; Rittenhouse, Jennifer L; King, Kevin W

2009-01-01

Pesticides applied to turf grass have been detected in surface waters raising concerns of their effect on water quality and interest in their source, hydrological transport and use of models to predict transport. TurfPQ, a pesticide runoff model for turf grass, predicts pesticide transport but has not been rigorously validated for larger storms. The objective of this study was to determine TurfPQ's ability to accurately predict the transport of pesticides with runoff following more intense precipitation. The study was conducted with creeping bentgrass [Agrostis palustris Huds.] turf managed as a golf course fairway. A pesticide mixture containing dicamba, 2,4-D, MCPP, flutolanil, and chlorpyrifos was applied to six adjacent 24.4 by 6.1 m plots. Controlled rainfall simulations were conducted using a rainfall simulator designed to deliver water droplets similar to natural rain. Runoff flow rates and volume were measured and water samples were collected for analysis of pesticide concentrations. Six simulations yielded 13 events with which to test TurfPQ. Measured mean percentage of applied pesticide recovered in the runoff for dicamba, 2,4-D, MCPP, flutolanil, and chlorpyrifos was 24.6, 20.7, 14.9, 5.9, and 0.8%, respectively. The predicted mean values produced by TurfPQ were 13.7, 15.6, 15.5, 2.5, and 0.2%, respectively. The model produced correlations of r=0.56 and 0.64 for curve number hydrology and measured hydrology, respectively. Comparisons of the model estimates with our field observations indicate that TurfPQ under predicted pesticide runoff during 69.5+/-11.4 mm, 1.9+/-0.2 h, simulated storms.

Using OPC technology to support the study of advanced process control.

PubMed

Mahmoud, Magdi S; Sabih, Muhammad; Elshafei, Moustafa

2015-03-01

OPC, originally the Object Linking and Embedding (OLE) for Process Control, brings a broad communication opportunity between different kinds of control systems. This paper investigates the use of OPC technology for the study of distributed control systems (DCS) as a cost effective and flexible research tool for the development and testing of advanced process control (APC) techniques in university research centers. Co-Simulation environment based on Matlab, LabVIEW and TCP/IP network is presented here. Several implementation issues and OPC based client/server control application have been addressed for TCP/IP network. A nonlinear boiler model is simulated as OPC server and OPC client is used for closed loop model identification, and to design a Model Predictive Controller. The MPC is able to control the NOx emissions in addition to drum water level and steam pressure. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Global sensitivity analysis of DRAINMOD-FOREST, an integrated forest ecosystem model

Treesearch

Shiying Tian; Mohamed A. Youssef; Devendra M. Amatya; Eric D. Vance

2014-01-01

Global sensitivity analysis is a useful tool to understand process-based ecosystem models by identifying key parameters and processes controlling model predictions. This study reported a comprehensive global sensitivity analysis for DRAINMOD-FOREST, an integrated model for simulating water, carbon (C), and nitrogen (N) cycles and plant growth in lowland forests. The...

A model predictive speed tracking control approach for autonomous ground vehicles

NASA Astrophysics Data System (ADS)

Zhu, Min; Chen, Huiyan; Xiong, Guangming

2017-03-01

This paper presents a novel speed tracking control approach based on a model predictive control (MPC) framework for autonomous ground vehicles. A switching algorithm without calibration is proposed to determine the drive or brake control. Combined with a simple inverse longitudinal vehicle model and adaptive regulation of MPC, this algorithm can make use of the engine brake torque for various driving conditions and avoid high frequency oscillations automatically. A simplified quadratic program (QP) solving algorithm is used to reduce the computational time, and the approach has been applied in a 16-bit microcontroller. The performance of the proposed approach is evaluated via simulations and vehicle tests, which were carried out in a range of speed-profile tracking tasks. With a well-designed system structure, high-precision speed control is achieved. The system can robustly model uncertainty and external disturbances, and yields a faster response with less overshoot than a PI controller.

FOOD RISK ANALYSIS

USDA-ARS?s Scientific Manuscript database

Food risk analysis is a holistic approach to food safety because it considers all aspects of the problem. Risk assessment modeling is the foundation of food risk analysis. Proper design and simulation of the risk assessment model is important to properly predict and control risk. Because of knowl...

Cognitive task load in a naval ship control centre: from identification to prediction.

PubMed

Grootjen, M; Neerincx, M A; Veltman, J A

Deployment of information and communication technology will lead to further automation of control centre tasks and an increasing amount of information to be processed. A method for establishing adequate levels of cognitive task load for the operators in such complex environments has been developed. It is based on a model distinguishing three load factors: time occupied, task-set switching, and level of information processing. Application of the method resulted in eight scenarios for eight extremes of task load (i.e. low and high values for each load factor). These scenarios were performed by 13 teams in a high-fidelity control centre simulator of the Royal Netherlands Navy. The results show that the method provides good prediction of the task load that will actually appear in the simulator. The model allowed identification of under- and overload situations showing negative effects on operator performance corresponding to controlled experiments in a less realistic task environment. Tools proposed to keep the operator at an optimum task load are (adaptive) task allocation and interface support.

Comparing in Cylinder Pressure Modelling of a DI Diesel Engine Fuelled on Alternative Fuel Using Two Tabulated Chemistry Approaches.

PubMed

Ngayihi Abbe, Claude Valery; Nzengwa, Robert; Danwe, Raidandi

2014-01-01

The present work presents the comparative simulation of a diesel engine fuelled on diesel fuel and biodiesel fuel. Two models, based on tabulated chemistry, were implemented for the simulation purpose and results were compared with experimental data obtained from a single cylinder diesel engine. The first model is a single zone model based on the Krieger and Bormann combustion model while the second model is a two-zone model based on Olikara and Bormann combustion model. It was shown that both models can predict well the engine's in-cylinder pressure as well as its overall performances. The second model showed a better accuracy than the first, while the first model was easier to implement and faster to compute. It was found that the first method was better suited for real time engine control and monitoring while the second one was better suited for engine design and emission prediction.

I-SAVE: AN INTERACTIVE REAL-TIME MONITOR AND CONTROLLER TO INFLUENCE ENERGY CONSERVATION BEHAVIOR BY IMPULSE SAVING

EPA Science Inventory

Simulation-based model to explore the benefits of monitoring and control to energy saving opportunities in residential homes; an adaptive algorithm to predict the type of electrical loads; a prototype user friendly interface monitoring and control device to save energy; a p...

The Impact of Trajectory Prediction Uncertainty on Air Traffic Controller Performance and Acceptability

NASA Technical Reports Server (NTRS)

Mercer, Joey S.; Bienert, Nancy; Gomez, Ashley; Hunt, Sarah; Kraut, Joshua; Martin, Lynne; Morey, Susan; Green, Steven M.; Prevot, Thomas; Wu, Minghong G.

2013-01-01

A Human-In-The-Loop air traffic control simulation investigated the impact of uncertainties in trajectory predictions on NextGen Trajectory-Based Operations concepts, seeking to understand when the automation would become unacceptable to controllers or when performance targets could no longer be met. Retired air traffic controllers staffed two en route transition sectors, delivering arrival traffic to the northwest corner-post of Atlanta approach control under time-based metering operations. Using trajectory-based decision-support tools, the participants worked the traffic under varying levels of wind forecast error and aircraft performance model error, impacting the ground automations ability to make accurate predictions. Results suggest that the controllers were able to maintain high levels of performance, despite even the highest levels of trajectory prediction errors.

Gryphon: A Hybrid Agent-Based Modeling and Simulation Platform for Infectious Diseases

NASA Astrophysics Data System (ADS)

Yu, Bin; Wang, Jijun; McGowan, Michael; Vaidyanathan, Ganesh; Younger, Kristofer

In this paper we present Gryphon, a hybrid agent-based stochastic modeling and simulation platform developed for characterizing the geographic spread of infectious diseases and the effects of interventions. We study both local and non-local transmission dynamics of stochastic simulations based on the published parameters and data for SARS. The results suggest that the expected numbers of infections and the timeline of control strategies predicted by our stochastic model are in reasonably good agreement with previous studies. These preliminary results indicate that Gryphon is able to characterize other future infectious diseases and identify endangered regions in advance.

Computational Models of the Cardiovascular System and Its Response to Microgravity

NASA Technical Reports Server (NTRS)

Kamm, Roger D.

1999-01-01

Computational models of the cardiovascular system are powerful adjuncts to ground-based and in-flight experiments. We will provide NSBRI with a model capable of simulating the short-term effects of gravity on cardiovascular function. The model from this project will: (1) provide a rational framework which quantitatively defines interactions among complex cardiovascular parameters and which supports the critical interpretation of experimental results and testing of hypotheses. (2) permit predictions of the impact of specific countermeasures in the context of various hypothetical cardiovascular abnormalities induced by microgravity. Major progress has been made during the first 18 months of the program: (1) We have developed an operational first-order computer model capable of simulating the cardiovascular response to orthostatic stress. The model consists of a lumped parameter hemodynamic model and a complete reflex control system. The latter includes cardiopulmonary and carotid sinus reflex limbs and interactions between the two. (2) We have modeled the physiologic stress of tilt table experiments and lower body negative pressure procedures (LBNP). We have verified our model's predictions by comparing them with experimental findings from the literature. (3) We have established collaborative efforts with leading investigators interested in experimental studies of orthostatic intolerance, cardiovascular control, and physiologic responses to space flight. (4) We have established a standardized method of transferring data to our laboratory from the ongoing NSBRI bedrest studies. We use this data to estimate input parameters to our model and compare our model predictions to actual data to further verify our model. (5) We are in the process of systematically simulating current hypotheses concerning the mechanism underlying orthostatic intolerance by matching our simulations to stand test data from astronauts pre- and post-flight. (6) We are in the process of developing a JAVA version of the simulator which will be distributed amongst the cardiovascular team members. Future work on this project involves modifications of the model to represent a rodent (rat) model, further evaluation of the bedrest astronaut and animal data, and systematic investigation of specific countermeasures.

Steering Angle Control of Car for Dubins Path-tracking Using Model Predictive Control

NASA Astrophysics Data System (ADS)

Kusuma Rahma Putri, Dian; Subchan; Asfihani, Tahiyatul

2018-03-01

Car as one of transportation is inseparable from technological developments. About ten years, there are a lot of research and development on lane keeping system(LKS) which is a system that automaticaly controls the steering to keep the vehicle especially car always on track. This system can be developed for unmanned cars. Unmanned system car requires navigation, guidance and control which is able to direct the vehicle to move toward the desired path. The guidance system is represented by using Dubins-Path that will be controlled by using Model Predictive Control. The control objective is to keep the car’s movement that represented by dinamic lateral motion model so car can move according to the path appropriately. The simulation control on the four types of trajectories that generate the value for steering angle and steering angle changes are at the specified interval.

Using micro-simulation to investigate the safety impacts of transit design alternatives at signalized intersections.

PubMed

Li, Lu; Persaud, Bhagwant; Shalaby, Amer

2017-03-01

This study investigates the use of crash prediction models and micro-simulation to develop an effective surrogate safety assessment measure at the intersection level. With the use of these tools, hypothetical scenarios can be developed and explored to evaluate the safety impacts of design alternatives in a controlled environment, in which factors not directly associated with the design alternatives can be fixed. Micro-simulation models are developed, calibrated, and validated. Traffic conflicts in the micro-simulation models are estimated and linked with observed crash frequency, which greatly alleviates the lengthy time needed to collect sufficient crash data for evaluating alternatives, due to the rare and infrequent nature of crash events. A set of generalized linear models with negative binomial error structure is developed to correlate the simulated conflicts with the observed crash frequency in Toronto, Ontario, Canada. Crash prediction models are also developed for crashes of different impact types and for transit-involved crashes. The resulting statistical significance and the goodness-of-fit of the models suggest adequate predictive ability. Based on the established correlation between simulated conflicts and observed crashes, scenarios are developed in the micro-simulation models to investigate the safety effects of individual transit line elements by making hypothetical modifications to such elements and estimating changes in crash frequency from the resulting changes in conflicts. The findings imply that the existing transit signal priority schemes can have a negative effect on safety performance, and that the existing near-side stop positioning and streetcar transit type can be safer at their current state than if they were to be replaced by their respective counterparts. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predictive Rotation Profile Control for the DIII-D Tokamak

NASA Astrophysics Data System (ADS)

Wehner, W. P.; Schuster, E.; Boyer, M. D.; Walker, M. L.; Humphreys, D. A.

2017-10-01

Control-oriented modeling and model-based control of the rotation profile are employed to build a suitable control capability for aiding rotation-related physics studies at DIII-D. To obtain a control-oriented model, a simplified version of the momentum balance equation is combined with empirical representations of the momentum sources. The control approach is rooted in a Model Predictive Control (MPC) framework to regulate the rotation profile while satisfying constraints associated with the desired plasma stored energy and/or βN limit. Simple modifications allow for alternative control objectives, such as maximizing the plasma rotation while maintaining a specified input torque. Because the MPC approach can explicitly incorporate various types of constraints, this approach is well suited to a variety of control objectives, and therefore serves as a valuable tool for experimental physics studies. Closed-loop TRANSP simulations are presented to demonstrate the effectiveness of the control approach. Supported by the US DOE under DE-SC0010661 and DE-FC02-04ER54698.

A Systems Model of Parkinson's Disease Using Biochemical Systems Theory.

PubMed

Sasidharakurup, Hemalatha; Melethadathil, Nidheesh; Nair, Bipin; Diwakar, Shyam

2017-08-01

Parkinson's disease (PD), a neurodegenerative disorder, affects millions of people and has gained attention because of its clinical roles affecting behaviors related to motor and nonmotor symptoms. Although studies on PD from various aspects are becoming popular, few rely on predictive systems modeling approaches. Using Biochemical Systems Theory (BST), this article attempts to model and characterize dopaminergic cell death and understand pathophysiology of progression of PD. PD pathways were modeled using stochastic differential equations incorporating law of mass action, and initial concentrations for the modeled proteins were obtained from literature. Simulations suggest that dopamine levels were reduced significantly due to an increase in dopaminergic quinones and 3,4-dihydroxyphenylacetaldehyde (DOPAL) relating to imbalances compared to control during PD progression. Associating to clinically observed PD-related cell death, simulations show abnormal parkin and reactive oxygen species levels with an increase in neurofibrillary tangles. While relating molecular mechanistic roles, the BST modeling helps predicting dopaminergic cell death processes involved in the progression of PD and provides a predictive understanding of neuronal dysfunction for translational neuroscience.

Flight Testing an Iced Business Jet for Flight Simulation Model Validation

NASA Technical Reports Server (NTRS)

Ratvasky, Thomas P.; Barnhart, Billy P.; Lee, Sam; Cooper, Jon

2007-01-01

A flight test of a business jet aircraft with various ice accretions was performed to obtain data to validate flight simulation models developed through wind tunnel tests. Three types of ice accretions were tested: pre-activation roughness, runback shapes that form downstream of the thermal wing ice protection system, and a wing ice protection system failure shape. The high fidelity flight simulation models of this business jet aircraft were validated using a software tool called "Overdrive." Through comparisons of flight-extracted aerodynamic forces and moments to simulation-predicted forces and moments, the simulation models were successfully validated. Only minor adjustments in the simulation database were required to obtain adequate match, signifying the process used to develop the simulation models was successful. The simulation models were implemented in the NASA Ice Contamination Effects Flight Training Device (ICEFTD) to enable company pilots to evaluate flight characteristics of the simulation models. By and large, the pilots confirmed good similarities in the flight characteristics when compared to the real airplane. However, pilots noted pitch up tendencies at stall with the flaps extended that were not representative of the airplane and identified some differences in pilot forces. The elevator hinge moment model and implementation of the control forces on the ICEFTD were identified as a driver in the pitch ups and control force issues, and will be an area for future work.

Modeling the influence of coupled mass transfer processes on mass flux downgradient of heterogeneous DNAPL source zones

NASA Astrophysics Data System (ADS)

Yang, Lurong; Wang, Xinyu; Mendoza-Sanchez, Itza; Abriola, Linda M.

2018-04-01

Sequestered mass in low permeability zones has been increasingly recognized as an important source of organic chemical contamination that acts to sustain downgradient plume concentrations above regulated levels. However, few modeling studies have investigated the influence of this sequestered mass and associated (coupled) mass transfer processes on plume persistence in complex dense nonaqueous phase liquid (DNAPL) source zones. This paper employs a multiphase flow and transport simulator (a modified version of the modular transport simulator MT3DMS) to explore the two- and three-dimensional evolution of source zone mass distribution and near-source plume persistence for two ensembles of highly heterogeneous DNAPL source zone realizations. Simulations reveal the strong influence of subsurface heterogeneity on the complexity of DNAPL and sequestered (immobile/sorbed) mass distribution. Small zones of entrapped DNAPL are shown to serve as a persistent source of low concentration plumes, difficult to distinguish from other (sorbed and immobile dissolved) sequestered mass sources. Results suggest that the presence of DNAPL tends to control plume longevity in the near-source area; for the examined scenarios, a substantial fraction (43.3-99.2%) of plume life was sustained by DNAPL dissolution processes. The presence of sorptive media and the extent of sorption non-ideality are shown to greatly affect predictions of near-source plume persistence following DNAPL depletion, with plume persistence varying one to two orders of magnitude with the selected sorption model. Results demonstrate the importance of sorption-controlled back diffusion from low permeability zones and reveal the importance of selecting the appropriate sorption model for accurate prediction of plume longevity. Large discrepancies for both DNAPL depletion time and plume longevity were observed between 2-D and 3-D model simulations. Differences between 2- and 3-D predictions increased in the presence of sorption, especially for the case of non-ideal sorption, demonstrating the limitations of employing 2-D predictions for field-scale modeling.

Dissertation Defense Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

NASA Technical Reports Server (NTRS)

Groves, Curtis Edward

2014-01-01

Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This paper describes an approach to quantify the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft without the use of test data. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional "validation by test only" mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions. Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computational Fluid Dynamics can be used to verify these requirements; however, the model must be validated by test data. This research includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available and open source solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT, STARCCM+, and OPENFOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid Dynamics model using the methodology found in "Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations". This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions. The method accounts for all uncertainty terms from both numerical and input variables. Objective three is to compile a table of uncertainty parameters that could be used to estimate the error in a Computational Fluid Dynamics model of the Environmental Control System /spacecraft system. Previous studies have looked at the uncertainty in a Computational Fluid Dynamics model for a single output variable at a single point, for example the re-attachment length of a backward facing step. For the flow regime being analyzed (turbulent, three-dimensional, incompressible), the error at a single point can propagate into the solution both via flow physics and numerical methods. Calculating the uncertainty in using Computational Fluid Dynamics to accurately predict airflow speeds around encapsulated spacecraft in is imperative to the success of future missions.

Dissertation Defense: Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

NASA Technical Reports Server (NTRS)

Groves, Curtis Edward

2014-01-01

Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This paper describes an approach to quantify the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft without the use of test data. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional validation by test only mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions.Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computational Fluid Dynamics can be used to verify these requirements; however, the model must be validated by test data. This research includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available and open source solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT, STARCCM+, and OPENFOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid Dynamics model using the methodology found in Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations. This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions. The method accounts for all uncertainty terms from both numerical and input variables. Objective three is to compile a table of uncertainty parameters that could be used to estimate the error in a Computational Fluid Dynamics model of the Environmental Control System spacecraft system.Previous studies have looked at the uncertainty in a Computational Fluid Dynamics model for a single output variable at a single point, for example the re-attachment length of a backward facing step. For the flow regime being analyzed (turbulent, three-dimensional, incompressible), the error at a single point can propagate into the solution both via flow physics and numerical methods. Calculating the uncertainty in using Computational Fluid Dynamics to accurately predict airflow speeds around encapsulated spacecraft in is imperative to the success of future missions.

Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

NASA Technical Reports Server (NTRS)

Groves, Curtis E.

2013-01-01

Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This proposal describes an approach to validate the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft. The research described here is absolutely cutting edge. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional"validation by test only'' mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions. Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computationaf Fluid Dynamics can be used to veritY these requirements; however, the model must be validated by test data. The proposed research project includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT and OPEN FOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid . . . Dynamics model using the methodology found in "Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations". This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions. The method accounts for all uncertainty terms from both numerical and input variables. Objective three is to compile a table of uncertainty parameters that could be used to estimate the error in a Computational Fluid Dynamics model of the Environmental Control System /spacecraft system. Previous studies have looked at the uncertainty in a Computational Fluid Dynamics model for a single output variable at a single point, for example the re-attachment length of a backward facing step. To date, the author is the only person to look at the uncertainty in the entire computational domain. For the flow regime being analyzed (turbulent, threedimensional, incompressible), the error at a single point can propagate into the solution both via flow physics and numerical methods. Calculating the uncertainty in using Computational Fluid Dynamics to accurately predict airflow speeds around encapsulated spacecraft in is imperative to the success of future missions.

Head-target tracking control of well drilling

NASA Astrophysics Data System (ADS)

Agzamov, Z. V.

2018-05-01

The method of directional drilling trajectory control for oil and gas wells using predictive models is considered in the paper. The developed method does not apply optimization and therefore there is no need for the high-performance computing. Nevertheless, it allows following the well-plan with high precision taking into account process input saturation. Controller output is calculated both from the present target reference point of the well-plan and from well trajectory prediction with using the analytical model. This method allows following a well-plan not only on angular, but also on the Cartesian coordinates. Simulation of the control system has confirmed the high precision and operation performance with a wide range of random disturbance action.

Likelihood of achieving air quality targets under model uncertainties.

PubMed

Digar, Antara; Cohan, Daniel S; Cox, Dennis D; Kim, Byeong-Uk; Boylan, James W

2011-01-01

Regulatory attainment demonstrations in the United States typically apply a bright-line test to predict whether a control strategy is sufficient to attain an air quality standard. Photochemical models are the best tools available to project future pollutant levels and are a critical part of regulatory attainment demonstrations. However, because photochemical models are uncertain and future meteorology is unknowable, future pollutant levels cannot be predicted perfectly and attainment cannot be guaranteed. This paper introduces a computationally efficient methodology for estimating the likelihood that an emission control strategy will achieve an air quality objective in light of uncertainties in photochemical model input parameters (e.g., uncertain emission and reaction rates, deposition velocities, and boundary conditions). The method incorporates Monte Carlo simulations of a reduced form model representing pollutant-precursor response under parametric uncertainty to probabilistically predict the improvement in air quality due to emission control. The method is applied to recent 8-h ozone attainment modeling for Atlanta, Georgia, to assess the likelihood that additional controls would achieve fixed (well-defined) or flexible (due to meteorological variability and uncertain emission trends) targets of air pollution reduction. The results show that in certain instances ranking of the predicted effectiveness of control strategies may differ between probabilistic and deterministic analyses.

Modelling and multi-parametric control for delivery of anaesthetic agents.

PubMed

Dua, Pinky; Dua, Vivek; Pistikopoulos, Efstratios N

2010-06-01

This article presents model predictive controllers (MPCs) and multi-parametric model-based controllers for delivery of anaesthetic agents. The MPC can take into account constraints on drug delivery rates and state of the patient but requires solving an optimization problem at regular time intervals. The multi-parametric controller has all the advantages of the MPC and does not require repetitive solution of optimization problem for its implementation. This is achieved by obtaining the optimal drug delivery rates as a set of explicit functions of the state of the patient. The derivation of the controllers relies on using detailed models of the system. A compartmental model for the delivery of three drugs for anaesthesia is developed. The key feature of this model is that mean arterial pressure, cardiac output and unconsciousness of the patient can be simultaneously regulated. This is achieved by using three drugs: dopamine (DP), sodium nitroprusside (SNP) and isoflurane. A number of dynamic simulation experiments are carried out for the validation of the model. The model is then used for the design of model predictive and multi-parametric controllers, and the performance of the controllers is analyzed.

Predictive modeling of mosquito abundance and dengue transmission in Kenya

NASA Astrophysics Data System (ADS)

Caldwell, J.; Krystosik, A.; Mutuku, F.; Ndenga, B.; LaBeaud, D.; Mordecai, E.

2017-12-01

Approximately 390 million people are exposed to dengue virus every year, and with no widely available treatments or vaccines, predictive models of disease risk are valuable tools for vector control and disease prevention. The aim of this study was to modify and improve climate-driven predictive models of dengue vector abundance (Aedes spp. mosquitoes) and viral transmission to people in Kenya. We simulated disease transmission using a temperature-driven mechanistic model and compared model predictions with vector trap data for larvae, pupae, and adult mosquitoes collected between 2014 and 2017 at four sites across urban and rural villages in Kenya. We tested predictive capacity of our models using four temperature measurements (minimum, maximum, range, and anomalies) across daily, weekly, and monthly time scales. Our results indicate seasonal temperature variation is a key driving factor of Aedes mosquito abundance and disease transmission. These models can help vector control programs target specific locations and times when vectors are likely to be present, and can be modified for other Aedes-transmitted diseases and arboviral endemic regions around the world.

The use of vestibular models for design and evaluation of flight simulator motion

NASA Technical Reports Server (NTRS)

Bussolari, Steven R.; Young, Laurence R.; Lee, Alfred T.

1989-01-01

Quantitative models for the dynamics of the human vestibular system are applied to the design and evaluation of flight simulator platform motion. An optimal simulator motion control algorithm is generated to minimize the vector difference between perceived spatial orientation estimated in flight and in simulation. The motion controller has been implemented on the Vertical Motion Simulator at NASA Ames Research Center and evaluated experimentally through measurement of pilot performance and subjective rating during VTOL aircraft simulation. In general, pilot performance in a longitudinal tracking task (formation flight) did not appear to be sensitive to variations in platform motion condition as long as motion was present. However, pilot assessment of motion fidelity by means of a rating scale designed for this purpose, were sensitive to motion controller design. Platform motion generated with the optimal motion controller was found to be generally equivalent to that generated by conventional linear crossfeed washout. The vestibular models are used to evaluate the motion fidelity of transport category aircraft (Boeing 727) simulation in a pilot performance and simulator acceptability study at the Man-Vehicle Systems Research Facility at NASA Ames Research Center. Eighteen airline pilots, currently flying B-727, were given a series of flight scenarios in the simulator under various conditions of simulator motion. The scenarios were chosen to reflect the flight maneuvers that these pilots might expect to be given during a routine pilot proficiency check. Pilot performance and subjective rating of simulator fidelity was relatively insensitive to the motion condition, despite large differences in the amplitude of motion provided. This lack of sensitivity may be explained by means of the vestibular models, which predict little difference in the modeled motion sensations of the pilots when different motion conditions are imposed.

Investigation of models for large-scale meteorological prediction experiments

NASA Technical Reports Server (NTRS)

Spar, J.

1981-01-01

An attempt is made to compute the contributions of various surface boundary conditions to the monthly mean states generated by the 7 layer, 8 x 10 GISS climate model (Hansen et al., 1980), and also to examine the influence of initial conditions on the model climate simulations. Obvious climatic controls as the shape and rotation of the Earth, the solar radiation, and the dry composition of the atmosphere are fixed, and only the surface boundary conditions are altered in the various climate simulations.

An Agent-Based Model for Analyzing Control Policies and the Dynamic Service-Time Performance of a Capacity-Constrained Air Traffic Management Facility

NASA Technical Reports Server (NTRS)

Conway, Sheila R.

2006-01-01

Simple agent-based models may be useful for investigating air traffic control strategies as a precursory screening for more costly, higher fidelity simulation. Of concern is the ability of the models to capture the essence of the system and provide insight into system behavior in a timely manner and without breaking the bank. The method is put to the test with the development of a model to address situations where capacity is overburdened and potential for propagation of the resultant delay though later flights is possible via flight dependencies. The resultant model includes primitive representations of principal air traffic system attributes, namely system capacity, demand, airline schedules and strategy, and aircraft capability. It affords a venue to explore their interdependence in a time-dependent, dynamic system simulation. The scope of the research question and the carefully-chosen modeling fidelity did allow for the development of an agent-based model in short order. The model predicted non-linear behavior given certain initial conditions and system control strategies. Additionally, a combination of the model and dimensionless techniques borrowed from fluid systems was demonstrated that can predict the system s dynamic behavior across a wide range of parametric settings.

Nonlinear Recurrent Neural Network Predictive Control for Energy Distribution of a Fuel Cell Powered Robot

PubMed Central

Chen, Qihong; Long, Rong; Quan, Shuhai

2014-01-01

This paper presents a neural network predictive control strategy to optimize power distribution for a fuel cell/ultracapacitor hybrid power system of a robot. We model the nonlinear power system by employing time variant auto-regressive moving average with exogenous (ARMAX), and using recurrent neural network to represent the complicated coefficients of the ARMAX model. Because the dynamic of the system is viewed as operating- state- dependent time varying local linear behavior in this frame, a linear constrained model predictive control algorithm is developed to optimize the power splitting between the fuel cell and ultracapacitor. The proposed algorithm significantly simplifies implementation of the controller and can handle multiple constraints, such as limiting substantial fluctuation of fuel cell current. Experiment and simulation results demonstrate that the control strategy can optimally split power between the fuel cell and ultracapacitor, limit the change rate of the fuel cell current, and so as to extend the lifetime of the fuel cell. PMID:24707206

Decisions on control of foot-and-mouth disease informed using model predictions.

PubMed

Halasa, T; Willeberg, P; Christiansen, L E; Boklund, A; Alkhamis, M; Perez, A; Enøe, C

2013-11-01

The decision on whether or not to change the control strategy, such as introducing emergency vaccination, is perhaps one of the most difficult decisions faced by the veterinary authorities during a foot-and-mouth disease (FMD) epidemic. A simple tool that may predict the epidemic outcome and consequences would be useful to assist the veterinary authorities in the decision-making process. A previously proposed simple quantitative tool based on the first 14 days outbreaks (FFO) of FMD was used with results from an FMD simulation exercise. Epidemic outcomes included the number of affected herds, epidemic duration, geographical size and costs. The first 14 days spatial spread (FFS) was also included to further support the prediction. The epidemic data was obtained from a Danish version (DTU-DADS) of a pre-existing FMD simulation model (Davis Animal Disease Spread - DADS) adapted to model the spread of FMD in Denmark. The European Union (EU) and Danish regulations for FMD control were used in the simulation. The correlations between FFO and FFS and the additional number of affected herds after day 14 following detection of the first infected herd were 0.66 and 0.82, respectively. The variation explained by the FFO at day 14 following detection was high (P-value<0.001). This indicates that the FFO may take a part in the decision of whether or not to intensify FMD control, for instance by introducing emergency vaccination and/or pre-emptive depopulation, which might prevent a "catastrophic situation". A significant part of the variation was explained by supplementing the model with the FFS (P-value<0.001). Furthermore, the type of the index-herd was also a significant predictor of the epidemic outcomes (P-value<0.05). The results of the current study suggest that national veterinary authorities should consider to model their national situation and to use FFO and FFS to help planning and updating their contingency plans and FMD emergency control strategies. Copyright © 2013 Elsevier B.V. All rights reserved.

Computational Model of Human and System Dynamics in Free Flight: Studies in Distributed Control Technologies

NASA Technical Reports Server (NTRS)

Corker, Kevin M.; Pisanich, Gregory; Lebacqz, J. Victor (Technical Monitor)

1998-01-01

This paper presents a set of studies in full mission simulation and the development of a predictive computational model of human performance in control of complex airspace operations. NASA and the FAA have initiated programs of research and development to provide flight crew, airline operations and air traffic managers with automation aids to increase capacity in en route and terminal area to support the goals of safe, flexible, predictable and efficient operations. In support of these developments, we present a computational model to aid design that includes representation of multiple cognitive agents (both human operators and intelligent aiding systems). The demands of air traffic management require representation of many intelligent agents sharing world-models, coordinating action/intention, and scheduling goals and actions in a potentially unpredictable world of operations. The operator-model structure includes attention functions, action priority, and situation assessment. The cognitive model has been expanded to include working memory operations including retrieval from long-term store, and interference. The operator's activity structures have been developed to provide for anticipation (knowledge of the intention and action of remote operators), and to respond to failures of the system and other operators in the system in situation-specific paradigms. System stability and operator actions can be predicted by using the model. The model's predictive accuracy was verified using the full-mission simulation data of commercial flight deck operations with advanced air traffic management techniques.

The effects of school closures on influenza outbreaks and pandemics: systematic review of simulation studies.

PubMed

Jackson, Charlotte; Mangtani, Punam; Hawker, Jeremy; Olowokure, Babatunde; Vynnycky, Emilia

2014-01-01

School closure is a potential intervention during an influenza pandemic and has been investigated in many modelling studies. To systematically review the effects of school closure on influenza outbreaks as predicted by simulation studies. We searched Medline and Embase for relevant modelling studies published by the end of October 2012, and handsearched key journals. We summarised the predicted effects of school closure on the peak and cumulative attack rates and the duration of the epidemic. We investigated how these predictions depended on the basic reproduction number, the timing and duration of closure and the assumed effects of school closures on contact patterns. School closures were usually predicted to be most effective if they caused large reductions in contact, if transmissibility was low (e.g. a basic reproduction number <2), and if attack rates were higher in children than in adults. The cumulative attack rate was expected to change less than the peak, but quantitative predictions varied (e.g. reductions in the peak were frequently 20-60% but some studies predicted >90% reductions or even increases under certain assumptions). This partly reflected differences in model assumptions, such as those regarding population contact patterns. Simulation studies suggest that school closure can be a useful control measure during an influenza pandemic, particularly for reducing peak demand on health services. However, it is difficult to accurately quantify the likely benefits. Further studies of the effects of reactive school closures on contact patterns are needed to improve the accuracy of model predictions.

Automated System Checkout to Support Predictive Maintenance for the Reusable Launch Vehicle

NASA Technical Reports Server (NTRS)

Patterson-Hine, Ann; Deb, Somnath; Kulkarni, Deepak; Wang, Yao; Lau, Sonie (Technical Monitor)

1998-01-01

The Propulsion Checkout and Control System (PCCS) is a predictive maintenance software system. The real-time checkout procedures and diagnostics are designed to detect components that need maintenance based on their condition, rather than using more conventional approaches such as scheduled or reliability centered maintenance. Predictive maintenance can reduce turn-around time and cost and increase safety as compared to conventional maintenance approaches. Real-time sensor validation, limit checking, statistical anomaly detection, and failure prediction based on simulation models are employed. Multi-signal models, useful for testability analysis during system design, are used during the operational phase to detect and isolate degraded or failed components. The TEAMS-RT real-time diagnostic engine was developed to utilize the multi-signal models by Qualtech Systems, Inc. Capability of predicting the maintenance condition was successfully demonstrated with a variety of data, from simulation to actual operation on the Integrated Propulsion Technology Demonstrator (IPTD) at Marshall Space Flight Center (MSFC). Playback of IPTD valve actuations for feature recognition updates identified an otherwise undetectable Main Propulsion System 12 inch prevalve degradation. The algorithms were loaded into the Propulsion Checkout and Control System for further development and are the first known application of predictive Integrated Vehicle Health Management to an operational cryogenic testbed. The software performed successfully in real-time, meeting the required performance goal of 1 second cycle time.

Predictive IP controller for robust position control of linear servo system.

PubMed

Lu, Shaowu; Zhou, Fengxing; Ma, Yajie; Tang, Xiaoqi

2016-07-01

Position control is a typical application of linear servo system. In this paper, to reduce the system overshoot, an integral plus proportional (IP) controller is used in the position control implementation. To further improve the control performance, a gain-tuning IP controller based on a generalized predictive control (GPC) law is proposed. Firstly, to represent the dynamics of the position loop, a second-order linear model is used and its model parameters are estimated on-line by using a recursive least squares method. Secondly, based on the GPC law, an optimal control sequence is obtained by using receding horizon, then directly supplies the IP controller with the corresponding control parameters in the real operations. Finally, simulation and experimental results are presented to show the efficiency of proposed scheme. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Lysine production from methanol at 50 degrees C using Bacillus methanolicus: Modeling volume control, lysine concentration, and productivity using a three-phase continuous simulation.

PubMed

Lee, G H; Hur, W; Bremmon, C E; Flickinger, M C

1996-03-20

A simulation was developed based on experimental data obtained in a 14-L reactor to predict the growth and L-lysine accumulation kinetics, and change in volume of a large-scale (250-m(3)) Bacillus methanolicus methanol-based process. Homoserine auxotrophs of B. methanolicus MGA3 are unique methylotrophs because of the ability to secrete lysine during aerobic growth and threonine starvation at 50 degrees C. Dissolved methanol (100 mM), pH, dissolved oxygen tension (0.063 atm), and threonine levels were controlled to obtain threonine-limited conditions and high-cell density (25 g dry cell weight/L) in a 14-L reactor. As a fed-batch process, the additions of neat methanol (fed on demand), threonine, and other nutrients cause the volume of the fermentation to increase and the final lysine concentration to decrease. In addition, water produced as a result of methanol metabolism contributes to the increase in the volume of the reactor. A three-phase approach was used to predict the rate of change of culture volume based on carbon dioxide production and methanol consumption. This model was used for the evaluation of volume control strategies to optimize lysine productivity. A constant volume reactor process with variable feeding and continuous removal of broth and cells (VF(cstr)) resulted in higher lysine productivity than a fed-batch process without volume control. This model predicts the variation in productivity of lysine with changes in growth and in specific lysine productivity. Simple modifications of the model allows one to investigate other high-lysine-secreting strains with different growth and lysine productivity characteristics. Strain NOA2#13A5-2 which secretes lysine and other end-products were modeled using both growth and non-growth-associated lysine productivity. A modified version of this model was used to simulate the change in culture volume of another L-lysine producing mutant (NOA2#13A52-8A66) with reduced secretion of end-products. The modified simulation indicated that growth-associated production dominates in strain NOA2#13A52-8A66. (c) 1996 John Wiley & Sons, Inc.

Short-term responses of leaf growth rate to water deficit scale up to whole-plant and crop levels: an integrated modelling approach in maize.

PubMed

Chenu, Karine; Chapman, Scott C; Hammer, Graeme L; McLean, Greg; Salah, Halim Ben Haj; Tardieu, François

2008-03-01

Physiological and genetic studies of leaf growth often focus on short-term responses, leaving a gap to whole-plant models that predict biomass accumulation, transpiration and yield at crop scale. To bridge this gap, we developed a model that combines an existing model of leaf 6 expansion in response to short-term environmental variations with a model coordinating the development of all leaves of a plant. The latter was based on: (1) rates of leaf initiation, appearance and end of elongation measured in field experiments; and (2) the hypothesis of an independence of the growth between leaves. The resulting whole-plant leaf model was integrated into the generic crop model APSIM which provided dynamic feedback of environmental conditions to the leaf model and allowed simulation of crop growth at canopy level. The model was tested in 12 field situations with contrasting temperature, evaporative demand and soil water status. In observed and simulated data, high evaporative demand reduced leaf area at the whole-plant level, and short water deficits affected only leaves developing during the stress, either visible or still hidden in the whorl. The model adequately simulated whole-plant profiles of leaf area with a single set of parameters that applied to the same hybrid in all experiments. It was also suitable to predict biomass accumulation and yield of a similar hybrid grown in different conditions. This model extends to field conditions existing knowledge of the environmental controls of leaf elongation, and can be used to simulate how their genetic controls flow through to yield.

Limit of validity of Ostwald's rule of stages in a statistical mechanical model of crystallization.

PubMed

Hedges, Lester O; Whitelam, Stephen

2011-10-28

We have only rules of thumb with which to predict how a material will crystallize, chief among which is Ostwald's rule of stages. It states that the first phase to appear upon transformation of a parent phase is the one closest to it in free energy. Although sometimes upheld, the rule is without theoretical foundation and is not universally obeyed, highlighting the need for microscopic understanding of crystallization controls. Here we study in detail the crystallization pathways of a prototypical model of patchy particles. The range of crystallization pathways it exhibits is richer than can be predicted by Ostwald's rule, but a combination of simulation and analytic theory reveals clearly how these pathways are selected by microscopic parameters. Our results suggest strategies for controlling self-assembly pathways in simulation and experiment.

Disturbance observer based model predictive control for accurate atmospheric entry of spacecraft

NASA Astrophysics Data System (ADS)

Wu, Chao; Yang, Jun; Li, Shihua; Li, Qi; Guo, Lei

2018-05-01

Facing the complex aerodynamic environment of Mars atmosphere, a composite atmospheric entry trajectory tracking strategy is investigated in this paper. External disturbances, initial states uncertainties and aerodynamic parameters uncertainties are the main problems. The composite strategy is designed to solve these problems and improve the accuracy of Mars atmospheric entry. This strategy includes a model predictive control for optimized trajectory tracking performance, as well as a disturbance observer based feedforward compensation for external disturbances and uncertainties attenuation. 500-run Monte Carlo simulations show that the proposed composite control scheme achieves more precise Mars atmospheric entry (3.8 km parachute deployment point distribution error) than the baseline control scheme (8.4 km) and integral control scheme (5.8 km).

Evaluation of wave runup predictions from numerical and parametric models

USGS Publications Warehouse

Stockdon, Hilary F.; Thompson, David M.; Plant, Nathaniel G.; Long, Joseph W.

2014-01-01

Wave runup during storms is a primary driver of coastal evolution, including shoreline and dune erosion and barrier island overwash. Runup and its components, setup and swash, can be predicted from a parameterized model that was developed by comparing runup observations to offshore wave height, wave period, and local beach slope. Because observations during extreme storms are often unavailable, a numerical model is used to simulate the storm-driven runup to compare to the parameterized model and then develop an approach to improve the accuracy of the parameterization. Numerically simulated and parameterized runup were compared to observations to evaluate model accuracies. The analysis demonstrated that setup was accurately predicted by both the parameterized model and numerical simulations. Infragravity swash heights were most accurately predicted by the parameterized model. The numerical model suffered from bias and gain errors that depended on whether a one-dimensional or two-dimensional spatial domain was used. Nonetheless, all of the predictions were significantly correlated to the observations, implying that the systematic errors can be corrected. The numerical simulations did not resolve the incident-band swash motions, as expected, and the parameterized model performed best at predicting incident-band swash heights. An assimilated prediction using a weighted average of the parameterized model and the numerical simulations resulted in a reduction in prediction error variance. Finally, the numerical simulations were extended to include storm conditions that have not been previously observed. These results indicated that the parameterized predictions of setup may need modification for extreme conditions; numerical simulations can be used to extend the validity of the parameterized predictions of infragravity swash; and numerical simulations systematically underpredict incident swash, which is relatively unimportant under extreme conditions.

Hybrid and electric advanced vehicle systems (heavy) simulation

NASA Technical Reports Server (NTRS)

Hammond, R. A.; Mcgehee, R. K.

1981-01-01

A computer program to simulate hybrid and electric advanced vehicle systems (HEAVY) is described. It is intended for use early in the design process: concept evaluation, alternative comparison, preliminary design, control and management strategy development, component sizing, and sensitivity studies. It allows the designer to quickly, conveniently, and economically predict the performance of a proposed drive train. The user defines the system to be simulated using a library of predefined component models that may be connected to represent a wide variety of propulsion systems. The development of three models are discussed as examples.

Simulating the elimination of sleeping sickness with an agent-based model.

PubMed

Grébaut, Pascal; Girardin, Killian; Fédérico, Valentine; Bousquet, François

2016-01-01

Although Human African Trypanosomiasis is largely considered to be in the process of extinction today, the persistence of human and animal reservoirs, as well as the vector, necessitates a laborious elimination process. In this context, modeling could be an effective tool to evaluate the ability of different public health interventions to control the disease. Using the Cormas ® system, we developed HATSim, an agent-based model capable of simulating the possible endemic evolutions of sleeping sickness and the ability of National Control Programs to eliminate the disease. This model takes into account the analysis of epidemiological, entomological, and ecological data from field studies conducted during the last decade, making it possible to predict the evolution of the disease within this area over a 5-year span. In this article, we first present HATSim according to the Overview, Design concepts, and Details (ODD) protocol that is classically used to describe agent-based models, then, in a second part, we present predictive results concerning the evolution of Human African Trypanosomiasis in the village of Lambi (Cameroon), in order to illustrate the interest of such a tool. Our results are consistent with what was observed in the field by the Cameroonian National Control Program (CNCP). Our simulations also revealed that regular screening can be sufficient, although vector control applied to all areas with human activities could be significantly more efficient. Our results indicate that the current model can already help decision-makers in planning the elimination of the disease in foci. © P. Grébaut et al., published by EDP Sciences, 2016.

Generalized Predictive and Neural Generalized Predictive Control of Aerospace Systems

NASA Technical Reports Server (NTRS)

Kelkar, Atul G.

2000-01-01

The research work presented in this thesis addresses the problem of robust control of uncertain linear and nonlinear systems using Neural network-based Generalized Predictive Control (NGPC) methodology. A brief overview of predictive control and its comparison with Linear Quadratic (LQ) control is given to emphasize advantages and drawbacks of predictive control methods. It is shown that the Generalized Predictive Control (GPC) methodology overcomes the drawbacks associated with traditional LQ control as well as conventional predictive control methods. It is shown that in spite of the model-based nature of GPC it has good robustness properties being special case of receding horizon control. The conditions for choosing tuning parameters for GPC to ensure closed-loop stability are derived. A neural network-based GPC architecture is proposed for the control of linear and nonlinear uncertain systems. A methodology to account for parametric uncertainty in the system is proposed using on-line training capability of multi-layer neural network. Several simulation examples and results from real-time experiments are given to demonstrate the effectiveness of the proposed methodology.

Impact of rain gauge quality control and interpolation on streamflow simulation: an application to the Warwick catchment, Australia

NASA Astrophysics Data System (ADS)

Liu, Shulun; Li, Yuan; Pauwels, Valentijn R. N.; Walker, Jeffrey P.

2017-12-01

Rain gauges are widely used to obtain temporally continuous point rainfall records, which are then interpolated into spatially continuous data to force hydrological models. However, rainfall measurements and interpolation procedure are subject to various uncertainties, which can be reduced by applying quality control and selecting appropriate spatial interpolation approaches. Consequently, the integrated impact of rainfall quality control and interpolation on streamflow simulation has attracted increased attention but not been fully addressed. This study applies a quality control procedure to the hourly rainfall measurements obtained in the Warwick catchment in eastern Australia. The grid-based daily precipitation from the Australian Water Availability Project was used as a reference. The Pearson correlation coefficient between the daily accumulation of gauged rainfall and the reference data was used to eliminate gauges with significant quality issues. The unrealistic outliers were censored based on a comparison between gauged rainfall and the reference. Four interpolation methods, including the inverse distance weighting (IDW), nearest neighbors (NN), linear spline (LN), and ordinary Kriging (OK), were implemented. The four methods were firstly assessed through a cross-validation using the quality-controlled rainfall data. The impacts of the quality control and interpolation on streamflow simulation were then evaluated through a semi-distributed hydrological model. The results showed that the Nash–Sutcliffe model efficiency coefficient (NSE) and Bias of the streamflow simulations were significantly improved after quality control. In the cross-validation, the IDW and OK methods resulted in good interpolation rainfall, while the NN led to the worst result. In term of the impact on hydrological prediction, the IDW led to the most consistent streamflow predictions with the observations, according to the validation at five streamflow-gauged locations. The OK method performed second best according to streamflow predictions at the five gauges in the calibration period (01/01/2007–31/12/2011) and four gauges during the validation period (01/01/2012–30/06/2014). However, NN produced the worst prediction at the outlet of the catchment in the validation period, indicating a low robustness. While the IDW exhibited the best performance in the study catchment in terms of accuracy, robustness and efficiency, more general recommendations on the selection of rainfall interpolation methods need to be further explored.

Impact of rain gauge quality control and interpolation on streamflow simulation: an application to the Warwick catchment, Australia

NASA Astrophysics Data System (ADS)

Liu, Shulun; Li, Yuan; Pauwels, Valentijn R. N.; Walker, Jeffrey P.

2018-01-01

Rain gauges are widely used to obtain temporally continuous point rainfall records, which are then interpolated into spatially continuous data to force hydrological models. However, rainfall measurements and interpolation procedure are subject to various uncertainties, which can be reduced by applying quality control and selecting appropriate spatial interpolation approaches. Consequently, the integrated impact of rainfall quality control and interpolation on streamflow simulation has attracted increased attention but not been fully addressed. This study applies a quality control procedure to the hourly rainfall measurements obtained in the Warwick catchment in eastern Australia. The grid-based daily precipitation from the Australian Water Availability Project was used as a reference. The Pearson correlation coefficient between the daily accumulation of gauged rainfall and the reference data was used to eliminate gauges with significant quality issues. The unrealistic outliers were censored based on a comparison between gauged rainfall and the reference. Four interpolation methods, including the inverse distance weighting (IDW), nearest neighbors (NN), linear spline (LN), and ordinary Kriging (OK), were implemented. The four methods were firstly assessed through a cross-validation using the quality-controlled rainfall data. The impacts of the quality control and interpolation on streamflow simulation were then evaluated through a semi-distributed hydrological model. The results showed that the Nash–Sutcliffe model efficiency coefficient (NSE) and Bias of the streamflow simulations were significantly improved after quality control. In the cross-validation, the IDW and OK methods resulted in good interpolation rainfall, while the NN led to the worst result. In term of the impact on hydrological prediction, the IDW led to the most consistent streamflow predictions with the observations, according to the validation at five streamflow-gauged locations. The OK method performed second best according to streamflow predictions at the five gauges in the calibration period (01/01/2007–31/12/2011) and four gauges during the validation period (01/01/2012–30/06/2014). However, NN produced the worst prediction at the outlet of the catchment in the validation period, indicating a low robustness. While the IDW exhibited the best performance in the study catchment in terms of accuracy, robustness and efficiency, more general recommendations on the selection of rainfall interpolation methods need to be further explored.

The Mira-Titan Universe. II. Matter Power Spectrum Emulation

NASA Astrophysics Data System (ADS)

Lawrence, Earl; Heitmann, Katrin; Kwan, Juliana; Upadhye, Amol; Bingham, Derek; Habib, Salman; Higdon, David; Pope, Adrian; Finkel, Hal; Frontiere, Nicholas

2017-09-01

We introduce a new cosmic emulator for the matter power spectrum covering eight cosmological parameters. Targeted at optical surveys, the emulator provides accurate predictions out to a wavenumber k˜ 5 Mpc-1 and redshift z≤slant 2. In addition to covering the standard set of ΛCDM parameters, massive neutrinos and a dynamical dark energy of state are included. The emulator is built on a sample set of 36 cosmological models, carefully chosen to provide accurate predictions over the wide and large parameter space. For each model, we have performed a high-resolution simulation, augmented with 16 medium-resolution simulations and TimeRG perturbation theory results to provide accurate coverage over a wide k-range; the data set generated as part of this project is more than 1.2Pbytes. With the current set of simulated models, we achieve an accuracy of approximately 4%. Because the sampling approach used here has established convergence and error-control properties, follow-up results with more than a hundred cosmological models will soon achieve ˜ 1 % accuracy. We compare our approach with other prediction schemes that are based on halo model ideas and remapping approaches. The new emulator code is publicly available.

The Mira-Titan Universe. II. Matter Power Spectrum Emulation

DOE PAGES

Lawrence, Earl; Heitmann, Katrin; Kwan, Juliana; ...

2017-09-20

We introduce a new cosmic emulator for the matter power spectrum covering eight cosmological parameters. Targeted at optical surveys, the emulator provides accurate predictions out to a wavenumber k ~ 5Mpc -1 and redshift z ≤ 2. Besides covering the standard set of CDM parameters, massive neutrinos and a dynamical dark energy of state are included. The emulator is built on a sample set of 36 cosmological models, carefully chosen to provide accurate predictions over the wide and large parameter space. For each model, we have performed a high-resolution simulation, augmented with sixteen medium-resolution simulations and TimeRG perturbation theory resultsmore » to provide accurate coverage of a wide k-range; the dataset generated as part of this project is more than 1.2Pbyte. With the current set of simulated models, we achieve an accuracy of approximately 4%. Because the sampling approach used here has established convergence and error-control properties, follow-on results with more than a hundred cosmological models will soon achieve ~1% accuracy. We compare our approach with other prediction schemes that are based on halo model ideas and remapping approaches. The new emulator code is publicly available.« less

The Mira-Titan Universe. II. Matter Power Spectrum Emulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lawrence, Earl; Heitmann, Katrin; Kwan, Juliana

We introduce a new cosmic emulator for the matter power spectrum covering eight cosmological parameters. Targeted at optical surveys, the emulator provides accurate predictions out to a wavenumber k similar to 5 Mpc(-1) and redshift z <= 2. In addition to covering the standard set of Lambda CDM parameters, massive neutrinos and a dynamical dark energy of state are included. The emulator is built on a sample set of 36 cosmological models, carefully chosen to provide accurate predictions over the wide and large parameter space. For each model, we have performed a high-resolution simulation, augmented with 16 medium-resolution simulations andmore » TimeRG perturbation theory results to provide accurate coverage over a wide k-range; the data set generated as part of this project is more than 1.2Pbytes. With the current set of simulated models, we achieve an accuracy of approximately 4%. Because the sampling approach used here has established convergence and error-control properties, follow-up results with more than a hundred cosmological models will soon achieve similar to 1% accuracy. We compare our approach with other prediction schemes that are based on halo model ideas and remapping approaches.« less

The Mira-Titan Universe. II. Matter Power Spectrum Emulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lawrence, Earl; Heitmann, Katrin; Kwan, Juliana

We introduce a new cosmic emulator for the matter power spectrum covering eight cosmological parameters. Targeted at optical surveys, the emulator provides accurate predictions out to a wavenumber k ~ 5Mpc -1 and redshift z ≤ 2. Besides covering the standard set of CDM parameters, massive neutrinos and a dynamical dark energy of state are included. The emulator is built on a sample set of 36 cosmological models, carefully chosen to provide accurate predictions over the wide and large parameter space. For each model, we have performed a high-resolution simulation, augmented with sixteen medium-resolution simulations and TimeRG perturbation theory resultsmore » to provide accurate coverage of a wide k-range; the dataset generated as part of this project is more than 1.2Pbyte. With the current set of simulated models, we achieve an accuracy of approximately 4%. Because the sampling approach used here has established convergence and error-control properties, follow-on results with more than a hundred cosmological models will soon achieve ~1% accuracy. We compare our approach with other prediction schemes that are based on halo model ideas and remapping approaches. The new emulator code is publicly available.« less

A combined PHREEQC-2/parallel fracture model for the simulation of laminar/non-laminar flow and contaminant transport with reactions

NASA Astrophysics Data System (ADS)

Masciopinto, Costantino; Volpe, Angela; Palmiotta, Domenico; Cherubini, Claudia

2010-09-01

A combination of a parallel fracture model with the PHREEQC-2 geochemical model was developed to simulate sequential flow and chemical transport with reactions in fractured media where both laminar and turbulent flows occur. The integration of non-laminar flow resistances in one model produced relevant effects on water flow velocities, thus improving model prediction capabilities on contaminant transport. The proposed conceptual model consists of 3D rock-blocks, separated by horizontal bedding plane fractures with variable apertures. Particle tracking solved the transport equations for conservative compounds and provided input for PHREEQC-2. For each cluster of contaminant pathways, PHREEQC-2 determined the concentration for mass-transfer, sorption/desorption, ion exchange, mineral dissolution/precipitation and biodegradation, under kinetically controlled reactive processes of equilibrated chemical species. Field tests have been performed for the code verification. As an example, the combined model has been applied to a contaminated fractured aquifer of southern Italy in order to simulate the phenol transport. The code correctly fitted the field available data and also predicted a possible rapid depletion of phenols as a result of an increased biodegradation rate induced by a simulated artificial injection of nitrates, upgradient to the sources.

Peak-Seeking Optimization of Trim for Reduced Fuel Consumption: Architecture and Performance Predictions

NASA Technical Reports Server (NTRS)

Schaefer, Jacob; Brown, Nelson

2013-01-01

A peak-seeking control approach for real-time trim configuration optimization for reduced fuel consumption has been developed by researchers at the National Aeronautics and Space Administration (NASA) Dryden Flight Research Center to address the goals of the NASA Environmentally Responsible Aviation project to reduce fuel burn and emissions. The peak-seeking control approach is based on a steepest-descent algorithm using a time-varying Kalman filter to estimate the gradient of a performance function of fuel flow versus control surface positions. In real-time operation, deflections of symmetric ailerons, trailing-edge flaps, and leading-edge flaps of an FA-18 airplane (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) are controlled for optimization of fuel flow. This presentation presents the design and integration of this peak-seeking controller on a modified NASA FA-18 airplane with research flight control computers. A research flight was performed to collect data to build a realistic model of the performance function and characterize measurement noise. This model was then implemented into a nonlinear six-degree-of-freedom FA-18 simulation along with the peak-seeking control algorithm. With the goal of eventual flight tests, the algorithm was first evaluated in the improved simulation environment. Results from the simulation predict good convergence on minimum fuel flow with a 2.5-percent reduction in fuel flow relative to the baseline trim of the aircraft.

Peak-Seeking Optimization of Trim for Reduced Fuel Consumption: Architecture and Performance Predictions

NASA Technical Reports Server (NTRS)

Schaefer, Jacob; Brown, Nelson A.

2013-01-01

A peak-seeking control approach for real-time trim configuration optimization for reduced fuel consumption has been developed by researchers at the National Aeronautics and Space Administration (NASA) Dryden Flight Research Center to address the goals of the NASA Environmentally Responsible Aviation project to reduce fuel burn and emissions. The peak-seeking control approach is based on a steepest-descent algorithm using a time-varying Kalman filter to estimate the gradient of a performance function of fuel flow versus control surface positions. In real-time operation, deflections of symmetric ailerons, trailing-edge flaps, and leading-edge flaps of an F/A-18 airplane (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) are controlled for optimization of fuel flow. This paper presents the design and integration of this peak-seeking controller on a modified NASA F/A-18 airplane with research flight control computers. A research flight was performed to collect data to build a realistic model of the performance function and characterize measurement noise. This model was then implemented into a nonlinear six-degree-of-freedom F/A-18 simulation along with the peak-seeking control algorithm. With the goal of eventual flight tests, the algorithm was first evaluated in the improved simulation environment. Results from the simulation predict good convergence on minimum fuel flow with a 2.5-percent reduction in fuel flow relative to the baseline trim of the aircraft.

Dissipative-particle-dynamics model of biofilm growth

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Zhijie; Meakin, Paul; Tartakovsky, Alexandre M.

2011-06-13

A dissipative particle dynamics (DPD) model for the quantitative simulation of biofilm growth controlled by substrate (nutrient) consumption, advective and diffusive substrate transport, and hydrodynamic interactions with fluid flow (including fragmentation and reattachment) is described. The model was used to simulate biomass growth, decay, and spreading. It predicts how the biofilm morphology depends on flow conditions, biofilm growth kinetics, the rheomechanical properties of the biofilm and adhesion to solid surfaces. The morphology of the model biofilm depends strongly on its rigidity and the magnitude of the body force that drives the fluid over the biofilm.

Quantitative modeling of soil genesis processes

NASA Technical Reports Server (NTRS)

Levine, E. R.; Knox, R. G.; Kerber, A. G.

1992-01-01

For fine spatial scale simulation, a model is being developed to predict changes in properties over short-, meso-, and long-term time scales within horizons of a given soil profile. Processes that control these changes can be grouped into five major process clusters: (1) abiotic chemical reactions; (2) activities of organisms; (3) energy balance and water phase transitions; (4) hydrologic flows; and (5) particle redistribution. Landscape modeling of soil development is possible using digitized soil maps associated with quantitative soil attribute data in a geographic information system (GIS) framework to which simulation models are applied.

Model-on-Demand Predictive Control for Nonlinear Hybrid Systems With Application to Adaptive Behavioral Interventions

PubMed Central

Nandola, Naresh N.; Rivera, Daniel E.

2011-01-01

This paper presents a data-centric modeling and predictive control approach for nonlinear hybrid systems. System identification of hybrid systems represents a challenging problem because model parameters depend on the mode or operating point of the system. The proposed algorithm applies Model-on-Demand (MoD) estimation to generate a local linear approximation of the nonlinear hybrid system at each time step, using a small subset of data selected by an adaptive bandwidth selector. The appeal of the MoD approach lies in the fact that model parameters are estimated based on a current operating point; hence estimation of locations or modes governed by autonomous discrete events is achieved automatically. The local MoD model is then converted into a mixed logical dynamical (MLD) system representation which can be used directly in a model predictive control (MPC) law for hybrid systems using multiple-degree-of-freedom tuning. The effectiveness of the proposed MoD predictive control algorithm for nonlinear hybrid systems is demonstrated on a hypothetical adaptive behavioral intervention problem inspired by Fast Track, a real-life preventive intervention for improving parental function and reducing conduct disorder in at-risk children. Simulation results demonstrate that the proposed algorithm can be useful for adaptive intervention problems exhibiting both nonlinear and hybrid character. PMID:21874087

Evolution of Bacterial Suicide

NASA Astrophysics Data System (ADS)

Tchernookov, Martin; Nemenman, Ilya

2013-03-01

While active, controlled cellular suicide (autolysis) in bacteria is commonly observed, it has been hard to argue that autolysis can be beneficial to an individual who commits it. We propose a theoretical model that predicts that bacterial autolysis is evolutionarily advantageous to an individualand would fixate in physically structured environments for stationary phase colonies. We perform spatially resolved agent-based simulations of the model, which predict that lower mixing in the environment results in fixation of a higher autolysis rate from a single mutated cell, regardless of the colony's genetic diversity. We argue that quorum sensing will fixate as well, even if initially rare, if it is coupled to controlling the autolysis rate. The model does not predict a strong additional competitive advantage for cells where autolysis is controlled by quorum sensing systems that distinguish self from nonself. These predictions are broadly supported by recent experimental results in B. subtilisand S. pneumoniae. Research partially supported by the James S McDonnell Foundation grant No. 220020321 and by HFSP grant No. RGY0084/2011.

Springback Simulation and Compensation for High Strength Parts Using JSTAMP

NASA Astrophysics Data System (ADS)

Shindo, Terumasa; Sugitomo, Nobuhiko; Ma, Ninshu

2011-08-01

The stamping parts made from high strength steel have a large springback which is difficult to control. With the development of simulation technology, the springback can be accurately predicted using advanced kinematic material models and CAE systems. In this paper, a stamping process for a pillar part made from several classes of high strength steel was simulated using a Yoshida-Uemori kinematic material model and the springback was well predicted. To obtain the desired part shape, CAD surfaces of the stamping tools were compensated by a CAE system JSTAMP. After applying the compensation 2 or 3 times, the dimension accuracy of the simulation for the part shape achieved was about 0.5 mm. The compensated CAD surfaces of the stamping tools were directly exported from JSTAMP to CAM for machining. The effectiveness of the compensation was verified by an experiment using the compensated tools.

Numerical simulation study on rolling-chemical milling process of aluminum-lithium alloy skin panel

NASA Astrophysics Data System (ADS)

Huang, Z. B.; Sun, Z. G.; Sun, X. F.; Li, X. Q.

2017-09-01

Single curvature parts such as aircraft fuselage skin panels are usually manufactured by rolling-chemical milling process, which is usually faced with the problem of geometric accuracy caused by springback. In most cases, the methods of manual adjustment and multiple roll bending are used to control or eliminate the springback. However, these methods can cause the increase of product cost and cycle, and lead to material performance degradation. Therefore, it is of significance to precisely control the springback of rolling-chemical milling process. In this paper, using the method of experiment and numerical simulation on rolling-chemical milling process, the simulation model for rolling-chemical milling process of 2060-T8 aluminum-lithium alloy skin was established and testified by the comparison between numerical simulation and experiment results for the validity. Then, based on the numerical simulation model, the relative technological parameters which influence on the curvature of the skin panel were analyzed. Finally, the prediction of springback and the compensation can be realized by controlling the process parameters.

Modeling temperature variations in a pilot plant thermophilic anaerobic digester.

PubMed

Valle-Guadarrama, Salvador; Espinosa-Solares, Teodoro; López-Cruz, Irineo L; Domaschko, Max

2011-05-01

A model that predicts temperature changes in a pilot plant thermophilic anaerobic digester was developed based on fundamental thermodynamic laws. The methodology utilized two simulation strategies. In the first, model equations were solved through a searching routine based on a minimal square optimization criterion, from which the overall heat transfer coefficient values, for both biodigester and heat exchanger, were determined. In the second, the simulation was performed with variable values of these overall coefficients. The prediction with both strategies allowed reproducing experimental data within 5% of the temperature span permitted in the equipment by the system control, which validated the model. The temperature variation was affected by the heterogeneity of the feeding and extraction processes, by the heterogeneity of the digestate recirculation through the heating system and by the lack of a perfect mixing inside the biodigester tank. The use of variable overall heat transfer coefficients improved the temperature change prediction and reduced the effect of a non-ideal performance of the pilot plant modeled.

Evaluating performance of risk identification methods through a large-scale simulation of observational data.

PubMed

Ryan, Patrick B; Schuemie, Martijn J

2013-10-01

There has been only limited evaluation of statistical methods for identifying safety risks of drug exposure in observational healthcare data. Simulations can support empirical evaluation, but have not been shown to adequately model the real-world phenomena that challenge observational analyses. To design and evaluate a probabilistic framework (OSIM2) for generating simulated observational healthcare data, and to use this data for evaluating the performance of methods in identifying associations between drug exposure and health outcomes of interest. Seven observational designs, including case-control, cohort, self-controlled case series, and self-controlled cohort design were applied to 399 drug-outcome scenarios in 6 simulated datasets with no effect and injected relative risks of 1.25, 1.5, 2, 4, and 10, respectively. Longitudinal data for 10 million simulated patients were generated using a model derived from an administrative claims database, with associated demographics, periods of drug exposure derived from pharmacy dispensings, and medical conditions derived from diagnoses on medical claims. Simulation validation was performed through descriptive comparison with real source data. Method performance was evaluated using Area Under ROC Curve (AUC), bias, and mean squared error. OSIM2 replicates prevalence and types of confounding observed in real claims data. When simulated data are injected with relative risks (RR) ≥ 2, all designs have good predictive accuracy (AUC > 0.90), but when RR < 2, no methods achieve 100 % predictions. Each method exhibits a different bias profile, which changes with the effect size. OSIM2 can support methodological research. Results from simulation suggest method operating characteristics are far from nominal properties.

IECON '87: Industrial applications of control and simulation; Proceedings of the 1987 International Conference on Industrial Electronics, Control, and Instrumentation, Cambridge, MA, Nov. 3, 4, 1987

NASA Technical Reports Server (NTRS)

Hartley, Tom T. (Editor)

1987-01-01

Recent advances in control-system design and simulation are discussed in reviews and reports. Among the topics considered are fast algorithms for generating near-optimal binary decision programs, trajectory control of robot manipulators with compensation of load effects via a six-axis force sensor, matrix integrators for real-time simulation, a high-level control language for an autonomous land vehicle, and a practical engineering design method for stable model-reference adaptive systems. Also addressed are the identification and control of flexible-limb robots with unknown loads, adaptive control and robust adaptive control for manipulators with feedforward compensation, adaptive pole-placement controllers with predictive action, variable-structure strategies for motion control, and digital signal-processor-based variable-structure controls.

A mechanistic pan-cancer pathway model informed by multi-omics data interprets stochastic cell fate responses to drugs and mitogens

PubMed Central

Bouhaddou, Mehdi; Koch, Rick J.; DiStefano, Matthew S.; Tan, Annie L.; Mertz, Alex E.

2018-01-01

Most cancer cells harbor multiple drivers whose epistasis and interactions with expression context clouds drug and drug combination sensitivity prediction. We constructed a mechanistic computational model that is context-tailored by omics data to capture regulation of stochastic proliferation and death by pan-cancer driver pathways. Simulations and experiments explore how the coordinated dynamics of RAF/MEK/ERK and PI-3K/AKT kinase activities in response to synergistic mitogen or drug combinations control cell fate in a specific cellular context. In this MCF10A cell context, simulations suggest that synergistic ERK and AKT inhibitor-induced death is likely mediated by BIM rather than BAD, which is supported by prior experimental studies. AKT dynamics explain S-phase entry synergy between EGF and insulin, but simulations suggest that stochastic ERK, and not AKT, dynamics seem to drive cell-to-cell proliferation variability, which in simulations is predictable from pre-stimulus fluctuations in C-Raf/B-Raf levels. Simulations suggest MEK alteration negligibly influences transformation, consistent with clinical data. Tailoring the model to an alternate cell expression and mutation context, a glioma cell line, allows prediction of increased sensitivity of cell death to AKT inhibition. Our model mechanistically interprets context-specific landscapes between driver pathways and cell fates, providing a framework for designing more rational cancer combination therapy. PMID:29579036

University of Maryland MRSEC - Research: Seed 3

Science.gov Websites

MRSEC Templates Opportunities Search Home » Research » Seed 3 Seed 3: Modeling Elastic Effects on controlling parameters and variables include temperature, deposition flux, external electric field and elastic simulating the effects of these controlling factors often lead to predictions that guide future experiments

Pre-launch Optical Characteristics of the Oculus-ASR Nanosatellite for Attitude and Shape Recognition Experiments

DTIC Science & Technology

2011-12-02

construction and validation of predictive computer models such as those used in Time-domain Analysis Simulation for Advanced Tracking (TASAT), a...characterization data, successful construction and validation of predictive computer models was accomplished. And an investigation in pose determination from...currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD-MM-YYYY) 2. REPORT TYPE 3. DATES

A human life-stage physiologically based pharmacokinetic and pharmacodynamic model for chlorpyrifos: development and validation.

PubMed

Smith, Jordan Ned; Hinderliter, Paul M; Timchalk, Charles; Bartels, Michael J; Poet, Torka S

2014-08-01

Sensitivity to some chemicals in animals and humans are known to vary with age. Age-related changes in sensitivity to chlorpyrifos have been reported in animal models. A life-stage physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model was developed to predict disposition of chlorpyrifos and its metabolites, chlorpyrifos-oxon (the ultimate toxicant) and 3,5,6-trichloro-2-pyridinol (TCPy), as well as B-esterase inhibition by chlorpyrifos-oxon in humans. In this model, previously measured age-dependent metabolism of chlorpyrifos and chlorpyrifos-oxon were integrated into age-related descriptions of human anatomy and physiology. The life-stage PBPK/PD model was calibrated and tested against controlled adult human exposure studies. Simulations suggest age-dependent pharmacokinetics and response may exist. At oral doses ⩾0.6mg/kg of chlorpyrifos (100- to 1000-fold higher than environmental exposure levels), 6months old children are predicted to have higher levels of chlorpyrifos-oxon in blood and higher levels of red blood cell cholinesterase inhibition compared to adults from equivalent doses. At lower doses more relevant to environmental exposures, simulations predict that adults will have slightly higher levels of chlorpyrifos-oxon in blood and greater cholinesterase inhibition. This model provides a computational framework for age-comparative simulations that can be utilized to predict chlorpyrifos disposition and biological response over various postnatal life stages. Copyright © 2013 Elsevier Inc. All rights reserved.

Simplified continuous simulation model for investigating effects of controlled drainage on long-term soil moisture dynamics with a shallow groundwater table.

PubMed

Sun, Huaiwei; Tong, Juxiu; Luo, Wenbing; Wang, Xiugui; Yang, Jinzhong

2016-08-01

Accurate modeling of soil water content is required for a reasonable prediction of crop yield and of agrochemical leaching in the field. However, complex mathematical models faced the difficult-to-calibrate parameters and the distinct knowledge between the developers and users. In this study, a deterministic model is presented and is used to investigate the effects of controlled drainage on soil moisture dynamics in a shallow groundwater area. This simplified one-dimensional model is formulated to simulate soil moisture in the field on a daily basis and takes into account only the vertical hydrological processes. A linear assumption is proposed and is used to calculate the capillary rise from the groundwater. The pipe drainage volume is calculated by using a steady-state approximation method and the leakage rate is calculated as a function of soil moisture. The model is successfully calibrated by using field experiment data from four different pipe drainage treatments with several field observations. The model was validated by comparing the simulations with observed soil water content during the experimental seasons. The comparison results demonstrated the robustness and effectiveness of the model in the prediction of average soil moisture values. The input data required to run the model are widely available and can be measured easily in the field. It is observed that controlled drainage results in lower groundwater contribution to the root zone and lower depth of percolation to the groundwater, thus helping in the maintenance of a low level of soil salinity in the root zone.

Inverse simulation system for manual-controlled rendezvous and docking based on artificial neural network

NASA Astrophysics Data System (ADS)

Zhou, Wanmeng; Wang, Hua; Tang, Guojin; Guo, Shuai

2016-09-01

The time-consuming experimental method for handling qualities assessment cannot meet the increasing fast design requirements for the manned space flight. As a tool for the aircraft handling qualities research, the model-predictive-control structured inverse simulation (MPC-IS) has potential applications in the aerospace field to guide the astronauts' operations and evaluate the handling qualities more effectively. Therefore, this paper establishes MPC-IS for the manual-controlled rendezvous and docking (RVD) and proposes a novel artificial neural network inverse simulation system (ANN-IS) to further decrease the computational cost. The novel system was obtained by replacing the inverse model of MPC-IS with the artificial neural network. The optimal neural network was trained by the genetic Levenberg-Marquardt algorithm, and finally determined by the Levenberg-Marquardt algorithm. In order to validate MPC-IS and ANN-IS, the manual-controlled RVD experiments on the simulator were carried out. The comparisons between simulation results and experimental data demonstrated the validity of two systems and the high computational efficiency of ANN-IS.

Modeling of the Human - Operator in a Complex System Functioning Under Extreme Conditions

NASA Astrophysics Data System (ADS)

Getzov, Peter; Hubenova, Zoia; Yordanov, Dimitar; Popov, Wiliam

2013-12-01

Problems, related to the explication of sophisticated control systems of objects, operating under extreme conditions, have been examined and the impact of the effectiveness of the operator's activity on the systems as a whole. The necessity of creation of complex simulation models, reflecting operator's activity, is discussed. Organizational and technical system of an unmanned aviation complex is described as a sophisticated ergatic system. Computer realization of main subsystems of algorithmic system of the man as a controlling system is implemented and specialized software for data processing and analysis is developed. An original computer model of a Man as a tracking system has been implemented. Model of unmanned complex for operators training and formation of a mental model in emergency situation, implemented in "matlab-simulink" environment, has been synthesized. As a unit of the control loop, the pilot (operator) is simplified viewed as an autocontrol system consisting of three main interconnected subsystems: sensitive organs (perception sensors); central nervous system; executive organs (muscles of the arms, legs, back). Theoretical-data model of prediction the level of operator's information load in ergatic systems is proposed. It allows the assessment and prediction of the effectiveness of a real working operator. Simulation model of operator's activity in takeoff based on the Petri nets has been synthesized.

Comment on "Advective transport in heterogeneous aquifers: Are proxy models predictive?" by A. Fiori, A. Zarlenga, H. Gotovac, I. Jankovic, E. Volpi, V. Cvetkovic, and G. Dagan

NASA Astrophysics Data System (ADS)

Neuman, Shlomo P.

2016-07-01

Fiori et al. (2015) examine the predictive capabilities of (among others) two "proxy" non-Fickian transport models, MRMT (Multi-Rate Mass Transfer) and CTRW (Continuous-Time Random Walk). In particular, they compare proxy model predictions of mean breakthrough curves (BTCs) at a sequence of control planes with near-ergodic BTCs generated through two- and three-dimensional simulations of nonreactive, mean-uniform advective transport in single realizations of stationary, randomly heterogeneous porous media. The authors find fitted proxy model parameters to be nonunique and devoid of clear physical meaning. This notwithstanding, they conclude optimistically that "i. Fitting the proxy models to match the BTC at [one control plane] automatically ensures prediction at downstream control planes [and thus] ii. … the measured BTC can be used directly for prediction, with no need to use models underlain by fitting." I show that (a) the authors' findings follow directly from (and thus confirm) theoretical considerations discussed earlier by Neuman and Tartakovsky (2009), which (b) additionally demonstrate that proxy models will lack similar predictive capabilities under more realistic, non-Markovian flow and transport conditions that prevail under flow through nonstationary (e.g., multiscale) media in the presence of boundaries and/or nonuniformly distributed sources, and/or when flow/transport are conditioned on measurements.

Trajectory control method of stratospheric airship based on the sliding mode control and prediction in wind field

NASA Astrophysics Data System (ADS)

Zhang, Jia-shi; Yang, Xi-xiang

2017-11-01

The stratospheric airship has the characteristics of large inertia, long time delay and large disturbance of wind field , so the trajectory control is very difficult .Build the lateral three degrees of freedom dynamic model which consider the wind interference , the dynamics equation is linearized by the small perturbation theory, propose a trajectory control method Combine with the sliding mode control and prediction, design the trajectory controller , takes the HAA airship as the reference to carry out simulation analysis. Results show that the improved sliding mode control with front-feedback method not only can solve well control problems of airship trajectory in wind field, but also can effectively improve the control accuracy of the traditional sliding mode control method, solved problems that using the traditional sliding mode control to control. It provides a useful reference for dynamic modeling and trajectory control of stratospheric airship.

Intelligent systems approach for automated identification of individual control behavior of a human operator

NASA Astrophysics Data System (ADS)

Zaychik, Kirill B.

Acceptable results have been obtained using conventional techniques to model the generic human operator's control behavior. However, little research has been done in an attempt to identify an individual based on his/her control behavior. The main hypothesis investigated in this dissertation is that different operators exhibit different control behavior when performing a given control task. Furthermore, inter-person differences are manifested in the amplitude and frequency content of the non-linear component of the control behavior. Two enhancements to the existing models of the human operator, which allow personalization of the modeled control behavior, are presented in this dissertation. One of the proposed enhancements accounts for the "testing" control signals, which are introduced by an operator for more accurate control of the system and/or to adjust his/her control strategy. Such enhancement uses the Artificial Neural Network (ANN), which can be fine-tuned to model the "testing" control behavior of a given individual. The other model enhancement took the form of an equiripple filter (EF), which conditions the power spectrum of the control signal before it is passed through the plant dynamics block. The filter design technique uses Parks-McClellan algorithm, which allows parameterization of the desired levels of power at certain frequencies. A novel automated parameter identification technique (APID) was developed to facilitate the identification process of the parameters of the selected models of the human operator. APID utilizes a Genetic Algorithm (GA) based optimization engine called the Bit-climbing Algorithm (BCA). Proposed model enhancements were validated using the experimental data obtained at three different sources: the Manual Control Laboratory software experiments, Unmanned Aerial Vehicle simulation, and NASA Langley Research Center Visual Motion Simulator studies. Validation analysis involves comparison of the actual and simulated control activity signals. Validation criteria used in this dissertation is based on comparing Power Spectral Densities of the control signals against that of the Precision model of the human operator. This dissertation also addresses the issue of applying the proposed human operator model augmentation to evaluate the effectiveness of the motion feedback when simulating the actual pilot control behavior in a flight simulator. The proposed modeling methodology allows for quantitative assessments and prediction of the need for platform motion, while performing aircraft/pilot simulation studies.

An assessment of the impact of ATMS and CrIS data assimilation on precipitation prediction over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Xue, Tong; Xu, Jianjun; Guan, Zhaoyong; Chen, Han-Ching; Chiu, Long S.; Shao, Min

2017-07-01

Using the National Oceanic and Atmospheric Administration's Gridpoint Statistical Interpolation data assimilation system and the National Center for Atmospheric Research's Advanced Research Weather Research and Forecasting (WRF-ARW) regional model, the impact of assimilating Advanced Technology Microwave Sounder (ATMS) and Cross-track Infrared Sounder (CrIS) satellite data on precipitation prediction over the Tibetan Plateau in July 2015 was evaluated. Four experiments were designed: a control experiment and three data assimilation experiments with different data sets injected: conventional data only, a combination of conventional and ATMS satellite data, and a combination of conventional and CrIS satellite data. The results showed that the monthly mean of precipitation is shifted northward in the simulations and showed an orographic bias described as an overestimation upwind of the mountains and an underestimation in the south of the rain belt. The rain shadow mainly influenced prediction of the quantity of precipitation, although the main rainfall pattern was well simulated. For the first 24 h and last 24 h of accumulated daily precipitation, the model generally overestimated the amount of precipitation, but it was underestimated in the heavy-rainfall periods of 3-5, 13-16, and 22-25 July. The observed water vapor conveyance from the southeastern Tibetan Plateau was larger than in the model simulations, which induced inaccuracies in the forecast of heavy rain on 3-5 July. The data assimilation experiments, particularly the ATMS assimilation, were closer to the observations for the heavy-rainfall process than the control. Overall, based on the experiments in July 2015, the satellite data assimilation improved to some extent the prediction of the precipitation pattern over the Tibetan Plateau, although the simulation of the rain belt without data assimilation shows the regional shifting.

Simulation of urinary excretion of 1-hydroxypyrene in various scenarios of exposure to polycyclic aromatic hydrocarbons with a generic, cross-chemical predictive PBTK-model.

PubMed

Jongeneelen, Frans; ten Berge, Wil

2012-08-01

A physiologically based toxicokinetic (PBTK) model can predict blood and urine concentrations, given a certain exposure scenario of inhalation, dermal and/or oral exposure. The recently developed PBTK-model IndusChemFate is a unified model that mimics the uptake, distribution, metabolism and elimination of a chemical in a reference human of 70 kg. Prediction of the uptake by inhalation is governed by pulmonary exchange to blood. Oral uptake is simulated as a bolus dose that is taken up at a first-order rate. Dermal uptake is estimated by the use of a novel dermal physiologically based module that considers dermal deposition rate and duration of deposition. Moreover, evaporation during skin contact is fully accounted for and related to the volatility of the substance. Partitioning of the chemical and metabolite(s) over blood and tissues is estimated by a Quantitative Structure-Property Relationship (QSPR) algorithm. The aim of this study was to test the generic PBTK-model by comparing measured urinary levels of 1-hydroxypyrene in various inhalation and dermal exposure scenarios with the result of model simulations. In the last three decades, numerous biomonitoring studies of PAH-exposed humans were published that used the bioindicator 1-hydroxypyrene (1-OH-pyrene) in urine. Longitudinal studies that encompass both dosimetry and biomonitoring with repeated sampling in time were selected to test the accuracy of the PBTK-model by comparing the reported concentrations of 1-OHP in urine with the model-predicted values. Two controlled human volunteer studies and three field studies of workers exposed to polycyclic aromatic hydrocarbons (PAH) were included. The urinary pyrene-metabolite levels of a controlled human inhalation study, a transdermal uptake study of bitumen fume, efficacy of respirator use in electrode paste workers, cokery workers in shale oil industry and a longitudinal study of five coke liquefaction workers were compared to the PBTK-predicted values. The simulations showed that the model-predicted concentrations of urinary pyrene and metabolites over time, as well as peak-concentrations and total excreted amount in different exposure scenarios of inhalation and transdermal exposure were in all comparisons within an order of magnitude. The model predicts that only a very small fraction is excreted in urine as parent pyrene and as free 1-OH-pyrene. The predominant urinary metabolite is 1-OH-pyrene-glucuronide. Enterohepatic circulation of 1-OH-pyrene-glucuronide seems the reason of the delayed release from the body. It appeared that urinary excretion of pyrene and pyrene-metabolites in humans is predictable with the PBTK-model. The model outcomes have a satisfying accuracy for early testing, in so-called 1st tier simulations and in range finding. This newly developed generic PBTK-model IndusChemFate is a tool that can be used to do early explorations of the significance of uptake of pyrene in the human body following industrial or environmental exposure scenarios. And it can be used to optimize the sampling time and urine sampling frequency of a biomonitoring program.

Predictive displays for a process-control schematic interface.

PubMed

Yin, Shanqing; Wickens, Christopher D; Helander, Martin; Laberge, Jason C

2015-02-01

Our objective was to examine the extent to which increasing precision of predictive (rate of change) information in process control will improve performance on a simulated process-control task. Predictive displays have been found to be useful in process control (as well as aviation and maritime industries). However, authors of prior research have not examined the extent to which predictive value is increased by increasing predictor resolution, nor has such research tied potential improvements to changes in process control strategy. Fifty nonprofessional participants each controlled a simulated chemical mixture process (honey mixer simulation) that simulated the operations found in process control. Participants in each of five groups controlled with either no predictor or a predictor ranging in the resolution of prediction of the process. Increasing detail resolution generally increased the benefit of prediction over the control condition although not monotonically so. The best overall performance, combining quality and predictive ability, was obtained by the display of intermediate resolution. The two displays with the lowest resolution were clearly inferior. Predictors with higher resolution are of value but may trade off enhanced sensitivity to variable change (lower-resolution discrete state predictor) with smoother control action (higher-resolution continuous predictors). The research provides guidelines to the process-control industry regarding displays that can most improve operator performance.

Model predictive direct power control for active power decoupled single-phase quasi- Z -source inverter

DOE PAGES

Liu, Yushan; Ge, Baoming; Abu-Rub, Haitham; ...

2016-06-14

In this study, the active power filter (APF) that consists of a half-bridge leg and an ac capacitor is integrated in the single-phase quasi-Z-source inverter (qZSI) in this paper to avoid the second harmonic power flowing into the dc side. The capacitor of APF buffers the second harmonic power of the load, and the ac capacitor allows highly pulsating ac voltage, so that the capacitances of both dc and ac sides can be small. A model predictive direct power control (DPC) is further proposed to achieve the purpose of this newtopology through predicting the capacitor voltage of APF at eachmore » sampling period and ensuring the APF power to track the second harmonic power of single-phase qZSI. Simulation and experimental results verify the model predictive DPC for the APF-integrated single-phase qZSI.« less

Model predictive direct power control for active power decoupled single-phase quasi- Z -source inverter

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Yushan; Ge, Baoming; Abu-Rub, Haitham

In this study, the active power filter (APF) that consists of a half-bridge leg and an ac capacitor is integrated in the single-phase quasi-Z-source inverter (qZSI) in this paper to avoid the second harmonic power flowing into the dc side. The capacitor of APF buffers the second harmonic power of the load, and the ac capacitor allows highly pulsating ac voltage, so that the capacitances of both dc and ac sides can be small. A model predictive direct power control (DPC) is further proposed to achieve the purpose of this newtopology through predicting the capacitor voltage of APF at eachmore » sampling period and ensuring the APF power to track the second harmonic power of single-phase qZSI. Simulation and experimental results verify the model predictive DPC for the APF-integrated single-phase qZSI.« less

A Discrete-Time Average Model Based Predictive Control for Quasi-Z-Source Inverter

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Yushan; Abu-Rub, Haitham; Xue, Yaosuo

A discrete-time average model-based predictive control (DTA-MPC) is proposed for a quasi-Z-source inverter (qZSI). As a single-stage inverter topology, the qZSI regulates the dc-link voltage and the ac output voltage through the shoot-through (ST) duty cycle and the modulation index. Several feedback strategies have been dedicated to produce these two control variables, among which the most popular are the proportional–integral (PI)-based control and the conventional model-predictive control (MPC). However, in the former, there are tradeoffs between fast response and stability; the latter is robust, but at the cost of high calculation burden and variable switching frequency. Moreover, they require anmore » elaborated design or fine tuning of controller parameters. The proposed DTA-MPC predicts future behaviors of the ST duty cycle and modulation signals, based on the established discrete-time average model of the quasi-Z-source (qZS) inductor current, the qZS capacitor voltage, and load currents. The prediction actions are applied to the qZSI modulator in the next sampling instant, without the need of other controller parameters’ design. A constant switching frequency and significantly reduced computations are achieved with high performance. Transient responses and steady-state accuracy of the qZSI system under the proposed DTA-MPC are investigated and compared with the PI-based control and the conventional MPC. Simulation and experimental results verify the effectiveness of the proposed approach for the qZSI.« less

A Discrete-Time Average Model Based Predictive Control for Quasi-Z-Source Inverter

DOE PAGES

Liu, Yushan; Abu-Rub, Haitham; Xue, Yaosuo; ...

2017-12-25

A discrete-time average model-based predictive control (DTA-MPC) is proposed for a quasi-Z-source inverter (qZSI). As a single-stage inverter topology, the qZSI regulates the dc-link voltage and the ac output voltage through the shoot-through (ST) duty cycle and the modulation index. Several feedback strategies have been dedicated to produce these two control variables, among which the most popular are the proportional–integral (PI)-based control and the conventional model-predictive control (MPC). However, in the former, there are tradeoffs between fast response and stability; the latter is robust, but at the cost of high calculation burden and variable switching frequency. Moreover, they require anmore » elaborated design or fine tuning of controller parameters. The proposed DTA-MPC predicts future behaviors of the ST duty cycle and modulation signals, based on the established discrete-time average model of the quasi-Z-source (qZS) inductor current, the qZS capacitor voltage, and load currents. The prediction actions are applied to the qZSI modulator in the next sampling instant, without the need of other controller parameters’ design. A constant switching frequency and significantly reduced computations are achieved with high performance. Transient responses and steady-state accuracy of the qZSI system under the proposed DTA-MPC are investigated and compared with the PI-based control and the conventional MPC. Simulation and experimental results verify the effectiveness of the proposed approach for the qZSI.« less

Prediction based active ramp metering control strategy with mobility and safety assessment

NASA Astrophysics Data System (ADS)

Fang, Jie; Tu, Lili

2018-04-01

Ramp metering is one of the most direct and efficient motorway traffic flow management measures so as to improve traffic conditions. However, owing to short of traffic conditions prediction, in earlier studies, the impact on traffic flow dynamics of the applied RM control was not quantitatively evaluated. In this study, a RM control algorithm adopting Model Predictive Control (MPC) framework to predict and assess future traffic conditions, which taking both the current traffic conditions and the RM-controlled future traffic states into consideration, was presented. The designed RM control algorithm targets at optimizing the network mobility and safety performance. The designed algorithm is evaluated in a field-data-based simulation. Through comparing the presented algorithm controlled scenario with the uncontrolled scenario, it was proved that the proposed RM control algorithm can effectively relieve the congestion of traffic network with no significant compromises in safety aspect.

Theoretical Tools and Software for Modeling, Simulation and Control Design of Rocket Test Facilities

NASA Technical Reports Server (NTRS)

Richter, Hanz

2004-01-01

A rocket test stand and associated subsystems are complex devices whose operation requires that certain preparatory calculations be carried out before a test. In addition, real-time control calculations must be performed during the test, and further calculations are carried out after a test is completed. The latter may be required in order to evaluate if a particular test conformed to specifications. These calculations are used to set valve positions, pressure setpoints, control gains and other operating parameters so that a desired system behavior is obtained and the test can be successfully carried out. Currently, calculations are made in an ad-hoc fashion and involve trial-and-error procedures that may involve activating the system with the sole purpose of finding the correct parameter settings. The goals of this project are to develop mathematical models, control methodologies and associated simulation environments to provide a systematic and comprehensive prediction and real-time control capability. The models and controller designs are expected to be useful in two respects: 1) As a design tool, a model is the only way to determine the effects of design choices without building a prototype, which is, in the context of rocket test stands, impracticable; 2) As a prediction and tuning tool, a good model allows to set system parameters off-line, so that the expected system response conforms to specifications. This includes the setting of physical parameters, such as valve positions, and the configuration and tuning of any feedback controllers in the loop.

Modeling take-over performance in level 3 conditionally automated vehicles.

PubMed

Gold, Christian; Happee, Riender; Bengler, Klaus

2018-07-01

Taking over vehicle control from a Level 3 conditionally automated vehicle can be a demanding task for a driver. The take-over determines the controllability of automated vehicle functions and thereby also traffic safety. This paper presents models predicting the main take-over performance variables take-over time, minimum time-to-collision, brake application and crash probability. These variables are considered in relation to the situational and driver-related factors time-budget, traffic density, non-driving-related task, repetition, the current lane and driver's age. Regression models were developed using 753 take-over situations recorded in a series of driving simulator experiments. The models were validated with data from five other driving simulator experiments of mostly unrelated authors with another 729 take-over situations. The models accurately captured take-over time, time-to-collision and crash probability, and moderately predicted the brake application. Especially the time-budget, traffic density and the repetition strongly influenced the take-over performance, while the non-driving-related tasks, the lane and drivers' age explained a minor portion of the variance in the take-over performances. Copyright © 2017 Elsevier Ltd. All rights reserved.

Control Strategy of Active Power Filter Based on Modular Multilevel Converter

NASA Astrophysics Data System (ADS)

Xie, Xifeng

2018-03-01

To improve the capacity, pressure resistance and the equivalent switching frequency of active power filter (APF), a control strategy of APF based on Modular Multilevel Converter (MMC) is presented. In this Control Strategy, the indirect current control method is used to achieve active current and reactive current decoupling control; Voltage Balance Control Strategy is to stabilize sub-module capacitor voltage, the predictive current control method is used to Track and control of harmonic currents. As a result, the harmonic current is restrained, and power quality is improved. Finally, the simulation model of active power filter controller based on MMC is established in Matlab/Simulink, the simulation proves that the proposed strategy is feasible and correct.

Simultaneous Simulations of Uptake in Plants and Leaching to Groundwater of Cadmium and Lead for Arable Land Amended with Compost or Farmyard Manure

PubMed Central

Legind, Charlotte N.; Rein, Arno; Serre, Jeanne; Brochier, Violaine; Haudin, Claire-Sophie; Cambier, Philippe; Houot, Sabine; Trapp, Stefan

2012-01-01

The water budget of soil, the uptake in plants and the leaching to groundwater of cadmium (Cd) and lead (Pb) were simulated simultaneously using a physiological plant uptake model and a tipping buckets water and solute transport model for soil. Simulations were compared to results from a ten-year experimental field study, where four organic amendments were applied every second year. Predicted concentrations slightly decreased (Cd) or stagnated (Pb) in control soils, but increased in amended soils by about 10% (Cd) and 6% to 18% (Pb). Estimated plant uptake was lower in amended plots, due to an increase of Kd (dry soil to water partition coefficient). Predicted concentrations in plants were close to measured levels in plant residues (straw), but higher than measured concentrations in grains. Initially, Pb was mainly predicted to deposit from air into plants (82% in 1998); the next years, uptake from soil became dominating (30% from air in 2006), because of decreasing levels in air. For Cd, predicted uptake from air into plants was negligible (1–5%). PMID:23056555

Nonlinear Dynamic Inversion Baseline Control Law: Flight-Test Results for the Full-scale Advanced Systems Testbed F/A-18 Airplane

NASA Technical Reports Server (NTRS)

Miller, Christopher J.

2011-01-01

A model reference nonlinear dynamic inversion control law has been developed to provide a baseline controller for research into simple adaptive elements for advanced flight control laws. This controller has been implemented and tested in a hardware-in-the-loop simulation and in flight. The flight results agree well with the simulation predictions and show good handling qualities throughout the tested flight envelope with some noteworthy deficiencies highlighted both by handling qualities metrics and pilot comments. Many design choices and implementation details reflect the requirements placed on the system by the nonlinear flight environment and the desire to keep the system as simple as possible to easily allow the addition of the adaptive elements. The flight-test results and how they compare to the simulation predictions are discussed, along with a discussion about how each element affected pilot opinions. Additionally, aspects of the design that performed better than expected are presented, as well as some simple improvements that will be suggested for follow-on work.

Prediction of wastewater treatment plants performance based on artificial fish school neural network

NASA Astrophysics Data System (ADS)

Zhang, Ruicheng; Li, Chong

2011-10-01

A reliable model for wastewater treatment plant is essential in providing a tool for predicting its performance and to form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. For the multi-variable, uncertainty, non-linear characteristics of the wastewater treatment system, an artificial fish school neural network prediction model is established standing on actual operation data in the wastewater treatment system. The model overcomes several disadvantages of the conventional BP neural network. The results of model calculation show that the predicted value can better match measured value, played an effect on simulating and predicting and be able to optimize the operation status. The establishment of the predicting model provides a simple and practical way for the operation and management in wastewater treatment plant, and has good research and engineering practical value.

Wind Plant Power Optimization through Yaw Control using a Parametric Model for Wake Effects -- A CFD Simulation Study

DOE PAGES

Gebraad, P. M. O.; Teeuwisse, F. W.; van Wingerden, J. W.; ...

2016-01-01

This article presents a wind plant control strategy that optimizes the yaw settings of wind turbines for improved energy production of the whole wind plant by taking into account wake effects. The optimization controller is based on a novel internal parametric model for wake effects, called the FLOw Redirection and Induction in Steady-state (FLORIS) model. The FLORIS model predicts the steady-state wake locations and the effective flow velocities at each turbine, and the resulting turbine electrical energy production levels, as a function of the axial induction and the yaw angle of the different rotors. The FLORIS model has a limitedmore » number of parameters that are estimated based on turbine electrical power production data. In high-fidelity computational fluid dynamics simulations of a small wind plant, we demonstrate that the optimization control based on the FLORIS model increases the energy production of the wind plant, with a reduction of loads on the turbines as an additional effect.« less

Calibration and simulation of two large wastewater treatment plants operated for nutrient removal.

PubMed

Ferrer, J; Morenilla, J J; Bouzas, A; García-Usach, F

2004-01-01

Control and optimisation of plant processes has become a priority for WWTP managers. The calibration and verification of a mathematical model provides an important tool for the investigation of advanced control strategies that may assist in the design or optimization of WWTPs. This paper describes the calibration of the ASM2d model for two full scale biological nitrogen and phosphorus removal plants in order to characterize the biological process and to upgrade the plants' performance. Results from simulation showed a good correspondence with experimental data demonstrating that the model and the calibrated parameters were able to predict the behaviour of both WWTPs. Once the calibration and simulation process was finished, a study for each WWTP was done with the aim of improving its performance. Modifications focused on reactor configuration and operation strategies were proposed.

Predictive Control of the Blood Glucose Level in Type I Diabetic Patient Using Delay Differential Equation Wang Model.

PubMed

Esna-Ashari, Mojgan; Zekri, Maryam; Askari, Masood; Khalili, Noushin

2017-01-01

Because of increasing risk of diabetes, the measurement along with control of blood sugar has been of great importance in recent decades. In type I diabetes, because of the lack of insulin secretion, the cells cannot absorb glucose leading to low level of glucose. To control blood glucose (BG), the insulin must be injected to the body. This paper proposes a method for BG level regulation in type I diabetes. The control strategy is based on nonlinear model predictive control. The aim of the proposed controller optimized with genetics algorithms is to measure BG level each time and predict it for the next time interval. This merit causes a less amount of control effort, which is the rate of insulin delivered to the patient body. Consequently, this method can decrease the risk of hypoglycemia, a lethal phenomenon in regulating BG level in diabetes caused by a low BG level. Two delay differential equation models, namely Wang model and Enhanced Wang model, are applied as controller model and plant, respectively. The simulation results exhibit an acceptable performance of the proposed controller in meal disturbance rejection and robustness against parameter changes. As a result, if the nutrition of the person decreases instantly, the hypoglycemia will not happen. Furthermore, comparing this method with other works, it was shown that the new method outperforms previous studies.

Predictive Control of the Blood Glucose Level in Type I Diabetic Patient Using Delay Differential Equation Wang Model

PubMed Central

Esna-Ashari, Mojgan; Zekri, Maryam; Askari, Masood; Khalili, Noushin

2017-01-01

Because of increasing risk of diabetes, the measurement along with control of blood sugar has been of great importance in recent decades. In type I diabetes, because of the lack of insulin secretion, the cells cannot absorb glucose leading to low level of glucose. To control blood glucose (BG), the insulin must be injected to the body. This paper proposes a method for BG level regulation in type I diabetes. The control strategy is based on nonlinear model predictive control. The aim of the proposed controller optimized with genetics algorithms is to measure BG level each time and predict it for the next time interval. This merit causes a less amount of control effort, which is the rate of insulin delivered to the patient body. Consequently, this method can decrease the risk of hypoglycemia, a lethal phenomenon in regulating BG level in diabetes caused by a low BG level. Two delay differential equation models, namely Wang model and Enhanced Wang model, are applied as controller model and plant, respectively. The simulation results exhibit an acceptable performance of the proposed controller in meal disturbance rejection and robustness against parameter changes. As a result, if the nutrition of the person decreases instantly, the hypoglycemia will not happen. Furthermore, comparing this method with other works, it was shown that the new method outperforms previous studies. PMID:28487828

Forecast and control of epidemics in a globalized world

PubMed Central

Hufnagel, L.; Brockmann, D.; Geisel, T.

2004-01-01

The rapid worldwide spread of severe acute respiratory syndrome demonstrated the potential threat an infectious disease poses in a closely interconnected and interdependent world. Here we introduce a probabilistic model that describes the worldwide spread of infectious diseases and demonstrate that a forecast of the geographical spread of epidemics is indeed possible. This model combines a stochastic local infection dynamics among individuals with stochastic transport in a worldwide network, taking into account national and international civil aviation traffic. Our simulations of the severe acute respiratory syndrome outbreak are in surprisingly good agreement with published case reports. We show that the high degree of predictability is caused by the strong heterogeneity of the network. Our model can be used to predict the worldwide spread of future infectious diseases and to identify endangered regions in advance. The performance of different control strategies is analyzed, and our simulations show that a quick and focused reaction is essential to inhibiting the global spread of epidemics. PMID:15477600

Stellar tracking attitude reference system

NASA Technical Reports Server (NTRS)

Klestadt, B.

1974-01-01

A satellite precision attitude control system was designed, based on the use of STARS as the principal sensing system. The entire system was analyzed and simulated in detail, considering the nonideal properties of the control and sensing components and realistic spacecraft mass properties. Experimental results were used to improve the star tracker noise model. The results of the simulation indicate that STARS performs in general as predicted in a realistic application and should be a strong contender in most precision earth pointing applications.

An analytical investigation of NO sub x control techniques for methanol fueled spark ignition engines

NASA Technical Reports Server (NTRS)

Browning, L. H.; Argenbright, L. A.

1983-01-01

A thermokinetic SI engine simulation was used to study the effects of simple nitrogen oxide control techniques on performance and emissions of a methanol fueled engine. As part of this simulation, a ring crevice storage model was formulated to predict UBF emissions. The study included spark retard, two methods of compression ratio increase and EGR. The study concludes that use of EGR in high turbulence, high compression engines will both maximize power and thermal efficiency while minimizing harmful exhaust pollutants.

Calibration and prediction of removal function in magnetorheological finishing.

PubMed

Dai, Yifan; Song, Ci; Peng, Xiaoqiang; Shi, Feng

2010-01-20

A calibrated and predictive model of the removal function has been established based on the analysis of a magnetorheological finishing (MRF) process. By introducing an efficiency coefficient of the removal function, the model can be used to calibrate the removal function in a MRF figuring process and to accurately predict the removal function of a workpiece to be polished whose material is different from the spot part. Its correctness and feasibility have been validated by simulations. Furthermore, applying this model to the MRF figuring experiments, the efficiency coefficient of the removal function can be identified accurately to make the MRF figuring process deterministic and controllable. Therefore, all the results indicate that the calibrated and predictive model of the removal function can improve the finishing determinacy and increase the model applicability in a MRF process.

Output-feedback control of combined sewer networks through receding horizon control with moving horizon estimation

NASA Astrophysics Data System (ADS)

Joseph-Duran, Bernat; Ocampo-Martinez, Carlos; Cembrano, Gabriela

2015-10-01

An output-feedback control strategy for pollution mitigation in combined sewer networks is presented. The proposed strategy provides means to apply model-based predictive control to large-scale sewer networks, in-spite of the lack of measurements at most of the network sewers. In previous works, the authors presented a hybrid linear control-oriented model for sewer networks together with the formulation of Optimal Control Problems (OCP) and State Estimation Problems (SEP). By iteratively solving these problems, preliminary Receding Horizon Control with Moving Horizon Estimation (RHC/MHE) results, based on flow measurements, were also obtained. In this work, the RHC/MHE algorithm has been extended to take into account both flow and water level measurements and the resulting control loop has been extensively simulated to assess the system performance according different measurement availability scenarios and rain events. All simulations have been carried out using a detailed physically based model of a real case-study network as virtual reality.

A Pressure Control Method for Emulsion Pump Station Based on Elman Neural Network

PubMed Central

Tan, Chao; Qi, Nan; Yao, Xingang; Wang, Zhongbin; Si, Lei

2015-01-01

In order to realize pressure control of emulsion pump station which is key equipment of coal mine in the safety production, the control requirements were analyzed and a pressure control method based on Elman neural network was proposed. The key techniques such as system framework, pressure prediction model, pressure control model, and the flowchart of proposed approach were presented. Finally, a simulation example was carried out and comparison results indicated that the proposed approach was feasible and efficient and outperformed others. PMID:25861253

A model of cerebrocerebello-spinomuscular interaction in the sagittal control of human walking.

PubMed

Jo, Sungho; Massaquoi, Steve G

2007-03-01

A computationally developed model of human upright balance control (Jo and Massaquoi on Biol cybern 91:188-202, 2004) has been enhanced to describe biped walking in the sagittal plane. The model incorporates (a) non-linear muscle mechanics having activation level -dependent impedance, (b) scheduled cerebrocerebellar interaction for control of center of mass position and trunk pitch angle, (c) rectangular pulse-like feedforward commands from a brainstem/ spinal pattern generator, and (d) segmental reflex modulation of muscular synergies to refine inter-joint coordination. The model can stand when muscles around the ankle are coactivated. When trigger signals activate, the model transitions from standing still to walking at 1.5 m/s. Simulated natural walking displays none of seven pathological gait features. The model can simulate different walking speeds by tuning the amplitude and frequency in spinal pattern generator. The walking is stable against forward and backward pushes of up to 70 and 75 N, respectively, and with sudden changes in trunk mass of up to 18%. The sensitivity of the model to changes in neural parameters and the predicted behavioral results of simulated neural system lesions are examined. The deficit gait simulations may be useful to support the functional and anatomical correspondences of the model. The model demonstrates that basic human-like walking can be achieved by a hierarchical structure of stabilized-long loop feedback and synergy-mediated feedforward controls. In particular, internal models of body dynamics are not required.

Assessing uncertainty in ecological systems using global sensitivity analyses: a case example of simulated wolf reintroduction effects on elk

USGS Publications Warehouse

Fieberg, J.; Jenkins, Kurt J.

2005-01-01

Often landmark conservation decisions are made despite an incomplete knowledge of system behavior and inexact predictions of how complex ecosystems will respond to management actions. For example, predicting the feasibility and likely effects of restoring top-level carnivores such as the gray wolf (Canis lupus) to North American wilderness areas is hampered by incomplete knowledge of the predator-prey system processes and properties. In such cases, global sensitivity measures, such as Sobola?? indices, allow one to quantify the effect of these uncertainties on model predictions. Sobola?? indices are calculated by decomposing the variance in model predictions (due to parameter uncertainty) into main effects of model parameters and their higher order interactions. Model parameters with large sensitivity indices can then be identified for further study in order to improve predictive capabilities. Here, we illustrate the use of Sobola?? sensitivity indices to examine the effect of parameter uncertainty on the predicted decline of elk (Cervus elaphus) population sizes following a hypothetical reintroduction of wolves to Olympic National Park, Washington, USA. The strength of density dependence acting on survival of adult elk and magnitude of predation were the most influential factors controlling elk population size following a simulated wolf reintroduction. In particular, the form of density dependence in natural survival rates and the per-capita predation rate together accounted for over 90% of variation in simulated elk population trends. Additional research on wolf predation rates on elk and natural compensations in prey populations is needed to reliably predict the outcome of predatora??prey system behavior following wolf reintroductions.

Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

NASA Technical Reports Server (NTRS)

Lee, Hanbong

2016-01-01

Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.

Micro-simulation of vehicle conflicts involving right-turn vehicles at signalized intersections based on cellular automata.

PubMed

Chai, C; Wong, Y D

2014-02-01

At intersection, vehicles coming from different directions conflict with each other. Improper geometric design and signal settings at signalized intersection will increase occurrence of conflicts between road users and results in a reduction of the safety level. This study established a cellular automata (CA) model to simulate vehicular interactions involving right-turn vehicles (as similar to left-turn vehicles in US). Through various simulation scenarios for four case cross-intersections, the relationships between conflict occurrences involving right-turn vehicles with traffic volume and right-turn movement control strategies are analyzed. Impacts of traffic volume, permissive right-turn compared to red-amber-green (RAG) arrow, shared straight-through and right-turn lane as well as signal setting are estimated from simulation results. The simulation model is found to be able to provide reasonable assessment of conflicts through comparison of existed simulation approach and observed accidents. Through the proposed approach, prediction models for occurrences and severity of vehicle conflicts can be developed for various geometric layouts and traffic control strategies. Copyright © 2013 Elsevier Ltd. All rights reserved.

Nutation control during precession of a spin-stabilized spacecraft

NASA Technical Reports Server (NTRS)

1974-01-01

Precession maneuver control laws for single-spin spacecraft are investigated so that nutation is concurrently controlled. Analysis has led to the development of two types of control laws employing precession modulation for concurrent nutation control. Results were verified through digital simulation of a Synchronous Meteorological Satellite (SMS) configuration. An addition research effort was undertaken to investigate the cause and elimination of nutation anomalies in dual-spin spacecraft. A literature search was conducted and a dual-spin configuration was simulated to verify that nutational anomalies are not predicted by the existing nonlinear model. No conclusions were drawn as to the cause of the observed nutational anomalies in dual-spin spacecraft.

Simulation of controllable permeation in PNIPAAm coated membranes

NASA Astrophysics Data System (ADS)

Ehrenhofer, Adrian; Wallmersperger, Thomas; Richter, Andreas

2016-04-01

Membranes separate fluid compartments and can comprise transport structures for selective permeation. In biology, channel proteins are specialized in their atomic structure to allow transport of specific compounds (selectivity). Conformational changes in protein structure allow the control of the permeation abilities by outer stimuli (gating). In polymeric membranes, the selectivity is due to electrostatic or size-exclusion. It can thus be controlled by size variation or electric charges. Controllable permeation can be useful to determine particle-size distributions in continuous flow, e.g. in microfluidics and biomedicine to gain cell diameter profiles in blood. The present approach uses patterned polyethylene terephthalate (PET) membranes with hydrogel surface coating for permeation control by size-exclusion. The thermosensitive hydrogel poly(N-isopropylacrylamide) (PNIPAAm) is structured with a cross-shaped pore geometry. A change in the temperature of the water flow through the membrane leads to a pore shape variation. The temperature dependent behavior of PNIPAAm can be numerically modeled with a temperature expansion model, where the swelling and deswelling is depicted by temperature dependent expansion coefficients. In the present study, the free swelling behavior was implemented to the Finite Element tool ABAQUS for the complex composite structure of the permeation control membrane. Experimental values of the geometry characteristics were derived from microscopy images with the tool Image J and compared to simulation results. Numerical simulations using the derived thermo-mechanical model for different pore geometries (circular, rectangle, cross and triangle) were performed. With this study, we show that the temperature expansion model with values from the free swelling behavior can be used to adequately predict the deformation behavior of the complex membrane system. The predictions can be used to optimize the behavior of the membrane pores and the overall performance of the smart membrane.

Reference governors for controlled belt restraint systems

NASA Astrophysics Data System (ADS)

van der Laan, E. P.; Heemels, W. P. M. H.; Luijten, H.; Veldpaus, F. E.; Steinbuch, M.

2010-07-01

Today's restraint systems typically include a number of airbags, and a three-point seat belt with load limiter and pretensioner. For the class of real-time controlled restraint systems, the restraint actuator settings are continuously manipulated during the crash. This paper presents a novel control strategy for these systems. The control strategy developed here is based on a combination of model predictive control and reference management, in which a non-linear device - a reference governor (RG) - is added to a primal closed-loop controlled system. This RG determines an optimal setpoint in terms of injury reduction and constraint satisfaction by solving a constrained optimisation problem. Prediction of the vehicle motion, required to predict future constraint violation, is included in the design and is based on past crash data, using linear regression techniques. Simulation results with MADYMO models show that, with ideal sensors and actuators, a significant reduction (45%) of the peak chest acceleration can be achieved, without prior knowledge of the crash. Furthermore, it is shown that the algorithms are sufficiently fast to be implemented online.

Process simulation and dynamic control for marine oily wastewater treatment using UV irradiation.

PubMed

Jing, Liang; Chen, Bing; Zhang, Baiyu; Li, Pu

2015-09-15

UV irradiation and advanced oxidation processes have been recently regarded as promising solutions in removing polycyclic aromatic hydrocarbons (PAHs) from marine oily wastewater. However, such treatment methods are generally not sufficiently understood in terms of reaction mechanisms, process simulation and process control. These deficiencies can drastically hinder their application in shipping and offshore petroleum industries which produce bilge/ballast water and produced water as the main streams of marine oily wastewater. In this study, the factorial design of experiment was carried out to investigate the degradation mechanism of a typical PAH, namely naphthalene, under UV irradiation in seawater. Based on the experimental results, a three-layer feed-forward artificial neural network simulation model was developed to simulate the treatment process and to forecast the removal performance. A simulation-based dynamic mixed integer nonlinear programming (SDMINP) approach was then proposed to intelligently control the treatment process by integrating the developed simulation model, genetic algorithm and multi-stage programming. The applicability and effectiveness of the developed approach were further tested though a case study. The experimental results showed that the influences of fluence rate and temperature on the removal of naphthalene were greater than those of salinity and initial concentration. The developed simulation model could well predict the UV-induced removal process under varying conditions. The case study suggested that the SDMINP approach, with the aid of the multi-stage control strategy, was able to significantly reduce treatment cost when comparing to the traditional single-stage process optimization. The developed approach and its concept/framework have high potential of applicability in other environmental fields where a treatment process is involved and experimentation and modeling are used for process simulation and control. Copyright © 2015 Elsevier Ltd. All rights reserved.

Anticipation from sensation: using anticipating synchronization to stabilize a system with inherent sensory delay.

PubMed

Eberle, Henry; Nasuto, Slawomir J; Hayashi, Yoshikatsu

2018-03-01

We present a novel way of using a dynamical model for predictive tracking control that can adapt to a wide range of delays without parameter update. This is achieved by incorporating the paradigm of anticipating synchronization (AS), where a 'slave' system predicts a 'master' via delayed self-feedback. By treating the delayed output of the plant as one half of a 'sensory' AS coupling, the plant and an internal dynamical model can be synchronized such that the plant consistently leads the target's motion. We use two simulated robotic systems with differing arrangements of the plant and internal model ('parallel' and 'serial') to demonstrate that this form of control adapts to a wide range of delays without requiring the parameters of the controller to be changed.

The UKC2 regional coupled environmental prediction system

NASA Astrophysics Data System (ADS)

Lewis, Huw W.; Castillo Sanchez, Juan Manuel; Graham, Jennifer; Saulter, Andrew; Bornemann, Jorge; Arnold, Alex; Fallmann, Joachim; Harris, Chris; Pearson, David; Ramsdale, Steven; Martínez-de la Torre, Alberto; Bricheno, Lucy; Blyth, Eleanor; Bell, Victoria A.; Davies, Helen; Marthews, Toby R.; O'Neill, Clare; Rumbold, Heather; O'Dea, Enda; Brereton, Ashley; Guihou, Karen; Hines, Adrian; Butenschon, Momme; Dadson, Simon J.; Palmer, Tamzin; Holt, Jason; Reynard, Nick; Best, Martin; Edwards, John; Siddorn, John

2018-01-01

It is hypothesized that more accurate prediction and warning of natural hazards, such as of the impacts of severe weather mediated through various components of the environment, require a more integrated Earth System approach to forecasting. This hypothesis can be explored using regional coupled prediction systems, in which the known interactions and feedbacks between different physical and biogeochemical components of the environment across sky, sea and land can be simulated. Such systems are becoming increasingly common research tools. This paper describes the development of the UKC2 regional coupled research system, which has been delivered under the UK Environmental Prediction Prototype project. This provides the first implementation of an atmosphere-land-ocean-wave modelling system focussed on the United Kingdom and surrounding seas at km-scale resolution. The UKC2 coupled system incorporates models of the atmosphere (Met Office Unified Model), land surface with river routing (JULES), shelf-sea ocean (NEMO) and ocean waves (WAVEWATCH III). These components are coupled, via OASIS3-MCT libraries, at unprecedentedly high resolution across the UK within a north-western European regional domain. A research framework has been established to explore the representation of feedback processes in coupled and uncoupled modes, providing a new research tool for UK environmental science. This paper documents the technical design and implementation of UKC2, along with the associated evaluation framework. An analysis of new results comparing the output of the coupled UKC2 system with relevant forced control simulations for six contrasting case studies of 5-day duration is presented. Results demonstrate that performance can be achieved with the UKC2 system that is at least comparable to its component control simulations. For some cases, improvements in air temperature, sea surface temperature, wind speed, significant wave height and mean wave period highlight the potential benefits of coupling between environmental model components. Results also illustrate that the coupling itself is not sufficient to address all known model issues. Priorities for future development of the UK Environmental Prediction framework and component systems are discussed.

Predictions of Cockpit Simulator Experimental Outcome Using System Models

NASA Technical Reports Server (NTRS)

Sorensen, J. A.; Goka, T.

1984-01-01

This study involved predicting the outcome of a cockpit simulator experiment where pilots used cockpit displays of traffic information (CDTI) to establish and maintain in-trail spacing behind a lead aircraft during approach. The experiments were run on the NASA Ames Research Center multicab cockpit simulator facility. Prior to the experiments, a mathematical model of the pilot/aircraft/CDTI flight system was developed which included relative in-trail and vertical dynamics between aircraft in the approach string. This model was used to construct a digital simulation of the string dynamics including response to initial position errors. The model was then used to predict the outcome of the in-trail following cockpit simulator experiments. Outcome included performance and sensitivity to different separation criteria. The experimental results were then used to evaluate the model and its prediction accuracy. Lessons learned in this modeling and prediction study are noted.

Using a Gaussian Process Emulator for Data-driven Surrogate Modelling of a Complex Urban Drainage Simulator

NASA Astrophysics Data System (ADS)

Bellos, V.; Mahmoodian, M.; Leopold, U.; Torres-Matallana, J. A.; Schutz, G.; Clemens, F.

2017-12-01

Surrogate models help to decrease the run-time of computationally expensive, detailed models. Recent studies show that Gaussian Process Emulators (GPE) are promising techniques in the field of urban drainage modelling. However, this study focusses on developing a GPE-based surrogate model for later application in Real Time Control (RTC) using input and output time series of a complex simulator. The case study is an urban drainage catchment in Luxembourg. A detailed simulator, implemented in InfoWorks ICM, is used to generate 120 input-output ensembles, from which, 100 are used for training the emulator and 20 for validation of the results. An ensemble of historical rainfall events with 2 hours duration and 10 minutes time steps are considered as the input data. Two example outputs, are selected as wastewater volume and total COD concentration in a storage tank in the network. The results of the emulator are tested with unseen random rainfall events from the ensemble dataset. The emulator is approximately 1000 times faster than the original simulator for this small case study. Whereas the overall patterns of the simulator are matched by the emulator, in some cases the emulator deviates from the simulator. To quantify the accuracy of the emulator in comparison with the original simulator, Nash-Sutcliffe efficiency (NSE) between the emulator and simulator is calculated for unseen rainfall scenarios. The range of NSE for the case of tank volume is from 0.88 to 0.99 with a mean value of 0.95, whereas for COD is from 0.71 to 0.99 with a mean value of 0.92. The emulator is able to predict the tank volume with higher accuracy as the relationship between rainfall intensity and tank volume is linear. For COD, which has a non-linear behaviour, the predictions are less accurate and more uncertain, in particular when rainfall intensity increases. This predictions were improved by including a larger amount of training data for the higher rainfall intensities. It was observed that, the accuracy of the emulator predictions depends on the ensemble training dataset design and the amount of data fed. Finally, more investigation is required to test the possibility of applying this type of fast emulators for model-based RTC applications in which limited number of inputs and outputs are considered in a short prediction horizon.

Comparing in Cylinder Pressure Modelling of a DI Diesel Engine Fuelled on Alternative Fuel Using Two Tabulated Chemistry Approaches

PubMed Central

Ngayihi Abbe, Claude Valery; Nzengwa, Robert; Danwe, Raidandi

2014-01-01

The present work presents the comparative simulation of a diesel engine fuelled on diesel fuel and biodiesel fuel. Two models, based on tabulated chemistry, were implemented for the simulation purpose and results were compared with experimental data obtained from a single cylinder diesel engine. The first model is a single zone model based on the Krieger and Bormann combustion model while the second model is a two-zone model based on Olikara and Bormann combustion model. It was shown that both models can predict well the engine's in-cylinder pressure as well as its overall performances. The second model showed a better accuracy than the first, while the first model was easier to implement and faster to compute. It was found that the first method was better suited for real time engine control and monitoring while the second one was better suited for engine design and emission prediction. PMID:27379306

SLiM 2: Flexible, Interactive Forward Genetic Simulations.

PubMed

Haller, Benjamin C; Messer, Philipp W

2017-01-01

Modern population genomic datasets hold immense promise for revealing the evolutionary processes operating in natural populations, but a crucial prerequisite for this goal is the ability to model realistic evolutionary scenarios and predict their expected patterns in genomic data. To that end, we present SLiM 2: an evolutionary simulation framework that combines a powerful, fast engine for forward population genetic simulations with the capability of modeling a wide variety of complex evolutionary scenarios. SLiM achieves this flexibility through scriptability, which provides control over most aspects of the simulated evolutionary scenarios with a simple R-like scripting language called Eidos. An example SLiM simulation is presented to illustrate the power of this approach. SLiM 2 also includes a graphical user interface for simulation construction, interactive runtime control, and dynamic visualization of simulation output, facilitating easy and fast model development with quick prototyping and visual debugging. We conclude with a performance comparison between SLiM and two other popular forward genetic simulation packages. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Reduction and prediction of N2O emission from an Anoxic/Oxic wastewater treatment plant upon DO control and model simulation.

PubMed

Sun, Shichang; Bao, Zhiyuan; Li, Ruoyu; Sun, Dezhi; Geng, Haihong; Huang, Xiaofei; Lin, Junhao; Zhang, Peixin; Ma, Rui; Fang, Lin; Zhang, Xianghua; Zhao, Xuxin

2017-11-01

In order to make a better understanding of the characteristics of N 2 O emission in A/O wastewater treatment plant, full-scale and pilot-scale experiments were carried out and a back propagation artificial neural network model based on the experimental data was constructed to make a precise prediction of N 2 O emission. Results showed that, N 2 O flux from different units followed a descending order: aerated grit tank>oxic zone≫anoxic zone>final clarifier>primary clarifier, but 99.4% of the total emission of N 2 O (1.60% of N-load) was monitored from the oxic zone due to its big surface area. A proper DO control could reduce N 2 O emission down to 0.21% of N-load in A/O process, and a two-hidden-layers back propagation model with an optimized structure of 4:3:9:1 could achieve a good simulation of N 2 O emission, which provided a new method for the prediction of N 2 O emission during wastewater treatment. Copyright © 2017. Published by Elsevier Ltd.

Characterizing Observed Limit Cycles in the Cassini Main Engine Guidance Control System

NASA Technical Reports Server (NTRS)

Rizvi, Farheen; Weitl, Raquel M.

2011-01-01

The Cassini spacecraft dynamics-related telemetry during long Main Engine (ME) burns has indicated the presence of stable limit cycles between 0.03-0.04 Hz frequencies. These stable limit cycles cause the spacecraft to possess non-zero oscillating rates for extended periods of time. This indicates that the linear ME guidance control system does not model the complete dynamics of the spacecraft. In this study, we propose that the observed limit cycles in the spacecraft dynamics telemetry appear from a stable interaction between the unmodeled nonlinear elements in the ME guidance control system. Many nonlinearities in the control system emerge from translating the linear engine gimbal actuator (EGA) motion into a spacecraft rotation. One such nonlinearity comes from the gear backlash in the EGA system, which is the focus of this paper. The limit cycle characteristics and behavior can be predicted by modeling this gear backlash nonlinear element via a describing function and studying the interaction of this describing function with the overall dynamics of the spacecraft. The linear ME guidance controller and gear backlash nonlinearity are modeled analytically. The frequency, magnitude, and nature of the limit cycle are obtained from the frequency response of the ME guidance controller and nonlinear element. In addition, the ME guidance controller along with the nonlinearity is simulated. The simulation response contains a limit cycle with similar characterstics as predicted analytically: 0.03-0.04 Hz frequency and stable, sustained oscillations. The analytical and simulated limit cycle responses are compared to the flight telemetry for long burns such as the Saturn Orbit Insertion and Main Engine Orbit Trim Maneuvers. The analytical and simulated limit cycle characteristics compare well with the actual observed limit cycles in the flight telemetry. Both have frequencies between 0.03-0.04 Hz and stable oscillations. This work shows that the stable limit cycles occur due to the interaction between the unmodeled nonlinear elements and linear ME guidance controller.

Simulation of nutrient and sediment concentrations and loads in the Delaware inland bays watershed: Extension of the hydrologic and water-quality model to ungaged segments

USGS Publications Warehouse

Gutierrez-Magness, Angelica L.

2006-01-01

Rapid population increases, agriculture, and industrial practices have been identified as important sources of excessive nutrients and sediments in the Delaware Inland Bays watershed. The amount and effect of excessive nutrients and sediments in the Inland Bays watershed have been well documented by the Delaware Geological Survey, the Delaware Department of Natural Resources and Environmental Control, the U.S. Environmental Protection Agency's National Estuary Program, the Delaware Center for Inland Bays, the University of Delaware, and other agencies. This documentation and data previously were used to develop a hydrologic and water-quality model of the Delaware Inland Bays watershed to simulate nutrients and sediment concentrations and loads, and to calibrate the model by comparing concentrations and streamflow data at six stations in the watershed over a limited period of time (October 1998 through April 2000). Although the model predictions of nutrient and sediment concentrations for the calibrated segments were fairly accurate, the predictions for the 28 ungaged segments located near tidal areas, where stream data were not available, were above the range of values measured in the area. The cooperative study established in 2000 by the Delaware Department of Natural Resources and Environmental Control, the Delaware Geological Survey, and the U.S. Geological Survey was extended to evaluate the model predictions in ungaged segments and to ensure that the model, developed as a planning and management tool, could accurately predict nutrient and sediment concentrations within the measured range of values in the area. The evaluation of the predictions was limited to the period of calibration (1999) of the 2003 model. To develop estimates on ungaged watersheds, parameter values from calibrated segments are transferred to the ungaged segments; however, accurate predictions are unlikely where parameter transference is subject to error. The unexpected nutrient and sediment concentrations simulated with the 2003 model were likely the result of inappropriate criteria for the transference of parameter values. From a model-simulation perspective, it is a common practice to transfer parameter values based on the similarity of soils or the similarity of land-use proportions between segments. For the Inland Bays model, the similarity of soils between segments was used as the basis to transfer parameter values. An alternative approach, which is documented in this report, is based on the similarity of the spatial distribution of the land use between segments and the similarity of land-use proportions, as these can be important factors for the transference of parameter values in lumped models. Previous work determined that the difference in the variation of runoff due to various spatial distributions of land use within a watershed can cause substantialloss of accuracy in the model predictions. The incorporation of the spatial distribution of land use to transfer parameter values from calibrated to uncalibrated segments provided more consistent and rational predictions of flow, especially during the summer, and consequently, predictions of lower nutrient concentrations during the same period. For the segments where the similarity of spatial distribution of land use was not clearly established with a calibrated segment, the similarity of the location of the most impervious areas was also used as a criterion for the transference of parameter values. The model predictions from the 28 ungaged segments were verified through comparison with measured in-stream concentrations from local and nearby streams provided by the Delaware Department of Natural Resources and Environmental Control. Model results indicated that the predicted edge-of-stream total suspended solids loads in the Inland Bays watershed were low in comparison to loads reported for the Eastern Shore of Maryland from the Chesapeake Bay watershed model. The flatness of the ter

Target controlled infusion for kids: trials and simulations.

PubMed

Mehta, Disha; McCormack, Jon; Fung, Parry; Dumont, Guy; Ansermino, J

2008-01-01

Target controlled infusion (TCI) for Kids is a computer controlled system designed to administer propofol for general anesthesia. A controller establishes infusion rates required to achieve a specified concentration at the drug's effect site (C(e)) by implementing a continuously updated pharmacokinetic-pharmacodymanic model. This manuscript provides an overview of the system's design, preclinical tests, and a clinical pilot study. In pre-clinical tests, predicted infusion rates for 20 simulated procedures displayed complete convergent validity between two software implementations, Labview and Matlab, at computational intervals of 5, 10, and 15s, but diverged with 20s intervals due to system rounding errors. The volume of drug delivered by the TCI system also displayed convergent validity with Tivatrainer, a widely used TCI simulation software. Further tests, were conducted for 50 random procedures to evaluate discrepancies between volumes reported and those actually delivered by the system. Accuracies were within clinically acceptable ranges and normally distributed with a mean of 0.08 +/- 0.01 ml. In the clinical study, propofol pharmacokinetics were simulated for 30 surgical procedures involving children aged 3 months to 9 years. Predicted C(e) values during standard clinical practice, the accuracy of wake-up times predicted by the system, and potential correlations between patient wake-up times, C(e), and state entropy (SE) were assessed. Neither Ce nor SE was a reliable predictor of wake-up time in children, but the small sample size of this study does not fully accommodate the noted variation in children's response to propofol. A C(e) value of 1.9 mug/ml was found to best predict emergence from anesthesia in children.

Modelling and Simulation of the Dynamics of the Antigen-Specific T Cell Response Using Variable Structure Control Theory.

PubMed

Anelone, Anet J N; Spurgeon, Sarah K

2016-01-01

Experimental and mathematical studies in immunology have revealed that the dynamics of the programmed T cell response to vigorous infection can be conveniently modelled using a sigmoidal or a discontinuous immune response function. This paper hypothesizes strong synergies between this existing work and the dynamical behaviour of engineering systems with a variable structure control (VSC) law. These findings motivate the interpretation of the immune system as a variable structure control system. It is shown that dynamical properties as well as conditions to analytically assess the transition from health to disease can be developed for the specific T cell response from the theory of variable structure control. In particular, it is shown that the robustness properties of the specific T cell response as observed in experiments can be explained analytically using a VSC perspective. Further, the predictive capacity of the VSC framework to determine the T cell help required to overcome chronic Lymphocytic Choriomeningitis Virus (LCMV) infection is demonstrated. The findings demonstrate that studying the immune system using variable structure control theory provides a new framework for evaluating immunological dynamics and experimental observations. A modelling and simulation tool results with predictive capacity to determine how to modify the immune response to achieve healthy outcomes which may have application in drug development and vaccine design.

Towards Assessing the Human Trajectory Planning Horizon

PubMed Central

Nitsch, Verena; Meinzer, Dominik; Wollherr, Dirk

2016-01-01

Mobile robots are envisioned to cooperate closely with humans and to integrate seamlessly into a shared environment. For locomotion, these environments resemble traversable areas which are shared between multiple agents like humans and robots. The seamless integration of mobile robots into these environments requires accurate predictions of human locomotion. This work considers optimal control and model predictive control approaches for accurate trajectory prediction and proposes to integrate aspects of human behavior to improve their performance. Recently developed models are not able to reproduce accurately trajectories that result from sudden avoidance maneuvers. Particularly, the human locomotion behavior when handling disturbances from other agents poses a problem. The goal of this work is to investigate whether humans alter their trajectory planning horizon, in order to resolve abruptly emerging collision situations. By modeling humans as model predictive controllers, the influence of the planning horizon is investigated in simulations. Based on these results, an experiment is designed to identify, whether humans initiate a change in their locomotion planning behavior while moving in a complex environment. The results support the hypothesis, that humans employ a shorter planning horizon to avoid collisions that are triggered by unexpected disturbances. Observations presented in this work are expected to further improve the generalizability and accuracy of prediction methods based on dynamic models. PMID:27936015

Towards Assessing the Human Trajectory Planning Horizon.

PubMed

Carton, Daniel; Nitsch, Verena; Meinzer, Dominik; Wollherr, Dirk

2016-01-01

Mobile robots are envisioned to cooperate closely with humans and to integrate seamlessly into a shared environment. For locomotion, these environments resemble traversable areas which are shared between multiple agents like humans and robots. The seamless integration of mobile robots into these environments requires accurate predictions of human locomotion. This work considers optimal control and model predictive control approaches for accurate trajectory prediction and proposes to integrate aspects of human behavior to improve their performance. Recently developed models are not able to reproduce accurately trajectories that result from sudden avoidance maneuvers. Particularly, the human locomotion behavior when handling disturbances from other agents poses a problem. The goal of this work is to investigate whether humans alter their trajectory planning horizon, in order to resolve abruptly emerging collision situations. By modeling humans as model predictive controllers, the influence of the planning horizon is investigated in simulations. Based on these results, an experiment is designed to identify, whether humans initiate a change in their locomotion planning behavior while moving in a complex environment. The results support the hypothesis, that humans employ a shorter planning horizon to avoid collisions that are triggered by unexpected disturbances. Observations presented in this work are expected to further improve the generalizability and accuracy of prediction methods based on dynamic models.

Atlantic Meridional Overturning Circulation Influence on North Atlantic Sector Surface Air Temperature and its Predictability in the Kiel Climate Model

NASA Astrophysics Data System (ADS)

Latif, M.

2017-12-01

We investigate the influence of the Atlantic Meridional Overturning Circulation (AMOC) on the North Atlantic sector surface air temperature (SAT) in two multi-millennial control integrations of the Kiel Climate Model (KCM). One model version employs a freshwater flux correction over the North Atlantic, while the other does not. A clear influence of the AMOC on North Atlantic sector SAT only is simulated in the corrected model that depicts much reduced upper ocean salinity and temperature biases in comparison to the uncorrected model. Further, the model with much reduced biases depicts significantly enhanced multiyear SAT predictability in the North Atlantic sector relative to the uncorrected model. The enhanced SAT predictability in the corrected model is due to a stronger and more variable AMOC and its enhanced influence on North Atlantic sea surface temperature (SST). Results obtained from preindustrial control integrations of models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) support the findings obtained from the KCM: models with large North Atlantic biases tend to have a weak AMOC influence on SST and exhibit a smaller SAT predictability over the North Atlantic sector.

Occupant-vehicle dynamics and the role of the internal model

NASA Astrophysics Data System (ADS)

Cole, David J.

2018-05-01

With the increasing need to reduce time and cost of vehicle development there is increasing advantage in simulating mathematically the dynamic interaction of a vehicle and its occupant. The larger design space arising from the introduction of automated vehicles further increases the potential advantage. The aim of the paper is to outline the role of the internal model hypothesis in understanding and modelling occupant-vehicle dynamics, specifically the dynamics associated with direction and speed control of the vehicle. The internal model is the driver's or passenger's understanding of the vehicle dynamics and is thought to be employed in the perception, cognition and action processes of the brain. The internal model aids the estimation of the states of the vehicle from noisy sensory measurements. It can also be used to optimise cognitive control action by predicting the consequence of the action; thus model predictive control (MPC) theory provides a foundation for modelling the cognition process. The stretch reflex of the neuromuscular system also makes use of the prediction of the internal model. Extensions to the MPC approach are described which account for: interaction with an automated vehicle; robust control; intermittent control; and cognitive workload. Further work to extend understanding of occupant-vehicle dynamic interaction is outlined. This paper is based on a keynote presentation given by the author to the 13th International Symposium on Advanced Vehicle Control (AVEC) conference held in Munich, September 2016.

Active vibration absorber for CSI evolutionary model: Design and experimental results

NASA Technical Reports Server (NTRS)

Bruner, Anne M.; Belvin, W. Keith; Horta, Lucas G.; Juang, Jer-Nan

1991-01-01

The development of control of large flexible structures technology must include practical demonstration to aid in the understanding and characterization of controlled structures in space. To support this effort, a testbed facility was developed to study practical implementation of new control technologies under realistic conditions. The design is discussed of a second order, acceleration feedback controller which acts as an active vibration absorber. This controller provides guaranteed stability margins for collocated sensor/actuator pairs in the absence of sensor/actuator dynamics and computational time delay. The primary performance objective considered is damping augmentation of the first nine structural modes. Comparison of experimental and predicted closed loop damping is presented, including test and simulation time histories for open and closed loop cases. Although the simulation and test results are not in full agreement, robustness of this design under model uncertainty is demonstrated. The basic advantage of this second order controller design is that the stability of the controller is model independent.

Predictive functional control for active queue management in congested TCP/IP networks.

PubMed

Bigdeli, N; Haeri, M

2009-01-01

Predictive functional control (PFC) as a new active queue management (AQM) method in dynamic TCP networks supporting explicit congestion notification (ECN) is proposed. The ability of the controller in handling system delay along with its simplicity and low computational load makes PFC a privileged AQM method in the high speed networks. Besides, considering the disturbance term (which represents model/process mismatches, external disturbances, and existing noise) in the control formulation adds some level of robustness into the PFC-AQM controller. This is an important and desired property in the control of dynamically-varying computer networks. In this paper, the controller is designed based on a small signal linearized fluid-flow model of the TCP/AQM networks. Then, closed-loop transfer function representation of the system is derived to analyze the robustness with respect to the network and controller parameters. The analytical as well as the packet-level ns-2 simulation results show the out-performance of the developed controller for both queue regulation and resource utilization. Fast response, low queue fluctuations (and consequently low delay jitter), high link utilization, good disturbance rejection, scalability, and low packet marking probability are other features of the developed method with respect to other well-known AQM methods such as RED, PI, and REM which are also simulated for comparison.

Predicted torque equilibrium attitude utilization for Space Station attitude control

NASA Technical Reports Server (NTRS)

Kumar, Renjith R.; Heck, Michael L.; Robertson, Brent P.

1990-01-01

An approximate knowledge of the torque equilibrium attitude (TEA) is shown to improve the performance of a control moment gyroscope (CMG) momentum management/attitude control law for Space Station Freedom. The linearized equations of motion are used in conjunction with a state transformation to obtain a control law which uses full state feedback and the predicted TEA to minimize both attitude excursions and CMG peak and secular momentum. The TEA can be computationally determined either by observing the steady state attitude of a 'controlled' spacecraft using arbitrary initial attitude, or by simulating a fixed attitude spacecraft flying in desired orbit subject to realistic environmental disturbance models.

LAMPS software

NASA Technical Reports Server (NTRS)

Perkey, D. J.; Kreitzberg, C. W.

1984-01-01

The dynamic prediction model along with its macro-processor capability and data flow system from the Drexel Limited-Area and Mesoscale Prediction System (LAMPS) were converted and recorded for the Perkin-Elmer 3220. The previous version of this model was written for Control Data Corporation 7600 and CRAY-1a computer environment which existed until recently at the National Center for Atmospheric Research. The purpose of this conversion was to prepare LAMPS for porting to computer environments other than that encountered at NCAR. The emphasis was shifted from programming tasks to model simulation and evaluation tests.

Improving Permafrost Hydrology Prediction Through Data-Model Integration

NASA Astrophysics Data System (ADS)

Wilson, C. J.; Andresen, C. G.; Atchley, A. L.; Bolton, W. R.; Busey, R.; Coon, E.; Charsley-Groffman, L.

2017-12-01

The CMIP5 Earth System Models were unable to adequately predict the fate of the 16GT of permafrost carbon in a warming climate due to poor representation of Arctic ecosystem processes. The DOE Office of Science Next Generation Ecosystem Experiment, NGEE-Arctic project aims to reduce uncertainty in the Arctic carbon cycle and its impact on the Earth's climate system by improved representation of the coupled physical, chemical and biological processes that drive how much buried carbon will be converted to CO2 and CH4, how fast this will happen, which form will dominate, and the degree to which increased plant productivity will offset increased soil carbon emissions. These processes fundamentally depend on permafrost thaw rate and its influence on surface and subsurface hydrology through thermal erosion, land subsidence and changes to groundwater flow pathways as soil, bedrock and alluvial pore ice and massive ground ice melts. LANL and its NGEE colleagues are co-developing data and models to better understand controls on permafrost degradation and improve prediction of the evolution of permafrost and its impact on Arctic hydrology. The LANL Advanced Terrestrial Simulator was built using a state of the art HPC software framework to enable the first fully coupled 3-dimensional surface-subsurface thermal-hydrology and land surface deformation simulations to simulate the evolution of the physical Arctic environment. Here we show how field data including hydrology, snow, vegetation, geochemistry and soil properties, are informing the development and application of the ATS to improve understanding of controls on permafrost stability and permafrost hydrology. The ATS is being used to inform parameterizations of complex coupled physical, ecological and biogeochemical processes for implementation in the DOE ACME land model, to better predict the role of changing Arctic hydrology on the global climate system. LA-UR-17-26566.

Planner-Based Control of Advanced Life Support Systems

NASA Technical Reports Server (NTRS)

Muscettola, Nicola; Kortenkamp, David; Fry, Chuck; Bell, Scott

2005-01-01

The paper describes an approach to the integration of qualitative and quantitative modeling techniques for advanced life support (ALS) systems. Developing reliable control strategies that scale up to fully integrated life support systems requires augmenting quantitative models and control algorithms with the abstractions provided by qualitative, symbolic models and their associated high-level control strategies. This will allow for effective management of the combinatorics due to the integration of a large number of ALS subsystems. By focusing control actions at different levels of detail and reactivity we can use faster: simpler responses at the lowest level and predictive but complex responses at the higher levels of abstraction. In particular, methods from model-based planning and scheduling can provide effective resource management over long time periods. We describe reference implementation of an advanced control system using the IDEA control architecture developed at NASA Ames Research Center. IDEA uses planning/scheduling as the sole reasoning method for predictive and reactive closed loop control. We describe preliminary experiments in planner-based control of ALS carried out on an integrated ALS simulation developed at NASA Johnson Space Center.

ISS Double-Gimbaled CMG Subsystem Simulation Using the Agile Development Method

NASA Technical Reports Server (NTRS)

Inampudi, Ravi

2016-01-01

This paper presents an evolutionary approach in simulating a cluster of 4 Control Moment Gyros (CMG) on the International Space Station (ISS) using a common sense approach (the agile development method) for concurrent mathematical modeling and simulation of the CMG subsystem. This simulation is part of Training systems for the 21st Century simulator which will provide training for crew members, instructors, and flight controllers. The basic idea of how the CMGs on the space station are used for its non-propulsive attitude control is briefly explained to set up the context for simulating a CMG subsystem. Next different reference frames and the detailed equations of motion (EOM) for multiple double-gimbal variable-speed control moment gyroscopes (DGVs) are presented. Fixing some of the terms in the EOM becomes the special case EOM for ISS's double-gimbaled fixed speed CMGs. CMG simulation development using the agile development method is presented in which customer's requirements and solutions evolve through iterative analysis, design, coding, unit testing and acceptance testing. At the end of the iteration a set of features implemented in that iteration are demonstrated to the flight controllers thus creating a short feedback loop and helping in creating adaptive development cycles. The unified modeling language (UML) tool is used in illustrating the user stories, class designs and sequence diagrams. This incremental development approach of mathematical modeling and simulating the CMG subsystem involved the development team and the customer early on, thus improving the quality of the working CMG system in each iteration and helping the team to accurately predict the cost, schedule and delivery of the software.

The Application of Satellite-Derived, High-Resolution Land Use/Land Cover Data to Improve Urban Air Quality Model Forecasts

NASA Technical Reports Server (NTRS)

Quattrochi, D. A.; Lapenta, W. M.; Crosson, W. L.; Estes, M. G., Jr.; Limaye, A.; Kahn, M.

2006-01-01

Local and state agencies are responsible for developing state implementation plans to meet National Ambient Air Quality Standards. Numerical models used for this purpose simulate the transport and transformation of criteria pollutants and their precursors. The specification of land use/land cover (LULC) plays an important role in controlling modeled surface meteorology and emissions. NASA researchers have worked with partners and Atlanta stakeholders to incorporate an improved high-resolution LULC dataset for the Atlanta area within their modeling system and to assess meteorological and air quality impacts of Urban Heat Island (UHI) mitigation strategies. The new LULC dataset provides a more accurate representation of land use, has the potential to improve model accuracy, and facilitates prediction of LULC changes. Use of the new LULC dataset for two summertime episodes improved meteorological forecasts, with an existing daytime cold bias of approx. equal to 3 C reduced by 30%. Model performance for ozone prediction did not show improvement. In addition, LULC changes due to Atlanta area urbanization were predicted through 2030, for which model simulations predict higher urban air temperatures. The incorporation of UHI mitigation strategies partially offset this warming trend. The data and modeling methods used are generally applicable to other U.S. cities.

Performance factors in associative learning: assessment of the sometimes competing retrieval model.

PubMed

Witnauer, James E; Wojick, Brittany M; Polack, Cody W; Miller, Ralph R

2012-09-01

Previous simulations revealed that the sometimes competing retrieval model (SOCR; Stout & Miller, Psychological Review, 114, 759-783, 2007), which assumes local error reduction, can explain many cue interaction phenomena that elude traditional associative theories based on total error reduction. Here, we applied SOCR to a new set of Pavlovian phenomena. Simulations used a single set of fixed parameters to simulate each basic effect (e.g., blocking) and, for specific experiments using different procedures, used fitted parameters discovered through hill climbing. In simulation 1, SOCR was successfully applied to basic acquisition, including the overtraining effect, which is context dependent. In simulation 2, we applied SOCR to basic extinction and renewal. SOCR anticipated these effects with both fixed parameters and best-fitting parameters, although the renewal effects were weaker than those observed in some experiments. In simulation 3a, feature-negative training was simulated, including the often observed transition from second-order conditioning to conditioned inhibition. In simulation 3b, SOCR predicted the observation that conditioned inhibition after feature-negative and differential conditioning depends on intertrial interval. In simulation 3c, SOCR successfully predicted failure of conditioned inhibition to extinguish with presentations of the inhibitor alone under most circumstances. In simulation 4, cue competition, including blocking (4a), recovery from relative validity (4b), and unblocking (4c), was simulated. In simulation 5, SOCR correctly predicted that inhibitors gain more behavioral control than do excitors when they are trained in compound. Simulation 6 demonstrated that SOCR explains the slower acquisition observed following CS-weak shock pairings.

Physics based modeling of a series parallel battery pack for asymmetry analysis, predictive control and life extension

NASA Astrophysics Data System (ADS)

Ganesan, Nandhini; Basu, Suman; Hariharan, Krishnan S.; Kolake, Subramanya Mayya; Song, Taewon; Yeo, Taejung; Sohn, Dong Kee; Doo, Seokgwang

2016-08-01

Lithium-Ion batteries used for electric vehicle applications are subject to large currents and various operation conditions, making battery pack design and life extension a challenging problem. With increase in complexity, modeling and simulation can lead to insights that ensure optimal performance and life extension. In this manuscript, an electrochemical-thermal (ECT) coupled model for a 6 series × 5 parallel pack is developed for Li ion cells with NCA/C electrodes and validated against experimental data. Contribution of the cathode to overall degradation at various operating conditions is assessed. Pack asymmetry is analyzed from a design and an operational perspective. Design based asymmetry leads to a new approach of obtaining the individual cell responses of the pack from an average ECT output. Operational asymmetry is demonstrated in terms of effects of thermal gradients on cycle life, and an efficient model predictive control technique is developed. Concept of reconfigurable battery pack is studied using detailed simulations that can be used for effective monitoring and extension of battery pack life.

Adaptive two-degree-of-freedom PI for speed control of permanent magnet synchronous motor based on fractional order GPC.

PubMed

Qiao, Wenjun; Tang, Xiaoqi; Zheng, Shiqi; Xie, Yuanlong; Song, Bao

2016-09-01

In this paper, an adaptive two-degree-of-freedom (2Dof) proportional-integral (PI) controller is proposed for the speed control of permanent magnet synchronous motor (PMSM). Firstly, an enhanced just-in-time learning technique consisting of two novel searching engines is presented to identify the model of the speed control system in a real-time manner. Secondly, a general formula is given to predict the future speed reference which is unavailable at the interval of two bus-communication cycles. Thirdly, the fractional order generalized predictive control (FOGPC) is introduced to improve the control performance of the servo drive system. Based on the identified model parameters and predicted speed reference, the optimal control law of FOGPC is derived. Finally, the designed 2Dof PI controller is auto-tuned by matching with the optimal control law. Simulations and real-time experimental results on the servo drive system of PMSM are provided to illustrate the effectiveness of the proposed strategy. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Cryogenic Pressure Control Modeling for Ellipsoidal Space Tanks in Reduced Gravity

NASA Technical Reports Server (NTRS)

Hedayat, Ali; Lopez, Alfredo; Grayson, Gary D.; Chandler, Frank O.; Hastings, Leon J.

2008-01-01

A computational fluid dynamics (CFD) model is developed to simulate pressure control of an ellipsoidal-shaped liquid hydrogen tank under external heating in low gravity. Pressure control is provided by an axial jet thermodynamic vent system (TVS) centered within the vessel that injects cooler liquid into the tank, mixing the contents and reducing tank pressure. The two-phase cryogenic tank model considers liquid hydrogen in its own vapor with liquid density varying with temperature only and a fully compressible ullage. The axisymmetric model is developed using a custom version of the commercially available FLOW-3D software and simulates low gravity extrapolations of engineering checkout tests performed at Marshall Space Flight Center in 1999 in support of the Solar Thermal Upper Stage Technology Demonstrator (STUSTD) program. Model results illustrate that stable low gravity liquid-gas interfaces are maintained during all phases of the pressure control cycle. Steady and relatively smooth ullage pressurization rates are predicted. This work advances current low gravity CFD modeling capabilities for cryogenic pressure control and aids the development of a low cost CFD-based design process for space hardware.

Lap time simulation and design optimisation of a brushed DC electric motorcycle for the Isle of Man TT Zero Challenge

NASA Astrophysics Data System (ADS)

Dal Bianco, N.; Lot, R.; Matthys, K.

2018-01-01

This works regards the design of an electric motorcycle for the annual Isle of Man TT Zero Challenge. Optimal control theory was used to perform lap time simulation and design optimisation. A bespoked model was developed, featuring 3D road topology, vehicle dynamics and electric power train, composed of a lithium battery pack, brushed DC motors and motor controller. The model runs simulations over the entire ? or ? of the Snaefell Mountain Course. The work is validated using experimental data from the BX chassis of the Brunel Racing team, which ran during the 2009 to 2015 TT Zero races. Optimal control is used to improve drive train and power train configurations. Findings demonstrate computational efficiency, good lap time prediction and design optimisation potential, achieving a 2 minutes reduction of the reference lap time through changes in final drive gear ratio, battery pack size and motor configuration.

Optical fiber sensors and signal processing for intelligent structure monitoring

NASA Technical Reports Server (NTRS)

Thomas, Daniel; Cox, Dave; Lindner, D. K.; Claus, R. O.

1989-01-01

Few mode optical fibers have been shown to produce predictable interference patterns when placed under strain. The use is described of a modal domain sensor in a vibration control experiment. An optical fiber is bonded along the length of a flexible beam. Output from the modal domain sensor is used to suppress vibrations induced in the beam. A distributed effect model for the modal domain sensor is developed. This model is combined with the beam and actuator dynamics to produce a system suitable for control design. Computer simulations predict open and closed loop dynamic responses. An experimental apparatus is described and experimental results are presented.

Dinucleotide controlled null models for comparative RNA gene prediction.

PubMed

Gesell, Tanja; Washietl, Stefan

2008-05-27

Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require randomization of multiple alignments can be considered. SISSIz is available as open source C code that can be compiled for every major platform and downloaded here: http://sourceforge.net/projects/sissiz.

Prediction of Bispectral Index during Target-controlled Infusion of Propofol and Remifentanil: A Deep Learning Approach.

PubMed

Lee, Hyung-Chul; Ryu, Ho-Geol; Chung, Eun-Jin; Jung, Chul-Woo

2018-03-01

The discrepancy between predicted effect-site concentration and measured bispectral index is problematic during intravenous anesthesia with target-controlled infusion of propofol and remifentanil. We hypothesized that bispectral index during total intravenous anesthesia would be more accurately predicted by a deep learning approach. Long short-term memory and the feed-forward neural network were sequenced to simulate the pharmacokinetic and pharmacodynamic parts of an empirical model, respectively, to predict intraoperative bispectral index during combined use of propofol and remifentanil. Inputs of long short-term memory were infusion histories of propofol and remifentanil, which were retrieved from target-controlled infusion pumps for 1,800 s at 10-s intervals. Inputs of the feed-forward network were the outputs of long short-term memory and demographic data such as age, sex, weight, and height. The final output of the feed-forward network was the bispectral index. The performance of bispectral index prediction was compared between the deep learning model and previously reported response surface model. The model hyperparameters comprised 8 memory cells in the long short-term memory layer and 16 nodes in the hidden layer of the feed-forward network. The model training and testing were performed with separate data sets of 131 and 100 cases. The concordance correlation coefficient (95% CI) were 0.561 (0.560 to 0.562) in the deep learning model, which was significantly larger than that in the response surface model (0.265 [0.263 to 0.266], P < 0.001). The deep learning model-predicted bispectral index during target-controlled infusion of propofol and remifentanil more accurately compared to the traditional model. The deep learning approach in anesthetic pharmacology seems promising because of its excellent performance and extensibility.

Path-integral method for the source apportionment of photochemical pollutants

NASA Astrophysics Data System (ADS)

Dunker, A. M.

2015-06-01

A new, path-integral method is presented for apportioning the concentrations of pollutants predicted by a photochemical model to emissions from different sources. A novel feature of the method is that it can apportion the difference in a species concentration between two simulations. For example, the anthropogenic ozone increment, which is the difference between a simulation with all emissions present and another simulation with only the background (e.g., biogenic) emissions included, can be allocated to the anthropogenic emission sources. The method is based on an existing, exact mathematical equation. This equation is applied to relate the concentration difference between simulations to line or path integrals of first-order sensitivity coefficients. The sensitivities describe the effects of changing the emissions and are accurately calculated by the decoupled direct method. The path represents a continuous variation of emissions between the two simulations, and each path can be viewed as a separate emission-control strategy. The method does not require auxiliary assumptions, e.g., whether ozone formation is limited by the availability of volatile organic compounds (VOCs) or nitrogen oxides (NOx), and can be used for all the species predicted by the model. A simplified configuration of the Comprehensive Air Quality Model with Extensions (CAMx) is used to evaluate the accuracy of different numerical integration procedures and the dependence of the source contributions on the path. A Gauss-Legendre formula using three or four points along the path gives good accuracy for apportioning the anthropogenic increments of ozone, nitrogen dioxide, formaldehyde, and nitric acid. Source contributions to these increments were obtained for paths representing proportional control of all anthropogenic emissions together, control of NOx emissions before VOC emissions, and control of VOC emissions before NOx emissions. There are similarities in the source contributions from the three paths but also differences due to the different chemical regimes resulting from the emission-control strategies.

Path-integral method for the source apportionment of photochemical pollutants

NASA Astrophysics Data System (ADS)

Dunker, A. M.

2014-12-01

A new, path-integral method is presented for apportioning the concentrations of pollutants predicted by a photochemical model to emissions from different sources. A novel feature of the method is that it can apportion the difference in a species concentration between two simulations. For example, the anthropogenic ozone increment, which is the difference between a simulation with all emissions present and another simulation with only the background (e.g., biogenic) emissions included, can be allocated to the anthropogenic emission sources. The method is based on an existing, exact mathematical equation. This equation is applied to relate the concentration difference between simulations to line or path integrals of first-order sensitivity coefficients. The sensitivities describe the effects of changing the emissions and are accurately calculated by the decoupled direct method. The path represents a continuous variation of emissions between the two simulations, and each path can be viewed as a separate emission-control strategy. The method does not require auxiliary assumptions, e.g., whether ozone formation is limited by the availability of volatile organic compounds (VOC's) or nitrogen oxides (NOx), and can be used for all the species predicted by the model. A simplified configuration of the Comprehensive Air Quality Model with Extensions is used to evaluate the accuracy of different numerical integration procedures and the dependence of the source contributions on the path. A Gauss-Legendre formula using 3 or 4 points along the path gives good accuracy for apportioning the anthropogenic increments of ozone, nitrogen dioxide, formaldehyde, and nitric acid. Source contributions to these increments were obtained for paths representing proportional control of all anthropogenic emissions together, control of NOx emissions before VOC emissions, and control of VOC emissions before NOx emissions. There are similarities in the source contributions from the three paths but also differences due to the different chemical regimes resulting from the emission-control strategies.

Water immersion and its computer simulation as analogs of weightlessness

NASA Technical Reports Server (NTRS)

Leonard, J. I.

1982-01-01

Experimental studies and computer simulations of water immersion are summarized and discussed with regard to their utility as analogs of weightlessness. Emphasis is placed on describing and interpreting the renal, endocrine, fluid, and circulatory changes that take place during immersion. A mathematical model, based on concepts of fluid volume regulation, is shown to be well suited to simulate the dynamic responses to water immersion. Further, it is shown that such a model provides a means to study specific mechanisms and pathways involved in the immersion response. A number of hypotheses are evaluated with the model related to the effects of dehydration, venous pressure disturbances, the control of ADH, and changes in plasma-interstitial volume. By inference, it is suggested that most of the model's responses to water immersion are plausible predictions of the acute changes expected, but not yet measured, during space flight. One important prediction of the model is that previous attempts to measure a diuresis during space flight failed because astronauts may have been dehydrated and urine samples were pooled over 24-hour periods.

Development of Aeroservoelastic Analytical Models and Gust Load Alleviation Control Laws of a SensorCraft Wind-Tunnel Model Using Measured Data

NASA Technical Reports Server (NTRS)

Silva, Walter A.; Vartio, Eric; Shimko, Anthony; Kvaternik, Raymond G.; Eure, Kenneth W.; Scott,Robert C.

2007-01-01

Aeroservoelastic (ASE) analytical models of a SensorCraft wind-tunnel model are generated using measured data. The data was acquired during the ASE wind-tunnel test of the HiLDA (High Lift-to-Drag Active) Wing model, tested in the NASA Langley Transonic Dynamics Tunnel (TDT) in late 2004. Two time-domain system identification techniques are applied to the development of the ASE analytical models: impulse response (IR) method and the Generalized Predictive Control (GPC) method. Using measured control surface inputs (frequency sweeps) and associated sensor responses, the IR method is used to extract corresponding input/output impulse response pairs. These impulse responses are then transformed into state-space models for use in ASE analyses. Similarly, the GPC method transforms measured random control surface inputs and associated sensor responses into an AutoRegressive with eXogenous input (ARX) model. The ARX model is then used to develop the gust load alleviation (GLA) control law. For the IR method, comparison of measured with simulated responses are presented to investigate the accuracy of the ASE analytical models developed. For the GPC method, comparison of simulated open-loop and closed-loop (GLA) time histories are presented.

Development of Aeroservoelastic Analytical Models and Gust Load Alleviation Control Laws of a SensorCraft Wind-Tunnel Model Using Measured Data

NASA Technical Reports Server (NTRS)

Silva, Walter A.; Shimko, Anthony; Kvaternik, Raymond G.; Eure, Kenneth W.; Scott, Robert C.

2006-01-01

Aeroservoelastic (ASE) analytical models of a SensorCraft wind-tunnel model are generated using measured data. The data was acquired during the ASE wind-tunnel test of the HiLDA (High Lift-to-Drag Active) Wing model, tested in the NASA Langley Transonic Dynamics Tunnel (TDT) in late 2004. Two time-domain system identification techniques are applied to the development of the ASE analytical models: impulse response (IR) method and the Generalized Predictive Control (GPC) method. Using measured control surface inputs (frequency sweeps) and associated sensor responses, the IR method is used to extract corresponding input/output impulse response pairs. These impulse responses are then transformed into state-space models for use in ASE analyses. Similarly, the GPC method transforms measured random control surface inputs and associated sensor responses into an AutoRegressive with eXogenous input (ARX) model. The ARX model is then used to develop the gust load alleviation (GLA) control law. For the IR method, comparison of measured with simulated responses are presented to investigate the accuracy of the ASE analytical models developed. For the GPC method, comparison of simulated open-loop and closed-loop (GLA) time histories are presented.

A multisensor evaluation of the asymmetric convective model, version 2, in southeast Texas.

PubMed

Kolling, Jenna S; Pleim, Jonathan E; Jeffries, Harvey E; Vizuete, William

2013-01-01

There currently exist a number of planetary boundary layer (PBL) schemes that can represent the effects of turbulence in daytime convective conditions, although these schemes remain a large source of uncertainty in meteorology and air quality model simulations. This study evaluates a recently developed combined local and nonlocal closure PBL scheme, the Asymmetric Convective Model, version 2 (ACM2), against PBL observations taken from radar wind profilers, a ground-based lidar, and multiple daytime radiosonde balloon launches. These observations were compared against predictions of PBLs from the Weather Research and Forecasting (WRF) model version 3.1 with the ACM2 PBL scheme option, and the Fifth-Generation Meteorological Model (MM5) version 3.7.3 with the Eta PBL scheme option that is currently being used to develop ozone control strategies in southeast Texas. MM5 and WRF predictions during the regulatory modeling episode were evaluated on their ability to predict the rise and fall of the PBL during daytime convective conditions across southeastern Texas. The MM5 predicted PBLs consistently underpredicted observations, and were also less than the WRF PBL predictions. The analysis reveals that the MM5 predicted a slower rising and shallower PBL not representative of the daytime urban boundary layer. Alternatively, the WRF model predicted a more accurate PBL evolution improving the root mean square error (RMSE), both temporally and spatially. The WRF model also more accurately predicted vertical profiles of temperature and moisture in the lowest 3 km of the atmosphere. Inspection of median surface temperature and moisture time-series plots revealed higher predicted surface temperatures in WRF and more surface moisture in MM5. These could not be attributed to surface heat fluxes, and thus the differences in performance of the WRF and MM5 models are likely due to the PBL schemes. An accurate depiction of the diurnal evolution of the planetary boundary layer (PBL) is necessary for realistic air quality simulations, and for formulating effective policy. The meteorological model used to support the southeast Texas 03 attainment demonstration made predictions of the PBL that were consistently less than those found in observations. The use of the Asymmetric Convective Model, version 2 (ACM2), predicted taller PBL heights and improved model predictions. A lower predicted PBL height in an air quality model would increase precursor concentrations and change the chemical production of O3 and possibly the response to control strategies.

A network of molecular switches controls the activation of the two-component response regulator NtrC

NASA Astrophysics Data System (ADS)

Vanatta, Dan K.; Shukla, Diwakar; Lawrenz, Morgan; Pande, Vijay S.

2015-06-01

Recent successes in simulating protein structure and folding dynamics have demonstrated the power of molecular dynamics to predict the long timescale behaviour of proteins. Here, we extend and improve these methods to predict molecular switches that characterize conformational change pathways between the active and inactive state of nitrogen regulatory protein C (NtrC). By employing unbiased Markov state model-based molecular dynamics simulations, we construct a dynamic picture of the activation pathways of this key bacterial signalling protein that is consistent with experimental observations and predicts new mutants that could be used for validation of the mechanism. Moreover, these results suggest a novel mechanistic paradigm for conformational switching.

The changing paradigm for integrated simulation in support of Command and Control (C2)

NASA Astrophysics Data System (ADS)

Riecken, Mark; Hieb, Michael

2016-05-01

Modern software and network technologies are on the verge of enabling what has eluded the simulation and operational communities for more than two decades, truly integrating simulation functionality into operational Command and Control (C2) capabilities. This deep integration will benefit multiple stakeholder communities from experimentation and test to training by providing predictive and advanced analytics. There is a new opportunity to support operations with simulation once a deep integration is achieved. While it is true that doctrinal and acquisition issues remain to be addressed, nonetheless it is increasingly obvious that few technical barriers persist. How will this change the way in which common simulation and operational data is stored and accessed? As the Services move towards single networks, will there be technical and policy issues associated with sharing those operational networks with simulation data, even if the simulation data is operational in nature (e.g., associated with planning)? How will data models that have traditionally been simulation only be merged in with operational data models? How will the issues of trust be addressed?

Aperiodic Robust Model Predictive Control for Constrained Continuous-Time Nonlinear Systems: An Event-Triggered Approach.

PubMed

Liu, Changxin; Gao, Jian; Li, Huiping; Xu, Demin

2018-05-01

The event-triggered control is a promising solution to cyber-physical systems, such as networked control systems, multiagent systems, and large-scale intelligent systems. In this paper, we propose an event-triggered model predictive control (MPC) scheme for constrained continuous-time nonlinear systems with bounded disturbances. First, a time-varying tightened state constraint is computed to achieve robust constraint satisfaction, and an event-triggered scheduling strategy is designed in the framework of dual-mode MPC. Second, the sufficient conditions for ensuring feasibility and closed-loop robust stability are developed, respectively. We show that robust stability can be ensured and communication load can be reduced with the proposed MPC algorithm. Finally, numerical simulations and comparison studies are performed to verify the theoretical results.

Discriminative Random Field Models for Subsurface Contamination Uncertainty Quantification

NASA Astrophysics Data System (ADS)

Arshadi, M.; Abriola, L. M.; Miller, E. L.; De Paolis Kaluza, C.

2017-12-01

Application of flow and transport simulators for prediction of the release, entrapment, and persistence of dense non-aqueous phase liquids (DNAPLs) and associated contaminant plumes is a computationally intensive process that requires specification of a large number of material properties and hydrologic/chemical parameters. Given its computational burden, this direct simulation approach is particularly ill-suited for quantifying both the expected performance and uncertainty associated with candidate remediation strategies under real field conditions. Prediction uncertainties primarily arise from limited information about contaminant mass distributions, as well as the spatial distribution of subsurface hydrologic properties. Application of direct simulation to quantify uncertainty would, thus, typically require simulating multiphase flow and transport for a large number of permeability and release scenarios to collect statistics associated with remedial effectiveness, a computationally prohibitive process. The primary objective of this work is to develop and demonstrate a methodology that employs measured field data to produce equi-probable stochastic representations of a subsurface source zone that capture the spatial distribution and uncertainty associated with key features that control remediation performance (i.e., permeability and contamination mass). Here we employ probabilistic models known as discriminative random fields (DRFs) to synthesize stochastic realizations of initial mass distributions consistent with known, and typically limited, site characterization data. Using a limited number of full scale simulations as training data, a statistical model is developed for predicting the distribution of contaminant mass (e.g., DNAPL saturation and aqueous concentration) across a heterogeneous domain. Monte-Carlo sampling methods are then employed, in conjunction with the trained statistical model, to generate realizations conditioned on measured borehole data. Performance of the statistical model is illustrated through comparisons of generated realizations with the `true' numerical simulations. Finally, we demonstrate how these realizations can be used to determine statistically optimal locations for further interrogation of the subsurface.

Neural Generalized Predictive Control: A Newton-Raphson Implementation

NASA Technical Reports Server (NTRS)

Soloway, Donald; Haley, Pamela J.

1997-01-01

An efficient implementation of Generalized Predictive Control using a multi-layer feedforward neural network as the plant's nonlinear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. The main cost of the Newton-Raphson algorithm is in the calculation of the Hessian, but even with this overhead the low iteration numbers make Newton-Raphson faster than other techniques and a viable algorithm for real-time control. This paper presents a detailed derivation of the Neural Generalized Predictive Control algorithm with Newton-Raphson as the minimization algorithm. Simulation results show convergence to a good solution within two iterations and timing data show that real-time control is possible. Comments about the algorithm's implementation are also included.

Thinking as the control of imagination: a conceptual framework for goal-directed systems.

PubMed

Pezzulo, Giovanni; Castelfranchi, Cristiano

2009-07-01

This paper offers a conceptual framework which (re)integrates goal-directed control, motivational processes, and executive functions, and suggests a developmental pathway from situated action to higher level cognition. We first illustrate a basic computational (control-theoretic) model of goal-directed action that makes use of internal modeling. We then show that by adding the problem of selection among multiple action alternatives motivation enters the scene, and that the basic mechanisms of executive functions such as inhibition, the monitoring of progresses, and working memory, are required for this system to work. Further, we elaborate on the idea that the off-line re-enactment of anticipatory mechanisms used for action control gives rise to (embodied) mental simulations, and propose that thinking consists essentially in controlling mental simulations rather than directly controlling behavior and perceptions. We conclude by sketching an evolutionary perspective of this process, proposing that anticipation leveraged cognition, and by highlighting specific predictions of our model.

Flight Dynamics Aspects of a Large Civil Tiltrotor Simulation Using Translational Rate Command

NASA Technical Reports Server (NTRS)

Lawrence, Ben; Malpica, Carlos A.; Theodore, Colin R.; Decker, William A.; Lindsey, James E.

2011-01-01

An in-depth analysis of a Large Civil Tiltrotor simulation with a Translational Rate Command control law that uses automatic nacelle deflections for longitudinal velocity control and lateral cyclic for lateral velocity control is presented. Results from piloted real-time simulation experiments and offline time and frequency domain analyses are used to investigate the fundamental flight dynamic and control mechanisms of the control law. The baseline Translational Rate Command conferred handling qualities improvements over an attitude command attitude hold control law but in some scenarios there was a tendency to enter PIO. Nacelle actuator rate limiting strongly influenced the PIO tendency and reducing the rate limits degraded the handling qualities further. Counterintuitively, increasing rate limits also led to a worsening of the handling qualities ratings. This led to the identification of a nacelle rate to rotor longitudinal flapping coupling effect that induced undesired pitching motions proportional to the allowable amount of nacelle rate. A modification that applied a counteracting amount of longitudinal cyclic proportional to the nacelle rate significantly improved the handling qualities. The lateral axis of the Translational Rate Command conferred Level 1 handling qualities in a Lateral Reposition maneuver. Analysis of the influence of the modeling fidelity on the lateral flapping angles is presented. It is showed that the linear modeling approximation is likely to have under-predicted the side-force and therefore under-predicted the lateral flapping at velocities above 15 ft/s. However, at lower velocities, and therefore more weakly influenced by the side force modeling, the accelerations that the control law commands also significantly influenced the peak levels of lateral flapping achieved.

Formalization, implementation, and modeling of institutional controllers for distributed robotic systems.

PubMed

Pereira, José N; Silva, Porfírio; Lima, Pedro U; Martinoli, Alcherio

2014-01-01

The work described is part of a long term program of introducing institutional robotics, a novel framework for the coordination of robot teams that stems from institutional economics concepts. Under the framework, institutions are cumulative sets of persistent artificial modifications made to the environment or to the internal mechanisms of a subset of agents, thought to be functional for the collective order. In this article we introduce a formal model of institutional controllers based on Petri nets. We define executable Petri nets-an extension of Petri nets that takes into account robot actions and sensing-to design, program, and execute institutional controllers. We use a generalized stochastic Petri net view of the robot team controlled by the institutional controllers to model and analyze the stochastic performance of the resulting distributed robotic system. The ability of our formalism to replicate results obtained using other approaches is assessed through realistic simulations of up to 40 e-puck robots. In particular, we model a robot swarm and its institutional controller with the goal of maintaining wireless connectivity, and successfully compare our model predictions and simulation results with previously reported results, obtained by using finite state automaton models and controllers.

Simulation-Based Prediction of Equivalent Continuous Noises during Construction Processes

PubMed Central

Zhang, Hong; Pei, Yun

2016-01-01

Quantitative prediction of construction noise is crucial to evaluate construction plans to help make decisions to address noise levels. Considering limitations of existing methods for measuring or predicting the construction noise and particularly the equivalent continuous noise level over a period of time, this paper presents a discrete-event simulation method for predicting the construction noise in terms of equivalent continuous level. The noise-calculating models regarding synchronization, propagation and equivalent continuous level are presented. The simulation framework for modeling the noise-affected factors and calculating the equivalent continuous noise by incorporating the noise-calculating models into simulation strategy is proposed. An application study is presented to demonstrate and justify the proposed simulation method in predicting the equivalent continuous noise during construction. The study contributes to provision of a simulation methodology to quantitatively predict the equivalent continuous noise of construction by considering the relevant uncertainties, dynamics and interactions. PMID:27529266

Simulation-Based Prediction of Equivalent Continuous Noises during Construction Processes.

PubMed

Zhang, Hong; Pei, Yun

2016-08-12

Quantitative prediction of construction noise is crucial to evaluate construction plans to help make decisions to address noise levels. Considering limitations of existing methods for measuring or predicting the construction noise and particularly the equivalent continuous noise level over a period of time, this paper presents a discrete-event simulation method for predicting the construction noise in terms of equivalent continuous level. The noise-calculating models regarding synchronization, propagation and equivalent continuous level are presented. The simulation framework for modeling the noise-affected factors and calculating the equivalent continuous noise by incorporating the noise-calculating models into simulation strategy is proposed. An application study is presented to demonstrate and justify the proposed simulation method in predicting the equivalent continuous noise during construction. The study contributes to provision of a simulation methodology to quantitatively predict the equivalent continuous noise of construction by considering the relevant uncertainties, dynamics and interactions.

Simulated annealing model of acupuncture

NASA Astrophysics Data System (ADS)

Shang, Charles; Szu, Harold

2015-05-01

The growth control singularity model suggests that acupuncture points (acupoints) originate from organizers in embryogenesis. Organizers are singular points in growth control. Acupuncture can cause perturbation of a system with effects similar to simulated annealing. In clinical trial, the goal of a treatment is to relieve certain disorder which corresponds to reaching certain local optimum in simulated annealing. The self-organizing effect of the system is limited and related to the person's general health and age. Perturbation at acupoints can lead a stronger local excitation (analogous to higher annealing temperature) compared to perturbation at non-singular points (placebo control points). Such difference diminishes as the number of perturbed points increases due to the wider distribution of the limited self-organizing activity. This model explains the following facts from systematic reviews of acupuncture trials: 1. Properly chosen single acupoint treatment for certain disorder can lead to highly repeatable efficacy above placebo 2. When multiple acupoints are used, the result can be highly repeatable if the patients are relatively healthy and young but are usually mixed if the patients are old, frail and have multiple disorders at the same time as the number of local optima or comorbidities increases. 3. As number of acupoints used increases, the efficacy difference between sham and real acupuncture often diminishes. It predicted that the efficacy of acupuncture is negatively correlated to the disease chronicity, severity and patient's age. This is the first biological - physical model of acupuncture which can predict and guide clinical acupuncture research.

Cutting process simulation of flat drill

NASA Astrophysics Data System (ADS)

Tamura, Shoichi; Matsumura, Takashi

2018-05-01

Flat drills at a point angle of 180 deg. have recently been developed for drilling of automobile parts with the inclination of the workpiece surfaces. The paper studies the cutting processes of the flat drills in the analytical simulation. A predictive force model is applied to simulation of the cutting force with the chip flow direction. The chip flow model is piled up with orthogonal cuttings in the plane containing the cutting velocities and the chip flow velocities, in which the chip flow direction is determined to minimize the cutting energy. Then, the cutting force is predicted in the determined in the chip flow model. The typical cutting force of the flat drill is discussed with comparing to that of the standard drill. The typical differences are confirmed in the cutting force change during the tool engagement and disengagement. The cutting force, then, is simulated in drilling for an inclined workpiece with a flat drill. The horizontal components in the cutting forces are simulated with changing the inclination angle of the plate. The horizontal force component in the flat drilling is stable to be controlled in terms of the machining accuracy and the tool breakage.

Optimization Control of the Color-Coating Production Process for Model Uncertainty

PubMed Central

He, Dakuo; Wang, Zhengsong; Yang, Le; Mao, Zhizhong

2016-01-01

Optimized control of the color-coating production process (CCPP) aims at reducing production costs and improving economic efficiency while meeting quality requirements. However, because optimization control of the CCPP is hampered by model uncertainty, a strategy that considers model uncertainty is proposed. Previous work has introduced a mechanistic model of CCPP based on process analysis to simulate the actual production process and generate process data. The partial least squares method is then applied to develop predictive models of film thickness and economic efficiency. To manage the model uncertainty, the robust optimization approach is introduced to improve the feasibility of the optimized solution. Iterative learning control is then utilized to further refine the model uncertainty. The constrained film thickness is transformed into one of the tracked targets to overcome the drawback that traditional iterative learning control cannot address constraints. The goal setting of economic efficiency is updated continuously according to the film thickness setting until this reaches its desired value. Finally, fuzzy parameter adjustment is adopted to ensure that the economic efficiency and film thickness converge rapidly to their optimized values under the constraint conditions. The effectiveness of the proposed optimization control strategy is validated by simulation results. PMID:27247563

Optimization Control of the Color-Coating Production Process for Model Uncertainty.

PubMed

He, Dakuo; Wang, Zhengsong; Yang, Le; Mao, Zhizhong

2016-01-01

Optimized control of the color-coating production process (CCPP) aims at reducing production costs and improving economic efficiency while meeting quality requirements. However, because optimization control of the CCPP is hampered by model uncertainty, a strategy that considers model uncertainty is proposed. Previous work has introduced a mechanistic model of CCPP based on process analysis to simulate the actual production process and generate process data. The partial least squares method is then applied to develop predictive models of film thickness and economic efficiency. To manage the model uncertainty, the robust optimization approach is introduced to improve the feasibility of the optimized solution. Iterative learning control is then utilized to further refine the model uncertainty. The constrained film thickness is transformed into one of the tracked targets to overcome the drawback that traditional iterative learning control cannot address constraints. The goal setting of economic efficiency is updated continuously according to the film thickness setting until this reaches its desired value. Finally, fuzzy parameter adjustment is adopted to ensure that the economic efficiency and film thickness converge rapidly to their optimized values under the constraint conditions. The effectiveness of the proposed optimization control strategy is validated by simulation results.

Optimal control analysis of Ebola disease with control strategies of quarantine and vaccination.

PubMed

Ahmad, Muhammad Dure; Usman, Muhammad; Khan, Adnan; Imran, Mudassar

2016-07-13

The 2014 Ebola epidemic is the largest in history, affecting multiple countries in West Africa. Some isolated cases were also observed in other regions of the world. In this paper, we introduce a deterministic SEIR type model with additional hospitalization, quarantine and vaccination components in order to understand the disease dynamics. Optimal control strategies, both in the case of hospitalization (with and without quarantine) and vaccination are used to predict the possible future outcome in terms of resource utilization for disease control and the effectiveness of vaccination on sick populations. Further, with the help of uncertainty and sensitivity analysis we also have identified the most sensitive parameters which effectively contribute to change the disease dynamics. We have performed mathematical analysis with numerical simulations and optimal control strategies on Ebola virus models. We used dynamical system tools with numerical simulations and optimal control strategies on our Ebola virus models. The original model, which allowed transmission of Ebola virus via human contact, was extended to include imperfect vaccination and quarantine. After the qualitative analysis of all three forms of Ebola model, numerical techniques, using MATLAB as a platform, were formulated and analyzed in detail. Our simulation results support the claims made in the qualitative section. Our model incorporates an important component of individuals with high risk level with exposure to disease, such as front line health care workers, family members of EVD patients and Individuals involved in burial of deceased EVD patients, rather than the general population in the affected areas. Our analysis suggests that in order for R 0 (i.e., the basic reproduction number) to be less than one, which is the basic requirement for the disease elimination, the transmission rate of isolated individuals should be less than one-fourth of that for non-isolated ones. Our analysis also predicts, we need high levels of medication and hospitalization at the beginning of an epidemic. Further, optimal control analysis of the model suggests the control strategies that may be adopted by public health authorities in order to reduce the impact of epidemics like Ebola.

Varying execution discipline to increase performance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Campbell, P.L.; Maccabe, A.B.

1993-12-22

This research investigates the relationship between execution discipline and performance. The hypothesis has two parts: 1. Different execution disciplines exhibit different performance for different computations, and 2. These differences can be effectively predicted by heuristics. A machine model is developed that can vary its execution discipline. That is, the model can execute a given program using either the control-driven, data-driven or demand-driven execution discipline. This model is referred to as a ``variable-execution-discipline`` machine. The instruction set for the model is the Program Dependence Web (PDW). The first part of the hypothesis will be tested by simulating the execution of themore » machine model on a suite of computations, based on the Livermore Fortran Kernel (LFK) Test (a.k.a. the Livermore Loops), using all three execution disciplines. Heuristics are developed to predict relative performance. These heuristics predict (a) the execution time under each discipline for one iteration of each loop and (b) the number of iterations taken by that loop; then the heuristics use those predictions to develop a prediction for the execution of the entire loop. Similar calculations are performed for branch statements. The second part of the hypothesis will be tested by comparing the results of the simulated execution with the predictions produced by the heuristics. If the hypothesis is supported, then the door is open for the development of machines that can vary execution discipline to increase performance.« less

Influence of disturbance on temperate forest productivity

USGS Publications Warehouse

Peters, Emily B.; Wythers, Kirk R.; Bradford, John B.; Reich, Peter B.

2013-01-01

Climate, tree species traits, and soil fertility are key controls on forest productivity. However, in most forest ecosystems, natural and human disturbances, such as wind throw, fire, and harvest, can also exert important and lasting direct and indirect influence over productivity. We used an ecosystem model, PnET-CN, to examine how disturbance type, intensity, and frequency influence net primary production (NPP) across a range of forest types from Minnesota and Wisconsin, USA. We assessed the importance of past disturbances on NPP, net N mineralization, foliar N, and leaf area index at 107 forest stands of differing types (aspen, jack pine, northern hardwood, black spruce) and disturbance history (fire, harvest) by comparing model simulations with observations. The model reasonably predicted differences among forest types in productivity, foliar N, leaf area index, and net N mineralization. Model simulations that included past disturbances minimally improved predictions compared to simulations without disturbance, suggesting the legacy of past disturbances played a minor role in influencing current forest productivity rates. Modeled NPP was more sensitive to the intensity of soil removal during a disturbance than the fraction of stand mortality or wood removal. Increasing crown fire frequency resulted in lower NPP, particularly for conifer forest types with longer leaf life spans and longer recovery times. These findings suggest that, over long time periods, moderate frequency disturbances are a relatively less important control on productivity than climate, soil, and species traits.

Experimental and Numerical Simulations of Phase Transformations Occurring During Continuous Annealing of DP Steel Strips

NASA Astrophysics Data System (ADS)

Wrożyna, Andrzej; Pernach, Monika; Kuziak, Roman; Pietrzyk, Maciej

2016-04-01

Due to their exceptional strength properties combined with good workability the Advanced High-Strength Steels (AHSS) are commonly used in automotive industry. Manufacturing of these steels is a complex process which requires precise control of technological parameters during thermo-mechanical treatment. Design of these processes can be significantly improved by the numerical models of phase transformations. Evaluation of predictive capabilities of models, as far as their applicability in simulation of thermal cycles thermal cycles for AHSS is considered, was the objective of the paper. Two models were considered. The former was upgrade of the JMAK equation while the latter was an upgrade of the Leblond model. The models can be applied to any AHSS though the examples quoted in the paper refer to the Dual Phase (DP) steel. Three series of experimental simulations were performed. The first included various thermal cycles going beyond limitations of the continuous annealing lines. The objective was to validate models behavior in more complex cooling conditions. The second set of tests included experimental simulations of the thermal cycle characteristic for the continuous annealing lines. Capability of the models to describe properly phase transformations in this process was evaluated. The third set included data from the industrial continuous annealing line. Validation and verification of models confirmed their good predictive capabilities. Since it does not require application of the additivity rule, the upgrade of the Leblond model was selected as the better one for simulation of industrial processes in AHSS production.

Double-null divertor configuration discharge and disruptive heat flux simulation using TSC on EAST

NASA Astrophysics Data System (ADS)

Bo, SHI; Jinhong, YANG; Cheng, YANG; Desheng, CHENG; Hui, WANG; Hui, ZHANG; Haifei, DENG; Junli, QI; Xianzu, GONG; Weihua, WANG

2018-07-01

The tokamak simulation code (TSC) is employed to simulate the complete evolution of a disruptive discharge in the experimental advanced superconducting tokamak. The multiplication factor of the anomalous transport coefficient was adjusted to model the major disruptive discharge with double-null divertor configuration based on shot 61 916. The real-time feed-back control system for the plasma displacement was employed. Modeling results of the evolution of the poloidal field coil currents, the plasma current, the major radius, the plasma configuration all show agreement with experimental measurements. Results from the simulation show that during disruption, heat flux about 8 MW m‑2 flows to the upper divertor target plate and about 6 MW m‑2 flows to the lower divertor target plate. Computations predict that different amounts of heat fluxes on the divertor target plate could result by adjusting the multiplication factor of the anomalous transport coefficient. This shows that TSC has high flexibility and predictability.

Testing prediction methods: Earthquake clustering versus the Poisson model

USGS Publications Warehouse

Michael, A.J.

1997-01-01

Testing earthquake prediction methods requires statistical techniques that compare observed success to random chance. One technique is to produce simulated earthquake catalogs and measure the relative success of predicting real and simulated earthquakes. The accuracy of these tests depends on the validity of the statistical model used to simulate the earthquakes. This study tests the effect of clustering in the statistical earthquake model on the results. Three simulation models were used to produce significance levels for a VLF earthquake prediction method. As the degree of simulated clustering increases, the statistical significance drops. Hence, the use of a seismicity model with insufficient clustering can lead to overly optimistic results. A successful method must pass the statistical tests with a model that fully replicates the observed clustering. However, a method can be rejected based on tests with a model that contains insufficient clustering. U.S. copyright. Published in 1997 by the American Geophysical Union.

Simulating the Current Water Cycle with the NASA Ames Mars Global Climate Model

NASA Astrophysics Data System (ADS)

Kahre, M. A.; Haberle, R. M.; Hollingsworth, J. L.; Brecht, A. S.; Urata, R. A.; Montmessin, F.

2017-12-01

The water cycle is a critical component of the current Mars climate system, and it is now widely recognized that water ice clouds significantly affect the nature of the simulated water cycle. Two processes are key to implementing clouds in a Mars global climate model (GCM): the microphysical processes of formation and dissipation, and their radiative effects on atmospheric heating/cooling rates. Together, these processes alter the thermal structure, change the atmospheric dynamics, and regulate inter-hemispheric transport. We have made considerable progress using the NASA Ames Mars GCM to simulate the current-day water cycle with radiatively active clouds. Cloud fields from our baseline simulation are in generally good agreement with observations. The predicted seasonal extent and peak IR optical depths are consistent MGS/TES observations. Additionally, the thermal response to the clouds in the aphelion cloud belt (ACB) is generally consistent with observations and other climate model predictions. Notably, there is a distinct gap in the predicted clouds over the North Residual Cap (NRC) during local summer, but the clouds reappear in this simulation over the NRC earlier than the observations indicate. Polar clouds are predicted near the seasonal CO2 ice caps, but the column thicknesses of these clouds are generally too thick compared to observations. Our baseline simulation is dry compared to MGS/TES-observed water vapor abundances, particularly in the tropics and subtropics. These areas of disagreement appear to be a consistent with other current water cycle GCMs. Future avenues of investigation will target improving our understanding of what controls the vertical extent of clouds and the apparent seasonal evolution of cloud particle sizes within the ACB.

High-Throughput Analysis of Ovarian Cycle Disruption by Mixtures of Aromatase Inhibitors

PubMed Central

Golbamaki-Bakhtyari, Nazanin; Kovarich, Simona; Tebby, Cleo; Gabb, Henry A.; Lemazurier, Emmanuel

2017-01-01

Background: Combining computational toxicology with ExpoCast exposure estimates and ToxCast™ assay data gives us access to predictions of human health risks stemming from exposures to chemical mixtures. Objectives: We explored, through mathematical modeling and simulations, the size of potential effects of random mixtures of aromatase inhibitors on the dynamics of women's menstrual cycles. Methods: We simulated random exposures to millions of potential mixtures of 86 aromatase inhibitors. A pharmacokinetic model of intake and disposition of the chemicals predicted their internal concentration as a function of time (up to 2 y). A ToxCast™ aromatase assay provided concentration–inhibition relationships for each chemical. The resulting total aromatase inhibition was input to a mathematical model of the hormonal hypothalamus–pituitary–ovarian control of ovulation in women. Results: Above 10% inhibition of estradiol synthesis by aromatase inhibitors, noticeable (eventually reversible) effects on ovulation were predicted. Exposures to individual chemicals never led to such effects. In our best estimate, ∼10% of the combined exposures simulated had mild to catastrophic impacts on ovulation. A lower bound on that figure, obtained using an optimistic exposure scenario, was 0.3%. Conclusions: These results demonstrate the possibility to predict large-scale mixture effects for endocrine disrupters with a predictive toxicology approach that is suitable for high-throughput ranking and risk assessment. The size of the effects predicted is consistent with an increased risk of infertility in women from everyday exposures to our chemical environment. https://doi.org/10.1289/EHP742 PMID:28886606

Improving Computational Efficiency of Prediction in Model-Based Prognostics Using the Unscented Transform

NASA Technical Reports Server (NTRS)

Daigle, Matthew John; Goebel, Kai Frank

2010-01-01

Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of end of life (EOL). EOL is predicted based on the estimated current state distribution of a component and expected profiles of future usage. In general, this requires simulations of the component using the underlying models. In this paper, we develop a simulation-based prediction methodology that achieves computational efficiency by performing only the minimal number of simulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented transform, which predicts the means and covariances of a distribution passed through a nonlinear transformation. In this case, the EOL simulation acts as that nonlinear transformation. In this paper, we review the unscented transform, and describe how this concept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experiments to demonstrate improved computational efficiency without sacrificing prediction accuracy.

Bayesian inference of physiologically meaningful parameters from body sway measurements.

PubMed

Tietäväinen, A; Gutmann, M U; Keski-Vakkuri, E; Corander, J; Hæggström, E

2017-06-19

The control of the human body sway by the central nervous system, muscles, and conscious brain is of interest since body sway carries information about the physiological status of a person. Several models have been proposed to describe body sway in an upright standing position, however, due to the statistical intractability of the more realistic models, no formal parameter inference has previously been conducted and the expressive power of such models for real human subjects remains unknown. Using the latest advances in Bayesian statistical inference for intractable models, we fitted a nonlinear control model to posturographic measurements, and we showed that it can accurately predict the sway characteristics of both simulated and real subjects. Our method provides a full statistical characterization of the uncertainty related to all model parameters as quantified by posterior probability density functions, which is useful for comparisons across subjects and test settings. The ability to infer intractable control models from sensor data opens new possibilities for monitoring and predicting body status in health applications.

Model predictive control of a lean-burn gasoline engine coupled with a passive selective catalytic reduction system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Pingen; Lin, Qinghua; Prikhodko, Vitaly Y.

Lean-burn gasoline engines have demonstrated 10–20% engine efficiency gain over stoichiometric engines and are widely considered as a promising technology for meeting the 54.5 miles-per-gallon (mpg) Corporate Average Fuel Economy standard by 2025. Nevertheless, NOx emissions control for lean-burn gasoline for meeting the stringent EPA Tier 3 emission standards has been one of the main challenges towards the commercialization of highly-efficient lean-burn gasoline engines in the United States. Passive selective catalytic reduction (SCR) systems, which consist of a three-way catalyst and SCR, have demonstrated great potentials of effectively reducing NOx emissions for lean gasoline engines but may cause significant fuelmore » penalty due to ammonia generation via rich engine combustion. The purpose of this study is to develop a model-predictive control (MPC) scheme for a lean-burn gasoline engine coupled with a passive SCR system to minimize the fuel penalty associated with passive SCR operation while satisfying stringent NOx and NH3 emissions requirements. Simulation results demonstrate that the MPC-based control can reduce the fuel penalty by 47.7% in a simulated US06 cycle and 32.0% in a simulated UDDS cycle, compared to the baseline control, while achieving over 96% deNOx efficiency and less than 15 ppm tailpipe ammonia slip. The proposed MPC control can potentially enable high engine efficiency gain for highly-efficient lean-burn gasoline engine while meeting the stringent EPA Tier 3 emission standards.« less

Chemoviscosity modeling for thermosetting resins - I

NASA Technical Reports Server (NTRS)

Hou, T. H.

1984-01-01

A new analytical model for chemoviscosity variation during cure of thermosetting resins was developed. This model is derived by modifying the widely used WLF (Williams-Landel-Ferry) Theory in polymer rheology. Major assumptions involved are that the rate of reaction is diffusion controlled and is linearly inversely proportional to the viscosity of the medium over the entire cure cycle. The resultant first order nonlinear differential equation is solved numerically, and the model predictions compare favorably with experimental data of EPON 828/Agent U obtained on a Rheometrics System 4 Rheometer. The model describes chemoviscosity up to a range of six orders of magnitude under isothermal curing conditions. The extremely non-linear chemoviscosity profile for a dynamic heating cure cycle is predicted as well. The model is also shown to predict changes of glass transition temperature for the thermosetting resin during cure. The physical significance of this prediction is unclear at the present time, however, and further research is required. From the chemoviscosity simulation point of view, the technique of establishing an analytical model as described here is easily applied to any thermosetting resin. The model thus obtained is used in real-time process controls for fabricating composite materials.

Simulation of an electrowetting solar concentration cell

NASA Astrophysics Data System (ADS)

Khan, Iftekhar; Rosengarten, Gary

2015-09-01

Electrowetting control of liquid lenses has emerged as a novel approach for solar tracking and concentration. Recent studies have demonstrated the concept of steering sunlight using thin electrowetting cells without the use of any bulky mechanical equipment. Effective application of this technique may facilitate designing thin and flat solar concentrators. Understanding the behavior of liquid-liquid and liquid-solid interface of the electrowetting cell through trial and error experimental processes is not efficient and is time consuming. In this paper, we present a simulation model to predict the liquid-liquid and liquid-solid interface behavior of electrowetting cell as a function of various parameters such as applied voltage, dielectric constant, cell size etc. We used Comsol Multiphysics simulations incorporating experimental data of different liquids. We have designed both two dimensional and three dimensional simulation models, which predict the shape of the liquid lenses. The model calculates the contact angle using the Young-Lippman equation and uses a moving mesh interface to solve the Navier-stokes equation with Navier slip wall boundary condition. Simulation of the electric field from the electrodes is coupled to the Young-Lippman equation. The model can also be used to determine operational characteristics of other MEMS electrowetting devices such as electrowetting display, optical switches, electronic paper, electrowetting Fresnel lens etc.

Model predictive control-based dynamic coordinate strategy for hydraulic hub-motor auxiliary system of a heavy commercial vehicle

NASA Astrophysics Data System (ADS)

Zeng, Xiaohua; Li, Guanghan; Yin, Guodong; Song, Dafeng; Li, Sheng; Yang, Nannan

2018-02-01

Equipping a hydraulic hub-motor auxiliary system (HHMAS), which mainly consists of a hydraulic variable pump, a hydraulic hub-motor, a hydraulic valve block and hydraulic accumulators, with part-time all-wheel-drive functions improves the power performance and fuel economy of heavy commercial vehicles. The coordinated control problem that occurs when HHMAS operates in the auxiliary drive mode is addressed in this paper; the solution to this problem is the key to the maximization of HHMAS. To achieve a reasonable distribution of the engine power between mechanical and hydraulic paths, a nonlinear control scheme based on model predictive control (MPC) is investigated. First, a nonlinear model of HHMAS with vehicle dynamics and tire slip characteristics is built, and a controller-design-oriented model is simplified. Then, a steady-state feedforward + dynamic MPC feedback controller (FMPC) is designed to calculate the control input sequence of engine torque and hydraulic variable pump displacement. Finally, the controller is tested in the MATLAB/Simulink and AMESim co-simulation platform and the hardware-in-the-loop experiment platform, and its performance is compared with that of the existing proportional-integral-derivative controller and the feedforward controller under the same conditions. Simulation results show that the designed FMPC has the best performance, and control performance can be guaranteed in a real-time environment. Compared with the tracking control error of the feedforward controller, that of the designed FMPC is decreased by 85% and the traction efficiency performance is improved by 23% under a low-friction-surface condition. Moreover, under common road conditions for heavy commercial vehicles, the traction force can increase up to 13.4-15.6%.

Models of volcanic eruption hazards

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wohletz, K.H.

1992-01-01

Volcanic eruptions pose an ever present but poorly constrained hazard to life and property for geothermal installations in volcanic areas. Because eruptions occur sporadically and may limit field access, quantitative and systematic field studies of eruptions are difficult to complete. Circumventing this difficulty, laboratory models and numerical simulations are pivotal in building our understanding of eruptions. For example, the results of fuel-coolant interaction experiments show that magma-water interaction controls many eruption styles. Applying these results, increasing numbers of field studies now document and interpret the role of external water eruptions. Similarly, numerical simulations solve the fundamental physics of high-speed fluidmore » flow and give quantitative predictions that elucidate the complexities of pyroclastic flows and surges. A primary goal of these models is to guide geologists in searching for critical field relationships and making their interpretations. Coupled with field work, modeling is beginning to allow more quantitative and predictive volcanic hazard assessments.« less

A model of phytoplankton blooms.

PubMed

Huppert, Amit; Blasius, Bernd; Stone, Lewi

2002-02-01

A simple model that describes the dynamics of nutrient-driven phytoplankton blooms is presented. Apart from complicated simulation studies, very few models reported in the literature have taken this "bottom-up" approach. Yet, as discussed and justified from a theoretical standpoint, many blooms are strongly controlled by nutrients rather than by higher trophic levels. The analysis identifies an important threshold effect: a bloom will only be triggered when nutrients exceed a certain defined level. This threshold effect should be generic to both natural blooms and most simulation models. Furthermore, predictions are given as to how the peak of the bloom Pmax is determined by initial conditions. A number of counterintuitive results are found. In particular, it is shown that increasing initial nutrient or phytoplankton levels can act to decrease Pmax. Correct predictions require an understanding of such factors as the timing of the bloom and the period of nutrient buildup before the bloom.

Anaerobic Digestion Model No. 1 Simulation of High Solids Anaerobic Digestion with Feasibility Study for El Gabal El Asfar Water Resource Recovery Facility.

PubMed

Aboulfotoh, Ahmed M

2018-03-01

  Performance of continuous mesophilic high solids anaerobic digestion (HSAD) was simulated using Anaerobic Digestion Model No. 1 (ADM1), under different conditions (solids concentrations, sludge retention time (SRT), organic loading rate (OLR), and type of sludge). Implementation of ADM1, using the proposed biochemical parameters, proved to be a useful tool for the prediction and control of HSAD as the model predicted the behavior of the tested sets of data with considerable accuracy, especially for SRT more than 13 days. The model was then used to investigate the possibility of changing the existing conventional anaerobic digestion (CAD) units in Gabal El Asfar water resource recovery facility into HSAD, instead of establishing new CAD units, and results show that the system will be feasible. HSAD will produce the same bioenergy combined with a decrease in capital, operational, and maintenance costs.

Models of volcanic eruption hazards

NASA Astrophysics Data System (ADS)

Wohletz, K. H.

Volcanic eruptions pose an ever present but poorly constrained hazard to life and property for geothermal installations in volcanic areas. Because eruptions occur sporadically and may limit field access, quantitative and systematic field studies of eruptions are difficult to complete. Circumventing this difficulty, laboratory models and numerical simulations are pivotal in building our understanding of eruptions. For example, the results of fuel-coolant interaction experiments show that magma-water interaction controls many eruption styles. Applying these results, increasing numbers of field studies now document and interpret the role of external water eruptions. Similarly, numerical simulations solve the fundamental physics of high-speed fluid flow and give quantitative predictions that elucidate the complexities of pyroclastic flows and surges. A primary goal of these models is to guide geologists in searching for critical field relationships and making their interpretations. Coupled with field work, modeling is beginning to allow more quantitative and predictive volcanic hazard assessments.

The predictive protective control of the heat exchanger

NASA Astrophysics Data System (ADS)

Nevriva, Pavel; Filipova, Blanka; Vilimec, Ladislav

2016-06-01

The paper deals with the predictive control applied to flexible cogeneration energy system FES. FES was designed and developed by the VITKOVICE POWER ENGINEERING joint-stock company and represents a new solution of decentralized cogeneration energy sources. In FES, the heating medium is flue gas generated by combustion of a solid fuel. The heated medium is power gas, which is a gas mixture of air and water steam. Power gas is superheated in the main heat exchanger and led to gas turbines. To protect the main heat exchanger against damage by overheating, the novel predictive protective control based on the mathematical model of exchanger was developed. The paper describes the principle, the design and the simulation of the predictive protective method applied to main heat exchanger of FES.

Implicit methods for efficient musculoskeletal simulation and optimal control

PubMed Central

van den Bogert, Antonie J.; Blana, Dimitra; Heinrich, Dieter

2011-01-01

The ordinary differential equations for musculoskeletal dynamics are often numerically stiff and highly nonlinear. Consequently, simulations require small time steps, and optimal control problems are slow to solve and have poor convergence. In this paper, we present an implicit formulation of musculoskeletal dynamics, which leads to new numerical methods for simulation and optimal control, with the expectation that we can mitigate some of these problems. A first order Rosenbrock method was developed for solving forward dynamic problems using the implicit formulation. It was used to perform real-time dynamic simulation of a complex shoulder arm system with extreme dynamic stiffness. Simulations had an RMS error of only 0.11 degrees in joint angles when running at real-time speed. For optimal control of musculoskeletal systems, a direct collocation method was developed for implicitly formulated models. The method was applied to predict gait with a prosthetic foot and ankle. Solutions were obtained in well under one hour of computation time and demonstrated how patients may adapt their gait to compensate for limitations of a specific prosthetic limb design. The optimal control method was also applied to a state estimation problem in sports biomechanics, where forces during skiing were estimated from noisy and incomplete kinematic data. Using a full musculoskeletal dynamics model for state estimation had the additional advantage that forward dynamic simulations, could be done with the same implicitly formulated model to simulate injuries and perturbation responses. While these methods are powerful and allow solution of previously intractable problems, there are still considerable numerical challenges, especially related to the convergence of gradient-based solvers. PMID:22102983

Anticipation from sensation: using anticipating synchronization to stabilize a system with inherent sensory delay

PubMed Central

Nasuto, Slawomir J.; Hayashi, Yoshikatsu

2018-01-01

We present a novel way of using a dynamical model for predictive tracking control that can adapt to a wide range of delays without parameter update. This is achieved by incorporating the paradigm of anticipating synchronization (AS), where a ‘slave’ system predicts a ‘master’ via delayed self-feedback. By treating the delayed output of the plant as one half of a ‘sensory’ AS coupling, the plant and an internal dynamical model can be synchronized such that the plant consistently leads the target’s motion. We use two simulated robotic systems with differing arrangements of the plant and internal model (‘parallel’ and ‘serial’) to demonstrate that this form of control adapts to a wide range of delays without requiring the parameters of the controller to be changed. PMID:29657750

Enhancing Flood Prediction Reliability Using Bayesian Model Averaging

NASA Astrophysics Data System (ADS)

Liu, Z.; Merwade, V.

2017-12-01

Uncertainty analysis is an indispensable part of modeling the hydrology and hydrodynamics of non-idealized environmental systems. Compared to reliance on prediction from one model simulation, using on ensemble of predictions that consider uncertainty from different sources is more reliable. In this study, Bayesian model averaging (BMA) is applied to Black River watershed in Arkansas and Missouri by combining multi-model simulations to get reliable deterministic water stage and probabilistic inundation extent predictions. The simulation ensemble is generated from 81 LISFLOOD-FP subgrid model configurations that include uncertainty from channel shape, channel width, channel roughness and discharge. Model simulation outputs are trained with observed water stage data during one flood event, and BMA prediction ability is validated for another flood event. Results from this study indicate that BMA does not always outperform all members in the ensemble, but it provides relatively robust deterministic flood stage predictions across the basin. Station based BMA (BMA_S) water stage prediction has better performance than global based BMA (BMA_G) prediction which is superior to the ensemble mean prediction. Additionally, high-frequency flood inundation extent (probability greater than 60%) in BMA_G probabilistic map is more accurate than the probabilistic flood inundation extent based on equal weights.

Environmental controls, oceanography and population dynamics of pathogens and harmful algal blooms: connecting sources to human exposure.

PubMed

Dyble, Julianne; Bienfang, Paul; Dusek, Eva; Hitchcock, Gary; Holland, Fred; Laws, Ed; Lerczak, James; McGillicuddy, Dennis J; Minnett, Peter; Moore, Stephanie K; O'Kelly, Charles; Solo-Gabriele, Helena; Wang, John D

2008-11-07

Coupled physical-biological models are capable of linking the complex interactions between environmental factors and physical hydrodynamics to simulate the growth, toxicity and transport of infectious pathogens and harmful algal blooms (HABs). Such simulations can be used to assess and predict the impact of pathogens and HABs on human health. Given the widespread and increasing reliance of coastal communities on aquatic systems for drinking water, seafood and recreation, such predictions are critical for making informed resource management decisions. Here we identify three challenges to making this connection between pathogens/HABs and human health: predicting concentrations and toxicity; identifying the spatial and temporal scales of population and ecosystem interactions; and applying the understanding of population dynamics of pathogens/HABs to management strategies. We elaborate on the need to meet each of these challenges, describe how modeling approaches can be used and discuss strategies for moving forward in addressing these challenges.

Machine Learning Estimates of Natural Product Conformational Energies

PubMed Central

Rupp, Matthias; Bauer, Matthias R.; Wilcken, Rainer; Lange, Andreas; Reutlinger, Michael; Boeckler, Frank M.; Schneider, Gisbert

2014-01-01

Machine learning has been used for estimation of potential energy surfaces to speed up molecular dynamics simulations of small systems. We demonstrate that this approach is feasible for significantly larger, structurally complex molecules, taking the natural product Archazolid A, a potent inhibitor of vacuolar-type ATPase, from the myxobacterium Archangium gephyra as an example. Our model estimates energies of new conformations by exploiting information from previous calculations via Gaussian process regression. Predictive variance is used to assess whether a conformation is in the interpolation region, allowing a controlled trade-off between prediction accuracy and computational speed-up. For energies of relaxed conformations at the density functional level of theory (implicit solvent, DFT/BLYP-disp3/def2-TZVP), mean absolute errors of less than 1 kcal/mol were achieved. The study demonstrates that predictive machine learning models can be developed for structurally complex, pharmaceutically relevant compounds, potentially enabling considerable speed-ups in simulations of larger molecular structures. PMID:24453952

Integrating Predictive Modeling with Control System Design for Managed Aquifer Recharge and Recovery Applications

NASA Astrophysics Data System (ADS)

Drumheller, Z. W.; Regnery, J.; Lee, J. H.; Illangasekare, T. H.; Kitanidis, P. K.; Smits, K. M.

2014-12-01

Aquifers around the world show troubling signs of irreversible depletion and seawater intrusion as climate change, population growth, and urbanization led to reduced natural recharge rates and overuse. Scientists and engineers have begun to re-investigate the technology of managed aquifer recharge and recovery (MAR) as a means to increase the reliability of the diminishing and increasingly variable groundwater supply. MAR systems offer the possibility of naturally increasing groundwater storage while improving the quality of impaired water used for recharge. Unfortunately, MAR systems remain wrought with operational challenges related to the quality and quantity of recharged and recovered water stemming from a lack of data-driven, real-time control. Our project seeks to ease the operational challenges of MAR facilities through the implementation of active sensor networks, adaptively calibrated flow and transport models, and simulation-based meta-heuristic control optimization methods. The developed system works by continually collecting hydraulic and water quality data from a sensor network embedded within the aquifer. The data is fed into an inversion algorithm, which calibrates the parameters and initial conditions of a predictive flow and transport model. The calibrated model is passed to a meta-heuristic control optimization algorithm (e.g. genetic algorithm) to execute the simulations and determine the best course of action, i.e., the optimal pumping policy for current aquifer conditions. The optimal pumping policy is manually or autonomously applied. During operation, sensor data are used to assess the accuracy of the optimal prediction and augment the pumping strategy as needed. At laboratory-scale, a small (18"H x 46"L) and an intermediate (6'H x 16'L) two-dimensional synthetic aquifer were constructed and outfitted with sensor networks. Data collection and model inversion components were developed and sensor data were validated by analytical measurements.

Space-based Doppler lidar sampling strategies: Algorithm development and simulated observation experiments

NASA Technical Reports Server (NTRS)

Emmitt, G. D.; Wood, S. A.; Morris, M.

1990-01-01

Lidar Atmospheric Wind Sounder (LAWS) Simulation Models (LSM) were developed to evaluate the potential impact of global wind observations on the basic understanding of the Earth's atmosphere and on the predictive skills of current forecast models (GCM and regional scale). Fully integrated top to bottom LAWS Simulation Models for global and regional scale simulations were developed. The algorithm development incorporated the effects of aerosols, water vapor, clouds, terrain, and atmospheric turbulence into the models. Other additions include a new satellite orbiter, signal processor, line of sight uncertainty model, new Multi-Paired Algorithm and wind error analysis code. An atmospheric wind field library containing control fields, meteorological fields, phenomena fields, and new European Center for Medium Range Weather Forecasting (ECMWF) data was also added. The LSM was used to address some key LAWS issues and trades such as accuracy and interpretation of LAWS information, data density, signal strength, cloud obscuration, and temporal data resolution.

Integration of Irma tactical scene generator into directed-energy weapon system simulation

NASA Astrophysics Data System (ADS)

Owens, Monte A.; Cole, Madison B., III; Laine, Mark R.

2003-08-01

Integrated high-fidelity physics-based simulations that include engagement models, image generation, electro-optical hardware models and control system algorithms have previously been developed by Boeing-SVS for various tracking and pointing systems. These simulations, however, had always used images with featureless or random backgrounds and simple target geometries. With the requirement to engage tactical ground targets in the presence of cluttered backgrounds, a new type of scene generation tool was required to fully evaluate system performance in this challenging environment. To answer this need, Irma was integrated into the existing suite of Boeing-SVS simulation tools, allowing scene generation capabilities with unprecedented realism. Irma is a US Air Force research tool used for high-resolution rendering and prediction of target and background signatures. The MATLAB/Simulink-based simulation achieves closed-loop tracking by running track algorithms on the Irma-generated images, processing the track errors through optical control algorithms, and moving simulated electro-optical elements. The geometry of these elements determines the sensor orientation with respect to the Irma database containing the three-dimensional background and target models. This orientation is dynamically passed to Irma through a Simulink S-function to generate the next image. This integrated simulation provides a test-bed for development and evaluation of tracking and control algorithms against representative images including complex background environments and realistic targets calibrated using field measurements.

Evaluation of simulated biospheric carbon dioxide fluxes and atmospheric concentrations using global in situ observations

NASA Astrophysics Data System (ADS)

Philip, S.; Johnson, M. S.; Potter, C. S.; Genovese, V. B.

2016-12-01

Atmospheric mixing ratios of carbon dioxide (CO2) are largely controlled by anthropogenic emission sources and biospheric sources/sinks. Global biospheric fluxes of CO2 are controlled by complex processes facilitating the exchange of carbon between terrestrial ecosystems and the atmosphere. These processes which play a key role in these terrestrial ecosystem-atmosphere carbon exchanges are currently not fully understood, resulting in large uncertainties in the quantification of biospheric CO2 fluxes. Current models with these inherent deficiencies have difficulties simulating the global carbon cycle with high accuracy. We are developing a new modeling platform, GEOS-Chem-CASA by integrating the year-specific NASA-CASA (National Aeronautics and Space Administration - Carnegie Ames Stanford Approach) biosphere model with the GEOS-Chem (Goddard Earth Observation System-Chemistry) chemical transport model to improve the simulation of atmosphere-terrestrial ecosystem carbon exchange. We use NASA-CASA to explicitly represent the exchange of CO2 between terrestrial ecosystem and atmosphere by replacing the baseline GEOS-Chem land net CO2 flux and forest biomass burning CO2 emissions. We will present the estimation and evaluation of these "bottom-up" land CO2 fluxes, simulated atmospheric mixing ratios, and forest disturbance changes over the last decade. In addition, we will present our initial comparison of atmospheric column-mean dry air mole fraction of CO2 predicted by the model and those retrieved from NASA's OCO-2 (Orbiting Carbon Observatory-2) satellite instrument and model-predicted surface CO2 mixing ratios with global in situ observations. This evaluation is the first step necessary for our future work planned to constrain the estimates of biospheric carbon fluxes through "top-down" inverse modeling, which will improve our understanding of the processes controlling atmosphere-terrestrial ecosystem greenhouse gas exchanges, especially over regions which lack in situ observations.

B33C-0612: Evaluation of Simulated Biospheric Carbon Dioxide Fluxes and Atmospheric Concentrations Using Global in Situ Observations

NASA Technical Reports Server (NTRS)

Philip, Sajeev; Johnson, Matthew S.; Potter, Christopher S.; Genovese, Vanessa

2016-01-01

Atmospheric mixing ratios of carbon dioxide (CO2) are largely controlled by anthropogenic emission sources and biospheric sources/sinks. Global biospheric fluxes of CO2 are controlled by complex processes facilitating the exchange of carbon between terrestrial ecosystems and the atmosphere. These processes which play a key role in these terrestrial ecosystem-atmosphere carbon exchanges are currently not fully understood, resulting in large uncertainties in the quantification of biospheric CO2 fluxes. Current models with these inherent deficiencies have difficulties simulating the global carbon cycle with high accuracy. We are developing a new modeling platform, GEOS-Chem-CASA by integrating the year-specific NASA-CASA (National Aeronautics and Space Administration - Carnegie Ames Stanford Approach) biosphere model with the GEOS-Chem (Goddard Earth Observation System-Chemistry) chemical transport model to improve the simulation of atmosphere-terrestrial ecosystem carbon exchange. We use NASA-CASA to explicitly represent the exchange of CO2 between terrestrial ecosystem and atmosphere by replacing the baseline GEOS-Chem land net CO2 flux and forest biomass burning CO2 emissions. We will present the estimation and evaluation of these "bottom-up" land CO2 fluxes, simulated atmospheric mixing ratios, and forest disturbance changes over the last decade. In addition, we will present our initial comparison of atmospheric column-mean dry air mole fraction of CO2 predicted by the model and those retrieved from NASA's OCO-2 (Orbiting Carbon Observatory-2) satellite instrument and model-predicted surface CO2 mixing ratios with global in situ observations. This evaluation is the first step necessary for our future work planned to constrain the estimates of biospheric carbon fluxes through "top-down" inverse modeling, which will improve our understanding of the processes controlling atmosphere-terrestrial ecosystem greenhouse gas exchanges, especially over regions which lack in situ observations.

The fatigue life prediction of aluminium alloy using genetic algorithm and neural network

NASA Astrophysics Data System (ADS)

Susmikanti, Mike

2013-09-01

The behavior of the fatigue life of the industrial materials is very important. In many cases, the material with experiencing fatigue life cannot be avoided, however, there are many ways to control their behavior. Many investigations of the fatigue life phenomena of alloys have been done, but it is high cost and times consuming computation. This paper report the modeling and simulation approaches to predict the fatigue life behavior of Aluminum Alloys and resolves some problems of computation. First, the simulation using genetic algorithm was utilized to optimize the load to obtain the stress values. These results can be used to provide N-cycle fatigue life of the material. Furthermore, the experimental data was applied as input data in the neural network learning, while the samples data were applied for testing of the training data. Finally, the multilayer perceptron algorithm is applied to predict whether the given data sets in accordance with the fatigue life of the alloy. To achieve rapid convergence, the Levenberg-Marquardt algorithm was also employed. The simulations results shows that the fatigue behaviors of aluminum under pressure can be predicted. In addition, implementation of neural networks successfully identified a model for material fatigue life.

Improving Numerical Weather Predictions of Summertime Precipitation Over the Southeastern U.S. Through a High-Resolution Initialization of the Surface State

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Kumar, Sujay V.; Krikishen, Jayanthi; Jedlovec, Gary J.

2011-01-01

It is hypothesized that high-resolution, accurate representations of surface properties such as soil moisture and sea surface temperature are necessary to improve simulations of summertime pulse-type convective precipitation in high resolution models. This paper presents model verification results of a case study period from June-August 2008 over the Southeastern U.S. using the Weather Research and Forecasting numerical weather prediction model. Experimental simulations initialized with high-resolution land surface fields from the NASA Land Information System (LIS) and sea surface temperature (SST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) are compared to a set of control simulations initialized with interpolated fields from the National Centers for Environmental Prediction 12-km North American Mesoscale model. The LIS land surface and MODIS SSTs provide a more detailed surface initialization at a resolution comparable to the 4-km model grid spacing. Soil moisture from the LIS spin-up run is shown to respond better to the extreme rainfall of Tropical Storm Fay in August 2008 over the Florida peninsula. The LIS has slightly lower errors and higher anomaly correlations in the top soil layer, but exhibits a stronger dry bias in the root zone. The model sensitivity to the alternative surface initial conditions is examined for a sample case, showing that the LIS/MODIS data substantially impact surface and boundary layer properties.

Dynamic Smagorinsky model on anisotropic grids

NASA Technical Reports Server (NTRS)

Scotti, A.; Meneveau, C.; Fatica, M.

1996-01-01

Large Eddy Simulation (LES) of complex-geometry flows often involves highly anisotropic meshes. To examine the performance of the dynamic Smagorinsky model in a controlled fashion on such grids, simulations of forced isotropic turbulence are performed using highly anisotropic discretizations. The resulting model coefficients are compared with a theoretical prediction (Scotti et al., 1993). Two extreme cases are considered: pancake-like grids, for which two directions are poorly resolved compared to the third, and pencil-like grids, where one direction is poorly resolved when compared to the other two. For pancake-like grids the dynamic model yields the results expected from the theory (increasing coefficient with increasing aspect ratio), whereas for pencil-like grids the dynamic model does not agree with the theoretical prediction (with detrimental effects only on smallest resolved scales). A possible explanation of the departure is attempted, and it is shown that the problem may be circumvented by using an isotropic test-filter at larger scales. Overall, all models considered give good large-scale results, confirming the general robustness of the dynamic and eddy-viscosity models. But in all cases, the predictions were poor for scales smaller than that of the worst resolved direction.

Experimental validation of a Lyapunov-based controller for the plasma safety factor and plasma pressure in the TCV tokamak

NASA Astrophysics Data System (ADS)

Mavkov, B.; Witrant, E.; Prieur, C.; Maljaars, E.; Felici, F.; Sauter, O.; the TCV-Team

2018-05-01

In this paper, model-based closed-loop algorithms are derived for distributed control of the inverse of the safety factor profile and the plasma pressure parameter β of the TCV tokamak. The simultaneous control of the two plasma quantities is performed by combining two different control methods. The control design of the plasma safety factor is based on an infinite-dimensional setting using Lyapunov analysis for partial differential equations, while the control of the plasma pressure parameter is designed using control techniques for single-input and single-output systems. The performance and robustness of the proposed controller is analyzed in simulations using the fast plasma transport simulator RAPTOR. The control is then implemented and tested in experiments in TCV L-mode discharges using the RAPTOR model predicted estimates for the q-profile. The distributed control in TCV is performed using one co-current and one counter-current electron cyclotron heating actuation.

Physiologically-based pharmacokinetic model of vaginally administered dapivirine ring and film formulations.

PubMed

Kay, Katherine; Shah, Dhaval K; Rohan, Lisa; Bies, Robert

2018-05-01

A physiologically-based pharmacokinetic (PBPK) model of the vaginal space was developed with the aim of predicting concentrations in the vaginal and cervical space. These predictions can be used to optimize the probability of success of vaginally administered dapivirine (DPV) for HIV prevention. We focus on vaginal delivery using either a ring or film. A PBPK model describing the physiological structure of the vaginal tissue and fluid was defined mathematically and implemented in MATLAB. Literature reviews provided estimates for relevant physiological and physiochemical parameters. Drug concentration-time profiles were simulated in luminal fluids, vaginal tissue and plasma after administration of ring or film. Patient data were extracted from published clinical trials and used to test model predictions. The DPV ring simulations tested the two dosing regimens and predicted PK profiles and area under the curve of luminal fluids (29 079 and 33 067 mg h l -1 in groups A and B, respectively) and plasma (0.177 and 0.211 mg h l -1 ) closely matched those reported (within one standard deviation). While the DPV film study reported drug concentration at only one time point per patient, our simulated profiles pass through reported concentration range. HIV is a major public health issue and vaginal microbicides have the potential to provide a crucial, female-controlled option for protection. The PBPK model successfully simulated realistic representations of drug PK. It provides a reliable, inexpensive and accessible platform where potential effectiveness of new compounds and the robustness of treatment modalities for pre-exposure prophylaxis can be evaluated. © 2018 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.

The Role of Public Policies in Reducing Smoking Prevalence in California: Results from the California Tobacco Policy Simulation Model

PubMed Central

Levy, David T.; Hyland, Andrew; Higbee, Cheryl; Remer, Lillian; Compton, Christine

2009-01-01

Summary Tobacco control policies are examined utilizing a simulation model for California, the state with the longest running comprehensive program. We assess the impact of the California Tobacco Control Program (CTCP) and surrounding price changes on smoking prevalence and smoking-attributable deaths. Modeling begins in 1988 and progresses chronologically to 2004, and considers four types of policies (taxes, mass media, clean air laws, and youth access policies) independently and as a package. The model is validated against existing smoking prevalence estimates. The difference in trends between predicted smoking rates from the model and other commonly used estimates of smoking prevalence for the overall period were generally small. The model also predicted some important changes in trend, which occurred with changes in policy. The California SimSmoke model estimates that tobacco control policies reduced smoking rates in California by an additional 25% relative to the level that they would have been if policies were kept at their 1988 level. By 2004, the model attributes over 60% of the reduction to price increases, over 25% of the overall effect to media policies, 10% to clean air laws, and only a small percent to youth access policies. The model estimates that over 5,000 lives will be saved in the year 2010 alone as a result of the CTCP and industry-initiated price increases, and that over 50,000 lives were saved over the period 1988-2010. Tobacco control policies implemented as comprehensive tobacco control strategies have significantly impacted smoking rates. Further tax increases should lead to additional lives saved, and additional policies may result in further impacts on smoking rates, and consequently on smoking-attributable health outcomes in the population. PMID:17055104

Accounting for control mislabeling in case-control biomarker studies.

PubMed

Rantalainen, Mattias; Holmes, Chris C

2011-12-02

In biomarker discovery studies, uncertainty associated with case and control labels is often overlooked. By omitting to take into account label uncertainty, model parameters and the predictive risk can become biased, sometimes severely. The most common situation is when the control set contains an unknown number of undiagnosed, or future, cases. This has a marked impact in situations where the model needs to be well-calibrated, e.g., when the prediction performance of a biomarker panel is evaluated. Failing to account for class label uncertainty may lead to underestimation of classification performance and bias in parameter estimates. This can further impact on meta-analysis for combining evidence from multiple studies. Using a simulation study, we outline how conventional statistical models can be modified to address class label uncertainty leading to well-calibrated prediction performance estimates and reduced bias in meta-analysis. We focus on the problem of mislabeled control subjects in case-control studies, i.e., when some of the control subjects are undiagnosed cases, although the procedures we report are generic. The uncertainty in control status is a particular situation common in biomarker discovery studies in the context of genomic and molecular epidemiology, where control subjects are commonly sampled from the general population with an established expected disease incidence rate.

Special Issue on Uncertainty Quantification in Multiscale System Design and Simulation

DOE PAGES

Wang, Yan; Swiler, Laura

2017-09-07

The importance of uncertainty has been recognized in various modeling, simulation, and analysis applications, where inherent assumptions and simplifications affect the accuracy of model predictions for physical phenomena. As model predictions are now heavily relied upon for simulation-based system design, which includes new materials, vehicles, mechanical and civil structures, and even new drugs, wrong model predictions could potentially cause catastrophic consequences. Therefore, uncertainty and associated risks due to model errors should be quantified to support robust systems engineering.

Special Issue on Uncertainty Quantification in Multiscale System Design and Simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Yan; Swiler, Laura

The importance of uncertainty has been recognized in various modeling, simulation, and analysis applications, where inherent assumptions and simplifications affect the accuracy of model predictions for physical phenomena. As model predictions are now heavily relied upon for simulation-based system design, which includes new materials, vehicles, mechanical and civil structures, and even new drugs, wrong model predictions could potentially cause catastrophic consequences. Therefore, uncertainty and associated risks due to model errors should be quantified to support robust systems engineering.

The dynamic simulation of the Progetto Energia combined cycle power plants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Giglio, R.; Cerabolini, M.; Pisacane, F.

1996-12-31

Over the next four years, the Progetto Energia project is building several cogeneration plants to satisfy the increasing demands of Italy`s industrial complex and the country`s demand for electrical power. Located at six different sites within Italy`s borders these Combined Cycle Cogeneration Plants will supply a total of 500 MW of electricity and 100 tons/hr of process steam to Italian industries and residences. To ensure project success, a dynamic model of the 50 MW base unit was developed. The goal established for the model was to predict the dynamic behavior of the complex thermodynamic system in order to assess equipmentmore » performance and control system effectiveness for normal operation and, more importantly, abrupt load changes. In addition to fulfilling its goals, the dynamic study guided modifications to controller logic that significantly improved steam drum pressure control and bypassed steam de-superheating performance. Simulations of normal and abrupt transient events allowed engineers to define optimum controller gain coefficients. The paper discusses the Combined Cycle plant configuration, its operating modes and control system, the dynamic model representation, the simulation results and project benefits.« less

Assessment of driver stopping prediction models before and after the onset of yellow using two driving simulator datasets.

PubMed

Ghanipoor Machiani, Sahar; Abbas, Montasir

2016-11-01

Accurate modeling of driver decisions in dilemma zones (DZ), where drivers are not sure whether to stop or go at the onset of yellow, can be used to increase safety at signalized intersections. This study utilized data obtained from two different driving simulator studies (VT-SCORES and NADS datasets) to investigate the possibility of developing accurate driver-decision prediction/classification models in DZ. Canonical discriminant analysis was used to construct the prediction models, and two timeframes were considered. The first timeframe used data collected during green immediately before the onset of yellow, and the second timeframe used data collected during the first three seconds after the onset of yellow. Signal protection algorithms could use the results of the prediction model during the first timeframe to decide the best time for ending the green signal, and could use the results of the prediction model during the first three seconds of yellow to extend the clearance interval. It was found that the discriminant model using data collected during the first three seconds of yellow was the most accurate, at 99% accuracy. It was also found that data collection should focus on variables that are related to speed, acceleration, time, and distance to intersection, as opposed to secondary variables, such as pavement conditions, since secondary variables did not significantly change the accuracy of the prediction models. The results reveal a promising possibility for incorporating the developed models in traffic-signal controllers to improve DZ-protection strategies. Copyright © 2015 Elsevier Ltd. All rights reserved.

On the required complexity of vehicle dynamic models for use in simulation-based highway design.

PubMed

Brown, Alexander; Brennan, Sean

2014-06-01

This paper presents the results of a comprehensive project whose goal is to identify roadway design practices that maximize the margin of safety between the friction supply and friction demand. This study is motivated by the concern for increased accident rates on curves with steep downgrades, geometries that contain features that interact in all three dimensions - planar curves, grade, and superelevation. This complexity makes the prediction of vehicle skidding quite difficult, particularly for simple simulation models that have historically been used for road geometry design guidance. To obtain estimates of friction margin, this study considers a range of vehicle models, including: a point-mass model used by the American Association of State Highway Transportation Officials (AASHTO) design policy, a steady-state "bicycle model" formulation that considers only per-axle forces, a transient formulation of the bicycle model commonly used in vehicle stability control systems, and finally, a full multi-body simulation (CarSim and TruckSim) regularly used in the automotive industry for high-fidelity vehicle behavior prediction. The presence of skidding--the friction demand exceeding supply--was calculated for each model considering a wide range of vehicles and road situations. The results indicate that the most complicated vehicle models are generally unnecessary for predicting skidding events. However, there are specific maneuvers, namely braking events within lane changes and curves, which consistently predict the worst-case friction margins across all models. This suggests that any vehicle model used for roadway safety analysis should include the effects of combined cornering and braking. The point-mass model typically used by highway design professionals may not be appropriate to predict vehicle behavior on high-speed curves during braking in low-friction situations. However, engineers can use the results of this study to help select the appropriate vehicle dynamic model complexity to use in the highway design process. Copyright © 2014 Elsevier Ltd. All rights reserved.

Functionality of empirical model-based predictive analytics for the early detection of hemodynamic instabilty.

PubMed

Summers, Richard L; Pipke, Matt; Wegerich, Stephan; Conkright, Gary; Isom, Kristen C

2014-01-01

Background. Monitoring cardiovascular hemodynamics in the modern clinical setting is a major challenge. Increasing amounts of physiologic data must be analyzed and interpreted in the context of the individual patient’s pathology and inherent biologic variability. Certain data-driven analytical methods are currently being explored for smart monitoring of data streams from patients as a first tier automated detection system for clinical deterioration. As a prelude to human clinical trials, an empirical multivariate machine learning method called Similarity-Based Modeling (“SBM”), was tested in an In Silico experiment using data generated with the aid of a detailed computer simulator of human physiology (Quantitative Circulatory Physiology or “QCP”) which contains complex control systems with realistic integrated feedback loops. Methods. SBM is a kernel-based, multivariate machine learning method that that uses monitored clinical information to generate an empirical model of a patient’s physiologic state. This platform allows for the use of predictive analytic techniques to identify early changes in a patient’s condition that are indicative of a state of deterioration or instability. The integrity of the technique was tested through an In Silico experiment using QCP in which the output of computer simulations of a slowly evolving cardiac tamponade resulted in progressive state of cardiovascular decompensation. Simulator outputs for the variables under consideration were generated at a 2-min data rate (0.083Hz) with the tamponade introduced at a point 420 minutes into the simulation sequence. The functionality of the SBM predictive analytics methodology to identify clinical deterioration was compared to the thresholds used by conventional monitoring methods. Results. The SBM modeling method was found to closely track the normal physiologic variation as simulated by QCP. With the slow development of the tamponade, the SBM model are seen to disagree while the simulated biosignals in the early stages of physiologic deterioration and while the variables are still within normal ranges. Thus, the SBM system was found to identify pathophysiologic conditions in a timeframe that would not have been detected in a usual clinical monitoring scenario. Conclusion. In this study the functionality of a multivariate machine learning predictive methodology that that incorporates commonly monitored clinical information was tested using a computer model of human physiology. SBM and predictive analytics were able to differentiate a state of decompensation while the monitored variables were still within normal clinical ranges. This finding suggests that the SBM could provide for early identification of a clinical deterioration using predictive analytic techniques. predictive analytics, hemodynamic, monitoring.

Predictive Analytical Model for Isolator Shock-Train Location in a Mach 2.2 Direct-Connect Supersonic Combustion Tunnel

NASA Astrophysics Data System (ADS)

Lingren, Joe; Vanstone, Leon; Hashemi, Kelley; Gogineni, Sivaram; Donbar, Jeffrey; Akella, Maruthi; Clemens, Noel

2016-11-01

This study develops an analytical model for predicting the leading shock of a shock-train in the constant area isolator section in a Mach 2.2 direct-connect scramjet simulation tunnel. The effective geometry of the isolator is assumed to be a weakly converging duct owing to boundary-layer growth. For some given pressure rise across the isolator, quasi-1D equations relating to isentropic or normal shock flows can be used to predict the normal shock location in the isolator. The surface pressure distribution through the isolator was measured during experiments and both the actual and predicted locations can be calculated. Three methods of finding the shock-train location are examined, one based on the measured pressure rise, one using a non-physics-based control model, and one using the physics-based analytical model. It is shown that the analytical model performs better than the non-physics-based model in all cases. The analytic model is less accurate than the pressure threshold method but requires significantly less information to compute. In contrast to other methods for predicting shock-train location, this method is relatively accurate and requires as little as a single pressure measurement. This makes this method potentially useful for unstart control applications.

Quasi-laminar stability and sensitivity analyses for turbulent flows: Prediction of low-frequency unsteadiness and passive control

NASA Astrophysics Data System (ADS)

Mettot, Clément; Sipp, Denis; Bézard, Hervé

2014-04-01

This article presents a quasi-laminar stability approach to identify in high-Reynolds number flows the dominant low-frequencies and to design passive control means to shift these frequencies. The approach is based on a global linear stability analysis of mean-flows, which correspond to the time-average of the unsteady flows. Contrary to the previous work by Meliga et al. ["Sensitivity of 2-D turbulent flow past a D-shaped cylinder using global stability," Phys. Fluids 24, 061701 (2012)], we use the linearized Navier-Stokes equations based solely on the molecular viscosity (leaving aside any turbulence model and any eddy viscosity) to extract the least stable direct and adjoint global modes of the flow. Then, we compute the frequency sensitivity maps of these modes, so as to predict before hand where a small control cylinder optimally shifts the frequency of the flow. In the case of the D-shaped cylinder studied by Parezanović and Cadot [J. Fluid Mech. 693, 115 (2012)], we show that the present approach well captures the frequency of the flow and recovers accurately the frequency control maps obtained experimentally. The results are close to those already obtained by Meliga et al., who used a more complex approach in which turbulence models played a central role. The present approach is simpler and may be applied to a broader range of flows since it is tractable as soon as mean-flows — which can be obtained either numerically from simulations (Direct Numerical Simulation (DNS), Large Eddy Simulation (LES), unsteady Reynolds-Averaged-Navier-Stokes (RANS), steady RANS) or from experimental measurements (Particle Image Velocimetry - PIV) — are available. We also discuss how the influence of the control cylinder on the mean-flow may be more accurately predicted by determining an eddy-viscosity from numerical simulations or experimental measurements. From a technical point of view, we finally show how an existing compressible numerical simulation code may be used in a black-box manner to extract the global modes and sensitivity maps.

Computer Simulation in Predicting Biochemical Processes and Energy Balance at WWTPs

NASA Astrophysics Data System (ADS)

Drewnowski, Jakub; Zaborowska, Ewa; Hernandez De Vega, Carmen

2018-02-01

Nowadays, the use of mathematical models and computer simulation allow analysis of many different technological solutions as well as testing various scenarios in a short time and at low financial budget in order to simulate the scenario under typical conditions for the real system and help to find the best solution in design or operation process. The aim of the study was to evaluate different concepts of biochemical processes and energy balance modelling using a simulation platform GPS-x and a comprehensive model Mantis2. The paper presents the example of calibration and validation processes in the biological reactor as well as scenarios showing an influence of operational parameters on the WWTP energy balance. The results of batch tests and full-scale campaign obtained in the former work were used to predict biochemical and operational parameters in a newly developed plant model. The model was extended with sludge treatment devices, including anaerobic digester. Primary sludge removal efficiency was found as a significant factor determining biogas production and further renewable energy production in cogeneration. Water and wastewater utilities, which run and control WWTP, are interested in optimizing the process in order to save environment, their budget and decrease the pollutant emissions to water and air. In this context, computer simulation can be the easiest and very useful tool to improve the efficiency without interfering in the actual process performance.

Are Droughts in the United States Great Plains Predictable on Seasonal and Longer Time Scales?

NASA Technical Reports Server (NTRS)

Schubert, Siegfried D.; Suarez, M.; Pegion, P.; Kistler, M.; Einaudi, Franco (Technical Monitor)

2001-01-01

The United States Great Plains has experienced numerous episodes of unusually dry conditions lasting anywhere from months to several years, In this presentation, we will examine the predictability of such episodes and the physical mechanisms controlling the variability of the summer climate of the continental United States. The analysis is based on ensembles of multi-year simulations and seasonal hindcasts generated with the NASA Seasonal to-Interannual Prediction Project (NSIPP-1) General Circulation Model.

The Effect of Visual Information on the Manual Approach and Landing

NASA Technical Reports Server (NTRS)

Wewerinke, P. H.

1982-01-01

The effect of visual information in combination with basic display information on the approach performance. A pre-experimental model analysis was performed in terms of the optimal control model. The resulting aircraft approach performance predictions were compared with the results of a moving base simulator program. The results illustrate that the model provides a meaningful description of the visual (scene) perception process involved in the complex (multi-variable, time varying) manual approach task with a useful predictive capability. The theoretical framework was shown to allow a straight-forward investigation of the complex interaction of a variety of task variables.

Development of Integrated Magnetic and Kinetic Control-oriented Transport Model for q-profile Response Prediction in EAST Discharges

NASA Astrophysics Data System (ADS)

Wang, Hexiang; Schuster, Eugenio; Rafiq, Tariq; Kritz, Arnold; Ding, Siye

2016-10-01

Extensive research has been conducted to find high-performance operating scenarios characterized by high fusion gain, good confinement, plasma stability and possible steady-state operation. A key plasma property that is related to both the stability and performance of these advanced plasma scenarios is the safety factor profile. A key component of the EAST research program is the exploration of non-inductively driven steady-state plasmas with the recently upgraded heating and current drive capabilities that include lower hybrid current drive and neutral beam injection. Anticipating the need for tight regulation of the safety factor profile in these plasma scenarios, a first-principles-driven (FPD)control-oriented model is proposed to describe the safety factor profile evolution in EAST in response to the different actuators. The TRANSP simulation code is employed to tailor the FPD model to the EAST tokamak geometry and to convert it into a form suitable for control design. The FPD control-oriented model's prediction capabilities are demonstrated by comparing predictions with experimental data from EAST. Supported by the US DOE under DE-SC0010537,DE-FG02-92ER54141 and DE-SC0013977.

Real-Time Monitoring and Prediction of the Pilot Vehicle System (PVS) Closed-Loop Stability

NASA Astrophysics Data System (ADS)

Mandal, Tanmay Kumar

Understanding human control behavior is an important step for improving the safety of future aircraft. Considerable resources are invested during the design phase of an aircraft to ensure that the aircraft has desirable handling qualities. However, human pilots exhibit a wide range of control behaviors that are a function of external stimulus, aircraft dynamics, and human psychological properties (such as workload, stress factor, confidence, and sense of urgency factor). This variability is difficult to address comprehensively during the design phase and may lead to undesirable pilot-aircraft interaction, such as pilot-induced oscillations (PIO). This creates the need to keep track of human pilot performance in real-time to monitor the pilot vehicle system (PVS) stability. This work focused on studying human pilot behavior for the longitudinal axis of a remotely controlled research aircraft and using human-in-the-loop (HuIL) simulations to obtain information about the human controlled system (HCS) stability. The work in this dissertation is divided into two main parts: PIO analysis and human control model parameters estimation. To replicate different flight conditions, this study included time delay and elevator rate limiting phenomena, typical of actuator dynamics during the experiments. To study human control behavior, this study employed the McRuer model for single-input single-output manual compensatory tasks. McRuer model is a lead-lag controller with time delay which has been shown to adequately model manual compensatory tasks. This dissertation presents a novel technique to estimate McRuer model parameters in real-time and associated validation using HuIL simulations to correctly predict HCS stability. The McRuer model parameters were estimated in real-time using a Kalman filter approach. The estimated parameters were then used to analyze the stability of the closed-loop HCS and verify them against the experimental data. Therefore, the main contribution of this dissertation is the design of an unscented Kalman filter-based algorithm to estimate McRuer model parameters in real time, and a framework to validate this algorithm for single-input single-output manual compensatory tasks to predict instabilities.

Intercomparison of Large-Eddy Simulations of Arctic Mixed-Phase Clouds: Importance of Ice Size Distribution Assumptions

NASA Technical Reports Server (NTRS)

Ovchinnikov, Mikhail; Ackerman, Andrew S.; Avramov, Alexander; Cheng, Anning; Fan, Jiwen; Fridlind, Ann M.; Ghan, Steven; Harrington, Jerry; Hoose, Corinna; Korolev, Alexei;

Development of Quality Assessment Techniques for Large Eddy Simulation of Propulsion and Power Systems in Complex Geometries

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lacaze, Guilhem; Oefelein, Joseph

Large-eddy-simulation (LES) is quickly becoming a method of choice for studying complex thermo-physics in a wide range of propulsion and power systems. It provides a means to study coupled turbulent combustion and flow processes in parameter spaces that are unattainable using direct-numerical-simulation (DNS), with a degree of fidelity that can be far more accurate than conventional engineering methods such as the Reynolds-averaged Navier-Stokes (RANS) approx- imation. However, development of predictive LES is complicated by the complex interdependence of different type of errors coming from numerical methods, algorithms, models and boundary con- ditions. On the other hand, control of accuracy hasmore » become a critical aspect in the development of predictive LES for design. The objective of this project is to create a framework of metrics aimed at quantifying the quality and accuracy of state-of-the-art LES in a manner that addresses the myriad of competing interdependencies. In a typical simulation cycle, only 20% of the computational time is actually usable. The rest is spent in case preparation, assessment, and validation, because of the lack of guidelines. This work increases confidence in the accuracy of a given solution while min- imizing the time obtaining the solution. The approach facilitates control of the tradeoffs between cost, accuracy, and uncertainties as a function of fidelity and methods employed. The analysis is coupled with advanced Uncertainty Quantification techniques employed to estimate confidence in model predictions and calibrate model's parameters. This work has provided positive conse- quences on the accuracy of the results delivered by LES and will soon have a broad impact on research supported both by the DOE and elsewhere.« less

Development and validation of a predictive model for the influences of selected product and process variables on ascorbic acid degradation in simulated fruit juice.

PubMed

Gabriel, Alonzo A; Cayabyab, Jochelle Elysse C; Tan, Athalie Kaye L; Corook, Mark Lester F; Ables, Errol John O; Tiangson-Bayaga, Cecile Leah P

2015-06-15

A predictive response surface model for the influences of product (soluble solids and titratable acidity) and process (temperature and heating time) parameters on the degradation of ascorbic acid (AA) in heated simulated fruit juices (SFJs) was established. Physicochemical property ranges of freshly squeezed and processed juices, and a previously established decimal reduction times of Escherichiacoli O157:H7 at different heating temperatures were used in establishing a Central Composite Design of Experiment that determined the combinations of product and process variable used in the model building. Only the individual linear effects of temperature and heating time significantly (P<0.05) affected AA reduction (%AAr). Validating systems either over- or underestimated actual %AAr with bias factors 0.80-1.20. However, all validating systems still resulted in acceptable predictive efficacy, with accuracy factor 1.00-1.26. The model may be useful in establishing unique process schedules for specific products, for the simultaneous control and improvement of food safety and quality. Copyright © 2015 Elsevier Ltd. All rights reserved.

Predicting knee replacement damage in a simulator machine using a computational model with a consistent wear factor.

PubMed

Zhao, Dong; Sakoda, Hideyuki; Sawyer, W Gregory; Banks, Scott A; Fregly, Benjamin J

2008-02-01

Wear of ultrahigh molecular weight polyethylene remains a primary factor limiting the longevity of total knee replacements (TKRs). However, wear testing on a simulator machine is time consuming and expensive, making it impractical for iterative design purposes. The objectives of this paper were first, to evaluate whether a computational model using a wear factor consistent with the TKR material pair can predict accurate TKR damage measured in a simulator machine, and second, to investigate how choice of surface evolution method (fixed or variable step) and material model (linear or nonlinear) affect the prediction. An iterative computational damage model was constructed for a commercial knee implant in an AMTI simulator machine. The damage model combined a dynamic contact model with a surface evolution model to predict how wear plus creep progressively alter tibial insert geometry over multiple simulations. The computational framework was validated by predicting wear in a cylinder-on-plate system for which an analytical solution was derived. The implant damage model was evaluated for 5 million cycles of simulated gait using damage measurements made on the same implant in an AMTI machine. Using a pin-on-plate wear factor for the same material pair as the implant, the model predicted tibial insert wear volume to within 2% error and damage depths and areas to within 18% and 10% error, respectively. Choice of material model had little influence, while inclusion of surface evolution affected damage depth and area but not wear volume predictions. Surface evolution method was important only during the initial cycles, where variable step was needed to capture rapid geometry changes due to the creep. Overall, our results indicate that accurate TKR damage predictions can be made with a computational model using a constant wear factor obtained from pin-on-plate tests for the same material pair, and furthermore, that surface evolution method matters only during the initial "break in" period of the simulation.

Ocean eddies and climate predictability

NASA Astrophysics Data System (ADS)

Kirtman, Ben P.; Perlin, Natalie; Siqueira, Leo

2017-12-01

A suite of coupled climate model simulations and experiments are used to examine how resolved mesoscale ocean features affect aspects of climate variability, air-sea interactions, and predictability. In combination with control simulations, experiments with the interactive ensemble coupling strategy are used to further amplify the role of the oceanic mesoscale field and the associated air-sea feedbacks and predictability. The basic intent of the interactive ensemble coupling strategy is to reduce the atmospheric noise at the air-sea interface, allowing an assessment of how noise affects the variability, and in this case, it is also used to diagnose predictability from the perspective of signal-to-noise ratios. The climate variability is assessed from the perspective of sea surface temperature (SST) variance ratios, and it is shown that, unsurprisingly, mesoscale variability significantly increases SST variance. Perhaps surprising is the fact that the presence of mesoscale ocean features even further enhances the SST variance in the interactive ensemble simulation beyond what would be expected from simple linear arguments. Changes in the air-sea coupling between simulations are assessed using pointwise convective rainfall-SST and convective rainfall-SST tendency correlations and again emphasize how the oceanic mesoscale alters the local association between convective rainfall and SST. Understanding the possible relationships between the SST-forced signal and the weather noise is critically important in climate predictability. We use the interactive ensemble simulations to diagnose this relationship, and we find that the presence of mesoscale ocean features significantly enhances this link particularly in ocean eddy rich regions. Finally, we use signal-to-noise ratios to show that the ocean mesoscale activity increases model estimated predictability in terms of convective precipitation and atmospheric upper tropospheric circulation.

Ocean eddies and climate predictability.

PubMed

Kirtman, Ben P; Perlin, Natalie; Siqueira, Leo

2017-12-01

A suite of coupled climate model simulations and experiments are used to examine how resolved mesoscale ocean features affect aspects of climate variability, air-sea interactions, and predictability. In combination with control simulations, experiments with the interactive ensemble coupling strategy are used to further amplify the role of the oceanic mesoscale field and the associated air-sea feedbacks and predictability. The basic intent of the interactive ensemble coupling strategy is to reduce the atmospheric noise at the air-sea interface, allowing an assessment of how noise affects the variability, and in this case, it is also used to diagnose predictability from the perspective of signal-to-noise ratios. The climate variability is assessed from the perspective of sea surface temperature (SST) variance ratios, and it is shown that, unsurprisingly, mesoscale variability significantly increases SST variance. Perhaps surprising is the fact that the presence of mesoscale ocean features even further enhances the SST variance in the interactive ensemble simulation beyond what would be expected from simple linear arguments. Changes in the air-sea coupling between simulations are assessed using pointwise convective rainfall-SST and convective rainfall-SST tendency correlations and again emphasize how the oceanic mesoscale alters the local association between convective rainfall and SST. Understanding the possible relationships between the SST-forced signal and the weather noise is critically important in climate predictability. We use the interactive ensemble simulations to diagnose this relationship, and we find that the presence of mesoscale ocean features significantly enhances this link particularly in ocean eddy rich regions. Finally, we use signal-to-noise ratios to show that the ocean mesoscale activity increases model estimated predictability in terms of convective precipitation and atmospheric upper tropospheric circulation.

The role of public policies in reducing smoking and deaths caused by smoking in Vietnam: results from the Vietnam tobacco policy simulation model.

PubMed

Levy, David T; Bales, Sarah; Lam, Nguyen T; Nikolayev, Leonid

2006-04-01

A simulation model is developed for Vietnam to project smoking prevalence and associated premature mortality. The model examines independently and as a package the effects of five types of tobacco control policies: tax increases, clean air laws, mass media campaigns, advertising bans, and youth access policies. Predictions suggest that the largest reductions in smoking rates will result from implementing a comprehensive tobacco control policy package. Significant inroads may be achieved through tax increases. A media campaign along with programs to publicize and enforce clean air laws, advertising bans and youth access laws would further reduce smoking rates. Tobacco control policies have the potential to make large dents in smoking rates, which in turn could lead to many lives saved. In the absence of these measures, deaths from smoking will increase. The model also helps to identify information gaps pertinent both to modeling and policy-making.

Closing the contrast gap between testbed and model prediction with WFIRST-CGI shaped pupil coronagraph

NASA Astrophysics Data System (ADS)

Zhou, Hanying; Nemati, Bijan; Krist, John; Cady, Eric; Prada, Camilo M.; Kern, Brian; Poberezhskiy, Ilya

2016-07-01

JPL has recently passed an important milestone in its technology development for a proposed NASA WFIRST mission coronagraph: demonstration of better than 1x10-8 contrast over broad bandwidth (10%) on both shaped pupil coronagraph (SPC) and hybrid Lyot coronagraph (HLC) testbeds with the WFIRST obscuration pattern. Challenges remain, however, in the technology readiness for the proposed mission. One is the discrepancies between the achieved contrasts on the testbeds and their corresponding model predictions. A series of testbed diagnoses and modeling activities were planned and carried out on the SPC testbed in order to close the gap. A very useful tool we developed was a derived "measured" testbed wavefront control Jacobian matrix that could be compared with the model-predicted "control" version that was used to generate the high contrast dark hole region in the image plane. The difference between these two is an estimate of the error in the control Jacobian. When the control matrix, which includes both amplitude and phase, was modified to reproduce the error, the simulated performance closely matched the SPC testbed behavior in both contrast floor and contrast convergence speed. This is a step closer toward model validation for high contrast coronagraphs. Further Jacobian analysis and modeling provided clues to the possible sources for the mismatch: DM misregistration and testbed optical wavefront error (WFE) and the deformable mirror (DM) setting for correcting this WFE. These analyses suggested that a high contrast coronagraph has a tight tolerance in the accuracy of its control Jacobian. Modifications to both testbed control model as well as prediction model are being implemented, and future works are discussed.

AnimatLab: a 3D graphics environment for neuromechanical simulations.

PubMed

Cofer, David; Cymbalyuk, Gennady; Reid, James; Zhu, Ying; Heitler, William J; Edwards, Donald H

2010-03-30

The nervous systems of animals evolved to exert dynamic control of behavior in response to the needs of the animal and changing signals from the environment. To understand the mechanisms of dynamic control requires a means of predicting how individual neural and body elements will interact to produce the performance of the entire system. AnimatLab is a software tool that provides an approach to this problem through computer simulation. AnimatLab enables a computational model of an animal's body to be constructed from simple building blocks, situated in a virtual 3D world subject to the laws of physics, and controlled by the activity of a multicellular, multicompartment neural circuit. Sensor receptors on the body surface and inside the body respond to external and internal signals and then excite central neurons, while motor neurons activate Hill muscle models that span the joints and generate movement. AnimatLab provides a common neuromechanical simulation environment in which to construct and test models of any skeletal animal, vertebrate or invertebrate. The use of AnimatLab is demonstrated in a neuromechanical simulation of human arm flexion and the myotactic and contact-withdrawal reflexes. Copyright (c) 2010 Elsevier B.V. All rights reserved.

Model Predictive Control of the Current Profile and the Internal Energy of DIII-D Plasmas

NASA Astrophysics Data System (ADS)

Lauret, M.; Wehner, W.; Schuster, E.

2015-11-01

For efficient and stable operation of tokamak plasmas it is important that the current density profile and the internal energy are jointly controlled by using the available heating and current-drive (H&CD) sources. The proposed approach is a version of nonlinear model predictive control in which the input set is restricted in size by the possible combinations of the H&CD on/off states. The controller uses real-time predictions over a receding-time horizon of both the current density profile (nonlinear partial differential equation) and the internal energy (nonlinear ordinary differential equation) evolutions. At every time instant the effect of every possible combination of H&CD sources on the current profile and internal energy is evaluated over the chosen time horizon. The combination that leads to the best result, which is assessed by a user-defined cost function, is then applied up until the next time instant. Simulations results based on a control-oriented transport code illustrate the effectiveness of the proposed control method. Supported by the US DOE under DE-FC02-04ER54698 & DE-SC0010661.

An Improved Model Predictive Current Controller of Switched Reluctance Machines Using Time-Multiplexed Current Sensor

PubMed Central

Li, Bingchu; Ling, Xiao; Huang, Yixiang; Gong, Liang; Liu, Chengliang

2017-01-01

This paper presents a fixed-switching-frequency model predictive current controller using multiplexed current sensor for switched reluctance machine (SRM) drives. The converter was modified to distinguish currents from simultaneously excited phases during the sampling period. The only current sensor installed in the converter was time division multiplexing for phase current sampling. During the commutation stage, the control steps of adjacent phases were shifted so that sampling time was staggered. The maximum and minimum duty ratio of pulse width modulation (PWM) was limited to keep enough sampling time for analog-to-digital (A/D) conversion. Current sensor multiplexing was realized without complex adjustment of either driver circuit nor control algorithms, while it helps to reduce the cost and errors introduced in current sampling due to inconsistency between sensors. The proposed controller is validated by both simulation and experimental results with a 1.5 kW three-phase 12/8 SRM. Satisfied current sampling is received with little difference compared with independent phase current sensors for each phase. The proposed controller tracks the reference current profile as accurately as the model predictive current controller with independent phase current sensors, while having minor tracking errors compared with a hysteresis current controller. PMID:28513554

A strain-, cow-, and herd-specific bio-economic simulation model of intramammary infections in dairy cattle herds.

PubMed

Gussmann, Maya; Kirkeby, Carsten; Græsbøll, Kaare; Farre, Michael; Halasa, Tariq

2018-07-14

Intramammary infections (IMI) in dairy cattle lead to economic losses for farmers, both through reduced milk production and disease control measures. We present the first strain-, cow- and herd-specific bio-economic simulation model of intramammary infections in a dairy cattle herd. The model can be used to investigate the cost-effectiveness of different prevention and control strategies against IMI. The objective of this study was to describe a transmission framework, which simulates spread of IMI causing pathogens through different transmission modes. These include the traditional contagious and environmental spread and a new opportunistic transmission mode. In addition, the within-herd transmission dynamics of IMI causing pathogens were studied. Sensitivity analysis was conducted to investigate the influence of input parameters on model predictions. The results show that the model is able to represent various within-herd levels of IMI prevalence, depending on the simulated pathogens and their parameter settings. The parameters can be adjusted to include different combinations of IMI causing pathogens at different prevalence levels, representing herd-specific situations. The model is most sensitive to varying the transmission rate parameters and the strain-specific recovery rates from IMI. It can be used for investigating both short term operational and long term strategic decisions for the prevention and control of IMI in dairy cattle herds. Copyright © 2018 Elsevier Ltd. All rights reserved.

Numerical modeling of incised-valley deposits in Tokyo lowland for the last 13 kyrs

NASA Astrophysics Data System (ADS)

Kubo, Y.; Syvitski, J. P.; Hutton, E. W.; Tanabe, S.

2006-12-01

A coupled-simulation by the hydrologic model HydroTrend and the stratigraphic model SedFlux is applied to the incised-valley-fill deposits in the Tokyo lowland for the last 13,000 years. The postglacial sediments supplied by paleo Tonegawa River have formed deltaic deposits controlled by eustatic sea-level rise after LGM. The effects of changes in sea level, climate, and morphology on the resultant architecture of the deposits are simulated and analyzed by the numerical models. Synthetic sediment flux from the paleo Tonegawa is computed by the hydrologic model HydroTrend. The model predicts variation in average rate of sediment production over geological time scale from changes in drainage area, precipitation, temperature and morphology. Random variation based on statistic climate data is added to the predicted average values to provide daily sediment discharge. The model prediction indicates that, despite 80% increase in drainage area in the past, competing effects of decreased precipitation resulted in relatively stable sediment discharge over the last 13,000 years. On the other hand, variation in daily sediment discharge shows drastic increase during infrequent storm events. Possible occurrence of hyperpycnal flows at the river mouth was indicated during such storms, which produced daily sediment load ten times larger than average yearly sediment discharge. The estimated sediment supply is used as input to the process-based forward-model 2D-SedFlux. SedFlux is able to simulate transport and deposition of sediments by such processes as river plume, bedload dumping and ocean storms with changing boundary conditions of sea level and basement morphology. The simulation is based on the initial paleo-morphology reconstructed from integrated core analysis from the area. 2D-SedFlux successfully predicts the formation of transgressive deposits and subsequent prograding delta deposits, and the results are comparable to general architecture of incised-valley fills in the area. Detailed comparison between the model predictions and field data shows some minor differences, which are then used to revise the local sea level curve.

Stochastic modelling of infectious diseases for heterogeneous populations.

PubMed

Ming, Rui-Xing; Liu, Ji-Ming; W Cheung, William K; Wan, Xiang

2016-12-22

Infectious diseases such as SARS and H1N1 can significantly impact people's lives and cause severe social and economic damages. Recent outbreaks have stressed the urgency of effective research on the dynamics of infectious disease spread. However, it is difficult to predict when and where outbreaks may emerge and how infectious diseases spread because many factors affect their transmission, and some of them may be unknown. One feasible means to promptly detect an outbreak and track the progress of disease spread is to implement surveillance systems in regional or national health and medical centres. The accumulated surveillance data, including temporal, spatial, clinical, and demographic information can provide valuable information that can be exploited to better understand and model the dynamics of infectious disease spread. The aim of this work is to develop and empirically evaluate a stochastic model that allows the investigation of transmission patterns of infectious diseases in heterogeneous populations. We test the proposed model on simulation data and apply it to the surveillance data from the 2009 H1N1 pandemic in Hong Kong. In the simulation experiment, our model achieves high accuracy in parameter estimation (less than 10.0 % mean absolute percentage error). In terms of the forward prediction of case incidence, the mean absolute percentage errors are 17.3 % for the simulation experiment and 20.0 % for the experiment on the real surveillance data. We propose a stochastic model to study the dynamics of infectious disease spread in heterogeneous populations from temporal-spatial surveillance data. The proposed model is evaluated using both simulated data and the real data from the 2009 H1N1 epidemic in Hong Kong and achieves acceptable prediction accuracy. We believe that our model can provide valuable insights for public health authorities to predict the effect of disease spread and analyse its underlying factors and to guide new control efforts.

Getting the biggest birch for the bang: restoring and expanding upland birchwoods in the Scottish Highlands by managing red deer

PubMed Central

Tanentzap, Andrew J; Zou, James; Coomes, David A

2013-01-01

High deer populations threaten the conservation value of woodlands and grasslands, but predicting the success of deer culling, in terms of allowing vegetation to recover, is difficult. Numerical simulation modeling is one approach to gain insight into the outcomes of management scenarios. We develop a spatially explicit model to predict the responses of Betula spp. to red deer (Cervus elaphus) and land management in the Scottish Highlands. Our model integrates a Bayesian stochastic stage-based matrix model within the framework of a widely used individual-based forest simulation model, using data collected along spatial and temporal gradients in deer browsing. By initializing our model with the historical spatial locations of trees, we find that densities of juvenile trees (<3 m tall) predicted after 9–13 years closely match counts observed in the field. This is among the first tests of the accuracy of a dynamical simulation model for predicting the responses of tree regeneration to herbivores. We then test the relative importance of deer browsing, ground cover vegetation, and seed availability in facilitating landscape-level birch regeneration using simulations in which we varied these three variables. We find that deer primarily control transitions of birch to taller (>3 m) height tiers over 30 years, but regeneration also requires suitable ground cover for seedling establishment. Densities of adult seed sources did not influence regeneration, nor did an active management scenario where we altered the spatial configuration of adults by creating “woodland islets”. Our results show that managers interested in maximizing tree regeneration cannot simply reduce deer densities but must also improve ground cover for seedling establishment, and the model we develop now enables managers to quantify explicitly how much both these factors need to be altered. More broadly, our findings emphasize the need for land managers to consider the impacts of large herbivores rather than their densities. PMID:23919137

Design and Testing of Flight Control Laws on the RASCAL Research Helicopter

NASA Technical Reports Server (NTRS)

Frost, Chad R.; Hindson, William S.; Moralez. Ernesto, III; Tucker, George E.; Dryfoos, James B.

2001-01-01

Two unique sets of flight control laws were designed, tested and flown on the Army/NASA Rotorcraft Aircrew Systems Concepts Airborne Laboratory (RASCAL) JUH-60A Black Hawk helicopter. The first set of control laws used a simple rate feedback scheme, intended to facilitate the first flight and subsequent flight qualification of the RASCAL research flight control system. The second set of control laws comprised a more sophisticated model-following architecture. Both sets of flight control laws were developed and tested extensively using desktop-to-flight modeling, analysis, and simulation tools. Flight test data matched the model predicted responses well, providing both evidence and confidence that future flight control development for RASCAL will be efficient and accurate.

Path-following in model predictive rollover prevention using front steering and braking

NASA Astrophysics Data System (ADS)

Ghazali, Mohammad; Durali, Mohammad; Salarieh, Hassan

2017-01-01

In this paper vehicle path-following in the presence of rollover risk is investigated. Vehicles with high centre of mass are prone to roll instability. Untripped rollover risk is increased in high centre of gravity vehicles and high-friction road condition. Researches introduce strategies to handle the short-duration rollover condition. In these researches, however, trajectory tracking is affected and not thoroughly investigated. This paper puts stress on tracking error from rollover prevention. A lower level model predictive front steering controller is adopted to deal with rollover and tracking error as a priority sequence. A brake control is included in lower level controller which directly obeys an upper level controller (ULC) command. The ULC manages vehicle speed regarding primarily tracking error. Simulation results show that the proposed control framework maintains roll stability while tracking error is confined to predefined error limit.

Perturbed cooperative-state feedback strategy for model predictive networked control of interconnected systems.

PubMed

Tran, Tri; Ha, Q P

2018-01-01

A perturbed cooperative-state feedback (PSF) strategy is presented for the control of interconnected systems in this paper. The subsystems of an interconnected system can exchange data via the communication network that has multiple connection topologies. The PSF strategy can resolve both issues, the sensor data losses and the communication network breaks, thanks to the two components of the control including a cooperative-state feedback and a perturbation variable, e.g., u i =K ij x j +w i . The PSF is implemented in a decentralized model predictive control scheme with a stability constraint and a non-monotonic storage function (ΔV(x(k))≥0), derived from the dissipative systems theory. Numerical simulation for the automatic generation control problem in power systems is studied to illustrate the effectiveness of the presented PSF strategy. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Modeling and simulation of enzymatic gluconic acid production using immobilized enzyme and CSTR-PFTR circulation reaction system.

PubMed

Li, Can; Lin, Jianqun; Gao, Ling; Lin, Huibin; Lin, Jianqiang

2018-04-01

Production of gluconic acid by using immobilized enzyme and continuous stirred tank reactor-plug flow tubular reactor (CSTR-PFTR) circulation reaction system. A production system is constructed for gluconic acid production, which consists of a continuous stirred tank reactor (CSTR) for pH control and liquid storage and a plug flow tubular reactor (PFTR) filled with immobilized glucose oxidase (GOD) for gluconic acid production. Mathematical model is developed for this production system and simulation is made for the enzymatic reaction process. The pH inhibition effect on GOD is modeled by using a bell-type curve. Gluconic acid can be efficiently produced by using the reaction system and the mathematical model developed for this system can simulate and predict the process well.

A Madden-Julian oscillation event realistically simulated by a global cloud-resolving model.

PubMed

Miura, Hiroaki; Satoh, Masaki; Nasuno, Tomoe; Noda, Akira T; Oouchi, Kazuyoshi

2007-12-14

A Madden-Julian Oscillation (MJO) is a massive weather event consisting of deep convection coupled with atmospheric circulation, moving slowly eastward over the Indian and Pacific Oceans. Despite its enormous influence on many weather and climate systems worldwide, it has proven very difficult to simulate an MJO because of assumptions about cumulus clouds in global meteorological models. Using a model that allows direct coupling of the atmospheric circulation and clouds, we successfully simulated the slow eastward migration of an MJO event. Topography, the zonal sea surface temperature gradient, and interplay between eastward- and westward-propagating signals controlled the timing of the eastward transition of the convective center. Our results demonstrate the potential making of month-long MJO predictions when global cloud-resolving models with realistic initial conditions are used.

Dynamic analysis of space structures including elastic, multibody, and control behavior

NASA Technical Reports Server (NTRS)

Pinson, Larry; Soosaar, Keto

1989-01-01

The problem is to develop analysis methods, modeling stategies, and simulation tools to predict with assurance the on-orbit performance and integrity of large complex space structures that cannot be verified on the ground. The problem must incorporate large reliable structural models, multi-body flexible dynamics, multi-tier controller interaction, environmental models including 1g and atmosphere, various on-board disturbances, and linkage to mission-level performance codes. All areas are in serious need of work, but the weakest link is multi-body flexible dynamics.

Objective Model Selection for Identifying the Human Feedforward Response in Manual Control.

PubMed

Drop, Frank M; Pool, Daan M; van Paassen, Marinus Rene M; Mulder, Max; Bulthoff, Heinrich H

2018-01-01

Realistic manual control tasks typically involve predictable target signals and random disturbances. The human controller (HC) is hypothesized to use a feedforward control strategy for target-following, in addition to feedback control for disturbance-rejection. Little is known about human feedforward control, partly because common system identification methods have difficulty in identifying whether, and (if so) how, the HC applies a feedforward strategy. In this paper, an identification procedure is presented that aims at an objective model selection for identifying the human feedforward response, using linear time-invariant autoregressive with exogenous input models. A new model selection criterion is proposed to decide on the model order (number of parameters) and the presence of feedforward in addition to feedback. For a range of typical control tasks, it is shown by means of Monte Carlo computer simulations that the classical Bayesian information criterion (BIC) leads to selecting models that contain a feedforward path from data generated by a pure feedback model: "false-positive" feedforward detection. To eliminate these false-positives, the modified BIC includes an additional penalty on model complexity. The appropriate weighting is found through computer simulations with a hypothesized HC model prior to performing a tracking experiment. Experimental human-in-the-loop data will be considered in future work. With appropriate weighting, the method correctly identifies the HC dynamics in a wide range of control tasks, without false-positive results.

Model Predictive Control considering Reachable Range of Wheels for Leg / Wheel Mobile Robots

NASA Astrophysics Data System (ADS)

Suzuki, Naito; Nonaka, Kenichiro; Sekiguchi, Kazuma

2016-09-01

Obstacle avoidance is one of the important tasks for mobile robots. In this paper, we study obstacle avoidance control for mobile robots equipped with four legs comprised of three DoF SCARA leg/wheel mechanism, which enables the robot to change its shape adapting to environments. Our previous method achieves obstacle avoidance by model predictive control (MPC) considering obstacle size and lateral wheel positions. However, this method does not ensure existence of joint angles which achieves reference wheel positions calculated by MPC. In this study, we propose a model predictive control considering reachable mobile ranges of wheels positions by combining multiple linear constraints, where each reachable mobile range is approximated as a convex trapezoid. Thus, we achieve to formulate a MPC as a quadratic problem with linear constraints for nonlinear problem of longitudinal and lateral wheel position control. By optimization of MPC, the reference wheel positions are calculated, while each joint angle is determined by inverse kinematics. Considering reachable mobile ranges explicitly, the optimal joint angles are calculated, which enables wheels to reach the reference wheel positions. We verify its advantages by comparing the proposed method with the previous method through numerical simulations.

Predictability of Subsurface Temperature and the AMOC

NASA Astrophysics Data System (ADS)

Chang, Y.; Schubert, S. D.

2013-12-01

GEOS 5 coupled model is extensively used for experimental decadal climate prediction. Understanding the limits of decadal ocean predictability is critical for making progress in these efforts. Using this model, we study the subsurface temperature initial value predictability, the variability of the Atlantic meridional overturning circulation (AMOC) and its impacts on the global climate. Our approach is to utilize the idealized data assimilation technology developed at the GMAO. The technique 'replay' allows us to assess, for example, the impact of the surface wind stresses and/or precipitation on the ocean in a very well controlled environment. By running the coupled model in replay mode we can in fact constrain the model using any existing reanalysis data set. We replay the model constraining (nudging) it to the MERRA reanalysis in various fields from 1948-2012. The fields, u,v,T,q,ps, are adjusted towards the 6-hourly analyzed fields in atmosphere. The simulated AMOC variability is studied with a 400-year-long segment of replay integration. The 84 cases of 10-year hindcasts are initialized from 4 different replay cycles. Here, the variability and predictability are examined further by a measure to quantify how much the subsurface temperature and AMOC variability has been influenced by atmospheric forcing and by ocean internal variability. The simulated impact of the AMOC on the multi-decadal variability of the SST, sea surface height (SSH) and sea ice extent is also studied.

Numerical simulations of the NREL S826 airfoil

NASA Astrophysics Data System (ADS)

Sagmo, KF; Bartl, J.; Sætran, L.

2016-09-01

2D and 3D steady state simulations were done using the commercial CFD package Star-CCM+ with three different RANS turbulence models. Lift and drag coefficients were simulated at different angles of attack for the NREL S826 airfoil at a Reynolds number of 100 000, and compared to experimental data obtained at NTNU and at DTU. The Spalart-Allmaras and the Realizable k-epsilon turbulence models reproduced experimental results for lift well in the 2D simulations. The 3D simulations with the Realizable two-layer k-epsilon model predicted essentially the same lift coefficients as the 2D Spalart-Allmaras simulations. A comparison between 2D and 3D simulations with the Realizable k-epsilon model showed a significantly lower prediction in drag by the 2D simulations. From the conducted 3D simulations surface pressure predictions along the wing span were presented, along with volumetric renderings of vorticity. Both showed a high degree of span wise flow variation when going into the stall region, and predicted a flow field resembling that of stall cells for angles of attack above peak lift.

Assimilating soil moisture into an Earth System Model

NASA Astrophysics Data System (ADS)

Stacke, Tobias; Hagemann, Stefan

2017-04-01

Several modelling studies reported potential impacts of soil moisture anomalies on regional climate. In particular for short prediction periods, perturbations of the soil moisture state may result in significant alteration of surface temperature in the following season. However, it is not clear yet whether or not soil moisture anomalies affect climate also on larger temporal and spatial scales. In an earlier study, we showed that soil moisture anomalies can persist for several seasons in the deeper soil layers of a land surface model. Additionally, those anomalies can influence root zone moisture, in particular during explicitly dry or wet periods. Thus, one prerequisite for predictability, namely the existence of long term memory, is evident for simulated soil moisture and might be exploited to improve climate predictions. The second prerequisite is the sensitivity of the climate system to soil moisture. In order to investigate this sensitivity for decadal simulations, we implemented a soil moisture assimilation scheme into the Max-Planck Institute for Meteorology's Earth System Model (MPI-ESM). The assimilation scheme is based on a simple nudging algorithm and updates the surface soil moisture state once per day. In our experiments, the MPI-ESM is used which includes model components for the interactive simulation of atmosphere, land and ocean. Artificial assimilation data is created from a control simulation to nudge the MPI-ESM towards predominantly dry and wet states. First analyses are focused on the impact of the assimilation on land surface variables and reveal distinct differences in the long-term mean values between wet and dry state simulations. Precipitation, evapotranspiration and runoff are larger in the wet state compared to the dry state, resulting in an increased moisture transport from the land to atmosphere and ocean. Consequently, surface temperatures are lower in the wet state simulations by more than one Kelvin. In terms of spatial pattern, the largest differences between both simulations are seen for continental areas, while regions with a maritime climate are least sensitive to soil moisture assimilation.

Statistical modeling of crystalline silica exposure by trade in the construction industry using a database compiled from the literature.

PubMed

Sauvé, Jean-François; Beaudry, Charles; Bégin, Denis; Dion, Chantal; Gérin, Michel; Lavoué, Jérôme

2012-09-01

A quantitative determinants-of-exposure analysis of respirable crystalline silica (RCS) levels in the construction industry was performed using a database compiled from an extensive literature review. Statistical models were developed to predict work-shift exposure levels by trade. Monte Carlo simulation was used to recreate exposures derived from summarized measurements which were combined with single measurements for analysis. Modeling was performed using Tobit models within a multimodel inference framework, with year, sampling duration, type of environment, project purpose, project type, sampling strategy and use of exposure controls as potential predictors. 1346 RCS measurements were included in the analysis, of which 318 were non-detects and 228 were simulated from summary statistics. The model containing all the variables explained 22% of total variability. Apart from trade, sampling duration, year and strategy were the most influential predictors of RCS levels. The use of exposure controls was associated with an average decrease of 19% in exposure levels compared to none, and increased concentrations were found for industrial, demolition and renovation projects. Predicted geometric means for year 1999 were the highest for drilling rig operators (0.238 mg m(-3)) and tunnel construction workers (0.224 mg m(-3)), while the estimated exceedance fraction of the ACGIH TLV by trade ranged from 47% to 91%. The predicted geometric means in this study indicated important overexposure compared to the TLV. However, the low proportion of variability explained by the models suggests that the construction trade is only a moderate predictor of work-shift exposure levels. The impact of the different tasks performed during a work shift should also be assessed to provide better management and control of RCS exposure levels on construction sites.

Tracing the source of numerical climate model uncertainties in precipitation simulations using a feature-oriented statistical model

NASA Astrophysics Data System (ADS)

Xu, Y.; Jones, A. D.; Rhoades, A.

2017-12-01

Precipitation is a key component in hydrologic cycles, and changing precipitation regimes contribute to more intense and frequent drought and flood events around the world. Numerical climate modeling is a powerful tool to study climatology and to predict future changes. Despite the continuous improvement in numerical models, long-term precipitation prediction remains a challenge especially at regional scales. To improve numerical simulations of precipitation, it is important to find out where the uncertainty in precipitation simulations comes from. There are two types of uncertainty in numerical model predictions. One is related to uncertainty in the input data, such as model's boundary and initial conditions. These uncertainties would propagate to the final model outcomes even if the numerical model has exactly replicated the true world. But a numerical model cannot exactly replicate the true world. Therefore, the other type of model uncertainty is related the errors in the model physics, such as the parameterization of sub-grid scale processes, i.e., given precise input conditions, how much error could be generated by the in-precise model. Here, we build two statistical models based on a neural network algorithm to predict long-term variation of precipitation over California: one uses "true world" information derived from observations, and the other uses "modeled world" information using model inputs and outputs from the North America Coordinated Regional Downscaling Project (NA CORDEX). We derive multiple climate feature metrics as the predictors for the statistical model to represent the impact of global climate on local hydrology, and include topography as a predictor to represent the local control. We first compare the predictors between the true world and the modeled world to determine the errors contained in the input data. By perturbing the predictors in the statistical model, we estimate how much uncertainty in the model's final outcomes is accounted for by each predictor. By comparing the statistical model derived from true world information and modeled world information, we assess the errors lying in the physics of the numerical models. This work provides a unique insight to assess the performance of numerical climate models, and can be used to guide improvement of precipitation prediction.

A Simulation Model for Studying Effects of Pollution and Freshwater Inflow on Secondary Productivity in an Ecosystem. Ph.D. Thesis - North Carolina State Univ.

NASA Technical Reports Server (NTRS)

Johnson, R. W.

1974-01-01

A mathematical model of an ecosystem is developed. Secondary productivity is evaluated in terms of man related and controllable factors. Information from an existing physical parameters model is used as well as pertinent biological measurements. Predictive information of value to estuarine management is presented. Biological, chemical, and physical parameters measured in order to develop models of ecosystems are identified.

Fault tolerant control of multivariable processes using auto-tuning PID controller.

PubMed

Yu, Ding-Li; Chang, T K; Yu, Ding-Wen

2005-02-01

Fault tolerant control of dynamic processes is investigated in this paper using an auto-tuning PID controller. A fault tolerant control scheme is proposed composing an auto-tuning PID controller based on an adaptive neural network model. The model is trained online using the extended Kalman filter (EKF) algorithm to learn system post-fault dynamics. Based on this model, the PID controller adjusts its parameters to compensate the effects of the faults, so that the control performance is recovered from degradation. The auto-tuning algorithm for the PID controller is derived with the Lyapunov method and therefore, the model predicted tracking error is guaranteed to converge asymptotically. The method is applied to a simulated two-input two-output continuous stirred tank reactor (CSTR) with various faults, which demonstrate the applicability of the developed scheme to industrial processes.

The use of dispersion modeling to determine the feasibility of vegetative environmental buffers (VEBS) at controlling odor dispersion

NASA Astrophysics Data System (ADS)

Weber, Eric E.

Concentrated animal feeding operations (CAFOs) have been experiencing increased resistance from surrounding residents making construction of new facilities or expansion of existing ones increasingly limited (Jacobson et al., 2002). Such concerns often include the impact of nuisance odor on peoples’ lives and on the environment (Huang and Miller, 2006). Vegetative environmental buffers (VEBs) have been suggested as a possible odor control technology. They have been found to impact odor plume dispersion and have shown the possibility of being an effective tool for odor abatement when used alone or in combination with other technologies (Lin et al., 2006). The main objective of this study was to use Gaussian-type dispersion modeling to determine the feasibility of use and the effectiveness of a VEB at controlling the spread of odor from a swine feeding operation. First, wind tunnel NH3 dispersion trends were compared to model generated dispersion trends to determine the accuracy of the model at handling VEB dispersion. Next, facility-scale (northern Missouri specific) model simulations with and without a VEB were run to determine its viability as an option for dispersion reduction. Finally, dispersion forecasts that integrated numerical weather forecasts were developed and compared to collected concentration data to determine forecast accuracy. The results of this study found that dispersion models can be used to simulate dispersion around a VEB. AERMOD-generated dispersion trends were found to follow similar patterns of decreasing downwind concentration to those of both wind tunnel simulations and previous research. This shows that a VEB can be incorporated into AERMOD and that the model can be used to determine its effectiveness as an odor control option. The results of this study also showed that a VEB has an effect on odor dispersion by reducing downwind concentrations. This was confirmed by both wind tunnel and AERMOD simulations of dispersion displaying decreased downwind concentrations from a control scenario. This shows that VEBs have the potential to act as an odor control option for CAFOs. This study also found that a forecast method that integrated numerical weather prediction into dispersion models could be developed to forecast areas of high concentration. Model-forecasted dispersion trends had a high spatial correlation with collected concentrations for days when the facility was emitting. This shows that dispersion models can accurately predict high concentration areas using forecasted weather data. The information provided by this study may ultimately prove useful for this particular facility and others and may help to lower tensions with surrounding residents.

Piloted simulation of a ground-based time-control concept for air traffic control

NASA Technical Reports Server (NTRS)

Davis, Thomas J.; Green, Steven M.

1989-01-01

A concept for aiding air traffic controllers in efficiently spacing traffic and meeting scheduled arrival times at a metering fix was developed and tested in a real time simulation. The automation aid, referred to as the ground based 4-D descent advisor (DA), is based on accurate models of aircraft performance and weather conditions. The DA generates suggested clearances, including both top-of-descent-point and speed-profile data, for one or more aircraft in order to achieve specific time or distance separation objectives. The DA algorithm is used by the air traffic controller to resolve conflicts and issue advisories to arrival aircraft. A joint simulation was conducted using a piloted simulator and an advanced concept air traffic control simulation to study the acceptability and accuracy of the DA automation aid from both the pilot's and the air traffic controller's perspectives. The results of the piloted simulation are examined. In the piloted simulation, airline crews executed controller issued descent advisories along standard curved path arrival routes, and were able to achieve an arrival time precision of + or - 20 sec at the metering fix. An analysis of errors generated in turns resulted in further enhancements of the algorithm to improve the predictive accuracy. Evaluations by pilots indicate general support for the concept and provide specific recommendations for improvement.

A Simulation Based Methodology to Examine the B-1B’s AN/ALQ-161 Maintenance Process

DTIC Science & Technology

2010-03-01

2001). “The Air Force does not think of, or advertise , bombers as interchangeable. The B-1, B-2 and B-52 all have a specific mission area and 2...Modeling ( CPM ), Goal Programming, EOQ, Nonlinear Programming Predictive Models unknown, ill-defined known or under the decision-maker’s control

Simulated thought insertion: Influencing the sense of agency using deception and magic.

PubMed

Olson, Jay A; Landry, Mathieu; Appourchaux, Krystèle; Raz, Amir

2016-07-01

In order to study the feeling of control over decisions, we told 60 participants that a neuroimaging machine could read and influence their thoughts. While inside a mock brain scanner, participants chose arbitrary numbers in two similar tasks. In the Mind-Reading Task, the scanner appeared to guess the participants' numbers; in the Mind-Influencing Task, it appeared to influence their choice of numbers. We predicted that participants would feel less voluntary control over their decisions when they believed that the scanner was influencing their choices. As predicted, participants felt less control and made slower decisions in the Mind-Influencing Task compared to the Mind-Reading Task. A second study replicated these findings. Participants' experience of the ostensible influence varied, with some reporting an unknown source directing them towards specific numbers. This simulated thought insertion paradigm can therefore influence feelings of voluntary control and may help model symptoms of mental disorders. Copyright © 2016 Elsevier Inc. All rights reserved.

Inlet Flow Control and Prediction Technologies for Embedded Propulsion Systems

NASA Technical Reports Server (NTRS)

McMillan, Michelle L.; Gissen, Abe; Vukasinovic, Bojan; Lakebrink, Matthew T.; Glezer, Ari; Mani, Mori; Mace, James

2010-01-01

Fail-safe inlet flow control may enable high-speed cruise efficiency, low noise signature, and reduced fuel-burn goals for hybrid wing-body aircraft. The objectives of this program are to develop flow control and prediction methodologies for boundary-layer ingesting (BLI) inlets used in these aircraft. This report covers the second of a three year program. The approach integrates experiments and numerical simulations. Both passive and active flow-control devices were tested in a small-scale wind tunnel. Hybrid actuation approaches, combining a passive microvane and active synthetic jet, were tested in various geometric arrangements. Detailed flow measurements were taken to provide insight into the flow physics. Results of the numerical simulations were correlated against experimental data. The sensitivity of results to grid resolution and turbulence models was examined. Aerodynamic benefits from microvanes and microramps were assessed when installed in an offset BLI inlet. Benefits were quantified in terms of recovery and distortion changes. Microvanes were more effective than microramps at improving recovery and distortion.

Synchronizing movements with the metronome: nonlinear error correction and unstable periodic orbits.

PubMed

Engbert, Ralf; Krampe, Ralf Th; Kurths, Jürgen; Kliegl, Reinhold

2002-02-01

The control of human hand movements is investigated in a simple synchronization task. We propose and analyze a stochastic model based on nonlinear error correction; a mechanism which implies the existence of unstable periodic orbits. This prediction is tested in an experiment with human subjects. We find that our experimental data are in good agreement with numerical simulations of our theoretical model. These results suggest that feedback control of the human motor systems shows nonlinear behavior. Copyright 2001 Elsevier Science (USA).

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dobson, Ian; Hiskens, Ian; Linderoth, Jeffrey

Building on models of electrical power systems, and on powerful mathematical techniques including optimization, model predictive control, and simluation, this project investigated important issues related to the stable operation of power grids. A topic of particular focus was cascading failures of the power grid: simulation, quantification, mitigation, and control. We also analyzed the vulnerability of networks to component failures, and the design of networks that are responsive to and robust to such failures. Numerous other related topics were investigated, including energy hubs and cascading stall of induction machines

Resin Film Infusion (RFI) Process Modeling for Large Transport Aircraft Wing Structures

NASA Technical Reports Server (NTRS)

Loos, Alfred C.; Caba, Aaron C.; Furrow, Keith W.

2000-01-01

This investigation completed the verification of a three-dimensional resin transfer molding/resin film infusion (RTM/RFI) process simulation model. The model incorporates resin flow through an anisotropic carbon fiber preform, cure kinetics of the resin, and heat transfer within the preform/tool assembly. The computer model can predict the flow front location, resin pressure distribution, and thermal profiles in the modeled part. The formulation for the flow model is given using the finite element/control volume (FE/CV) technique based on Darcy's Law of creeping flow through a porous media. The FE/CV technique is a numerically efficient method for finding the flow front location and the fluid pressure. The heat transfer model is based on the three-dimensional, transient heat conduction equation, including heat generation. Boundary conditions include specified temperature and convection. The code was designed with a modular approach so the flow and/or the thermal module may be turned on or off as desired. Both models are solved sequentially in a quasi-steady state fashion. A mesh refinement study was completed on a one-element thick model to determine the recommended size of elements that would result in a converged model for a typical RFI analysis. Guidelines are established for checking the convergence of a model, and the recommended element sizes are listed. Several experiments were conducted and computer simulations of the experiments were run to verify the simulation model. Isothermal, non-reacting flow in a T-stiffened section was simulated to verify the flow module. Predicted infiltration times were within 12-20% of measured times. The predicted pressures were approximately 50% of the measured pressures. A study was performed to attempt to explain the difference in pressures. Non-isothermal experiments with a reactive resin were modeled to verify the thermal module and the resin model. Two panels were manufactured using the RFI process. One was a stepped panel and the other was a panel with two 'T' stiffeners. The difference between the predicted infiltration times and the experimental times was 4% to 23%.

Operational flood control of a low-lying delta system using large time step Model Predictive Control

NASA Astrophysics Data System (ADS)

Tian, Xin; van Overloop, Peter-Jules; Negenborn, Rudy R.; van de Giesen, Nick

2015-01-01

The safety of low-lying deltas is threatened not only by riverine flooding but by storm-induced coastal flooding as well. For the purpose of flood control, these deltas are mostly protected in a man-made environment, where dikes, dams and other adjustable infrastructures, such as gates, barriers and pumps are widely constructed. Instead of always reinforcing and heightening these structures, it is worth considering making the most of the existing infrastructure to reduce the damage and manage the delta in an operational and overall way. In this study, an advanced real-time control approach, Model Predictive Control, is proposed to operate these structures in the Dutch delta system (the Rhine-Meuse delta). The application covers non-linearity in the dynamic behavior of the water system and the structures. To deal with the non-linearity, a linearization scheme is applied which directly uses the gate height instead of the structure flow as the control variable. Given the fact that MPC needs to compute control actions in real-time, we address issues regarding computational time. A new large time step scheme is proposed in order to save computation time, in which different control variables can have different control time steps. Simulation experiments demonstrate that Model Predictive Control with the large time step setting is able to control a delta system better and much more efficiently than the conventional operational schemes.

Prediction of Aerodynamic Coefficient using Genetic Algorithm Optimized Neural Network for Sparse Data

NASA Technical Reports Server (NTRS)

Rajkumar, T.; Bardina, Jorge; Clancy, Daniel (Technical Monitor)

2002-01-01

Wind tunnels use scale models to characterize aerodynamic coefficients, Wind tunnel testing can be slow and costly due to high personnel overhead and intensive power utilization. Although manual curve fitting can be done, it is highly efficient to use a neural network to define the complex relationship between variables. Numerical simulation of complex vehicles on the wide range of conditions required for flight simulation requires static and dynamic data. Static data at low Mach numbers and angles of attack may be obtained with simpler Euler codes. Static data of stalled vehicles where zones of flow separation are usually present at higher angles of attack require Navier-Stokes simulations which are costly due to the large processing time required to attain convergence. Preliminary dynamic data may be obtained with simpler methods based on correlations and vortex methods; however, accurate prediction of the dynamic coefficients requires complex and costly numerical simulations. A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation I'S presented using a neural network. The training data for the neural network are derived from numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as functions of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation. Genetic Algorithms (GA) are used to optimize a previously built Artificial Neural Network (ANN) that reliably predicts aerodynamic coefficients. Results indicate that the GA provided an efficient method of optimizing the ANN model to predict aerodynamic coefficients. The reliability of the ANN using the GA includes prediction of aerodynamic coefficients to an accuracy of 110% . In our problem, we would like to get an optimized neural network architecture and minimum data set. This has been accomplished within 500 training cycles of a neural network. After removing training pairs (outliers), the GA has produced much better results. The neural network constructed is a feed forward neural network with a back propagation learning mechanism. The main goal has been to free the network design process from constraints of human biases, and to discover better forms of neural network architectures. The automation of the network architecture search by genetic algorithms seems to have been the best way to achieve this goal.

Mechanics of rolling of nanoribbon on tube and sphere.

PubMed

Yin, Qifang; Shi, Xinghua

2013-06-21

The configuration of graphene nano-ribbon (GNR) assembly on carbon nanotube (CNT) and sphere is studied through theoretical modeling and molecular simulation. The GNR can spontaneously wind onto the CNT due to van der Waals (vdW) interaction and form two basic configurations: helix and scroll. The final configuration arises from the competition among three energy terms: the bending energy of the GNR, the vdW interaction between GNR and CNT, the vdW between the GNR itself. We derive analytical solutions by accounting for the three energy parts, with which we draw phase diagrams and predict the final configuration (helix or scroll) based on the selected parameters. The molecular simulations are conducted to verify the model with the results agree well with the model predicted. Our work can be used to actively control and transfer the tube-like nanoparticles and viruses as well as to assemble ribbon-like nanomaterials.

Artificial neural network modelling of a large-scale wastewater treatment plant operation.

PubMed

Güçlü, Dünyamin; Dursun, Sükrü

2010-11-01

Artificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41 mg/L and 5.03% for COD; 1.59, 1.21 mg/L and 17.10% for SS; 52.51, 44.91 mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.

Simulating the Historical Process To Create Laboratory Exercises That Teach Research Methods.

ERIC Educational Resources Information Center

Alcock, James

1994-01-01

Explains how controlling student access to data can be used as a strategy enabling students to take the role of a research geologist. Students develop models based on limited data and conduct field tests by comparing their predictions with the additional data. (DDR)

Analysis of three-phase equilibrium conditions for methane hydrate by isometric-isothermal molecular dynamics simulations.

PubMed

Yuhara, Daisuke; Brumby, Paul E; Wu, David T; Sum, Amadeu K; Yasuoka, Kenji

2018-05-14

To develop prediction methods of three-phase equilibrium (coexistence) conditions of methane hydrate by molecular simulations, we examined the use of NVT (isometric-isothermal) molecular dynamics (MD) simulations. NVT MD simulations of coexisting solid hydrate, liquid water, and vapor methane phases were performed at four different temperatures, namely, 285, 290, 295, and 300 K. NVT simulations do not require complex pressure control schemes in multi-phase systems, and the growth or dissociation of the hydrate phase can lead to significant pressure changes in the approach toward equilibrium conditions. We found that the calculated equilibrium pressures tended to be higher than those reported by previous NPT (isobaric-isothermal) simulation studies using the same water model. The deviations of equilibrium conditions from previous simulation studies are mainly attributable to the employed calculation methods of pressure and Lennard-Jones interactions. We monitored the pressure in the methane phase, far from the interfaces with other phases, and confirmed that it was higher than the total pressure of the system calculated by previous studies. This fact clearly highlights the difficulties associated with the pressure calculation and control for multi-phase systems. The treatment of Lennard-Jones interactions without tail corrections in MD simulations also contributes to the overestimation of equilibrium pressure. Although improvements are still required to obtain accurate equilibrium conditions, NVT MD simulations exhibit potential for the prediction of equilibrium conditions of multi-phase systems.

Analysis of three-phase equilibrium conditions for methane hydrate by isometric-isothermal molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Yuhara, Daisuke; Brumby, Paul E.; Wu, David T.; Sum, Amadeu K.; Yasuoka, Kenji

2018-05-01

To develop prediction methods of three-phase equilibrium (coexistence) conditions of methane hydrate by molecular simulations, we examined the use of NVT (isometric-isothermal) molecular dynamics (MD) simulations. NVT MD simulations of coexisting solid hydrate, liquid water, and vapor methane phases were performed at four different temperatures, namely, 285, 290, 295, and 300 K. NVT simulations do not require complex pressure control schemes in multi-phase systems, and the growth or dissociation of the hydrate phase can lead to significant pressure changes in the approach toward equilibrium conditions. We found that the calculated equilibrium pressures tended to be higher than those reported by previous NPT (isobaric-isothermal) simulation studies using the same water model. The deviations of equilibrium conditions from previous simulation studies are mainly attributable to the employed calculation methods of pressure and Lennard-Jones interactions. We monitored the pressure in the methane phase, far from the interfaces with other phases, and confirmed that it was higher than the total pressure of the system calculated by previous studies. This fact clearly highlights the difficulties associated with the pressure calculation and control for multi-phase systems. The treatment of Lennard-Jones interactions without tail corrections in MD simulations also contributes to the overestimation of equilibrium pressure. Although improvements are still required to obtain accurate equilibrium conditions, NVT MD simulations exhibit potential for the prediction of equilibrium conditions of multi-phase systems.

Design of a data-driven predictive controller for start-up process of AMT vehicles.

PubMed

Lu, Xiaohui; Chen, Hong; Wang, Ping; Gao, Bingzhao

2011-12-01

In this paper, a data-driven predictive controller is designed for the start-up process of vehicles with automated manual transmissions (AMTs). It is obtained directly from the input-output data of a driveline simulation model constructed by the commercial software AMESim. In order to obtain offset-free control for the reference input, the predictor equation is gained with incremental inputs and outputs. Because of the physical characteristics, the input and output constraints are considered explicitly in the problem formulation. The contradictory requirements of less friction losses and less driveline shock are included in the objective function. The designed controller is tested under nominal conditions and changed conditions. The simulation results show that, during the start-up process, the AMT clutch with the proposed controller works very well, and the process meets the control objectives: fast clutch lockup time, small friction losses, and the preservation of driver comfort, i.e., smooth acceleration of the vehicle. At the same time, the closed-loop system has the ability to reject uncertainties, such as the vehicle mass and road grade.

Predicting landscape vegetation dynamics using state-and-transition simulation models

Treesearch

Colin J. Daniel; Leonardo Frid

2012-01-01

This paper outlines how state-and-transition simulation models (STSMs) can be used to project changes in vegetation over time across a landscape. STSMs are stochastic, empirical simulation models that use an adapted Markov chain approach to predict how vegetation will transition between states over time, typically in response to interactions between succession,...

Controls on SOC across space and time: Models with different acclimation schemes make similar spatial predictions but divergent warming predictions

NASA Astrophysics Data System (ADS)

Abramoff, R. Z.; Torn, M. S.; Georgiou, K.; Tang, J.; Riley, W. J.

2017-12-01

Researchers use spatial gradients to estimate long-term ecosystem responses to perturbations. This approach is commonly applied to soil organic carbon (SOC) stocks which change slowly but store the majority of terrestrial carbon. Climate warming may reduce SOC stocks if higher temperatures increase decomposition rates. Yet, it is uncertain how vulnerable SOC is to warming, and whether the same factors - such as organo-mineral associations, climate, or plant inputs - determine SOC stocks across space and time. In order to test the "space for time" concept, we developed two versions of the Substrate-Mineral-Microbe Soil (SuMMS) model - one with microbial temperature acclimation and one without - to analyze observed SOC stocks at 24 sites spanning a wide range of soil types and climate. Both model predictions of SOC were strongly correlated with observations (R2 > 0.9), because mineral sorption capacity was the dominant control over steady-state SOC stock as determined by a Random Forest model. However, the two model versions made fundamentally different predictions of the change in SOC following 5°C soil warming from 2016 to 2100 because the initial mean annual temperature (MAT) was the dominant control over the SOC response. The model with microbial acclimation predicted that SOC would decline 10% at all sites along the transect, while the model with no acclimation predicted large surface SOC losses at high latitude sites and SOC gains at low latitude sites where microbial exoenzymes were already at or near their temperature optimum. These simulations suggest that gradient studies cannot be used to infer site-level responses to warming, because the dominant controls on SOC at steady state (i.e., mineral sorption capacity) are different than the dominant controls on the SOC response to a warming perturbation (i.e., initial MAT, capacity for acclimation).

Impact of assimilation of conventional and satellite meteorological observations on the numerical simulation of a Bay of Bengal Tropical Cyclone of November 2008 near Tamilnadu using WRF model

NASA Astrophysics Data System (ADS)

Srinivas, C. V.; Yesubabu, V.; Venkatesan, R.; Ramarkrishna, S. S. V. S.

2010-12-01

The objective of this study is to examine the impact of assimilation of conventional and satellite data on the prediction of a severe cyclonic storm that formed in the Bay of Bengal during November 2008 with the four-dimensional data assimilation (FDDA) technique. The Weather Research and Forecasting (WRF ARW) model was used to study the structure, evolution, and intensification of the storm. Five sets of numerical simulations were performed using the WRF. In the first one, called Control run, the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) was used for the initial and boundary conditions. In the remaining experiments available observations were used to obtain an improved analysis and FDDA grid nudging was performed for a pre-forecast period of 24 h. The second simulation (FDDAALL) was performed with all the data of the Quick Scatterometer (QSCAT), Special Sensor Microwave Imager (SSM/I) winds, conventional surface, and upper air meteorological observations. QSCAT wind alone was used in the third simulation (FDDAQSCAT), the SSM/I wind alone in the fourth (FDDASSMI) and the conventional observations alone in the fifth (FDDAAWS). The FDDAALL with assimilation of all observations, produced sea level pressure pattern closely agreeing with the analysis. Examination of various parameters indicated that the Control run over predicted the intensity of the storm with large error in its track and landfall position. The assimilation experiment with QSCAT winds performed marginally better than the one with SSM/I winds due to better representation of surface wind vectors. The FDDAALL outperformed all the simulations for the intensity, movement, and rainfall associated with the storm. Results suggest that the combination of land-based surface, upper air observations along with satellite winds for assimilation produced better prediction than the assimilation with individual data sets.

Decentralized robust nonlinear model predictive controller for unmanned aerial systems

NASA Astrophysics Data System (ADS)

Garcia Garreton, Gonzalo A.

The nonlinear and unsteady nature of aircraft aerodynamics together with limited practical range of controls and state variables make the use of the linear control theory inadequate especially in the presence of external disturbances, such as wind. In the classical approach, aircraft are controlled by multiple inner and outer loops, designed separately and sequentially. For unmanned aerial systems in particular, control technology must evolve to a point where autonomy is extended to the entire mission flight envelope. This requires advanced controllers that have sufficient robustness, track complex trajectories, and use all the vehicles control capabilities at higher levels of accuracy. In this work, a robust nonlinear model predictive controller is designed to command and control an unmanned aerial system to track complex tight trajectories in the presence of internal and external perturbance. The Flight System developed in this work achieves the above performance by using: 1. A nonlinear guidance algorithm that enables the vehicle to follow an arbitrary trajectory shaped by moving points; 2. A formulation that embeds the guidance logic and trajectory information in the aircraft model, avoiding cross coupling and control degradation; 3. An artificial neural network, designed to adaptively estimate and provide aerodynamic and propulsive forces in real-time; and 4. A mixed sensitivity approach that enhances the robustness for a nonlinear model predictive controller overcoming the effect of un-modeled dynamics, external disturbances such as wind, and measurement additive perturbations, such as noise and biases. These elements have been integrated and tested in simulation and with previously stored flight test data and shown to be feasible.

Evaluation of plant-wide WWTP control strategies including the effects of filamentous bulking sludge.

PubMed

Flores-Alsina, Xavier; Comas, Joaquim; Rodríguez Roda, Ignasi; Poch, Manel; Gernaey, Krist V; Jeppsson, Ulf

2009-01-01

The main objective of this paper is to evaluate the effect of filamentous bulking sludge on the predicted performance of simulated plant-wide WWTP control strategies. First, as a reference case, several control strategies are implemented, simulated and evaluated using the IWA Benchmark Simulation Model No. 2 (BSM2). In a second series of simulations the parameters of the secondary settler model in the BSM2 are automatically changed on the basis of an on-line calculated risk of filamentous bulking, in order to mimic the effect of growth of filamentous bacteria in the plant. The results are presented using multivariate analysis. Including the effects of filamentous bulking in the simulation model gives a-more realistic-deterioration of the plant performance during periods when the conditions for development of filamentous bulking sludge are favourable: compared to the reference case where bulking effects are not considered. Thus, there is a decrease of the overall settling velocity, an accumulation of the total suspended solids (TSS) in the middle layers of the settler with a consequent reduction of their degree of compaction in the bottom. As a consequence there is a lower TSS concentration in both return and waste flow, less biomass in the bioreactors and a reduction of the TSS removal efficiency. The control alternatives using a TSS controller substantially increase the food to microorganisms (F/M) ratio in the bioreactor, thereby reducing both risk and effects of bulking sludge. The effects of ammonium (NH(4)(+)), nitrate (NO(3)(-)) and reject water control strategies are rather poor when it comes to handling solids separation problems.

GM(1,N) method for the prediction of anaerobic digestion system and sensitivity analysis of influential factors.

PubMed

Ren, Jingzheng

2018-01-01

Anaerobic digestion process has been recognized as a promising way for waste treatment and energy recovery in a sustainable way. Modelling of anaerobic digestion system is significantly important for effectively and accurately controlling, adjusting, and predicting the system for higher methane yield. The GM(1,N) approach which does not need the mechanism or a large number of samples was employed to model the anaerobic digestion system to predict methane yield. In order to illustrate the proposed model, an illustrative case about anaerobic digestion of municipal solid waste for methane yield was studied, and the results demonstrate that GM(1,N) model can effectively simulate anaerobic digestion system at the cases of poor information with less computational expense. Copyright © 2017 Elsevier Ltd. All rights reserved.

ITER Simulations Using the PEDESTAL Module in the PTRANSP Code

NASA Astrophysics Data System (ADS)

Halpern, F. D.; Bateman, G.; Kritz, A. H.; Pankin, A. Y.; Budny, R. V.; Kessel, C.; McCune, D.; Onjun, T.

2006-10-01

PTRANSP simulations with a computed pedestal height are carried out for ITER scenarios including a standard ELMy H-mode (15 MA discharge) and a hybrid scenario (12MA discharge). It has been found that fusion power production predicted in simulations of ITER discharges depends sensitively on the height of the H-mode temperature pedestal [1]. In order to study this effect, the NTCC PEDESTAL module [2] has been implemented in PTRANSP code to provide boundary conditions used for the computation of the projected performance of ITER. The PEDESTAL module computes both the temperature and width of the pedestal at the edge of type I ELMy H-mode discharges once the threshold conditions for the H-mode are satisfied. The anomalous transport in the plasma core is predicted using the GLF23 or MMM95 transport models. To facilitate the steering of lengthy PTRANSP computations, the PTRANSP code has been modified to allow changes in the transport model when simulations are restarted. The PTRANSP simulation results are compared with corresponding results obtained using other integrated modeling codes.[1] G. Bateman, T. Onjun and A.H. Kritz, Plasma Physics and Controlled Fusion, 45, 1939 (2003).[2] T. Onjun, G. Bateman, A.H. Kritz, and G. Hammett, Phys. Plasmas 9, 5018 (2002).

Fate and transport of phenol in a packed bed reactor containing simulated solid waste

DOE Office of Scientific and Technical Information (OSTI.GOV)

Saquing, Jovita M., E-mail: jmsaquing@gmail.com; Knappe, Detlef R.U., E-mail: knappe@ncsu.edu; Barlaz, Morton A., E-mail: barlaz@ncsu.edu

Highlights: Black-Right-Pointing-Pointer Anaerobic column experiments were conducted at 37 Degree-Sign C using a simulated waste mixture. Black-Right-Pointing-Pointer Sorption and biodegradation model parameters were determined from batch tests. Black-Right-Pointing-Pointer HYDRUS simulated well the fate and transport of phenol in a fully saturated waste column. Black-Right-Pointing-Pointer The batch biodegradation rate and the rate obtained by inverse modeling differed by a factor of {approx}2. Black-Right-Pointing-Pointer Tracer tests showed the importance of hydrodynamic parameters to improve model estimates. - Abstract: An assessment of the risk to human health and the environment associated with the presence of organic contaminants (OCs) in landfills necessitates reliable predictivemore » models. The overall objectives of this study were to (1) conduct column experiments to measure the fate and transport of an OC in a simulated solid waste mixture, (2) compare the results of column experiments to model predictions using HYDRUS-1D (version 4.13), a contaminant fate and transport model that can be parameterized to simulate the laboratory experimental system, and (3) determine model input parameters from independently conducted batch experiments. Experiments were conducted in which sorption only and sorption plus biodegradation influenced OC transport. HYDRUS-1D can reasonably simulate the fate and transport of phenol in an anaerobic and fully saturated waste column in which biodegradation and sorption are the prevailing fate processes. The agreement between model predictions and column data was imperfect (i.e., within a factor of two) for the sorption plus biodegradation test and the error almost certainly lies in the difficulty of measuring a biodegradation rate that is applicable to the column conditions. Nevertheless, a biodegradation rate estimate that is within a factor of two or even five may be adequate in the context of a landfill, given the extended retention time and the fact that leachate release will be controlled by the infiltration rate which can be minimized by engineering controls.« less

Influence of feedbacks from simulated crop growth on integrated regional hydrologic simulations under climate scenarios

NASA Astrophysics Data System (ADS)

van Walsum, P. E. V.

2011-11-01

Climate change impact modelling of hydrologic responses is hampered by climate-dependent model parameterizations. Reducing this dependency was one of the goals of extending the regional hydrologic modelling system SIMGRO with a two-way coupling to the crop growth simulation model WOFOST. The coupling includes feedbacks to the hydrologic model in terms of the root zone depth, soil cover, leaf area index, interception storage capacity, crop height and crop factor. For investigating whether such feedbacks lead to significantly different simulation results, two versions of the model coupling were set up for a test region: one with exogenous vegetation parameters, the "static" model, and one with endogenous simulation of the crop growth, the "dynamic" model WOFOST. The used parameterization methods of the static/dynamic vegetation models ensure that for the current climate the simulated long-term average of the actual evapotranspiration is the same for both models. Simulations were made for two climate scenarios. Owing to the higher temperatures in combination with a higher CO2-concentration of the atmosphere, a forward time shift of the crop development is simulated in the dynamic model; the used arable land crop, potatoes, also shows a shortening of the growing season. For this crop, a significant reduction of the potential transpiration is simulated compared to the static model, in the example by 15% in a warm, dry year. In consequence, the simulated crop water stress (the unit minus the relative transpiration) is lower when the dynamic model is used; also the simulated increase of crop water stress due to climate change is lower; in the example, the simulated increase is 15 percentage points less (of 55) than when a static model is used. The static/dynamic models also simulate different absolute values of the transpiration. The difference is most pronounced for potatoes at locations with ample moisture supply; this supply can either come from storage release of a good soil or from capillary rise. With good supply of moisture, the dynamic model simulates up to 10% less actual evapotranspiration than the static one in the example. This can lead to cases where the dynamic model predicts a slight increase of the recharge in a climate scenario, where the static model predicts a decrease. The use of a dynamic model also affects the simulated demand for surface water from external sources; especially the timing is affected. The proposed modelling approach uses postulated relationships that require validation with controlled field trials. In the Netherlands there is a lack of experimental facilities for performing such validations.

A mathematical tool to generate complex whole body motor tasks and test hypotheses on underlying motor planning.

PubMed

Tagliabue, Michele; Pedrocchi, Alessandra; Pozzo, Thierry; Ferrigno, Giancarlo

2008-01-01

In spite of the complexity of human motor behavior, difficulties in mathematical modeling have restricted to rather simple movements attempts to identify the motor planning criterion used by the central nervous system. This paper presents a novel-simulation technique able to predict the "desired trajectory" corresponding to a wide range of kinematic and kinetic optimality criteria for tasks involving many degrees of freedom and the coordination between goal achievement and balance maintenance. Employment of proper time discretization, inverse dynamic methods and constrained optimization technique are combined. The application of this simulator to a planar whole body pointing movement shows its effectiveness in managing system nonlinearities and instability as well as in ensuring the anatomo-physiological feasibility of predicted motor plans. In addition, the simulator's capability to simultaneously optimize competing movement aspects represents an interesting opportunity for the motor control community, in which the coexistence of several controlled variables has been hypothesized.

Constraining 3-PG with a new δ13C submodel: a test using the δ13C of tree rings.

PubMed

Wei, Liang; Marshall, John D; Link, Timothy E; Kavanagh, Kathleen L; DU, Enhao; Pangle, Robert E; Gag, Peter J; Ubierna, Nerea

2014-01-01

A semi-mechanistic forest growth model, 3-PG (Physiological Principles Predicting Growth), was extended to calculate δ(13)C in tree rings. The δ(13)C estimates were based on the model's existing description of carbon assimilation and canopy conductance. The model was tested in two ~80-year-old natural stands of Abies grandis (grand fir) in northern Idaho. We used as many independent measurements as possible to parameterize the model. Measured parameters included quantum yield, specific leaf area, soil water content and litterfall rate. Predictions were compared with measurements of transpiration by sap flux, stem biomass, tree diameter growth, leaf area index and δ(13)C. Sensitivity analysis showed that the model's predictions of δ(13)C were sensitive to key parameters controlling carbon assimilation and canopy conductance, which would have allowed it to fail had the model been parameterized or programmed incorrectly. Instead, the simulated δ(13)C of tree rings was no different from measurements (P > 0.05). The δ(13)C submodel provides a convenient means of constraining parameter space and avoiding model artefacts. This δ(13)C test may be applied to any forest growth model that includes realistic simulations of carbon assimilation and transpiration. © 2013 John Wiley & Sons Ltd.

Real-Time Adaptive Control Allocation Applied to a High Performance Aircraft

NASA Technical Reports Server (NTRS)

Davidson, John B.; Lallman, Frederick J.; Bundick, W. Thomas

2001-01-01

Abstract This paper presents the development and application of one approach to the control of aircraft with large numbers of control effectors. This approach, referred to as real-time adaptive control allocation, combines a nonlinear method for control allocation with actuator failure detection and isolation. The control allocator maps moment (or angular acceleration) commands into physical control effector commands as functions of individual control effectiveness and availability. The actuator failure detection and isolation algorithm is a model-based approach that uses models of the actuators to predict actuator behavior and an adaptive decision threshold to achieve acceptable false alarm/missed detection rates. This integrated approach provides control reconfiguration when an aircraft is subjected to actuator failure, thereby improving maneuverability and survivability of the degraded aircraft. This method is demonstrated on a next generation military aircraft Lockheed-Martin Innovative Control Effector) simulation that has been modified to include a novel nonlinear fluid flow control control effector based on passive porosity. Desktop and real-time piloted simulation results demonstrate the performance of this integrated adaptive control allocation approach.

Characterization of Aerodynamic Interactions with the Mars Science Laboratory Reaction Control System Using Computation and Experiment

NASA Technical Reports Server (NTRS)

Schoenenberger, Mark; VanNorman, John; Rhode, Matthew; Paulson, John

2013-01-01

On August 5 , 2012, the Mars Science Laboratory (MSL) entry capsule successfully entered Mars' atmosphere and landed the Curiosity rover in Gale Crater. The capsule used a reaction control system (RCS) consisting of four pairs of hydrazine thrusters to fly a guided entry. The RCS provided bank control to fly along a flight path commanded by an onboard computer and also damped unwanted rates due to atmospheric disturbances and any dynamic instabilities of the capsule. A preliminary assessment of the MSL's flight data from entry showed that the capsule flew much as predicted. This paper will describe how the MSL aerodynamics team used engineering analyses, computational codes and wind tunnel testing in concert to develop the RCS system and certify it for flight. Over the course of MSL's development, the RCS configuration underwent a number of design iterations to accommodate mechanical constraints, aeroheating concerns and excessive aero/RCS interactions. A brief overview of the MSL RCS configuration design evolution is provided. Then, a brief description is presented of how the computational predictions of RCS jet interactions were validated. The primary work to certify that the RCS interactions were acceptable for flight was centered on validating computational predictions at hypersonic speeds. A comparison of computational fluid dynamics (CFD) predictions to wind tunnel force and moment data gathered in the NASA Langley 31-Inch Mach 10 Tunnel was the lynch pin to validating the CFD codes used to predict aero/RCS interactions. Using the CFD predictions and experimental data, an interaction model was developed for Monte Carlo analyses using 6-degree-of-freedom trajectory simulation. The interaction model used in the flight simulation is presented.

Tools for Evaluating Fault Detection and Diagnostic Methods for HVAC Secondary Systems

NASA Astrophysics Data System (ADS)

Pourarian, Shokouh

Although modern buildings are using increasingly sophisticated energy management and control systems that have tremendous control and monitoring capabilities, building systems routinely fail to perform as designed. More advanced building control, operation, and automated fault detection and diagnosis (AFDD) technologies are needed to achieve the goal of net-zero energy commercial buildings. Much effort has been devoted to develop such technologies for primary heating ventilating and air conditioning (HVAC) systems, and some secondary systems. However, secondary systems, such as fan coil units and dual duct systems, although widely used in commercial, industrial, and multifamily residential buildings, have received very little attention. This research study aims at developing tools that could provide simulation capabilities to develop and evaluate advanced control, operation, and AFDD technologies for these less studied secondary systems. In this study, HVACSIM+ is selected as the simulation environment. Besides developing dynamic models for the above-mentioned secondary systems, two other issues related to the HVACSIM+ environment are also investigated. One issue is the nonlinear equation solver used in HVACSIM+ (Powell's Hybrid method in subroutine SNSQ). It has been found from several previous research projects (ASRHAE RP 825 and 1312) that SNSQ is especially unstable at the beginning of a simulation and sometimes unable to converge to a solution. Another issue is related to the zone model in the HVACSIM+ library of components. Dynamic simulation of secondary HVAC systems unavoidably requires an interacting zone model which is systematically and dynamically interacting with building surrounding. Therefore, the accuracy and reliability of the building zone model affects operational data generated by the developed dynamic tool to predict HVAC secondary systems function. The available model does not simulate the impact of direct solar radiation that enters a zone through glazing and the study of zone model is conducted in this direction to modify the existing zone model. In this research project, the following tasks are completed and summarized in this report: 1. Develop dynamic simulation models in the HVACSIM+ environment for common fan coil unit and dual duct system configurations. The developed simulation models are able to produce both fault-free and faulty operational data under a wide variety of faults and severity levels for advanced control, operation, and AFDD technology development and evaluation purposes; 2. Develop a model structure, which includes the grouping of blocks and superblocks, treatment of state variables, initial and boundary conditions, and selection of equation solver, that can simulate a dual duct system efficiently with satisfactory stability; 3. Design and conduct a comprehensive and systematic validation procedure using collected experimental data to validate the developed simulation models under both fault-free and faulty operational conditions; 4. Conduct a numerical study to compare two solution techniques: Powell's Hybrid (PH) and Levenberg-Marquardt (LM) in terms of their robustness and accuracy. 5. Modification of the thermal state of the existing building zone model in HVACSIM+ library of component. This component is revised to consider the transmitted heat through glazing as a heat source for transient building zone load prediction In this report, literature, including existing HVAC dynamic modeling environment and models, HVAC model validation methodologies, and fault modeling and validation methodologies, are reviewed. The overall methodologies used for fault free and fault model development and validation are introduced. Detailed model development and validation results for the two secondary systems, i.e., fan coil unit and dual duct system are summarized. Experimental data mostly from the Iowa Energy Center Energy Resource Station are used to validate the models developed in this project. Satisfactory model performance in both fault free and fault simulation studies is observed for all studied systems.

Effect of vergence adaptation on convergence-accommodation: model simulations.

PubMed

Sreenivasan, Vidhyapriya; Bobier, William R; Irving, Elizabeth L; Lakshminarayanan, Vasudevan

2009-10-01

Several theoretical control models depict the adaptation effects observed in the accommodation and vergence mechanisms of the human visual system. Two current quantitative models differ in their approach of defining adaptation and in identifying the effect of controller adaptation on their respective cross-links between the vergence and accommodative systems. Here, we compare the simulation results of these adaptation models with empirical data obtained from emmetropic adults when they performed sustained near task through + 2D lens addition. The results of our experimental study showed an initial increase in exophoria (a divergent open-loop vergence position) and convergence-accommodation (CA) when viewing through +2D lenses. Prolonged fixation through the near addition lenses initiated vergence adaptation, which reduced the lens-induced exophoria and resulted in a concurrent reduction of CA. Both models showed good agreement with empirical measures of vergence adaptation. However, only one model predicted the experimental time course of reduction in CA. The pattern of our empirical results seem to be best described by the adaptation model that indicates the total vergence response to be a sum of two controllers, phasic and tonic, with the output of phasic controller providing input to the cross-link interactions.

Analysis of helicopter flight dynamics through modeling and simulation of primary flight control actuation system

NASA Astrophysics Data System (ADS)

Nelson, Hunter Barton

A simplified second-order transfer function actuator model used in most flight dynamics applications cannot easily capture the effects of different actuator parameters. The present work integrates a nonlinear actuator model into a nonlinear state space rotorcraft model to determine the effect of actuator parameters on key flight dynamics. The completed actuator model was integrated with a swashplate kinematics where step responses were generated over a range of key hydraulic parameters. The actuator-swashplate system was then introduced into a nonlinear state space rotorcraft simulation where flight dynamics quantities such as bandwidth and phase delay analyzed. Frequency sweeps were simulated for unique actuator configurations using the coupled nonlinear actuator-rotorcraft system. The software package CIFER was used for system identification and compared directly to the linearized models. As the actuator became rate saturated, the effects on bandwidth and phase delay were apparent on the predicted handling qualities specifications.

Strategies for Controlling Non-Transmissible Infection Outbreaks Using a Large Human Movement Data Set

PubMed Central

Hancock, Penelope A.; Rehman, Yasmin; Hall, Ian M.; Edeghere, Obaghe; Danon, Leon; House, Thomas A.; Keeling, Matthew J.

2014-01-01

Prediction and control of the spread of infectious disease in human populations benefits greatly from our growing capacity to quantify human movement behavior. Here we develop a mathematical model for non-transmissible infections contracted from a localized environmental source, informed by a detailed description of movement patterns of the population of Great Britain. The model is applied to outbreaks of Legionnaires' disease, a potentially life-threatening form of pneumonia caused by the bacteria Legionella pneumophilia. We use case-report data from three recent outbreaks that have occurred in Great Britain where the source has already been identified by public health agencies. We first demonstrate that the amount of individual-level heterogeneity incorporated in the movement data greatly influences our ability to predict the source location. The most accurate predictions were obtained using reported travel histories to describe movements of infected individuals, but using detailed simulation models to estimate movement patterns offers an effective fast alternative. Secondly, once the source is identified, we show that our model can be used to accurately determine the population likely to have been exposed to the pathogen, and hence predict the residential locations of infected individuals. The results give rise to an effective control strategy that can be implemented rapidly in response to an outbreak. PMID:25211122

Improving the Forecast Accuracy of an Ocean Observation and Prediction System by Adaptive Control of the Sensor Network

NASA Astrophysics Data System (ADS)

Talukder, A.; Panangadan, A. V.; Blumberg, A. F.; Herrington, T.; Georgas, N.

2008-12-01

The New York Harbor Observation and Prediction System (NYHOPS) is a real-time, estuarine and coastal ocean observing and modeling system for the New York Harbor and surrounding waters. Real-time measurements from in-situ mobile and stationary sensors in the NYHOPS networks are assimilated into marine forecasts in order to reduce the discrepancy with ground truth. The forecasts are obtained from the ECOMSED hydrodynamic model, a shallow water derivative of the Princeton Ocean Model. Currently, all sensors in the NYHOPS system are operated in a fixed mode with uniform sampling rates. This technology infusion effort demonstrates the use of Model Predictive Control (MPC) to autonomously adapt the operation of both mobile and stationary sensors in response to changing events that are -automatically detected from the ECOMSED forecasts. The controller focuses sensing resources on those regions that are expected to be impacted by the detected events. The MPC approach involves formulating the problem of calculating the optimal sensor parameters as a constrained multi-objective optimization problem. We have developed an objective function that takes into account the spatiotemporal relationship of the in-situ sensor locations and the locations of events detected by the model. Experiments in simulation were carried out using data collected during a freshwater flooding event. The location of the resulting freshwater plume was calculated from the corresponding model forecasts and was used by the MPC controller to derive control parameters for the sensing assets. The operational parameters that are controlled include the sampling rates of stationary sensors, paths of unmanned underwater vehicles (UUVs), and data transfer routes between sensors and the central modeling computer. The simulation experiments show that MPC-based sensor control reduces the RMS error in the forecast by a factor of 380% as compared to uniform sampling. The paths of multiple UUVs were simultaneously calculated such that measurements from on-board sensors would lead to maximal reduction in the forecast error after data assimilation. The MPC controller also reduces the consumption of system resources such as energy expended in sampling and wireless communication. The MPC-based control approach can be generalized to accept data from remote sensing satellites. This will enable in-situ sensors to be regulated using forecasts generated by assimilating local high resolution in-situ measurements with wide-area observations from remote sensing satellites.

First results of coupled IPS/NIMROD/GENRAY simulations

NASA Astrophysics Data System (ADS)

Jenkins, Thomas; Kruger, S. E.; Held, E. D.; Harvey, R. W.; Elwasif, W. R.; Schnack, D. D.

2010-11-01

The Integrated Plasma Simulator (IPS) framework, developed by the SWIM Project Team, facilitates self-consistent simulations of complicated plasma behavior via the coupling of various codes modeling different spatial/temporal scales in the plasma. Here, we apply this capability to investigate the stabilization of tearing modes by ECCD. Under IPS control, the NIMROD code (MHD) evolves fluid equations to model bulk plasma behavior, while the GENRAY code (RF) calculates the self-consistent propagation and deposition of RF power in the resulting plasma profiles. GENRAY data is then used to construct moments of the quasilinear diffusion tensor (induced by the RF) which influence the dynamics of momentum/energy evolution in NIMROD's equations. We present initial results from these coupled simulations and demonstrate that they correctly capture the physics of magnetic island stabilization [Jenkins et al, PoP 17, 012502 (2010)] in the low-beta limit. We also discuss the process of code verification in these simulations, demonstrating good agreement between NIMROD and GENRAY predictions for the flux-surface-averaged, RF-induced currents. An overview of ongoing model development (synthetic diagnostics/plasma control systems; neoclassical effects; etc.) is also presented. Funded by US DoE.

Adaptive neural network motion control for aircraft under uncertainty conditions

NASA Astrophysics Data System (ADS)

Efremov, A. V.; Tiaglik, M. S.; Tiumentsev, Yu V.

2018-02-01

We need to provide motion control of modern and advanced aircraft under diverse uncertainty conditions. This problem can be solved by using adaptive control laws. We carry out an analysis of the capabilities of these laws for such adaptive systems as MRAC (Model Reference Adaptive Control) and MPC (Model Predictive Control). In the case of a nonlinear control object, the most efficient solution to the adaptive control problem is the use of neural network technologies. These technologies are suitable for the development of both a control object model and a control law for the object. The approximate nature of the ANN model was taken into account by introducing additional compensating feedback into the control system. The capabilities of adaptive control laws under uncertainty in the source data are considered. We also conduct simulations to assess the contribution of adaptivity to the behavior of the system.

Descriptive and sensitivity analyses of WATBALI: A dynamic soil water model

NASA Technical Reports Server (NTRS)

Hildreth, W. W. (Principal Investigator)

1981-01-01

A soil water computer model that uses the IBM Continuous System Modeling Program III to solve the dynamic equations representing the soil, plant, and atmospheric physical or physiological processes considered is presented and discussed. Using values describing the soil-plant-atmosphere characteristics, the model predicts evaporation, transpiration, drainage, and soil water profile changes from an initial soil water profile and daily meteorological data. The model characteristics and simulations that were performed to determine the nature of the response to controlled variations in the input are described the results of the simulations are included and a change that makes the response of the model more closely represent the observed characteristics of evapotranspiration and profile changes for dry soil conditions is examined.

Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

USGS Publications Warehouse

Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

1997-01-01

One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

Petri Net computational modelling of Langerhans cell Interferon Regulatory Factor Network predicts their role in T cell activation.

PubMed

Polak, Marta E; Ung, Chuin Ying; Masapust, Joanna; Freeman, Tom C; Ardern-Jones, Michael R

2017-04-06

Langerhans cells (LCs) are able to orchestrate adaptive immune responses in the skin by interpreting the microenvironmental context in which they encounter foreign substances, but the regulatory basis for this has not been established. Utilising systems immunology approaches combining in silico modelling of a reconstructed gene regulatory network (GRN) with in vitro validation of the predictions, we sought to determine the mechanisms of regulation of immune responses in human primary LCs. The key role of Interferon regulatory factors (IRFs) as controllers of the human Langerhans cell response to epidermal cytokines was revealed by whole transcriptome analysis. Applying Boolean logic we assembled a Petri net-based model of the IRF-GRN which provides molecular pathway predictions for the induction of different transcriptional programmes in LCs. In silico simulations performed after model parameterisation with transcription factor expression values predicted that human LC activation of antigen-specific CD8 T cells would be differentially regulated by epidermal cytokine induction of specific IRF-controlled pathways. This was confirmed by in vitro measurement of IFN-γ production by activated T cells. As a proof of concept, this approach shows that stochastic modelling of a specific immune networks renders transcriptome data valuable for the prediction of functional outcomes of immune responses.

Modelling of the Human Knee Joint Supported by Active Orthosis

NASA Astrophysics Data System (ADS)

Musalimov, V.; Monahov, Y.; Tamre, M.; Rõbak, D.; Sivitski, A.; Aryassov, G.; Penkov, I.

2018-02-01

The article discusses motion of a healthy knee joint in the sagittal plane and motion of an injured knee joint supported by an active orthosis. A kinematic scheme of a mechanism for the simulation of a knee joint motion is developed and motion of healthy and injured knee joints are modelled in Matlab. Angles between links, which simulate the femur and tibia are controlled by Simulink block of Model predictive control (MPC). The results of simulation have been compared with several samples of real motion of the human knee joint obtained from motion capture systems. On the basis of these analyses and also of the analysis of the forces in human lower limbs created at motion, an active smart orthosis is developed. The orthosis design was optimized to achieve an energy saving system with sufficient anatomy, necessary reliability, easy exploitation and low cost. With the orthosis it is possible to unload the knee joint, and also partially or fully compensate muscle forces required for the bending of the lower limb.

Network-level reproduction number and extinction threshold for vector-borne diseases.

PubMed

Xue, Ling; Scoglio, Caterina

2015-06-01

The basic reproduction number of deterministic models is an essential quantity to predict whether an epidemic will spread or not. Thresholds for disease extinction contribute crucial knowledge of disease control, elimination, and mitigation of infectious diseases. Relationships between basic reproduction numbers of two deterministic network-based ordinary differential equation vector-host models, and extinction thresholds of corresponding stochastic continuous-time Markov chain models are derived under some assumptions. Numerical simulation results for malaria and Rift Valley fever transmission on heterogeneous networks are in agreement with analytical results without any assumptions, reinforcing that the relationships may always exist and proposing a mathematical problem for proving existence of the relationships in general. Moreover, numerical simulations show that the basic reproduction number does not monotonically increase or decrease with the extinction threshold. Consistent trends of extinction probability observed through numerical simulations provide novel insights into mitigation strategies to increase the disease extinction probability. Research findings may improve understandings of thresholds for disease persistence in order to control vector-borne diseases.

Prediction of pilot opinion ratings using an optimal pilot model. [of aircraft handling qualities in multiaxis tasks

NASA Technical Reports Server (NTRS)

Hess, R. A.

1977-01-01

A brief review of some of the more pertinent applications of analytical pilot models to the prediction of aircraft handling qualities is undertaken. The relative ease with which multiloop piloting tasks can be modeled via the optimal control formulation makes the use of optimal pilot models particularly attractive for handling qualities research. To this end, a rating hypothesis is introduced which relates the numerical pilot opinion rating assigned to a particular vehicle and task to the numerical value of the index of performance resulting from an optimal pilot modeling procedure as applied to that vehicle and task. This hypothesis is tested using data from piloted simulations and is shown to be reasonable. An example concerning a helicopter landing approach is introduced to outline the predictive capability of the rating hypothesis in multiaxis piloting tasks.

Correlation of Wissler Human Thermal Model Blood Flow and Shiver Algorithms

NASA Technical Reports Server (NTRS)

Bue, Grant; Makinen, Janice; Cognata, Thomas

2010-01-01

The Wissler Human Thermal Model (WHTM) is a thermal math model of the human body that has been widely used to predict the human thermoregulatory response to a variety of cold and hot environments. The model has been shown to predict core temperature and skin temperatures higher and lower, respectively, than in tests of subjects in crew escape suit working in a controlled hot environments. Conversely the model predicts core temperature and skin temperatures lower and higher, respectively, than in tests of lightly clad subjects immersed in cold water conditions. The blood flow algorithms of the model has been investigated to allow for more and less flow, respectively, for the cold and hot case. These changes in the model have yielded better correlation of skin and core temperatures in the cold and hot cases. The algorithm for onset of shiver did not need to be modified to achieve good agreement in cold immersion simulations

A generalised individual-based algorithm for modelling the evolution of quantitative herbicide resistance in arable weed populations.

PubMed

Liu, Chun; Bridges, Melissa E; Kaundun, Shiv S; Glasgow, Les; Owen, Micheal Dk; Neve, Paul

2017-02-01

Simulation models are useful tools for predicting and comparing the risk of herbicide resistance in weed populations under different management strategies. Most existing models assume a monogenic mechanism governing herbicide resistance evolution. However, growing evidence suggests that herbicide resistance is often inherited in a polygenic or quantitative fashion. Therefore, we constructed a generalised modelling framework to simulate the evolution of quantitative herbicide resistance in summer annual weeds. Real-field management parameters based on Amaranthus tuberculatus (Moq.) Sauer (syn. rudis) control with glyphosate and mesotrione in Midwestern US maize-soybean agroecosystems demonstrated that the model can represent evolved herbicide resistance in realistic timescales. Sensitivity analyses showed that genetic and management parameters were impactful on the rate of quantitative herbicide resistance evolution, whilst biological parameters such as emergence and seed bank mortality were less important. The simulation model provides a robust and widely applicable framework for predicting the evolution of quantitative herbicide resistance in summer annual weed populations. The sensitivity analyses identified weed characteristics that would favour herbicide resistance evolution, including high annual fecundity, large resistance phenotypic variance and pre-existing herbicide resistance. Implications for herbicide resistance management and potential use of the model are discussed. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Model-based analysis of environmental controls over ecosystem primary production in an alpine tundra dry meadow

DOE PAGES

Fan, Zhaosheng; Neff, Jason C.; Wieder, William R.

2016-02-10

We investigated several key limiting factors that control alpine tundra productivity by developing an ecosystem biogeochemistry model. The model simulates the coupled cycling of carbon (C), nitrogen (N), and phosphorus (P) and their interactions with gross primary production (GPP). It was parameterized with field observations from an alpine dry meadow ecosystem using a global optimization strategy to estimate the unknown parameters. The model, along with the estimated parameters, was first validated against independent data and then used to examine the environmental controls over plant productivity. Our results show that air temperature is the strongest limiting factor to GPP in themore » early growing season, N availability becomes important during the middle portion of the growing season, and soil moisture is the strongest limiting factors by late in the growing season. Overall, the controls over GPP during the growing season, from strongest to weakest, are soil moisture content, air temperature, N availability, and P availability. This simulation provides testable predictions of the shifting nature of physical and nutrient limitations on plant growth. The model also indicates that changing environmental conditions in the alpine will likely lead to changes in productivity. For example, warming eliminates the control of P availability on GPP and makes N availability surpass air temperature to become the second strongest limiting factor. In contrast, an increase in atmospheric nutrient deposition eliminates the control of N availability and enhances the importance of P availability. Furthermore, these analyses provide a quantitative and conceptual framework that can be used to test predictions and refine ecological analyses at this long-term ecological research site.« less

Model-based analysis of environmental controls over ecosystem primary production in an alpine tundra dry meadow

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fan, Zhaosheng; Neff, Jason C.; Wieder, William R.

We investigated several key limiting factors that control alpine tundra productivity by developing an ecosystem biogeochemistry model. The model simulates the coupled cycling of carbon (C), nitrogen (N), and phosphorus (P) and their interactions with gross primary production (GPP). It was parameterized with field observations from an alpine dry meadow ecosystem using a global optimization strategy to estimate the unknown parameters. The model, along with the estimated parameters, was first validated against independent data and then used to examine the environmental controls over plant productivity. Our results show that air temperature is the strongest limiting factor to GPP in themore » early growing season, N availability becomes important during the middle portion of the growing season, and soil moisture is the strongest limiting factors by late in the growing season. Overall, the controls over GPP during the growing season, from strongest to weakest, are soil moisture content, air temperature, N availability, and P availability. This simulation provides testable predictions of the shifting nature of physical and nutrient limitations on plant growth. The model also indicates that changing environmental conditions in the alpine will likely lead to changes in productivity. For example, warming eliminates the control of P availability on GPP and makes N availability surpass air temperature to become the second strongest limiting factor. In contrast, an increase in atmospheric nutrient deposition eliminates the control of N availability and enhances the importance of P availability. Furthermore, these analyses provide a quantitative and conceptual framework that can be used to test predictions and refine ecological analyses at this long-term ecological research site.« less

Dopamine, Affordance and Active Inference

PubMed Central

Friston, Karl J.; Shiner, Tamara; FitzGerald, Thomas; Galea, Joseph M.; Adams, Rick; Brown, Harriet; Dolan, Raymond J.; Moran, Rosalyn; Stephan, Klaas Enno; Bestmann, Sven

2012-01-01

The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or salience of (external or internal) cues that engender action. In other words, dopamine balances bottom-up sensory information and top-down prior beliefs when making hierarchical inferences (predictions) about cues that have affordance. In this paper, we focus on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements. Crucially, the predictions driving movements are based upon a hierarchical generative model that infers the context in which movements are made. This means that we can confuse agents by changing the context (order) in which cues are presented. These simulations provide a (Bayes-optimal) model of contextual uncertainty and set switching that can be quantified in terms of behavioural and electrophysiological responses. Furthermore, one can simulate dopaminergic lesions (by changing the precision of prediction errors) to produce pathological behaviours that are reminiscent of those seen in neurological disorders such as Parkinson's disease. We use these simulations to demonstrate how a single functional role for dopamine at the synaptic level can manifest in different ways at the behavioural level. PMID:22241972

TU-F-17A-03: An Analytical Respiratory Perturbation Model for Lung Motion Prediction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, G; Yuan, A; Wei, J

2014-06-15

Purpose: Breathing irregularity is common, causing unreliable prediction in tumor motion for correlation-based surrogates. Both tidal volume (TV) and breathing pattern (BP=ΔVthorax/TV, where TV=ΔVthorax+ΔVabdomen) affect lung motion in anterior-posterior and superior-inferior directions. We developed a novel respiratory motion perturbation (RMP) model in analytical form to account for changes in TV and BP in motion prediction from simulation to treatment. Methods: The RMP model is an analytical function of patient-specific anatomic and physiologic parameters. It contains a base-motion trajectory d(x,y,z) derived from a 4-dimensional computed tomography (4DCT) at simulation and a perturbation term Δd(ΔTV,ΔBP) accounting for deviation at treatment from simulation.more » The perturbation is dependent on tumor-specific location and patient-specific anatomy. Eleven patients with simulation and treatment 4DCT images were used to assess the RMP method in motion prediction from 4DCT1 to 4DCT2, and vice versa. For each patient, ten motion trajectories of corresponding points in the lower lobes were measured in both 4DCTs: one served as the base-motion trajectory and the other as the ground truth for comparison. In total, 220 motion trajectory predictions were assessed. The motion discrepancy between two 4DCTs for each patient served as a control. An established 5D motion model was used for comparison. Results: The average absolute error of RMP model prediction in superior-inferior direction is 1.6±1.8 mm, similar to 1.7±1.6 mm from the 5D model (p=0.98). Some uncertainty is associated with limited spatial resolution (2.5mm slice thickness) and temporal resolution (10-phases). Non-corrected motion discrepancy between two 4DCTs is 2.6±2.7mm, with the maximum of ±20mm, and correction is necessary (p=0.01). Conclusion: The analytical motion model predicts lung motion with accuracy similar to the 5D model. The analytical model is based on physical relationships, requires no training, and therefore is potentially more resilient to breathing irregularities. On-going investigation introduces airflow into the RMP model for improvement. This research is in part supported by NIH (U54CA137788/132378). AY would like to thank MSKCC summer medical student research program supported by National Cancer Institute and hosted by Department of Medical Physics at MSKCC.« less

A SEIR model for transmission of tuberculosis

NASA Astrophysics Data System (ADS)

Side, Syafruddin; Mulbar, Usman; Sidjara, Sahlan; Sanusi, Wahidah

2017-04-01

In this paper will be described Tuberculosis (TB) transmission using Susceptible-Exposed-Infected-Recovered (SEIR) model. SEIR model for transmission of TB were analyzed and performed simulations using data on the number of TB cases in South Sulawesi. The results showed that the levels of the basic reproduction ratio R0 using the model of SEIR is R0 ≤ 1, it means that the status of TB disease in South Sulawesi is at a stage that is not alarming, but based on simulation results using MatLab, predicted that the number of infection cases will continue to increase therefore government needs to take preventive measures to control and reduce the number of TB infections in South Sulawesi.

High-fidelity Simulation of Jet Noise from Rectangular Nozzles . [Large Eddy Simulation (LES) Model for Noise Reduction in Advanced Jet Engines and Automobiles

NASA Technical Reports Server (NTRS)

Sinha, Neeraj

2014-01-01

This Phase II project validated a state-of-the-art LES model, coupled with a Ffowcs Williams-Hawkings (FW-H) far-field acoustic solver, to support the development of advanced engine concepts. These concepts include innovative flow control strategies to attenuate jet noise emissions. The end-to-end LES/ FW-H noise prediction model was demonstrated and validated by applying it to rectangular nozzle designs with a high aspect ratio. The model also was validated against acoustic and flow-field data from a realistic jet-pylon experiment, thereby significantly advancing the state of the art for LES.

Computational Modeling and Simulation of Developmental ...

EPA Pesticide Factsheets

SYNOPSIS: The question of how tissues and organs are shaped during development is crucial for understanding human birth defects. Data from high-throughput screening assays on human stem cells may be utilized predict developmental toxicity with reasonable accuracy. Other types of models are necessary, however, for mechanism-specific analysis because embryogenesis requires precise timing and control. Agent-based modeling and simulation (ABMS) is an approach to virtually reconstruct these dynamics, cell-by-cell and interaction-by-interaction. Using ABMS, HTS lesions from ToxCast can be integrated with patterning systems heuristically to propagate key events This presentation to FDA-CFSAN will update progress on the applications of in silico modeling tools and approaches for assessing developmental toxicity.

Using an Informative Missing Data Model to Predict the Ability to Assess Recovery of Balance Control after Spaceflight

NASA Technical Reports Server (NTRS)

Feiveson, Alan H.; Wood, Scott J.; Jain, Varsha

2008-01-01

Astronauts show degraded balance control immediately after spaceflight. To assess this change, astronauts' ability to maintain a fixed stance under several challenging stimuli on a movable platform is quantified by "equilibrium" scores (EQs) on a scale of 0 to 100, where 100 represents perfect control (sway angle of 0) and 0 represents data loss where no sway angle is observed because the subject has to be restrained from falling. By comparing post- to pre-flight EQs for actual astronauts vs. controls, we built a classifier for deciding when an astronaut has recovered. Future diagnostic performance depends both on the sampling distribution of the classifier as well as the distribution of its input data. Taking this into consideration, we constructed a predictive ROC by simulation after modeling P(EQ = 0) in terms of a latent EQ-like beta-distributed random variable with random effects.

Final report for the DOE Early Career Award #DE-SC0003912

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jayaraman, Arthi

This DoE supported early career project was aimed at developing computational models, theory and simulation methods that would be then be used to predict assembly and morphology in polymer nanocomposites. In particular, the focus was on composites in active layers of devices, containing conducting polymers that act as electron donors and nanoscale additives that act as electron acceptors. During the course this work, we developed the first of its kind molecular models to represent conducting polymers enabling simulations at the experimentally relevant length and time scales. By comparison with experimentally observed morphologies we validated these models. Furthermore, using these modelsmore » and molecular dynamics simulations on graphical processing units (GPUs) we predicted the molecular level design features in polymers and additive that lead to morphologies with optimal features for charge carrier behavior in solar cells. Additionally, we also predicted computationally new design rules for better dispersion of additives in polymers that have been confirmed through experiments. Achieving dispersion in polymer nanocomposites is valuable to achieve controlled macroscopic properties of the composite. The results obtained during the course of this DOE funded project enables optimal design of higher efficiency organic electronic and photovoltaic devices and improve every day life with engineering of these higher efficiency devices.« less

A new controller for battery-powered electric vehicles

NASA Technical Reports Server (NTRS)

Belsterling, C. A.; Stone, J.

1980-01-01

This paper describes the development, under a NASA/DOE contract, of a new concept for efficient and reliable control of battery-powered vehicles. It avoids the detrimental effects of pulsed-power controllers like the SCR 'chopper' by using rotating machines to meter continuous currents to the traction motor. The concept is validated in a proof-of-principle demonstration system and a complete vehicle is simulated on an analog computer. Test results show exceptional promise for a full-scale system. Optimum control strategies to minimize controller weight are developed by means of the simulated vehicle. The design for an Engineering Model is then prepared in the form of a practical, compact two-bearing package with forced air cooling. Predicted performance is outstanding, with controller efficiency of over 90% at high speed.

The Australian Computational Earth Systems Simulator

NASA Astrophysics Data System (ADS)

Mora, P.; Muhlhaus, H.; Lister, G.; Dyskin, A.; Place, D.; Appelbe, B.; Nimmervoll, N.; Abramson, D.

2001-12-01

Numerical simulation of the physics and dynamics of the entire earth system offers an outstanding opportunity for advancing earth system science and technology but represents a major challenge due to the range of scales and physical processes involved, as well as the magnitude of the software engineering effort required. However, new simulation and computer technologies are bringing this objective within reach. Under a special competitive national funding scheme to establish new Major National Research Facilities (MNRF), the Australian government together with a consortium of Universities and research institutions have funded construction of the Australian Computational Earth Systems Simulator (ACcESS). The Simulator or computational virtual earth will provide the research infrastructure to the Australian earth systems science community required for simulations of dynamical earth processes at scales ranging from microscopic to global. It will consist of thematic supercomputer infrastructure and an earth systems simulation software system. The Simulator models and software will be constructed over a five year period by a multi-disciplinary team of computational scientists, mathematicians, earth scientists, civil engineers and software engineers. The construction team will integrate numerical simulation models (3D discrete elements/lattice solid model, particle-in-cell large deformation finite-element method, stress reconstruction models, multi-scale continuum models etc) with geophysical, geological and tectonic models, through advanced software engineering and visualization technologies. When fully constructed, the Simulator aims to provide the software and hardware infrastructure needed to model solid earth phenomena including global scale dynamics and mineralisation processes, crustal scale processes including plate tectonics, mountain building, interacting fault system dynamics, and micro-scale processes that control the geological, physical and dynamic behaviour of earth systems. ACcESS represents a part of Australia's contribution to the APEC Cooperation for Earthquake Simulation (ACES) international initiative. Together with other national earth systems science initiatives including the Japanese Earth Simulator and US General Earthquake Model projects, ACcESS aims to provide a driver for scientific advancement and technological breakthroughs including: quantum leaps in understanding of earth evolution at global, crustal, regional and microscopic scales; new knowledge of the physics of crustal fault systems required to underpin the grand challenge of earthquake prediction; new understanding and predictive capabilities of geological processes such as tectonics and mineralisation.

The Airspace Concepts Evaluation System Architecture and System Plant

NASA Technical Reports Server (NTRS)

Windhorst, Robert; Meyn, Larry; Manikonda, Vikram; Carlos, Patrick; Capozzi, Brian

2006-01-01

The Airspace Concepts Evaluation System is a simulation of the National Airspace System. It includes models of flights, airports, airspaces, air traffic controls, traffic flow managements, and airline operation centers operating throughout the United States. It is used to predict system delays in response to future capacity and demand scenarios and perform benefits assessments of current and future airspace technologies and operational concepts. Facilitation of these studies requires that the simulation architecture supports plug and play of different air traffic control, traffic flow management, and airline operation center models and multi-fidelity modeling of flights, airports, and airspaces. The simulation is divided into two parts that are named, borrowing from classical control theory terminology, control and plant. The control consists of air traffic control, traffic flow management, and airline operation center models, and the plant consists of flight, airport, and airspace models. The plant can run open loop, in the absence of the control. However, undesired affects, such as conflicts and over congestions in the airspaces and airports, can occur. Different controls are applied, "plug and played", to the plant. A particular control is evaluated by analyzing how well it managed conflicts and congestions. Furthermore, the terminal area plants consist of models of airports and terminal airspaces. Each model consists of a set of nodes and links which are connected by the user to form a network. Nodes model runways, fixes, taxi intersections, gates, and/or other points of interest, and links model taxiways, departure paths, and arrival paths. Metering, flow distribution, and sequencing functions can be applied at nodes. Different fidelity model of how a flight transits are can be used by links. The fidelity of the model can be adjusted by the user by either changing the complexity of the node/link network-or the way that the link models how the flights transit from one node to the other.

Coupling a Mesoscale Numerical Weather Prediction Model with Large-Eddy Simulation for Realistic Wind Plant Aerodynamics Simulations (Poster)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Draxl, C.; Churchfield, M.; Mirocha, J.

Wind plant aerodynamics are influenced by a combination of microscale and mesoscale phenomena. Incorporating mesoscale atmospheric forcing (e.g., diurnal cycles and frontal passages) into wind plant simulations can lead to a more accurate representation of microscale flows, aerodynamics, and wind turbine/plant performance. Our goal is to couple a numerical weather prediction model that can represent mesoscale flow [specifically the Weather Research and Forecasting model] with a microscale LES model (OpenFOAM) that can predict microscale turbulence and wake losses.

Optimal Control of Distributed Energy Resources using Model Predictive Control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayhorn, Ebony T.; Kalsi, Karanjit; Elizondo, Marcelo A.

2012-07-22

In an isolated power system (rural microgrid), Distributed Energy Resources (DERs) such as renewable energy resources (wind, solar), energy storage and demand response can be used to complement fossil fueled generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation. The problem is formulated as a multi-objective optimization problem with the goals of minimizing fuel costs and changes in power output of diesel generators, minimizingmore » costs associated with low battery life of energy storage and maintaining system frequency at the nominal operating value. Two control modes are considered for controlling the energy storage to compensate either net load variability or wind variability. Model predictive control (MPC) is used to solve the aforementioned problem and the performance is compared to an open-loop look-ahead dispatch problem. Simulation studies using high and low wind profiles, as well as, different MPC prediction horizons demonstrate the efficacy of the closed-loop MPC in compensating for uncertainties in wind and demand.« less

Forcing and variability of nonstationary rip currents

USGS Publications Warehouse

Long, Joseph W.; H.T. Özkan-Haller,

2016-01-01

Surface wave transformation and the resulting nearshore circulation along a section of coast with strong alongshore bathymetric gradients outside the surf zone are modeled for a consecutive 4 week time period. The modeled hydrodynamics are compared to in situ measurements of waves and currents collected during the Nearshore Canyon Experiment and indicate that for the entire range of observed conditions, the model performance is similar to other studies along this stretch of coast. Strong alongshore wave height gradients generate rip currents that are observed by remote sensing data and predicted qualitatively well by the numerical model. Previous studies at this site have used idealized scenarios to link the rip current locations to undulations in the offshore bathymetry but do not explain the dichotomy between permanent offshore bathymetric features and intermittent rip current development. Model results from the month‐long simulation are used to track the formation and location of rip currents using hourly statistics, and results show that the direction of the incoming wave energy strongly controls whether rip currents form. In particular, most of the offshore wave spectra were bimodal and we find that the ratio of energy contained in each mode dictates rip current development, and the alongshore rip current position is controlled by the incident wave period. Additionally, model simulations performed with and without updating the nearshore morphology yield no significant change in the accuracy of the predicted surf zone hydrodyanmics indicating that the large‐scale offshore features (e.g., submarine canyon) predominately control the nearshore wave‐circulation system.

Population Dynamics of a Salmonella Lytic Phage and Its Host: Implications of the Host Bacterial Growth Rate in Modelling

PubMed Central

Santos, Sílvio B.; Carvalho, Carla; Azeredo, Joana; Ferreira, Eugénio C.

2014-01-01

The prevalence and impact of bacteriophages in the ecology of bacterial communities coupled with their ability to control pathogens turn essential to understand and predict the dynamics between phage and bacteria populations. To achieve this knowledge it is essential to develop mathematical models able to explain and simulate the population dynamics of phage and bacteria. We have developed an unstructured mathematical model using delay-differential equations to predict the interactions between a broad-host-range Salmonella phage and its pathogenic host. The model takes into consideration the main biological parameters that rule phage-bacteria interactions likewise the adsorption rate, latent period, burst size, bacterial growth rate, and substrate uptake rate, among others. The experimental validation of the model was performed with data from phage-interaction studies in a 5 L bioreactor. The key and innovative aspect of the model was the introduction of variations in the latent period and adsorption rate values that are considered as constants in previous developed models. By modelling the latent period as a normal distribution of values and the adsorption rate as a function of the bacterial growth rate it was possible to accurately predict the behaviour of the phage-bacteria population. The model was shown to predict simulated data with a good agreement with the experimental observations and explains how a lytic phage and its host bacteria are able to coexist. PMID:25051248