Interplanetary Program to Optimize Simulated Trajectories (IPOST). Volume 2: Analytic manual

NASA Technical Reports Server (NTRS)

Hong, P. E.; Kent, P. D.; Olson, D. W.; Vallado, C. A.

1992-01-01

The Interplanetary Program to Optimize Space Trajectories (IPOST) is intended to support many analysis phases, from early interplanetary feasibility studies through spacecraft development and operations. The IPOST output provides information for sizing and understanding mission impacts related to propulsion, guidance, communications, sensor/actuators, payload, and other dynamic and geometric environments. IPOST models three degree of freedom trajectory events, such as launch/ascent, orbital coast, propulsive maneuvering (impulsive and finite burn), gravity assist, and atmospheric entry. Trajectory propagation is performed using a choice of Cowell, Encke, Multiconic, Onestep, or Conic methods. The user identifies a desired sequence of trajectory events, and selects which parameters are independent (controls) and dependent (targets), as well as other constraints and the cost function. Targeting and optimization is performed using the Stanford NPSOL algorithm. IPOST structure allows subproblems within a master optimization problem to aid in the general constrained parameter optimization solution. An alternate optimization method uses implicit simulation and collocation techniques.

Operations research investigations of satellite power stations

NASA Technical Reports Server (NTRS)

Cole, J. W.; Ballard, J. L.

1976-01-01

A systems model reflecting the design concepts of Satellite Power Stations (SPS) was developed. The model is of sufficient scope to include the interrelationships of the following major design parameters: the transportation to and between orbits; assembly of the SPS; and maintenance of the SPS. The systems model is composed of a set of equations that are nonlinear with respect to the system parameters and decision variables. The model determines a figure of merit from which alternative concepts concerning transportation, assembly, and maintenance of satellite power stations are studied. A hybrid optimization model was developed to optimize the system's decision variables. The optimization model consists of a random search procedure and the optimal-steepest descent method. A FORTRAN computer program was developed to enable the user to optimize nonlinear functions using the model. Specifically, the computer program was used to optimize Satellite Power Station system components.

Optimal lunar soft landing trajectories using taboo evolutionary programming

NASA Astrophysics Data System (ADS)

Mutyalarao, M.; Raj, M. Xavier James

A safe lunar landing is a key factor to undertake an effective lunar exploration. Lunar lander consists of four phases such as launch phase, the earth-moon transfer phase, circumlunar phase and landing phase. The landing phase can be either hard landing or soft landing. Hard landing means the vehicle lands under the influence of gravity without any deceleration measures. However, soft landing reduces the vertical velocity of the vehicle before landing. Therefore, for the safety of the astronauts as well as the vehicle lunar soft landing with an acceptable velocity is very much essential. So it is important to design the optimal lunar soft landing trajectory with minimum fuel consumption. Optimization of Lunar Soft landing is a complex optimal control problem. In this paper, an analysis related to lunar soft landing from a parking orbit around Moon has been carried out. A two-dimensional trajectory optimization problem is attempted. The problem is complex due to the presence of system constraints. To solve the time-history of control parameters, the problem is converted into two point boundary value problem by using the maximum principle of Pontrygen. Taboo Evolutionary Programming (TEP) technique is a stochastic method developed in recent years and successfully implemented in several fields of research. It combines the features of taboo search and single-point mutation evolutionary programming. Identifying the best unknown parameters of the problem under consideration is the central idea for many space trajectory optimization problems. The TEP technique is used in the present methodology for the best estimation of initial unknown parameters by minimizing objective function interms of fuel requirements. The optimal estimation subsequently results into an optimal trajectory design of a module for soft landing on the Moon from a lunar parking orbit. Numerical simulations demonstrate that the proposed approach is highly efficient and it reduces the minimum fuel consumption. The results are compared with the available results in literature shows that the solution of present algorithm is better than some of the existing algorithms. Keywords: soft landing, trajectory optimization, evolutionary programming, control parameters, Pontrygen principle.

Optimization for minimum sensitivity to uncertain parameters

NASA Technical Reports Server (NTRS)

Pritchard, Jocelyn I.; Adelman, Howard M.; Sobieszczanski-Sobieski, Jaroslaw

1994-01-01

A procedure to design a structure for minimum sensitivity to uncertainties in problem parameters is described. The approach is to minimize directly the sensitivity derivatives of the optimum design with respect to fixed design parameters using a nested optimization procedure. The procedure is demonstrated for the design of a bimetallic beam for minimum weight with insensitivity to uncertainties in structural properties. The beam is modeled with finite elements based on two dimensional beam analysis. A sequential quadratic programming procedure used as the optimizer supplies the Lagrange multipliers that are used to calculate the optimum sensitivity derivatives. The method was perceived to be successful from comparisons of the optimization results with parametric studies.

Series Hybrid Electric Vehicle Power System Optimization Based on Genetic Algorithm

NASA Astrophysics Data System (ADS)

Zhu, Tianjun; Li, Bin; Zong, Changfu; Wu, Yang

2017-09-01

Hybrid electric vehicles (HEV), compared with conventional vehicles, have complex structures and more component parameters. If variables optimization designs are carried on all these parameters, it will increase the difficulty and the convergence of algorithm program, so this paper chooses the parameters which has a major influence on the vehicle fuel consumption to make it all work at maximum efficiency. First, HEV powertrain components modelling are built. Second, taking a tandem hybrid structure as an example, genetic algorithm is used in this paper to optimize fuel consumption and emissions. Simulation results in ADVISOR verify the feasibility of the proposed genetic optimization algorithm.

Modeling and Analysis of Power Processing Systems (MAPPS). Volume 2: Appendices

NASA Technical Reports Server (NTRS)

Lee, F. C.; Radman, S.; Carter, R. A.; Wu, C. H.; Yu, Y.; Chang, R.

1980-01-01

The computer programs and derivations generated in support of the modeling and design optimization program are presented. Programs for the buck regulator, boost regulator, and buck-boost regulator are described. The computer program for the design optimization calculations is presented. Constraints for the boost and buck-boost converter were derived. Derivations of state-space equations and transfer functions are presented. Computer lists for the converters are presented, and the input parameters justified.

Analysis of parameter estimation and optimization application of ant colony algorithm in vehicle routing problem

NASA Astrophysics Data System (ADS)

Xu, Quan-Li; Cao, Yu-Wei; Yang, Kun

2018-03-01

Ant Colony Optimization (ACO) is the most widely used artificial intelligence algorithm at present. This study introduced the principle and mathematical model of ACO algorithm in solving Vehicle Routing Problem (VRP), and designed a vehicle routing optimization model based on ACO, then the vehicle routing optimization simulation system was developed by using c ++ programming language, and the sensitivity analyses, estimations and improvements of the three key parameters of ACO were carried out. The results indicated that the ACO algorithm designed in this paper can efficiently solve rational planning and optimization of VRP, and the different values of the key parameters have significant influence on the performance and optimization effects of the algorithm, and the improved algorithm is not easy to locally converge prematurely and has good robustness.

SEEK: A FORTRAN optimization program using a feasible directions gradient search

NASA Technical Reports Server (NTRS)

Savage, M.

1995-01-01

This report describes the use of computer program 'SEEK' which works in conjunction with two user-written subroutines and an input data file to perform an optimization procedure on a user's problem. The optimization method uses a modified feasible directions gradient technique. SEEK is written in ANSI standard Fortran 77, has an object size of about 46K bytes, and can be used on a personal computer running DOS. This report describes the use of the program and discusses the optimizing method. The program use is illustrated with four example problems: a bushing design, a helical coil spring design, a gear mesh design, and a two-parameter Weibull life-reliability curve fit.

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization.

PubMed

Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

2017-03-01

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors' memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

PubMed Central

Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

2017-01-01

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm. PMID:28257060

Shape optimization of the modular press body

NASA Astrophysics Data System (ADS)

Pabiszczak, Stanisław

2016-12-01

A paper contains an optimization algorithm of cross-sectional dimensions of a modular press body for the minimum mass criterion. Parameters of the wall thickness and the angle of their inclination relative to the base of section are assumed as the decision variables. The overall dimensions are treated as a constant. The optimal values of parameters were calculated using numerical method of the tool Solver in the program Microsoft Excel. The results of the optimization procedure helped reduce body weight by 27% while maintaining the required rigidity of the body.

Reducing the Volume of NASA Earth-Science Data

NASA Technical Reports Server (NTRS)

Lee, Seungwon; Braverman, Amy J.; Guillaume, Alexandre

2010-01-01

A computer program reduces data generated by NASA Earth-science missions into representative clusters characterized by centroids and membership information, thereby reducing the large volume of data to a level more amenable to analysis. The program effects an autonomous data-reduction/clustering process to produce a representative distribution and joint relationships of the data, without assuming a specific type of distribution and relationship and without resorting to domain-specific knowledge about the data. The program implements a combination of a data-reduction algorithm known as the entropy-constrained vector quantization (ECVQ) and an optimization algorithm known as the differential evolution (DE). The combination of algorithms generates the Pareto front of clustering solutions that presents the compromise between the quality of the reduced data and the degree of reduction. Similar prior data-reduction computer programs utilize only a clustering algorithm, the parameters of which are tuned manually by users. In the present program, autonomous optimization of the parameters by means of the DE supplants the manual tuning of the parameters. Thus, the program determines the best set of clustering solutions without human intervention.

Interplanetary program to optimize simulated trajectories (IPOST). Volume 4: Sample cases

NASA Technical Reports Server (NTRS)

Hong, P. E.; Kent, P. D; Olson, D. W.; Vallado, C. A.

1992-01-01

The Interplanetary Program to Optimize Simulated Trajectories (IPOST) is intended to support many analysis phases, from early interplanetary feasibility studies through spacecraft development and operations. The IPOST output provides information for sizing and understanding mission impacts related to propulsion, guidance, communications, sensor/actuators, payload, and other dynamic and geometric environments. IPOST models three degree of freedom trajectory events, such as launch/ascent, orbital coast, propulsive maneuvering (impulsive and finite burn), gravity assist, and atmospheric entry. Trajectory propagation is performed using a choice of Cowell, Encke, Multiconic, Onestep, or Conic methods. The user identifies a desired sequence of trajectory events, and selects which parameters are independent (controls) and dependent (targets), as well as other constraints and the cost function. Targeting and optimization are performed using the Standard NPSOL algorithm. The IPOST structure allows sub-problems within a master optimization problem to aid in the general constrained parameter optimization solution. An alternate optimization method uses implicit simulation and collocation techniques.

Finding Bayesian Optimal Designs for Nonlinear Models: A Semidefinite Programming-Based Approach.

PubMed

Duarte, Belmiro P M; Wong, Weng Kee

2015-08-01

This paper uses semidefinite programming (SDP) to construct Bayesian optimal design for nonlinear regression models. The setup here extends the formulation of the optimal designs problem as an SDP problem from linear to nonlinear models. Gaussian quadrature formulas (GQF) are used to compute the expectation in the Bayesian design criterion, such as D-, A- or E-optimality. As an illustrative example, we demonstrate the approach using the power-logistic model and compare results in the literature. Additionally, we investigate how the optimal design is impacted by different discretising schemes for the design space, different amounts of uncertainty in the parameter values, different choices of GQF and different prior distributions for the vector of model parameters, including normal priors with and without correlated components. Further applications to find Bayesian D-optimal designs with two regressors for a logistic model and a two-variable generalised linear model with a gamma distributed response are discussed, and some limitations of our approach are noted.

Finding Bayesian Optimal Designs for Nonlinear Models: A Semidefinite Programming-Based Approach

PubMed Central

Duarte, Belmiro P. M.; Wong, Weng Kee

2014-01-01

Summary This paper uses semidefinite programming (SDP) to construct Bayesian optimal design for nonlinear regression models. The setup here extends the formulation of the optimal designs problem as an SDP problem from linear to nonlinear models. Gaussian quadrature formulas (GQF) are used to compute the expectation in the Bayesian design criterion, such as D-, A- or E-optimality. As an illustrative example, we demonstrate the approach using the power-logistic model and compare results in the literature. Additionally, we investigate how the optimal design is impacted by different discretising schemes for the design space, different amounts of uncertainty in the parameter values, different choices of GQF and different prior distributions for the vector of model parameters, including normal priors with and without correlated components. Further applications to find Bayesian D-optimal designs with two regressors for a logistic model and a two-variable generalised linear model with a gamma distributed response are discussed, and some limitations of our approach are noted. PMID:26512159

Direct Digital Control Study.

DTIC Science & Technology

1985-02-01

Deck - Cold Deck Reset Reheat Coil Reset Steam Boiler Optimization [lot Water Outside Air Reset Chiller Optimization Chiller Water Temperature Reset...with programming techniques for each type of installed DDC in order to effect changes in operating setpoints and application programs. *Communication...can be changed without recailbration of instrumentation devices. Changes to the application software, operating setpoints and parameters require the

Application of Layered Perforation Profile Control Technique to Low Permeable Reservoir

NASA Astrophysics Data System (ADS)

Wei, Sun

2018-01-01

it is difficult to satisfy the demand of profile control of complex well section and multi-layer reservoir by adopting the conventional profile control technology, therefore, a research is conducted on adjusting the injection production profile with layered perforating parameters optimization. i.e. in the case of coproduction for multi-layer, water absorption of each layer is adjusted by adjusting the perforating parameters, thus to balance the injection production profile of the whole well section, and ultimately enhance the oil displacement efficiency of water flooding. By applying the relationship between oil-water phase percolation theory/perforating damage and capacity, a mathematic model of adjusting the injection production profile with layered perforating parameters optimization, besides, perforating parameters optimization software is programmed. Different types of optimization design work are carried out according to different geological conditions and construction purposes by using the perforating optimization design software; furthermore, an application test is done for low permeable reservoir, and the water injection profile tends to be balanced significantly after perforation with optimized parameters, thereby getting a good application effect on site.

Robust Neighboring Optimal Guidance for the Advanced Launch System

NASA Technical Reports Server (NTRS)

Hull, David G.

1993-01-01

In recent years, optimization has become an engineering tool through the availability of numerous successful nonlinear programming codes. Optimal control problems are converted into parameter optimization (nonlinear programming) problems by assuming the control to be piecewise linear, making the unknowns the nodes or junction points of the linear control segments. Once the optimal piecewise linear control (suboptimal) control is known, a guidance law for operating near the suboptimal path is the neighboring optimal piecewise linear control (neighboring suboptimal control). Research conducted under this grant has been directed toward the investigation of neighboring suboptimal control as a guidance scheme for an advanced launch system.

A flexible, interactive software tool for fitting the parameters of neuronal models.

PubMed

Friedrich, Péter; Vella, Michael; Gulyás, Attila I; Freund, Tamás F; Káli, Szabolcs

2014-01-01

The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool.

Optimization for high-dose-rate brachytherapy of cervical cancer with adaptive simulated annealing and gradient descent.

PubMed

Yao, Rui; Templeton, Alistair K; Liao, Yixiang; Turian, Julius V; Kiel, Krystyna D; Chu, James C H

2014-01-01

To validate an in-house optimization program that uses adaptive simulated annealing (ASA) and gradient descent (GD) algorithms and investigate features of physical dose and generalized equivalent uniform dose (gEUD)-based objective functions in high-dose-rate (HDR) brachytherapy for cervical cancer. Eight Syed/Neblett template-based cervical cancer HDR interstitial brachytherapy cases were used for this study. Brachytherapy treatment plans were first generated using inverse planning simulated annealing (IPSA). Using the same dwell positions designated in IPSA, plans were then optimized with both physical dose and gEUD-based objective functions, using both ASA and GD algorithms. Comparisons were made between plans both qualitatively and based on dose-volume parameters, evaluating each optimization method and objective function. A hybrid objective function was also designed and implemented in the in-house program. The ASA plans are higher on bladder V75% and D2cc (p=0.034) and lower on rectum V75% and D2cc (p=0.034) than the IPSA plans. The ASA and GD plans are not significantly different. The gEUD-based plans have higher homogeneity index (p=0.034), lower overdose index (p=0.005), and lower rectum gEUD and normal tissue complication probability (p=0.005) than the physical dose-based plans. The hybrid function can produce a plan with dosimetric parameters between the physical dose-based and gEUD-based plans. The optimized plans with the same objective value and dose-volume histogram could have different dose distributions. Our optimization program based on ASA and GD algorithms is flexible on objective functions, optimization parameters, and can generate optimized plans comparable with IPSA. Copyright © 2014 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.

A flexible, interactive software tool for fitting the parameters of neuronal models

PubMed Central

Friedrich, Péter; Vella, Michael; Gulyás, Attila I.; Freund, Tamás F.; Káli, Szabolcs

2014-01-01

The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool. PMID:25071540

A generalized fuzzy credibility-constrained linear fractional programming approach for optimal irrigation water allocation under uncertainty

NASA Astrophysics Data System (ADS)

Zhang, Chenglong; Guo, Ping

2017-10-01

The vague and fuzzy parametric information is a challenging issue in irrigation water management problems. In response to this problem, a generalized fuzzy credibility-constrained linear fractional programming (GFCCFP) model is developed for optimal irrigation water allocation under uncertainty. The model can be derived from integrating generalized fuzzy credibility-constrained programming (GFCCP) into a linear fractional programming (LFP) optimization framework. Therefore, it can solve ratio optimization problems associated with fuzzy parameters, and examine the variation of results under different credibility levels and weight coefficients of possibility and necessary. It has advantages in: (1) balancing the economic and resources objectives directly; (2) analyzing system efficiency; (3) generating more flexible decision solutions by giving different credibility levels and weight coefficients of possibility and (4) supporting in-depth analysis of the interrelationships among system efficiency, credibility level and weight coefficient. The model is applied to a case study of irrigation water allocation in the middle reaches of Heihe River Basin, northwest China. Therefore, optimal irrigation water allocation solutions from the GFCCFP model can be obtained. Moreover, factorial analysis on the two parameters (i.e. λ and γ) indicates that the weight coefficient is a main factor compared with credibility level for system efficiency. These results can be effective for support reasonable irrigation water resources management and agricultural production.

Analysis and selection of optimal function implementations in massively parallel computer

DOEpatents

Archer, Charles Jens [Rochester, MN; Peters, Amanda [Rochester, MN; Ratterman, Joseph D [Rochester, MN

2011-05-31

An apparatus, program product and method optimize the operation of a parallel computer system by, in part, collecting performance data for a set of implementations of a function capable of being executed on the parallel computer system based upon the execution of the set of implementations under varying input parameters in a plurality of input dimensions. The collected performance data may be used to generate selection program code that is configured to call selected implementations of the function in response to a call to the function under varying input parameters. The collected performance data may be used to perform more detailed analysis to ascertain the comparative performance of the set of implementations of the function under the varying input parameters.

Expected value based fuzzy programming approach to solve integrated supplier selection and inventory control problem with fuzzy demand

NASA Astrophysics Data System (ADS)

Sutrisno; Widowati; Sunarsih; Kartono

2018-01-01

In this paper, a mathematical model in quadratic programming with fuzzy parameter is proposed to determine the optimal strategy for integrated inventory control and supplier selection problem with fuzzy demand. To solve the corresponding optimization problem, we use the expected value based fuzzy programming. Numerical examples are performed to evaluate the model. From the results, the optimal amount of each product that have to be purchased from each supplier for each time period and the optimal amount of each product that have to be stored in the inventory for each time period were determined with minimum total cost and the inventory level was sufficiently closed to the reference level.

Rocket ascent G-limited moment-balanced optimization program (RAGMOP)

NASA Technical Reports Server (NTRS)

Lyons, J. T.; Woltosz, W. S.; Abercrombie, G. E.; Gottlieb, R. G.

1972-01-01

This document describes the RAGMOP (Rocket Ascent G-limited Momentbalanced Optimization Program) computer program for parametric ascent trajectory optimization. RAGMOP computes optimum polynomial-form attitude control histories, launch azimuth, engine burn-time, and gross liftoff weight for space shuttle type vehicles using a search-accelerated, gradient projection parameter optimization technique. The trajectory model available in RAGMOP includes a rotating oblate earth model, the option of input wind tables, discrete and/or continuous throttling for the purposes of limiting the thrust acceleration and/or the maximum dynamic pressure, limitation of the structural load indicators (the product of dynamic pressure with angle-of-attack and sideslip angle), and a wide selection of intermediate and terminal equality constraints.

Probability distribution functions for unit hydrographs with optimization using genetic algorithm

NASA Astrophysics Data System (ADS)

Ghorbani, Mohammad Ali; Singh, Vijay P.; Sivakumar, Bellie; H. Kashani, Mahsa; Atre, Atul Arvind; Asadi, Hakimeh

2017-05-01

A unit hydrograph (UH) of a watershed may be viewed as the unit pulse response function of a linear system. In recent years, the use of probability distribution functions (pdfs) for determining a UH has received much attention. In this study, a nonlinear optimization model is developed to transmute a UH into a pdf. The potential of six popular pdfs, namely two-parameter gamma, two-parameter Gumbel, two-parameter log-normal, two-parameter normal, three-parameter Pearson distribution, and two-parameter Weibull is tested on data from the Lighvan catchment in Iran. The probability distribution parameters are determined using the nonlinear least squares optimization method in two ways: (1) optimization by programming in Mathematica; and (2) optimization by applying genetic algorithm. The results are compared with those obtained by the traditional linear least squares method. The results show comparable capability and performance of two nonlinear methods. The gamma and Pearson distributions are the most successful models in preserving the rising and recession limbs of the unit hydographs. The log-normal distribution has a high ability in predicting both the peak flow and time to peak of the unit hydrograph. The nonlinear optimization method does not outperform the linear least squares method in determining the UH (especially for excess rainfall of one pulse), but is comparable.

A VLBI variance-covariance analysis interactive computer program. M.S. Thesis

NASA Technical Reports Server (NTRS)

Bock, Y.

1980-01-01

An interactive computer program (in FORTRAN) for the variance covariance analysis of VLBI experiments is presented for use in experiment planning, simulation studies and optimal design problems. The interactive mode is especially suited to these types of analyses providing ease of operation as well as savings in time and cost. The geodetic parameters include baseline vector parameters and variations in polar motion and Earth rotation. A discussion of the theroy on which the program is based provides an overview of the VLBI process emphasizing the areas of interest to geodesy. Special emphasis is placed on the problem of determining correlations between simultaneous observations from a network of stations. A model suitable for covariance analyses is presented. Suggestions towards developing optimal observation schedules are included.

Optimization of seismic isolation systems via harmony search

NASA Astrophysics Data System (ADS)

Melih Nigdeli, Sinan; Bekdaş, Gebrail; Alhan, Cenk

2014-11-01

In this article, the optimization of isolation system parameters via the harmony search (HS) optimization method is proposed for seismically isolated buildings subjected to both near-fault and far-fault earthquakes. To obtain optimum values of isolation system parameters, an optimization program was developed in Matlab/Simulink employing the HS algorithm. The objective was to obtain a set of isolation system parameters within a defined range that minimizes the acceleration response of a seismically isolated structure subjected to various earthquakes without exceeding a peak isolation system displacement limit. Several cases were investigated for different isolation system damping ratios and peak displacement limitations of seismic isolation devices. Time history analyses were repeated for the neighbouring parameters of optimum values and the results proved that the parameters determined via HS were true optima. The performance of the optimum isolation system was tested under a second set of earthquakes that was different from the first set used in the optimization process. The proposed optimization approach is applicable to linear isolation systems. Isolation systems composed of isolation elements that are inherently nonlinear are the subject of a future study. Investigation of the optimum isolation system parameters has been considered in parametric studies. However, obtaining the best performance of a seismic isolation system requires a true optimization by taking the possibility of both near-fault and far-fault earthquakes into account. HS optimization is proposed here as a viable solution to this problem.

Adaptable structural synthesis using advanced analysis and optimization coupled by a computer operating system

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, J.; Bhat, R. B.

1979-01-01

A finite element program is linked with a general purpose optimization program in a 'programing system' which includes user supplied codes that contain problem dependent formulations of the design variables, objective function and constraints. The result is a system adaptable to a wide spectrum of structural optimization problems. In a sample of numerical examples, the design variables are the cross-sectional dimensions and the parameters of overall shape geometry, constraints are applied to stresses, displacements, buckling and vibration characteristics, and structural mass is the objective function. Thin-walled, built-up structures and frameworks are included in the sample. Details of the system organization and characteristics of the component programs are given.

An Information-theoretic Approach to Optimize JWST Observations and Retrievals of Transiting Exoplanet Atmospheres

NASA Astrophysics Data System (ADS)

Howe, Alex R.; Burrows, Adam; Deming, Drake

2017-01-01

We provide an example of an analysis to explore the optimization of observations of transiting hot Jupiters with the James Webb Space Telescope (JWST) to characterize their atmospheres based on a simple three-parameter forward model. We construct expansive forward model sets for 11 hot Jupiters, 10 of which are relatively well characterized, exploring a range of parameters such as equilibrium temperature and metallicity, as well as considering host stars over a wide range in brightness. We compute posterior distributions of our model parameters for each planet with all of the available JWST spectroscopic modes and several programs of combined observations and compute their effectiveness using the metric of estimated mutual information per degree of freedom. From these simulations, clear trends emerge that provide guidelines for designing a JWST observing program. We demonstrate that these guidelines apply over a wide range of planet parameters and target brightnesses for our simple forward model.

Method of optimization onboard communication network

NASA Astrophysics Data System (ADS)

Platoshin, G. A.; Selvesuk, N. I.; Semenov, M. E.; Novikov, V. M.

2018-02-01

In this article the optimization levels of onboard communication network (OCN) are proposed. We defined the basic parameters, which are necessary for the evaluation and comparison of modern OCN, we identified also a set of initial data for possible modeling of the OCN. We also proposed a mathematical technique for implementing the OCN optimization procedure. This technique is based on the principles and ideas of binary programming. It is shown that the binary programming technique allows to obtain an inherently optimal solution for the avionics tasks. An example of the proposed approach implementation to the problem of devices assignment in OCN is considered.

An optimal system design process for a Mars roving vehicle

NASA Technical Reports Server (NTRS)

Pavarini, C.; Baker, J.; Goldberg, A.

1971-01-01

The problem of determining the optimal design for a Mars roving vehicle is considered. A system model is generated by consideration of the physical constraints on the design parameters and the requirement that the system be deliverable to the Mars surface. An expression which evaluates system performance relative to mission goals as a function of the design parameters only is developed. The use of nonlinear programming techniques to optimize the design is proposed and an example considering only two of the vehicle subsystems is formulated and solved.

A dynamic programming-based particle swarm optimization algorithm for an inventory management problem under uncertainty

NASA Astrophysics Data System (ADS)

Xu, Jiuping; Zeng, Ziqiang; Han, Bernard; Lei, Xiao

2013-07-01

This article presents a dynamic programming-based particle swarm optimization (DP-based PSO) algorithm for solving an inventory management problem for large-scale construction projects under a fuzzy random environment. By taking into account the purchasing behaviour and strategy under rules of international bidding, a multi-objective fuzzy random dynamic programming model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform fuzzy random parameters into fuzzy variables that are subsequently defuzzified by using an expected value operator with optimistic-pessimistic index. The iterative nature of the authors' model motivates them to develop a DP-based PSO algorithm. More specifically, their approach treats the state variables as hidden parameters. This in turn eliminates many redundant feasibility checks during initialization and particle updates at each iteration. Results and sensitivity analysis are presented to highlight the performance of the authors' optimization method, which is very effective as compared to the standard PSO algorithm.

Fine-Tuning ADAS Algorithm Parameters for Optimizing Traffic ...

EPA Pesticide Factsheets

With the development of the Connected Vehicle technology that facilitates wirelessly communication among vehicles and road-side infrastructure, the Advanced Driver Assistance Systems (ADAS) can be adopted as an effective tool for accelerating traffic safety and mobility optimization at various highway facilities. To this end, the traffic management centers identify the optimal ADAS algorithm parameter set that enables the maximum improvement of the traffic safety and mobility performance, and broadcast the optimal parameter set wirelessly to individual ADAS-equipped vehicles. After adopting the optimal parameter set, the ADAS-equipped drivers become active agents in the traffic stream that work collectively and consistently to prevent traffic conflicts, lower the intensity of traffic disturbances, and suppress the development of traffic oscillations into heavy traffic jams. Successful implementation of this objective requires the analysis capability of capturing the impact of the ADAS on driving behaviors, and measuring traffic safety and mobility performance under the influence of the ADAS. To address this challenge, this research proposes a synthetic methodology that incorporates the ADAS-affected driving behavior modeling and state-of-the-art microscopic traffic flow modeling into a virtually simulated environment. Building on such an environment, the optimal ADAS algorithm parameter set is identified through an optimization programming framework to enable th

Multi-objective optimization of composite structures. A review

NASA Astrophysics Data System (ADS)

Teters, G. A.; Kregers, A. F.

1996-05-01

Studies performed on the optimization of composite structures by coworkers of the Institute of Polymers Mechanics of the Latvian Academy of Sciences in recent years are reviewed. The possibility of controlling the geometry and anisotropy of laminar composite structures will make it possible to design articles that best satisfy the requirements established for them. Conflicting requirements such as maximum bearing capacity, minimum weight and/or cost, prescribed thermal conductivity and thermal expansion, etc. usually exist for optimal design. This results in the multi-objective compromise optimization of structures. Numerical methods have been developed for solution of problems of multi-objective optimization of composite structures; parameters of the structure of the reinforcement and the geometry of the design are assigned as controlling parameters. Programs designed to run on personal computers have been compiled for multi-objective optimization of the properties of composite materials, plates, and shells. Solutions are obtained for both linear and nonlinear models. The programs make it possible to establish the Pareto compromise region and special multicriterial solutions. The problem of the multi-objective optimization of the elastic moduli of a spatially reinforced fiberglass with stochastic stiffness parameters has been solved. The region of permissible solutions and the Pareto region have been found for the elastic moduli. The dimensions of the scatter ellipse have been determined for a multidimensional Gaussian probability distribution where correlation between the composite's properties being optimized are accounted for. Two types of problems involving the optimization of a laminar rectangular composite plate are considered: the plate is considered elastic and anisotropic in the first case, and viscoelastic properties are accounted for in the second. The angle of reinforcement and the relative amount of fibers in the longitudinal direction are controlling parameters. The optimized properties are the critical stresses, thermal conductivity, and thermal expansion. The properties of a plate are determined by the properties of the components in the composite, eight of which are stochastic. The region of multi-objective compromise solutions is presented, and the parameters of the scatter ellipses of the properties are given.

Finite Set Control Transcription for Optimal Control Applications

DTIC Science & Technology

2009-05-01

Figures 1.1 The Parameters of x . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.1 Categories of Optimization Algorithms ...Programming (NLP) algorithm , such as SNOPT2 (hereafter, called the optimizer). The Finite Set Control Transcription (FSCT) method is essentially a...artificial neural networks, ge- netic algorithms , or combinations thereof for analysis.4,5 Indeed, an actual biological neural network is an example of

Model-based optimal design of experiments - semidefinite and nonlinear programming formulations

PubMed Central

Duarte, Belmiro P.M.; Wong, Weng Kee; Oliveira, Nuno M.C.

2015-01-01

We use mathematical programming tools, such as Semidefinite Programming (SDP) and Nonlinear Programming (NLP)-based formulations to find optimal designs for models used in chemistry and chemical engineering. In particular, we employ local design-based setups in linear models and a Bayesian setup in nonlinear models to find optimal designs. In the latter case, Gaussian Quadrature Formulas (GQFs) are used to evaluate the optimality criterion averaged over the prior distribution for the model parameters. Mathematical programming techniques are then applied to solve the optimization problems. Because such methods require the design space be discretized, we also evaluate the impact of the discretization scheme on the generated design. We demonstrate the techniques for finding D–, A– and E–optimal designs using design problems in biochemical engineering and show the method can also be directly applied to tackle additional issues, such as heteroscedasticity in the model. Our results show that the NLP formulation produces highly efficient D–optimal designs but is computationally less efficient than that required for the SDP formulation. The efficiencies of the generated designs from the two methods are generally very close and so we recommend the SDP formulation in practice. PMID:26949279

Model-based optimal design of experiments - semidefinite and nonlinear programming formulations.

PubMed

Duarte, Belmiro P M; Wong, Weng Kee; Oliveira, Nuno M C

2016-02-15

We use mathematical programming tools, such as Semidefinite Programming (SDP) and Nonlinear Programming (NLP)-based formulations to find optimal designs for models used in chemistry and chemical engineering. In particular, we employ local design-based setups in linear models and a Bayesian setup in nonlinear models to find optimal designs. In the latter case, Gaussian Quadrature Formulas (GQFs) are used to evaluate the optimality criterion averaged over the prior distribution for the model parameters. Mathematical programming techniques are then applied to solve the optimization problems. Because such methods require the design space be discretized, we also evaluate the impact of the discretization scheme on the generated design. We demonstrate the techniques for finding D -, A - and E -optimal designs using design problems in biochemical engineering and show the method can also be directly applied to tackle additional issues, such as heteroscedasticity in the model. Our results show that the NLP formulation produces highly efficient D -optimal designs but is computationally less efficient than that required for the SDP formulation. The efficiencies of the generated designs from the two methods are generally very close and so we recommend the SDP formulation in practice.

Optimization-Based Image Reconstruction with Artifact Reduction in C-Arm CBCT

PubMed Central

Xia, Dan; Langan, David A.; Solomon, Stephen B.; Zhang, Zheng; Chen, Buxin; Lai, Hao; Sidky, Emil Y.; Pan, Xiaochuan

2016-01-01

We investigate an optimization-based reconstruction, with an emphasis on image-artifact reduction, from data collected in C-arm cone-beam computed tomography (CBCT) employed in image-guided interventional procedures. In the study, an image to be reconstructed is formulated as a solution to a convex optimization program in which a weighted data divergence is minimized subject to a constraint on the image total variation (TV); a data-derivative fidelity is introduced in the program specifically for effectively suppressing dominant, low-frequency data artifact caused by, e.g., data truncation; and the Chambolle-Pock (CP) algorithm is tailored to reconstruct an image through solving the program. Like any other reconstructions, the optimization-based reconstruction considered depends upon numerous parameters. We elucidate the parameters, illustrate their determination, and demonstrate their impact on the reconstruction. The optimization-based reconstruction, when applied to data collected from swine and patient subjects, yields images with visibly reduced artifacts in contrast to the reference reconstruction, and it also appears to exhibit a high degree of robustness against distinctively different anatomies of imaged subjects and scanning conditions of clinical significance. Knowledge and insights gained in the study may be exploited for aiding in the design of practical reconstructions of truly clinical-application utility. PMID:27694700

Optimization-based image reconstruction with artifact reduction in C-arm CBCT

NASA Astrophysics Data System (ADS)

Xia, Dan; Langan, David A.; Solomon, Stephen B.; Zhang, Zheng; Chen, Buxin; Lai, Hao; Sidky, Emil Y.; Pan, Xiaochuan

2016-10-01

We investigate an optimization-based reconstruction, with an emphasis on image-artifact reduction, from data collected in C-arm cone-beam computed tomography (CBCT) employed in image-guided interventional procedures. In the study, an image to be reconstructed is formulated as a solution to a convex optimization program in which a weighted data divergence is minimized subject to a constraint on the image total variation (TV); a data-derivative fidelity is introduced in the program specifically for effectively suppressing dominant, low-frequency data artifact caused by, e.g. data truncation; and the Chambolle-Pock (CP) algorithm is tailored to reconstruct an image through solving the program. Like any other reconstructions, the optimization-based reconstruction considered depends upon numerous parameters. We elucidate the parameters, illustrate their determination, and demonstrate their impact on the reconstruction. The optimization-based reconstruction, when applied to data collected from swine and patient subjects, yields images with visibly reduced artifacts in contrast to the reference reconstruction, and it also appears to exhibit a high degree of robustness against distinctively different anatomies of imaged subjects and scanning conditions of clinical significance. Knowledge and insights gained in the study may be exploited for aiding in the design of practical reconstructions of truly clinical-application utility.

Development of a parameter optimization technique for the design of automatic control systems

NASA Technical Reports Server (NTRS)

Whitaker, P. H.

1977-01-01

Parameter optimization techniques for the design of linear automatic control systems that are applicable to both continuous and digital systems are described. The model performance index is used as the optimization criterion because of the physical insight that can be attached to it. The design emphasis is to start with the simplest system configuration that experience indicates would be practical. Design parameters are specified, and a digital computer program is used to select that set of parameter values which minimizes the performance index. The resulting design is examined, and complexity, through the use of more complex information processing or more feedback paths, is added only if performance fails to meet operational specifications. System performance specifications are assumed to be such that the desired step function time response of the system can be inferred.

A Semi-Infinite Programming based algorithm for determining T-optimum designs for model discrimination

PubMed Central

Duarte, Belmiro P.M.; Wong, Weng Kee; Atkinson, Anthony C.

2016-01-01

T-optimum designs for model discrimination are notoriously difficult to find because of the computational difficulty involved in solving an optimization problem that involves two layers of optimization. Only a handful of analytical T-optimal designs are available for the simplest problems; the rest in the literature are found using specialized numerical procedures for a specific problem. We propose a potentially more systematic and general way for finding T-optimal designs using a Semi-Infinite Programming (SIP) approach. The strategy requires that we first reformulate the original minimax or maximin optimization problem into an equivalent semi-infinite program and solve it using an exchange-based method where lower and upper bounds produced by solving the outer and the inner programs, are iterated to convergence. A global Nonlinear Programming (NLP) solver is used to handle the subproblems, thus finding the optimal design and the least favorable parametric configuration that minimizes the residual sum of squares from the alternative or test models. We also use a nonlinear program to check the global optimality of the SIP-generated design and automate the construction of globally optimal designs. The algorithm is successfully used to produce results that coincide with several T-optimal designs reported in the literature for various types of model discrimination problems with normally distributed errors. However, our method is more general, merely requiring that the parameters of the model be estimated by a numerical optimization. PMID:27330230

A Semi-Infinite Programming based algorithm for determining T-optimum designs for model discrimination.

PubMed

Duarte, Belmiro P M; Wong, Weng Kee; Atkinson, Anthony C

2015-03-01

T-optimum designs for model discrimination are notoriously difficult to find because of the computational difficulty involved in solving an optimization problem that involves two layers of optimization. Only a handful of analytical T-optimal designs are available for the simplest problems; the rest in the literature are found using specialized numerical procedures for a specific problem. We propose a potentially more systematic and general way for finding T-optimal designs using a Semi-Infinite Programming (SIP) approach. The strategy requires that we first reformulate the original minimax or maximin optimization problem into an equivalent semi-infinite program and solve it using an exchange-based method where lower and upper bounds produced by solving the outer and the inner programs, are iterated to convergence. A global Nonlinear Programming (NLP) solver is used to handle the subproblems, thus finding the optimal design and the least favorable parametric configuration that minimizes the residual sum of squares from the alternative or test models. We also use a nonlinear program to check the global optimality of the SIP-generated design and automate the construction of globally optimal designs. The algorithm is successfully used to produce results that coincide with several T-optimal designs reported in the literature for various types of model discrimination problems with normally distributed errors. However, our method is more general, merely requiring that the parameters of the model be estimated by a numerical optimization.

A sequential linear optimization approach for controller design

NASA Technical Reports Server (NTRS)

Horta, L. G.; Juang, J.-N.; Junkins, J. L.

1985-01-01

A linear optimization approach with a simple real arithmetic algorithm is presented for reliable controller design and vibration suppression of flexible structures. Using first order sensitivity of the system eigenvalues with respect to the design parameters in conjunction with a continuation procedure, the method converts a nonlinear optimization problem into a maximization problem with linear inequality constraints. The method of linear programming is then applied to solve the converted linear optimization problem. The general efficiency of the linear programming approach allows the method to handle structural optimization problems with a large number of inequality constraints on the design vector. The method is demonstrated using a truss beam finite element model for the optimal sizing and placement of active/passive-structural members for damping augmentation. Results using both the sequential linear optimization approach and nonlinear optimization are presented and compared. The insensitivity to initial conditions of the linear optimization approach is also demonstrated.

Empirical valence bond models for reactive potential energy surfaces: a parallel multilevel genetic program approach.

PubMed

Bellucci, Michael A; Coker, David F

2011-07-28

We describe a new method for constructing empirical valence bond potential energy surfaces using a parallel multilevel genetic program (PMLGP). Genetic programs can be used to perform an efficient search through function space and parameter space to find the best functions and sets of parameters that fit energies obtained by ab initio electronic structure calculations. Building on the traditional genetic program approach, the PMLGP utilizes a hierarchy of genetic programming on two different levels. The lower level genetic programs are used to optimize coevolving populations in parallel while the higher level genetic program (HLGP) is used to optimize the genetic operator probabilities of the lower level genetic programs. The HLGP allows the algorithm to dynamically learn the mutation or combination of mutations that most effectively increase the fitness of the populations, causing a significant increase in the algorithm's accuracy and efficiency. The algorithm's accuracy and efficiency is tested against a standard parallel genetic program with a variety of one-dimensional test cases. Subsequently, the PMLGP is utilized to obtain an accurate empirical valence bond model for proton transfer in 3-hydroxy-gamma-pyrone in gas phase and protic solvent. © 2011 American Institute of Physics

AN INFORMATION-THEORETIC APPROACH TO OPTIMIZE JWST OBSERVATIONS AND RETRIEVALS OF TRANSITING EXOPLANET ATMOSPHERES

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

Howe, Alex R.; Burrows, Adam; Deming, Drake, E-mail: arhowe@umich.edu, E-mail: burrows@astro.princeton.edu, E-mail: ddeming@astro.umd.edu

We provide an example of an analysis to explore the optimization of observations of transiting hot Jupiters with the James Webb Space Telescope ( JWST ) to characterize their atmospheres based on a simple three-parameter forward model. We construct expansive forward model sets for 11 hot Jupiters, 10 of which are relatively well characterized, exploring a range of parameters such as equilibrium temperature and metallicity, as well as considering host stars over a wide range in brightness. We compute posterior distributions of our model parameters for each planet with all of the available JWST spectroscopic modes and several programs ofmore » combined observations and compute their effectiveness using the metric of estimated mutual information per degree of freedom. From these simulations, clear trends emerge that provide guidelines for designing a JWST observing program. We demonstrate that these guidelines apply over a wide range of planet parameters and target brightnesses for our simple forward model.« less

Optimal Strategy for Integrated Dynamic Inventory Control and Supplier Selection in Unknown Environment via Stochastic Dynamic Programming

NASA Astrophysics Data System (ADS)

Sutrisno; Widowati; Solikhin

2016-06-01

In this paper, we propose a mathematical model in stochastic dynamic optimization form to determine the optimal strategy for an integrated single product inventory control problem and supplier selection problem where the demand and purchasing cost parameters are random. For each time period, by using the proposed model, we decide the optimal supplier and calculate the optimal product volume purchased from the optimal supplier so that the inventory level will be located at some point as close as possible to the reference point with minimal cost. We use stochastic dynamic programming to solve this problem and give several numerical experiments to evaluate the model. From the results, for each time period, the proposed model was generated the optimal supplier and the inventory level was tracked the reference point well.

Optimal Trajectories Generation in Robotic Fiber Placement Systems

NASA Astrophysics Data System (ADS)

Gao, Jiuchun; Pashkevich, Anatol; Caro, Stéphane

2017-06-01

The paper proposes a methodology for optimal trajectories generation in robotic fiber placement systems. A strategy to tune the parameters of the optimization algorithm at hand is also introduced. The presented technique transforms the original continuous problem into a discrete one where the time-optimal motions are generated by using dynamic programming. The developed strategy for the optimization algorithm tuning allows essentially reducing the computing time and obtaining trajectories satisfying industrial constraints. Feasibilities and advantages of the proposed methodology are confirmed by an application example.

Automatic Phase Picker for Local and Teleseismic Events Using Wavelet Transform and Simulated Annealing

NASA Astrophysics Data System (ADS)

Gaillot, P.; Bardaine, T.; Lyon-Caen, H.

2004-12-01

Since recent years, various automatic phase pickers based on the wavelet transform have been developed. The main motivation for using wavelet transform is that they are excellent at finding the characteristics of transient signals, they have good time resolution at all periods, and they are easy to program for fast execution. Thus, the time-scale properties and flexibility of the wavelets allow detection of P and S phases in a broad frequency range making their utilization possible in various context. However, the direct application of an automatic picking program in a different context/network than the one for which it has been initially developed is quickly tedious. In fact, independently of the strategy involved in automatic picking algorithms (window average, autoregressive, beamforming, optimization filtering, neuronal network), all developed algorithms use different parameters that depend on the objective of the seismological study, the region and the seismological network. Classically, these parameters are manually defined by trial-error or calibrated learning stage. In order to facilitate this laborious process, we have developed an automated method that provide optimal parameters for the picking programs. The set of parameters can be explored using simulated annealing which is a generic name for a family of optimization algorithms based on the principle of stochastic relaxation. The optimization process amounts to systematically modifying an initial realization so as to decrease the value of the objective function, getting the realization acceptably close to the target statistics. Different formulations of the optimization problem (objective function) are discussed using (1) world seismicity data recorded by the French national seismic monitoring network (ReNass), (2) regional seismicity data recorded in the framework of the Corinth Rift Laboratory (CRL) experiment, (3) induced seismicity data from the gas field of Lacq (Western Pyrenees), and (4) micro-seismicity data from glacier monitoring. The developed method is discussed and tested using our wavelet version of the standard STA-LTA algorithm.

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis :

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

Adams, Brian M.; Ebeida, Mohamed Salah; Eldred, Michael S.

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components requiredmore » for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.« less

Nonlinear programming extensions to rational function approximation methods for unsteady aerodynamic forces

NASA Technical Reports Server (NTRS)

Tiffany, Sherwood H.; Adams, William M., Jr.

1988-01-01

The approximation of unsteady generalized aerodynamic forces in the equations of motion of a flexible aircraft are discussed. Two methods of formulating these approximations are extended to include the same flexibility in constraining the approximations and the same methodology in optimizing nonlinear parameters as another currently used extended least-squares method. Optimal selection of nonlinear parameters is made in each of the three methods by use of the same nonlinear, nongradient optimizer. The objective of the nonlinear optimization is to obtain rational approximations to the unsteady aerodynamics whose state-space realization is lower order than that required when no optimization of the nonlinear terms is performed. The free linear parameters are determined using the least-squares matrix techniques of a Lagrange multiplier formulation of an objective function which incorporates selected linear equality constraints. State-space mathematical models resulting from different approaches are described and results are presented that show comparative evaluations from application of each of the extended methods to a numerical example.

Flight instrumentation specification for parameter identification: Program user's guide. [instrument errors/error analysis

NASA Technical Reports Server (NTRS)

Mohr, R. L.

1975-01-01

A set of four digital computer programs is presented which can be used to investigate the effects of instrumentation errors on the accuracy of aircraft and helicopter stability-and-control derivatives identified from flight test data. The programs assume that the differential equations of motion are linear and consist of small perturbations about a quasi-steady flight condition. It is also assumed that a Newton-Raphson optimization technique is used for identifying the estimates of the parameters. Flow charts and printouts are included.

PLUTONIUM PROCESSING OPTIMIZATION IN SUPPORT OF THE MOX FUEL PROGRAM

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

GRAY, DEVIN W.; COSTA, DAVID A.

2007-02-02

After Los Alamos National Laboratory (LANL) personnel completed polishing 125 Kg of plutonium as highly purified PuO{sub 2} from surplus nuclear weapons, Duke, COGEMA, Stone, and Webster (DCS) required as the next process stage, the validation and optimization of all phases of the plutonium polishing flow sheet. Personnel will develop the optimized parameters for use in the upcoming 330 kg production mission.

An interval programming model for continuous improvement in micro-manufacturing

NASA Astrophysics Data System (ADS)

Ouyang, Linhan; Ma, Yizhong; Wang, Jianjun; Tu, Yiliu; Byun, Jai-Hyun

2018-03-01

Continuous quality improvement in micro-manufacturing processes relies on optimization strategies that relate an output performance to a set of machining parameters. However, when determining the optimal machining parameters in a micro-manufacturing process, the economics of continuous quality improvement and decision makers' preference information are typically neglected. This article proposes an economic continuous improvement strategy based on an interval programming model. The proposed strategy differs from previous studies in two ways. First, an interval programming model is proposed to measure the quality level, where decision makers' preference information is considered in order to determine the weight of location and dispersion effects. Second, the proposed strategy is a more flexible approach since it considers the trade-off between the quality level and the associated costs, and leaves engineers a larger decision space through adjusting the quality level. The proposed strategy is compared with its conventional counterparts using an Nd:YLF laser beam micro-drilling process.

The solution of private problems for optimization heat exchangers parameters

NASA Astrophysics Data System (ADS)

Melekhin, A.

2017-11-01

The relevance of the topic due to the decision of problems of the economy of resources in heating systems of buildings. To solve this problem we have developed an integrated method of research which allows solving tasks on optimization of parameters of heat exchangers. This method decides multicriteria optimization problem with the program nonlinear optimization on the basis of software with the introduction of an array of temperatures obtained using thermography. The author have developed a mathematical model of process of heat exchange in heat exchange surfaces of apparatuses with the solution of multicriteria optimization problem and check its adequacy to the experimental stand in the visualization of thermal fields, an optimal range of managed parameters influencing the process of heat exchange with minimal metal consumption and the maximum heat output fin heat exchanger, the regularities of heat exchange process with getting generalizing dependencies distribution of temperature on the heat-release surface of the heat exchanger vehicles, defined convergence of the results of research in the calculation on the basis of theoretical dependencies and solving mathematical model.

Development and application of computer assisted optimal method for treatment of femoral neck fracture.

PubMed

Wang, Monan; Zhang, Kai; Yang, Ning

2018-04-09

To help doctors decide their treatment from the aspect of mechanical analysis, the work built a computer assisted optimal system for treatment of femoral neck fracture oriented to clinical application. The whole system encompassed the following three parts: Preprocessing module, finite element mechanical analysis module, post processing module. Preprocessing module included parametric modeling of bone, parametric modeling of fracture face, parametric modeling of fixed screw and fixed position and input and transmission of model parameters. Finite element mechanical analysis module included grid division, element type setting, material property setting, contact setting, constraint and load setting, analysis method setting and batch processing operation. Post processing module included extraction and display of batch processing operation results, image generation of batch processing operation, optimal program operation and optimal result display. The system implemented the whole operations from input of fracture parameters to output of the optimal fixed plan according to specific patient real fracture parameter and optimal rules, which demonstrated the effectiveness of the system. Meanwhile, the system had a friendly interface, simple operation and could improve the system function quickly through modifying single module.

Evaluation of a new parallel numerical parameter optimization algorithm for a dynamical system

NASA Astrophysics Data System (ADS)

Duran, Ahmet; Tuncel, Mehmet

2016-10-01

It is important to have a scalable parallel numerical parameter optimization algorithm for a dynamical system used in financial applications where time limitation is crucial. We use Message Passing Interface parallel programming and present such a new parallel algorithm for parameter estimation. For example, we apply the algorithm to the asset flow differential equations that have been developed and analyzed since 1989 (see [3-6] and references contained therein). We achieved speed-up for some time series to run up to 512 cores (see [10]). Unlike [10], we consider more extensive financial market situations, for example, in presence of low volatility, high volatility and stock market price at a discount/premium to its net asset value with varying magnitude, in this work. Moreover, we evaluated the convergence of the model parameter vector, the nonlinear least squares error and maximum improvement factor to quantify the success of the optimization process depending on the number of initial parameter vectors.

Contributions to optimization of storage and transporting industrial goods

NASA Astrophysics Data System (ADS)

Babanatsas, T.; Babanatis Merce, R. M.; Glăvan, D. O.; Glăvan, A.

2018-01-01

Optimization of storage and transporting industrial goods in a factory either from a constructive, functional, or technological point of view is a determinant parameter in programming the manufacturing process, the performance of the whole process being determined by the correlation realized taking in consideration those two factors (optimization and programming the process). It is imperative to take into consideration each type of production program (range), to restrain as much as possible the area that we are using and to minimize the times of execution, all of these in order to satisfy the client’s needs, to try to classify them in order to be able to define a global software (with general rules) that is expected to fulfil each client’s needs.

LATTICE/hor ellipsis/a beam transport program

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

Staples, J.

1987-06-01

LATTICE is a computer program that calculates the first order characteristics of synchrotrons and beam transport systems. The program uses matrix algebra to calculate the propagation of the betatron (Twiss) parameters along a beam line. The program draws on ideas from several older programs, notably Transport and Synch, adds many new ones and incorporates them into an interactive, user-friendly program. LATTICE will calculate the matched functions of a synchrotron lattice and display them in a number of ways, including a high resolution Tektronix graphics display. An optimizer is included to adjust selected element parameters so the beam meets a setmore » of constraints. LATTICE is a first order program, but the effect of sextupoles on the chromaticity of a synchrotron lattice is included, and the optimizer will set the sextupole strengths for zero chromaticity. The program will also calculate the characteristics of beam transport systems. In this mode, the beam parameters, defined at the start of the transport line, are propagated through to the end. LATTICE has two distinct modes: the lattice mode which finds the matched functions of a synchrotron, and the transport mode which propagates a predefined beam through a beam line. However, each mode can be used for either type of problem: the transport mode may be used to calculate an insertion for a synchrotron lattice, and the lattice mode may be used to calculate the characteristics of a long periodic beam transport system.« less

Coupling Network Computing Applications in Air-cooled Turbine Blades Optimization

NASA Astrophysics Data System (ADS)

Shi, Liang; Yan, Peigang; Xie, Ming; Han, Wanjin

2018-05-01

Through establishing control parameters from blade outside to inside, the parametric design of air-cooled turbine blade based on airfoil has been implemented. On the basis of fast updating structure features and generating solid model, a complex cooling system has been created. Different flow units are modeled into a complex network topology with parallel and serial connection. Applying one-dimensional flow theory, programs have been composed to get pipeline network physical quantities along flow path, including flow rate, pressure, temperature and other parameters. These inner units parameters set as inner boundary conditions for external flow field calculation program HIT-3D by interpolation, thus to achieve full field thermal coupling simulation. Referring the studies in literatures to verify the effectiveness of pipeline network program and coupling algorithm. After that, on the basis of a modified design, and with the help of iSIGHT-FD, an optimization platform had been established. Through MIGA mechanism, the target of enhancing cooling efficiency has been reached, and the thermal stress has been effectively reduced. Research work in this paper has significance for rapid deploying the cooling structure design.

Software Analytical Instrument for Assessment of the Process of Casting Slabs

NASA Astrophysics Data System (ADS)

Franěk, Zdeněk; Kavička, František; Štětina, Josef; Masarik, Miloš

2010-06-01

The paper describes the original proposal of ways of solution and function of the program equipment for assessment of the process of casting slabs. The program system LITIOS was developed and implemented in EVRAZ Vitkovice Steel Ostrava on the equipment of continuous casting of steel (further only ECC). This program system works on the data warehouse of technological parameters of casting and quality parameters of slabs. It enables an ECC technologist to analyze the course of casting melt and with using statistics methods to set the influence of single technological parameters on the duality of final slabs. The system also enables long term monitoring and optimization of the production.

AN OVERVIEW OF THE UNCERTAINTY ANALYSIS, SENSITIVITY ANALYSIS, AND PARAMETER ESTIMATION (UA/SA/PE) API AND HOW TO IMPLEMENT IT

EPA Science Inventory

The Application Programming Interface (API) for Uncertainty Analysis, Sensitivity Analysis, and
Parameter Estimation (UA/SA/PE API) (also known as Calibration, Optimization and Sensitivity and Uncertainty (CUSO)) was developed in a joint effort between several members of both ...

Multi-objective optimal design of magnetorheological engine mount based on an improved non-dominated sorting genetic algorithm

NASA Astrophysics Data System (ADS)

Zheng, Ling; Duan, Xuwei; Deng, Zhaoxue; Li, Yinong

2014-03-01

A novel flow-mode magneto-rheological (MR) engine mount integrated a diaphragm de-coupler and the spoiler plate is designed and developed to isolate engine and the transmission from the chassis in a wide frequency range and overcome the stiffness in high frequency. A lumped parameter model of the MR engine mount in single degree of freedom system is further developed based on bond graph method to predict the performance of the MR engine mount accurately. The optimization mathematical model is established to minimize the total of force transmissibility over several frequency ranges addressed. In this mathematical model, the lumped parameters are considered as design variables. The maximum of force transmissibility and the corresponding frequency in low frequency range as well as individual lumped parameter are limited as constraints. The multiple interval sensitivity analysis method is developed to select the optimized variables and improve the efficiency of optimization process. An improved non-dominated sorting genetic algorithm (NSGA-II) is used to solve the multi-objective optimization problem. The synthesized distance between the individual in Pareto set and the individual in possible set in engineering is defined and calculated. A set of real design parameters is thus obtained by the internal relationship between the optimal lumped parameters and practical design parameters for the MR engine mount. The program flowchart for the improved non-dominated sorting genetic algorithm (NSGA-II) is given. The obtained results demonstrate the effectiveness of the proposed optimization approach in minimizing the total of force transmissibility over several frequency ranges addressed.

Six-degree-of-freedom program to optimize simulated trajectories (6D POST). Volume 1: Formulation manual

NASA Technical Reports Server (NTRS)

Brauer, G. L.; Habeger, A. R.; Stevenson, R.

1974-01-01

The basic equations and models used in a computer program (6D POST) to optimize simulated trajectories with six degrees of freedom were documented. The 6D POST program was conceived as a direct extension of the program POST, which dealt with point masses, and considers the general motion of a rigid body with six degrees of freedom. It may be used to solve a wide variety of atmospheric flight mechanics and orbital transfer problems for powered or unpowered vehicles operating near a rotating oblate planet. Its principal features are: an easy to use NAMELIST type input procedure, an integrated set of Flight Control System (FCS) modules, and a general-purpose discrete parameter targeting and optimization capability. It was written in FORTRAN 4 for the CDC 6000 series computers.

Hearing AIDS and music.

PubMed

Chasin, Marshall; Russo, Frank A

2004-01-01

Historically, the primary concern for hearing aid design and fitting is optimization for speech inputs. However, increasingly other types of inputs are being investigated and this is certainly the case for music. Whether the hearing aid wearer is a musician or merely someone who likes to listen to music, the electronic and electro-acoustic parameters described can be optimized for music as well as for speech. That is, a hearing aid optimally set for music can be optimally set for speech, even though the converse is not necessarily true. Similarities and differences between speech and music as inputs to a hearing aid are described. Many of these lead to the specification of a set of optimal electro-acoustic characteristics. Parameters such as the peak input-limiting level, compression issues-both compression ratio and knee-points-and number of channels all can deleteriously affect music perception through hearing aids. In other cases, it is not clear how to set other parameters such as noise reduction and feedback control mechanisms. Regardless of the existence of a "music program,'' unless the various electro-acoustic parameters are available in a hearing aid, music fidelity will almost always be less than optimal. There are many unanswered questions and hypotheses in this area. Future research by engineers, researchers, clinicians, and musicians will aid in the clarification of these questions and their ultimate solutions.

ParFit: A Python-Based Object-Oriented Program for Fitting Molecular Mechanics Parameters to ab Initio Data.

PubMed

Zahariev, Federico; De Silva, Nuwan; Gordon, Mark S; Windus, Theresa L; Dick-Perez, Marilu

2017-03-27

A newly created object-oriented program for automating the process of fitting molecular-mechanics parameters to ab initio data, termed ParFit, is presented. ParFit uses a hybrid of deterministic and stochastic genetic algorithms. ParFit can simultaneously handle several molecular-mechanics parameters in multiple molecules and can also apply symmetric and antisymmetric constraints on the optimized parameters. The simultaneous handling of several molecules enhances the transferability of the fitted parameters. ParFit is written in Python, uses a rich set of standard and nonstandard Python libraries, and can be run in parallel on multicore computer systems. As an example, a series of phosphine oxides, important for metal extraction chemistry, are parametrized using ParFit. ParFit is in an open source program available for free on GitHub ( https://github.com/fzahari/ParFit ).

Capacity planning for waste management systems: an interval fuzzy robust dynamic programming approach.

PubMed

Nie, Xianghui; Huang, Guo H; Li, Yongping

2009-11-01

This study integrates the concepts of interval numbers and fuzzy sets into optimization analysis by dynamic programming as a means of accounting for system uncertainty. The developed interval fuzzy robust dynamic programming (IFRDP) model improves upon previous interval dynamic programming methods. It allows highly uncertain information to be effectively communicated into the optimization process through introducing the concept of fuzzy boundary interval and providing an interval-parameter fuzzy robust programming method for an embedded linear programming problem. Consequently, robustness of the optimization process and solution can be enhanced. The modeling approach is applied to a hypothetical problem for the planning of waste-flow allocation and treatment/disposal facility expansion within a municipal solid waste (MSW) management system. Interval solutions for capacity expansion of waste management facilities and relevant waste-flow allocation are generated and interpreted to provide useful decision alternatives. The results indicate that robust and useful solutions can be obtained, and the proposed IFRDP approach is applicable to practical problems that are associated with highly complex and uncertain information.

Integrated planning for regional development planning and water resources management under uncertainty: A case study of Xining, China

NASA Astrophysics Data System (ADS)

Fu, Z. H.; Zhao, H. J.; Wang, H.; Lu, W. T.; Wang, J.; Guo, H. C.

2017-11-01

Economic restructuring, water resources management, population planning and environmental protection are subjects to inner uncertainties of a compound system with objectives which are competitive alternatives. Optimization model and water quality model are usually used to solve problems in a certain aspect. To overcome the uncertainty and coupling in reginal planning management, an interval fuzzy program combined with water quality model for regional planning and management has been developed to obtain the absolutely ;optimal; solution in this study. The model is a hybrid methodology of interval parameter programming (IPP), fuzzy programing (FP), and a general one-dimensional water quality model. The method extends on the traditional interval parameter fuzzy programming method by integrating water quality model into the optimization framework. Meanwhile, as an abstract concept, water resources carrying capacity has been transformed into specific and calculable index. Besides, unlike many of the past studies about water resource management, population as a significant factor has been considered. The results suggested that the methodology was applicable for reflecting the complexities of the regional planning and management systems within the planning period. The government policy makers could establish effective industrial structure, water resources utilization patterns and population planning, and to better understand the tradeoffs among economic, water resources, population and environmental objectives.

Optimization of self-acting step thrust bearings for load capacity and stiffness.

NASA Technical Reports Server (NTRS)

Hamrock, B. J.

1972-01-01

Linearized analysis of a finite-width rectangular step thrust bearing. Dimensionless load capacity and stiffness are expressed in terms of a Fourier cosine series. The dimensionless load capacity and stiffness were found to be a function of the dimensionless bearing number, the pad length-to-width ratio, the film thickness ratio, the step location parameter, and the feed groove parameter. The equations obtained in the analysis were verified. The assumptions imposed were substantiated by comparing the results with an existing exact solution for the infinite width bearing. A digital computer program was developed which determines optimal bearing configuration for maximum load capacity or stiffness. Simple design curves are presented. Results are shown for both compressible and incompressible lubrication. Through a parameter transformation the results are directly usable in designing optimal step sector thrust bearings.

An algorithm for control system design via parameter optimization. M.S. Thesis

NASA Technical Reports Server (NTRS)

Sinha, P. K.

1972-01-01

An algorithm for design via parameter optimization has been developed for linear-time-invariant control systems based on the model reference adaptive control concept. A cost functional is defined to evaluate the system response relative to nominal, which involves in general the error between the system and nominal response, its derivatives and the control signals. A program for the practical implementation of this algorithm has been developed, with the computational scheme for the evaluation of the performance index based on Lyapunov's theorem for stability of linear invariant systems.

Automatic design optimization tool for passive structural control systems

NASA Astrophysics Data System (ADS)

Mojolic, Cristian; Hulea, Radu; Parv, Bianca Roxana

2017-07-01

The present paper proposes an automatic dynamic process in order to find the parameters of the seismic isolation systems applied to large span structures. Three seismic isolation solutions are proposed for the model of the new Slatina Sport Hall. The first case uses friction pendulum system (FP), the second one uses High Damping Rubber Bearing (HDRB) and Lead Rubber Bearings, while (LRB) are used for the last case of isolation. The placement of the isolation level is at the top end of the roof supporting columns. The aim is to calculate the parameters of each isolation system so that the whole's structure first vibration periods is the one desired by the user. The model is computed with the use of SAP2000 software. In order to find the best solution for the optimization problem, an optimization process based on Genetic Algorithms (GA) has been developed in Matlab. With the use of the API (Application Programming Interface) libraries a two way link is created between the two programs in order to exchange results and link parameters. The main goal is to find the best seismic isolation method for each desired modal period so that the bending moment on the supporting columns should be minimum.

Optimization of cutting parameters for machining time in turning process

NASA Astrophysics Data System (ADS)

Mavliutov, A. R.; Zlotnikov, E. G.

2018-03-01

This paper describes the most effective methods for nonlinear constraint optimization of cutting parameters in the turning process. Among them are Linearization Programming Method with Dual-Simplex algorithm, Interior Point method, and Augmented Lagrangian Genetic Algorithm (ALGA). Every each of them is tested on an actual example – the minimization of production rate in turning process. The computation was conducted in the MATLAB environment. The comparative results obtained from the application of these methods show: The optimal value of the linearized objective and the original function are the same. ALGA gives sufficiently accurate values, however, when the algorithm uses the Hybrid function with Interior Point algorithm, the resulted values have the maximal accuracy.

User's manual for the BNW-I optimization code for dry-cooled power plants. [AMCIRC

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

Braun, D.J.; Daniel, D.J.; De Mier, W.V.

1977-01-01

This appendix provides a listing, called Program AMCIRC, of the BNW-1 optimization code for determining, for a particular size power plant, the optimum dry cooling tower design using ammonia flow in the heat exchanger tubes. The optimum design is determined by repeating the design of the cooling system over a range of design conditions in order to find the cooling system with the smallest incremental cost. This is accomplished by varying five parameters of the plant and cooling system over ranges of values. These parameters are varied systematically according to techniques that perform pattern and gradient searches. The dry coolingmore » system optimized by program AMCIRC is composed of a condenser/reboiler (condensation of steam and boiling of ammonia), piping system (transports ammonia vapor out and ammonia liquid from the dry cooling towers), and circular tower system (vertical one-pass heat exchangers situated in circular configurations with cocurrent ammonia flow in the tubes of the heat exchanger). (LCL)« less

Computer-aided linear-circuit design.

NASA Technical Reports Server (NTRS)

Penfield, P.

1971-01-01

Usually computer-aided design (CAD) refers to programs that analyze circuits conceived by the circuit designer. Among the services such programs should perform are direct network synthesis, analysis, optimization of network parameters, formatting, storage of miscellaneous data, and related calculations. The program should be embedded in a general-purpose conversational language such as BASIC, JOSS, or APL. Such a program is MARTHA, a general-purpose linear-circuit analyzer embedded in APL.

Interplanetary Program to Optimize Simulated Trajectories (IPOST). Volume 1: User's guide

NASA Technical Reports Server (NTRS)

Hong, P. E.; Kent, P. D.; Olson, D. W.; Vallado, C. A.

1992-01-01

IPOST is intended to support many analysis phases, from early interplanetary feasibility studies through spacecraft development and operations. The IPOST output provides information for sizing and understanding mission impacts related to propulsion, guidance, communications, sensor/actuators, payload, and other dynamic and geometric environments. IPOST models three degree of freedom trajectory events, such as launch/ascent, orbital coast, propulsive maneuvering (impulsive and finite burn), gravity assist, and atmospheric entry. Trajectory propagation is performed using a choice of Cowell, Encke, Multiconic, Onestep, or Conic methods. The user identifies a desired sequence fo trajectory events, and selects which parameters are independent (controls) and dependent (targets), as well as other constraints and the coat function. Targeting and optimization is performed using the Stanford NPSOL algorithm. IPOST structure allows sub-problems within a master optimization problem to aid in the general constrained parameter optimization solution. An alternate optimization method uses implicit simulation and collocation techniques.

A generalized interval fuzzy mixed integer programming model for a multimodal transportation problem under uncertainty

NASA Astrophysics Data System (ADS)

Tian, Wenli; Cao, Chengxuan

2017-03-01

A generalized interval fuzzy mixed integer programming model is proposed for the multimodal freight transportation problem under uncertainty, in which the optimal mode of transport and the optimal amount of each type of freight transported through each path need to be decided. For practical purposes, three mathematical methods, i.e. the interval ranking method, fuzzy linear programming method and linear weighted summation method, are applied to obtain equivalents of constraints and parameters, and then a fuzzy expected value model is presented. A heuristic algorithm based on a greedy criterion and the linear relaxation algorithm are designed to solve the model.

A new technique for rapid assessment of eutrophication status of coastal waters using a support vector machine

NASA Astrophysics Data System (ADS)

Kong, Xianyu; Che, Xiaowei; Su, Rongguo; Zhang, Chuansong; Yao, Qingzhen; Shi, Xiaoyong

2017-05-01

There is an urgent need to develop efficient evaluation tools that use easily measured variables to make rapid and timely eutrophication assessments, which are important for marine health management, and to implement eutrophication monitoring programs. In this study, an approach for rapidly assessing the eutrophication status of coastal waters with three easily measured parameters (turbidity, chlorophyll a and dissolved oxygen) was developed by the grid search (GS) optimized support vector machine (SVM), with trophic index TRIX classification results as the reference. With the optimized penalty parameter C =64 and the kernel parameter γ =1, the classification accuracy rates reached 89.3% for the training data, 88.3% for the cross-validation, and 88.5% for the validation dataset. Because the developed approach only used three easy-to-measure variables, its application could facilitate the rapid assessment of the eutrophication status of coastal waters, resulting in potential cost savings in marine monitoring programs and assisting in the provision of timely advice for marine management.

Use of multilevel modeling for determining optimal parameters of heat supply systems

NASA Astrophysics Data System (ADS)

Stennikov, V. A.; Barakhtenko, E. A.; Sokolov, D. V.

2017-07-01

The problem of finding optimal parameters of a heat-supply system (HSS) is in ensuring the required throughput capacity of a heat network by determining pipeline diameters and characteristics and location of pumping stations. Effective methods for solving this problem, i.e., the method of stepwise optimization based on the concept of dynamic programming and the method of multicircuit optimization, were proposed in the context of the hydraulic circuit theory developed at Melentiev Energy Systems Institute (Siberian Branch, Russian Academy of Sciences). These methods enable us to determine optimal parameters of various types of piping systems due to flexible adaptability of the calculation procedure to intricate nonlinear mathematical models describing features of used equipment items and methods of their construction and operation. The new and most significant results achieved in developing methodological support and software for finding optimal parameters of complex heat supply systems are presented: a new procedure for solving the problem based on multilevel decomposition of a heat network model that makes it possible to proceed from the initial problem to a set of interrelated, less cumbersome subproblems with reduced dimensionality; a new algorithm implementing the method of multicircuit optimization and focused on the calculation of a hierarchical model of a heat supply system; the SOSNA software system for determining optimum parameters of intricate heat-supply systems and implementing the developed methodological foundation. The proposed procedure and algorithm enable us to solve engineering problems of finding the optimal parameters of multicircuit heat supply systems having large (real) dimensionality, and are applied in solving urgent problems related to the optimal development and reconstruction of these systems. The developed methodological foundation and software can be used for designing heat supply systems in the Central and the Admiralty regions in St. Petersburg, the city of Bratsk, and the Magistral'nyi settlement.

An efficient and accurate solution methodology for bilevel multi-objective programming problems using a hybrid evolutionary-local-search algorithm.

PubMed

Deb, Kalyanmoy; Sinha, Ankur

2010-01-01

Bilevel optimization problems involve two optimization tasks (upper and lower level), in which every feasible upper level solution must correspond to an optimal solution to a lower level optimization problem. These problems commonly appear in many practical problem solving tasks including optimal control, process optimization, game-playing strategy developments, transportation problems, and others. However, they are commonly converted into a single level optimization problem by using an approximate solution procedure to replace the lower level optimization task. Although there exist a number of theoretical, numerical, and evolutionary optimization studies involving single-objective bilevel programming problems, not many studies look at the context of multiple conflicting objectives in each level of a bilevel programming problem. In this paper, we address certain intricate issues related to solving multi-objective bilevel programming problems, present challenging test problems, and propose a viable and hybrid evolutionary-cum-local-search based algorithm as a solution methodology. The hybrid approach performs better than a number of existing methodologies and scales well up to 40-variable difficult test problems used in this study. The population sizing and termination criteria are made self-adaptive, so that no additional parameters need to be supplied by the user. The study indicates a clear niche of evolutionary algorithms in solving such difficult problems of practical importance compared to their usual solution by a computationally expensive nested procedure. The study opens up many issues related to multi-objective bilevel programming and hopefully this study will motivate EMO and other researchers to pay more attention to this important and difficult problem solving activity.

Optimizations of geothermal cycle shell and tube exchangers of various configurations with variable fluid properties and site specific fouling. [SIZEHX

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

Pope, W.L.; Pines, H.S.; Silvester, L.F.

1978-03-01

A new heat exchanger program, SIZEHX, is described. This program allows single step multiparameter cost optimizations on single phase or supercritical exchanger arrays with variable properties and arbitrary fouling for a multitude of matrix configurations and fluids. SIZEHX uses a simplified form of Tinker's method for characterization of shell side performance; the Starling modified BWR equation for thermodynamic properties of hydrocarbons; and transport properties developed by NBS. Results of four parameter cost optimizations on exchangers for specific geothermal applications are included. The relative mix of capital cost, pumping cost, and brine cost ($/Btu) is determined for geothermal exchangers illustrating themore » invariant nature of the optimal cost distribution for fixed unit costs.« less

DE and NLP Based QPLS Algorithm

NASA Astrophysics Data System (ADS)

Yu, Xiaodong; Huang, Dexian; Wang, Xiong; Liu, Bo

As a novel evolutionary computing technique, Differential Evolution (DE) has been considered to be an effective optimization method for complex optimization problems, and achieved many successful applications in engineering. In this paper, a new algorithm of Quadratic Partial Least Squares (QPLS) based on Nonlinear Programming (NLP) is presented. And DE is used to solve the NLP so as to calculate the optimal input weights and the parameters of inner relationship. The simulation results based on the soft measurement of diesel oil solidifying point on a real crude distillation unit demonstrate that the superiority of the proposed algorithm to linear PLS and QPLS which is based on Sequential Quadratic Programming (SQP) in terms of fitting accuracy and computational costs.

ADMAP (automatic data manipulation program)

NASA Technical Reports Server (NTRS)

Mann, F. I.

1971-01-01

Instructions are presented on the use of ADMAP, (automatic data manipulation program) an aerospace data manipulation computer program. The program was developed to aid in processing, reducing, plotting, and publishing electric propulsion trajectory data generated by the low thrust optimization program, HILTOP. The program has the option of generating SC4020 electric plots, and therefore requires the SC4020 routines to be available at excution time (even if not used). Several general routines are present, including a cubic spline interpolation routine, electric plotter dash line drawing routine, and single parameter and double parameter sorting routines. Many routines are tailored for the manipulation and plotting of electric propulsion data, including an automatic scale selection routine, an automatic curve labelling routine, and an automatic graph titling routine. Data are accepted from either punched cards or magnetic tape.

ParFit: A Python-Based Object-Oriented Program for Fitting Molecular Mechanics Parameters to ab Initio Data

DOE PAGES

Zahariev, Federico; De Silva, Nuwan; Gordon, Mark S.; ...

2017-02-23

Here, a newly created object-oriented program for automating the process of fitting molecular-mechanics parameters to ab initio data, termed ParFit, is presented. ParFit uses a hybrid of deterministic and stochastic genetic algorithms. ParFit can simultaneously handle several molecular-mechanics parameters in multiple molecules and can also apply symmetric and antisymmetric constraints on the optimized parameters. The simultaneous handling of several molecules enhances the transferability of the fitted parameters. ParFit is written in Python, uses a rich set of standard and nonstandard Python libraries, and can be run in parallel on multicore computer systems. As an example, a series of phosphine oxides,more » important for metal extraction chemistry, are parametrized using ParFit.« less

ParFit: A Python-Based Object-Oriented Program for Fitting Molecular Mechanics Parameters to ab Initio Data

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

Zahariev, Federico; De Silva, Nuwan; Gordon, Mark S.

Here, a newly created object-oriented program for automating the process of fitting molecular-mechanics parameters to ab initio data, termed ParFit, is presented. ParFit uses a hybrid of deterministic and stochastic genetic algorithms. ParFit can simultaneously handle several molecular-mechanics parameters in multiple molecules and can also apply symmetric and antisymmetric constraints on the optimized parameters. The simultaneous handling of several molecules enhances the transferability of the fitted parameters. ParFit is written in Python, uses a rich set of standard and nonstandard Python libraries, and can be run in parallel on multicore computer systems. As an example, a series of phosphine oxides,more » important for metal extraction chemistry, are parametrized using ParFit.« less

Hearing Aids and Music

PubMed Central

Chasin, Marshall; Russo, Frank A.

2004-01-01

Historically, the primary concern for hearing aid design and fitting is optimization for speech inputs. However, increasingly other types of inputs are being investigated and this is certainly the case for music. Whether the hearing aid wearer is a musician or merely someone who likes to listen to music, the electronic and electro-acoustic parameters described can be optimized for music as well as for speech. That is, a hearing aid optimally set for music can be optimally set for speech, even though the converse is not necessarily true. Similarities and differences between speech and music as inputs to a hearing aid are described. Many of these lead to the specification of a set of optimal electro-acoustic characteristics. Parameters such as the peak input-limiting level, compression issues—both compression ratio and knee-points—and number of channels all can deleteriously affect music perception through hearing aids. In other cases, it is not clear how to set other parameters such as noise reduction and feedback control mechanisms. Regardless of the existence of a “music program,” unless the various electro-acoustic parameters are available in a hearing aid, music fidelity will almost always be less than optimal. There are many unanswered questions and hypotheses in this area. Future research by engineers, researchers, clinicians, and musicians will aid in the clarification of these questions and their ultimate solutions. PMID:15497032

A new software for deformation source optimization, the Bayesian Earthquake Analysis Tool (BEAT)

NASA Astrophysics Data System (ADS)

Vasyura-Bathke, H.; Dutta, R.; Jonsson, S.; Mai, P. M.

2017-12-01

Modern studies of crustal deformation and the related source estimation, including magmatic and tectonic sources, increasingly use non-linear optimization strategies to estimate geometric and/or kinematic source parameters and often consider both jointly, geodetic and seismic data. Bayesian inference is increasingly being used for estimating posterior distributions of deformation source model parameters, given measured/estimated/assumed data and model uncertainties. For instance, some studies consider uncertainties of a layered medium and propagate these into source parameter uncertainties, while others use informative priors to reduce the model parameter space. In addition, innovative sampling algorithms have been developed to efficiently explore the high-dimensional parameter spaces. Compared to earlier studies, these improvements have resulted in overall more robust source model parameter estimates that include uncertainties. However, the computational burden of these methods is high and estimation codes are rarely made available along with the published results. Even if the codes are accessible, it is usually challenging to assemble them into a single optimization framework as they are typically coded in different programing languages. Therefore, further progress and future applications of these methods/codes are hampered, while reproducibility and validation of results has become essentially impossible. In the spirit of providing open-access and modular codes to facilitate progress and reproducible research in deformation source estimations, we undertook the effort of developing BEAT, a python package that comprises all the above-mentioned features in one single programing environment. The package builds on the pyrocko seismological toolbox (www.pyrocko.org), and uses the pymc3 module for Bayesian statistical model fitting. BEAT is an open-source package (https://github.com/hvasbath/beat), and we encourage and solicit contributions to the project. Here, we present our strategy for developing BEAT and show application examples; especially the effect of including the model prediction uncertainty of the velocity model in following source optimizations: full moment tensor, Mogi source, moderate strike-slip earth-quake.

Optimal pricing and marketing planning for deteriorating items.

PubMed

Moosavi Tabatabaei, Seyed Reza; Sadjadi, Seyed Jafar; Makui, Ahmad

2017-01-01

Optimal pricing and marketing planning plays an essential role in production decisions on deteriorating items. This paper presents a mathematical model for a three-level supply chain, which includes one producer, one distributor and one retailer. The proposed study considers the production of a deteriorating item where demand is influenced by price, marketing expenditure, quality of product and after-sales service expenditures. The proposed model is formulated as a geometric programming with 5 degrees of difficulty and the problem is solved using the recent advances in optimization techniques. The study is supported by several numerical examples and sensitivity analysis is performed to analyze the effects of the changes in different parameters on the optimal solution. The preliminary results indicate that with the change in parameters influencing on demand, inventory holding, inventory deteriorating and set-up costs change and also significantly affect total revenue.

OPDOT: A computer program for the optimum preliminary design of a transport airplane

NASA Technical Reports Server (NTRS)

Sliwa, S. M.; Arbuckle, P. D.

1980-01-01

A description of a computer program, OPDOT, for the optimal preliminary design of transport aircraft is given. OPDOT utilizes constrained parameter optimization to minimize a performance index (e.g., direct operating cost per block hour) while satisfying operating constraints. The approach in OPDOT uses geometric descriptors as independent design variables. The independent design variables are systematically iterated to find the optimum design. The technical development of the program is provided and a program listing with sample input and output are utilized to illustrate its use in preliminary design. It is not meant to be a user's guide, but rather a description of a useful design tool developed for studying the application of new technologies to transport airplanes.

A study of application of remote sensing to river forecasting. Volume 2: Detailed technical report, NASA-IBM streamflow forecast model user's guide

NASA Technical Reports Server (NTRS)

1975-01-01

The Model is described along with data preparation, determining model parameters, initializing and optimizing parameters (calibration) selecting control options and interpreting results. Some background information is included, and appendices contain a dictionary of variables, a source program listing, and flow charts. The model was operated on an IBM System/360 Model 44, using a model 2250 keyboard/graphics terminal for interactive operation. The model can be set up and operated in a batch processing mode on any System/360 or 370 that has the memory capacity. The model requires 210K bytes of core storage, and the optimization program, OPSET (which was used previous to but not in this study), requires 240K bytes. The data band for one small watershed requires approximately 32 tracks of disk storage.

A reduced successive quadratic programming strategy for errors-in-variables estimation.

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

Tjoa, I.-B.; Biegler, L. T.; Carnegie-Mellon Univ.

Parameter estimation problems in process engineering represent a special class of nonlinear optimization problems, because the maximum likelihood structure of the objective function can be exploited. Within this class, the errors in variables method (EVM) is particularly interesting. Here we seek a weighted least-squares fit to the measurements with an underdetermined process model. Thus, both the number of variables and degrees of freedom available for optimization increase linearly with the number of data sets. Large optimization problems of this type can be particularly challenging and expensive to solve because, for general-purpose nonlinear programming (NLP) algorithms, the computational effort increases atmore » least quadratically with problem size. In this study we develop a tailored NLP strategy for EVM problems. The method is based on a reduced Hessian approach to successive quadratic programming (SQP), but with the decomposition performed separately for each data set. This leads to the elimination of all variables but the model parameters, which are determined by a QP coordination step. In this way the computational effort remains linear in the number of data sets. Moreover, unlike previous approaches to the EVM problem, global and superlinear properties of the SQP algorithm apply naturally. Also, the method directly incorporates inequality constraints on the model parameters (although not on the fitted variables). This approach is demonstrated on five example problems with up to 102 degrees of freedom. Compared to general-purpose NLP algorithms, large improvements in computational performance are observed.« less

A knowledge-based approach to improving optimization techniques in system planning

NASA Technical Reports Server (NTRS)

Momoh, J. A.; Zhang, Z. Z.

1990-01-01

A knowledge-based (KB) approach to improve mathematical programming techniques used in the system planning environment is presented. The KB system assists in selecting appropriate optimization algorithms, objective functions, constraints and parameters. The scheme is implemented by integrating symbolic computation of rules derived from operator and planner's experience and is used for generalized optimization packages. The KB optimization software package is capable of improving the overall planning process which includes correction of given violations. The method was demonstrated on a large scale power system discussed in the paper.

Optimization of structures undergoing harmonic or stochastic excitation. Ph.D. Thesis; [atmospheric turbulence and white noise

NASA Technical Reports Server (NTRS)

Johnson, E. H.

1975-01-01

The optimal design was investigated of simple structures subjected to dynamic loads, with constraints on the structures' responses. Optimal designs were examined for one dimensional structures excited by harmonically oscillating loads, similar structures excited by white noise, and a wing in the presence of continuous atmospheric turbulence. The first has constraints on the maximum allowable stress while the last two place bounds on the probability of failure of the structure. Approximations were made to replace the time parameter with a frequency parameter. For the first problem, this involved the steady state response, and in the remaining cases, power spectral techniques were employed to find the root mean square values of the responses. Optimal solutions were found by using computer algorithms which combined finite elements methods with optimization techniques based on mathematical programming. It was found that the inertial loads for these dynamic problems result in optimal structures that are radically different from those obtained for structures loaded statically by forces of comparable magnitude.

Applications of wavelet-based compression to multidimensional Earth science data

NASA Technical Reports Server (NTRS)

Bradley, Jonathan N.; Brislawn, Christopher M.

1993-01-01

A data compression algorithm involving vector quantization (VQ) and the discrete wavelet transform (DWT) is applied to two different types of multidimensional digital earth-science data. The algorithms (WVQ) is optimized for each particular application through an optimization procedure that assigns VQ parameters to the wavelet transform subbands subject to constraints on compression ratio and encoding complexity. Preliminary results of compressing global ocean model data generated on a Thinking Machines CM-200 supercomputer are presented. The WVQ scheme is used in both a predictive and nonpredictive mode. Parameters generated by the optimization algorithm are reported, as are signal-to-noise (SNR) measurements of actual quantized data. The problem of extrapolating hydrodynamic variables across the continental landmasses in order to compute the DWT on a rectangular grid is discussed. Results are also presented for compressing Landsat TM 7-band data using the WVQ scheme. The formulation of the optimization problem is presented along with SNR measurements of actual quantized data. Postprocessing applications are considered in which the seven spectral bands are clustered into 256 clusters using a k-means algorithm and analyzed using the Los Alamos multispectral data analysis program, SPECTRUM, both before and after being compressed using the WVQ program.

Best-Fit Conic Approximation of Spacecraft Trajectory

NASA Technical Reports Server (NTRS)

Singh, Gurkipal

2005-01-01

A computer program calculates a best conic fit of a given spacecraft trajectory. Spacecraft trajectories are often propagated as conics onboard. The conic-section parameters as a result of the best-conic-fit are uplinked to computers aboard the spacecraft for use in updating predictions of the spacecraft trajectory for operational purposes. In the initial application for which this program was written, there is a requirement to fit a single conic section (necessitated by onboard memory constraints) accurate within 200 microradians to a sequence of positions measured over a 4.7-hour interval. The present program supplants a prior one that could not cover the interval with fewer than four successive conic sections. The present program is based on formulating the best-fit conic problem as a parameter-optimization problem and solving the problem numerically, on the ground, by use of a modified steepest-descent algorithm. For the purpose of this algorithm, optimization is defined as minimization of the maximum directional propagation error across the fit interval. In the specific initial application, the program generates a single 4.7-hour conic, the directional propagation of which is accurate to within 34 microradians easily exceeding the mission constraints by a wide margin.

Optimization of Equation of State and Burn Model Parameters for Explosives

NASA Astrophysics Data System (ADS)

Bergh, Magnus; Wedberg, Rasmus; Lundgren, Jonas

2017-06-01

A reactive burn model implemented in a multi-dimensional hydrocode can be a powerful tool for predicting non-ideal effects as well as initiation phenomena in explosives. Calibration against experiment is, however, critical and non-trivial. Here, a procedure is presented for calibrating the Ignition and Growth Model utilizing hydrocode simulation in conjunction with the optimization program LS-OPT. The model is applied to the explosive PBXN-109. First, a cylinder expansion test is presented together with a new automatic routine for product equation of state calibration. Secondly, rate stick tests and instrumented gap tests are presented. Data from these experiments are used to calibrate burn model parameters. Finally, we discuss the applicability and development of this optimization routine.

Optimal parameter estimation with a fixed rate of abstention

NASA Astrophysics Data System (ADS)

Gendra, B.; Ronco-Bonvehi, E.; Calsamiglia, J.; Muñoz-Tapia, R.; Bagan, E.

2013-07-01

The problems of optimally estimating a phase, a direction, and the orientation of a Cartesian frame (or trihedron) with general pure states are addressed. Special emphasis is put on estimation schemes that allow for inconclusive answers or abstention. It is shown that such schemes enable drastic improvements, up to the extent of attaining the Heisenberg limit in some cases, and the required amount of abstention is quantified. A general mathematical framework to deal with the asymptotic limit of many qubits or large angular momentum is introduced and used to obtain analytical results for all the relevant cases under consideration. Parameter estimation with abstention is also formulated as a semidefinite programming problem, for which very efficient numerical optimization techniques exist.

A testbed to explore the optimal electrical stimulation parameters for suppressing inter-ictal spikes in human hippocampal slices.

PubMed

Min-Chi Hsiao; Pen-Ning Yu; Dong Song; Liu, Charles Y; Heck, Christi N; Millett, David; Berger, Theodore W

2014-01-01

New interventions using neuromodulatory devices such as vagus nerve stimulation, deep brain stimulation and responsive neurostimulation are available or under study for the treatment of refractory epilepsy. Since the actual mechanisms of the onset and termination of the seizure are still unclear, most researchers or clinicians determine the optimal stimulation parameters through trial-and-error procedures. It is necessary to further explore what types of electrical stimulation parameters (these may include stimulation frequency, amplitude, duration, interval pattern, and location) constitute a set of optimal stimulation paradigms to suppress seizures. In a previous study, we developed an in vitro epilepsy model using hippocampal slices from patients suffering from mesial temporal lobe epilepsy. Using a planar multi-electrode array system, inter-ictal activity from human hippocampal slices was consistently recorded. In this study, we have further transferred this in vitro seizure model to a testbed for exploring the possible neurostimulation paradigms to inhibit inter-ictal spikes. The methodology used to collect the electrophysiological data, the approach to apply different electrical stimulation parameters to the slices are provided in this paper. The results show that this experimental testbed will provide a platform for testing the optimal stimulation parameters of seizure cessation. We expect this testbed will expedite the process for identifying the most effective parameters, and may ultimately be used to guide programming of new stimulating paradigms for neuromodulatory devices.

Research on design method of the full form ship with minimum thrust deduction factor

NASA Astrophysics Data System (ADS)

Zhang, Bao-ji; Miao, Ai-qin; Zhang, Zhu-xin

2015-04-01

In the preliminary design stage of the full form ships, in order to obtain a hull form with low resistance and maximum propulsion efficiency, an optimization design program for a full form ship with the minimum thrust deduction factor has been developed, which combined the potential flow theory and boundary layer theory with the optimization technique. In the optimization process, the Sequential Unconstrained Minimization Technique (SUMT) interior point method of Nonlinear Programming (NLP) was proposed with the minimum thrust deduction factor as the objective function. An appropriate displacement is a basic constraint condition, and the boundary layer separation is an additional one. The parameters of the hull form modification function are used as design variables. At last, the numerical optimization example for lines of after-body of 50000 DWT product oil tanker was provided, which indicated that the propulsion efficiency was improved distinctly by this optimal design method.

Development and Application of a Tool for Optimizing Composite Matrix Viscoplastic Material Parameters

NASA Technical Reports Server (NTRS)

Murthy, Pappu L. N.; Naghipour Ghezeljeh, Paria; Bednarcyk, Brett A.

2018-01-01

This document describes a recently developed analysis tool that enhances the resident capabilities of the Micromechanics Analysis Code with the Generalized Method of Cells (MAC/GMC) and its application. MAC/GMC is a composite material and laminate analysis software package developed at NASA Glenn Research Center. The primary focus of the current effort is to provide a graphical user interface (GUI) capability that helps users optimize highly nonlinear viscoplastic constitutive law parameters by fitting experimentally observed/measured stress-strain responses under various thermo-mechanical conditions for braided composites. The tool has been developed utilizing the MATrix LABoratory (MATLAB) (The Mathworks, Inc., Natick, MA) programming language. Illustrative examples shown are for a specific braided composite system wherein the matrix viscoplastic behavior is represented by a constitutive law described by seven parameters. The tool is general enough to fit any number of experimentally observed stress-strain responses of the material. The number of parameters to be optimized, as well as the importance given to each stress-strain response, are user choice. Three different optimization algorithms are included: (1) Optimization based on gradient method, (2) Genetic algorithm (GA) based optimization and (3) Particle Swarm Optimization (PSO). The user can mix and match the three algorithms. For example, one can start optimization with either 2 or 3 and then use the optimized solution to further fine tune with approach 1. The secondary focus of this paper is to demonstrate the application of this tool to optimize/calibrate parameters for a nonlinear viscoplastic matrix to predict stress-strain curves (for constituent and composite levels) at different rates, temperatures and/or loading conditions utilizing the Generalized Method of Cells. After preliminary validation of the tool through comparison with experimental results, a detailed virtual parametric study is presented wherein the combined effects of temperature and loading rate on the predicted response of a braided composite is investigated.

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis version 6.0 theory manual

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

Adams, Brian M.; Ebeida, Mohamed Salah; Eldred, Michael S

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components requiredmore » for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quanti cation, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.« less

Computer programs for generation and evaluation of near-optimum vertical flight profiles

NASA Technical Reports Server (NTRS)

Sorensen, J. A.; Waters, M. H.; Patmore, L. C.

1983-01-01

Two extensive computer programs were developed. The first, called OPTIM, generates a reference near-optimum vertical profile, and it contains control options so that the effects of various flight constraints on cost performance can be examined. The second, called TRAGEN, is used to simulate an aircraft flying along an optimum or any other vertical reference profile. TRAGEN is used to verify OPTIM's output, examine the effects of uncertainty in the values of parameters (such as prevailing wind) which govern the optimum profile, or compare the cost performance of profiles generated by different techniques. A general description of these programs, the efforts to add special features to them, and sample results of their usage are presented.

Inexact nonlinear improved fuzzy chance-constrained programming model for irrigation water management under uncertainty

NASA Astrophysics Data System (ADS)

Zhang, Chenglong; Zhang, Fan; Guo, Shanshan; Liu, Xiao; Guo, Ping

2018-01-01

An inexact nonlinear mλ-measure fuzzy chance-constrained programming (INMFCCP) model is developed for irrigation water allocation under uncertainty. Techniques of inexact quadratic programming (IQP), mλ-measure, and fuzzy chance-constrained programming (FCCP) are integrated into a general optimization framework. The INMFCCP model can deal with not only nonlinearities in the objective function, but also uncertainties presented as discrete intervals in the objective function, variables and left-hand side constraints and fuzziness in the right-hand side constraints. Moreover, this model improves upon the conventional fuzzy chance-constrained programming by introducing a linear combination of possibility measure and necessity measure with varying preference parameters. To demonstrate its applicability, the model is then applied to a case study in the middle reaches of Heihe River Basin, northwest China. An interval regression analysis method is used to obtain interval crop water production functions in the whole growth period under uncertainty. Therefore, more flexible solutions can be generated for optimal irrigation water allocation. The variation of results can be examined by giving different confidence levels and preference parameters. Besides, it can reflect interrelationships among system benefits, preference parameters, confidence levels and the corresponding risk levels. Comparison between interval crop water production functions and deterministic ones based on the developed INMFCCP model indicates that the former is capable of reflecting more complexities and uncertainties in practical application. These results can provide more reliable scientific basis for supporting irrigation water management in arid areas.

On the robust optimization to the uncertain vaccination strategy problem

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

Chaerani, D., E-mail: d.chaerani@unpad.ac.id; Anggriani, N., E-mail: d.chaerani@unpad.ac.id; Firdaniza, E-mail: d.chaerani@unpad.ac.id

2014-02-21

In order to prevent an epidemic of infectious diseases, the vaccination coverage needs to be minimized and also the basic reproduction number needs to be maintained below 1. This means that as we get the vaccination coverage as minimum as possible, thus we need to prevent the epidemic to a small number of people who already get infected. In this paper, we discuss the case of vaccination strategy in term of minimizing vaccination coverage, when the basic reproduction number is assumed as an uncertain parameter that lies between 0 and 1. We refer to the linear optimization model for vaccinationmore » strategy that propose by Becker and Starrzak (see [2]). Assuming that there is parameter uncertainty involved, we can see Tanner et al (see [9]) who propose the optimal solution of the problem using stochastic programming. In this paper we discuss an alternative way of optimizing the uncertain vaccination strategy using Robust Optimization (see [3]). In this approach we assume that the parameter uncertainty lies within an ellipsoidal uncertainty set such that we can claim that the obtained result will be achieved in a polynomial time algorithm (as it is guaranteed by the RO methodology). The robust counterpart model is presented.« less

Case studies on optimization problems in MATLAB and COMSOL multiphysics by means of the livelink

NASA Astrophysics Data System (ADS)

Ozana, Stepan; Pies, Martin; Docekal, Tomas

2016-06-01

LiveLink for COMSOL is a tool that integrates COMSOL Multiphysics with MATLAB to extend one's modeling with scripting programming in the MATLAB environment. It allows user to utilize the full power of MATLAB and its toolboxes in preprocessing, model manipulation, and post processing. At first, the head script launches COMSOL with MATLAB and defines initial value of all parameters, refers to the objective function J described in the objective function and creates and runs the defined optimization task. Once the task is launches, the COMSOL model is being called in the iteration loop (from MATLAB environment by use of API interface), changing defined optimization parameters so that the objective function is minimized, using fmincon function to find a local or global minimum of constrained linear or nonlinear multivariable function. Once the minimum is found, it returns exit flag, terminates optimization and returns the optimized values of the parameters. The cooperation with MATLAB via LiveLink enhances a powerful computational environment with complex multiphysics simulations. The paper will introduce using of the LiveLink for COMSOL for chosen case studies in the field of technical cybernetics and bioengineering.

Dynamical modeling and multi-experiment fitting with PottersWheel

PubMed Central

Maiwald, Thomas; Timmer, Jens

2008-01-01

Motivation: Modelers in Systems Biology need a flexible framework that allows them to easily create new dynamic models, investigate their properties and fit several experimental datasets simultaneously. Multi-experiment-fitting is a powerful approach to estimate parameter values, to check the validity of a given model, and to discriminate competing model hypotheses. It requires high-performance integration of ordinary differential equations and robust optimization. Results: We here present the comprehensive modeling framework Potters-Wheel (PW) including novel functionalities to satisfy these requirements with strong emphasis on the inverse problem, i.e. data-based modeling of partially observed and noisy systems like signal transduction pathways and metabolic networks. PW is designed as a MATLAB toolbox and includes numerous user interfaces. Deterministic and stochastic optimization routines are combined by fitting in logarithmic parameter space allowing for robust parameter calibration. Model investigation includes statistical tests for model-data-compliance, model discrimination, identifiability analysis and calculation of Hessian- and Monte-Carlo-based parameter confidence limits. A rich application programming interface is available for customization within own MATLAB code. Within an extensive performance analysis, we identified and significantly improved an integrator–optimizer pair which decreases the fitting duration for a realistic benchmark model by a factor over 3000 compared to MATLAB with optimization toolbox. Availability: PottersWheel is freely available for academic usage at http://www.PottersWheel.de/. The website contains a detailed documentation and introductory videos. The program has been intensively used since 2005 on Windows, Linux and Macintosh computers and does not require special MATLAB toolboxes. Contact: maiwald@fdm.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:18614583

Multiobjective optimization techniques for structural design

NASA Technical Reports Server (NTRS)

Rao, S. S.

1984-01-01

The multiobjective programming techniques are important in the design of complex structural systems whose quality depends generally on a number of different and often conflicting objective functions which cannot be combined into a single design objective. The applicability of multiobjective optimization techniques is studied with reference to simple design problems. Specifically, the parameter optimization of a cantilever beam with a tip mass and a three-degree-of-freedom vabration isolation system and the trajectory optimization of a cantilever beam are considered. The solutions of these multicriteria design problems are attempted by using global criterion, utility function, game theory, goal programming, goal attainment, bounded objective function, and lexicographic methods. It has been observed that the game theory approach required the maximum computational effort, but it yielded better optimum solutions with proper balance of the various objective functions in all the cases.

Optimization strategies based on sequential quadratic programming applied for a fermentation process for butanol production.

PubMed

Pinto Mariano, Adriano; Bastos Borba Costa, Caliane; de Franceschi de Angelis, Dejanira; Maugeri Filho, Francisco; Pires Atala, Daniel Ibraim; Wolf Maciel, Maria Regina; Maciel Filho, Rubens

2009-11-01

In this work, the mathematical optimization of a continuous flash fermentation process for the production of biobutanol was studied. The process consists of three interconnected units, as follows: fermentor, cell-retention system (tangential microfiltration), and vacuum flash vessel (responsible for the continuous recovery of butanol from the broth). The objective of the optimization was to maximize butanol productivity for a desired substrate conversion. Two strategies were compared for the optimization of the process. In one of them, the process was represented by a deterministic model with kinetic parameters determined experimentally and, in the other, by a statistical model obtained using the factorial design technique combined with simulation. For both strategies, the problem was written as a nonlinear programming problem and was solved with the sequential quadratic programming technique. The results showed that despite the very similar solutions obtained with both strategies, the problems found with the strategy using the deterministic model, such as lack of convergence and high computational time, make the use of the optimization strategy with the statistical model, which showed to be robust and fast, more suitable for the flash fermentation process, being recommended for real-time applications coupling optimization and control.

Reduction of shock induced noise in imperfectly expanded supersonic jets using convex optimization

NASA Astrophysics Data System (ADS)

Adhikari, Sam

2007-11-01

Imperfectly expanded jets generate screech noise. The imbalance between the backpressure and the exit pressure of the imperfectly expanded jets produce shock cells and expansion or compression waves from the nozzle. The instability waves and the shock cells interact to generate the screech sound. The mathematical model consists of cylindrical coordinate based full Navier-Stokes equations and large-eddy-simulation turbulence modeling. Analytical and computational analysis of the three-dimensional helical effects provide a model that relates several parameters with shock cell patterns, screech frequency and distribution of shock generation locations. Convex optimization techniques minimize the shock cell patterns and the instability waves. The objective functions are (convex) quadratic and the constraint functions are affine. In the quadratic optimization programs, minimization of the quadratic functions over a set of polyhedrons provides the optimal result. Various industry standard methods like regression analysis, distance between polyhedra, bounding variance, Markowitz optimization, and second order cone programming is used for Quadratic Optimization.

Quantum Optimization of Fully Connected Spin Glasses

NASA Astrophysics Data System (ADS)

Venturelli, Davide; Mandrà, Salvatore; Knysh, Sergey; O'Gorman, Bryan; Biswas, Rupak; Smelyanskiy, Vadim

2015-07-01

Many NP-hard problems can be seen as the task of finding a ground state of a disordered highly connected Ising spin glass. If solutions are sought by means of quantum annealing, it is often necessary to represent those graphs in the annealer's hardware by means of the graph-minor embedding technique, generating a final Hamiltonian consisting of coupled chains of ferromagnetically bound spins, whose binding energy is a free parameter. In order to investigate the effect of embedding on problems of interest, the fully connected Sherrington-Kirkpatrick model with random ±1 couplings is programmed on the D-Wave TwoTM annealer using up to 270 qubits interacting on a Chimera-type graph. We present the best embedding prescriptions for encoding the Sherrington-Kirkpatrick problem in the Chimera graph. The results indicate that the optimal choice of embedding parameters could be associated with the emergence of the spin-glass phase of the embedded problem, whose presence was previously uncertain. This optimal parameter setting allows the performance of the quantum annealer to compete with (and potentially outperform, in the absence of analog control errors) optimized simulated annealing algorithms.

Nonlinear programming extensions to rational function approximations of unsteady aerodynamics

NASA Technical Reports Server (NTRS)

Tiffany, Sherwood H.; Adams, William M., Jr.

1987-01-01

This paper deals with approximating unsteady generalized aerodynamic forces in the equations of motion of a flexible aircraft. Two methods of formulating these approximations are extended to include both the same flexibility in constraining them and the same methodology in optimizing nonlinear parameters as another currently used 'extended least-squares' method. Optimal selection of 'nonlinear' parameters is made in each of the three methods by use of the same nonlinear (nongradient) optimizer. The objective of the nonlinear optimization is to obtain rational approximations to the unsteady aerodynamics whose state-space realization is of lower order than that required when no optimization of the nonlinear terms is performed. The free 'linear' parameters are determined using least-squares matrix techniques on a Lagrange multiplier formulation of an objective function which incorporates selected linear equality constraints. State-space mathematical models resulting from the different approaches are described, and results are presented which show comparative evaluations from application of each of the extended methods to a numerical example. The results obtained for the example problem show a significant (up to 63 percent) reduction in the number of differential equations used to represent the unsteady aerodynamic forces in linear time-invariant equations of motion as compared to a conventional method in which nonlinear terms are not optimized.

Use of optimization to predict the effect of selected parameters on commuter aircraft performance

NASA Technical Reports Server (NTRS)

Wells, V. L.; Shevell, R. S.

1982-01-01

An optimizing computer program determined the turboprop aircraft with lowest direct operating cost for various sets of cruise speed and field length constraints. External variables included wing area, wing aspect ratio and engine sea level static horsepower; tail sizes, climb speed and cruise altitude were varied within the function evaluation program. Direct operating cost was minimized for a 150 n.mi typical mission. Generally, DOC increased with increasing speed and decreasing field length but not by a large amount. Ride roughness, however, increased considerably as speed became higher and field length became shorter.

Optimal pricing and marketing planning for deteriorating items

PubMed Central

Moosavi Tabatabaei, Seyed Reza; Sadjadi, Seyed Jafar; Makui, Ahmad

2017-01-01

Optimal pricing and marketing planning plays an essential role in production decisions on deteriorating items. This paper presents a mathematical model for a three-level supply chain, which includes one producer, one distributor and one retailer. The proposed study considers the production of a deteriorating item where demand is influenced by price, marketing expenditure, quality of product and after-sales service expenditures. The proposed model is formulated as a geometric programming with 5 degrees of difficulty and the problem is solved using the recent advances in optimization techniques. The study is supported by several numerical examples and sensitivity analysis is performed to analyze the effects of the changes in different parameters on the optimal solution. The preliminary results indicate that with the change in parameters influencing on demand, inventory holding, inventory deteriorating and set-up costs change and also significantly affect total revenue. PMID:28306750

Drug efficiency: a new concept to guide lead optimization programs towards the selection of better clinical candidates.

PubMed

Braggio, Simone; Montanari, Dino; Rossi, Tino; Ratti, Emiliangelo

2010-07-01

As a result of their wide acceptance and conceptual simplicity, drug-like concepts are having a major influence on the drug discovery process, particularly in the selection of the 'optimal' absorption, distribution, metabolism, excretion and toxicity and physicochemical parameters space. While they have an undisputable value when assessing the potential of lead series or in evaluating inherent risk of a portfolio of drug candidates, they result much less useful in weighing up compounds for the selection of the best potential clinical candidate. We introduce the concept of drug efficiency as a new tool both to guide the drug discovery program teams during the lead optimization phase and to better assess the developability potential of a drug candidate.

Optimization of brain PET imaging for a multicentre trial: the French CATI experience.

PubMed

Habert, Marie-Odile; Marie, Sullivan; Bertin, Hugo; Reynal, Moana; Martini, Jean-Baptiste; Diallo, Mamadou; Kas, Aurélie; Trébossen, Régine

2016-12-01

CATI is a French initiative launched in 2010 to handle the neuroimaging of a large cohort of subjects recruited for an Alzheimer's research program called MEMENTO. This paper presents our test protocol and results obtained for the 22 PET centres (overall 13 different scanners) involved in the MEMENTO cohort. We determined acquisition parameters using phantom experiments prior to patient studies, with the aim of optimizing PET quantitative values to the highest possible per site, while reducing, if possible, variability across centres. Jaszczak's and 3D-Hoffman's phantom measurements were used to assess image spatial resolution (ISR), recovery coefficients (RC) in hot and cold spheres, and signal-to-noise ratio (SNR). For each centre, the optimal reconstruction parameters were chosen as those maximizing ISR and RC without a noticeable decrease in SNR. Point-spread-function (PSF) modelling reconstructions were discarded. The three figures of merit extracted from the images reconstructed with optimized parameters and routine schemes were compared, as were volumes of interest ratios extracted from Hoffman acquisitions. The net effect of the 3D-OSEM reconstruction parameter optimization was investigated on a subset of 18 scanners without PSF modelling reconstruction. Compared to the routine parameters of the 22 PET centres, average RC in the two smallest hot and cold spheres and average ISR remained stable or were improved with the optimized reconstruction, at the expense of slight SNR degradation, while the dispersion of values was reduced. For the subset of scanners without PSF modelling, the mean RC of the smallest hot sphere obtained with the optimized reconstruction was significantly higher than with routine reconstruction. The putamen and caudate-to-white matter ratios measured on 3D-Hoffman acquisitions of all centres were also significantly improved by the optimization, while the variance was reduced. This study provides guidelines for optimizing quantitative results for multicentric PET neuroimaging trials.

Vibrational energy distribution analysis (VEDA): scopes and limitations.

PubMed

Jamróz, Michał H

2013-10-01

The principle of operations of the VEDA program written by the author for Potential Energy Distribution (PED) analysis of theoretical vibrational spectra is described. Nowadays, the PED analysis is indispensible tool in serious analysis of the vibrational spectra. To perform the PED analysis it is necessary to define 3N-6 linearly independent local mode coordinates. Already for 20-atomic molecules it is a difficult task. The VEDA program reads the input data automatically from the Gaussian program output files. Then, VEDA automatically proposes an introductory set of local mode coordinates. Next, the more adequate coordinates are proposed by the program and optimized to obtain maximal elements of each column (internal coordinate) of the PED matrix (the EPM parameter). The possibility for an automatic optimization of PED contributions is a unique feature of the VEDA program absent in any other programs performing PED analysis. Copyright © 2013 Elsevier B.V. All rights reserved.

Vibrational Energy Distribution Analysis (VEDA): Scopes and limitations

NASA Astrophysics Data System (ADS)

Jamróz, Michał H.

2013-10-01

The principle of operations of the VEDA program written by the author for Potential Energy Distribution (PED) analysis of theoretical vibrational spectra is described. Nowadays, the PED analysis is indispensible tool in serious analysis of the vibrational spectra. To perform the PED analysis it is necessary to define 3N-6 linearly independent local mode coordinates. Already for 20-atomic molecules it is a difficult task. The VEDA program reads the input data automatically from the Gaussian program output files. Then, VEDA automatically proposes an introductory set of local mode coordinates. Next, the more adequate coordinates are proposed by the program and optimized to obtain maximal elements of each column (internal coordinate) of the PED matrix (the EPM parameter). The possibility for an automatic optimization of PED contributions is a unique feature of the VEDA program absent in any other programs performing PED analysis.

Development of an Algorithm for Automatic Analysis of the Impedance Spectrum Based on a Measurement Model

NASA Astrophysics Data System (ADS)

Kobayashi, Kiyoshi; Suzuki, Tohru S.

2018-03-01

A new algorithm for the automatic estimation of an equivalent circuit and the subsequent parameter optimization is developed by combining the data-mining concept and complex least-squares method. In this algorithm, the program generates an initial equivalent-circuit model based on the sampling data and then attempts to optimize the parameters. The basic hypothesis is that the measured impedance spectrum can be reproduced by the sum of the partial-impedance spectra presented by the resistor, inductor, resistor connected in parallel to a capacitor, and resistor connected in parallel to an inductor. The adequacy of the model is determined by using a simple artificial-intelligence function, which is applied to the output function of the Levenberg-Marquardt module. From the iteration of model modifications, the program finds an adequate equivalent-circuit model without any user input to the equivalent-circuit model.

Automated design and optimization of flexible booster autopilots via linear programming, volume 1

NASA Technical Reports Server (NTRS)

Hauser, F. D.

1972-01-01

A nonlinear programming technique was developed for the automated design and optimization of autopilots for large flexible launch vehicles. This technique, which resulted in the COEBRA program, uses the iterative application of linear programming. The method deals directly with the three main requirements of booster autopilot design: to provide (1) good response to guidance commands; (2) response to external disturbances (e.g. wind) to minimize structural bending moment loads and trajectory dispersions; and (3) stability with specified tolerances on the vehicle and flight control system parameters. The method is applicable to very high order systems (30th and greater per flight condition). Examples are provided that demonstrate the successful application of the employed algorithm to the design of autopilots for both single and multiple flight conditions.

Optimized multiple quantum MAS lineshape simulations in solid state NMR

NASA Astrophysics Data System (ADS)

Brouwer, William J.; Davis, Michael C.; Mueller, Karl T.

2009-10-01

The majority of nuclei available for study in solid state Nuclear Magnetic Resonance have half-integer spin I>1/2, with corresponding electric quadrupole moment. As such, they may couple with a surrounding electric field gradient. This effect introduces anisotropic line broadening to spectra, arising from distinct chemical species within polycrystalline solids. In Multiple Quantum Magic Angle Spinning (MQMAS) experiments, a second frequency dimension is created, devoid of quadrupolar anisotropy. As a result, the center of gravity of peaks in the high resolution dimension is a function of isotropic second order quadrupole and chemical shift alone. However, for complex materials, these parameters take on a stochastic nature due in turn to structural and chemical disorder. Lineshapes may still overlap in the isotropic dimension, complicating the task of assignment and interpretation. A distributed computational approach is presented here which permits simulation of the two-dimensional MQMAS spectrum, generated by random variates from model distributions of isotropic chemical and quadrupole shifts. Owing to the non-convex nature of the residual sum of squares (RSS) function between experimental and simulated spectra, simulated annealing is used to optimize the simulation parameters. In this manner, local chemical environments for disordered materials may be characterized, and via a re-sampling approach, error estimates for parameters produced. Program summaryProgram title: mqmasOPT Catalogue identifier: AEEC_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEEC_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 3650 No. of bytes in distributed program, including test data, etc.: 73 853 Distribution format: tar.gz Programming language: C, OCTAVE Computer: UNIX/Linux Operating system: UNIX/Linux Has the code been vectorised or parallelized?: Yes RAM: Example: (1597 powder angles) × (200 Samples) × (81 F2 frequency pts) × (31 F1 frequency points) = 3.5M, SMP AMD opteron Classification: 2.3 External routines: OCTAVE ( http://www.gnu.org/software/octave/), GNU Scientific Library ( http://www.gnu.org/software/gsl/), OPENMP ( http://openmp.org/wp/) Nature of problem: The optimal simulation and modeling of multiple quantum magic angle spinning NMR spectra, for general systems, especially those with mild to significant disorder. The approach outlined and implemented in C and OCTAVE also produces model parameter error estimates. Solution method: A model for each distinct chemical site is first proposed, for the individual contribution of crystallite orientations to the spectrum. This model is averaged over all powder angles [1], as well as the (stochastic) parameters; isotropic chemical shift and quadrupole coupling constant. The latter is accomplished via sampling from a bi-variate Gaussian distribution, using the Box-Muller algorithm to transform Sobol (quasi) random numbers [2]. A simulated annealing optimization is performed, and finally the non-linear jackknife [3] is applied in developing model parameter error estimates. Additional comments: The distribution contains a script, mqmasOpt.m, which runs in the OCTAVE language workspace. Running time: Example: (1597 powder angles) × (200 Samples) × (81 F2 frequency pts) × (31 F1 frequency points) = 58.35 seconds, SMP AMD opteron. References:S.K. Zaremba, Annali di Matematica Pura ed Applicata 73 (1966) 293. H. Niederreiter, Random Number Generation and Quasi-Monte Carlo Methods, SIAM, 1992. T. Fox, D. Hinkley, K. Larntz, Technometrics 22 (1980) 29.

Development Optimization and Uncertainty Analysis Methods for Oil and Gas Reservoirs

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

Ettehadtavakkol, Amin, E-mail: amin.ettehadtavakkol@ttu.edu; Jablonowski, Christopher; Lake, Larry

Uncertainty complicates the development optimization of oil and gas exploration and production projects, but methods have been devised to analyze uncertainty and its impact on optimal decision-making. This paper compares two methods for development optimization and uncertainty analysis: Monte Carlo (MC) simulation and stochastic programming. Two example problems for a gas field development and an oilfield development are solved and discussed to elaborate the advantages and disadvantages of each method. Development optimization involves decisions regarding the configuration of initial capital investment and subsequent operational decisions. Uncertainty analysis involves the quantification of the impact of uncertain parameters on the optimum designmore » concept. The gas field development problem is designed to highlight the differences in the implementation of the two methods and to show that both methods yield the exact same optimum design. The results show that both MC optimization and stochastic programming provide unique benefits, and that the choice of method depends on the goal of the analysis. While the MC method generates more useful information, along with the optimum design configuration, the stochastic programming method is more computationally efficient in determining the optimal solution. Reservoirs comprise multiple compartments and layers with multiphase flow of oil, water, and gas. We present a workflow for development optimization under uncertainty for these reservoirs, and solve an example on the design optimization of a multicompartment, multilayer oilfield development.« less

An efficient hybrid approach for multiobjective optimization of water distribution systems

NASA Astrophysics Data System (ADS)

Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.

2014-05-01

An efficient hybrid approach for the design of water distribution systems (WDSs) with multiple objectives is described in this paper. The objectives are the minimization of the network cost and maximization of the network resilience. A self-adaptive multiobjective differential evolution (SAMODE) algorithm has been developed, in which control parameters are automatically adapted by means of evolution instead of the presetting of fine-tuned parameter values. In the proposed method, a graph algorithm is first used to decompose a looped WDS into a shortest-distance tree (T) or forest, and chords (Ω). The original two-objective optimization problem is then approximated by a series of single-objective optimization problems of the T to be solved by nonlinear programming (NLP), thereby providing an approximate Pareto optimal front for the original whole network. Finally, the solutions at the approximate front are used to seed the SAMODE algorithm to find an improved front for the original entire network. The proposed approach is compared with two other conventional full-search optimization methods (the SAMODE algorithm and the NSGA-II) that seed the initial population with purely random solutions based on three case studies: a benchmark network and two real-world networks with multiple demand loading cases. Results show that (i) the proposed NLP-SAMODE method consistently generates better-quality Pareto fronts than the full-search methods with significantly improved efficiency; and (ii) the proposed SAMODE algorithm (no parameter tuning) exhibits better performance than the NSGA-II with calibrated parameter values in efficiently offering optimal fronts.

MIDAS - Mission design and analysis software for the optimization of ballistic interplanetary trajectories

NASA Technical Reports Server (NTRS)

Sauer, Carl G., Jr.

1989-01-01

A patched conic trajectory optimization program MIDAS is described that was developed to investigate a wide variety of complex ballistic heliocentric transfer trajectories. MIDAS includes the capability of optimizing trajectory event times such as departure date, arrival date, and intermediate planetary flyby dates and is able to both add and delete deep space maneuvers when dictated by the optimization process. Both powered and unpowered flyby or gravity assist trajectories of intermediate bodies can be handled and capability is included to optimize trajectories having a rendezvous with an intermediate body such as for a sample return mission. Capability is included in the optimization process to constrain launch energy and launch vehicle parking orbit parameters.

PSC algorithm description

NASA Technical Reports Server (NTRS)

Nobbs, Steven G.

1995-01-01

An overview of the performance seeking control (PSC) algorithm and details of the important components of the algorithm are given. The onboard propulsion system models, the linear programming optimization, and engine control interface are described. The PSC algorithm receives input from various computers on the aircraft including the digital flight computer, digital engine control, and electronic inlet control. The PSC algorithm contains compact models of the propulsion system including the inlet, engine, and nozzle. The models compute propulsion system parameters, such as inlet drag and fan stall margin, which are not directly measurable in flight. The compact models also compute sensitivities of the propulsion system parameters to change in control variables. The engine model consists of a linear steady state variable model (SSVM) and a nonlinear model. The SSVM is updated with efficiency factors calculated in the engine model update logic, or Kalman filter. The efficiency factors are used to adjust the SSVM to match the actual engine. The propulsion system models are mathematically integrated to form an overall propulsion system model. The propulsion system model is then optimized using a linear programming optimization scheme. The goal of the optimization is determined from the selected PSC mode of operation. The resulting trims are used to compute a new operating point about which the optimization process is repeated. This process is continued until an overall (global) optimum is reached before applying the trims to the controllers.

Discrete-State Simulated Annealing For Traveling-Wave Tube Slow-Wave Circuit Optimization

NASA Technical Reports Server (NTRS)

Wilson, Jeffrey D.; Bulson, Brian A.; Kory, Carol L.; Williams, W. Dan (Technical Monitor)

2001-01-01

Algorithms based on the global optimization technique of simulated annealing (SA) have proven useful in designing traveling-wave tube (TWT) slow-wave circuits for high RF power efficiency. The characteristic of SA that enables it to determine a globally optimized solution is its ability to accept non-improving moves in a controlled manner. In the initial stages of the optimization, the algorithm moves freely through configuration space, accepting most of the proposed designs. This freedom of movement allows non-intuitive designs to be explored rather than restricting the optimization to local improvement upon the initial configuration. As the optimization proceeds, the rate of acceptance of non-improving moves is gradually reduced until the algorithm converges to the optimized solution. The rate at which the freedom of movement is decreased is known as the annealing or cooling schedule of the SA algorithm. The main disadvantage of SA is that there is not a rigorous theoretical foundation for determining the parameters of the cooling schedule. The choice of these parameters is highly problem dependent and the designer needs to experiment in order to determine values that will provide a good optimization in a reasonable amount of computational time. This experimentation can absorb a large amount of time especially when the algorithm is being applied to a new type of design. In order to eliminate this disadvantage, a variation of SA known as discrete-state simulated annealing (DSSA), was recently developed. DSSA provides the theoretical foundation for a generic cooling schedule which is problem independent, Results of similar quality to SA can be obtained, but without the extra computational time required to tune the cooling parameters. Two algorithm variations based on DSSA were developed and programmed into a Microsoft Excel spreadsheet graphical user interface (GUI) to the two-dimensional nonlinear multisignal helix traveling-wave amplifier analysis program TWA3. The algorithms were used to optimize the computed RF efficiency of a TWT by determining the phase velocity profile of the slow-wave circuit. The mathematical theory and computational details of the DSSA algorithms will be presented and results will be compared to those obtained with a SA algorithm.

Atomic modeling of cryo-electron microscopy reconstructions--joint refinement of model and imaging parameters.

PubMed

Chapman, Michael S; Trzynka, Andrew; Chapman, Brynmor K

2013-04-01

When refining the fit of component atomic structures into electron microscopic reconstructions, use of a resolution-dependent atomic density function makes it possible to jointly optimize the atomic model and imaging parameters of the microscope. Atomic density is calculated by one-dimensional Fourier transform of atomic form factors convoluted with a microscope envelope correction and a low-pass filter, allowing refinement of imaging parameters such as resolution, by optimizing the agreement of calculated and experimental maps. A similar approach allows refinement of atomic displacement parameters, providing indications of molecular flexibility even at low resolution. A modest improvement in atomic coordinates is possible following optimization of these additional parameters. Methods have been implemented in a Python program that can be used in stand-alone mode for rigid-group refinement, or embedded in other optimizers for flexible refinement with stereochemical restraints. The approach is demonstrated with refinements of virus and chaperonin structures at resolutions of 9 through 4.5 Å, representing regimes where rigid-group and fully flexible parameterizations are appropriate. Through comparisons to known crystal structures, flexible fitting by RSRef is shown to be an improvement relative to other methods and to generate models with all-atom rms accuracies of 1.5-2.5 Å at resolutions of 4.5-6 Å. Copyright © 2013 Elsevier Inc. All rights reserved.

SOL - SIZING AND OPTIMIZATION LANGUAGE COMPILER

NASA Technical Reports Server (NTRS)

Scotti, S. J.

1994-01-01

SOL is a computer language which is geared to solving design problems. SOL includes the mathematical modeling and logical capabilities of a computer language like FORTRAN but also includes the additional power of non-linear mathematical programming methods (i.e. numerical optimization) at the language level (as opposed to the subroutine level). The language-level use of optimization has several advantages over the traditional, subroutine-calling method of using an optimizer: first, the optimization problem is described in a concise and clear manner which closely parallels the mathematical description of optimization; second, a seamless interface is automatically established between the optimizer subroutines and the mathematical model of the system being optimized; third, the results of an optimization (objective, design variables, constraints, termination criteria, and some or all of the optimization history) are output in a form directly related to the optimization description; and finally, automatic error checking and recovery from an ill-defined system model or optimization description is facilitated by the language-level specification of the optimization problem. Thus, SOL enables rapid generation of models and solutions for optimum design problems with greater confidence that the problem is posed correctly. The SOL compiler takes SOL-language statements and generates the equivalent FORTRAN code and system calls. Because of this approach, the modeling capabilities of SOL are extended by the ability to incorporate existing FORTRAN code into a SOL program. In addition, SOL has a powerful MACRO capability. The MACRO capability of the SOL compiler effectively gives the user the ability to extend the SOL language and can be used to develop easy-to-use shorthand methods of generating complex models and solution strategies. The SOL compiler provides syntactic and semantic error-checking, error recovery, and detailed reports containing cross-references to show where each variable was used. The listings summarize all optimizations, listing the objective functions, design variables, and constraints. The compiler offers error-checking specific to optimization problems, so that simple mistakes will not cost hours of debugging time. The optimization engine used by and included with the SOL compiler is a version of Vanderplatt's ADS system (Version 1.1) modified specifically to work with the SOL compiler. SOL allows the use of the over 100 ADS optimization choices such as Sequential Quadratic Programming, Modified Feasible Directions, interior and exterior penalty function and variable metric methods. Default choices of the many control parameters of ADS are made for the user, however, the user can override any of the ADS control parameters desired for each individual optimization. The SOL language and compiler were developed with an advanced compiler-generation system to ensure correctness and simplify program maintenance. Thus, SOL's syntax was defined precisely by a LALR(1) grammar and the SOL compiler's parser was generated automatically from the LALR(1) grammar with a parser-generator. Hence unlike ad hoc, manually coded interfaces, the SOL compiler's lexical analysis insures that the SOL compiler recognizes all legal SOL programs, can recover from and correct for many errors and report the location of errors to the user. This version of the SOL compiler has been implemented on VAX/VMS computer systems and requires 204 KB of virtual memory to execute. Since the SOL compiler produces FORTRAN code, it requires the VAX FORTRAN compiler to produce an executable program. The SOL compiler consists of 13,000 lines of Pascal code. It was developed in 1986 and last updated in 1988. The ADS and other utility subroutines amount to 14,000 lines of FORTRAN code and were also updated in 1988.

MODOPTIM: A general optimization program for ground-water flow model calibration and ground-water management with MODFLOW

USGS Publications Warehouse

Halford, Keith J.

2006-01-01

MODOPTIM is a non-linear ground-water model calibration and management tool that simulates flow with MODFLOW-96 as a subroutine. A weighted sum-of-squares objective function defines optimal solutions for calibration and management problems. Water levels, discharges, water quality, subsidence, and pumping-lift costs are the five direct observation types that can be compared in MODOPTIM. Differences between direct observations of the same type can be compared to fit temporal changes and spatial gradients. Water levels in pumping wells, wellbore storage in the observation wells, and rotational translation of observation wells also can be compared. Negative and positive residuals can be weighted unequally so inequality constraints such as maximum chloride concentrations or minimum water levels can be incorporated in the objective function. Optimization parameters are defined with zones and parameter-weight matrices. Parameter change is estimated iteratively with a quasi-Newton algorithm and is constrained to a user-defined maximum parameter change per iteration. Parameters that are less sensitive than a user-defined threshold are not estimated. MODOPTIM facilitates testing more conceptual models by expediting calibration of each conceptual model. Examples of applying MODOPTIM to aquifer-test analysis, ground-water management, and parameter estimation problems are presented.

Proceedings of the Workshop on Computational Aspects in the Control of Flexible Systems, part 2

NASA Technical Reports Server (NTRS)

Taylor, Lawrence W., Jr. (Compiler)

1989-01-01

The Control/Structures Integration Program, a survey of available software for control of flexible structures, computational efficiency and capability, modeling and parameter estimation, and control synthesis and optimization software are discussed.

Multiobjective optimization in structural design with uncertain parameters and stochastic processes

NASA Technical Reports Server (NTRS)

Rao, S. S.

1984-01-01

The application of multiobjective optimization techniques to structural design problems involving uncertain parameters and random processes is studied. The design of a cantilever beam with a tip mass subjected to a stochastic base excitation is considered for illustration. Several of the problem parameters are assumed to be random variables and the structural mass, fatigue damage, and negative of natural frequency of vibration are considered for minimization. The solution of this three-criteria design problem is found by using global criterion, utility function, game theory, goal programming, goal attainment, bounded objective function, and lexicographic methods. It is observed that the game theory approach is superior in finding a better optimum solution, assuming the proper balance of the various objective functions. The procedures used in the present investigation are expected to be useful in the design of general dynamic systems involving uncertain parameters, stochastic process, and multiple objectives.

Natural Environmental Service Support to NASA Vehicle, Technology, and Sensor Development Programs

NASA Technical Reports Server (NTRS)

1993-01-01

The research performed under this contract involved definition of the natural environmental parameters affecting the design, development, and operation of space and launch vehicles. The Universities Space Research Association (USRA) provided the manpower and resources to accomplish the following tasks: defining environmental parameters critical for design, development, and operation of launch vehicles; defining environmental forecasts required to assure optimal utilization of launch vehicles; and defining orbital environments of operation and developing models on environmental parameters affecting launch vehicle operations.

Swarm intelligence-based approach for optimal design of CMOS differential amplifier and comparator circuit using a hybrid salp swarm algorithm

NASA Astrophysics Data System (ADS)

Asaithambi, Sasikumar; Rajappa, Muthaiah

2018-05-01

In this paper, an automatic design method based on a swarm intelligence approach for CMOS analog integrated circuit (IC) design is presented. The hybrid meta-heuristics optimization technique, namely, the salp swarm algorithm (SSA), is applied to the optimal sizing of a CMOS differential amplifier and the comparator circuit. SSA is a nature-inspired optimization algorithm which mimics the navigating and hunting behavior of salp. The hybrid SSA is applied to optimize the circuit design parameters and to minimize the MOS transistor sizes. The proposed swarm intelligence approach was successfully implemented for an automatic design and optimization of CMOS analog ICs using Generic Process Design Kit (GPDK) 180 nm technology. The circuit design parameters and design specifications are validated through a simulation program for integrated circuit emphasis simulator. To investigate the efficiency of the proposed approach, comparisons have been carried out with other simulation-based circuit design methods. The performances of hybrid SSA based CMOS analog IC designs are better than the previously reported studies.

Swarm intelligence-based approach for optimal design of CMOS differential amplifier and comparator circuit using a hybrid salp swarm algorithm.

PubMed

Asaithambi, Sasikumar; Rajappa, Muthaiah

2018-05-01

In this paper, an automatic design method based on a swarm intelligence approach for CMOS analog integrated circuit (IC) design is presented. The hybrid meta-heuristics optimization technique, namely, the salp swarm algorithm (SSA), is applied to the optimal sizing of a CMOS differential amplifier and the comparator circuit. SSA is a nature-inspired optimization algorithm which mimics the navigating and hunting behavior of salp. The hybrid SSA is applied to optimize the circuit design parameters and to minimize the MOS transistor sizes. The proposed swarm intelligence approach was successfully implemented for an automatic design and optimization of CMOS analog ICs using Generic Process Design Kit (GPDK) 180 nm technology. The circuit design parameters and design specifications are validated through a simulation program for integrated circuit emphasis simulator. To investigate the efficiency of the proposed approach, comparisons have been carried out with other simulation-based circuit design methods. The performances of hybrid SSA based CMOS analog IC designs are better than the previously reported studies.

DAKOTA Design Analysis Kit for Optimization and Terascale

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

Adams, Brian M.; Dalbey, Keith R.; Eldred, Michael S.

2010-02-24

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes (computational models) and iterative analysis methods. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and analysis of computational models on high performance computers.A user provides a set of DAKOTA commands in an input file and launches DAKOTA. DAKOTA invokes instances of the computational models, collects their results, and performs systems analyses. DAKOTA contains algorithms for optimization with gradient and nongradient-basedmore » methods; uncertainty quantification with sampling, reliability, polynomial chaos, stochastic collocation, and epistemic methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as hybrid optimization, surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. Services for parallel computing, simulation interfacing, approximation modeling, fault tolerance, restart, and graphics are also included.« less

MM Algorithms for Geometric and Signomial Programming

PubMed Central

Lange, Kenneth; Zhou, Hua

2013-01-01

This paper derives new algorithms for signomial programming, a generalization of geometric programming. The algorithms are based on a generic principle for optimization called the MM algorithm. In this setting, one can apply the geometric-arithmetic mean inequality and a supporting hyperplane inequality to create a surrogate function with parameters separated. Thus, unconstrained signomial programming reduces to a sequence of one-dimensional minimization problems. Simple examples demonstrate that the MM algorithm derived can converge to a boundary point or to one point of a continuum of minimum points. Conditions under which the minimum point is unique or occurs in the interior of parameter space are proved for geometric programming. Convergence to an interior point occurs at a linear rate. Finally, the MM framework easily accommodates equality and inequality constraints of signomial type. For the most important special case, constrained quadratic programming, the MM algorithm involves very simple updates. PMID:24634545

MM Algorithms for Geometric and Signomial Programming.

PubMed

Lange, Kenneth; Zhou, Hua

2014-02-01

This paper derives new algorithms for signomial programming, a generalization of geometric programming. The algorithms are based on a generic principle for optimization called the MM algorithm. In this setting, one can apply the geometric-arithmetic mean inequality and a supporting hyperplane inequality to create a surrogate function with parameters separated. Thus, unconstrained signomial programming reduces to a sequence of one-dimensional minimization problems. Simple examples demonstrate that the MM algorithm derived can converge to a boundary point or to one point of a continuum of minimum points. Conditions under which the minimum point is unique or occurs in the interior of parameter space are proved for geometric programming. Convergence to an interior point occurs at a linear rate. Finally, the MM framework easily accommodates equality and inequality constraints of signomial type. For the most important special case, constrained quadratic programming, the MM algorithm involves very simple updates.

Computerized optimization of radioimmunoassays for hCG and estradiol: an experimental evaluation

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

Yanagishita, M.; Rodbard, D.

1978-07-15

The mathematical and statistical theory of radioimmunoassays (RIAs) has been used to develop a series of computer programs to optimize sensitivity or precision at any desired dose level for either equilibrium or nonequilibrium assays. These computer programs provide for the calculation of the equilibrium constants of association and binding capacities for antisera (parameters of Scatchard plots), the association and dissociation rate constants, and prediction of optimum concentration of labeled ligand and antibody and optimum incubation times for the assay. This paper presents an experimental evaluation of the use of these computer programs applied to RIAs for human chorionic gonadotropin (hCG)more » and estradiol. The experimental results are in reasonable semiquantitative agreement with the predictions of the computer simulations (usually within a factor of two) and thus partially validate the use of computer techniques to optimize RIAs that are reasonably well behaved, as in the case of the hCG and estradiol RIAs. Further, these programs can provide insights into the nature of the RIA system, e.g., the general nature of the sensitivity and precision surfaces. This facilitates empirical optimization of conditions.« less

Optimization technique of wavefront coding system based on ZEMAX externally compiled programs

NASA Astrophysics Data System (ADS)

Han, Libo; Dong, Liquan; Liu, Ming; Zhao, Yuejin; Liu, Xiaohua

2016-10-01

Wavefront coding technique as a means of athermalization applied to infrared imaging system, the design of phase plate is the key to system performance. This paper apply the externally compiled programs of ZEMAX to the optimization of phase mask in the normal optical design process, namely defining the evaluation function of wavefront coding system based on the consistency of modulation transfer function (MTF) and improving the speed of optimization by means of the introduction of the mathematical software. User write an external program which computes the evaluation function on account of the powerful computing feature of the mathematical software in order to find the optimal parameters of phase mask, and accelerate convergence through generic algorithm (GA), then use dynamic data exchange (DDE) interface between ZEMAX and mathematical software to realize high-speed data exchanging. The optimization of the rotational symmetric phase mask and the cubic phase mask have been completed by this method, the depth of focus increases nearly 3 times by inserting the rotational symmetric phase mask, while the other system with cubic phase mask can be increased to 10 times, the consistency of MTF decrease obviously, the maximum operating temperature of optimized system range between -40°-60°. Results show that this optimization method can be more convenient to define some unconventional optimization goals and fleetly to optimize optical system with special properties due to its externally compiled function and DDE, there will be greater significance for the optimization of unconventional optical system.

An experimental evaluation of S-duct inlet-diffuser configurations for turboprop offset gearbox applications

NASA Technical Reports Server (NTRS)

Mcdill, Paul L.

1986-01-01

A test program, utilizing a large scale model, was run in the NASA Lewis Research Center 10- by 10-ft wind tunnel to examine the influence on performance of design parameters of turboprop S-duct inlet/diffuser systems. The parametric test program investigated inlet lip thickness, inlet/diffuser cross-sectional geometry, throat design Mach number, and shaft fairing shape. The test program was run at angles of attack to 15 deg and tunnel Mach numbers to 0.35. Results of the program indicate that current design techniques can be used to design inlet/diffuser systems with acceptable total pressure recovery, but several of the design parameters, notably lip thickness (contraction ratio) and shaft fairing cross section, must be optimized to prevent excessive distortion at the compressor face.

An assessment of separable fluid connector system parameters to perform a connector system design optimization study

NASA Technical Reports Server (NTRS)

Prasthofer, W. P.

1974-01-01

The key to optimization of design where there are a large number of variables, all of which may not be known precisely, lies in the mathematical tool of dynamic programming developed by Bellman. This methodology can lead to optimized solutions to the design of critical systems in a minimum amount of time, even when there are a great number of acceptable configurations to be considered. To demonstrate the usefulness of dynamic programming, an analytical method is developed for evaluating the relationship among existing numerous connector designs to find the optimum configuration. The data utilized in the study were generated from 900 flanges designed for six subsystems of the S-1B stage of the Saturn 1B space carrier vehicle.

An optimization program based on the method of feasible directions: Theory and users guide

NASA Technical Reports Server (NTRS)

Belegundu, Ashok D.; Berke, Laszlo; Patnaik, Surya N.

1994-01-01

The theory and user instructions for an optimization code based on the method of feasible directions are presented. The code was written for wide distribution and ease of attachment to other simulation software. Although the theory of the method of feasible direction was developed in the 1960's, many considerations are involved in its actual implementation as a computer code. Included in the code are a number of features to improve robustness in optimization. The search direction is obtained by solving a quadratic program using an interior method based on Karmarkar's algorithm. The theory is discussed focusing on the important and often overlooked role played by the various parameters guiding the iterations within the program. Also discussed is a robust approach for handling infeasible starting points. The code was validated by solving a variety of structural optimization test problems that have known solutions obtained by other optimization codes. It has been observed that this code is robust: it has solved a variety of problems from different starting points. However, the code is inefficient in that it takes considerable CPU time as compared with certain other available codes. Further work is required to improve its efficiency while retaining its robustness.

Refraction law and Fermat principle: a project using the ant colony optimization algorithm for undergraduate students in physics

NASA Astrophysics Data System (ADS)

Vuong, Q. L.; Rigaut, C.; Gossuin, Y.

2018-07-01

A programming project for undergraduate students in physics is proposed in this work. Its goal is to check the Snell–Descartes law of refraction using the Fermat principle and the ant colony optimization algorithm. The project involves basic mathematics and physics and is adapted to students with basic programming skills. More advanced tools can be used (but are not mandatory) as parallelization or object-oriented programming, which makes the project also suitable for more experienced students. We propose two tests to validate the program. Our algorithm is able to find solutions which are close to the theoretical predictions. Two quantities are defined to study its convergence and the quality of the solutions. It is also shown that the choice of the values of the simulation parameters is important to efficiently obtain precise results.

Optimization of programming parameters in children with the advanced bionics cochlear implant.

PubMed

Baudhuin, Jacquelyn; Cadieux, Jamie; Firszt, Jill B; Reeder, Ruth M; Maxson, Jerrica L

2012-05-01

Cochlear implants provide access to soft intensity sounds and therefore improved audibility for children with severe-to-profound hearing loss. Speech processor programming parameters, such as threshold (or T-level), input dynamic range (IDR), and microphone sensitivity, contribute to the recipient's program and influence audibility. When soundfield thresholds obtained through the speech processor are elevated, programming parameters can be modified to improve soft sound detection. Adult recipients show improved detection for low-level sounds when T-levels are set at raised levels and show better speech understanding in quiet when wider IDRs are used. Little is known about the effects of parameter settings on detection and speech recognition in children using today's cochlear implant technology. The overall study aim was to assess optimal T-level, IDR, and sensitivity settings in pediatric recipients of the Advanced Bionics cochlear implant. Two experiments were conducted. Experiment 1 examined the effects of two T-level settings on soundfield thresholds and detection of the Ling 6 sounds. One program set T-levels at 10% of most comfortable levels (M-levels) and another at 10 current units (CUs) below the level judged as "soft." Experiment 2 examined the effects of IDR and sensitivity settings on speech recognition in quiet and noise. Participants were 11 children 7-17 yr of age (mean 11.3) implanted with the Advanced Bionics High Resolution 90K or CII cochlear implant system who had speech recognition scores of 20% or greater on a monosyllabic word test. Two T-level programs were compared for detection of the Ling sounds and frequency modulated (FM) tones. Differing IDR/sensitivity programs (50/0, 50/10, 70/0, 70/10) were compared using Ling and FM tone detection thresholds, CNC (consonant-vowel nucleus-consonant) words at 50 dB SPL, and Hearing in Noise Test for Children (HINT-C) sentences at 65 dB SPL in the presence of four-talker babble (+8 signal-to-noise ratio). Outcomes were analyzed using a paired t-test and a mixed-model repeated measures analysis of variance (ANOVA). T-levels set 10 CUs below "soft" resulted in significantly lower detection thresholds for all six Ling sounds and FM tones at 250, 1000, 3000, 4000, and 6000 Hz. When comparing programs differing by IDR and sensitivity, a 50 dB IDR with a 0 sensitivity setting showed significantly poorer thresholds for low frequency FM tones and voiced Ling sounds. Analysis of group mean scores for CNC words in quiet or HINT-C sentences in noise indicated no significant differences across IDR/sensitivity settings. Individual data, however, showed significant differences between IDR/sensitivity programs in noise; the optimal program differed across participants. In pediatric recipients of the Advanced Bionics cochlear implant device, manually setting T-levels with ascending loudness judgments should be considered when possible or when low-level sounds are inaudible. Study findings confirm the need to determine program settings on an individual basis as well as the importance of speech recognition verification measures in both quiet and noise. Clinical guidelines are suggested for selection of programming parameters in both young and older children. American Academy of Audiology.

Optimization techniques applied to spectrum management for communications satellites

NASA Astrophysics Data System (ADS)

Ottey, H. R.; Sullivan, T. M.; Zusman, F. S.

This paper describes user requirements, algorithms and software design features for the application of optimization techniques to the management of the geostationary orbit/spectrum resource. Relevant problems include parameter sensitivity analyses, frequency and orbit position assignment coordination, and orbit position allotment planning. It is shown how integer and nonlinear programming as well as heuristic search techniques can be used to solve these problems. Formalized mathematical objective functions that define the problems are presented. Constraint functions that impart the necessary solution bounds are described. A versatile program structure is outlined, which would allow problems to be solved in stages while varying the problem space, solution resolution, objective function and constraints.

Parametric modeling and stagger angle optimization of an axial flow fan

NASA Astrophysics Data System (ADS)

Li, M. X.; Zhang, C. H.; Liu, Y.; Y Zheng, S.

2013-12-01

Axial flow fans are widely used in every field of social production. Improving their efficiency is a sustained and urgent demand of domestic industry. The optimization of stagger angle is an important method to improve fan performance. Parametric modeling and calculation process automation are realized in this paper to improve optimization efficiency. Geometric modeling and mesh division are parameterized based on GAMBIT. Parameter setting and flow field calculation are completed in the batch mode of FLUENT. A control program is developed in Visual C++ to dominate the data exchange of mentioned software. It also extracts calculation results for optimization algorithm module (provided by Matlab) to generate directive optimization control parameters, which as feedback are transferred upwards to modeling module. The center line of the blade airfoil, based on CLARK y profile, is constructed by non-constant circulation and triangle discharge method. Stagger angles of six airfoil sections are optimized, to reduce the influence of inlet shock loss as well as gas leak in blade tip clearance and hub resistance at blade root. Finally an optimal solution is obtained, which meets the total pressure requirement under given conditions and improves total pressure efficiency by about 6%.

Computer-Aided Process Model For Carbon/Phenolic Materials

NASA Technical Reports Server (NTRS)

Letson, Mischell A.; Bunker, Robert C.

1996-01-01

Computer program implements thermochemical model of processing of carbon-fiber/phenolic-matrix composite materials into molded parts of various sizes and shapes. Directed toward improving fabrication of rocket-engine-nozzle parts, also used to optimize fabrication of other structural components, and material-property parameters changed to apply to other materials. Reduces costs by reducing amount of laboratory trial and error needed to optimize curing processes and to predict properties of cured parts.

An Improved Evolutionary Programming with Voting and Elitist Dispersal Scheme

NASA Astrophysics Data System (ADS)

Maity, Sayan; Gunjan, Kumar; Das, Swagatam

Although initially conceived for evolving finite state machines, Evolutionary Programming (EP), in its present form, is largely used as a powerful real parameter optimizer. For function optimization, EP mainly relies on its mutation operators. Over past few years several mutation operators have been proposed to improve the performance of EP on a wide variety of numerical benchmarks. However, unlike real-coded GAs, there has been no fitness-induced bias in parent selection for mutation in EP. That means the i-th population member is selected deterministically for mutation and creation of the i-th offspring in each generation. In this article we present an improved EP variant called Evolutionary Programming with Voting and Elitist Dispersal (EPVE). The scheme encompasses a voting process which not only gives importance to best solutions but also consider those solutions which are converging fast. By introducing Elitist Dispersal Scheme we maintain the elitism by keeping the potential solutions intact and other solutions are perturbed accordingly, so that those come out of the local minima. By applying these two techniques we can be able to explore those regions which have not been explored so far that may contain optima. Comparison with the recent and best-known versions of EP over 25 benchmark functions from the CEC (Congress on Evolutionary Computation) 2005 test-suite for real parameter optimization reflects the superiority of the new scheme in terms of final accuracy, speed, and robustness.

‘tripleint_cc’: A program for 2-centre variational leptonic Coulomb potential matrix elements using Hylleraas-type trial functions, with a performance optimization study

NASA Astrophysics Data System (ADS)

Plummer, M.; Armour, E. A. G.; Todd, A. C.; Franklin, C. P.; Cooper, J. N.

2009-12-01

We present a program used to calculate intricate three-particle integrals for variational calculations of solutions to the leptonic Schrödinger equation with two nuclear centres in which inter-leptonic distances (electron-electron and positron-electron) are included directly in the trial functions. The program has been used so far in calculations of He-H¯ interactions and positron H 2 scattering, however the precisely defined integrals are applicable to other situations. We include a summary discussion of how the program has been optimized from a 'legacy'-type code to a more modern high-performance code with a performance improvement factor of up to 1000. Program summaryProgram title: tripleint.cc Catalogue identifier: AEEV_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEEV_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 12 829 No. of bytes in distributed program, including test data, etc.: 91 798 Distribution format: tar.gz Programming language: Fortran 95 (fixed format) Computer: Modern PC (tested on AMD processor) [1], IBM Power5 [2] Cray XT4 [3], similar Operating system: Red Hat Linux [1], IBM AIX [2], UNICOS [3] Has the code been vectorized or parallelized?: Serial (multi-core shared memory may be needed for some large jobs) RAM: Dependent on parameter sizes and option to use intermediate I/O. Estimates for practical use: 0.5-2 GBytes (with intermediate I/O); 1-4 GBytes (all-memory: the preferred option). Classification: 2.4, 2.6, 2.7, 2.9, 16.5, 16.10, 20 Nature of problem: The 'tripleint.cc' code evaluates three-particle integrals needed in certain variational (in particular: Rayleigh-Ritz and generalized-Kohn) matrix elements for solution of the Schrödinger equation with two fixed centres (the solutions may then be used in subsequent dynamic nuclear calculations). Specifically the integrals are defined by Eq. (16) in the main text and contain terms proportional to r×r/r,i≠j,i≠k,j≠k, with r the distance between leptons i and j. The article also briefly describes the performance optimizations used to increase the speed of evaluation of the integrals enough to allow detailed testing and mapping of the effect of varying non-linear parameters in the variational trial functions. Solution method: Each integral is solved using prolate spheroidal coordinates and series expansions (with cut-offs) of the many-lepton expressions. 1-d integrals and sub-integrals are solved analytically by various means (the program automatically chooses the most accurate of the available methods for each set of parameters and function arguments), while two of the three integrations over the prolate spheroidal coordinates ' λ' are carried out numerically. Many similar integrals with identical non-linear variational parameters may be calculated with one call of the code. Restrictions: There are limits to the number of points for the numerical integrations, to the cut-off variable itaumax for the many-lepton series expansions, and to the maximum powers of Slater-like input functions. For runs near the limit of the cut-off variable and with certain small-magnitude values of variational non-linear parameters, the code can require large amounts of memory (an option using some intermediate I/O is included to offset this). Unusual features: In addition to the program, we also present a summary description of the techniques and ideology used to optimize the code, together with accuracy tests and indications of performance improvement. Running time: The test runs take 1-15 minutes on HPCx [2] as indicated in Section 5 of the main text. A practical run with 729 integrals, 40 quadrature points per dimension and itaumax = 8 took 150 minutes on a PC (e.g., [1]): a similar run with 'medium' accuracy, e.g. for parameter optimization (see Section 2 of the main text), with 30 points per dimension and itaumax = 6 took 35 minutes. References:PC: Memory: 2.72 GB, CPU: AMD Opteron 246 dual-core, 2×2 GHz, OS: GNU/Linux, kernel: Linux 2.6.9-34.0.2.ELsmp. HPCx, IBM eServer 575 running IBM AIX, http://www.hpcx.ac.uk/ (visited May 2009). HECToR, CRAY XT4 running UNICOS/lc, http://www.hector.ac.uk/ (visited May 2009).

Automatic design of optical systems by digital computer

NASA Technical Reports Server (NTRS)

Casad, T. A.; Schmidt, L. F.

1967-01-01

Computer program uses geometrical optical techniques and a least squares optimization method employing computing equipment for the automatic design of optical systems. It evaluates changes in various optical parameters, provides comprehensive ray-tracing, and generally determines the acceptability of the optical system characteristics.

Automated Calibration For Numerical Models Of Riverflow

NASA Astrophysics Data System (ADS)

Fernandez, Betsaida; Kopmann, Rebekka; Oladyshkin, Sergey

2017-04-01

Calibration of numerical models is fundamental since the beginning of all types of hydro system modeling, to approximate the parameters that can mimic the overall system behavior. Thus, an assessment of different deterministic and stochastic optimization methods is undertaken to compare their robustness, computational feasibility, and global search capacity. Also, the uncertainty of the most suitable methods is analyzed. These optimization methods minimize the objective function that comprises synthetic measurements and simulated data. Synthetic measurement data replace the observed data set to guarantee an existing parameter solution. The input data for the objective function derivate from a hydro-morphological dynamics numerical model which represents an 180-degree bend channel. The hydro- morphological numerical model shows a high level of ill-posedness in the mathematical problem. The minimization of the objective function by different candidate methods for optimization indicates a failure in some of the gradient-based methods as Newton Conjugated and BFGS. Others reveal partial convergence, such as Nelder-Mead, Polak und Ribieri, L-BFGS-B, Truncated Newton Conjugated, and Trust-Region Newton Conjugated Gradient. Further ones indicate parameter solutions that range outside the physical limits, such as Levenberg-Marquardt and LeastSquareRoot. Moreover, there is a significant computational demand for genetic optimization methods, such as Differential Evolution and Basin-Hopping, as well as for Brute Force methods. The Deterministic Sequential Least Square Programming and the scholastic Bayes Inference theory methods present the optimal optimization results. keywords: Automated calibration of hydro-morphological dynamic numerical model, Bayesian inference theory, deterministic optimization methods.

Practical input optimization for aircraft parameter estimation experiments. Ph.D. Thesis, 1990

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

1993-01-01

The object of this research was to develop an algorithm for the design of practical, optimal flight test inputs for aircraft parameter estimation experiments. A general, single pass technique was developed which allows global optimization of the flight test input design for parameter estimation using the principles of dynamic programming with the input forms limited to square waves only. Provision was made for practical constraints on the input, including amplitude constraints, control system dynamics, and selected input frequency range exclusions. In addition, the input design was accomplished while imposing output amplitude constraints required by model validity and considerations of safety during the flight test. The algorithm has multiple input design capability, with optional inclusion of a constraint that only one control move at a time, so that a human pilot can implement the inputs. It is shown that the technique can be used to design experiments for estimation of open loop model parameters from closed loop flight test data. The report includes a new formulation of the optimal input design problem, a description of a new approach to the solution, and a summary of the characteristics of the algorithm, followed by three example applications of the new technique which demonstrate the quality and expanded capabilities of the input designs produced by the new technique. In all cases, the new input design approach showed significant improvement over previous input design methods in terms of achievable parameter accuracies.

Measurement of the main and critical parameters for optimal laser treatment of heart disease

NASA Astrophysics Data System (ADS)

Kabeya, FB; Abrahamse, H.; Karsten, AE

2017-10-01

Laser light is frequently used in the diagnosis and treatment of patients. As in traditional treatments such as medication, bypass surgery, and minimally invasive ways, laser treatment can also fail and present serious side effects. The true reason for laser treatment failure or the side effects thereof, remains unknown. From the literature review conducted, and experimental results generated we conclude that an optimal laser treatment for coronary artery disease (named heart disease) can be obtained if certain critical parameters are correctly measured and understood. These parameters include the laser power, the laser beam profile, the fluence rate, the treatment time, as well as the absorption and scattering coefficients of the target treatment tissue. Therefore, this paper proposes different, accurate methods for the measurement of these critical parameters to determine the optimal laser treatment of heart disease with a minimal risk of side effects. The results from the measurement of absorption and scattering properties can be used in a computer simulation package to predict the fluence rate. The computing technique is a program based on the random number (Monte Carlo) process and probability statistics to track the propagation of photons through a biological tissue.

Vortex generator design for aircraft inlet distortion as a numerical optimization problem

NASA Technical Reports Server (NTRS)

Anderson, Bernhard H.; Levy, Ralph

1991-01-01

Aerodynamic compatibility of aircraft/inlet/engine systems is a difficult design problem for aircraft that must operate in many different flight regimes. Takeoff, subsonic cruise, supersonic cruise, transonic maneuvering, and high altitude loiter each place different constraints on inlet design. Vortex generators, small wing like sections mounted on the inside surfaces of the inlet duct, are used to control flow separation and engine face distortion. The design of vortex generator installations in an inlet is defined as a problem addressable by numerical optimization techniques. A performance parameter is suggested to account for both inlet distortion and total pressure loss at a series of design flight conditions. The resulting optimization problem is difficult since some of the design parameters take on integer values. If numerical procedures could be used to reduce multimillion dollar development test programs to a small set of verification tests, numerical optimization could have a significant impact on both cost and elapsed time to design new aircraft.

Optimization of space manufacturing systems

NASA Technical Reports Server (NTRS)

Akin, D. L.

1979-01-01

Four separate analyses are detailed: transportation to low earth orbit, orbit-to-orbit optimization, parametric analysis of SPS logistics based on earth and lunar source locations, and an overall program option optimization implemented with linear programming. It is found that smaller vehicles are favored for earth launch, with the current Space Shuttle being right at optimum payload size. Fully reusable launch vehicles represent a savings of 50% over the Space Shuttle; increased reliability with less maintenance could further double the savings. An optimization of orbit-to-orbit propulsion systems using lunar oxygen for propellants shows that ion propulsion is preferable by a 3:1 cost margin over a mass driver reaction engine at optimum values; however, ion engines cannot yet operate in the lower exhaust velocity range where the optimum lies, and total program costs between the two systems are ambiguous. Heavier payloads favor the use of a MDRE. A parametric model of a space manufacturing facility is proposed, and used to analyze recurring costs, total costs, and net present value discounted cash flows. Parameters studied include productivity, effects of discounting, materials source tradeoffs, economic viability of closed-cycle habitats, and effects of varying degrees of nonterrestrial SPS materials needed from earth. Finally, candidate optimal scenarios are chosen, and implemented in a linear program with external constraints in order to arrive at an optimum blend of SPS production strategies in order to maximize returns.

Constraint Optimization Problem For The Cutting Of A Cobalt Chrome Refractory Material

NASA Astrophysics Data System (ADS)

Lebaal, Nadhir; Schlegel, Daniel; Folea, Milena

2011-05-01

This paper shows a complete approach to solve a given problem, from the experimentation to the optimization of different cutting parameters. In response to an industrial problem of slotting FSX 414, a Cobalt-based refractory material, we have implemented a design of experiment to determine the most influent parameters on the tool life, the surface roughness and the cutting forces. After theses trials, an optimization approach has been implemented to find the lowest manufacturing cost while respecting the roughness constraints and cutting force limitation constraints. The optimization approach is based on the Response Surface Method (RSM) using the Sequential Quadratic programming algorithm (SQP) for a constrained problem. To avoid a local optimum and to obtain an accurate solution at low cost, an efficient strategy, which allows improving the RSM accuracy in the vicinity of the global optimum, is presented. With these models and these trials, we could apply and compare our optimization methods in order to get the lowest cost for the best quality, i.e. a satisfying surface roughness and limited cutting forces.

Integrated optimization of location assignment and sequencing in multi-shuttle automated storage and retrieval systems under modified 2n-command cycle pattern

NASA Astrophysics Data System (ADS)

Yang, Peng; Peng, Yongfei; Ye, Bin; Miao, Lixin

2017-09-01

This article explores the integrated optimization problem of location assignment and sequencing in multi-shuttle automated storage/retrieval systems under the modified 2n-command cycle pattern. The decision of storage and retrieval (S/R) location assignment and S/R request sequencing are jointly considered. An integer quadratic programming model is formulated to describe this integrated optimization problem. The optimal travel cycles for multi-shuttle S/R machines can be obtained to process S/R requests in the storage and retrieval request order lists by solving the model. The small-sized instances are optimally solved using CPLEX. For large-sized problems, two tabu search algorithms are proposed, in which the first come, first served and nearest neighbour are used to generate initial solutions. Various numerical experiments are conducted to examine the heuristics' performance and the sensitivity of algorithm parameters. Furthermore, the experimental results are analysed from the viewpoint of practical application, and a parameter list for applying the proposed heuristics is recommended under different real-life scenarios.

Searching for globally optimal functional forms for interatomic potentials using genetic programming with parallel tempering.

PubMed

Slepoy, A; Peters, M D; Thompson, A P

2007-11-30

Molecular dynamics and other molecular simulation methods rely on a potential energy function, based only on the relative coordinates of the atomic nuclei. Such a function, called a force field, approximately represents the electronic structure interactions of a condensed matter system. Developing such approximate functions and fitting their parameters remains an arduous, time-consuming process, relying on expert physical intuition. To address this problem, a functional programming methodology was developed that may enable automated discovery of entirely new force-field functional forms, while simultaneously fitting parameter values. The method uses a combination of genetic programming, Metropolis Monte Carlo importance sampling and parallel tempering, to efficiently search a large space of candidate functional forms and parameters. The methodology was tested using a nontrivial problem with a well-defined globally optimal solution: a small set of atomic configurations was generated and the energy of each configuration was calculated using the Lennard-Jones pair potential. Starting with a population of random functions, our fully automated, massively parallel implementation of the method reproducibly discovered the original Lennard-Jones pair potential by searching for several hours on 100 processors, sampling only a minuscule portion of the total search space. This result indicates that, with further improvement, the method may be suitable for unsupervised development of more accurate force fields with completely new functional forms. Copyright (c) 2007 Wiley Periodicals, Inc.

Design of microstrip components by computer

NASA Technical Reports Server (NTRS)

Cisco, T. C.

1972-01-01

A number of computer programs are presented for use in the synthesis of microwave components in microstrip geometries. The programs compute the electrical and dimensional parameters required to synthesize couplers, filters, circulators, transformers, power splitters, diode switches, multipliers, diode attenuators and phase shifters. Additional programs are included to analyze and optimize cascaded transmission lines and lumped element networks, to analyze and synthesize Chebyshev and Butterworth filter prototypes, and to compute mixer intermodulation products. The programs are written in FORTRAN and the emphasis of the study is placed on the use of these programs and not on the theoretical aspects of the structures.

Improved model reduction and tuning of fractional-order PI(λ)D(μ) controllers for analytical rule extraction with genetic programming.

PubMed

Das, Saptarshi; Pan, Indranil; Das, Shantanu; Gupta, Amitava

2012-03-01

Genetic algorithm (GA) has been used in this study for a new approach of suboptimal model reduction in the Nyquist plane and optimal time domain tuning of proportional-integral-derivative (PID) and fractional-order (FO) PI(λ)D(μ) controllers. Simulation studies show that the new Nyquist-based model reduction technique outperforms the conventional H(2)-norm-based reduced parameter modeling technique. With the tuned controller parameters and reduced-order model parameter dataset, optimum tuning rules have been developed with a test-bench of higher-order processes via genetic programming (GP). The GP performs a symbolic regression on the reduced process parameters to evolve a tuning rule which provides the best analytical expression to map the data. The tuning rules are developed for a minimum time domain integral performance index described by a weighted sum of error index and controller effort. From the reported Pareto optimal front of the GP-based optimal rule extraction technique, a trade-off can be made between the complexity of the tuning formulae and the control performance. The efficacy of the single-gene and multi-gene GP-based tuning rules has been compared with the original GA-based control performance for the PID and PI(λ)D(μ) controllers, handling four different classes of representative higher-order processes. These rules are very useful for process control engineers, as they inherit the power of the GA-based tuning methodology, but can be easily calculated without the requirement for running the computationally intensive GA every time. Three-dimensional plots of the required variation in PID/fractional-order PID (FOPID) controller parameters with reduced process parameters have been shown as a guideline for the operator. Parametric robustness of the reported GP-based tuning rules has also been shown with credible simulation examples. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

BEARKIMPE-2: A VBA Excel program for characterizing granular iron in treatability studies

NASA Astrophysics Data System (ADS)

Firdous, R.; Devlin, J. F.

2014-02-01

The selection of a suitable kinetic model to investigate the reaction rate of a contaminant with granular iron (GI) is essential to optimize the permeable reactive barrier (PRB) performance in terms of its reactivity. The newly developed Kinetic Iron Model (KIM) determines the surface rate constant (k) and sorption parameters (Cmax &J) which were not possible to uniquely identify previously. The code was written in Visual Basic (VBA), within Microsoft Excel, was adapted from earlier command line FORTRAN codes, BEARPE and KIMPE. The program is organized with several user interface screens (UserForms) that guide the user step by step through the analysis. BEARKIMPE-2 uses a non-linear optimization algorithm to calculate transport and chemical kinetic parameters. Both reactive and non-reactive sites are considered. A demonstration of the functionality of BEARKIMPE-2, with three nitroaromatic compounds showed that the differences in reaction rates for these compounds could be attributed to differences in their sorption behavior rather than their propensities to accept electrons in the reduction process.

Effect of Processing Parameters on Reliability of VARTM/SCRIMP Composite Panels - Phase 1

DTIC Science & Technology

2007-07-01

used in this program (Hess and Beach, 2000). The structural risks associated with new FRP composite ship structures can be mitigated by...reliability calibration of new designs. This program focuses on addressing the first and second tasks outlined above. 1.2. Phase I - Objectives The...Accomplishments: Tension Testing An optimized geometry for tension coupon testing was developed for marine grade composites. The new geometry reduces

Mass drivers. 3: Engineering

NASA Technical Reports Server (NTRS)

Arnold, W.; Bowen, S.; Cohen, S.; Fine, K.; Kaplan, D.; Kolm, M.; Kolm, H.; Newman, J.; Oneill, G. K.; Snow, W.

1979-01-01

The last of a series of three papers by the Mass-Driver Group of the 1977 Ames Summer Study is presented. It develops the engineering principles required to implement the basic mass-driver. Optimum component mass trade-offs are derived from a set of four input parameters, and the program used to design a lunar launcher. The mass optimization procedures is then incorporated into a more comprehensive mission optimization program called OPT-4, which evaluates an optimized mass-driver reaction engine and its performance in a range of specified missions. Finally, this paper discusses, to the extent that time permitted, certain peripheral problems: heating effects in buckets due to magnetic field ripple; an approximate derivation of guide force profiles; the mechanics of inserting and releasing payloads; the reaction mass orbits; and a proposed research and development plan for implementing mass drivers.

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

Zhang, Z.; Pike, R.W.; Hertwig, T.A.

An effective approach for source reduction in chemical plants has been demonstrated using on-line optimization with flowsheeting (ASPEN PLUS) for process optimization and parameter estimation and the Tjao-Biegler algorithm implemented in a mathematical programming language (GAMS/MINOS) for data reconciliation and gross error detection. Results for a Monsanto sulfuric acid plant with a Bailey distributed control system showed a 25% reduction in the sulfur dioxide emissions and a 17% improvement in the profit over the current operating conditions. Details of the methods used are described.

A new Bayesian Earthquake Analysis Tool (BEAT)

NASA Astrophysics Data System (ADS)

Vasyura-Bathke, Hannes; Dutta, Rishabh; Jónsson, Sigurjón; Mai, Martin

2017-04-01

Modern earthquake source estimation studies increasingly use non-linear optimization strategies to estimate kinematic rupture parameters, often considering geodetic and seismic data jointly. However, the optimization process is complex and consists of several steps that need to be followed in the earthquake parameter estimation procedure. These include pre-describing or modeling the fault geometry, calculating the Green's Functions (often assuming a layered elastic half-space), and estimating the distributed final slip and possibly other kinematic source parameters. Recently, Bayesian inference has become popular for estimating posterior distributions of earthquake source model parameters given measured/estimated/assumed data and model uncertainties. For instance, some research groups consider uncertainties of the layered medium and propagate these to the source parameter uncertainties. Other groups make use of informative priors to reduce the model parameter space. In addition, innovative sampling algorithms have been developed that efficiently explore the often high-dimensional parameter spaces. Compared to earlier studies, these improvements have resulted in overall more robust source model parameter estimates that include uncertainties. However, the computational demands of these methods are high and estimation codes are rarely distributed along with the published results. Even if codes are made available, it is often difficult to assemble them into a single optimization framework as they are typically coded in different programing languages. Therefore, further progress and future applications of these methods/codes are hampered, while reproducibility and validation of results has become essentially impossible. In the spirit of providing open-access and modular codes to facilitate progress and reproducible research in earthquake source estimations, we undertook the effort of producing BEAT, a python package that comprises all the above-mentioned features in one single programing environment. The package is build on top of the pyrocko seismological toolbox (www.pyrocko.org) and makes use of the pymc3 module for Bayesian statistical model fitting. BEAT is an open-source package (https://github.com/hvasbath/beat) and we encourage and solicit contributions to the project. In this contribution, we present our strategy for developing BEAT, show application examples, and discuss future developments.

An export coefficient based inexact fuzzy bi-level multi-objective programming model for the management of agricultural nonpoint source pollution under uncertainty

NASA Astrophysics Data System (ADS)

Cai, Yanpeng; Rong, Qiangqiang; Yang, Zhifeng; Yue, Wencong; Tan, Qian

2018-02-01

In this research, an export coefficient based inexact fuzzy bi-level multi-objective programming (EC-IFBLMOP) model was developed through integrating export coefficient model (ECM), interval parameter programming (IPP) and fuzzy parameter programming (FPP) within a bi-level multi-objective programming framework. The proposed EC-IFBLMOP model can effectively deal with the multiple uncertainties expressed as discrete intervals and fuzzy membership functions. Also, the complexities in agricultural systems, such as the cooperation and gaming relationship between the decision makers at different levels, can be fully considered in the model. The developed model was then applied to identify the optimal land use patterns and BMP implementing levels for agricultural nonpoint source (NPS) pollution management in a subcatchment in the upper stream watershed of the Miyun Reservoir in north China. The results of the model showed that the desired optimal land use patterns and implementing levels of best management of practices (BMPs) would be obtained. It is the gaming result between the upper- and lower-level decision makers, when the allowable discharge amounts of NPS pollutants were limited. Moreover, results corresponding to different decision scenarios could provide a set of decision alternatives for the upper- and lower-level decision makers to identify the most appropriate management strategy. The model has a good applicability and can be effectively utilized for agricultural NPS pollution management.

Computer-Guided Deep Brain Stimulation Programming for Parkinson's Disease.

PubMed

Heldman, Dustin A; Pulliam, Christopher L; Urrea Mendoza, Enrique; Gartner, Maureen; Giuffrida, Joseph P; Montgomery, Erwin B; Espay, Alberto J; Revilla, Fredy J

2016-02-01

Pilot study to evaluate computer-guided deep brain stimulation (DBS) programming designed to optimize stimulation settings using objective motion sensor-based motor assessments. Seven subjects (five males; 54-71 years) with Parkinson's disease (PD) and recently implanted DBS systems participated in this pilot study. Within two months of lead implantation, the subject returned to the clinic to undergo computer-guided programming and parameter selection. A motion sensor was placed on the index finger of the more affected hand. Software guided a monopolar survey during which monopolar stimulation on each contact was iteratively increased followed by an automated assessment of tremor and bradykinesia. After completing assessments at each setting, a software algorithm determined stimulation settings designed to minimize symptom severities, side effects, and battery usage. Optimal DBS settings were chosen based on average severity of motor symptoms measured by the motion sensor. Settings chosen by the software algorithm identified a therapeutic window and improved tremor and bradykinesia by an average of 35.7% compared with baseline in the "off" state (p < 0.01). Motion sensor-based computer-guided DBS programming identified stimulation parameters that significantly improved tremor and bradykinesia with minimal clinician involvement. Automated motion sensor-based mapping is worthy of further investigation and may one day serve to extend programming to populations without access to specialized DBS centers. © 2015 International Neuromodulation Society.

VIBA-Lab 3.0: Computer program for simulation and semi-quantitative analysis of PIXE and RBS spectra and 2D elemental maps

NASA Astrophysics Data System (ADS)

Orlić, Ivica; Mekterović, Darko; Mekterović, Igor; Ivošević, Tatjana

2015-11-01

VIBA-Lab is a computer program originally developed by the author and co-workers at the National University of Singapore (NUS) as an interactive software package for simulation of Particle Induced X-ray Emission and Rutherford Backscattering Spectra. The original program is redeveloped to a VIBA-Lab 3.0 in which the user can perform semi-quantitative analysis by comparing simulated and measured spectra as well as simulate 2D elemental maps for a given 3D sample composition. The latest version has a new and more versatile user interface. It also has the latest data set of fundamental parameters such as Coster-Kronig transition rates, fluorescence yields, mass absorption coefficients and ionization cross sections for K and L lines in a wider energy range than the original program. Our short-term plan is to introduce routine for quantitative analysis for multiple PIXE and XRF excitations. VIBA-Lab is an excellent teaching tool for students and researchers in using PIXE and RBS techniques. At the same time the program helps when planning an experiment and when optimizing experimental parameters such as incident ions, their energy, detector specifications, filters, geometry, etc. By "running" a virtual experiment the user can test various scenarios until the optimal PIXE and BS spectra are obtained and in this way save a lot of expensive machine time.

Design of a multiple kernel learning algorithm for LS-SVM by convex programming.

PubMed

Jian, Ling; Xia, Zhonghang; Liang, Xijun; Gao, Chuanhou

2011-06-01

As a kernel based method, the performance of least squares support vector machine (LS-SVM) depends on the selection of the kernel as well as the regularization parameter (Duan, Keerthi, & Poo, 2003). Cross-validation is efficient in selecting a single kernel and the regularization parameter; however, it suffers from heavy computational cost and is not flexible to deal with multiple kernels. In this paper, we address the issue of multiple kernel learning for LS-SVM by formulating it as semidefinite programming (SDP). Furthermore, we show that the regularization parameter can be optimized in a unified framework with the kernel, which leads to an automatic process for model selection. Extensive experimental validations are performed and analyzed. Copyright © 2011 Elsevier Ltd. All rights reserved.

Nozzle Mounting Method Optimization Based on Robot Kinematic Analysis

NASA Astrophysics Data System (ADS)

Chen, Chaoyue; Liao, Hanlin; Montavon, Ghislain; Deng, Sihao

2016-08-01

Nowadays, the application of industrial robots in thermal spray is gaining more and more importance. A desired coating quality depends on factors such as a balanced robot performance, a uniform scanning trajectory and stable parameters (e.g. nozzle speed, scanning step, spray angle, standoff distance). These factors also affect the mass and heat transfer as well as the coating formation. Thus, the kinematic optimization of all these aspects plays a key role in order to obtain an optimal coating quality. In this study, the robot performance was optimized from the aspect of nozzle mounting on the robot. An optimized nozzle mounting for a type F4 nozzle was designed, based on the conventional mounting method from the point of view of robot kinematics validated on a virtual robot. Robot kinematic parameters were obtained from the simulation by offline programming software and analyzed by statistical methods. The energy consumptions of different nozzle mounting methods were also compared. The results showed that it was possible to reasonably assign the amount of robot motion to each axis during the process, so achieving a constant nozzle speed. Thus, it is possible optimize robot performance and to economize robot energy.

Optimal sampling theory and population modelling - Application to determination of the influence of the microgravity environment on drug distribution and elimination

NASA Technical Reports Server (NTRS)

Drusano, George L.

1991-01-01

The optimal sampling theory is evaluated in applications to studies related to the distribution and elimination of several drugs (including ceftazidime, piperacillin, and ciprofloxacin), using the SAMPLE module of the ADAPT II package of programs developed by D'Argenio and Schumitzky (1979, 1988) and comparing the pharmacokinetic parameter values with results obtained by traditional ten-sample design. The impact of the use of optimal sampling was demonstrated in conjunction with NONMEM (Sheiner et al., 1977) approach, in which the population is taken as the unit of analysis, allowing even fragmentary patient data sets to contribute to population parameter estimates. It is shown that this technique is applicable in both the single-dose and the multiple-dose environments. The ability to study real patients made it possible to show that there was a bimodal distribution in ciprofloxacin nonrenal clearance.

Direct Multiple Shooting Optimization with Variable Problem Parameters

NASA Technical Reports Server (NTRS)

Whitley, Ryan J.; Ocampo, Cesar A.

2009-01-01

Taking advantage of a novel approach to the design of the orbital transfer optimization problem and advanced non-linear programming algorithms, several optimal transfer trajectories are found for problems with and without known analytic solutions. This method treats the fixed known gravitational constants as optimization variables in order to reduce the need for an advanced initial guess. Complex periodic orbits are targeted with very simple guesses and the ability to find optimal transfers in spite of these bad guesses is successfully demonstrated. Impulsive transfers are considered for orbits in both the 2-body frame as well as the circular restricted three-body problem (CRTBP). The results with this new approach demonstrate the potential for increasing robustness for all types of orbit transfer problems.

Accuracy Analysis and Parameters Optimization in Urban Flood Simulation by PEST Model

NASA Astrophysics Data System (ADS)

Keum, H.; Han, K.; Kim, H.; Ha, C.

2017-12-01

The risk of urban flooding has been increasing due to heavy rainfall, flash flooding and rapid urbanization. Rainwater pumping stations, underground reservoirs are used to actively take measures against flooding, however, flood damage from lowlands continues to occur. Inundation in urban areas has resulted in overflow of sewer. Therefore, it is important to implement a network system that is intricately entangled within a city, similar to the actual physical situation and accurate terrain due to the effects on buildings and roads for accurate two-dimensional flood analysis. The purpose of this study is to propose an optimal scenario construction procedure watershed partitioning and parameterization for urban runoff analysis and pipe network analysis, and to increase the accuracy of flooded area prediction through coupled model. The establishment of optimal scenario procedure was verified by applying it to actual drainage in Seoul. In this study, optimization was performed by using four parameters such as Manning's roughness coefficient for conduits, watershed width, Manning's roughness coefficient for impervious area, Manning's roughness coefficient for pervious area. The calibration range of the parameters was determined using the SWMM manual and the ranges used in the previous studies, and the parameters were estimated using the automatic calibration method PEST. The correlation coefficient showed a high correlation coefficient for the scenarios using PEST. The RPE and RMSE also showed high accuracy for the scenarios using PEST. In the case of RPE, error was in the range of 13.9-28.9% in the no-parameter estimation scenarios, but in the scenario using the PEST, the error range was reduced to 6.8-25.7%. Based on the results of this study, it can be concluded that more accurate flood analysis is possible when the optimum scenario is selected by determining the appropriate reference conduit for future urban flooding analysis and if the results is applied to various rainfall event scenarios and parameter optimization. Keywords: Parameters Optimization; PEST model; Urban area Acknowledgement This research was supported by a grant (17AWMP-B079625-04) from Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

Stan : A Probabilistic Programming Language

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

Carpenter, Bob; Gelman, Andrew; Hoffman, Matthew D.

Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectationmore » propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can also be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.« less

Stan : A Probabilistic Programming Language

DOE PAGES

Carpenter, Bob; Gelman, Andrew; Hoffman, Matthew D.; ...

2017-01-01

Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectationmore » propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can also be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.« less

Proceedings of the Workshop on Computational Aspects in the Control of Flexible Systems, part 1

NASA Technical Reports Server (NTRS)

Taylor, Lawrence W., Jr. (Compiler)

1989-01-01

Control/Structures Integration program software needs, computer aided control engineering for flexible spacecraft, computer aided design, computational efficiency and capability, modeling and parameter estimation, and control synthesis and optimization software for flexible structures and robots are among the topics discussed.

Optimization methods applied to hybrid vehicle design

NASA Technical Reports Server (NTRS)

Donoghue, J. F.; Burghart, J. H.

1983-01-01

The use of optimization methods as an effective design tool in the design of hybrid vehicle propulsion systems is demonstrated. Optimization techniques were used to select values for three design parameters (battery weight, heat engine power rating and power split between the two on-board energy sources) such that various measures of vehicle performance (acquisition cost, life cycle cost and petroleum consumption) were optimized. The apporach produced designs which were often significant improvements over hybrid designs already reported on in the literature. The principal conclusions are as follows. First, it was found that the strategy used to split the required power between the two on-board energy sources can have a significant effect on life cycle cost and petroleum consumption. Second, the optimization program should be constructed so that performance measures and design variables can be easily changed. Third, the vehicle simulation program has a significant effect on the computer run time of the overall optimization program; run time can be significantly reduced by proper design of the types of trips the vehicle takes in a one year period. Fourth, care must be taken in designing the cost and constraint expressions which are used in the optimization so that they are relatively smooth functions of the design variables. Fifth, proper handling of constraints on battery weight and heat engine rating, variables which must be large enough to meet power demands, is particularly important for the success of an optimization study. Finally, the principal conclusion is that optimization methods provide a practical tool for carrying out the design of a hybrid vehicle propulsion system.

Infinite horizon optimal impulsive control with applications to Internet congestion control

NASA Astrophysics Data System (ADS)

Avrachenkov, Konstantin; Habachi, Oussama; Piunovskiy, Alexey; Zhang, Yi

2015-04-01

We investigate infinite-horizon deterministic optimal control problems with both gradual and impulsive controls, where any finitely many impulses are allowed simultaneously. Both discounted and long-run time-average criteria are considered. We establish very general and at the same time natural conditions, under which the dynamic programming approach results in an optimal feedback policy. The established theoretical results are applied to the Internet congestion control, and by solving analytically and nontrivially the underlying optimal control problems, we obtain a simple threshold-based active queue management scheme, which takes into account the main parameters of the transmission control protocols, and improves the fairness among the connections in a given network.

Rapid determination of thermodynamic parameters from one-dimensional programmed-temperature gas chromatography for use in retention time prediction in comprehensive multidimensional chromatography.

PubMed

McGinitie, Teague M; Ebrahimi-Najafabadi, Heshmatollah; Harynuk, James J

2014-01-17

A new method for estimating the thermodynamic parameters of ΔH(T0), ΔS(T0), and ΔCP for use in thermodynamic modeling of GC×GC separations has been developed. The method is an alternative to the traditional isothermal separations required to fit a three-parameter thermodynamic model to retention data. Herein, a non-linear optimization technique is used to estimate the parameters from a series of temperature-programmed separations using the Nelder-Mead simplex algorithm. With this method, the time required to obtain estimates of thermodynamic parameters a series of analytes is significantly reduced. This new method allows for precise predictions of retention time with the average error being only 0.2s for 1D separations. Predictions for GC×GC separations were also in agreement with experimental measurements; having an average relative error of 0.37% for (1)tr and 2.1% for (2)tr. Copyright © 2013 Elsevier B.V. All rights reserved.

Channel Simulation in Quantum Metrology

NASA Astrophysics Data System (ADS)

Laurenza, Riccardo; Lupo, Cosmo; Spedalieri, Gaetana; Braunstein, Samuel L.; Pirandola, Stefano

2018-04-01

In this review we discuss how channel simulation can be used to simplify the most general protocols of quantum parameter estimation, where unlimited entanglement and adaptive joint operations may be employed. Whenever the unknown parameter encoded in a quantum channel is completely transferred in an environmental program state simulating the channel, the optimal adaptive estimation cannot beat the standard quantum limit. In this setting, we elucidate the crucial role of quantum teleportation as a primitive operation which allows one to completely reduce adaptive protocols over suitable teleportation-covariant channels and derive matching upper and lower bounds for parameter estimation. For these channels,wemay express the quantum Cramér Rao bound directly in terms of their Choi matrices. Our review considers both discrete- and continuous-variable systems, also presenting some new results for bosonic Gaussian channels using an alternative sub-optimal simulation. It is an open problem to design simulations for quantum channels that achieve the Heisenberg limit.

Identification of inelastic parameters based on deep drawing forming operations using a global-local hybrid Particle Swarm approach

NASA Astrophysics Data System (ADS)

Vaz, Miguel; Luersen, Marco A.; Muñoz-Rojas, Pablo A.; Trentin, Robson G.

2016-04-01

Application of optimization techniques to the identification of inelastic material parameters has substantially increased in recent years. The complex stress-strain paths and high nonlinearity, typical of this class of problems, require the development of robust and efficient techniques for inverse problems able to account for an irregular topography of the fitness surface. Within this framework, this work investigates the application of the gradient-based Sequential Quadratic Programming method, of the Nelder-Mead downhill simplex algorithm, of Particle Swarm Optimization (PSO), and of a global-local PSO-Nelder-Mead hybrid scheme to the identification of inelastic parameters based on a deep drawing operation. The hybrid technique has shown to be the best strategy by combining the good PSO performance to approach the global minimum basin of attraction with the efficiency demonstrated by the Nelder-Mead algorithm to obtain the minimum itself.

Optimal design study of high efficiency indium phosphide space solar cells

NASA Technical Reports Server (NTRS)

Jain, Raj K.; Flood, Dennis J.

1990-01-01

Recently indium phosphide solar cells have achieved beginning of life AMO efficiencies in excess of 19 pct. at 25 C. The high efficiency prospects along with superb radiation tolerance make indium phosphide a leading material for space power requirements. To achieve cost effectiveness, practical cell efficiencies have to be raised to near theoretical limits and thin film indium phosphide cells need to be developed. The optimal design study is described of high efficiency indium phosphide solar cells for space power applications using the PC-1D computer program. It is shown that cells with efficiencies over 22 pct. AMO at 25 C could be fabricated by achieving proper material and process parameters. It is observed that further improvements in cell material and process parameters could lead to experimental cell efficiencies near theoretical limits. The effect of various emitter and base parameters on cell performance was studied.

Gear optimization

NASA Technical Reports Server (NTRS)

Vanderplaats, G. N.; Chen, Xiang; Zhang, Ning-Tian

1988-01-01

The use of formal numerical optimization methods for the design of gears is investigated. To achieve this, computer codes were developed for the analysis of spur gears and spiral bevel gears. These codes calculate the life, dynamic load, bending strength, surface durability, gear weight and size, and various geometric parameters. It is necessary to calculate all such important responses because they all represent competing requirements in the design process. The codes developed here were written in subroutine form and coupled to the COPES/ADS general purpose optimization program. This code allows the user to define the optimization problem at the time of program execution. Typical design variables include face width, number of teeth and diametral pitch. The user is free to choose any calculated response as the design objective to minimize or maximize and may impose lower and upper bounds on any calculated responses. Typical examples include life maximization with limits on dynamic load, stress, weight, etc. or minimization of weight subject to limits on life, dynamic load, etc. The research codes were written in modular form for easy expansion and so that they could be combined to create a multiple reduction optimization capability in future.

Optimization of Residual Stresses in MMC's Using Compensating/Compliant Interfacial Layers. Part 2: OPTCOMP User's Guide

NASA Technical Reports Server (NTRS)

Pindera, Marek-Jerzy; Salzar, Robert S.; Williams, Todd O.

1994-01-01

A user's guide for the computer program OPTCOMP is presented in this report. This program provides a capability to optimize the fabrication or service-induced residual stresses in uni-directional metal matrix composites subjected to combined thermo-mechanical axisymmetric loading using compensating or compliant layers at the fiber/matrix interface. The user specifies the architecture and the initial material parameters of the interfacial region, which can be either elastic or elastoplastic, and defines the design variables, together with the objective function, the associated constraints and the loading history through a user-friendly data input interface. The optimization procedure is based on an efficient solution methodology for the elastoplastic response of an arbitrarily layered multiple concentric cylinder model that is coupled to the commercial optimization package DOT. The solution methodology for the arbitrarily layered cylinder is based on the local-global stiffness matrix formulation and Mendelson's iterative technique of successive elastic solutions developed for elastoplastic boundary-value problems. The optimization algorithm employed in DOT is based on the method of feasible directions.

Multi-objective trajectory optimization for the space exploration vehicle

NASA Astrophysics Data System (ADS)

Qin, Xiaoli; Xiao, Zhen

2016-07-01

The research determines temperature-constrained optimal trajectory for the space exploration vehicle by developing an optimal control formulation and solving it using a variable order quadrature collocation method with a Non-linear Programming(NLP) solver. The vehicle is assumed to be the space reconnaissance aircraft that has specified takeoff/landing locations, specified no-fly zones, and specified targets for sensor data collections. A three degree of freedom aircraft model is adapted from previous work and includes flight dynamics, and thermal constraints.Vehicle control is accomplished by controlling angle of attack, roll angle, and propellant mass flow rate. This model is incorporated into an optimal control formulation that includes constraints on both the vehicle and mission parameters, such as avoidance of no-fly zones and exploration of space targets. In addition, the vehicle models include the environmental models(gravity and atmosphere). How these models are appropriately employed is key to gaining confidence in the results and conclusions of the research. Optimal trajectories are developed using several performance costs in the optimal control formation,minimum time,minimum time with control penalties,and maximum distance.The resulting analysis demonstrates that optimal trajectories that meet specified mission parameters and constraints can be quickly determined and used for large-scale space exloration.

Inter and intra-modal deformable registration: continuous deformations meet efficient optimal linear programming.

PubMed

Glocker, Ben; Paragios, Nikos; Komodakis, Nikos; Tziritas, Georgios; Navab, Nassir

2007-01-01

In this paper we propose a novel non-rigid volume registration based on discrete labeling and linear programming. The proposed framework reformulates registration as a minimal path extraction in a weighted graph. The space of solutions is represented using a set of a labels which are assigned to predefined displacements. The graph topology corresponds to a superimposed regular grid onto the volume. Links between neighborhood control points introduce smoothness, while links between the graph nodes and the labels (end-nodes) measure the cost induced to the objective function through the selection of a particular deformation for a given control point once projected to the entire volume domain, Higher order polynomials are used to express the volume deformation from the ones of the control points. Efficient linear programming that can guarantee the optimal solution up to (a user-defined) bound is considered to recover the optimal registration parameters. Therefore, the method is gradient free, can encode various similarity metrics (simple changes on the graph construction), can guarantee a globally sub-optimal solution and is computational tractable. Experimental validation using simulated data with known deformation, as well as manually segmented data demonstrate the extreme potentials of our approach.

X-48B Preliminary Flight Test Results

NASA Technical Reports Server (NTRS)

Taylor, Brian R.

2009-01-01

This slide presentation reviews the preliminary Flight tests of the X-48B development program. The X-48B is a blended wing body aircraft that is being used to test various features of the BWB concept. The research concerns the following: (1) Turbofan Development, (2) Intelligent Flight Control and Optimization, (3) Airdata Calibration (4) Parameter Identification (i.e., Determination of the parameters of a mathematical model of a system based on observation of the system inputs and response.)

Interdependent figure-of-merit software development

NASA Technical Reports Server (NTRS)

Ramohalli, K.; Kirsch, T.

1989-01-01

This program was undertaken in order to understand the complex nature of interdependent performance in space missions. At the first step in a planned sequence of progress, a spread sheet program was developed to evaluate different fuel/oxidizer combinations for a specific Martian mission. This program is to be linked with output attained using sophisticated software produced by Gordon and McBride. The programming to date makes use of 11 independent parameters. Optimization is essential when faced with the incredible magnitude of costs, risks, and benefits involved with space exploration. A system of weights needs to be devised on which to measure the options. It was the goal to devise a Figure of Merit (FoM) on which different choices can be presented and made. The plan was to model typical missions to Mars, identify the parameters, and vary them until the best one is found. Initially, most of the focus was placed on propellant selection.

A Poisson nonnegative matrix factorization method with parameter subspace clustering constraint for endmember extraction in hyperspectral imagery

NASA Astrophysics Data System (ADS)

Sun, Weiwei; Ma, Jun; Yang, Gang; Du, Bo; Zhang, Liangpei

2017-06-01

A new Bayesian method named Poisson Nonnegative Matrix Factorization with Parameter Subspace Clustering Constraint (PNMF-PSCC) has been presented to extract endmembers from Hyperspectral Imagery (HSI). First, the method integrates the liner spectral mixture model with the Bayesian framework and it formulates endmember extraction into a Bayesian inference problem. Second, the Parameter Subspace Clustering Constraint (PSCC) is incorporated into the statistical program to consider the clustering of all pixels in the parameter subspace. The PSCC could enlarge differences among ground objects and helps finding endmembers with smaller spectrum divergences. Meanwhile, the PNMF-PSCC method utilizes the Poisson distribution as the prior knowledge of spectral signals to better explain the quantum nature of light in imaging spectrometer. Third, the optimization problem of PNMF-PSCC is formulated into maximizing the joint density via the Maximum A Posterior (MAP) estimator. The program is finally solved by iteratively optimizing two sub-problems via the Alternating Direction Method of Multipliers (ADMM) framework and the FURTHESTSUM initialization scheme. Five state-of-the art methods are implemented to make comparisons with the performance of PNMF-PSCC on both the synthetic and real HSI datasets. Experimental results show that the PNMF-PSCC outperforms all the five methods in Spectral Angle Distance (SAD) and Root-Mean-Square-Error (RMSE), and especially it could identify good endmembers for ground objects with smaller spectrum divergences.

Optimal land use management for soil erosion control by using an interval-parameter fuzzy two-stage stochastic programming approach.

PubMed

Han, Jing-Cheng; Huang, Guo-He; Zhang, Hua; Li, Zhong

2013-09-01

Soil erosion is one of the most serious environmental and public health problems, and such land degradation can be effectively mitigated through performing land use transitions across a watershed. Optimal land use management can thus provide a way to reduce soil erosion while achieving the maximum net benefit. However, optimized land use allocation schemes are not always successful since uncertainties pertaining to soil erosion control are not well presented. This study applied an interval-parameter fuzzy two-stage stochastic programming approach to generate optimal land use planning strategies for soil erosion control based on an inexact optimization framework, in which various uncertainties were reflected. The modeling approach can incorporate predefined soil erosion control policies, and address inherent system uncertainties expressed as discrete intervals, fuzzy sets, and probability distributions. The developed model was demonstrated through a case study in the Xiangxi River watershed, China's Three Gorges Reservoir region. Land use transformations were employed as decision variables, and based on these, the land use change dynamics were yielded for a 15-year planning horizon. Finally, the maximum net economic benefit with an interval value of [1.197, 6.311] × 10(9) $ was obtained as well as corresponding land use allocations in the three planning periods. Also, the resulting soil erosion amount was found to be decreased and controlled at a tolerable level over the watershed. Thus, results confirm that the developed model is a useful tool for implementing land use management as not only does it allow local decision makers to optimize land use allocation, but can also help to answer how to accomplish land use changes.

Optimal Land Use Management for Soil Erosion Control by Using an Interval-Parameter Fuzzy Two-Stage Stochastic Programming Approach

NASA Astrophysics Data System (ADS)

Han, Jing-Cheng; Huang, Guo-He; Zhang, Hua; Li, Zhong

2013-09-01

Soil erosion is one of the most serious environmental and public health problems, and such land degradation can be effectively mitigated through performing land use transitions across a watershed. Optimal land use management can thus provide a way to reduce soil erosion while achieving the maximum net benefit. However, optimized land use allocation schemes are not always successful since uncertainties pertaining to soil erosion control are not well presented. This study applied an interval-parameter fuzzy two-stage stochastic programming approach to generate optimal land use planning strategies for soil erosion control based on an inexact optimization framework, in which various uncertainties were reflected. The modeling approach can incorporate predefined soil erosion control policies, and address inherent system uncertainties expressed as discrete intervals, fuzzy sets, and probability distributions. The developed model was demonstrated through a case study in the Xiangxi River watershed, China's Three Gorges Reservoir region. Land use transformations were employed as decision variables, and based on these, the land use change dynamics were yielded for a 15-year planning horizon. Finally, the maximum net economic benefit with an interval value of [1.197, 6.311] × 109 was obtained as well as corresponding land use allocations in the three planning periods. Also, the resulting soil erosion amount was found to be decreased and controlled at a tolerable level over the watershed. Thus, results confirm that the developed model is a useful tool for implementing land use management as not only does it allow local decision makers to optimize land use allocation, but can also help to answer how to accomplish land use changes.

DORCA computer program. Volume 1: User's guide

NASA Technical Reports Server (NTRS)

Wray, S. T., Jr.

1971-01-01

The Dynamic Operational Requirements and Cost Analysis Program (DORCA) was written to provide a top level analysis tool for NASA. DORCA relies on a man-machine interaction to optimize results based on external criteria. DORCA relies heavily on outside sources to provide cost information and vehicle performance parameters as the program does not determine these quantities but rather uses them. Given data describing missions, vehicles, payloads, containers, space facilities, schedules, cost values and costing procedures, the program computes flight schedules, cargo manifests, vehicle fleet requirements, acquisition schedules and cost summaries. The program is designed to consider the Earth Orbit, Lunar, Interplanetary and Automated Satellite Programs. A general outline of the capabilities of the program are provided.

An improved computer program for calculating the theoretical performance parameters of a propeller type wind turbine. An appendix to the final report on feasibility of using wind power to pump irrigation water (Texas). [PROP Code

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

Barieau, R.E.

1977-03-01

The PROP Program of Wilson and Lissaman has been modified by adding the Newton-Raphson Method and a Step Wise Search Method, as options for the method of solution. In addition, an optimization method is included. Twist angles, tip speed ratio and the pitch angle may be varied to produce maximum power coefficient. The computer program listing is presented along with sample input and output data. Further improvements to the program are discussed.

Search Planning Under Incomplete Information Using Stochastic Optimization and Regression

DTIC Science & Technology

2011-09-01

solve since they involve un- certainty and unknown parameters (see for example Shapiro et al., 2009; Wallace & Ziemba , 2005). One application area is...M16130.2E. 43 Wallace, S. W., & Ziemba , W. T. (2005). Applications of stochastic programming. Philadelphia, PA: Society for Industrial and Applied

Space Shuttle propulsion parameter estimation using optimal estimation techniques

NASA Technical Reports Server (NTRS)

1983-01-01

The fifth monthly progress report includes corrections and additions to the previously submitted reports. The addition of the SRB propellant thickness as a state variable is included with the associated partial derivatives. During this reporting period, preliminary results of the estimation program checkout was presented to NASA technical personnel.

Parameter optimization of an inerter-based isolator for passive vibration control of Michelangelo's Rondanini Pietà

NASA Astrophysics Data System (ADS)

Siami, A.; Karimi, H. R.; Cigada, A.; Zappa, E.; Sabbioni, E.

2018-01-01

Preserving cultural heritage against earthquake and ambient vibrations can be an attractive topic in the field of vibration control. This paper proposes a passive vibration isolator methodology based on inerters for improving the performance of the isolation system of the famous statue of Michelangelo Buonarroti Pietà Rondanini. More specifically, a five-degree-of-freedom (5DOF) model of the statue and the anti-seismic and anti-vibration base is presented and experimentally validated. The parameters of this model are tuned according to the experimental tests performed on the assembly of the isolator and the structure. Then, the developed model is used to investigate the impact of actuation devices such as tuned mass-damper (TMD) and tuned mass-damper-inerter (TMDI) in vibration reduction of the structure. The effect of implementation of TMDI on the 5DOF model is shown based on physical limitations of the system parameters. Simulation results are provided to illustrate effectiveness of the passive element of TMDI in reduction of the vibration transmitted to the statue in vertical direction. Moreover, the optimal design parameters of the passive system such as frequency and damping coefficient will be calculated using two different performance indexes. The obtained optimal parameters have been evaluated by using two different optimization algorithms: the sequential quadratic programming method and the Firefly algorithm. The results prove significant reduction in the transmitted vibration to the structure in the presence of the proposed tuned TMDI, without imposing a large amount of mass or modification to the structure of the isolator.

Active model-based balancing strategy for self-reconfigurable batteries

NASA Astrophysics Data System (ADS)

Bouchhima, Nejmeddine; Schnierle, Marc; Schulte, Sascha; Birke, Kai Peter

2016-08-01

This paper describes a novel balancing strategy for self-reconfigurable batteries where the discharge and charge rates of each cell can be controlled. While much effort has been focused on improving the hardware architecture of self-reconfigurable batteries, energy equalization algorithms have not been systematically optimized in terms of maximizing the efficiency of the balancing system. Our approach includes aspects of such optimization theory. We develop a balancing strategy for optimal control of the discharge rate of battery cells. We first formulate the cell balancing as a nonlinear optimal control problem, which is modeled afterward as a network program. Using dynamic programming techniques and MATLAB's vectorization feature, we solve the optimal control problem by generating the optimal battery operation policy for a given drive cycle. The simulation results show that the proposed strategy efficiently balances the cells over the life of the battery, an obvious advantage that is absent in the other conventional approaches. Our algorithm is shown to be robust when tested against different influencing parameters varying over wide spectrum on different drive cycles. Furthermore, due to the little computation time and the proved low sensitivity to the inaccurate power predictions, our strategy can be integrated in a real-time system.

Cost Minimization for Joint Energy Management and Production Scheduling Using Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Shah, Rahul H.

Production costs account for the largest share of the overall cost of manufacturing facilities. With the U.S. industrial sector becoming more and more competitive, manufacturers are looking for more cost and resource efficient working practices. Operations management and production planning have shown their capability to dramatically reduce manufacturing costs and increase system robustness. When implementing operations related decision making and planning, two fields that have shown to be most effective are maintenance and energy. Unfortunately, the current research that integrates both is limited. Additionally, these studies fail to consider parameter domains and optimization on joint energy and maintenance driven production planning. Accordingly, production planning methodology that considers maintenance and energy is investigated. Two models are presented to achieve well-rounded operating strategy. The first is a joint energy and maintenance production scheduling model. The second is a cost per part model considering maintenance, energy, and production. The proposed methodology will involve a Time-of-Use electricity demand response program, buffer and holding capacity, station reliability, production rate, station rated power, and more. In practice, the scheduling problem can be used to determine a joint energy, maintenance, and production schedule. Meanwhile, the cost per part model can be used to: (1) test the sensitivity of the obtained optimal production schedule and its corresponding savings by varying key production system parameters; and (2) to determine optimal system parameter combinations when using the joint energy, maintenance, and production planning model. Additionally, a factor analysis on the system parameters is conducted and the corresponding performance of the production schedule under variable parameter conditions, is evaluated. Also, parameter optimization guidelines that incorporate maintenance and energy parameter decision making in the production planning framework are discussed. A modified Particle Swarm Optimization solution technique is adopted to solve the proposed scheduling problem. The algorithm is described in detail and compared to Genetic Algorithm. Case studies are presented to illustrate the benefits of using the proposed model and the effectiveness of the Particle Swarm Optimization approach. Numerical Experiments are implemented and analyzed to test the effectiveness of the proposed model. The proposed scheduling strategy can achieve savings of around 19 to 27 % in cost per part when compared to the baseline scheduling scenarios. By optimizing key production system parameters from the cost per part model, the baseline scenarios can obtain around 20 to 35 % in savings for the cost per part. These savings further increase by 42 to 55 % when system parameter optimization is integrated with the proposed scheduling problem. Using this method, the most influential parameters on the cost per part are the rated power from production, the production rate, and the initial machine reliabilities. The modified Particle Swarm Optimization algorithm adopted allows greater diversity and exploration compared to Genetic Algorithm for the proposed joint model which results in it being more computationally efficient in determining the optimal scheduling. While Genetic Algorithm could achieve a solution quality of 2,279.63 at an expense of 2,300 seconds in computational effort. In comparison, the proposed Particle Swarm Optimization algorithm achieved a solution quality of 2,167.26 in less than half the computation effort which is required by Genetic Algorithm.

AutoBayes Program Synthesis System Users Manual

NASA Technical Reports Server (NTRS)

Schumann, Johann; Jafari, Hamed; Pressburger, Tom; Denney, Ewen; Buntine, Wray; Fischer, Bernd

2008-01-01

Program synthesis is the systematic, automatic construction of efficient executable code from high-level declarative specifications. AutoBayes is a fully automatic program synthesis system for the statistical data analysis domain; in particular, it solves parameter estimation problems. It has seen many successful applications at NASA and is currently being used, for example, to analyze simulation results for Orion. The input to AutoBayes is a concise description of a data analysis problem composed of a parameterized statistical model and a goal that is a probability term involving parameters and input data. The output is optimized and fully documented C/C++ code computing the values for those parameters that maximize the probability term. AutoBayes can solve many subproblems symbolically rather than having to rely on numeric approximation algorithms, thus yielding effective, efficient, and compact code. Statistical analysis is faster and more reliable, because effort can be focused on model development and validation rather than manual development of solution algorithms and code.

Application of the sequential quadratic programming algorithm for reconstructing the distribution of optical parameters based on the time-domain radiative transfer equation.

PubMed

Qi, Hong; Qiao, Yao-Bin; Ren, Ya-Tao; Shi, Jing-Wen; Zhang, Ze-Yu; Ruan, Li-Ming

2016-10-17

Sequential quadratic programming (SQP) is used as an optimization algorithm to reconstruct the optical parameters based on the time-domain radiative transfer equation (TD-RTE). Numerous time-resolved measurement signals are obtained using the TD-RTE as forward model. For a high computational efficiency, the gradient of objective function is calculated using an adjoint equation technique. SQP algorithm is employed to solve the inverse problem and the regularization term based on the generalized Gaussian Markov random field (GGMRF) model is used to overcome the ill-posed problem. Simulated results show that the proposed reconstruction scheme performs efficiently and accurately.

NLSCIDNT user's guide maximum likehood parameter identification computer program with nonlinear rotorcraft model

NASA Technical Reports Server (NTRS)

1979-01-01

A nonlinear, maximum likelihood, parameter identification computer program (NLSCIDNT) is described which evaluates rotorcraft stability and control coefficients from flight test data. The optimal estimates of the parameters (stability and control coefficients) are determined (identified) by minimizing the negative log likelihood cost function. The minimization technique is the Levenberg-Marquardt method, which behaves like the steepest descent method when it is far from the minimum and behaves like the modified Newton-Raphson method when it is nearer the minimum. Twenty-one states and 40 measurement variables are modeled, and any subset may be selected. States which are not integrated may be fixed at an input value, or time history data may be substituted for the state in the equations of motion. Any aerodynamic coefficient may be expressed as a nonlinear polynomial function of selected 'expansion variables'.

Application of Monte Carlo techniques to optimization of high-energy beam transport in a stochastic environment

NASA Technical Reports Server (NTRS)

Parrish, R. V.; Dieudonne, J. E.; Filippas, T. A.

1971-01-01

An algorithm employing a modified sequential random perturbation, or creeping random search, was applied to the problem of optimizing the parameters of a high-energy beam transport system. The stochastic solution of the mathematical model for first-order magnetic-field expansion allows the inclusion of state-variable constraints, and the inclusion of parameter constraints allowed by the method of algorithm application eliminates the possibility of infeasible solutions. The mathematical model and the algorithm were programmed for a real-time simulation facility; thus, two important features are provided to the beam designer: (1) a strong degree of man-machine communication (even to the extent of bypassing the algorithm and applying analog-matching techniques), and (2) extensive graphics for displaying information concerning both algorithm operation and transport-system behavior. Chromatic aberration was also included in the mathematical model and in the optimization process. Results presented show this method as yielding better solutions (in terms of resolutions) to the particular problem than those of a standard analog program as well as demonstrating flexibility, in terms of elements, constraints, and chromatic aberration, allowed by user interaction with both the algorithm and the stochastic model. Example of slit usage and a limited comparison of predicted results and actual results obtained with a 600 MeV cyclotron are given.

Selection of optimal oligonucleotide probes for microarrays usingmultiple criteria, global alignment and parameter estimation.

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

Li, Xingyuan; He, Zhili; Zhou, Jizhong

2005-10-30

The oligonucleotide specificity for microarray hybridizationcan be predicted by its sequence identity to non-targets, continuousstretch to non-targets, and/or binding free energy to non-targets. Mostcurrently available programs only use one or two of these criteria, whichmay choose 'false' specific oligonucleotides or miss 'true' optimalprobes in a considerable proportion. We have developed a software tool,called CommOligo using new algorithms and all three criteria forselection of optimal oligonucleotide probes. A series of filters,including sequence identity, free energy, continuous stretch, GC content,self-annealing, distance to the 3'-untranslated region (3'-UTR) andmelting temperature (Tm), are used to check each possibleoligonucleotide. A sequence identity is calculated based onmore » gapped globalalignments. A traversal algorithm is used to generate alignments for freeenergy calculation. The optimal Tm interval is determined based on probecandidates that have passed all other filters. Final probes are pickedusing a combination of user-configurable piece-wise linear functions andan iterative process. The thresholds for identity, stretch and freeenergy filters are automatically determined from experimental data by anaccessory software tool, CommOligo_PE (CommOligo Parameter Estimator).The program was used to design probes for both whole-genome and highlyhomologous sequence data. CommOligo and CommOligo_PE are freely availableto academic users upon request.« less

Assessing the applicability of WRF optimal parameters under the different precipitation simulations in the Greater Beijing Area

NASA Astrophysics Data System (ADS)

Di, Zhenhua; Duan, Qingyun; Wang, Chen; Ye, Aizhong; Miao, Chiyuan; Gong, Wei

2018-03-01

Forecasting skills of the complex weather and climate models have been improved by tuning the sensitive parameters that exert the greatest impact on simulated results based on more effective optimization methods. However, whether the optimal parameter values are still work when the model simulation conditions vary, which is a scientific problem deserving of study. In this study, a highly-effective optimization method, adaptive surrogate model-based optimization (ASMO), was firstly used to tune nine sensitive parameters from four physical parameterization schemes of the Weather Research and Forecasting (WRF) model to obtain better summer precipitation forecasting over the Greater Beijing Area in China. Then, to assess the applicability of the optimal parameter values, simulation results from the WRF model with default and optimal parameter values were compared across precipitation events, boundary conditions, spatial scales, and physical processes in the Greater Beijing Area. The summer precipitation events from 6 years were used to calibrate and evaluate the optimal parameter values of WRF model. Three boundary data and two spatial resolutions were adopted to evaluate the superiority of the calibrated optimal parameters to default parameters under the WRF simulations with different boundary conditions and spatial resolutions, respectively. Physical interpretations of the optimal parameters indicating how to improve precipitation simulation results were also examined. All the results showed that the optimal parameters obtained by ASMO are superior to the default parameters for WRF simulations for predicting summer precipitation in the Greater Beijing Area because the optimal parameters are not constrained by specific precipitation events, boundary conditions, and spatial resolutions. The optimal values of the nine parameters were determined from 127 parameter samples using the ASMO method, which showed that the ASMO method is very highly-efficient for optimizing WRF model parameters.

Improved Evolutionary Programming with Various Crossover Techniques for Optimal Power Flow Problem

NASA Astrophysics Data System (ADS)

Tangpatiphan, Kritsana; Yokoyama, Akihiko

This paper presents an Improved Evolutionary Programming (IEP) for solving the Optimal Power Flow (OPF) problem, which is considered as a non-linear, non-smooth, and multimodal optimization problem in power system operation. The total generator fuel cost is regarded as an objective function to be minimized. The proposed method is an Evolutionary Programming (EP)-based algorithm with making use of various crossover techniques, normally applied in Real Coded Genetic Algorithm (RCGA). The effectiveness of the proposed approach is investigated on the IEEE 30-bus system with three different types of fuel cost functions; namely the quadratic cost curve, the piecewise quadratic cost curve, and the quadratic cost curve superimposed by sine component. These three cost curves represent the generator fuel cost functions with a simplified model and more accurate models of a combined-cycle generating unit and a thermal unit with value-point loading effect respectively. The OPF solutions by the proposed method and Pure Evolutionary Programming (PEP) are observed and compared. The simulation results indicate that IEP requires less computing time than PEP with better solutions in some cases. Moreover, the influences of important IEP parameters on the OPF solution are described in details.

Development and Testing of Control Laws for the Active Aeroelastic Wing Program

NASA Technical Reports Server (NTRS)

Dibley, Ryan P.; Allen, Michael J.; Clarke, Robert; Gera, Joseph; Hodgkinson, John

2005-01-01

The Active Aeroelastic Wing research program was a joint program between the U.S. Air Force Research Laboratory and NASA established to investigate the characteristics of an aeroelastic wing and the technique of using wing twist for roll control. The flight test program employed the use of an F/A-18 aircraft modified by reducing the wing torsional stiffness and adding a custom research flight control system. The research flight control system was optimized to maximize roll rate using only wing surfaces to twist the wing while simultaneously maintaining design load limits, stability margins, and handling qualities. NASA Dryden Flight Research Center developed control laws using the software design tool called CONDUIT, which employs a multi-objective function optimization to tune selected control system design parameters. Modifications were made to the Active Aeroelastic Wing implementation in this new software design tool to incorporate the NASA Dryden Flight Research Center nonlinear F/A-18 simulation for time history analysis. This paper describes the design process, including how the control law requirements were incorporated into constraints for the optimization of this specific software design tool. Predicted performance is also compared to results from flight.

A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization.

PubMed

Liu, Qingshan; Guo, Zhishan; Wang, Jun

2012-02-01

In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable of solving more general pseudoconvex optimization problems with equality and bound constraints. Moreover, it is capable of solving constrained fractional programming problems as a special case. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds. Numerical examples with simulation results illustrate the effectiveness and characteristics of the proposed neural network. In addition, an application for dynamic portfolio optimization is discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.

Ground state of the time-independent Gross Pitaevskii equation

NASA Astrophysics Data System (ADS)

Dion, Claude M.; Cancès, Eric

2007-11-01

We present a suite of programs to determine the ground state of the time-independent Gross-Pitaevskii equation, used in the simulation of Bose-Einstein condensates. The calculation is based on the Optimal Damping Algorithm, ensuring a fast convergence to the true ground state. Versions are given for the one-, two-, and three-dimensional equation, using either a spectral method, well suited for harmonic trapping potentials, or a spatial grid. Program summaryProgram title: GPODA Catalogue identifier: ADZN_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADZN_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 5339 No. of bytes in distributed program, including test data, etc.: 19 426 Distribution format: tar.gz Programming language: Fortran 90 Computer: ANY (Compilers under which the program has been tested: Absoft Pro Fortran, The Portland Group Fortran 90/95 compiler, Intel Fortran Compiler) RAM: From <1 MB in 1D to ˜10 MB for a large 3D grid Classification: 2.7, 4.9 External routines: LAPACK, BLAS, DFFTPACK Nature of problem: The order parameter (or wave function) of a Bose-Einstein condensate (BEC) is obtained, in a mean field approximation, by the Gross-Pitaevskii equation (GPE) [F. Dalfovo, S. Giorgini, L.P. Pitaevskii, S. Stringari, Rev. Mod. Phys. 71 (1999) 463]. The GPE is a nonlinear Schrödinger-like equation, including here a confining potential. The stationary state of a BEC is obtained by finding the ground state of the time-independent GPE, i.e., the order parameter that minimizes the energy. In addition to the standard three-dimensional GPE, tight traps can lead to effective two- or even one-dimensional BECs, so the 2D and 1D GPEs are also considered. Solution method: The ground state of the time-independent of the GPE is calculated using the Optimal Damping Algorithm [E. Cancès, C. Le Bris, Int. J. Quantum Chem. 79 (2000) 82]. Two sets of programs are given, using either a spectral representation of the order parameter [C.M. Dion, E. Cancès, Phys. Rev. E 67 (2003) 046706], suitable for a (quasi) harmonic trapping potential, or by discretizing the order parameter on a spatial grid. Running time: From seconds in 1D to a few hours for large 3D grids

Fabrication of aluminum-carbon composites

NASA Technical Reports Server (NTRS)

Novak, R. C.

1973-01-01

A screening, optimization, and evaluation program is reported of unidirectional carbon-aluminum composites. During the screening phase both large diameter monofilament and small diameter multifilament reinforcements were utilized to determine optimum precursor tape making and consolidation techniques. Difficulty was encountered in impregnating and consolidating the multifiber reinforcements. Large diameter monofilament reinforcement was found easier to fabricate into composites and was selected to carry into the optimization phase in which the hot pressing parameters were refined and the size of the fabricated panels was scaled up. After process optimization the mechanical properties of the carbon-aluminum composites were characterized in tension, stress-rupture and creep, mechanical fatigue, thermal fatigue, thermal aging, thermal expansion, and impact.

Design optimization of beta- and photovoltaic conversion devices

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

Wichner, R.; Blum, A.; Fischer-Colbrie, E.

1976-01-08

This report presents the theoretical and experimental results of an LLL Electronics Engineering research program aimed at optimizing the design and electronic-material parameters of beta- and photovoltaic p-n junction conversion devices. To meet this objective, a comprehensive computer code has been developed that can handle a broad range of practical conditions. The physical model upon which the code is based is described first. Then, an example is given of a set of optimization calculations along with the resulting optimized efficiencies for silicon (Si) and gallium-arsenide (GaAs) devices. The model we have developed, however, is not limited to these materials. Itmore » can handle any appropriate material--single or polycrystalline-- provided energy absorption and electron-transport data are available. To check code validity, the performance of experimental silicon p-n junction devices (produced in-house) were measured under various light intensities and spectra as well as under tritium beta irradiation. The results of these tests were then compared with predicted results based on the known or best estimated device parameters. The comparison showed very good agreement between the calculated and the measured results.« less

Cost related sensitivity analysis for optimal operation of a grid-parallel PEM fuel cell power plant

NASA Astrophysics Data System (ADS)

El-Sharkh, M. Y.; Tanrioven, M.; Rahman, A.; Alam, M. S.

Fuel cell power plants (FCPP) as a combined source of heat, power and hydrogen (CHP&H) can be considered as a potential option to supply both thermal and electrical loads. Hydrogen produced from the FCPP can be stored for future use of the FCPP or can be sold for profit. In such a system, tariff rates for purchasing or selling electricity, the fuel cost for the FCPP/thermal load, and hydrogen selling price are the main factors that affect the operational strategy. This paper presents a hybrid evolutionary programming and Hill-Climbing based approach to evaluate the impact of change of the above mentioned cost parameters on the optimal operational strategy of the FCPP. The optimal operational strategy of the FCPP for different tariffs is achieved through the estimation of the following: hourly generated power, the amount of thermal power recovered, power trade with the local grid, and the quantity of hydrogen that can be produced. Results show the importance of optimizing system cost parameters in order to minimize overall operating cost.

Optimal integration of a hybrid solar-battery power source into smart home nanogrid with plug-in electric vehicle

NASA Astrophysics Data System (ADS)

Wu, Xiaohua; Hu, Xiaosong; Teng, Yanqiong; Qian, Shide; Cheng, Rui

2017-09-01

Hybrid solar-battery power source is essential in the nexus of plug-in electric vehicle (PEV), renewables, and smart building. This paper devises an optimization framework for efficient energy management and components sizing of a single smart home with home battery, PEV, and potovoltatic (PV) arrays. We seek to maximize the home economy, while satisfying home power demand and PEV driving. Based on the structure and system models of the smart home nanogrid, a convex programming (CP) problem is formulated to rapidly and efficiently optimize both the control decision and parameters of the home battery energy storage system (BESS). Considering different time horizons of optimization, home BESS prices, types and control modes of PEVs, the parameters of home BESS and electric cost are systematically investigated. Based on the developed CP control law in home to vehicle (H2V) mode and vehicle to home (V2H) mode, the home with BESS does not buy electric energy from the grid during the electric price's peak periods.

Statistical mechanical analysis of linear programming relaxation for combinatorial optimization problems

NASA Astrophysics Data System (ADS)

Takabe, Satoshi; Hukushima, Koji

2016-05-01

Typical behavior of the linear programming (LP) problem is studied as a relaxation of the minimum vertex cover (min-VC), a type of integer programming (IP) problem. A lattice-gas model on the Erdös-Rényi random graphs of α -uniform hyperedges is proposed to express both the LP and IP problems of the min-VC in the common statistical mechanical model with a one-parameter family. Statistical mechanical analyses reveal for α =2 that the LP optimal solution is typically equal to that given by the IP below the critical average degree c =e in the thermodynamic limit. The critical threshold for good accuracy of the relaxation extends the mathematical result c =1 and coincides with the replica symmetry-breaking threshold of the IP. The LP relaxation for the minimum hitting sets with α ≥3 , minimum vertex covers on α -uniform random graphs, is also studied. Analytic and numerical results strongly suggest that the LP relaxation fails to estimate optimal values above the critical average degree c =e /(α -1 ) where the replica symmetry is broken.

Statistical mechanical analysis of linear programming relaxation for combinatorial optimization problems.

PubMed

Takabe, Satoshi; Hukushima, Koji

2016-05-01

Typical behavior of the linear programming (LP) problem is studied as a relaxation of the minimum vertex cover (min-VC), a type of integer programming (IP) problem. A lattice-gas model on the Erdös-Rényi random graphs of α-uniform hyperedges is proposed to express both the LP and IP problems of the min-VC in the common statistical mechanical model with a one-parameter family. Statistical mechanical analyses reveal for α=2 that the LP optimal solution is typically equal to that given by the IP below the critical average degree c=e in the thermodynamic limit. The critical threshold for good accuracy of the relaxation extends the mathematical result c=1 and coincides with the replica symmetry-breaking threshold of the IP. The LP relaxation for the minimum hitting sets with α≥3, minimum vertex covers on α-uniform random graphs, is also studied. Analytic and numerical results strongly suggest that the LP relaxation fails to estimate optimal values above the critical average degree c=e/(α-1) where the replica symmetry is broken.

a Novel Discrete Optimal Transport Method for Bayesian Inverse Problems

NASA Astrophysics Data System (ADS)

Bui-Thanh, T.; Myers, A.; Wang, K.; Thiery, A.

2017-12-01

We present the Augmented Ensemble Transform (AET) method for generating approximate samples from a high-dimensional posterior distribution as a solution to Bayesian inverse problems. Solving large-scale inverse problems is critical for some of the most relevant and impactful scientific endeavors of our time. Therefore, constructing novel methods for solving the Bayesian inverse problem in more computationally efficient ways can have a profound impact on the science community. This research derives the novel AET method for exploring a posterior by solving a sequence of linear programming problems, resulting in a series of transport maps which map prior samples to posterior samples, allowing for the computation of moments of the posterior. We show both theoretical and numerical results, indicating this method can offer superior computational efficiency when compared to other SMC methods. Most of this efficiency is derived from matrix scaling methods to solve the linear programming problem and derivative-free optimization for particle movement. We use this method to determine inter-well connectivity in a reservoir and the associated uncertainty related to certain parameters. The attached file shows the difference between the true parameter and the AET parameter in an example 3D reservoir problem. The error is within the Morozov discrepancy allowance with lower computational cost than other particle methods.

Investigation of sludge re-circulating clarifiers design and optimization through numerical simulation.

PubMed

Davari, S; Lichayee, M J

2003-01-01

In steam thermal power plants (TPP) with open re-circulating wet cooling towers, elimination of water hardness and suspended solids (SS) is performed in clarifiers. Most of these clarifiers are of high efficiency sludge re-circulating type (SRC) with capacity between 500-1,500 m3/hr. Improper design and/or mal-operation of clarifiers in TPPs results in working conditions below design capacity or production of soft water with improper quality (hardness and S.S.). This causes accumulation of deposits in heat exchangers, condenser tubes, cooling and service water pipes and boiler tubes as well as increasing the ionic load of water at the demineralizing system inlet. It also increases the amount of chemical consumptions and produces more liquid and solid waste. In this regard, a software program for optimal design and simulation of SRCs has been developed. Then design parameters of existing SRCs in four TPPs in Iran were used as inputs to developed software program and resulting technical specifications were compared with existing ones. In some cases improper design was the main cause of poor outlet water quality. In order to achieve proper efficiency, further investigations were made to obtain control parameters as well as design parameters for both mal-designed and/or mal-operated SRCs.

Coastal aquifer management under parameter uncertainty: Ensemble surrogate modeling based simulation-optimization

NASA Astrophysics Data System (ADS)

Janardhanan, S.; Datta, B.

2011-12-01

Surrogate models are widely used to develop computationally efficient simulation-optimization models to solve complex groundwater management problems. Artificial intelligence based models are most often used for this purpose where they are trained using predictor-predictand data obtained from a numerical simulation model. Most often this is implemented with the assumption that the parameters and boundary conditions used in the numerical simulation model are perfectly known. However, in most practical situations these values are uncertain. Under these circumstances the application of such approximation surrogates becomes limited. In our study we develop a surrogate model based coupled simulation optimization methodology for determining optimal pumping strategies for coastal aquifers considering parameter uncertainty. An ensemble surrogate modeling approach is used along with multiple realization optimization. The methodology is used to solve a multi-objective coastal aquifer management problem considering two conflicting objectives. Hydraulic conductivity and the aquifer recharge are considered as uncertain values. Three dimensional coupled flow and transport simulation model FEMWATER is used to simulate the aquifer responses for a number of scenarios corresponding to Latin hypercube samples of pumping and uncertain parameters to generate input-output patterns for training the surrogate models. Non-parametric bootstrap sampling of this original data set is used to generate multiple data sets which belong to different regions in the multi-dimensional decision and parameter space. These data sets are used to train and test multiple surrogate models based on genetic programming. The ensemble of surrogate models is then linked to a multi-objective genetic algorithm to solve the pumping optimization problem. Two conflicting objectives, viz, maximizing total pumping from beneficial wells and minimizing the total pumping from barrier wells for hydraulic control of saltwater intrusion are considered. The salinity levels resulting at strategic locations due to these pumping are predicted using the ensemble surrogates and are constrained to be within pre-specified levels. Different realizations of the concentration values are obtained from the ensemble predictions corresponding to each candidate solution of pumping. Reliability concept is incorporated as the percent of the total number of surrogate models which satisfy the imposed constraints. The methodology was applied to a realistic coastal aquifer system in Burdekin delta area in Australia. It was found that all optimal solutions corresponding to a reliability level of 0.99 satisfy all the constraints and as reducing reliability level decreases the constraint violation increases. Thus ensemble surrogate model based simulation-optimization was found to be useful in deriving multi-objective optimal pumping strategies for coastal aquifers under parameter uncertainty.

The role of under-determined approximations in engineering and science application

NASA Technical Reports Server (NTRS)

Carpenter, William C.

1992-01-01

There is currently a great deal of interest in using response surfaces in the optimization of aircraft performance. The objective function and/or constraint equations involved in these optimization problems may come from numerous disciplines such as structures, aerodynamics, environmental engineering, etc. In each of these disciplines, the mathematical complexity of the governing equations usually dictates that numerical results be obtained from large computer programs such as a finite element method program. Thus, when performing optimization studies, response surfaces are a convenient way of transferring information from the various disciplines to the optimization algorithm as opposed to bringing all the sundry computer programs together in a massive computer code. Response surfaces offer another advantage in the optimization of aircraft structures. A characteristic of these types of optimization problems is that evaluation of the objective function and response equations (referred to as a functional evaluation) can be very expensive in a computational sense. Because of the computational expense in obtaining functional evaluations, the present study was undertaken to investigate under-determinined approximations. An under-determined approximation is one in which there are fewer training pairs (pieces of information about a function) than there are undetermined parameters (coefficients or weights) associated with the approximation. Both polynomial approximations and neural net approximations were examined. Three main example problems were investigated: (1) a function of one design variable was considered; (2) a function of two design variables was considered; and (3) a 35 bar truss with 4 design variables was considered.

The Capability Portfolio Analysis Tool (CPAT): A Mixed Integer Linear Programming Formulation for Fleet Modernization Analysis (Version 2.0.2).

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

Waddell, Lucas; Muldoon, Frank; Henry, Stephen Michael

In order to effectively plan the management and modernization of their large and diverse fleets of vehicles, Program Executive Office Ground Combat Systems (PEO GCS) and Program Executive Office Combat Support and Combat Service Support (PEO CS&CSS) commis- sioned the development of a large-scale portfolio planning optimization tool. This software, the Capability Portfolio Analysis Tool (CPAT), creates a detailed schedule that optimally prioritizes the modernization or replacement of vehicles within the fleet - respecting numerous business rules associated with fleet structure, budgets, industrial base, research and testing, etc., while maximizing overall fleet performance through time. This paper contains a thor-more » ough documentation of the terminology, parameters, variables, and constraints that comprise the fleet management mixed integer linear programming (MILP) mathematical formulation. This paper, which is an update to the original CPAT formulation document published in 2015 (SAND2015-3487), covers the formulation of important new CPAT features.« less

Software Would Largely Automate Design of Kalman Filter

NASA Technical Reports Server (NTRS)

Chuang, Jason C. H.; Negast, William J.

2005-01-01

Embedded Navigation Filter Automatic Designer (ENFAD) is a computer program being developed to automate the most difficult tasks in designing embedded software to implement a Kalman filter in a navigation system. The most difficult tasks are selection of error states of the filter and tuning of filter parameters, which are timeconsuming trial-and-error tasks that require expertise and rarely yield optimum results. An optimum selection of error states and filter parameters depends on navigation-sensor and vehicle characteristics, and on filter processing time. ENFAD would include a simulation module that would incorporate all possible error states with respect to a given set of vehicle and sensor characteristics. The first of two iterative optimization loops would vary the selection of error states until the best filter performance was achieved in Monte Carlo simulations. For a fixed selection of error states, the second loop would vary the filter parameter values until an optimal performance value was obtained. Design constraints would be satisfied in the optimization loops. Users would supply vehicle and sensor test data that would be used to refine digital models in ENFAD. Filter processing time and filter accuracy would be computed by ENFAD.

Multi-Objective Trajectory Optimization of a Hypersonic Reconnaissance Vehicle with Temperature Constraints

NASA Astrophysics Data System (ADS)

Masternak, Tadeusz J.

This research determines temperature-constrained optimal trajectories for a scramjet-based hypersonic reconnaissance vehicle by developing an optimal control formulation and solving it using a variable order Gauss-Radau quadrature collocation method with a Non-Linear Programming (NLP) solver. The vehicle is assumed to be an air-breathing reconnaissance aircraft that has specified takeoff/landing locations, airborne refueling constraints, specified no-fly zones, and specified targets for sensor data collections. A three degree of freedom scramjet aircraft model is adapted from previous work and includes flight dynamics, aerodynamics, and thermal constraints. Vehicle control is accomplished by controlling angle of attack, roll angle, and propellant mass flow rate. This model is incorporated into an optimal control formulation that includes constraints on both the vehicle and mission parameters, such as avoidance of no-fly zones and coverage of high-value targets. To solve the optimal control formulation, a MATLAB-based package called General Pseudospectral Optimal Control Software (GPOPS-II) is used, which transcribes continuous time optimal control problems into an NLP problem. In addition, since a mission profile can have varying vehicle dynamics and en-route imposed constraints, the optimal control problem formulation can be broken up into several "phases" with differing dynamics and/or varying initial/final constraints. Optimal trajectories are developed using several different performance costs in the optimal control formulation: minimum time, minimum time with control penalties, and maximum range. The resulting analysis demonstrates that optimal trajectories that meet specified mission parameters and constraints can be quickly determined and used for larger-scale operational and campaign planning and execution.

Random sampling and validation of covariance matrices of resonance parameters

NASA Astrophysics Data System (ADS)

Plevnik, Lucijan; Zerovnik, Gašper

2017-09-01

Analytically exact methods for random sampling of arbitrary correlated parameters are presented. Emphasis is given on one hand on the possible inconsistencies in the covariance data, concentrating on the positive semi-definiteness and consistent sampling of correlated inherently positive parameters, and on the other hand on optimization of the implementation of the methods itself. The methods have been applied in the program ENDSAM, written in the Fortran language, which from a file from a nuclear data library of a chosen isotope in ENDF-6 format produces an arbitrary number of new files in ENDF-6 format which contain values of random samples of resonance parameters (in accordance with corresponding covariance matrices) in places of original values. The source code for the program ENDSAM is available from the OECD/NEA Data Bank. The program works in the following steps: reads resonance parameters and their covariance data from nuclear data library, checks whether the covariance data is consistent, and produces random samples of resonance parameters. The code has been validated with both realistic and artificial data to show that the produced samples are statistically consistent. Additionally, the code was used to validate covariance data in existing nuclear data libraries. A list of inconsistencies, observed in covariance data of resonance parameters in ENDF-VII.1, JEFF-3.2 and JENDL-4.0 is presented. For now, the work has been limited to resonance parameters, however the methods presented are general and can in principle be extended to sampling and validation of any nuclear data.

Prepositioning emergency supplies under uncertainty: a parametric optimization method

NASA Astrophysics Data System (ADS)

Bai, Xuejie; Gao, Jinwu; Liu, Yankui

2018-07-01

Prepositioning of emergency supplies is an effective method for increasing preparedness for disasters and has received much attention in recent years. In this article, the prepositioning problem is studied by a robust parametric optimization method. The transportation cost, supply, demand and capacity are unknown prior to the extraordinary event, which are represented as fuzzy parameters with variable possibility distributions. The variable possibility distributions are obtained through the credibility critical value reduction method for type-2 fuzzy variables. The prepositioning problem is formulated as a fuzzy value-at-risk model to achieve a minimum total cost incurred in the whole process. The key difficulty in solving the proposed optimization model is to evaluate the quantile of the fuzzy function in the objective and the credibility in the constraints. The objective function and constraints can be turned into their equivalent parametric forms through chance constrained programming under the different confidence levels. Taking advantage of the structural characteristics of the equivalent optimization model, a parameter-based domain decomposition method is developed to divide the original optimization problem into six mixed-integer parametric submodels, which can be solved by standard optimization solvers. Finally, to explore the viability of the developed model and the solution approach, some computational experiments are performed on realistic scale case problems. The computational results reported in the numerical example show the credibility and superiority of the proposed parametric optimization method.

FASTER 3: A generalized-geometry Monte Carlo computer program for the transport of neutrons and gamma rays. Volume 2: Users manual

NASA Technical Reports Server (NTRS)

Jordan, T. M.

1970-01-01

A description of the FASTER-III program for Monte Carlo Carlo calculation of photon and neutron transport in complex geometries is presented. Major revisions include the capability of calculating minimum weight shield configurations for primary and secondary radiation and optimal importance sampling parameters. The program description includes a users manual describing the preparation of input data cards, the printout from a sample problem including the data card images, definitions of Fortran variables, the program logic, and the control cards required to run on the IBM 7094, IBM 360, UNIVAC 1108 and CDC 6600 computers.

Nuclear Electric Vehicle Optimization Toolset (NEVOT)

NASA Technical Reports Server (NTRS)

Tinker, Michael L.; Steincamp, James W.; Stewart, Eric T.; Patton, Bruce W.; Pannell, William P.; Newby, Ronald L.; Coffman, Mark E.; Kos, Larry D.; Qualls, A. Lou; Greene, Sherrell

2004-01-01

The Nuclear Electric Vehicle Optimization Toolset (NEVOT) optimizes the design of all major nuclear electric propulsion (NEP) vehicle subsystems for a defined mission within constraints and optimization parameters chosen by a user. The tool uses a genetic algorithm (GA) search technique to combine subsystem designs and evaluate the fitness of the integrated design to fulfill a mission. The fitness of an individual is used within the GA to determine its probability of survival through successive generations in which the designs with low fitness are eliminated and replaced with combinations or mutations of designs with higher fitness. The program can find optimal solutions for different sets of fitness metrics without modification and can create and evaluate vehicle designs that might never be considered through traditional design techniques. It is anticipated that the flexible optimization methodology will expand present knowledge of the design trade-offs inherent in designing nuclear powered space vehicles and lead to improved NEP designs.

Research on crude oil storage and transportation based on optimization algorithm

NASA Astrophysics Data System (ADS)

Yuan, Xuhua

2018-04-01

At present, the optimization theory and method have been widely used in the optimization scheduling and optimal operation scheme of complex production systems. Based on C++Builder 6 program development platform, the theoretical research results are implemented by computer. The simulation and intelligent decision system of crude oil storage and transportation inventory scheduling are designed. The system includes modules of project management, data management, graphics processing, simulation of oil depot operation scheme. It can realize the optimization of the scheduling scheme of crude oil storage and transportation system. A multi-point temperature measuring system for monitoring the temperature field of floating roof oil storage tank is developed. The results show that by optimizing operating parameters such as tank operating mode and temperature, the total transportation scheduling costs of the storage and transportation system can be reduced by 9.1%. Therefore, this method can realize safe and stable operation of crude oil storage and transportation system.

Cost minimizing of cutting process for CNC thermal and water-jet machines

NASA Astrophysics Data System (ADS)

Tavaeva, Anastasia; Kurennov, Dmitry

2015-11-01

This paper deals with optimization problem of cutting process for CNC thermal and water-jet machines. The accuracy of objective function parameters calculation for optimization problem is investigated. This paper shows that working tool path speed is not constant value. One depends on some parameters that are described in this paper. The relations of working tool path speed depending on the numbers of NC programs frames, length of straight cut, configuration part are presented. Based on received results the correction coefficients for working tool speed are defined. Additionally the optimization problem may be solved by using mathematical model. Model takes into account the additional restrictions of thermal cutting (choice of piercing and output tool point, precedence condition, thermal deformations). At the second part of paper the non-standard cutting techniques are considered. Ones may lead to minimizing of cutting cost and time compared with standard cutting techniques. This paper considers the effectiveness of non-standard cutting techniques application. At the end of the paper the future research works are indicated.

GVIPS Models and Software

NASA Technical Reports Server (NTRS)

Arnold, Steven M.; Gendy, Atef; Saleeb, Atef F.; Mark, John; Wilt, Thomas E.

2007-01-01

Two reports discuss, respectively, (1) the generalized viscoplasticity with potential structure (GVIPS) class of mathematical models and (2) the Constitutive Material Parameter Estimator (COMPARE) computer program. GVIPS models are constructed within a thermodynamics- and potential-based theoretical framework, wherein one uses internal state variables and derives constitutive equations for both the reversible (elastic) and the irreversible (viscoplastic) behaviors of materials. Because of the underlying potential structure, GVIPS models not only capture a variety of material behaviors but also are very computationally efficient. COMPARE comprises (1) an analysis core and (2) a C++-language subprogram that implements a Windows-based graphical user interface (GUI) for controlling the core. The GUI relieves the user of the sometimes tedious task of preparing data for the analysis core, freeing the user to concentrate on the task of fitting experimental data and ultimately obtaining a set of material parameters. The analysis core consists of three modules: one for GVIPS material models, an analysis module containing a specialized finite-element solution algorithm, and an optimization module. COMPARE solves the problem of finding GVIPS material parameters in the manner of a design-optimization problem in which the parameters are the design variables.

Hierarchical Bayesian Model Averaging for Chance Constrained Remediation Designs

NASA Astrophysics Data System (ADS)

Chitsazan, N.; Tsai, F. T.

2012-12-01

Groundwater remediation designs are heavily relying on simulation models which are subjected to various sources of uncertainty in their predictions. To develop a robust remediation design, it is crucial to understand the effect of uncertainty sources. In this research, we introduce a hierarchical Bayesian model averaging (HBMA) framework to segregate and prioritize sources of uncertainty in a multi-layer frame, where each layer targets a source of uncertainty. The HBMA framework provides an insight to uncertainty priorities and propagation. In addition, HBMA allows evaluating model weights in different hierarchy levels and assessing the relative importance of models in each level. To account for uncertainty, we employ a chance constrained (CC) programming for stochastic remediation design. Chance constrained programming was implemented traditionally to account for parameter uncertainty. Recently, many studies suggested that model structure uncertainty is not negligible compared to parameter uncertainty. Using chance constrained programming along with HBMA can provide a rigorous tool for groundwater remediation designs under uncertainty. In this research, the HBMA-CC was applied to a remediation design in a synthetic aquifer. The design was to develop a scavenger well approach to mitigate saltwater intrusion toward production wells. HBMA was employed to assess uncertainties from model structure, parameter estimation and kriging interpolation. An improved harmony search optimization method was used to find the optimal location of the scavenger well. We evaluated prediction variances of chloride concentration at the production wells through the HBMA framework. The results showed that choosing the single best model may lead to a significant error in evaluating prediction variances for two reasons. First, considering the single best model, variances that stem from uncertainty in the model structure will be ignored. Second, considering the best model with non-dominant model weight may underestimate or overestimate prediction variances by ignoring other plausible propositions. Chance constraints allow developing a remediation design with a desirable reliability. However, considering the single best model, the calculated reliability will be different from the desirable reliability. We calculated the reliability of the design for the models at different levels of HBMA. The results showed that by moving toward the top layers of HBMA, the calculated reliability converges to the chosen reliability. We employed the chance constrained optimization along with the HBMA framework to find the optimal location and pumpage for the scavenger well. The results showed that using models at different levels in the HBMA framework, the optimal location of the scavenger well remained the same, but the optimal extraction rate was altered. Thus, we concluded that the optimal pumping rate was sensitive to the prediction variance. Also, the prediction variance was changed by using different extraction rate. Using very high extraction rate will cause prediction variances of chloride concentration at the production wells to approach zero regardless of which HBMA models used.

Analysis Balance Parameter of Optimal Ramp metering

NASA Astrophysics Data System (ADS)

Li, Y.; Duan, N.; Yang, X.

2018-05-01

Ramp metering is a motorway control method to avoid onset congestion through limiting the access of ramp inflows into the main road of the motorway. The optimization model of ramp metering is developed based upon cell transmission model (CTM). With the piecewise linear structure of CTM, the corresponding motorway traffic optimization problem can be formulated as a linear programming (LP) problem. It is known that LP problem can be solved by established solution algorithms such as SIMPLEX or interior-point methods for the global optimal solution. The commercial software (CPLEX) is adopted in this study to solve the LP problem within reasonable computational time. The concept is illustrated through a case study of the United Kingdom M25 Motorway. The optimal solution provides useful insights and guidances on how to manage motorway traffic in order to maximize the corresponding efficiency.

A new approach of optimal control for a class of continuous-time chaotic systems by an online ADP algorithm

NASA Astrophysics Data System (ADS)

Song, Rui-Zhuo; Xiao, Wen-Dong; Wei, Qing-Lai

2014-05-01

We develop an online adaptive dynamic programming (ADP) based optimal control scheme for continuous-time chaotic systems. The idea is to use the ADP algorithm to obtain the optimal control input that makes the performance index function reach an optimum. The expression of the performance index function for the chaotic system is first presented. The online ADP algorithm is presented to achieve optimal control. In the ADP structure, neural networks are used to construct a critic network and an action network, which can obtain an approximate performance index function and the control input, respectively. It is proven that the critic parameter error dynamics and the closed-loop chaotic systems are uniformly ultimately bounded exponentially. Our simulation results illustrate the performance of the established optimal control method.

Characterization of Al 2219 material for the application of the spin-forming-process

NASA Astrophysics Data System (ADS)

Mueller-Wiesner, D.; Sieger, E.; Ernsberger, K.

1991-10-01

The shells of the propellant tanks of the Ariane 5 EPS stage are to be manufactured by the spin forming process. The material for the shells (hemispheres) is the aluminum alloy 2219. By a material characterization program optimized parameters for the application of the forming process starting from different material conditions (T31 temper and '0' condition) are determined. Based on the results of this program it was decided to start spin forming in the '0' condition for flight hardware.

SLC injector modeling

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

Hanerfeld, H; Herrmannsfeldt, W.B.; James, M.B.

1985-03-01

The injector for the Stanford Linear Collider is being studied using the fully electromagnetic particle-in-cell program MASK. The program takes account of cylindrically symmetrical rf fields from the external source, as well as fields produced by the beam and dc magnetic fields. It calculates the radial and longitudinal motion of electrons and plots their positions in various planes in phase space. Bunching parameters can be optimized and insights into the bunching process and emittance growth have been gained. The results of the simulations are compared to the experimental results.

Interactive software tool to comprehend the calculation of optimal sequence alignments with dynamic programming.

PubMed

Ibarra, Ignacio L; Melo, Francisco

2010-07-01

Dynamic programming (DP) is a general optimization strategy that is successfully used across various disciplines of science. In bioinformatics, it is widely applied in calculating the optimal alignment between pairs of protein or DNA sequences. These alignments form the basis of new, verifiable biological hypothesis. Despite its importance, there are no interactive tools available for training and education on understanding the DP algorithm. Here, we introduce an interactive computer application with a graphical interface, for the purpose of educating students about DP. The program displays the DP scoring matrix and the resulting optimal alignment(s), while allowing the user to modify key parameters such as the values in the similarity matrix, the sequence alignment algorithm version and the gap opening/extension penalties. We hope that this software will be useful to teachers and students of bioinformatics courses, as well as researchers who implement the DP algorithm for diverse applications. The software is freely available at: http:/melolab.org/sat. The software is written in the Java computer language, thus it runs on all major platforms and operating systems including Windows, Mac OS X and LINUX. All inquiries or comments about this software should be directed to Francisco Melo at fmelo@bio.puc.cl.

Eigenvectors phase correction in inverse modal problem

NASA Astrophysics Data System (ADS)

Qiao, Guandong; Rahmatalla, Salam

2017-12-01

The solution of the inverse modal problem for the spatial parameters of mechanical and structural systems is heavily dependent on the quality of the modal parameters obtained from the experiments. While experimental and environmental noises will always exist during modal testing, the resulting modal parameters are expected to be corrupted with different levels of noise. A novel methodology is presented in this work to mitigate the errors in the eigenvectors when solving the inverse modal problem for the spatial parameters. The phases of the eigenvector component were utilized as design variables within an optimization problem that minimizes the difference between the calculated and experimental transfer functions. The equation of motion in terms of the modal and spatial parameters was used as a constraint in the optimization problem. Constraints that reserve the positive and semi-positive definiteness and the inter-connectivity of the spatial matrices were implemented using semi-definite programming. Numerical examples utilizing noisy eigenvectors with augmented Gaussian white noise of 1%, 5%, and 10% were used to demonstrate the efficacy of the proposed method. The results showed that the proposed method is superior when compared with a known method in the literature.

A Module Experimental Process System Development Unit (MEPSDU)

NASA Technical Reports Server (NTRS)

1981-01-01

The purpose of this program is to demonstrate the technical readiness of a cost effective process sequence that has the potential for the production of flat plate photovoltaic modules which met the price goal in 1986 of $.70 or less per watt peak. Program efforts included: preliminary design review, preliminary cell fabrication using the proposed process sequence, verification of sandblasting back cleanup, study of resist parameters, evaluation of pull strength of the proposed metallization, measurement of contact resistance of Electroless Ni contacts, optimization of process parameter, design of the MEPSDU module, identification and testing of insulator tapes, development of a lamination process sequence, identification, discussions, demonstrations and visits with candidate equipment vendors, evaluation of proposals for tabbing and stringing machine.

Determination of the optimal mesh parameters for Iguassu centrifuge flow and separation calculations

NASA Astrophysics Data System (ADS)

Romanihin, S. M.; Tronin, I. V.

2016-09-01

We present the method and the results of the determination for optimal computational mesh parameters for axisymmetric modeling of flow and separation in the Iguasu gas centrifuge. The aim of this work was to determine the mesh parameters which provide relatively low computational cost whithout loss of accuracy. We use direct search optimization algorithm to calculate optimal mesh parameters. Obtained parameters were tested by the calculation of the optimal working regime of the Iguasu GC. Separative power calculated using the optimal mesh parameters differs less than 0.5% from the result obtained on the detailed mesh. Presented method can be used to determine optimal mesh parameters of the Iguasu GC with different rotor speeds.

Solving iTOUGH2 simulation and optimization problems using the PEST protocol

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

Finsterle, S.A.; Zhang, Y.

2011-02-01

The PEST protocol has been implemented into the iTOUGH2 code, allowing the user to link any simulation program (with ASCII-based inputs and outputs) to iTOUGH2's sensitivity analysis, inverse modeling, and uncertainty quantification capabilities. These application models can be pre- or post-processors of the TOUGH2 non-isothermal multiphase flow and transport simulator, or programs that are unrelated to the TOUGH suite of codes. PEST-style template and instruction files are used, respectively, to pass input parameters updated by the iTOUGH2 optimization routines to the model, and to retrieve the model-calculated values that correspond to observable variables. We summarize the iTOUGH2 capabilities and demonstratemore » the flexibility added by the PEST protocol for the solution of a variety of simulation-optimization problems. In particular, the combination of loosely coupled and tightly integrated simulation and optimization routines provides both the flexibility and control needed to solve challenging inversion problems for the analysis of multiphase subsurface flow and transport systems.« less

Boundary Control of Linear Uncertain 1-D Parabolic PDE Using Approximate Dynamic Programming.

PubMed

Talaei, Behzad; Jagannathan, Sarangapani; Singler, John

2018-04-01

This paper develops a near optimal boundary control method for distributed parameter systems governed by uncertain linear 1-D parabolic partial differential equations (PDE) by using approximate dynamic programming. A quadratic surface integral is proposed to express the optimal cost functional for the infinite-dimensional state space. Accordingly, the Hamilton-Jacobi-Bellman (HJB) equation is formulated in the infinite-dimensional domain without using any model reduction. Subsequently, a neural network identifier is developed to estimate the unknown spatially varying coefficient in PDE dynamics. Novel tuning law is proposed to guarantee the boundedness of identifier approximation error in the PDE domain. A radial basis network (RBN) is subsequently proposed to generate an approximate solution for the optimal surface kernel function online. The tuning law for near optimal RBN weights is created, such that the HJB equation error is minimized while the dynamics are identified and closed-loop system remains stable. Ultimate boundedness (UB) of the closed-loop system is verified by using the Lyapunov theory. The performance of the proposed controller is successfully confirmed by simulation on an unstable diffusion-reaction process.

Parameter optimization for constructing competing endogenous RNA regulatory network in glioblastoma multiforme and other cancers.

PubMed

Chiu, Yu-Chiao; Hsiao, Tzu-Hung; Chen, Yidong; Chuang, Eric Y

2015-01-01

In addition to direct targeting and repressing mRNAs, recent studies reported that microRNAs (miRNAs) can bridge up an alternative layer of post-transcriptional gene regulatory networks. The competing endogenous RNA (ceRNA) regulation depicts the scenario where pairs of genes (ceRNAs) sharing, fully or partially, common binding miRNAs (miRNA program) can establish coexpression through competition for a limited pool of the miRNA program. While the dynamics of ceRNA regulation among cellular conditions have been verified based on in silico and in vitro experiments, comprehensive investigation into the strength of ceRNA regulation in human datasets remains largely unexplored. Furthermore, pan-cancer analysis of ceRNA regulation, to our knowledge, has not been systematically investigated. In the present study we explored optimal conditions for ceRNA regulation, investigated functions governed by ceRNA regulation, and evaluated pan-cancer effects. We started by investigating how essential factors, such as the size of miRNA programs, the number of miRNA program binding sites, and expression levels of miRNA programs and ceRNAs affect the ceRNA regulation capacity in tumors derived from glioblastoma multiforme patients captured by The Cancer Genome Atlas (TCGA). We demonstrated that increased numbers of common targeting miRNAs as well as the abundance of binding sites enhance ceRNA regulation and strengthen coexpression of ceRNA pairs. Also, our investigation revealed that the strength of ceRNA regulation is dependent on expression levels of both miRNA programs and ceRNAs. Through functional annotation analysis, our results indicated that ceRNA regulation is highly associated with essential cellular functions and diseases including cancer. Furthermore, the highly intertwined ceRNA regulatory relationship enables constitutive and effective intra-function regulation of genes in diverse types of cancer. Using gene and microRNA expression datasets from TCGA, we successfully quantified the optimal conditions for ceRNA regulation, which hinge on four essential parameters of ceRNAs. Our analysis suggests optimized ceRNA regulation is related to disease pathways and essential cellular functions. Furthermore, although the strength of ceRNA regulation is dynamic among cancers, its governing functions are stably maintained. The findings of this report contribute to better understanding of ceRNA dynamics and its crucial roles in cancers.

SU-E-T-254: Optimization of GATE and PHITS Monte Carlo Code Parameters for Uniform Scanning Proton Beam Based On Simulation with FLUKA General-Purpose Code

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

Kurosu, K; Department of Medical Physics ' Engineering, Osaka University Graduate School of Medicine, Osaka; Takashina, M

Purpose: Monte Carlo codes are becoming important tools for proton beam dosimetry. However, the relationships between the customizing parameters and percentage depth dose (PDD) of GATE and PHITS codes have not been reported which are studied for PDD and proton range compared to the FLUKA code and the experimental data. Methods: The beam delivery system of the Indiana University Health Proton Therapy Center was modeled for the uniform scanning beam in FLUKA and transferred identically into GATE and PHITS. This computational model was built from the blue print and validated with the commissioning data. Three parameters evaluated are the maximummore » step size, cut off energy and physical and transport model. The dependence of the PDDs on the customizing parameters was compared with the published results of previous studies. Results: The optimal parameters for the simulation of the whole beam delivery system were defined by referring to the calculation results obtained with each parameter. Although the PDDs from FLUKA and the experimental data show a good agreement, those of GATE and PHITS obtained with our optimal parameters show a minor discrepancy. The measured proton range R90 was 269.37 mm, compared to the calculated range of 269.63 mm, 268.96 mm, and 270.85 mm with FLUKA, GATE and PHITS, respectively. Conclusion: We evaluated the dependence of the results for PDDs obtained with GATE and PHITS Monte Carlo generalpurpose codes on the customizing parameters by using the whole computational model of the treatment nozzle. The optimal parameters for the simulation were then defined by referring to the calculation results. The physical model, particle transport mechanics and the different geometrybased descriptions need accurate customization in three simulation codes to agree with experimental data for artifact-free Monte Carlo simulation. This study was supported by Grants-in Aid for Cancer Research (H22-3rd Term Cancer Control-General-043) from the Ministry of Health, Labor and Welfare of Japan, Grants-in-Aid for Scientific Research (No. 23791419), and JSPS Core-to-Core program (No. 23003). The authors have no conflict of interest.« less

Optimization of Residual Stresses in MMC's through Process Parameter Control and the use of Heterogeneous Compensating/Compliant Interfacial Layers. OPTCOMP2 User's Guide

NASA Technical Reports Server (NTRS)

Pindera, Marek-Jerzy; Salzar, Robert S.

1996-01-01

A user's guide for the computer program OPTCOMP2 is presented in this report. This program provides a capability to optimize the fabrication or service-induced residual stresses in unidirectional metal matrix composites subjected to combined thermomechanical axisymmetric loading by altering the processing history, as well as through the microstructural design of interfacial fiber coatings. The user specifies the initial architecture of the composite and the load history, with the constituent materials being elastic, plastic, viscoplastic, or as defined by the 'user-defined' constitutive model, in addition to the objective function and constraints, through a user-friendly data input interface. The optimization procedure is based on an efficient solution methodology for the inelastic response of a fiber/interface layer(s)/matrix concentric cylinder model where the interface layers can be either homogeneous or heterogeneous. The response of heterogeneous layers is modeled using Aboudi's three-dimensional method of cells micromechanics model. The commercial optimization package DOT is used for the nonlinear optimization problem. The solution methodology for the arbitrarily layered cylinder is based on the local-global stiffness matrix formulation and Mendelson's iterative technique of successive elastic solutions developed for elastoplastic boundary-value problems. The optimization algorithm employed in DOT is based on the method of feasible directions.

Mechanical behavior and shape optimization of lining structure for subsea tunnel excavated in weathered slot

NASA Astrophysics Data System (ADS)

Li, Peng-fei; Zhou, Xiao-jun

2015-12-01

Subsea tunnel lining structures should be designed to sustain the loads transmitted from surrounding ground and groundwater during excavation. Extremely high pore-water pressure reduces the effective strength of the country rock that surrounds a tunnel, thereby lowering the arching effect and stratum stability of the structure. In this paper, the mechanical behavior and shape optimization of the lining structure for the Xiang'an tunnel excavated in weathered slots are examined. Eight cross sections with different geometric parameters are adopted to study the mechanical behavior and shape optimization of the lining structure. The hyperstatic reaction method is used through finite element analysis software ANSYS. The mechanical behavior of the lining structure is evidently affected by the geometric parameters of crosssectional shape. The minimum safety factor of the lining structure elements is set to be the objective function. The efficient tunnel shape to maximize the minimum safety factor is identified. The minimum safety factor increases significantly after optimization. The optimized cross section significantly improves the mechanical characteristics of the lining structure and effectively reduces its deformation. Force analyses of optimization process and program are conducted parametrically so that the method can be applied to the optimization design of other similar structures. The results obtained from this study enhance our understanding of the mechanical behavior of the lining structure for subsea tunnels. These results are also beneficial to the optimal design of lining structures in general.

Optimal control of LQG problem with an explicit trade-off between mean and variance

NASA Astrophysics Data System (ADS)

Qian, Fucai; Xie, Guo; Liu, Ding; Xie, Wenfang

2011-12-01

For discrete-time linear-quadratic Gaussian (LQG) control problems, a utility function on the expectation and the variance of the conventional performance index is considered. The utility function is viewed as an overall objective of the system and can perform the optimal trade-off between the mean and the variance of performance index. The nonlinear utility function is first converted into an auxiliary parameters optimisation problem about the expectation and the variance. Then an optimal closed-loop feedback controller for the nonseparable mean-variance minimisation problem is designed by nonlinear mathematical programming. Finally, simulation results are given to verify the algorithm's effectiveness obtained in this article.

Equivalent Air Spring Suspension Model for Quarter-Passive Model of Passenger Vehicles.

PubMed

Abid, Haider J; Chen, Jie; Nassar, Ameen A

2015-01-01

This paper investigates the GENSIS air spring suspension system equivalence to a passive suspension system. The SIMULINK simulation together with the OptiY optimization is used to obtain the air spring suspension model equivalent to passive suspension system, where the car body response difference from both systems with the same road profile inputs is used as the objective function for optimization (OptiY program). The parameters of air spring system such as initial pressure, volume of bag, length of surge pipe, diameter of surge pipe, and volume of reservoir are obtained from optimization. The simulation results show that the air spring suspension equivalent system can produce responses very close to the passive suspension system.

A Review of the Impacts of Different Approaches for Diabetes Prevention and a Framework for Making Investment Decisions

PubMed Central

2018-01-01

This paper selectively reviews the economic research on individual (i.e., diabetes prevention programs and financial rewards for weight loss) and population-wide based diabetes prevention interventions (such as food taxes, nutritional labeling, and worksite wellness programs) that demonstrate a direct reduction in diabetes incidence or improvements in diabetes risk factors such as weight, glucose or glycated hemoglobin. The paper suggests a framework to guide decision makers on how to use the available evidence to determine the optimal allocation of resources across population-wide and individual-based interventions. This framework should also assist in the discussion of what parameters are needed from research to inform decision-making on what might be the optimal mix of strategies to reduce diabetes prevalence. PMID:29543711

A Bell-Curved Based Algorithm for Mixed Continuous and Discrete Structural Optimization

NASA Technical Reports Server (NTRS)

Kincaid, Rex K.; Weber, Michael; Sobieszczanski-Sobieski, Jaroslaw

2001-01-01

An evolutionary based strategy utilizing two normal distributions to generate children is developed to solve mixed integer nonlinear programming problems. This Bell-Curve Based (BCB) evolutionary algorithm is similar in spirit to (mu + mu) evolutionary strategies and evolutionary programs but with fewer parameters to adjust and no mechanism for self adaptation. First, a new version of BCB to solve purely discrete optimization problems is described and its performance tested against a tabu search code for an actuator placement problem. Next, the performance of a combined version of discrete and continuous BCB is tested on 2-dimensional shape problems and on a minimum weight hub design problem. In the latter case the discrete portion is the choice of the underlying beam shape (I, triangular, circular, rectangular, or U).

A Review of the Impacts of Different Approaches for Diabetes Prevention and a Framework for Making Investment Decisions.

PubMed

Alva, Maria L

2018-03-15

This paper selectively reviews the economic research on individual (i.e., diabetes prevention programs and financial rewards for weight loss) and population-wide based diabetes prevention interventions (such as food taxes, nutritional labeling, and worksite wellness programs) that demonstrate a direct reduction in diabetes incidence or improvements in diabetes risk factors such as weight, glucose or glycated hemoglobin. The paper suggests a framework to guide decision makers on how to use the available evidence to determine the optimal allocation of resources across population-wide and individual-based interventions. This framework should also assist in the discussion of what parameters are needed from research to inform decision-making on what might be the optimal mix of strategies to reduce diabetes prevalence.

An improved hierarchical A * algorithm in the optimization of parking lots

NASA Astrophysics Data System (ADS)

Wang, Yong; Wu, Junjuan; Wang, Ying

2017-08-01

In the parking lot parking path optimization, the traditional evaluation index is the shortest distance as the best index and it does not consider the actual road conditions. Now, the introduction of a more practical evaluation index can not only simplify the hardware design of the boot system but also save the software overhead. Firstly, we establish the parking lot network graph RPCDV mathematical model and all nodes in the network is divided into two layers which were constructed using different evaluation function base on the improved hierarchical A * algorithm which improves the time optimal path search efficiency and search precision of the evaluation index. The final results show that for different sections of the program attribute parameter algorithm always faster the time to find the optimal path.

Dose responses in a normoxic polymethacrylic acid gel dosimeter using optimal CT scanning parameters

NASA Astrophysics Data System (ADS)

Cho, K. H.; Cho, S. J.; Lee, S.; Lee, S. H.; Min, C. K.; Kim, Y. H.; Moon, S. K.; Kim, E. S.; Chang, A. R.; Kwon, S. I.

2012-05-01

The dosimetric characteristics of normoxic polymethacrylic acid gels are investigated using optimal CT scanning parameters and the possibility of their clinical application is also considered. The effects of CT scanning parameters (tube voltage, tube current, scan time, slick thickness, field of view, and reconstruction algorithm) are experimentally investigated to determine the optimal parameters for minimizing the amount of noise in images obtained using normoxic polymethacrylic acid gel. In addition, the dose sensitivity, dose response, accuracy, and reproducibility of the normoxic polymethacrylic acid gel are evaluated. CT images are obtained using a head phantom that is fabricated for clinical applications. In addition, IMRT treatment planning is performed using a Tomotherapy radiation treatment planning system. A program for analyzing the results is produced using Visual C. A comparison between the treatment planning and the CT images of irradiated gels is performed. The dose sensitivity is found to be 2.41±0.04 HGy-1. The accuracies of dose evaluation at doses of 2 Gy and 4 Gy are 3.0% and 2.6%, respectively, and their reproducibilities are 2.0% and 2.1%, respectively. In the comparison of gel and Tomotherpay planning, the pass rate of the γ-index, based on the reference values of a dose error of 3% and a DTA of 3 mm, is 93.7%.

Hash Bit Selection for Nearest Neighbor Search.

PubMed

Xianglong Liu; Junfeng He; Shih-Fu Chang

2017-11-01

To overcome the barrier of storage and computation when dealing with gigantic-scale data sets, compact hashing has been studied extensively to approximate the nearest neighbor search. Despite the recent advances, critical design issues remain open in how to select the right features, hashing algorithms, and/or parameter settings. In this paper, we address these by posing an optimal hash bit selection problem, in which an optimal subset of hash bits are selected from a pool of candidate bits generated by different features, algorithms, or parameters. Inspired by the optimization criteria used in existing hashing algorithms, we adopt the bit reliability and their complementarity as the selection criteria that can be carefully tailored for hashing performance in different tasks. Then, the bit selection solution is discovered by finding the best tradeoff between search accuracy and time using a modified dynamic programming method. To further reduce the computational complexity, we employ the pairwise relationship among hash bits to approximate the high-order independence property, and formulate it as an efficient quadratic programming method that is theoretically equivalent to the normalized dominant set problem in a vertex- and edge-weighted graph. Extensive large-scale experiments have been conducted under several important application scenarios of hash techniques, where our bit selection framework can achieve superior performance over both the naive selection methods and the state-of-the-art hashing algorithms, with significant accuracy gains ranging from 10% to 50%, relatively.

Flight instrument and telemetry response and its inversion

NASA Technical Reports Server (NTRS)

Weinberger, M. R.

1971-01-01

Mathematical models of rate gyros, servo accelerometers, pressure transducers, and telemetry systems were derived and their parameters were obtained from laboratory tests. Analog computer simulations were used extensively for verification of the validity for fast and large input signals. An optimal inversion method was derived to reconstruct input signals from noisy output signals and a computer program was prepared.

Teaching learning algorithm based optimization of kerf deviations in pulsed Nd:YAG laser cutting of Kevlar-29 composite laminates

NASA Astrophysics Data System (ADS)

Gautam, Girish Dutt; Pandey, Arun Kumar

2018-03-01

Kevlar is the most popular aramid fiber and most commonly used in different technologically advanced industries for various applications. But the precise cutting of Kevlar composite laminates is a difficult task. The conventional cutting methods face various defects such as delamination, burr formation, fiber pullout with poor surface quality and their mechanical performance is greatly affected by these defects. The laser beam machining may be an alternative of the conventional cutting processes due to its non-contact nature, requirement of low specific energy with higher production rate. But this process also faces some problems that may be minimized by operating the machine at optimum parameters levels. This research paper examines the effective utilization of the Nd:YAG laser cutting system on difficult-to-cut Kevlar-29 composite laminates. The objective of the proposed work is to find the optimum process parameters settings for getting the minimum kerf deviations at both sides. The experiments have been conducted on Kevlar-29 composite laminates having thickness 1.25 mm by using Box-Benkhen design with two center points. The experimental data have been used for the optimization by using the proposed methodology. For the optimization, Teaching learning Algorithm based approach has been employed to obtain the minimum kerf deviation at bottom and top sides. A self coded Matlab program has been developed by using the proposed methodology and this program has been used for the optimization. Finally, the confirmation tests have been performed to compare the experimental and optimum results obtained by the proposed methodology. The comparison results show that the machining performance in the laser beam cutting process has been remarkably improved through proposed approach. Finally, the influence of different laser cutting parameters such as lamp current, pulse frequency, pulse width, compressed air pressure and cutting speed on top kerf deviation and bottom kerf deviation during the Nd:YAG laser cutting of Kevlar-29 laminates have been discussed.

Calibrating a Soil-Vegetation-Atmosphere system with a genetical algorithm

NASA Astrophysics Data System (ADS)

Schneider, S.; Jacques, D.; Mallants, D.

2009-04-01

Accuracy of model prediction is well known for being very sensitive to the quality of the calibration of the model. It is also known that quantifying soil hydraulic parameters in a Soil-Vegetation-Atmosphere (SVA) system is a highly non-linear parameter estimation problem, and that robust methods are needed to avoid the optimization process to lead to non-optimal parameters. Evolutionary algorithms and specifically genetic algorithms (GAs) are very well suited for those complex parameter optimization problems. The SVA system in this study concerns a pine stand on a heterogeneous sandy soil (podzol) in the north of Belgium (Campine region). Throughfall and other meteorological data and water contents at different soil depths have been recorded during one year at a daily time step. The water table level, which is varying between 95 and 170 cm, has been recorded with a frequency of 0.5 hours. Based on the profile description, four soil layers have been distinguished in the podzol and used for the numerical simulation with the hydrus1D model (Simunek and al., 2005). For the inversion procedure the MYGA program (Yedder, 2002), which is an elitism GA, was used. Optimization was based on the water content measurements realized at the depths of 10, 20, 40, 50, 60, 70, 90, 110, and 120 cm to estimate parameters describing the unsaturated hydraulic soil properties of the different soil layers. Comparison between the modeled and measured water contents shows a good similarity during the simulated year. Impacts of short and intensive events (rainfall) on the water content of the soil are also well reproduced. Errors on predictions are on average equal to 5%, which is considered as a good result. A. Ben Haj Yedder. Numerical optimization and optimal control : (molecular chemistry applications). PhD thesis, Ecole Nationale des Ponts et Chaussées, 2002. Šimůnek, J., M. Th. van Genuchten, and M. Šejna, The HYDRUS-1D software package for simulating the one-dimensional movement of water, heat, and multiple solutes in variably saturated media. Version 3.0, HYDRUS Software Series 1, Department of Environmental Sciences, University of California Riverside, Riverside, CA, 270 pp., 2005.

MCPB.py: A Python Based Metal Center Parameter Builder.

PubMed

Li, Pengfei; Merz, Kenneth M

2016-04-25

MCPB.py, a python based metal center parameter builder, has been developed to build force fields for the simulation of metal complexes employing the bonded model approach. It has an optimized code structure, with far fewer required steps than the previous developed MCPB program. It supports various AMBER force fields and more than 80 metal ions. A series of parametrization schemes to derive force constants and charge parameters are available within the program. We give two examples (one metalloprotein example and one organometallic compound example), indicating the program's ability to build reliable force fields for different metal ion containing complexes. The original version was released with AmberTools15. It is provided via the GNU General Public License v3.0 (GNU_GPL_v3) agreement and is free to download and distribute. MCPB.py provides a bridge between quantum mechanical calculations and molecular dynamics simulation software packages thereby enabling the modeling of metal ion centers. It offers an entry into simulating metal ions in a number of situations by providing an efficient way for researchers to handle the vagaries and difficulties associated with metal ion modeling.

Experimental Research Regarding The Motion Capacity Of A Robotic Arm

NASA Astrophysics Data System (ADS)

Dumitru, Violeta Cristina

2015-09-01

This paper refers to the development of necessary experiments which obtained dynamic parameters (force, displacement) for a modular mechanism with multiple vertebrae. This mechanism performs functions of inspection and intervention in small spaces. Mechanical structure allows functional parameters to achieve precise movements to an imposed target. Will be analyzed the dynamic of the mechanisms using simulation instruments DimamicaRobot.tst under TestPoint programming environment and the elasticity of the tension cables. It will be changes on the mechanism so that spatial movement of the robotic arm is optimal.

Optimized production planning model for a multi-plant cultivation system under uncertainty

NASA Astrophysics Data System (ADS)

Ke, Shunkui; Guo, Doudou; Niu, Qingliang; Huang, Danfeng

2015-02-01

An inexact multi-constraint programming model under uncertainty was developed by incorporating a production plan algorithm into the crop production optimization framework under the multi-plant collaborative cultivation system. In the production plan, orders from the customers are assigned to a suitable plant under the constraints of plant capabilities and uncertainty parameters to maximize profit and achieve customer satisfaction. The developed model and solution method were applied to a case study of a multi-plant collaborative cultivation system to verify its applicability. As determined in the case analysis involving different orders from customers, the period of plant production planning and the interval between orders can significantly affect system benefits. Through the analysis of uncertain parameters, reliable and practical decisions can be generated using the suggested model of a multi-plant collaborative cultivation system.

A supplier selection and order allocation problem with stochastic demands

NASA Astrophysics Data System (ADS)

Zhou, Yun; Zhao, Lei; Zhao, Xiaobo; Jiang, Jianhua

2011-08-01

We consider a system comprising a retailer and a set of candidate suppliers that operates within a finite planning horizon of multiple periods. The retailer replenishes its inventory from the suppliers and satisfies stochastic customer demands. At the beginning of each period, the retailer makes decisions on the replenishment quantity, supplier selection and order allocation among the selected suppliers. An optimisation problem is formulated to minimise the total expected system cost, which includes an outer level stochastic dynamic program for the optimal replenishment quantity and an inner level integer program for supplier selection and order allocation with a given replenishment quantity. For the inner level subproblem, we develop a polynomial algorithm to obtain optimal decisions. For the outer level subproblem, we propose an efficient heuristic for the system with integer-valued inventory, based on the structural properties of the system with real-valued inventory. We investigate the efficiency of the proposed solution approach, as well as the impact of parameters on the optimal replenishment decision with numerical experiments.

Optimization of sound absorbing performance for gradient multi-layer-assembled sintered fibrous absorbers

NASA Astrophysics Data System (ADS)

Zhang, Bo; Zhang, Weiyong; Zhu, Jian

2012-04-01

The transfer matrix method, based on plane wave theory, of multi-layer equivalent fluid is employed to evaluate the sound absorbing properties of two-layer-assembled and three-layer-assembled sintered fibrous sheets (generally regarded as a kind of compound absorber or structures). Two objective functions which are more suitable for the optimization of sound absorption properties of multi-layer absorbers within the wider frequency ranges are developed and the optimized results of using two objective functions are also compared with each other. It is found that using the two objective functions, especially the second one, may be more helpful to exert the sound absorbing properties of absorbers at lower frequencies to the best of their abilities. Then the calculation and optimization of sound absorption properties of multi-layer-assembled structures are performed by developing a simulated annealing genetic arithmetic program and using above-mentioned objective functions. Finally, based on the optimization in this work the thoughts of the gradient design over the acoustic parameters- the porosity, the tortuosity, the viscous and thermal characteristic lengths and the thickness of each samples- of porous metals are put forth and thereby some useful design criteria upon the acoustic parameters of each layer of porous fibrous metals are given while applying the multi-layer-assembled compound absorbers in noise control engineering.

A review of distributed parameter groundwater management modeling methods

USGS Publications Warehouse

Gorelick, Steven M.

1983-01-01

Models which solve the governing groundwater flow or solute transport equations in conjunction with optimization techniques, such as linear and quadratic programing, are powerful aquifer management tools. Groundwater management models fall in two general categories: hydraulics or policy evaluation and water allocation. Groundwater hydraulic management models enable the determination of optimal locations and pumping rates of numerous wells under a variety of restrictions placed upon local drawdown, hydraulic gradients, and water production targets. Groundwater policy evaluation and allocation models can be used to study the influence upon regional groundwater use of institutional policies such as taxes and quotas. Furthermore, fairly complex groundwater-surface water allocation problems can be handled using system decomposition and multilevel optimization. Experience from the few real world applications of groundwater optimization-management techniques is summarized. Classified separately are methods for groundwater quality management aimed at optimal waste disposal in the subsurface. This classification is composed of steady state and transient management models that determine disposal patterns in such a way that water quality is protected at supply locations. Classes of research missing from the literature are groundwater quality management models involving nonlinear constraints, models which join groundwater hydraulic and quality simulations with political-economic management considerations, and management models that include parameter uncertainty.

A Review of Distributed Parameter Groundwater Management Modeling Methods

NASA Astrophysics Data System (ADS)

Gorelick, Steven M.

1983-04-01

Models which solve the governing groundwater flow or solute transport equations in conjunction with optimization techniques, such as linear and quadratic programing, are powerful aquifer management tools. Groundwater management models fall in two general categories: hydraulics or policy evaluation and water allocation. Groundwater hydraulic management models enable the determination of optimal locations and pumping rates of numerous wells under a variety of restrictions placed upon local drawdown, hydraulic gradients, and water production targets. Groundwater policy evaluation and allocation models can be used to study the influence upon regional groundwater use of institutional policies such as taxes and quotas. Furthermore, fairly complex groundwater-surface water allocation problems can be handled using system decomposition and multilevel optimization. Experience from the few real world applications of groundwater optimization-management techniques is summarized. Classified separately are methods for groundwater quality management aimed at optimal waste disposal in the subsurface. This classification is composed of steady state and transient management models that determine disposal patterns in such a way that water quality is protected at supply locations. Classes of research missing from the literature are groundwater quality management models involving nonlinear constraints, models which join groundwater hydraulic and quality simulations with political-economic management considerations, and management models that include parameter uncertainty.

Matrix elements of N-particle explicitly correlated Gaussian basis functions with complex exponential parameters

NASA Astrophysics Data System (ADS)

Bubin, Sergiy; Adamowicz, Ludwik

2006-06-01

In this work we present analytical expressions for Hamiltonian matrix elements with spherically symmetric, explicitly correlated Gaussian basis functions with complex exponential parameters for an arbitrary number of particles. The expressions are derived using the formalism of matrix differential calculus. In addition, we present expressions for the energy gradient that includes derivatives of the Hamiltonian integrals with respect to the exponential parameters. The gradient is used in the variational optimization of the parameters. All the expressions are presented in the matrix form suitable for both numerical implementation and theoretical analysis. The energy and gradient formulas have been programed and used to calculate ground and excited states of the He atom using an approach that does not involve the Born-Oppenheimer approximation.

Matrix elements of N-particle explicitly correlated Gaussian basis functions with complex exponential parameters.

PubMed

Bubin, Sergiy; Adamowicz, Ludwik

2006-06-14

In this work we present analytical expressions for Hamiltonian matrix elements with spherically symmetric, explicitly correlated Gaussian basis functions with complex exponential parameters for an arbitrary number of particles. The expressions are derived using the formalism of matrix differential calculus. In addition, we present expressions for the energy gradient that includes derivatives of the Hamiltonian integrals with respect to the exponential parameters. The gradient is used in the variational optimization of the parameters. All the expressions are presented in the matrix form suitable for both numerical implementation and theoretical analysis. The energy and gradient formulas have been programmed and used to calculate ground and excited states of the He atom using an approach that does not involve the Born-Oppenheimer approximation.

A multi-objective programming model for assessment the GHG emissions in MSW management

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

Mavrotas, George, E-mail: mavrotas@chemeng.ntua.gr; Skoulaxinou, Sotiria; Gakis, Nikos

2013-09-15

Highlights: • The multi-objective multi-period optimization model. • The solution approach for the generation of the Pareto front with mathematical programming. • The very detailed description of the model (decision variables, parameters, equations). • The use of IPCC 2006 guidelines for landfill emissions (first order decay model) in the mathematical programming formulation. - Abstract: In this study a multi-objective mathematical programming model is developed for taking into account GHG emissions for Municipal Solid Waste (MSW) management. Mathematical programming models are often used for structure, design and operational optimization of various systems (energy, supply chain, processes, etc.). The last twenty yearsmore » they are used all the more often in Municipal Solid Waste (MSW) management in order to provide optimal solutions with the cost objective being the usual driver of the optimization. In our work we consider the GHG emissions as an additional criterion, aiming at a multi-objective approach. The Pareto front (Cost vs. GHG emissions) of the system is generated using an appropriate multi-objective method. This information is essential to the decision maker because he can explore the trade-offs in the Pareto curve and select his most preferred among the Pareto optimal solutions. In the present work a detailed multi-objective, multi-period mathematical programming model is developed in order to describe the waste management problem. Apart from the bi-objective approach, the major innovations of the model are (1) the detailed modeling considering 34 materials and 42 technologies, (2) the detailed calculation of the energy content of the various streams based on the detailed material balances, and (3) the incorporation of the IPCC guidelines for the CH{sub 4} generated in the landfills (first order decay model). The equations of the model are described in full detail. Finally, the whole approach is illustrated with a case study referring to the application of the model in a Greek region.« less

BISIP I: A program for Bayesian inference of spectral induced polarization parameters, and application to mineral exploration at the Canadian Malartic gold deposit, Québec, CA

NASA Astrophysics Data System (ADS)

Lafrenière-Bérubé, Charles; Chouteau, Michel; Shamsipour, Pejman; Olivo, Gema R.

2016-04-01

Spectral induced polarization (SIP) parameters can be extracted from field or laboratory complex resistivity measurements, and even airborne or ground frequency domain electromagnetic data. With the growing interest in application of complex resistivity measurements to environmental and mineral exploration problems, there is a need for accurate and easy-to-use inversion tools to estimate SIP parameters. These parameters, which often include chargeability and relaxation time may then be studied and related to other rock attributes such as porosity or metallic grain content, in the case of mineral exploration. We present an open source program, available both as a standalone application or Python module, to estimate SIP parameters using Markov-chain Monte Carlo (MCMC) sampling. The Python language is a high level, open source language that is now widely used in scientific computing. Our program allows the user to choose between the more common Cole-Cole (Pelton), Dias, or Debye decomposition models. Simple circuits composed of resistances and constant phase elements may also be used to represent SIP data. Initial guesses are required when using more classic inversion techniques such as the least-squares formulation, and wrong estimates are often the cause of bad curve fitting. In stochastic optimization using MCMC, the effect of the starting values disappears as the simulation proceeds. Our program is then optimized to do batch inversion over large data sets with as little user-interaction as possible. Additionally, the Bayesian formulation allows the user to do quality control by fully propagating the measurement errors in the inversion process, providing an estimation of the SIP parameters uncertainty. This information is valuable when trying to relate chargeability or relaxation time to other physical properties. We test the inversion program on complex resistivity measurements of 12 core samples from the world-class gold deposit of Canadian Malartic. Results show that the Cole-Cole and Debye decomposition models converge quickly to a solution and often provide the best fit with experimental data. The Dias model requires the least amount of iterations to fully converge, but we note a small discrepancy between experimental data and mathematical model for most samples. Using petrographic analysis we test possible relationships between porosity, sulfur content and grain size with parameters obtained from the different models, and note that sulfur content influences both the chargeability and frequency dependence of the Cole-Cole model. Finally, we use our program to compare the different definitions of chargeability and relaxation time given by the three models. We note that these parameters tend to be correlated from one model to another. However, they have different electrochemical definitions and a single sample may possess different chargeability or relaxation time values depending on the model used. In the near future, the program will be used on a more extensive collection of samples from the Canadian Malartic gold deposit, the Highland Valley copper deposit, and the Millennium-McArthur uranium deposits. CMIC-NSERC Exploration Footprints Network Contribution 082

Stochastic optimization of GeantV code by use of genetic algorithms

DOE PAGES

Amadio, G.; Apostolakis, J.; Bandieramonte, M.; ...

2017-10-01

GeantV is a complex system based on the interaction of different modules needed for detector simulation, which include transport of particles in fields, physics models simulating their interactions with matter and a geometrical modeler library for describing the detector and locating the particles and computing the path length to the current volume boundary. The GeantV project is recasting the classical simulation approach to get maximum benefit from SIMD/MIMD computational architectures and highly massive parallel systems. This involves finding the appropriate balance between several aspects influencing computational performance (floating-point performance, usage of off-chip memory bandwidth, specification of cache hierarchy, etc.) andmore » handling a large number of program parameters that have to be optimized to achieve the best simulation throughput. This optimization task can be treated as a black-box optimization problem, which requires searching the optimum set of parameters using only point-wise function evaluations. Here, the goal of this study is to provide a mechanism for optimizing complex systems (high energy physics particle transport simulations) with the help of genetic algorithms and evolution strategies as tuning procedures for massive parallel simulations. One of the described approaches is based on introducing a specific multivariate analysis operator that could be used in case of resource expensive or time consuming evaluations of fitness functions, in order to speed-up the convergence of the black-box optimization problem.« less

Stochastic optimization of GeantV code by use of genetic algorithms

NASA Astrophysics Data System (ADS)

Amadio, G.; Apostolakis, J.; Bandieramonte, M.; Behera, S. P.; Brun, R.; Canal, P.; Carminati, F.; Cosmo, G.; Duhem, L.; Elvira, D.; Folger, G.; Gheata, A.; Gheata, M.; Goulas, I.; Hariri, F.; Jun, S. Y.; Konstantinov, D.; Kumawat, H.; Ivantchenko, V.; Lima, G.; Nikitina, T.; Novak, M.; Pokorski, W.; Ribon, A.; Seghal, R.; Shadura, O.; Vallecorsa, S.; Wenzel, S.

2017-10-01

GeantV is a complex system based on the interaction of different modules needed for detector simulation, which include transport of particles in fields, physics models simulating their interactions with matter and a geometrical modeler library for describing the detector and locating the particles and computing the path length to the current volume boundary. The GeantV project is recasting the classical simulation approach to get maximum benefit from SIMD/MIMD computational architectures and highly massive parallel systems. This involves finding the appropriate balance between several aspects influencing computational performance (floating-point performance, usage of off-chip memory bandwidth, specification of cache hierarchy, etc.) and handling a large number of program parameters that have to be optimized to achieve the best simulation throughput. This optimization task can be treated as a black-box optimization problem, which requires searching the optimum set of parameters using only point-wise function evaluations. The goal of this study is to provide a mechanism for optimizing complex systems (high energy physics particle transport simulations) with the help of genetic algorithms and evolution strategies as tuning procedures for massive parallel simulations. One of the described approaches is based on introducing a specific multivariate analysis operator that could be used in case of resource expensive or time consuming evaluations of fitness functions, in order to speed-up the convergence of the black-box optimization problem.

Stochastic optimization of GeantV code by use of genetic algorithms

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

Amadio, G.; Apostolakis, J.; Bandieramonte, M.

GeantV is a complex system based on the interaction of different modules needed for detector simulation, which include transport of particles in fields, physics models simulating their interactions with matter and a geometrical modeler library for describing the detector and locating the particles and computing the path length to the current volume boundary. The GeantV project is recasting the classical simulation approach to get maximum benefit from SIMD/MIMD computational architectures and highly massive parallel systems. This involves finding the appropriate balance between several aspects influencing computational performance (floating-point performance, usage of off-chip memory bandwidth, specification of cache hierarchy, etc.) andmore » handling a large number of program parameters that have to be optimized to achieve the best simulation throughput. This optimization task can be treated as a black-box optimization problem, which requires searching the optimum set of parameters using only point-wise function evaluations. Here, the goal of this study is to provide a mechanism for optimizing complex systems (high energy physics particle transport simulations) with the help of genetic algorithms and evolution strategies as tuning procedures for massive parallel simulations. One of the described approaches is based on introducing a specific multivariate analysis operator that could be used in case of resource expensive or time consuming evaluations of fitness functions, in order to speed-up the convergence of the black-box optimization problem.« less

MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics.

PubMed

Egea, Jose A; Henriques, David; Cokelaer, Thomas; Villaverde, Alejandro F; MacNamara, Aidan; Danciu, Diana-Patricia; Banga, Julio R; Saez-Rodriguez, Julio

2014-05-10

Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods.

MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics

PubMed Central

2014-01-01

Background Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. Results We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. Conclusions MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods. PMID:24885957

Genome-Wide Comparative Gene Family Classification

PubMed Central

Frech, Christian; Chen, Nansheng

2010-01-01

Correct classification of genes into gene families is important for understanding gene function and evolution. Although gene families of many species have been resolved both computationally and experimentally with high accuracy, gene family classification in most newly sequenced genomes has not been done with the same high standard. This project has been designed to develop a strategy to effectively and accurately classify gene families across genomes. We first examine and compare the performance of computer programs developed for automated gene family classification. We demonstrate that some programs, including the hierarchical average-linkage clustering algorithm MC-UPGMA and the popular Markov clustering algorithm TRIBE-MCL, can reconstruct manual curation of gene families accurately. However, their performance is highly sensitive to parameter setting, i.e. different gene families require different program parameters for correct resolution. To circumvent the problem of parameterization, we have developed a comparative strategy for gene family classification. This strategy takes advantage of existing curated gene families of reference species to find suitable parameters for classifying genes in related genomes. To demonstrate the effectiveness of this novel strategy, we use TRIBE-MCL to classify chemosensory and ABC transporter gene families in C. elegans and its four sister species. We conclude that fully automated programs can establish biologically accurate gene families if parameterized accordingly. Comparative gene family classification finds optimal parameters automatically, thus allowing rapid insights into gene families of newly sequenced species. PMID:20976221

Optimization design of wind turbine drive train based on Matlab genetic algorithm toolbox

NASA Astrophysics Data System (ADS)

Li, R. N.; Liu, X.; Liu, S. J.

2013-12-01

In order to ensure the high efficiency of the whole flexible drive train of the front-end speed adjusting wind turbine, the working principle of the main part of the drive train is analyzed. As critical parameters, rotating speed ratios of three planetary gear trains are selected as the research subject. The mathematical model of the torque converter speed ratio is established based on these three critical variable quantity, and the effect of key parameters on the efficiency of hydraulic mechanical transmission is analyzed. Based on the torque balance and the energy balance, refer to hydraulic mechanical transmission characteristics, the transmission efficiency expression of the whole drive train is established. The fitness function and constraint functions are established respectively based on the drive train transmission efficiency and the torque converter rotating speed ratio range. And the optimization calculation is carried out by using MATLAB genetic algorithm toolbox. The optimization method and results provide an optimization program for exact match of wind turbine rotor, gearbox, hydraulic mechanical transmission, hydraulic torque converter and synchronous generator, ensure that the drive train work with a high efficiency, and give a reference for the selection of the torque converter and hydraulic mechanical transmission.

Comparison of global optimization approaches for robust calibration of hydrologic model parameters

NASA Astrophysics Data System (ADS)

Jung, I. W.

2015-12-01

Robustness of the calibrated parameters of hydrologic models is necessary to provide a reliable prediction of future performance of watershed behavior under varying climate conditions. This study investigated calibration performances according to the length of calibration period, objective functions, hydrologic model structures and optimization methods. To do this, the combination of three global optimization methods (i.e. SCE-UA, Micro-GA, and DREAM) and four hydrologic models (i.e. SAC-SMA, GR4J, HBV, and PRMS) was tested with different calibration periods and objective functions. Our results showed that three global optimization methods provided close calibration performances under different calibration periods, objective functions, and hydrologic models. However, using the agreement of index, normalized root mean square error, Nash-Sutcliffe efficiency as the objective function showed better performance than using correlation coefficient and percent bias. Calibration performances according to different calibration periods from one year to seven years were hard to generalize because four hydrologic models have different levels of complexity and different years have different information content of hydrological observation. Acknowledgements This research was supported by a grant (14AWMP-B082564-01) from Advanced Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

Hierarchical winner-take-all particle swarm optimization social network for neural model fitting.

PubMed

Coventry, Brandon S; Parthasarathy, Aravindakshan; Sommer, Alexandra L; Bartlett, Edward L

2017-02-01

Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models.

Optimization of residual stresses in MMC's through the variation of interfacial layer architectures and processing parameters

NASA Technical Reports Server (NTRS)

Pindera, Marek-Jerzy; Salzar, Robert S.

1996-01-01

The objective of this work was the development of efficient, user-friendly computer codes for optimizing fabrication-induced residual stresses in metal matrix composites through the use of homogeneous and heterogeneous interfacial layer architectures and processing parameter variation. To satisfy this objective, three major computer codes have been developed and delivered to the NASA-Lewis Research Center, namely MCCM, OPTCOMP, and OPTCOMP2. MCCM is a general research-oriented code for investigating the effects of microstructural details, such as layered morphology of SCS-6 SiC fibers and multiple homogeneous interfacial layers, on the inelastic response of unidirectional metal matrix composites under axisymmetric thermomechanical loading. OPTCOMP and OPTCOMP2 combine the major analysis module resident in MCCM with a commercially-available optimization algorithm and are driven by user-friendly interfaces which facilitate input data construction and program execution. OPTCOMP enables the user to identify those dimensions, geometric arrangements and thermoelastoplastic properties of homogeneous interfacial layers that minimize thermal residual stresses for the specified set of constraints. OPTCOMP2 provides additional flexibility in the residual stress optimization through variation of the processing parameters (time, temperature, external pressure and axial load) as well as the microstructure of the interfacial region which is treated as a heterogeneous two-phase composite. Overviews of the capabilities of these codes are provided together with a summary of results that addresses the effects of various microstructural details of the fiber, interfacial layers and matrix region on the optimization of fabrication-induced residual stresses in metal matrix composites.

Shuttle cryogenic supply system. Optimization study. Volume 5 B-1: Programmers manual for math models

NASA Technical Reports Server (NTRS)

1973-01-01

A computer program for rapid parametric evaluation of various types of cryogenics spacecraft systems is presented. The mathematical techniques of the program provide the capability for in-depth analysis combined with rapid problem solution for the production of a large quantity of soundly based trade-study data. The program requires a large data bank capable of providing characteristics performance data for a wide variety of component assemblies used in cryogenic systems. The program data requirements are divided into: (1) the semipermanent data tables and source data for performance characteristics and (2) the variable input data which contains input parameters which may be perturbated for parametric system studies.

Control and optimization system

DOEpatents

Xinsheng, Lou

2013-02-12

A system for optimizing a power plant includes a chemical loop having an input for receiving an input parameter (270) and an output for outputting an output parameter (280), a control system operably connected to the chemical loop and having a multiple controller part (230) comprising a model-free controller. The control system receives the output parameter (280), optimizes the input parameter (270) based on the received output parameter (280), and outputs an optimized input parameter (270) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.

Parallel computing and first-principles calculations: Applications to complex ceramics and Vitamin B12

NASA Astrophysics Data System (ADS)

Ouyang, Lizhi

A systematic improvement and extension of the orthogonalized linear combinations of atomic orbitals method was carried out using a combined computational and theoretical approach. For high performance parallel computing, a Beowulf class personal computer cluster was constructed. It also served as a parallel program development platform that helped us to port the programs of the method to the national supercomputer facilities. The program, received a language upgrade from Fortran 77 to Fortran 90, and a dynamic memory allocation feature. A preliminary parallel High Performance Fortran version of the program has been developed as well. To be of more benefit though, scalability improvements are needed. In order to circumvent the difficulties of the analytical force calculation in the method, we developed a geometry optimization scheme using the finite difference approximation based on the total energy calculation. The implementation of this scheme was facilitated by the powerful general utility lattice program, which offers many desired features such as multiple optimization schemes and usage of space group symmetry. So far, many ceramic oxides have been tested with the geometry optimization program. Their optimized geometries were in excellent agreement with the experimental data. For nine ceramic oxide crystals, the optimized cell parameters differ from the experimental ones within 0.5%. Moreover, the geometry optimization was recently used to predict a new phase of TiNx. The method has also been used to investigate a complex Vitamin B12-derivative, the OHCbl crystals. In order to overcome the prohibitive disk I/O demand, an on-demand version of the method was developed. Based on the electronic structure calculation of the OHCbl crystal, a partial density of states analysis and a bond order analysis were carried out. The calculated bonding of the corrin ring of OHCbl model was coincident with the big open-ring pi bond. One interesting find of the calculation was that the Co-OH bond was weak. This, together with the ongoing projects studying different Vitamin B12 derivatives, might help us to answer questions about the Co-C cleavage of the B12 coenzyme, which is involved in many important B12 enzymatic reactions.

Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation

NASA Astrophysics Data System (ADS)

Janahiraman, Tiagrajah V.; Ahmad, Nooraziah; Hani Nordin, Farah

2018-04-01

The CNC machine is controlled by manipulating cutting parameters that could directly influence the process performance. Many optimization methods has been applied to obtain the optimal cutting parameters for the desired performance function. Nonetheless, the industry still uses the traditional technique to obtain those values. Lack of knowledge on optimization techniques is the main reason for this issue to be prolonged. Therefore, the simple yet easy to implement, Optimal Cutting Parameters Selection System is introduced to help the manufacturer to easily understand and determine the best optimal parameters for their turning operation. This new system consists of two stages which are modelling and optimization. In modelling of input-output and in-process parameters, the hybrid of Extreme Learning Machine and Particle Swarm Optimization is applied. This modelling technique tend to converge faster than other artificial intelligent technique and give accurate result. For the optimization stage, again the Particle Swarm Optimization is used to get the optimal cutting parameters based on the performance function preferred by the manufacturer. Overall, the system can reduce the gap between academic world and the industry by introducing a simple yet easy to implement optimization technique. This novel optimization technique can give accurate result besides being the fastest technique.

Development of an LSI maximum-likelihood convolutional decoder for advanced forward error correction capability on the NASA 30/20 GHz program

NASA Technical Reports Server (NTRS)

Clark, R. T.; Mccallister, R. D.

1982-01-01

The particular coding option identified as providing the best level of coding gain performance in an LSI-efficient implementation was the optimal constraint length five, rate one-half convolutional code. To determine the specific set of design parameters which optimally matches this decoder to the LSI constraints, a breadboard MCD (maximum-likelihood convolutional decoder) was fabricated and used to generate detailed performance trade-off data. The extensive performance testing data gathered during this design tradeoff study are summarized, and the functional and physical MCD chip characteristics are presented.

Optimal pre-scheduling of problem remappings

NASA Technical Reports Server (NTRS)

Nicol, David M.; Saltz, Joel H.

1987-01-01

A large class of scientific computational problems can be characterized as a sequence of steps where a significant amount of computation occurs each step, but the work performed at each step is not necessarily identical. Two good examples of this type of computation are: (1) regridding methods which change the problem discretization during the course of the computation, and (2) methods for solving sparse triangular systems of linear equations. Recent work has investigated a means of mapping such computations onto parallel processors; the method defines a family of static mappings with differing degrees of importance placed on the conflicting goals of good load balance and low communication/synchronization overhead. The performance tradeoffs are controllable by adjusting the parameters of the mapping method. To achieve good performance it may be necessary to dynamically change these parameters at run-time, but such changes can impose additional costs. If the computation's behavior can be determined prior to its execution, it can be possible to construct an optimal parameter schedule using a low-order-polynomial-time dynamic programming algorithm. Since the latter can be expensive, the performance is studied of the effect of a linear-time scheduling heuristic on one of the model problems, and it is shown to be effective and nearly optimal.

Preliminary tests of the electrostatic plasma accelerator

NASA Technical Reports Server (NTRS)

Aston, G.; Acker, T.

1990-01-01

This report describes the results of a program to verify an electrostatic plasma acceleration concept and to identify those parameters most important in optimizing an Electrostatic Plasma Accelerator (EPA) thruster based upon this thrust mechanism. Preliminary performance measurements of thrust, specific impulse and efficiency were obtained using a unique plasma exhaust momentum probe. Reliable EPA thruster operation was achieved using one power supply.

Performance enhancement of linear stirling cryocoolers

NASA Astrophysics Data System (ADS)

Korf, Herbert; Ruehlich, Ingo; Wiedmann, Th.

2000-12-01

Performance and reliability parameters of the AIM Stirling coolers have been presented in several previous publications. This paper focuses on recent developments at AIM for the COP improvement of cryocoolers in IR-detectors and systems applications. Improved COP of cryocoolers is a key for optimized form factors, weight and reliability. In addition, some systems are critical for minimum input power and consequently minimum electromagnetic interference or magnetic stray fields, heat sinking or minimum stress under high g-level, etc. Although performance parameters and loss mechanism are well understood and can be calculated precisely, several losses still had been excessive and needed to be minimized. The AIM program is based on the SADA I cryocooler, which now is optimized to carry 4.3 W net heat load at 77K. As this program will lead into applications on a space platform, in a next step AIM is introducing flexure bearings and in a final step, an advanced pulse tube cold head will be implemented. The performance of the SADA II cooler is also improved by using the same tools and methods than used for the performance increase of the SADA I cooler by a factor of two. The main features are summarized together with measured or calculated performance data.

An embedded Simplified Fuzzy ARTMAP implemented on a microcontroller for food classification.

PubMed

Garcia-Breijo, Eduardo; Garrigues, Jose; Sanchez, Luis Gil; Laguarda-Miro, Nicolas

2013-08-13

In the present study, a portable system based on a microcontroller has been developed to classify different kinds of honeys. In order to do this classification, a Simplified Fuzzy ARTMAP network (SFA) implemented in a microcontroller has been used. Due to memory limits when working with microcontrollers, it is necessary to optimize the use of both program and data memory. Thus, a Graphical User Interface (GUI) for MATLAB® has been developed in order to optimize the necessary parameters to programme the SFA in a microcontroller. The measures have been carried out by potentiometric techniques using a multielectrode made of seven different metals. Next, the neural network has been trained on a PC by means of the GUI in Matlab using the data obtained in the experimental phase. The microcontroller has been programmed with the obtained parameters and then, new samples have been analysed using the portable system in order to test the model. Results are very promising, as an 87.5% recognition rate has been achieved in the training phase, which suggests that this kind of procedures can be successfully used not only for honey classification, but also for many other kinds of food.

An Embedded Simplified Fuzzy ARTMAP Implemented on a Microcontroller for Food Classification

PubMed Central

Garcia-Breijo, Eduardo; Garrigues, Jose; Sanchez, Luis Gil; Laguarda-Miro, Nicolas

2013-01-01

In the present study, a portable system based on a microcontroller has been developed to classify different kinds of honeys. In order to do this classification, a Simplified Fuzzy ARTMAP network (SFA) implemented in a microcontroller has been used. Due to memory limits when working with microcontrollers, it is necessary to optimize the use of both program and data memory. Thus, a Graphical User Interface (GUI) for MATLAB® has been developed in order to optimize the necessary parameters to programme the SFA in a microcontroller. The measures have been carried out by potentiometric techniques using a multielectrode made of seven different metals. Next, the neural network has been trained on a PC by means of the GUI in Matlab using the data obtained in the experimental phase. The microcontroller has been programmed with the obtained parameters and then, new samples have been analysed using the portable system in order to test the model. Results are very promising, as an 87.5% recognition rate has been achieved in the training phase, which suggests that this kind of procedures can be successfully used not only for honey classification, but also for many other kinds of food. PMID:23945736

Characterization and optimization of the HyperV PLX- α coaxial-gun plasma jet

NASA Astrophysics Data System (ADS)

Case, Andrew; Brockington, Sam; Cruz, Edward; Witherspoon, F. Douglas

2017-10-01

We present results from characterizing and optimizing performance of the contoured gap coaxial plasma guns under development for the ARPA-E Accelerating Low-Cost Plasma Heating And Assembly (ALPHA) program. Plasma jet diagnostics include fast photodiodes for velocimetry and interferometry for line integrated density. Additionally we present results from spectroscopy, both time resolved high resolution spectroscopy using a novel detector and time integrated survey spectroscopy, for measurements of velocity and temperature as well as impurities. Fast imaging gives plume geometry and time integrated imaging gives overall light emission. Results from a novel long record length camera developed by HyperV will also be presented. Experimental results are compared to the desired target parameters for the plasma jets. The target values for the plasmoid are velocity of 50 km/s, mass of 3.5 mg, and length of 10 cm. The best results so far from the exploration of parameter space for gun operation are: 4 mg at >50 km/s, with a length of 10 cm. Peak axial density 34 cm downstream from the muzzle is 2 ×1016 cm-3. This work supported by the ARPA-E ALPHA Program under contract DE-AR0000566.

Optimized calculation of the synergy conditions between electron cyclotron current drive and lower hybrid current drive on EAST

NASA Astrophysics Data System (ADS)

Wei, Wei; Bo-Jiang, Ding; Y, Peysson; J, Decker; Miao-Hui, Li; Xin-Jun, Zhang; Xiao-Jie, Wang; Lei, Zhang

2016-01-01

The optimized synergy conditions between electron cyclotron current drive (ECCD) and lower hybrid current drive (LHCD) with normal parameters of the EAST tokamak are studied by using the C3PO/LUKE code based on the understanding of the synergy mechanisms so as to obtain a higher synergistic current and provide theoretical reference for the synergistic effect in the EAST experiment. The dependences of the synergistic effect on the parameters of two waves (lower hybrid wave (LHW) and electron cyclotron wave (ECW)), including the radial position of the power deposition, the power value of the LH and EC waves, and the parallel refractive indices of the LHW (N∥) are presented and discussed. Project supported by the National Magnetic Confinement Fusion Science Program of China (Grant Nos. 2011GB102000, 2012GB103000, and 2013GB106001), the National Natural Science Foundation of China (Grant Nos. 11175206 and 11305211), the JSPS-NRF-NSFC A3 Foresight Program in the Field of Plasma Physics (Grant No. 11261140328), and the Fundamental Research Funds for the Central Universities of China (Grant No. JZ2015HGBZ0472).

Energy solutions in rural Africa: mapping electrification costs of distributed solar and diesel generation versus grid extension

NASA Astrophysics Data System (ADS)

Szabó, S.; Bódis, K.; Huld, T.; Moner-Girona, M.

2011-07-01

Three rural electrification options are analysed showing the cost optimal conditions for a sustainable energy development applying renewable energy sources in Africa. A spatial electricity cost model has been designed to point out whether diesel generators, photovoltaic systems or extension of the grid are the least-cost option in off-grid areas. The resulting mapping application offers support to decide in which regions the communities could be electrified either within the grid or in an isolated mini-grid. Donor programs and National Rural Electrification Agencies (or equivalent governmental departments) could use this type of delineation for their program boundaries and then could use the local optimization tools adapted to the prevailing parameters. The views expressed in this paper are those of the authors and do not necessarily represent European Commission and UNEP policy.

Design of oligonucleotides for microarrays and perspectives for design of multi-transcriptome arrays.

PubMed

Nielsen, Henrik Bjørn; Wernersson, Rasmus; Knudsen, Steen

2003-07-01

Optimal design of oligonucleotides for microarrays involves tedious and laborious work evaluating potential oligonucleotides relative to a series of parameters. The currently available tools for this purpose are limited in their flexibility and do not present the oligonucleotide designer with an overview of these parameters. We present here a flexible tool named OligoWiz for designing oligonucleotides for multiple purposes. OligoWiz presents a set of parameter scores in a graphical interface to facilitate an overview for the user. Additional custom parameter scores can easily be added to the program to extend the default parameters: homology, DeltaTm, low-complexity, position and GATC-only. Furthermore we present an analysis of the limitations in designing oligonucleotide sets that can detect transcripts from multiple organisms. OligoWiz is available at www.cbs.dtu.dk/services/OligoWiz/.

Are We There Yet? Applying Thermodynamic and Kinetic Profiling on Embryonic Ectoderm Development (EED) Hit-to-Lead Program.

PubMed

Wang, Ying; Edalji, Rohinton P; Panchal, Sanjay C; Sun, Chaohong; Djuric, Stevan W; Vasudevan, Anil

2017-10-26

It is advocated that kinetic and thermodynamic profiling of bioactive compounds should be incorporated and utilized as complementary tools for hit and lead optimizations in drug discovery. To assess their applications in the EED hit-to-lead optimization process, large amount of thermodynamic and kinetic data were collected and analyzed via isothermal titration calorimetry (ITC) and surface plasmon resonance (SPR), respectively. Slower dissociation rates (k off ) of the lead compounds were observed as the program progressed. Analysis of the kinetic data indicated that compound cellular activity correlated with both K i and k off . Our analysis revealed that ITC data should be interpreted in the context of chiral purity of the compounds. The thermodynamic signatures of the EED aminopyrrolidine compounds were found to be mainly enthalpy driven with improved enthalpic contributions as the program progressed. Our study also demonstrated that significant challenges still exist in utilizing kinetic and thermodynamic parameters for hit selection.

Robust design of microchannel cooler

NASA Astrophysics Data System (ADS)

He, Ye; Yang, Tao; Hu, Li; Li, Leimin

2005-12-01

Microchannel cooler has offered a new method for the cooling of high power diode lasers, with the advantages of small volume, high efficiency of thermal dissipation and low cost when mass-produced. In order to reduce the sensitivity of design to manufacture errors or other disturbances, Taguchi method that is one of robust design method was chosen to optimize three parameters important to the cooling performance of roof-like microchannel cooler. The hydromechanical and thermal mathematical model of varying section microchannel was calculated using finite volume method by FLUENT. A special program was written to realize the automation of the design process for improving efficiency. The optimal design is presented which compromises between optimal cooling performance and its robustness. This design method proves to be available.

Hybrid Genetic Agorithms and Line Search Method for Industrial Production Planning with Non-Linear Fitness Function

NASA Astrophysics Data System (ADS)

Vasant, Pandian; Barsoum, Nader

2008-10-01

Many engineering, science, information technology and management optimization problems can be considered as non linear programming real world problems where the all or some of the parameters and variables involved are uncertain in nature. These can only be quantified using intelligent computational techniques such as evolutionary computation and fuzzy logic. The main objective of this research paper is to solve non linear fuzzy optimization problem where the technological coefficient in the constraints involved are fuzzy numbers which was represented by logistic membership functions by using hybrid evolutionary optimization approach. To explore the applicability of the present study a numerical example is considered to determine the production planning for the decision variables and profit of the company.

The DOPEX code: An application of the method of steepest descent to laminated-shield-weight optimization with several constraints

NASA Technical Reports Server (NTRS)

Lahti, G. P.

1972-01-01

A two- or three-constraint, two-dimensional radiation shield weight optimization procedure and a computer program, DOPEX, is described. The DOPEX code uses the steepest descent method to alter a set of initial (input) thicknesses for a shield configuration to achieve a minimum weight while simultaneously satisfying dose constaints. The code assumes an exponential dose-shield thickness relation with parameters specified by the user. The code also assumes that dose rates in each principal direction are dependent only on thicknesses in that direction. Code input instructions, FORTRAN 4 listing, and a sample problem are given. Typical computer time required to optimize a seven-layer shield is about 0.1 minute on an IBM 7094-2.

Automated optimization of an aspheric light-emitting diode lens for uniform illumination.

PubMed

Luo, Xiaoxia; Liu, Hua; Lu, Zhenwu; Wang, Yao

2011-07-10

In this paper, an automated optimization method in the sequential mode of ZEMAX is proposed in the design of an aspheric lens with uniform illuminance for an LED source. A feedback modification is introduced in the design for the LED extended source. The user-defined merit function is written out by using ZEMAX programming language macros language and, as an example, optimum parameters of an aspheric lens are obtained via running an optimization. The optical simulation results show that the illumination efficiency and uniformity can reach 83% and 90%, respectively, on a target surface of 40 mm diameter and at 60 mm away for a 1×1 mm LED source. © 2011 Optical Society of America

Equivalent Air Spring Suspension Model for Quarter-Passive Model of Passenger Vehicles

PubMed Central

Abid, Haider J.; Chen, Jie; Nassar, Ameen A.

2015-01-01

This paper investigates the GENSIS air spring suspension system equivalence to a passive suspension system. The SIMULINK simulation together with the OptiY optimization is used to obtain the air spring suspension model equivalent to passive suspension system, where the car body response difference from both systems with the same road profile inputs is used as the objective function for optimization (OptiY program). The parameters of air spring system such as initial pressure, volume of bag, length of surge pipe, diameter of surge pipe, and volume of reservoir are obtained from optimization. The simulation results show that the air spring suspension equivalent system can produce responses very close to the passive suspension system. PMID:27351020

Optimization Design of Minimum Total Resistance Hull Form Based on CFD Method

NASA Astrophysics Data System (ADS)

Zhang, Bao-ji; Zhang, Sheng-long; Zhang, Hui

2018-06-01

In order to reduce the resistance and improve the hydrodynamic performance of a ship, two hull form design methods are proposed based on the potential flow theory and viscous flow theory. The flow fields are meshed using body-fitted mesh and structured grids. The parameters of the hull modification function are the design variables. A three-dimensional modeling method is used to alter the geometry. The Non-Linear Programming (NLP) method is utilized to optimize a David Taylor Model Basin (DTMB) model 5415 ship under the constraints, including the displacement constraint. The optimization results show an effective reduction of the resistance. The two hull form design methods developed in this study can provide technical support and theoretical basis for designing green ships.

A Modified Penalty Parameter Approach for Optimal Estimation of UH with Simultaneous Estimation of Infiltration Parameters

NASA Astrophysics Data System (ADS)

Bhattacharjya, Rajib Kumar

2018-05-01

The unit hydrograph and the infiltration parameters of a watershed can be obtained from observed rainfall-runoff data by using inverse optimization technique. This is a two-stage optimization problem. In the first stage, the infiltration parameters are obtained and the unit hydrograph ordinates are estimated in the second stage. In order to combine this two-stage method into a single stage one, a modified penalty parameter approach is proposed for converting the constrained optimization problem to an unconstrained one. The proposed approach is designed in such a way that the model initially obtains the infiltration parameters and then searches the optimal unit hydrograph ordinates. The optimization model is solved using Genetic Algorithms. A reduction factor is used in the penalty parameter approach so that the obtained optimal infiltration parameters are not destroyed during subsequent generation of genetic algorithms, required for searching optimal unit hydrograph ordinates. The performance of the proposed methodology is evaluated by using two example problems. The evaluation shows that the model is superior, simple in concept and also has the potential for field application.

Topology-changing shape optimization with the genetic algorithm

NASA Astrophysics Data System (ADS)

Lamberson, Steven E., Jr.

The goal is to take a traditional shape optimization problem statement and modify it slightly to allow for prescribed changes in topology. This modification enables greater flexibility in the choice of parameters for the topology optimization problem, while improving the direct physical relevance of the results. This modification involves changing the optimization problem statement from a nonlinear programming problem into a form of mixed-discrete nonlinear programing problem. The present work demonstrates one possible way of using the Genetic Algorithm (GA) to solve such a problem, including the use of "masking bits" and a new modification to the bit-string affinity (BSA) termination criterion specifically designed for problems with "masking bits." A simple ten-bar truss problem proves the utility of the modified BSA for this type of problem. A more complicated two dimensional bracket problem is solved using both the proposed approach and a more traditional topology optimization approach (Solid Isotropic Microstructure with Penalization or SIMP) to enable comparison. The proposed approach is able to solve problems with both local and global constraints, which is something traditional methods cannot do. The proposed approach has a significantly higher computational burden --- on the order of 100 times larger than SIMP, although the proposed approach is able to offset this with parallel computing.

Visualization of logistic algorithm in Wilson model

NASA Astrophysics Data System (ADS)

Glushchenko, A. S.; Rodin, V. A.; Sinegubov, S. V.

2018-05-01

Economic order quantity (EOQ), defined by the Wilson's model, is widely used at different stages of production and distribution of different products. It is useful for making decisions in the management of inventories, providing a more efficient business operation and thus bringing more economic benefits. There is a large amount of reference material and extensive computer shells that help solving various logistics problems. However, the use of large computer environments is not always justified and requires special user training. A tense supply schedule in a logistics model is optimal, if, and only if, the planning horizon coincides with the beginning of the next possible delivery. For all other possible planning horizons, this plan is not optimal. It is significant that when the planning horizon changes, the plan changes immediately throughout the entire supply chain. In this paper, an algorithm and a program for visualizing models of the optimal value of supplies and their number, depending on the magnitude of the planned horizon, have been obtained. The program allows one to trace (visually and quickly) all main parameters of the optimal plan on the charts. The results of the paper represent a part of the authors’ research work in the field of optimization of protection and support services of ports in the Russian North.

Swainsonine, a novel fungal metabolite: optimization of fermentative production and bioreactor operations using evolutionary programming.

PubMed

Singh, Digar; Kaur, Gurvinder

2014-08-01

The optimization of bioreactor operations towards swainsonine production was performed using an artificial neural network coupled evolutionary program (EP)-based optimization algorithm fitted with experimental one-factor-at-a-time (OFAT) results. The effects of varying agitation (300-500 rpm) and aeration (0.5-2.0 vvm) rates for different incubation hours (72-108 h) were evaluated in bench top bioreactor. Prominent scale-up parameters, gassed power per unit volume (P g/V L, W/m(3)) and volumetric oxygen mass transfer coefficient (K L a, s(-1)) were correlated with optimized conditions. A maximum of 6.59 ± 0.10 μg/mL of swainsonine production was observed at 400 rpm-1.5 vvm at 84 h in OFAT experiments with corresponding P g/VL and K L a values of 91.66 W/m(3) and 341.48 × 10(-4) s(-1), respectively. The EP optimization algorithm predicted a maximum of 10.08 μg/mL of swainsonine at 325.47 rpm, 1.99 vvm and 80.75 h against the experimental production of 7.93 ± 0.52 μg/mL at constant K L a (349.25 × 10(-4) s(-1)) and significantly reduced P g/V L (33.33 W/m(3)) drawn by the impellers.

Optimizing Environmental Flow Operation Rules based on Explicit IHA Constraints

NASA Astrophysics Data System (ADS)

Dongnan, L.; Wan, W.; Zhao, J.

2017-12-01

Multi-objective operation of reservoirs are increasingly asked to consider the environmental flow to support ecosystem health. Indicators of Hydrologic Alteration (IHA) is widely used to describe environmental flow regimes, but few studies have explicitly formulated it into optimization models and thus is difficult to direct reservoir release. In an attempt to incorporate the benefit of environmental flow into economic achievement, a two-objective reservoir optimization model is developed and all 33 hydrologic parameters of IHA are explicitly formulated into constraints. The benefit of economic is defined by Hydropower Production (HP) while the benefit of environmental flow is transformed into Eco-Index (EI) that combined 5 of the 33 IHA parameters chosen by principal component analysis method. Five scenarios (A to E) with different constraints are tested and solved by nonlinear programming. The case study of Jing Hong reservoir, located in the upstream of Mekong basin, China, shows: 1. A Pareto frontier is formed by maximizing on only HP objective in scenario A and on only EI objective in scenario B. 2. Scenario D using IHA parameters as constraints obtains the optimal benefits of both economic and ecological. 3. A sensitive weight coefficient is found in scenario E, but the trade-offs between HP and EI objectives are not within the Pareto frontier. 4. When the fraction of reservoir utilizable capacity reaches 0.8, both HP and EI capture acceptable values. At last, to make this modelmore conveniently applied to everyday practice, a simplified operation rule curve is extracted.

Methods of Optimal Control of Laser-Plasma Instabilities Using Spike Trains of Uneven Duration and Delay (STUD Pulses)

NASA Astrophysics Data System (ADS)

Afeyan, Bedros

2013-10-01

We have recently introduced and extensively studied a new adaptive method of LPI control. It promises to extend the effectiveness of laser as inertial fusion drivers by allowing active control of stimulated Raman and Brillouin scattering and crossed beam energy transfer. It breaks multi-nanosecond pulses into a series of picosecond (ps) time scale spikes with comparable gaps in between. The height and width of each spike as well as their separations are optimization parameters. In addition, the spatial speckle patterns are changed after a number of successive spikes as needed (from every spike to never). The combination of these parameters allows the taming of parametric instabilities to conform to any desired reduced reflectivity profile, within the bounds of the performance limitations of the lasers. Instead of pulse shaping on hydrodynamical time scales, far faster (from 1 ps to 10 ps) modulations of the laser profile will be needed to implement the STUD pulse program for full LPI control. We will show theoretical and computational evidence for the effectiveness of the STUD pulse program to control LPI. The physics of why STUD pulses work and how optimization can be implemented efficiently using statistical nonlinear optical models and techniques will be explained. We will also discuss a novel diagnostic system employing STUD pulses that will allow the boosted measurement of velocity distribution function slopes on a ps time scale in the small crossing volume of a pump and a probe beam. Various regimes from weak to strong coupling and weak to strong damping will be treated. Novel pulse modulation schemes and diagnostic tools based on time-lenses used in both microscope and telescope modes will be suggested for the execution of the STUD pule program. Work Supported by the DOE NNSA-OFES Joint Program on HEDLP and DOE OFES SBIR Phase I Grants.

Finite-time convergent recurrent neural network with a hard-limiting activation function for constrained optimization with piecewise-linear objective functions.

PubMed

Liu, Qingshan; Wang, Jun

2011-04-01

This paper presents a one-layer recurrent neural network for solving a class of constrained nonsmooth optimization problems with piecewise-linear objective functions. The proposed neural network is guaranteed to be globally convergent in finite time to the optimal solutions under a mild condition on a derived lower bound of a single gain parameter in the model. The number of neurons in the neural network is the same as the number of decision variables of the optimization problem. Compared with existing neural networks for optimization, the proposed neural network has a couple of salient features such as finite-time convergence and a low model complexity. Specific models for two important special cases, namely, linear programming and nonsmooth optimization, are also presented. In addition, applications to the shortest path problem and constrained least absolute deviation problem are discussed with simulation results to demonstrate the effectiveness and characteristics of the proposed neural network.

Nuclear Electric Vehicle Optimization Toolset (NEVOT): Integrated System Design Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Tinker, Michael L.; Steincamp, James W.; Stewart, Eric T.; Patton, Bruce W.; Pannell, William P.; Newby, Ronald L.; Coffman, Mark E.; Qualls, A. L.; Bancroft, S.; Molvik, Greg

2003-01-01

The Nuclear Electric Vehicle Optimization Toolset (NEVOT) optimizes the design of all major Nuclear Electric Propulsion (NEP) vehicle subsystems for a defined mission within constraints and optimization parameters chosen by a user. The tool uses a Genetic Algorithm (GA) search technique to combine subsystem designs and evaluate the fitness of the integrated design to fulfill a mission. The fitness of an individual is used within the GA to determine its probability of survival through successive generations in which the designs with low fitness are eliminated and replaced with combinations or mutations of designs with higher fitness. The program can find optimal solutions for different sets of fitness metrics without modification and can create and evaluate vehicle designs that might never be conceived of through traditional design techniques. It is anticipated that the flexible optimization methodology will expand present knowledge of the design trade-offs inherent in designing nuclear powered space vehicles and lead to improved NEP designs.

Branch and bound algorithm for accurate estimation of analytical isotropic bidirectional reflectance distribution function models.

PubMed

Yu, Chanki; Lee, Sang Wook

2016-05-20

We present a reliable and accurate global optimization framework for estimating parameters of isotropic analytical bidirectional reflectance distribution function (BRDF) models. This approach is based on a branch and bound strategy with linear programming and interval analysis. Conventional local optimization is often very inefficient for BRDF estimation since its fitting quality is highly dependent on initial guesses due to the nonlinearity of analytical BRDF models. The algorithm presented in this paper employs L₁-norm error minimization to estimate BRDF parameters in a globally optimal way and interval arithmetic to derive our feasibility problem and lower bounding function. Our method is developed for the Cook-Torrance model but with several normal distribution functions such as the Beckmann, Berry, and GGX functions. Experiments have been carried out to validate the presented method using 100 isotropic materials from the MERL BRDF database, and our experimental results demonstrate that the L₁-norm minimization provides a more accurate and reliable solution than the L₂-norm minimization.

Simultaneous versus sequential optimal experiment design for the identification of multi-parameter microbial growth kinetics as a function of temperature.

PubMed

Van Derlinden, E; Bernaerts, K; Van Impe, J F

2010-05-21

Optimal experiment design for parameter estimation (OED/PE) has become a popular tool for efficient and accurate estimation of kinetic model parameters. When the kinetic model under study encloses multiple parameters, different optimization strategies can be constructed. The most straightforward approach is to estimate all parameters simultaneously from one optimal experiment (single OED/PE strategy). However, due to the complexity of the optimization problem or the stringent limitations on the system's dynamics, the experimental information can be limited and parameter estimation convergence problems can arise. As an alternative, we propose to reduce the optimization problem to a series of two-parameter estimation problems, i.e., an optimal experiment is designed for a combination of two parameters while presuming the other parameters known. Two different approaches can be followed: (i) all two-parameter optimal experiments are designed based on identical initial parameter estimates and parameters are estimated simultaneously from all resulting experimental data (global OED/PE strategy), and (ii) optimal experiments are calculated and implemented sequentially whereby the parameter values are updated intermediately (sequential OED/PE strategy). This work exploits OED/PE for the identification of the Cardinal Temperature Model with Inflection (CTMI) (Rosso et al., 1993). This kinetic model describes the effect of temperature on the microbial growth rate and encloses four parameters. The three OED/PE strategies are considered and the impact of the OED/PE design strategy on the accuracy of the CTMI parameter estimation is evaluated. Based on a simulation study, it is observed that the parameter values derived from the sequential approach deviate more from the true parameters than the single and global strategy estimates. The single and global OED/PE strategies are further compared based on experimental data obtained from design implementation in a bioreactor. Comparable estimates are obtained, but global OED/PE estimates are, in general, more accurate and reliable. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Multi-parameter optimization of piezoelectric actuators for multi-mode active vibration control of cylindrical shells

NASA Astrophysics Data System (ADS)

Hu, K. M.; Li, Hua

2018-07-01

A novel technique for the multi-parameter optimization of distributed piezoelectric actuators is presented in this paper. The proposed method is designed to improve the performance of multi-mode vibration control in cylindrical shells. The optimization parameters of actuator patch configuration include position, size, and tilt angle. The modal control force of tilted orthotropic piezoelectric actuators is derived and the multi-parameter cylindrical shell optimization model is established. The linear quadratic energy index is employed as the optimization criterion. A geometric constraint is proposed to prevent overlap between tilted actuators, which is plugged into a genetic algorithm to search the optimal configuration parameters. A simply-supported closed cylindrical shell with two actuators serves as a case study. The vibration control efficiencies of various parameter sets are evaluated via frequency response and transient response simulations. The results show that the linear quadratic energy indexes of position and size optimization decreased by 14.0% compared to position optimization; those of position and tilt angle optimization decreased by 16.8%; and those of position, size, and tilt angle optimization decreased by 25.9%. It indicates that, adding configuration optimization parameters is an efficient approach to improving the vibration control performance of piezoelectric actuators on shells.

An inventory-theory-based interval-parameter two-stage stochastic programming model for water resources management

NASA Astrophysics Data System (ADS)

Suo, M. Q.; Li, Y. P.; Huang, G. H.

2011-09-01

In this study, an inventory-theory-based interval-parameter two-stage stochastic programming (IB-ITSP) model is proposed through integrating inventory theory into an interval-parameter two-stage stochastic optimization framework. This method can not only address system uncertainties with complex presentation but also reflect transferring batch (the transferring quantity at once) and period (the corresponding cycle time) in decision making problems. A case of water allocation problems in water resources management planning is studied to demonstrate the applicability of this method. Under different flow levels, different transferring measures are generated by this method when the promised water cannot be met. Moreover, interval solutions associated with different transferring costs also have been provided. They can be used for generating decision alternatives and thus help water resources managers to identify desired policies. Compared with the ITSP method, the IB-ITSP model can provide a positive measure for solving water shortage problems and afford useful information for decision makers under uncertainty.

Optimal control of an invasive species using a reaction-diffusion model and linear programming

USGS Publications Warehouse

Bonneau, Mathieu; Johnson, Fred A.; Smith, Brian J.; Romagosa, Christina M.; Martin, Julien; Mazzotti, Frank J.

2017-01-01

Managing an invasive species is particularly challenging as little is generally known about the species’ biological characteristics in its new habitat. In practice, removal of individuals often starts before the species is studied to provide the information that will later improve control. Therefore, the locations and the amount of control have to be determined in the face of great uncertainty about the species characteristics and with a limited amount of resources. We propose framing spatial control as a linear programming optimization problem. This formulation, paired with a discrete reaction-diffusion model, permits calculation of an optimal control strategy that minimizes the remaining number of invaders for a fixed cost or that minimizes the control cost for containment or protecting specific areas from invasion. We propose computing the optimal strategy for a range of possible model parameters, representing current uncertainty on the possible invasion scenarios. Then, a best strategy can be identified depending on the risk attitude of the decision-maker. We use this framework to study the spatial control of the Argentine black and white tegus (Salvator merianae) in South Florida. There is uncertainty about tegu demography and we considered several combinations of model parameters, exhibiting various dynamics of invasion. For a fixed one-year budget, we show that the risk-averse strategy, which optimizes the worst-case scenario of tegus’ dynamics, and the risk-neutral strategy, which optimizes the expected scenario, both concentrated control close to the point of introduction. A risk-seeking strategy, which optimizes the best-case scenario, focuses more on models where eradication of the species in a cell is possible and consists of spreading control as much as possible. For the establishment of a containment area, assuming an exponential growth we show that with current control methods it might not be possible to implement such a strategy for some of the models that we considered. Including different possible models allows an examination of how the strategy is expected to perform in different scenarios. Then, a strategy that accounts for the risk attitude of the decision-maker can be designed.

Development of the Symbolic Manipulator Laboratory modeling package for the kinematic design and optimization of the Future Armor Rearm System robot. Ammunition Logistics Program

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

March-Leuba, S.; Jansen, J.F.; Kress, R.L.

1992-08-01

A new program package, Symbolic Manipulator Laboratory (SML), for the automatic generation of both kinematic and static manipulator models in symbolic form is presented. Critical design parameters may be identified and optimized using symbolic models as shown in the sample application presented for the Future Armor Rearm System (FARS) arm. The computer-aided development of the symbolic models yields equations with reduced numerical complexity. Important considerations have been placed on the closed form solutions simplification and on the user friendly operation. The main emphasis of this research is the development of a methodology which is implemented in a computer program capablemore » of generating symbolic kinematic and static forces models of manipulators. The fact that the models are obtained trigonometrically reduced is among the most significant results of this work and the most difficult to implement. Mathematica, a commercial program that allows symbolic manipulation, is used to implement the program package. SML is written such that the user can change any of the subroutines or create new ones easily. To assist the user, an on-line help has been written to make of SML a user friendly package. Some sample applications are presented. The design and optimization of the 5-degrees-of-freedom (DOF) FARS manipulator using SML is discussed. Finally, the kinematic and static models of two different 7-DOF manipulators are calculated symbolically.« less

Evaluating Thermodynamic Integration Performance of the New Amber Molecular Dynamics Package and Assess Potential Halogen Bonds of Enoyl-ACP Reductase (FabI) Benzimidazole Inhibitors

PubMed Central

Su, Pin-Chih; Johnson, Michael E.

2015-01-01

Thermodynamic integration (TI) can provide accurate binding free energy insights in a lead optimization program, but its high computational expense has limited its usage. In the effort of developing an efficient and accurate TI protocol for FabI inhibitors lead optimization program, we carefully compared TI with different Amber molecular dynamics (MD) engines (sander and pmemd), MD simulation lengths, the number of intermediate states and transformation steps, and the Lennard-Jones and Coulomb Softcore potentials parameters in the one-step TI, using eleven benzimidazole inhibitors in complex with Francisella tularensis enoyl acyl reductase (FtFabI). To our knowledge, this is the first study to extensively test the new AMBER MD engine, pmemd, on TI and compare the parameters of the Softcore potentials in the one-step TI in a protein-ligand binding system. The best performing model, the one-step pmemd TI, using 6 intermediate states and 1 ns MD simulations, provides better agreement with experimental results (RMSD = 0.52 kcal/mol) than the best performing implicit solvent method, QM/MM-GBSA from our previous study (RMSD = 3.00 kcal/mol), while maintaining similar efficiency. Briefly, we show the optimized TI protocol to be highly accurate and affordable for the FtFabI system. This approach can be implemented in a larger scale benzimidazole scaffold lead optimization against FtFabI. Lastly, the TI results here also provide structure-activity relationship insights, and suggest the para-halogen in benzimidazole compounds might form a weak halogen bond with FabI, which is a well-known halogen bond favoring enzyme. PMID:26666582

Evaluating thermodynamic integration performance of the new amber molecular dynamics package and assess potential halogen bonds of enoyl-ACP reductase (FabI) benzimidazole inhibitors.

PubMed

Su, Pin-Chih; Johnson, Michael E

2016-04-05

Thermodynamic integration (TI) can provide accurate binding free energy insights in a lead optimization program, but its high computational expense has limited its usage. In the effort of developing an efficient and accurate TI protocol for FabI inhibitors lead optimization program, we carefully compared TI with different Amber molecular dynamics (MD) engines (sander and pmemd), MD simulation lengths, the number of intermediate states and transformation steps, and the Lennard-Jones and Coulomb Softcore potentials parameters in the one-step TI, using eleven benzimidazole inhibitors in complex with Francisella tularensis enoyl acyl reductase (FtFabI). To our knowledge, this is the first study to extensively test the new AMBER MD engine, pmemd, on TI and compare the parameters of the Softcore potentials in the one-step TI in a protein-ligand binding system. The best performing model, the one-step pmemd TI, using 6 intermediate states and 1 ns MD simulations, provides better agreement with experimental results (RMSD = 0.52 kcal/mol) than the best performing implicit solvent method, QM/MM-GBSA from our previous study (RMSD = 3.00 kcal/mol), while maintaining similar efficiency. Briefly, we show the optimized TI protocol to be highly accurate and affordable for the FtFabI system. This approach can be implemented in a larger scale benzimidazole scaffold lead optimization against FtFabI. Lastly, the TI results here also provide structure-activity relationship insights, and suggest the parahalogen in benzimidazole compounds might form a weak halogen bond with FabI, which is a well-known halogen bond favoring enzyme. © 2015 Wiley Periodicals, Inc.

HyPEP FY06 Report: Models and Methods

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

DOE report

2006-09-01

The Department of Energy envisions the next generation very high-temperature gas-cooled reactor (VHTR) as a single-purpose or dual-purpose facility that produces hydrogen and electricity. The Ministry of Science and Technology (MOST) of the Republic of Korea also selected VHTR for the Nuclear Hydrogen Development and Demonstration (NHDD) Project. This research project aims at developing a user-friendly program for evaluating and optimizing cycle efficiencies of producing hydrogen and electricity in a Very-High-Temperature Reactor (VHTR). Systems for producing electricity and hydrogen are complex and the calculations associated with optimizing these systems are intensive, involving a large number of operating parameter variations andmore » many different system configurations. This research project will produce the HyPEP computer model, which is specifically designed to be an easy-to-use and fast running tool for evaluating nuclear hydrogen and electricity production facilities. The model accommodates flexible system layouts and its cost models will enable HyPEP to be well-suited for system optimization. Specific activities of this research are designed to develop the HyPEP model into a working tool, including (a) identifying major systems and components for modeling, (b) establishing system operating parameters and calculation scope, (c) establishing the overall calculation scheme, (d) developing component models, (e) developing cost and optimization models, and (f) verifying and validating the program. Once the HyPEP model is fully developed and validated, it will be used to execute calculations on candidate system configurations. FY-06 report includes a description of reference designs, methods used in this study, models and computational strategies developed for the first year effort. Results from computer codes such as HYSYS and GASS/PASS-H used by Idaho National Laboratory and Argonne National Laboratory, respectively will be benchmarked with HyPEP results in the following years.« less

Comparative modeling of InP solar cell structures

NASA Technical Reports Server (NTRS)

Jain, R. K.; Weinberg, I.; Flood, D. J.

1991-01-01

The comparative modeling of p(+)n and n(+)p indium phosphide solar cell structures is studied using a numerical program PC-1D. The optimal design study has predicted that the p(+)n structure offers improved cell efficiencies as compared to n(+)p structure, due to higher open-circuit voltage. The various cell material and process parameters to achieve the maximum cell efficiencies are reported. The effect of some of the cell parameters on InP cell I-V characteristics was studied. The available radiation resistance data on n(+)p and p(+)p InP solar cells are also critically discussed.

Design of a broadband band-pass filter with notch-band using new models of coupled transmission lines.

PubMed

Daryasafar, Navid; Baghbani, Somaye; Moghaddasi, Mohammad Naser; Sadeghzade, Ramezanali

2014-01-01

We intend to design a broadband band-pass filter with notch-band, which uses coupled transmission lines in the structure, using new models of coupled transmission lines. In order to realize and present the new model, first, previous models will be simulated in the ADS program. Then, according to the change of their equations and consequently change of basic parameters of these models, optimization and dependency among these parameters and also their frequency response are attended and results of these changes in order to design a new filter are converged.

Evaluating performances of simplified physically based landslide susceptibility models.

NASA Astrophysics Data System (ADS)

Capparelli, Giovanna; Formetta, Giuseppe; Versace, Pasquale

2015-04-01

Rainfall induced shallow landslides cause significant damages involving loss of life and properties. Prediction of shallow landslides susceptible locations is a complex task that involves many disciplines: hydrology, geotechnical science, geomorphology, and statistics. Usually to accomplish this task two main approaches are used: statistical or physically based model. This paper presents a package of GIS based models for landslide susceptibility analysis. It was integrated in the NewAge-JGrass hydrological model using the Object Modeling System (OMS) modeling framework. The package includes three simplified physically based models for landslides susceptibility analysis (M1, M2, and M3) and a component for models verifications. It computes eight goodness of fit indices (GOF) by comparing pixel-by-pixel model results and measurements data. Moreover, the package integration in NewAge-JGrass allows the use of other components such as geographic information system tools to manage inputs-output processes, and automatic calibration algorithms to estimate model parameters. The system offers the possibility to investigate and fairly compare the quality and the robustness of models and models parameters, according a procedure that includes: i) model parameters estimation by optimizing each of the GOF index separately, ii) models evaluation in the ROC plane by using each of the optimal parameter set, and iii) GOF robustness evaluation by assessing their sensitivity to the input parameter variation. This procedure was repeated for all three models. The system was applied for a case study in Calabria (Italy) along the Salerno-Reggio Calabria highway, between Cosenza and Altilia municipality. The analysis provided that among all the optimized indices and all the three models, Average Index (AI) optimization coupled with model M3 is the best modeling solution for our test case. This research was funded by PON Project No. 01_01503 "Integrated Systems for Hydrogeological Risk Monitoring, Early Warning and Mitigation Along the Main Lifelines", CUP B31H11000370005, in the framework of the National Operational Program for "Research and Competitiveness" 2007-2013.

Optimization of a centrifugal compressor impeller using CFD: the choice of simulation model parameters

NASA Astrophysics Data System (ADS)

Neverov, V. V.; Kozhukhov, Y. V.; Yablokov, A. M.; Lebedev, A. A.

2017-08-01

Nowadays the optimization using computational fluid dynamics (CFD) plays an important role in the design process of turbomachines. However, for the successful and productive optimization it is necessary to define a simulation model correctly and rationally. The article deals with the choice of a grid and computational domain parameters for optimization of centrifugal compressor impellers using computational fluid dynamics. Searching and applying optimal parameters of the grid model, the computational domain and solver settings allows engineers to carry out a high-accuracy modelling and to use computational capability effectively. The presented research was conducted using Numeca Fine/Turbo package with Spalart-Allmaras and Shear Stress Transport turbulence models. Two radial impellers was investigated: the high-pressure at ψT=0.71 and the low-pressure at ψT=0.43. The following parameters of the computational model were considered: the location of inlet and outlet boundaries, type of mesh topology, size of mesh and mesh parameter y+. Results of the investigation demonstrate that the choice of optimal parameters leads to the significant reduction of the computational time. Optimal parameters in comparison with non-optimal but visually similar parameters can reduce the calculation time up to 4 times. Besides, it is established that some parameters have a major impact on the result of modelling.

High-Level Connectionist Models

DTIC Science & Technology

1993-10-01

subject of intense research since Axelrod (1984) showed that two agents engaged in a prisoner’s dilemma ( Poundstone , 1992) can evolve into mutually...The various parameter values for the program are set as described above unless otherwise noted. 4.1 Williams ’ Trigger Problem As an initial test...M. P. Vecchi. Optimization by simulated annealing. Sci- ence, 220:671-680, 1983. [39] R. J. Williams . Adaptive State Representation and Estimation

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

Spycher, Nicolas; Peiffer, Loic; Finsterle, Stefan

GeoT implements the multicomponent geothermometry method developed by Reed and Spycher (1984, Geochim. Cosmichim. Acta 46 513–528) into a stand-alone computer program, to ease the application of this method and to improve the prediction of geothermal reservoir temperatures using full and integrated chemical analyses of geothermal fluids. Reservoir temperatures are estimated from statistical analyses of mineral saturation indices computed as a function of temperature. The reconstruction of the deep geothermal fluid compositions, and geothermometry computations, are all implemented into the same computer program, allowing unknown or poorly constrained input parameters to be estimated by numerical optimization using existing parameter estimationmore » software, such as iTOUGH2, PEST, or UCODE. This integrated geothermometry approach presents advantages over classical geothermometers for fluids that have not fully equilibrated with reservoir minerals and/or that have been subject to processes such as dilution and gas loss.« less

Flight contaminant trace analyser. Phase 1: Chromatographic input system

NASA Technical Reports Server (NTRS)

1975-01-01

The development of a chromatographic column capable of resolving compounds associated with spacecraft atmospheres is presented. Consideration is given to sampling techniques, column parameters and operation, and column interface with a mass spectrometer. A capillary column coated with a mixture of polyalkylene glycols is found to provide the best selectivity for resolving multicomponent mixtures found in spacecraft atmospheres. Temperature programming and isothermal operation of the column are evaluated and it is found that temperature programming has a shorter analysis time for a given carrier gas flow rate and overall superior resolution. It is observed that hydrogen provides a 15% savings in analysis time over helium. Following the optimization of column operational parameters, a mixed phase Ucon capillary is prepared for evaluation during the column test period in which the test sample is automatically analyzed. Analysis of the multicomponent test mixture is completed within 45 minutes provided temperature programming is used. All but two of the test compounds are well resolved.

Building model analysis applications with the Joint Universal Parameter IdenTification and Evaluation of Reliability (JUPITER) API

USGS Publications Warehouse

Banta, E.R.; Hill, M.C.; Poeter, E.; Doherty, J.E.; Babendreier, J.

2008-01-01

The open-source, public domain JUPITER (Joint Universal Parameter IdenTification and Evaluation of Reliability) API (Application Programming Interface) provides conventions and Fortran-90 modules to develop applications (computer programs) for analyzing process models. The input and output conventions allow application users to access various applications and the analysis methods they embody with a minimum of time and effort. Process models simulate, for example, physical, chemical, and (or) biological systems of interest using phenomenological, theoretical, or heuristic approaches. The types of model analyses supported by the JUPITER API include, but are not limited to, sensitivity analysis, data needs assessment, calibration, uncertainty analysis, model discrimination, and optimization. The advantages provided by the JUPITER API for users and programmers allow for rapid programming and testing of new ideas. Application-specific coding can be in languages other than the Fortran-90 of the API. This article briefly describes the capabilities and utility of the JUPITER API, lists existing applications, and uses UCODE_2005 as an example.

Fitting Nonlinear Curves by use of Optimization Techniques

NASA Technical Reports Server (NTRS)

Hill, Scott A.

2005-01-01

MULTIVAR is a FORTRAN 77 computer program that fits one of the members of a set of six multivariable mathematical models (five of which are nonlinear) to a multivariable set of data. The inputs to MULTIVAR include the data for the independent and dependent variables plus the user s choice of one of the models, one of the three optimization engines, and convergence criteria. By use of the chosen optimization engine, MULTIVAR finds values for the parameters of the chosen model so as to minimize the sum of squares of the residuals. One of the optimization engines implements a routine, developed in 1982, that utilizes the Broydon-Fletcher-Goldfarb-Shanno (BFGS) variable-metric method for unconstrained minimization in conjunction with a one-dimensional search technique that finds the minimum of an unconstrained function by polynomial interpolation and extrapolation without first finding bounds on the solution. The second optimization engine is a faster and more robust commercially available code, denoted Design Optimization Tool, that also uses the BFGS method. The third optimization engine is a robust and relatively fast routine that implements the Levenberg-Marquardt algorithm.

SU-E-T-562: Motion Tracking Optimization for Conformal Arc Radiotherapy Plans: A QUASAR Phantom Based Study

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

Xu, Z; Wang, I; Yao, R

Purpose: This study is to use plan parameters optimization (Dose rate, collimator angle, couch angle, initial starting phase) to improve the performance of conformal arc radiotherapy plans with motion tracking by increasing the plan performance score (PPS). Methods: Two types of 3D conformal arc plans were created based on QUASAR respiratory motion phantom with spherical and cylindrical targets. Sinusoidal model was applied to the MLC leaves to generate motion tracking plans. A MATLAB program was developed to calculate PPS of each plan (ranges from 0–1) and optimize plan parameters. We first selected the dose rate for motion tracking plans andmore » then used simulated annealing algorithm to search for the combination of the other parameters that resulted in the plan of the maximal PPS. The optimized motion tracking plan was delivered by Varian Truebeam Linac. In-room cameras and stopwatch were used for starting phase selection and synchronization between phantom motion and plan delivery. Gaf-EBT2 dosimetry films were used to measure the dose delivered to the target in QUASAR phantom. Dose profiles and Truebeam trajectory log files were used for plan delivery performance evaluation. Results: For spherical target, the maximal PPS (PPSsph) of the optimized plan was 0.79: (Dose rate: 500MU/min, Collimator: 90°, Couch: +10°, starting phase: 0.83π). For cylindrical target, the maximal PPScyl was 0.75 (Dose rate: 300MU/min, Collimator: 87°, starting phase: 0.97π) with couch at 0°. Differences of dose profiles between motion tracking plans (with the maximal and the minimal PPS) and 3D conformal plans were as follows: PPSsph=0.79: %ΔFWHM: 8.9%, %Dmax: 3.1%; PPSsph=0.52: %ΔFWHM: 10.4%, %Dmax: 6.1%. PPScyl=0.75: %ΔFWHM: 4.7%, %Dmax: 3.6%; PPScyl=0.42: %ΔFWHM: 12.5%, %Dmax: 9.6%. Conclusion: By achieving high plan performance score through parameters optimization, we can improve target dose conformity of motion tracking plan by decreasing total MLC leaf travel distance and leaf speed.« less

Portfolio optimization with skewness and kurtosis

NASA Astrophysics Data System (ADS)

Lam, Weng Hoe; Jaaman, Saiful Hafizah Hj.; Isa, Zaidi

2013-04-01

Mean and variance of return distributions are two important parameters of the mean-variance model in portfolio optimization. However, the mean-variance model will become inadequate if the returns of assets are not normally distributed. Therefore, higher moments such as skewness and kurtosis cannot be ignored. Risk averse investors prefer portfolios with high skewness and low kurtosis so that the probability of getting negative rates of return will be reduced. The objective of this study is to compare the portfolio compositions as well as performances between the mean-variance model and mean-variance-skewness-kurtosis model by using the polynomial goal programming approach. The results show that the incorporation of skewness and kurtosis will change the optimal portfolio compositions. The mean-variance-skewness-kurtosis model outperforms the mean-variance model because the mean-variance-skewness-kurtosis model takes skewness and kurtosis into consideration. Therefore, the mean-variance-skewness-kurtosis model is more appropriate for the investors of Malaysia in portfolio optimization.

Photovoltaic design optimization for terrestrial applications

NASA Technical Reports Server (NTRS)

Ross, R. G., Jr.

1978-01-01

As part of the Jet Propulsion Laboratory's Low-Cost Solar Array Project, a comprehensive program of module cost-optimization has been carried out. The objective of these studies has been to define means of reducing the cost and improving the utility and reliability of photovoltaic modules for the broad spectrum of terrestrial applications. This paper describes one of the methods being used for module optimization, including the derivation of specific equations which allow the optimization of various module design features. The method is based on minimizing the life-cycle cost of energy for the complete system. Comparison of the life-cycle energy cost with the marginal cost of energy each year allows the logical plant lifetime to be determined. The equations derived allow the explicit inclusion of design parameters such as tracking, site variability, and module degradation with time. An example problem involving the selection of an optimum module glass substrate is presented.

Technology forecasting for space communication. Task one report: Cost and weight tradeoff studies for EOS and TDRS

NASA Technical Reports Server (NTRS)

1974-01-01

Weight and cost optimized EOS communication links are determined for 2.25, 7.25, 14.5, 21, and 60 GHz systems and for a 10.6 micron homodyne detection laser system. EOS to ground links are examined for 556, 834, and 1112 km EOS orbits, with ground terminals at the Network Test and Tracking Facility and at Goldstone. Optimized 21 GHz and 10.6 micron links are also examined. For the EOS to Tracking and Data Relay Satellite to ground link, signal-to-noise ratios of the uplink and downlink are also optimized for minimum overall cost or spaceborne weight. Finally, the optimized 21 GHz EOS to ground link is determined for various precipitation rates. All system performance parameters and mission dependent constraints are presented, as are the system cost and weight functional dependencies. The features and capabilities of the computer program to perform the foregoing analyses are described.

Optimal Policy of Cross-Layer Design for Channel Access and Transmission Rate Adaptation in Cognitive Radio Networks

NASA Astrophysics Data System (ADS)

He, Hao; Wang, Jun; Zhu, Jiang; Li, Shaoqian

2010-12-01

In this paper, we investigate the cross-layer design of joint channel access and transmission rate adaptation in CR networks with multiple channels for both centralized and decentralized cases. Our target is to maximize the throughput of CR network under transmission power constraint by taking spectrum sensing errors into account. In centralized case, this problem is formulated as a special constrained Markov decision process (CMDP), which can be solved by standard linear programming (LP) method. As the complexity of finding the optimal policy by LP increases exponentially with the size of action space and state space, we further apply action set reduction and state aggregation to reduce the complexity without loss of optimality. Meanwhile, for the convenience of implementation, we also consider the pure policy design and analyze the corresponding characteristics. In decentralized case, where only local information is available and there is no coordination among the CR users, we prove the existence of the constrained Nash equilibrium and obtain the optimal decentralized policy. Finally, in the case that the traffic load parameters of the licensed users are unknown for the CR users, we propose two methods to estimate the parameters for two different cases. Numerical results validate the theoretic analysis.

Optimization of a hardware implementation for pulse coupled neural networks for image applications

NASA Astrophysics Data System (ADS)

Gimeno Sarciada, Jesús; Lamela Rivera, Horacio; Warde, Cardinal

2010-04-01

Pulse Coupled Neural Networks are a very useful tool for image processing and visual applications, since it has the advantages of being invariant to image changes as rotation, scale, or certain distortion. Among other characteristics, the PCNN changes a given image input into a temporal representation which can be easily later analyzed for pattern recognition. The structure of a PCNN though, makes it necessary to determine all of its parameters very carefully in order to function optimally, so that the responses to the kind of inputs it will be subjected are clearly discriminated allowing for an easy and fast post-processing yielding useful results. This tweaking of the system is a taxing process. In this paper we analyze and compare two methods for modeling PCNNs. A purely mathematical model is programmed and a similar circuital model is also designed. Both are then used to determine the optimal values of the several parameters of a PCNN: gain, threshold, time constants for feed-in and threshold and linking leading to an optimal design for image recognition. The results are compared for usefulness, accuracy and speed, as well as the performance and time requirements for fast and easy design, thus providing a tool for future ease of management of a PCNN for different tasks.

Protofit: A program for determining surface protonation constants from titration data

NASA Astrophysics Data System (ADS)

Turner, Benjamin F.; Fein, Jeremy B.

2006-11-01

Determining the surface protonation behavior of natural adsorbents is essential to understand how they interact with their environments. ProtoFit is a tool for analysis of acid-base titration data and optimization of surface protonation models. The program offers a number of useful features including: (1) enables visualization of adsorbent buffering behavior; (2) uses an optimization approach independent of starting titration conditions or initial surface charge; (3) does not require an initial surface charge to be defined or to be treated as an optimizable parameter; (4) includes an error analysis intrinsically as part of the computational methods; and (5) generates simulated titration curves for comparison with observation. ProtoFit will typically be run through ProtoFit-GUI, a graphical user interface providing user-friendly control of model optimization, simulation, and data visualization. ProtoFit calculates an adsorbent proton buffering value as a function of pH from raw titration data (including pH and volume of acid or base added). The data is reduced to a form where the protons required to change the pH of the solution are subtracted out, leaving protons exchanged between solution and surface per unit mass of adsorbent as a function of pH. The buffering intensity function Qads* is calculated as the instantaneous slope of this reduced titration curve. Parameters for a surface complexation model are obtained by minimizing the sum of squares between the modeled (i.e. simulated) buffering intensity curve and the experimental data. The variance in the slope estimate, intrinsically produced as part of the Qads* calculation, can be used to weight the sum of squares calculation between the measured buffering intensity and a simulated curve. Effects of analytical error on data visualization and model optimization are discussed. Examples are provided of using ProtoFit for data visualization, model optimization, and model evaluation.

A hybrid inventory management system respondingto regular demand and surge demand

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

Mohammad S. Roni; Mingzhou Jin; Sandra D. Eksioglu

2014-06-01

This paper proposes a hybrid policy for a stochastic inventory system facing regular demand and surge demand. The combination of two different demand patterns can be observed in many areas, such as healthcare inventory and humanitarian supply chain management. The surge demand has a lower arrival rate but higher demand volume per arrival. The solution approach proposed in this paper incorporates the level crossing method and mixed integer programming technique to optimize the hybrid inventory policy with both regular orders and emergency orders. The level crossing method is applied to obtain the equilibrium distributions of inventory levels under a givenmore » policy. The model is further transformed into a mixed integer program to identify an optimal hybrid policy. A sensitivity analysis is conducted to investigate the impact of parameters on the optimal inventory policy and minimum cost. Numerical results clearly show the benefit of using the proposed hybrid inventory model. The model and solution approach could help healthcare providers or humanitarian logistics providers in managing their emergency supplies in responding to surge demands.« less

Algorithms for Maneuvering Spacecraft Around Small Bodies

NASA Technical Reports Server (NTRS)

Acikmese, A. Bechet; Bayard, David

2006-01-01

A document describes mathematical derivations and applications of autonomous guidance algorithms for maneuvering spacecraft in the vicinities of small astronomical bodies like comets or asteroids. These algorithms compute fuel- or energy-optimal trajectories for typical maneuvers by solving the associated optimal-control problems with relevant control and state constraints. In the derivations, these problems are converted from their original continuous (infinite-dimensional) forms to finite-dimensional forms through (1) discretization of the time axis and (2) spectral discretization of control inputs via a finite number of Chebyshev basis functions. In these doubly discretized problems, the Chebyshev coefficients are the variables. These problems are, variously, either convex programming problems or programming problems that can be convexified. The resulting discrete problems are convex parameter-optimization problems; this is desirable because one can take advantage of very efficient and robust algorithms that have been developed previously and are well established for solving such problems. These algorithms are fast, do not require initial guesses, and always converge to global optima. Following the derivations, the algorithms are demonstrated by applying them to numerical examples of flyby, descent-to-hover, and ascent-from-hover maneuvers.

Machining Parameters Optimization using Hybrid Firefly Algorithm and Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Farahlina Johari, Nur; Zain, Azlan Mohd; Haszlinna Mustaffa, Noorfa; Udin, Amirmudin

2017-09-01

Firefly Algorithm (FA) is a metaheuristic algorithm that is inspired by the flashing behavior of fireflies and the phenomenon of bioluminescent communication and the algorithm is used to optimize the machining parameters (feed rate, depth of cut, and spindle speed) in this research. The algorithm is hybridized with Particle Swarm Optimization (PSO) to discover better solution in exploring the search space. Objective function of previous research is used to optimize the machining parameters in turning operation. The optimal machining cutting parameters estimated by FA that lead to a minimum surface roughness are validated using ANOVA test.

Bayesian-MCMC-based parameter estimation of stealth aircraft RCS models

NASA Astrophysics Data System (ADS)

Xia, Wei; Dai, Xiao-Xia; Feng, Yuan

2015-12-01

When modeling a stealth aircraft with low RCS (Radar Cross Section), conventional parameter estimation methods may cause a deviation from the actual distribution, owing to the fact that the characteristic parameters are estimated via directly calculating the statistics of RCS. The Bayesian-Markov Chain Monte Carlo (Bayesian-MCMC) method is introduced herein to estimate the parameters so as to improve the fitting accuracies of fluctuation models. The parameter estimations of the lognormal and the Legendre polynomial models are reformulated in the Bayesian framework. The MCMC algorithm is then adopted to calculate the parameter estimates. Numerical results show that the distribution curves obtained by the proposed method exhibit improved consistence with the actual ones, compared with those fitted by the conventional method. The fitting accuracy could be improved by no less than 25% for both fluctuation models, which implies that the Bayesian-MCMC method might be a good candidate among the optimal parameter estimation methods for stealth aircraft RCS models. Project supported by the National Natural Science Foundation of China (Grant No. 61101173), the National Basic Research Program of China (Grant No. 613206), the National High Technology Research and Development Program of China (Grant No. 2012AA01A308), the State Scholarship Fund by the China Scholarship Council (CSC), and the Oversea Academic Training Funds, and University of Electronic Science and Technology of China (UESTC).

Optimizing noise control strategy in a forging workshop.

PubMed

Razavi, Hamideh; Ramazanifar, Ehsan; Bagherzadeh, Jalal

2014-01-01

In this paper, a computer program based on a genetic algorithm is developed to find an economic solution for noise control in a forging workshop. Initially, input data, including characteristics of sound sources, human exposure, abatement techniques, and production plans are inserted into the model. Using sound pressure levels at working locations, the operators who are at higher risk are identified and picked out for the next step. The program is devised in MATLAB such that the parameters can be easily defined and changed for comparison. The final results are structured into 4 sections that specify an appropriate abatement method for each operator and machine, minimum allowance time for high-risk operators, required damping material for enclosures, and minimum total cost of these treatments. The validity of input data in addition to proper settings in the optimization model ensures the final solution is practical and economically reasonable.

River water quality management considering agricultural return flows: application of a nonlinear two-stage stochastic fuzzy programming.

PubMed

Tavakoli, Ali; Nikoo, Mohammad Reza; Kerachian, Reza; Soltani, Maryam

2015-04-01

In this paper, a new fuzzy methodology is developed to optimize water and waste load allocation (WWLA) in rivers under uncertainty. An interactive two-stage stochastic fuzzy programming (ITSFP) method is utilized to handle parameter uncertainties, which are expressed as fuzzy boundary intervals. An iterative linear programming (ILP) is also used for solving the nonlinear optimization model. To accurately consider the impacts of the water and waste load allocation strategies on the river water quality, a calibrated QUAL2Kw model is linked with the WWLA optimization model. The soil, water, atmosphere, and plant (SWAP) simulation model is utilized to determine the quantity and quality of each agricultural return flow. To control pollution loads of agricultural networks, it is assumed that a part of each agricultural return flow can be diverted to an evaporation pond and also another part of it can be stored in a detention pond. In detention ponds, contaminated water is exposed to solar radiation for disinfecting pathogens. Results of applying the proposed methodology to the Dez River system in the southwestern region of Iran illustrate its effectiveness and applicability for water and waste load allocation in rivers. In the planning phase, this methodology can be used for estimating the capacities of return flow diversion system and evaporation and detention ponds.

Optimizing decentralized production-distribution planning problem in a multi-period supply chain network under uncertainty

NASA Astrophysics Data System (ADS)

Nourifar, Raheleh; Mahdavi, Iraj; Mahdavi-Amiri, Nezam; Paydar, Mohammad Mahdi

2017-09-01

Decentralized supply chain management is found to be significantly relevant in today's competitive markets. Production and distribution planning is posed as an important optimization problem in supply chain networks. Here, we propose a multi-period decentralized supply chain network model with uncertainty. The imprecision related to uncertain parameters like demand and price of the final product is appropriated with stochastic and fuzzy numbers. We provide mathematical formulation of the problem as a bi-level mixed integer linear programming model. Due to problem's convolution, a structure to solve is developed that incorporates a novel heuristic algorithm based on Kth-best algorithm, fuzzy approach and chance constraint approach. Ultimately, a numerical example is constructed and worked through to demonstrate applicability of the optimization model. A sensitivity analysis is also made.

Cyber-Physical Attacks With Control Objectives

DOE PAGES

Chen, Yuan; Kar, Soummya; Moura, Jose M. F.

2017-08-18

This study studies attackers with control objectives against cyber-physical systems (CPSs). The goal of the attacker is to counteract the CPS's controller and move the system to a target state while evading detection. We formulate a cost function that reflects the attacker's goals, and, using dynamic programming, we show that the optimal attack strategy reduces to a linear feedback of the attacker's state estimate. By changing the parameters of the cost function, we show how an attacker can design optimal attacks to balance the control objective and the detection avoidance objective. In conclusion, we provide a numerical illustration based onmore » a remotely controlled helicopter under attack.« less

Cyber-Physical Attacks With Control Objectives

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

Chen, Yuan; Kar, Soummya; Moura, Jose M. F.

This study studies attackers with control objectives against cyber-physical systems (CPSs). The goal of the attacker is to counteract the CPS's controller and move the system to a target state while evading detection. We formulate a cost function that reflects the attacker's goals, and, using dynamic programming, we show that the optimal attack strategy reduces to a linear feedback of the attacker's state estimate. By changing the parameters of the cost function, we show how an attacker can design optimal attacks to balance the control objective and the detection avoidance objective. In conclusion, we provide a numerical illustration based onmore » a remotely controlled helicopter under attack.« less

Simultaneous Intrinsic and Extrinsic Parameter Identification of a Hand-Mounted Laser-Vision Sensor

PubMed Central

Lee, Jong Kwang; Kim, Kiho; Lee, Yongseok; Jeong, Taikyeong

2011-01-01

In this paper, we propose a simultaneous intrinsic and extrinsic parameter identification of a hand-mounted laser-vision sensor (HMLVS). A laser-vision sensor (LVS), consisting of a camera and a laser stripe projector, is used as a sensor component of the robotic measurement system, and it measures the range data with respect to the robot base frame using the robot forward kinematics and the optical triangulation principle. For the optimal estimation of the model parameters, we applied two optimization techniques: a nonlinear least square optimizer and a particle swarm optimizer. Best-fit parameters, including both the intrinsic and extrinsic parameters of the HMLVS, are simultaneously obtained based on the least-squares criterion. From the simulation and experimental results, it is shown that the parameter identification problem considered was characterized by a highly multimodal landscape; thus, the global optimization technique such as a particle swarm optimization can be a promising tool to identify the model parameters for a HMLVS, while the nonlinear least square optimizer often failed to find an optimal solution even when the initial candidate solutions were selected close to the true optimum. The proposed optimization method does not require good initial guesses of the system parameters to converge at a very stable solution and it could be applied to a kinematically dissimilar robot system without loss of generality. PMID:22164104

Aircraft wing structural design optimization based on automated finite element modelling and ground structure approach

NASA Astrophysics Data System (ADS)

Yang, Weizhu; Yue, Zhufeng; Li, Lei; Wang, Peiyan

2016-01-01

An optimization procedure combining an automated finite element modelling (AFEM) technique with a ground structure approach (GSA) is proposed for structural layout and sizing design of aircraft wings. The AFEM technique, based on CATIA VBA scripting and PCL programming, is used to generate models automatically considering the arrangement of inner systems. GSA is used for local structural topology optimization. The design procedure is applied to a high-aspect-ratio wing. The arrangement of the integral fuel tank, landing gear and control surfaces is considered. For the landing gear region, a non-conventional initial structural layout is adopted. The positions of components, the number of ribs and local topology in the wing box and landing gear region are optimized to obtain a minimum structural weight. Constraints include tank volume, strength, buckling and aeroelastic parameters. The results show that the combined approach leads to a greater weight saving, i.e. 26.5%, compared with three additional optimizations based on individual design approaches.

Practical automated glass selection and the design of apochromats with large field of view.

PubMed

Siew, Ronian

2016-11-10

This paper presents an automated approach to the selection of optical glasses for the design of an apochromatic lens with large field of view, based on a design originally provided by Yang et al. [Appl. Opt.55, 5977 (2016)APOPAI0003-693510.1364/AO.55.005977]. Following from this reference's preliminary optimized structure, it is shown that the effort of glass selection is significantly reduced by using the global optimization feature in the Zemax optical design program. The glass selection process is very fast, complete within minutes, and the key lies in automating the substitution of glasses found from the global search without the need to simultaneously optimize any other lens parameter during the glass search. The result is an alternate optimized version of the lens from the above reference possessing zero axial secondary color within the visible spectrum and a large field of view. Supplementary material is provided in the form of Zemax and text files, before and after final optimization.

Optimization of transfer trajectories to the Apophis asteroid for spacecraft with high and low thrust

NASA Astrophysics Data System (ADS)

Ivashkin, V. V.; Krylov, I. V.

2014-03-01

The problem of optimization of a spacecraft transfer to the Apophis asteroid is investigated. The scheme of transfer under analysis includes a geocentric stage of boosting the spacecraft with high thrust, a heliocentric stage of control by a low thrust engine, and a stage of deceleration with injection to an orbit of the asteroid's satellite. In doing this, the problem of optimal control is solved for cases of ideal and piecewise-constant low thrust, and the optimal magnitude and direction of spacecraft's hyperbolic velocity "at infinity" during departure from the Earth are determined. The spacecraft trajectories are found based on a specially developed comprehensive method of optimization. This method combines the method of dynamic programming at the first stage of analysis and the Pontryagin maximum principle at the concluding stage, together with the parameter continuation method. The estimates are obtained for the spacecraft's final mass and for the payload mass that can be delivered to the asteroid using the Soyuz-Fregat carrier launcher.

Hierarchical Winner-Take-All Particle Swarm Optimization Social Network for Neural Model Fitting

PubMed Central

Coventry, Brandon S.; Parthasarathy, Aravindakshan; Sommer, Alexandra L.; Bartlett, Edward L.

2016-01-01

Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models. PMID:27726048

Parameter meta-optimization of metaheuristics of solving specific NP-hard facility location problem

NASA Astrophysics Data System (ADS)

Skakov, E. S.; Malysh, V. N.

2018-03-01

The aim of the work is to create an evolutionary method for optimizing the values of the control parameters of metaheuristics of solving the NP-hard facility location problem. A system analysis of the tuning process of optimization algorithms parameters is carried out. The problem of finding the parameters of a metaheuristic algorithm is formulated as a meta-optimization problem. Evolutionary metaheuristic has been chosen to perform the task of meta-optimization. Thus, the approach proposed in this work can be called “meta-metaheuristic”. Computational experiment proving the effectiveness of the procedure of tuning the control parameters of metaheuristics has been performed.

Basic research for the geodynamics program

NASA Technical Reports Server (NTRS)

1984-01-01

Some objectives of this geodynamic program are: (1) optimal utilization of laser and VLBI observations as reference frames for geodynamics, (2) utilization of range difference observations in geodynamics, and (3) estimation techniques in crustal deformation analysis. The determination of Earth rotation parameters from different space geodetic systems is studied. Also reported on is the utilization of simultaneous laser range differences for the determination of baseline variation. An algorithm for the analysis of regional or local crustal deformation measurements is proposed along with other techniques and testing procedures. Some results of the reference from comparisons in terms of the pole coordinates from different techniques are presented.

Mutually unbiased bases and semi-definite programming

NASA Astrophysics Data System (ADS)

Brierley, Stephen; Weigert, Stefan

2010-11-01

A complex Hilbert space of dimension six supports at least three but not more than seven mutually unbiased bases. Two computer-aided analytical methods to tighten these bounds are reviewed, based on a discretization of parameter space and on Gröbner bases. A third algorithmic approach is presented: the non-existence of more than three mutually unbiased bases in composite dimensions can be decided by a global optimization method known as semidefinite programming. The method is used to confirm that the spectral matrix cannot be part of a complete set of seven mutually unbiased bases in dimension six.

Research on degradation of omethoate with Y2O3:Er3+ and TiO2

NASA Astrophysics Data System (ADS)

Liu, Zhiping; Mai, Yanling; Yan, Aiguo; Fan, Hailu; Yuan, Taidou

2018-06-01

Application of visible light excited photocatalytic degradation reagent of pesticide residues is not only suitable for the farmers, can also be used for city residents for daily use. Up conversion material Y2O3:Er3+ was prepared by sol gel method, then mixed with anatase TiO2 sol solution, to carry out the research of omethoate degradation under visible light. In order to get the higher degradability, it's important to study the technological parameters. Among so many parameters, four parameters were selected. They were vegetable surface omethoate concentration, photocatalytic degradation reagent dosage, pH value and degradation time. Utilizing orthogonal experimental design program, all parameters were optimized. The results showed that: the degradation rate was the largest concerned with the vegetable surface omethoate concentration, and then the degradation time.

Multi-objective optimization of combustion, performance and emission parameters in a jatropha biodiesel engine using Non-dominated sorting genetic algorithm-II

NASA Astrophysics Data System (ADS)

Dhingra, Sunil; Bhushan, Gian; Dubey, Kashyap Kumar

2014-03-01

The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition (CI) engine using jatropha biodiesel. Response surface methodology based on Central composite design (CCD) is used to design the experiments. Mathematical models are developed for combustion parameters (Brake specific fuel consumption (BSFC) and peak cylinder pressure (Pmax)), performance parameter brake thermal efficiency (BTE) and emission parameters (CO, NO x , unburnt HC and smoke) using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion (BSFC, Pmax), performance (BTE) and emission (CO, NO x , HC, smoke) parameters. As the objective is to maximize BTE and minimize BSFC, Pmax, CO, NO x , HC, smoke, a multiobjective optimization problem is formulated. Nondominated sorting genetic algorithm-II is used in predicting the Pareto optimal sets of solution. Experiments are performed at suitable optimal solutions for predicting the combustion, performance and emission parameters to check the adequacy of the proposed model. The Pareto optimal sets of solution can be used as guidelines for the end users to select optimal combination of engine output and emission parameters depending upon their own requirements.

JPL control/structure interaction test bed real-time control computer architecture

NASA Technical Reports Server (NTRS)

Briggs, Hugh C.

1989-01-01

The Control/Structure Interaction Program is a technology development program for spacecraft that exhibit interactions between the control system and structural dynamics. The program objectives include development and verification of new design concepts - such as active structure - and new tools - such as combined structure and control optimization algorithm - and their verification in ground and possibly flight test. A focus mission spacecraft was designed based upon a space interferometer and is the basis for design of the ground test article. The ground test bed objectives include verification of the spacecraft design concepts, the active structure elements and certain design tools such as the new combined structures and controls optimization tool. In anticipation of CSI technology flight experiments, the test bed control electronics must emulate the computation capacity and control architectures of space qualifiable systems as well as the command and control networks that will be used to connect investigators with the flight experiment hardware. The Test Bed facility electronics were functionally partitioned into three units: a laboratory data acquisition system for structural parameter identification and performance verification; an experiment supervisory computer to oversee the experiment, monitor the environmental parameters and perform data logging; and a multilevel real-time control computing system. The design of the Test Bed electronics is presented along with hardware and software component descriptions. The system should break new ground in experimental control electronics and is of interest to anyone working in the verification of control concepts for large structures.

Optimal Designs for the Rasch Model

ERIC Educational Resources Information Center

Grasshoff, Ulrike; Holling, Heinz; Schwabe, Rainer

2012-01-01

In this paper, optimal designs will be derived for estimating the ability parameters of the Rasch model when difficulty parameters are known. It is well established that a design is locally D-optimal if the ability and difficulty coincide. But locally optimal designs require that the ability parameters to be estimated are known. To attenuate this…

Optimization Under Uncertainty for Electronics Cooling Design

NASA Astrophysics Data System (ADS)

Bodla, Karthik K.; Murthy, Jayathi Y.; Garimella, Suresh V.

Optimization under uncertainty is a powerful methodology used in design and optimization to produce robust, reliable designs. Such an optimization methodology, employed when the input quantities of interest are uncertain, produces output uncertainties, helping the designer choose input parameters that would result in satisfactory thermal solutions. Apart from providing basic statistical information such as mean and standard deviation in the output quantities, auxiliary data from an uncertainty based optimization, such as local and global sensitivities, help the designer decide the input parameter(s) to which the output quantity of interest is most sensitive. This helps the design of experiments based on the most sensitive input parameter(s). A further crucial output of such a methodology is the solution to the inverse problem - finding the allowable uncertainty range in the input parameter(s), given an acceptable uncertainty range in the output quantity of interest...

Design and Evaluation of a Dynamic Programming Flight Routing Algorithm Using the Convective Weather Avoidance Model

NASA Technical Reports Server (NTRS)

Ng, Hok K.; Grabbe, Shon; Mukherjee, Avijit

2010-01-01

The optimization of traffic flows in congested airspace with varying convective weather is a challenging problem. One approach is to generate shortest routes between origins and destinations while meeting airspace capacity constraint in the presence of uncertainties, such as weather and airspace demand. This study focuses on development of an optimal flight path search algorithm that optimizes national airspace system throughput and efficiency in the presence of uncertainties. The algorithm is based on dynamic programming and utilizes the predicted probability that an aircraft will deviate around convective weather. It is shown that the running time of the algorithm increases linearly with the total number of links between all stages. The optimal routes minimize a combination of fuel cost and expected cost of route deviation due to convective weather. They are considered as alternatives to the set of coded departure routes which are predefined by FAA to reroute pre-departure flights around weather or air traffic constraints. A formula, which calculates predicted probability of deviation from a given flight path, is also derived. The predicted probability of deviation is calculated for all path candidates. Routes with the best probability are selected as optimal. The predicted probability of deviation serves as a computable measure of reliability in pre-departure rerouting. The algorithm can also be extended to automatically adjust its design parameters to satisfy the desired level of reliability.

Study on loading path optimization of internal high pressure forming process

NASA Astrophysics Data System (ADS)

Jiang, Shufeng; Zhu, Hengda; Gao, Fusheng

2017-09-01

In the process of internal high pressure forming, there is no formula to describe the process parameters and forming results. The article use numerical simulation to obtain several input parameters and corresponding output result, use the BP neural network to found their mapping relationship, and with weighted summing method make each evaluating parameters to set up a formula which can evaluate quality. Then put the training BP neural network into the particle swarm optimization, and take the evaluating formula of the quality as adapting formula of particle swarm optimization, finally do the optimization and research at the range of each parameters. The results show that the parameters obtained by the BP neural network algorithm and the particle swarm optimization algorithm can meet the practical requirements. The method can solve the optimization of the process parameters in the internal high pressure forming process.

Aspects of job scheduling

NASA Technical Reports Server (NTRS)

Phillips, K.

1976-01-01

A mathematical model for job scheduling in a specified context is presented. The model uses both linear programming and combinatorial methods. While designed with a view toward optimization of scheduling of facility and plant operations at the Deep Space Communications Complex, the context is sufficiently general to be widely applicable. The general scheduling problem including options for scheduling objectives is discussed and fundamental parameters identified. Mathematical algorithms for partitioning problems germane to scheduling are presented.

Controllability of semi-infinite rod heating by a point source

NASA Astrophysics Data System (ADS)

Khurshudyan, A.

2018-04-01

The possibility of control over heating of a semi-infinite thin rod by a point source concentrated at an inner point of the rod, is studied. Quadratic and piecewise constant solutions of the problem are derived, and the possibilities of solving appropriate problems of optimal control are indicated. Determining of the parameters of the piecewise constant solution is reduced to a problem of nonlinear programming. Numerical examples are considered.

Automatic control of finite element models for temperature-controlled radiofrequency ablation.

PubMed

Haemmerich, Dieter; Webster, John G

2005-07-14

The finite element method (FEM) has been used to simulate cardiac and hepatic radiofrequency (RF) ablation. The FEM allows modeling of complex geometries that cannot be solved by analytical methods or finite difference models. In both hepatic and cardiac RF ablation a common control mode is temperature-controlled mode. Commercial FEM packages don't support automating temperature control. Most researchers manually control the applied power by trial and error to keep the tip temperature of the electrodes constant. We implemented a PI controller in a control program written in C++. The program checks the tip temperature after each step and controls the applied voltage to keep temperature constant. We created a closed loop system consisting of a FEM model and the software controlling the applied voltage. The control parameters for the controller were optimized using a closed loop system simulation. We present results of a temperature controlled 3-D FEM model of a RITA model 30 electrode. The control software effectively controlled applied voltage in the FEM model to obtain, and keep electrodes at target temperature of 100 degrees C. The closed loop system simulation output closely correlated with the FEM model, and allowed us to optimize control parameters. The closed loop control of the FEM model allowed us to implement temperature controlled RF ablation with minimal user input.

Legacy model integration for enhancing hydrologic interdisciplinary research

NASA Astrophysics Data System (ADS)

Dozier, A.; Arabi, M.; David, O.

2013-12-01

Many challenges are introduced to interdisciplinary research in and around the hydrologic science community due to advances in computing technology and modeling capabilities in different programming languages, across different platforms and frameworks by researchers in a variety of fields with a variety of experience in computer programming. Many new hydrologic models as well as optimization, parameter estimation, and uncertainty characterization techniques are developed in scripting languages such as Matlab, R, Python, or in newer languages such as Java and the .Net languages, whereas many legacy models have been written in FORTRAN and C, which complicates inter-model communication for two-way feedbacks. However, most hydrologic researchers and industry personnel have little knowledge of the computing technologies that are available to address the model integration process. Therefore, the goal of this study is to address these new challenges by utilizing a novel approach based on a publish-subscribe-type system to enhance modeling capabilities of legacy socio-economic, hydrologic, and ecologic software. Enhancements include massive parallelization of executions and access to legacy model variables at any point during the simulation process by another program without having to compile all the models together into an inseparable 'super-model'. Thus, this study provides two-way feedback mechanisms between multiple different process models that can be written in various programming languages and can run on different machines and operating systems. Additionally, a level of abstraction is given to the model integration process that allows researchers and other technical personnel to perform more detailed and interactive modeling, visualization, optimization, calibration, and uncertainty analysis without requiring deep understanding of inter-process communication. To be compatible, a program must be written in a programming language with bindings to a common implementation of the message passing interface (MPI), which includes FORTRAN, C, Java, the .NET languages, Python, R, Matlab, and many others. The system is tested on a longstanding legacy hydrologic model, the Soil and Water Assessment Tool (SWAT), to observe and enhance speed-up capabilities for various optimization, parameter estimation, and model uncertainty characterization techniques, which is particularly important for computationally intensive hydrologic simulations. Initial results indicate that the legacy extension system significantly decreases developer time, computation time, and the cost of purchasing commercial parallel processing licenses, while enhancing interdisciplinary research by providing detailed two-way feedback mechanisms between various process models with minimal changes to legacy code.

Chaos minimization in DC-DC boost converter using circuit parameter optimization

NASA Astrophysics Data System (ADS)

Sudhakar, N.; Natarajan, Rajasekar; Gourav, Kumar; Padmavathi, P.

2017-11-01

DC-DC converters are prone to several types of nonlinear phenomena including bifurcation, quasi periodicity, intermittency and chaos. These undesirable effects must be controlled for periodic operation of the converter to ensure the stability. In this paper an effective solution to control of chaos in solar fed DC-DC boost converter is proposed. Controlling of chaos is significantly achieved using optimal circuit parameters obtained through Bacterial Foraging Optimization Algorithm. The optimization renders the suitable parameters in minimum computational time. The obtained results are compared with the operation of traditional boost converter. Further the obtained results with BFA optimized parameter ensures the operations of the converter are within the controllable region. To elaborate the study of bifurcation analysis with optimized and unoptimized parameters are also presented.

Adaptive Self-Tuning Networks

NASA Astrophysics Data System (ADS)

Knox, H. A.; Draelos, T.; Young, C. J.; Lawry, B.; Chael, E. P.; Faust, A.; Peterson, M. G.

2015-12-01

The quality of automatic detections from seismic sensor networks depends on a large number of data processing parameters that interact in complex ways. The largely manual process of identifying effective parameters is painstaking and does not guarantee that the resulting controls are the optimal configuration settings. Yet, achieving superior automatic detection of seismic events is closely related to these parameters. We present an automated sensor tuning (AST) system that learns near-optimal parameter settings for each event type using neuro-dynamic programming (reinforcement learning) trained with historic data. AST learns to test the raw signal against all event-settings and automatically self-tunes to an emerging event in real-time. The overall goal is to reduce the number of missed legitimate event detections and the number of false event detections. Reducing false alarms early in the seismic pipeline processing will have a significant impact on this goal. Applicable both for existing sensor performance boosting and new sensor deployment, this system provides an important new method to automatically tune complex remote sensing systems. Systems tuned in this way will achieve better performance than is currently possible by manual tuning, and with much less time and effort devoted to the tuning process. With ground truth on detections in seismic waveforms from a network of stations, we show that AST increases the probability of detection while decreasing false alarms.

Analytical investigations in aircraft and spacecraft trajectory optimization and optimal guidance

NASA Technical Reports Server (NTRS)

Markopoulos, Nikos; Calise, Anthony J.

1995-01-01

A collection of analytical studies is presented related to unconstrained and constrained aircraft (a/c) energy-state modeling and to spacecraft (s/c) motion under continuous thrust. With regard to a/c unconstrained energy-state modeling, the physical origin of the singular perturbation parameter that accounts for the observed 2-time-scale behavior of a/c during energy climbs is identified and explained. With regard to the constrained energy-state modeling, optimal control problems are studied involving active state-variable inequality constraints. Departing from the practical deficiencies of the control programs for such problems that result from the traditional formulations, a complete reformulation is proposed for these problems which, in contrast to the old formulation, will presumably lead to practically useful controllers that can track an inequality constraint boundary asymptotically, and even in the presence of 2-sided perturbations about it. Finally, with regard to s/c motion under continuous thrust, a thrust program is proposed for which the equations of 2-dimensional motion of a space vehicle in orbit, viewed as a point mass, afford an exact analytic solution. The thrust program arises under the assumption of tangential thrust from the costate system corresponding to minimum-fuel, power-limited, coplanar transfers between two arbitrary conics. The thrust program can be used not only with power-limited propulsion systems, but also with any propulsion system capable of generating continuous thrust of controllable magnitude, and, for propulsion types and classes of transfers for which it is sufficiently optimal the results of this report suggest a method of maneuvering during planetocentric or heliocentric orbital operations, requiring a minimum amount of computation; thus uniquely suitable for real-time feedback guidance implementations.

Simulation of Planetary Formation using Python

NASA Astrophysics Data System (ADS)

Bufkin, James; Bixler, David

2015-03-01

A program to simulate planetary formation was developed in the Python programming language. The program consists of randomly placed and massed bodies surrounding a central massive object in order to approximate a protoplanetary disk. The orbits of these bodies are time-stepped, with accelerations, velocities and new positions calculated in each step. Bodies are allowed to merge if their disks intersect. Numerous parameters (orbital distance, masses, number of particles, etc.) were varied in order to optimize the program. The program uses an iterative difference equation approach to solve the equations of motion using a kinematic model. Conservation of energy and angular momentum are not specifically forced, but conservation of momentum is forced during the merging of bodies. The initial program was created in Visual Python (VPython) but the current intention is to allow for higher particle count and faster processing by utilizing PyOpenCl and PyOpenGl. Current results and progress will be reported.

Optimal Bayesian Adaptive Design for Test-Item Calibration.

PubMed

van der Linden, Wim J; Ren, Hao

2015-06-01

An optimal adaptive design for test-item calibration based on Bayesian optimality criteria is presented. The design adapts the choice of field-test items to the examinees taking an operational adaptive test using both the information in the posterior distributions of their ability parameters and the current posterior distributions of the field-test parameters. Different criteria of optimality based on the two types of posterior distributions are possible. The design can be implemented using an MCMC scheme with alternating stages of sampling from the posterior distributions of the test takers' ability parameters and the parameters of the field-test items while reusing samples from earlier posterior distributions of the other parameters. Results from a simulation study demonstrated the feasibility of the proposed MCMC implementation for operational item calibration. A comparison of performances for different optimality criteria showed faster calibration of substantial numbers of items for the criterion of D-optimality relative to A-optimality, a special case of c-optimality, and random assignment of items to the test takers.

Application of dragonfly algorithm for optimal performance analysis of process parameters in turn-mill operations- A case study

NASA Astrophysics Data System (ADS)

Vikram, K. Arun; Ratnam, Ch; Lakshmi, VVK; Kumar, A. Sunny; Ramakanth, RT

2018-02-01

Meta-heuristic multi-response optimization methods are widely in use to solve multi-objective problems to obtain Pareto optimal solutions during optimization. This work focuses on optimal multi-response evaluation of process parameters in generating responses like surface roughness (Ra), surface hardness (H) and tool vibration displacement amplitude (Vib) while performing operations like tangential and orthogonal turn-mill processes on A-axis Computer Numerical Control vertical milling center. Process parameters like tool speed, feed rate and depth of cut are considered as process parameters machined over brass material under dry condition with high speed steel end milling cutters using Taguchi design of experiments (DOE). Meta-heuristic like Dragonfly algorithm is used to optimize the multi-objectives like ‘Ra’, ‘H’ and ‘Vib’ to identify the optimal multi-response process parameters combination. Later, the results thus obtained from multi-objective dragonfly algorithm (MODA) are compared with another multi-response optimization technique Viz. Grey relational analysis (GRA).

Empirical Performance Model-Driven Data Layout Optimization and Library Call Selection for Tensor Contraction Expressions

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

Lu, Qingda; Gao, Xiaoyang; Krishnamoorthy, Sriram

Empirical optimizers like ATLAS have been very effective in optimizing computational kernels in libraries. The best choice of parameters such as tile size and degree of loop unrolling is determined by executing different versions of the computation. In contrast, optimizing compilers use a model-driven approach to program transformation. While the model-driven approach of optimizing compilers is generally orders of magnitude faster than ATLAS-like library generators, its effectiveness can be limited by the accuracy of the performance models used. In this paper, we describe an approach where a class of computations is modeled in terms of constituent operations that are empiricallymore » measured, thereby allowing modeling of the overall execution time. The performance model with empirically determined cost components is used to perform data layout optimization together with the selection of library calls and layout transformations in the context of the Tensor Contraction Engine, a compiler for a high-level domain-specific language for expressing computational models in quantum chemistry. The effectiveness of the approach is demonstrated through experimental measurements on representative computations from quantum chemistry.« less

Optimization Model for Web Based Multimodal Interactive Simulations.

PubMed

Halic, Tansel; Ahn, Woojin; De, Suvranu

2015-07-15

This paper presents a technique for optimizing the performance of web based multimodal interactive simulations. For such applications where visual quality and the performance of simulations directly influence user experience, overloading of hardware resources may result in unsatisfactory reduction in the quality of the simulation and user satisfaction. However, optimization of simulation performance on individual hardware platforms is not practical. Hence, we present a mixed integer programming model to optimize the performance of graphical rendering and simulation performance while satisfying application specific constraints. Our approach includes three distinct phases: identification, optimization and update . In the identification phase, the computing and rendering capabilities of the client device are evaluated using an exploratory proxy code. This data is utilized in conjunction with user specified design requirements in the optimization phase to ensure best possible computational resource allocation. The optimum solution is used for rendering (e.g. texture size, canvas resolution) and simulation parameters (e.g. simulation domain) in the update phase. Test results are presented on multiple hardware platforms with diverse computing and graphics capabilities to demonstrate the effectiveness of our approach.

Optimization Model for Web Based Multimodal Interactive Simulations

PubMed Central

Halic, Tansel; Ahn, Woojin; De, Suvranu

2015-01-01

This paper presents a technique for optimizing the performance of web based multimodal interactive simulations. For such applications where visual quality and the performance of simulations directly influence user experience, overloading of hardware resources may result in unsatisfactory reduction in the quality of the simulation and user satisfaction. However, optimization of simulation performance on individual hardware platforms is not practical. Hence, we present a mixed integer programming model to optimize the performance of graphical rendering and simulation performance while satisfying application specific constraints. Our approach includes three distinct phases: identification, optimization and update. In the identification phase, the computing and rendering capabilities of the client device are evaluated using an exploratory proxy code. This data is utilized in conjunction with user specified design requirements in the optimization phase to ensure best possible computational resource allocation. The optimum solution is used for rendering (e.g. texture size, canvas resolution) and simulation parameters (e.g. simulation domain) in the update phase. Test results are presented on multiple hardware platforms with diverse computing and graphics capabilities to demonstrate the effectiveness of our approach. PMID:26085713

Application of genetic algorithm in modeling on-wafer inductors for up to 110 Ghz

NASA Astrophysics Data System (ADS)

Liu, Nianhong; Fu, Jun; Liu, Hui; Cui, Wenpu; Liu, Zhihong; Liu, Linlin; Zhou, Wei; Wang, Quan; Guo, Ao

2018-05-01

In this work, the genetic algorithm has been introducted into parameter extraction for on-wafer inductors for up to 110 GHz millimeter-wave operations, and nine independent parameters of the equivalent circuit model are optimized together. With the genetic algorithm, the model with the optimized parameters gives a better fitting accuracy than the preliminary parameters without optimization. Especially, the fitting accuracy of the Q value achieves a significant improvement after the optimization.

Evidence of soft bound behaviour in analogue memristive devices for neuromorphic computing.

PubMed

Frascaroli, Jacopo; Brivio, Stefano; Covi, Erika; Spiga, Sabina

2018-05-08

The development of devices that can modulate their conductance under the application of electrical stimuli constitutes a fundamental step towards the realization of synaptic connectivity in neural networks. Optimization of synaptic functionality requires the understanding of the analogue conductance update under different programming conditions. Moreover, properties of physical devices such as bounded conductance values and state-dependent modulation should be considered as they affect storage capacity and performance of the network. This work provides a study of the conductance dynamics produced by identical pulses as a function of the programming parameters in an HfO 2 memristive device. The application of a phenomenological model that considers a soft approach to the conductance boundaries allows the identification of different operation regimes and to quantify conductance modulation in the analogue region. Device non-linear switching kinetics is recognized as the physical origin of the transition between different dynamics and motivates the crucial trade-off between degree of analog modulation and memory window. Different kinetics for the processes of conductance increase and decrease account for device programming asymmetry. The identification of programming trade-off together with an evaluation of device variations provide a guideline for the optimization of the analogue programming in view of hardware implementation of neural networks.

Vibrational analysis of 4-chloro-3-nitrobenzonitrile by quantum chemical calculations

NASA Astrophysics Data System (ADS)

Sert, Yusuf; Çırak, Çağrı; Ucun, Fatih

2013-04-01

In the present study, the experimental and theoretical harmonic and anharmonic vibrational frequencies of 4-chloro-3-nitrobenzonitrile were investigated. The experimental FT-IR (400-4000 cm-1) and μ-Raman spectra (100-4000 cm-1) of the molecule in the solid phase were recorded. Theoretical vibrational frequencies and geometric parameters (bond lengths and bond angles) were calculated using ab initio Hartree Fock (HF), density functional B3LYP and M06-2X methods with 6-311++G(d,p) basis set by Gaussian 09 W program, for the first time. The assignments of the vibrational frequencies were performed by potential energy distribution (PED) analysis by using VEDA 4 program. The theoretical optimized geometric parameters and vibrational frequencies were compared with the corresponding experimental data, and they were seen to be in a good agreement with each other. Also, the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies were found.

Calculation and analysis of cross-sections for p+184W reactions up to 200 MeV

NASA Astrophysics Data System (ADS)

Sun, Jian-Ping; Zhang, Zheng-Jun; Han, Yin-Lu

2015-08-01

A set of optimal proton optical potential parameters for p+ 184W reactions are obtained at incident proton energy up to 250 MeV. Based on these parameters, the reaction cross-sections, elastic scattering angular distributions, energy spectra and double differential cross sections of proton-induced reactions on 184W are calculated and analyzed by using theoretical models which integrate the optical model, distorted Born wave approximation theory, intra-nuclear cascade model, exciton model, Hauser-Feshbach theory and evaporation model. The calculated results are compared with existing experimental data and good agreement is achieved. Supported by National Basic Research Program of China, Technology Research of Accelerator Driven Sub-critical System for Nuclear Waste Transmutation (2007CB209903) and Strategic Priority Research Program of Chinese Academy of Sciences, Thorium Molten Salt Reactor Nuclear Energy System (XDA02010100)

Analysis of Parallel Algorithms on SMP Node and Cluster of Workstations Using Parallel Programming Models with New Tile-based Method for Large Biological Datasets.

PubMed

Shrimankar, D D; Sathe, S R

2016-01-01

Sequence alignment is an important tool for describing the relationships between DNA sequences. Many sequence alignment algorithms exist, differing in efficiency, in their models of the sequences, and in the relationship between sequences. The focus of this study is to obtain an optimal alignment between two sequences of biological data, particularly DNA sequences. The algorithm is discussed with particular emphasis on time, speedup, and efficiency optimizations. Parallel programming presents a number of critical challenges to application developers. Today's supercomputer often consists of clusters of SMP nodes. Programming paradigms such as OpenMP and MPI are used to write parallel codes for such architectures. However, the OpenMP programs cannot be scaled for more than a single SMP node. However, programs written in MPI can have more than single SMP nodes. But such a programming paradigm has an overhead of internode communication. In this work, we explore the tradeoffs between using OpenMP and MPI. We demonstrate that the communication overhead incurs significantly even in OpenMP loop execution and increases with the number of cores participating. We also demonstrate a communication model to approximate the overhead from communication in OpenMP loops. Our results are astonishing and interesting to a large variety of input data files. We have developed our own load balancing and cache optimization technique for message passing model. Our experimental results show that our own developed techniques give optimum performance of our parallel algorithm for various sizes of input parameter, such as sequence size and tile size, on a wide variety of multicore architectures.

Analysis of Parallel Algorithms on SMP Node and Cluster of Workstations Using Parallel Programming Models with New Tile-based Method for Large Biological Datasets

PubMed Central

Shrimankar, D. D.; Sathe, S. R.

2016-01-01

Sequence alignment is an important tool for describing the relationships between DNA sequences. Many sequence alignment algorithms exist, differing in efficiency, in their models of the sequences, and in the relationship between sequences. The focus of this study is to obtain an optimal alignment between two sequences of biological data, particularly DNA sequences. The algorithm is discussed with particular emphasis on time, speedup, and efficiency optimizations. Parallel programming presents a number of critical challenges to application developers. Today’s supercomputer often consists of clusters of SMP nodes. Programming paradigms such as OpenMP and MPI are used to write parallel codes for such architectures. However, the OpenMP programs cannot be scaled for more than a single SMP node. However, programs written in MPI can have more than single SMP nodes. But such a programming paradigm has an overhead of internode communication. In this work, we explore the tradeoffs between using OpenMP and MPI. We demonstrate that the communication overhead incurs significantly even in OpenMP loop execution and increases with the number of cores participating. We also demonstrate a communication model to approximate the overhead from communication in OpenMP loops. Our results are astonishing and interesting to a large variety of input data files. We have developed our own load balancing and cache optimization technique for message passing model. Our experimental results show that our own developed techniques give optimum performance of our parallel algorithm for various sizes of input parameter, such as sequence size and tile size, on a wide variety of multicore architectures. PMID:27932868

ConvAn: a convergence analyzing tool for optimization of biochemical networks.

PubMed

Kostromins, Andrejs; Mozga, Ivars; Stalidzans, Egils

2012-01-01

Dynamic models of biochemical networks usually are described as a system of nonlinear differential equations. In case of optimization of models for purpose of parameter estimation or design of new properties mainly numerical methods are used. That causes problems of optimization predictability as most of numerical optimization methods have stochastic properties and the convergence of the objective function to the global optimum is hardly predictable. Determination of suitable optimization method and necessary duration of optimization becomes critical in case of evaluation of high number of combinations of adjustable parameters or in case of large dynamic models. This task is complex due to variety of optimization methods, software tools and nonlinearity features of models in different parameter spaces. A software tool ConvAn is developed to analyze statistical properties of convergence dynamics for optimization runs with particular optimization method, model, software tool, set of optimization method parameters and number of adjustable parameters of the model. The convergence curves can be normalized automatically to enable comparison of different methods and models in the same scale. By the help of the biochemistry adapted graphical user interface of ConvAn it is possible to compare different optimization methods in terms of ability to find the global optima or values close to that as well as the necessary computational time to reach them. It is possible to estimate the optimization performance for different number of adjustable parameters. The functionality of ConvAn enables statistical assessment of necessary optimization time depending on the necessary optimization accuracy. Optimization methods, which are not suitable for a particular optimization task, can be rejected if they have poor repeatability or convergence properties. The software ConvAn is freely available on www.biosystems.lv/convan. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Note: Characteristic beam parameter for the line electron gun

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

Iqbal, M.; Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049; Islam, G. U.

We have optimized the beam parameters of line source electron gun using Stanford Linear Accelerator Centre electron beam trajectory program (EGUN), utilizing electrostatic focusing only. We measured minimum beam diameter as 0.5 mm that corresponds to power density of 68.9 kW/cm{sup 2} at 13.5 mm in the post-anode region which is more than two-fold (33 kW/cm{sup 2}), of the previously reported results. The gun was operated for the validation of the theoretical results and found in good agreement. The gun is now without any magnetic and electrostatic focusing thus much simpler and more powerful.

Note: Characteristic beam parameter for the line electron gun.

PubMed

Iqbal, M; Islam, G U; Zhou, Z; Chi, Y

2013-11-01

We have optimized the beam parameters of line source electron gun using Stanford Linear Accelerator Centre electron beam trajectory program (EGUN), utilizing electrostatic focusing only. We measured minimum beam diameter as 0.5 mm that corresponds to power density of 68.9 kW/cm(2) at 13.5 mm in the post-anode region which is more than two-fold (33 kW/cm(2)), of the previously reported results. The gun was operated for the validation of the theoretical results and found in good agreement. The gun is now without any magnetic and electrostatic focusing thus much simpler and more powerful.

Note: Characteristic beam parameter for the line electron gun

NASA Astrophysics Data System (ADS)

Iqbal, M.; Islam, G. U.; Zhou, Z.; Chi, Y.

2013-11-01

We have optimized the beam parameters of line source electron gun using Stanford Linear Accelerator Centre electron beam trajectory program (EGUN), utilizing electrostatic focusing only. We measured minimum beam diameter as 0.5 mm that corresponds to power density of 68.9 kW/cm2 at 13.5 mm in the post-anode region which is more than two-fold (33 kW/cm2), of the previously reported results. The gun was operated for the validation of the theoretical results and found in good agreement. The gun is now without any magnetic and electrostatic focusing thus much simpler and more powerful.

Transportation optimization with fuzzy trapezoidal numbers based on possibility theory.

PubMed

He, Dayi; Li, Ran; Huang, Qi; Lei, Ping

2014-01-01

In this paper, a parametric method is introduced to solve fuzzy transportation problem. Considering that parameters of transportation problem have uncertainties, this paper develops a generalized fuzzy transportation problem with fuzzy supply, demand and cost. For simplicity, these parameters are assumed to be fuzzy trapezoidal numbers. Based on possibility theory and consistent with decision-makers' subjectiveness and practical requirements, the fuzzy transportation problem is transformed to a crisp linear transportation problem by defuzzifying fuzzy constraints and objectives with application of fractile and modality approach. Finally, a numerical example is provided to exemplify the application of fuzzy transportation programming and to verify the validity of the proposed methods.

Multi-Response Parameter Interval Sensitivity and Optimization for the Composite Tape Winding Process.

PubMed

Deng, Bo; Shi, Yaoyao; Yu, Tao; Kang, Chao; Zhao, Pan

2018-01-31

The composite tape winding process, which utilizes a tape winding machine and prepreg tapes, provides a promising way to improve the quality of composite products. Nevertheless, the process parameters of composite tape winding have crucial effects on the tensile strength and void content, which are closely related to the performances of the winding products. In this article, two different object values of winding products, including mechanical performance (tensile strength) and a physical property (void content), were respectively calculated. Thereafter, the paper presents an integrated methodology by combining multi-parameter relative sensitivity analysis and single-parameter sensitivity analysis to obtain the optimal intervals of the composite tape winding process. First, the global multi-parameter sensitivity analysis method was applied to investigate the sensitivity of each parameter in the tape winding processing. Then, the local single-parameter sensitivity analysis method was employed to calculate the sensitivity of a single parameter within the corresponding range. Finally, the stability and instability ranges of each parameter were distinguished. Meanwhile, the authors optimized the process parameter ranges and provided comprehensive optimized intervals of the winding parameters. The verification test validated that the optimized intervals of the process parameters were reliable and stable for winding products manufacturing.

Multi-Response Parameter Interval Sensitivity and Optimization for the Composite Tape Winding Process

PubMed Central

Yu, Tao; Kang, Chao; Zhao, Pan

2018-01-01

The composite tape winding process, which utilizes a tape winding machine and prepreg tapes, provides a promising way to improve the quality of composite products. Nevertheless, the process parameters of composite tape winding have crucial effects on the tensile strength and void content, which are closely related to the performances of the winding products. In this article, two different object values of winding products, including mechanical performance (tensile strength) and a physical property (void content), were respectively calculated. Thereafter, the paper presents an integrated methodology by combining multi-parameter relative sensitivity analysis and single-parameter sensitivity analysis to obtain the optimal intervals of the composite tape winding process. First, the global multi-parameter sensitivity analysis method was applied to investigate the sensitivity of each parameter in the tape winding processing. Then, the local single-parameter sensitivity analysis method was employed to calculate the sensitivity of a single parameter within the corresponding range. Finally, the stability and instability ranges of each parameter were distinguished. Meanwhile, the authors optimized the process parameter ranges and provided comprehensive optimized intervals of the winding parameters. The verification test validated that the optimized intervals of the process parameters were reliable and stable for winding products manufacturing. PMID:29385048

Optimization of microphysics in the Unified Model, using the Micro-genetic algorithm.

NASA Astrophysics Data System (ADS)

Jang, J.; Lee, Y.; Lee, H.; Lee, J.; Joo, S.

2016-12-01

This study focuses on parameter optimization of microphysics in the Unified Model (UM) using the Micro-genetic algorithm (Micro-GA). We need the optimization of microphysics in UM. Because, Microphysics in the Numerical Weather Prediction (NWP) model is important to Quantitative Precipitation Forecasting (QPF). The Micro-GA searches for optimal parameters on the basis of fitness function. The five parameters are chosen. The target parameters include x1, x2 related to raindrop size distribution, Cloud-rain correlation coefficient, Surface droplet number and Droplet taper height. The fitness function is based on the skill score that is BIAS and Critical Successive Index (CSI). An interface between UM and Micro-GA is developed and applied to three precipitation cases in Korea. The cases are (ⅰ) heavy rainfall in the Southern area because of typhoon NAKRI, (ⅱ) heavy rainfall in the Youngdong area, and (ⅲ) heavy rainfall in the Seoul metropolitan area. When the optimized result is compared to the control result (using the UM default value, CNTL), the optimized result leads to improvements in precipitation forecast, especially for heavy rainfall of the late forecast time. Also, we analyze the skill score of precipitation forecasts in terms of various thresholds of CNTL, Optimized result, and experiments on each optimized parameter for five parameters. Generally, the improvement is maximized when the five optimized parameters are used simultaneously. Therefore, this study demonstrates the ability to improve Korean precipitation forecasts by optimizing microphysics in UM.

Analytical sizing methods for behind-the-meter battery storage

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

Wu, Di; Kintner-Meyer, Michael; Yang, Tao

In behind-the-meter application, battery storage system (BSS) is utilized to reduce a commercial or industrial customer’s payment for electricity use, including energy charge and demand charge. The potential value of BSS in payment reduction and the most economic size can be determined by formulating and solving standard mathematical programming problems. In this method, users input system information such as load profiles, energy/demand charge rates, and battery characteristics to construct a standard programming problem that typically involve a large number of constraints and decision variables. Such a large scale programming problem is then solved by optimization solvers to obtain numerical solutions.more » Such a method cannot directly link the obtained optimal battery sizes to input parameters and requires case-by-case analysis. In this paper, we present an objective quantitative analysis of costs and benefits of customer-side energy storage, and thereby identify key factors that affect battery sizing. Based on the analysis, we then develop simple but effective guidelines that can be used to determine the most cost-effective battery size or guide utility rate design for stimulating energy storage development. The proposed analytical sizing methods are innovative, and offer engineering insights on how the optimal battery size varies with system characteristics. We illustrate the proposed methods using practical building load profile and utility rate. The obtained results are compared with the ones using mathematical programming based methods for validation.« less

Dynamic remapping decisions in multi-phase parallel computations

NASA Technical Reports Server (NTRS)

Nicol, D. M.; Reynolds, P. F., Jr.

1986-01-01

The effectiveness of any given mapping of workload to processors in a parallel system is dependent on the stochastic behavior of the workload. Program behavior is often characterized by a sequence of phases, with phase changes occurring unpredictably. During a phase, the behavior is fairly stable, but may become quite different during the next phase. Thus a workload assignment generated for one phase may hinder performance during the next phase. We consider the problem of deciding whether to remap a paralled computation in the face of uncertainty in remapping's utility. Fundamentally, it is necessary to balance the expected remapping performance gain against the delay cost of remapping. This paper treats this problem formally by constructing a probabilistic model of a computation with at most two phases. We use stochastic dynamic programming to show that the remapping decision policy which minimizes the expected running time of the computation has an extremely simple structure: the optimal decision at any step is followed by comparing the probability of remapping gain against a threshold. This theoretical result stresses the importance of detecting a phase change, and assessing the possibility of gain from remapping. We also empirically study the sensitivity of optimal performance to imprecise decision threshold. Under a wide range of model parameter values, we find nearly optimal performance if remapping is chosen simply when the gain probability is high. These results strongly suggest that except in extreme cases, the remapping decision problem is essentially that of dynamically determining whether gain can be achieved by remapping after a phase change; precise quantification of the decision model parameters is not necessary.

[Temporal and spatial heterogeneity analysis of optimal value of sensitive parameters in ecological process model: The BIOME-BGC model as an example.

PubMed

Li, Yi Zhe; Zhang, Ting Long; Liu, Qiu Yu; Li, Ying

2018-01-01

The ecological process models are powerful tools for studying terrestrial ecosystem water and carbon cycle at present. However, there are many parameters for these models, and weather the reasonable values of these parameters were taken, have important impact on the models simulation results. In the past, the sensitivity and the optimization of model parameters were analyzed and discussed in many researches. But the temporal and spatial heterogeneity of the optimal parameters is less concerned. In this paper, the BIOME-BGC model was used as an example. In the evergreen broad-leaved forest, deciduous broad-leaved forest and C3 grassland, the sensitive parameters of the model were selected by constructing the sensitivity judgment index with two experimental sites selected under each vegetation type. The objective function was constructed by using the simulated annealing algorithm combined with the flux data to obtain the monthly optimal values of the sensitive parameters at each site. Then we constructed the temporal heterogeneity judgment index, the spatial heterogeneity judgment index and the temporal and spatial heterogeneity judgment index to quantitatively analyze the temporal and spatial heterogeneity of the optimal values of the model sensitive parameters. The results showed that the sensitivity of BIOME-BGC model parameters was different under different vegetation types, but the selected sensitive parameters were mostly consistent. The optimal values of the sensitive parameters of BIOME-BGC model mostly presented time-space heterogeneity to different degrees which varied with vegetation types. The sensitive parameters related to vegetation physiology and ecology had relatively little temporal and spatial heterogeneity while those related to environment and phenology had generally larger temporal and spatial heterogeneity. In addition, the temporal heterogeneity of the optimal values of the model sensitive parameters showed a significant linear correlation with the spatial heterogeneity under the three vegetation types. According to the temporal and spatial heterogeneity of the optimal values, the parameters of the BIOME-BGC model could be classified in order to adopt different parameter strategies in practical application. The conclusion could help to deeply understand the parameters and the optimal values of the ecological process models, and provide a way or reference for obtaining the reasonable values of parameters in models application.

Constraining neutron guide optimizations with phase-space considerations

NASA Astrophysics Data System (ADS)

Bertelsen, Mads; Lefmann, Kim

2016-09-01

We introduce a method named the Minimalist Principle that serves to reduce the parameter space for neutron guide optimization when the required beam divergence is limited. The reduced parameter space will restrict the optimization to guides with a minimal neutron intake that are still theoretically able to deliver the maximal possible performance. The geometrical constraints are derived using phase-space propagation from moderator to guide and from guide to sample, while assuming that the optimized guides will achieve perfect transport of the limited neutron intake. Guide systems optimized using these constraints are shown to provide performance close to guides optimized without any constraints, however the divergence received at the sample is limited to the desired interval, even when the neutron transport is not limited by the supermirrors used in the guide. As the constraints strongly limit the parameter space for the optimizer, two control parameters are introduced that can be used to adjust the selected subspace, effectively balancing between maximizing neutron transport and avoiding background from unnecessary neutrons. One parameter is needed to describe the expected focusing abilities of the guide to be optimized, going from perfectly focusing to no correlation between position and velocity. The second parameter controls neutron intake into the guide, so that one can select exactly how aggressively the background should be limited. We show examples of guides optimized using these constraints which demonstrates the higher signal to noise than conventional optimizations. Furthermore the parameter controlling neutron intake is explored which shows that the simulated optimal neutron intake is close to the analytically predicted, when assuming that the guide is dominated by multiple scattering events.

Optimal dietary patterns designed from local foods to achieve maternal nutritional goals.

PubMed

Raymond, Jofrey; Kassim, Neema; Rose, Jerman W; Agaba, Morris

2018-04-04

Achieving nutritional requirements for pregnant and lactating mothers in rural households while maintaining the intake of local and culture-specific foods can be a difficult task. Deploying a linear goal programming approach can effectively generate optimal dietary patterns that incorporate local and culturally acceptable diets. The primary objective of this study was to determine whether a realistic and affordable diet that achieves nutritional goals for rural pregnant and lactating women can be formulated from locally available foods in Tanzania. A cross sectional study was conducted to assess dietary intakes of 150 pregnant and lactating women using a weighed dietary record (WDR), 24 h dietary recalls and a 7-days food record. A market survey was also carried out to estimate the cost per 100 g of edible portion of foods that are frequently consumed in the study population. Dietary survey and market data were then used to define linear programming (LP) model parameters for diet optimisation. All LP analyses were done using linear program solver to generate optimal dietary patterns. Our findings showed that optimal dietary patterns designed from locally available foods would improve dietary adequacy for 15 and 19 selected nutrients in pregnant and lactating women, respectively, but inadequacies remained for iron, zinc, folate, pantothenic acid, and vitamin E, indicating that these are problem nutrients (nutrients that did not achieve 100% of their RNIs in optimised diets) in the study population. These findings suggest that optimal use of local foods can improve dietary adequacy for rural pregnant and lactating women aged 19-50 years. However, additional cost-effective interventions are needed to ensure adequate intakes for the identified problem nutrients.

Enabling Parametric Optimal Ascent Trajectory Modeling During Early Phases of Design

NASA Technical Reports Server (NTRS)

Holt, James B.; Dees, Patrick D.; Diaz, Manuel J.

2015-01-01

During the early phases of engineering design, the costs committed are high, costs incurred are low, and the design freedom is high. It is well documented that decisions made in these early design phases drive the entire design's life cycle. In a traditional paradigm, key design decisions are made when little is known about the design. As the design matures, design changes become more difficult -- in both cost and schedule -- to enact. Indeed, the current capability-based paradigm that has emerged because of the constrained economic environment calls for the infusion of knowledge acquired during later design phases into earlier design phases, i.e. bring knowledge acquired during preliminary and detailed design into pre-conceptual and conceptual design. An area of critical importance to launch vehicle design is the optimization of its ascent trajectory, as the optimal trajectory will be able to take full advantage of the launch vehicle's capability to deliver a maximum amount of payload into orbit. Hence, the optimal ascent trajectory plays an important role in the vehicle's affordability posture as the need for more economically viable access to space solutions are needed in today's constrained economic environment. The problem of ascent trajectory optimization is not a new one. There are several programs that are widely used in industry that allows trajectory analysts to, based on detailed vehicle and insertion orbit parameters, determine the optimal ascent trajectory. Yet, little information is known about the launch vehicle early in the design phase - information that is required of many different disciplines in order to successfully optimize the ascent trajectory. Thus, the current paradigm of optimizing ascent trajectories involves generating point solutions for every change in a vehicle's design parameters. This is often a very tedious, manual, and time-consuming task for the analysts. Moreover, the trajectory design space is highly non-linear and multi-modal due to the interaction of various constraints. Additionally, when these obstacles are coupled with The Program to Optimize Simulated Trajectories [1] (POST), an industry standard program to optimize ascent trajectories that is difficult to use, it requires expert trajectory analysts to effectively optimize a vehicle's ascent trajectory. As it has been pointed out, the paradigm of trajectory optimization is still a very manual one because using modern computational resources on POST is still a challenging problem. The nuances and difficulties involved in correctly utilizing, and therefore automating, the program presents a large problem. In order to address these issues, the authors will discuss a methodology that has been developed. The methodology is two-fold: first, a set of heuristics will be introduced and discussed that were captured while working with expert analysts to replicate the current state-of-the-art; secondly, leveraging the power of modern computing to evaluate multiple trajectories simultaneously, and therefore, enable the exploration of the trajectory's design space early during the pre-conceptual and conceptual phases of design. When this methodology is coupled with design of experiments in order to train surrogate models, the authors were able to visualize the trajectory design space, enabling parametric optimal ascent trajectory information to be introduced with other pre-conceptual and conceptual design tools. The potential impact of this methodology's success would be a fully automated POST evaluation suite for the purpose of conceptual and preliminary design trade studies. This will enable engineers to characterize the ascent trajectory's sensitivity to design changes in an arbitrary number of dimensions and for finding settings for trajectory specific variables, which result in optimal performance for a "dialed-in" launch vehicle design. The effort described in this paper was developed for the Advanced Concepts Office [2] at NASA Marshall Space Flight Center

Real-time parameter optimization based on neural network for smart injection molding

NASA Astrophysics Data System (ADS)

Lee, H.; Liau, Y.; Ryu, K.

2018-03-01

The manufacturing industry has been facing several challenges, including sustainability, performance and quality of production. Manufacturers attempt to enhance the competitiveness of companies by implementing CPS (Cyber-Physical Systems) through the convergence of IoT(Internet of Things) and ICT(Information & Communication Technology) in the manufacturing process level. Injection molding process has a short cycle time and high productivity. This features have been making it suitable for mass production. In addition, this process is used to produce precise parts in various industry fields such as automobiles, optics and medical devices. Injection molding process has a mixture of discrete and continuous variables. In order to optimized the quality, variables that is generated in the injection molding process must be considered. Furthermore, Optimal parameter setting is time-consuming work to predict the optimum quality of the product. Since the process parameter cannot be easily corrected during the process execution. In this research, we propose a neural network based real-time process parameter optimization methodology that sets optimal process parameters by using mold data, molding machine data, and response data. This paper is expected to have academic contribution as a novel study of parameter optimization during production compare with pre - production parameter optimization in typical studies.

CHEETAH: A fast thermochemical code for detonation

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

Fried, L.E.

1993-11-01

For more than 20 years, TIGER has been the benchmark thermochemical code in the energetic materials community. TIGER has been widely used because it gives good detonation parameters in a very short period of time. Despite its success, TIGER is beginning to show its age. The program`s chemical equilibrium solver frequently crashes, especially when dealing with many chemical species. It often fails to find the C-J point. Finally, there are many inconveniences for the user stemming from the programs roots in pre-modern FORTRAN. These inconveniences often lead to mistakes in preparing input files and thus erroneous results. We are producingmore » a modern version of TIGER, which combines the best features of the old program with new capabilities, better computational algorithms, and improved packaging. The new code, which will evolve out of TIGER in the next few years, will be called ``CHEETAH.`` Many of the capabilities that will be put into CHEETAH are inspired by the thermochemical code CHEQ. The new capabilities of CHEETAH are: calculate trace levels of chemical compounds for environmental analysis; kinetics capability: CHEETAH will predict chemical compositions as a function of time given individual chemical reaction rates. Initial application: carbon condensation; CHEETAH will incorporate partial reactions; CHEETAH will be based on computer-optimized JCZ3 and BKW parameters. These parameters will be fit to over 20 years of data collected at LLNL. We will run CHEETAH thousands of times to determine the best possible parameter sets; CHEETAH will fit C-J data to JWL`s,and also predict full-wall and half-wall cylinder velocities.« less

Predicting the scanning branches of hysteretic soil water-retention capacity with use of the method of mathematical modeling

NASA Astrophysics Data System (ADS)

Terleev, V.; Ginevsky, R.; Lazarev, V.; Nikonorov, A.; Togo, I.; Topaj, A.; Moiseev, K.; Abakumov, E.; Melnichuk, A.; Dunaieva, I.

2017-10-01

A mathematical model of the hysteresis of the water-retention capacity of the soil is proposed. The parameters of the model are interpreted within the framework of physical concepts of the structure and capillary properties of soil pores. On the basis of the model, a computer program with an interface that allows for dialogue with the user is developed. The program has some of options: visualization of experimental data; identification of the model parameters with use of measured data by means of an optimizing algorithm; graphical presentation of the hysteresis loop with application of the assigned parameters. Using the program, computational experiments were carried out, which consisted in verifying the identifiability of the model parameters from data on the main branches, and also in testing the ability to predict the scanning branches of the hysteresis loop. For the experiments, literature data on two sandy soils were used. The absence of an “artificial pump effect” is proved. A sufficiently high accuracy of the prediction of the scanning branches of the hysteresis loop has been achieved in comparison with the three models of the precursors. The practical importance of the proposed model and computer program, which is developed on its basis, is to ensure the calculation of precision irrigation rates. The application of such rates in irrigation farming will help to prevent excess moisture from flowing beyond the root layer of the soil and, thus, minimize the unproductive loss of irrigation water and agrochemicals, as well as reduce the risk of groundwater contamination and natural water eutrophication.

Approximate optimal guidance for the advanced launch system

NASA Technical Reports Server (NTRS)

Feeley, T. S.; Speyer, J. L.

1993-01-01

A real-time guidance scheme for the problem of maximizing the payload into orbit subject to the equations of motion for a rocket over a spherical, non-rotating earth is presented. An approximate optimal launch guidance law is developed based upon an asymptotic expansion of the Hamilton - Jacobi - Bellman or dynamic programming equation. The expansion is performed in terms of a small parameter, which is used to separate the dynamics of the problem into primary and perturbation dynamics. For the zeroth-order problem the small parameter is set to zero and a closed-form solution to the zeroth-order expansion term of Hamilton - Jacobi - Bellman equation is obtained. Higher-order terms of the expansion include the effects of the neglected perturbation dynamics. These higher-order terms are determined from the solution of first-order linear partial differential equations requiring only the evaluation of quadratures. This technique is preferred as a real-time, on-line guidance scheme to alternative numerical iterative optimization schemes because of the unreliable convergence properties of these iterative guidance schemes and because the quadratures needed for the approximate optimal guidance law can be performed rapidly and by parallel processing. Even if the approximate solution is not nearly optimal, when using this technique the zeroth-order solution always provides a path which satisfies the terminal constraints. Results for two-degree-of-freedom simulations are presented for the simplified problem of flight in the equatorial plane and compared to the guidance scheme generated by the shooting method which is an iterative second-order technique.

Parameterized CAD techniques implementation for the fatigue behaviour optimization of a service chamber

NASA Astrophysics Data System (ADS)

Sánchez, H. T.; Estrems, M.; Franco, P.; Faura, F.

2009-11-01

In recent years, the market of heat exchangers is increasingly demanding new products in short cycle time, which means that both the design and manufacturing stages must be extremely reduced. The design stage can be reduced by means of CAD-based parametric design techniques. The methodology presented in this proceeding is based on the optimized control of geometric parameters of a service chamber of a heat exchanger by means of the Application Programming Interface (API) provided by the Solidworks CAD package. Using this implementation, a set of different design configurations of the service chamber made of stainless steel AISI 316 are studied by means of the FE method. As a result of this study, a set of knowledge rules based on the fatigue behaviour are constructed and integrated into the design optimization process.

Research on theoretical optimization and experimental verification of minimum resistance hull form based on Rankine source method

NASA Astrophysics Data System (ADS)

Zhang, Bao-Ji; Zhang, Zhu-Xin

2015-09-01

To obtain low resistance and high efficiency energy-saving ship, minimum total resistance hull form design method is studied based on potential flow theory of wave-making resistance and considering the effects of tail viscous separation. With the sum of wave resistance and viscous resistance as objective functions and the parameters of B-Spline function as design variables, mathematical models are built using Nonlinear Programming Method (NLP) ensuring the basic limit of displacement and considering rear viscous separation. We develop ship lines optimization procedures with intellectual property rights. Series60 is used as parent ship in optimization design to obtain improved ship (Series60-1) theoretically. Then drag tests for the improved ship (Series60-1) is made to get the actual minimum total resistance hull form.

Optimization of Gas Metal Arc Welding Process Parameters

NASA Astrophysics Data System (ADS)

Kumar, Amit; Khurana, M. K.; Yadav, Pradeep K.

2016-09-01

This study presents the application of Taguchi method combined with grey relational analysis to optimize the process parameters of gas metal arc welding (GMAW) of AISI 1020 carbon steels for multiple quality characteristics (bead width, bead height, weld penetration and heat affected zone). An orthogonal array of L9 has been implemented to fabrication of joints. The experiments have been conducted according to the combination of voltage (V), current (A) and welding speed (Ws). The results revealed that the welding speed is most significant process parameter. By analyzing the grey relational grades, optimal parameters are obtained and significant factors are known using ANOVA analysis. The welding parameters such as speed, welding current and voltage have been optimized for material AISI 1020 using GMAW process. To fortify the robustness of experimental design, a confirmation test was performed at selected optimal process parameter setting. Observations from this method may be useful for automotive sub-assemblies, shipbuilding and vessel fabricators and operators to obtain optimal welding conditions.

OPTIM: Computer program to generate a vertical profile which minimizes aircraft fuel burn or direct operating cost. User's guide

NASA Technical Reports Server (NTRS)

1983-01-01

A profile of altitude, airspeed, and flight path angle as a function of range between a given set of origin and destination points for particular models of transport aircraft provided by NASA is generated. Inputs to the program include the vertical wind profile, the aircraft takeoff weight, the costs of time and fuel, certain constraint parameters and control flags. The profile can be near optimum in the sense of minimizing: (1) fuel, (2) time, or (3) a combination of fuel and time (direct operating cost (DOC)). The user can also, as an option, specify the length of time the flight is to span. The theory behind the technical details of this program is also presented.

Estimation of the longitudinal and lateral-directional aerodynamic parameters from flight data for the NASA F/A-18 HARV

NASA Technical Reports Server (NTRS)

Napolitano, Marcello R.

1996-01-01

This progress report presents the results of an investigation focused on parameter identification for the NASA F/A-18 HARV. This aircraft was used in the high alpha research program at the NASA Dryden Flight Research Center. In this study the longitudinal and lateral-directional stability derivatives are estimated from flight data using the Maximum Likelihood method coupled with a Newton-Raphson minimization technique. The objective is to estimate an aerodynamic model describing the aircraft dynamics over a range of angle of attack from 5 deg to 60 deg. The mathematical model is built using the traditional static and dynamic derivative buildup. Flight data used in this analysis were from a variety of maneuvers. The longitudinal maneuvers included large amplitude multiple doublets, optimal inputs, frequency sweeps, and pilot pitch stick inputs. The lateral-directional maneuvers consisted of large amplitude multiple doublets, optimal inputs and pilot stick and rudder inputs. The parameter estimation code pEst, developed at NASA Dryden, was used in this investigation. Results of the estimation process from alpha = 5 deg to alpha = 60 deg are presented and discussed.

Idealized Experiments for Optimizing Model Parameters Using a 4D-Variational Method in an Intermediate Coupled Model of ENSO

NASA Astrophysics Data System (ADS)

Gao, Chuan; Zhang, Rong-Hua; Wu, Xinrong; Sun, Jichang

2018-04-01

Large biases exist in real-time ENSO prediction, which can be attributed to uncertainties in initial conditions and model parameters. Previously, a 4D variational (4D-Var) data assimilation system was developed for an intermediate coupled model (ICM) and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer ( T e), which is empirically and explicitly related to sea level (SL) variation. The strength of the thermocline effect on SST (referred to simply as "the thermocline effect") is represented by an introduced parameter, α Te. A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having their initial condition optimized only, and having their initial condition plus this additional model parameter optimized, are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameters and initial conditions together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Var data assimilation system implemented in the ICM are also discussed.

Using a 4D-Variational Method to Optimize Model Parameters in an Intermediate Coupled Model of ENSO

NASA Astrophysics Data System (ADS)

Gao, C.; Zhang, R. H.

2017-12-01

Large biases exist in real-time ENSO prediction, which is attributed to uncertainties in initial conditions and model parameters. Previously, a four dimentional variational (4D-Var) data assimilation system was developed for an intermediate coupled model (ICM) and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer (Te), which is empirically and explicitly related to sea level (SL) variation, written as Te=αTe×FTe (SL). The introduced parameter, αTe, represents the strength of the thermocline effect on sea surface temperature (SST; referred as the thermocline effect). A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having initial condition optimized only and having initial condition plus this additional model parameter optimized both are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameter and initial condition together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Var data assimilation system implemented in the ICM are also discussed.

Automated optimization techniques for aircraft synthesis

NASA Technical Reports Server (NTRS)

Vanderplaats, G. N.

1976-01-01

Application of numerical optimization techniques to automated conceptual aircraft design is examined. These methods are shown to be a general and efficient way to obtain quantitative information for evaluating alternative new vehicle projects. Fully automated design is compared with traditional point design methods and time and resource requirements for automated design are given. The NASA Ames Research Center aircraft synthesis program (ACSYNT) is described with special attention to calculation of the weight of a vehicle to fly a specified mission. The ACSYNT procedures for automatically obtaining sensitivity of the design (aircraft weight, performance and cost) to various vehicle, mission, and material technology parameters are presented. Examples are used to demonstrate the efficient application of these techniques.

Inventory-transportation integrated optimization for maintenance spare parts of high-speed trains

PubMed Central

Wang, Jiaxi; Wang, Huasheng; Wang, Zhongkai; Li, Jian; Lin, Ruixi; Xiao, Jie; Wu, Jianping

2017-01-01

This paper presents a 0–1 programming model aimed at obtaining the optimal inventory policy and transportation mode for maintenance spare parts of high-speed trains. To obtain the model parameters for occasionally-replaced spare parts, a demand estimation method based on the maintenance strategies of China’s high-speed railway system is proposed. In addition, we analyse the shortage time using PERT, and then calculate the unit time shortage cost from the viewpoint of train operation revenue. Finally, a real-world case study from Shanghai Depot is conducted to demonstrate our method. Computational results offer an effective and efficient decision support for inventory managers. PMID:28472097

Risk management for sulfur dioxide abatement under multiple uncertainties

NASA Astrophysics Data System (ADS)

Dai, C.; Sun, W.; Tan, Q.; Liu, Y.; Lu, W. T.; Guo, H. C.

2016-03-01

In this study, interval-parameter programming, two-stage stochastic programming (TSP), and conditional value-at-risk (CVaR) were incorporated into a general optimization framework, leading to an interval-parameter CVaR-based two-stage programming (ICTP) method. The ICTP method had several advantages: (i) its objective function simultaneously took expected cost and risk cost into consideration, and also used discrete random variables and discrete intervals to reflect uncertain properties; (ii) it quantitatively evaluated the right tail of distributions of random variables which could better calculate the risk of violated environmental standards; (iii) it was useful for helping decision makers to analyze the trade-offs between cost and risk; and (iv) it was effective to penalize the second-stage costs, as well as to capture the notion of risk in stochastic programming. The developed model was applied to sulfur dioxide abatement in an air quality management system. The results indicated that the ICTP method could be used for generating a series of air quality management schemes under different risk-aversion levels, for identifying desired air quality management strategies for decision makers, and for considering a proper balance between system economy and environmental quality.

Support Resources Demand Parameters - Aircraft. Revision A

DTIC Science & Technology

1980-01-15

Assurance Sciences - Reliability and Maintainability, 1969. Livers , Paul J., PREDICTION AND OPTIMIZATION OF FAILURE RATES (PROF 200) PROGRAMMING MANUAL...0CDC V3. -gr om In 0 0 V -40 LU -cc00L I- W! - OPC -i40 cc-4 qr - CA IL- 0cc 41 C3 C-1 I I=IJ o M c ccL) - In. r-c 0 0C LLA- Iii "" -c Ok CD 0D co- CUs

High-Reflection Coatings for Gravitational-Wave Detectors: State of The Art and Future Developments

NASA Astrophysics Data System (ADS)

Amato, Alex; Cagnoli, Gianpietro; Canepa, Maurizio; Coillet, Elodie; Degallaix, Jerome; Dolique, Vincent; Forest, Daniele; Granata, Massimo; Martinez, Valérie; Michel, Christophe; Pinard, Laurent; Sassolas, Benoit; Teillon, Julien

2018-02-01

We report on the optical, mechanical and structural characterization of the sputtered coating materials of Advanced LIGO, Advanced Virgo and KAGRA gravitational- waves detectors. We present the latest results of our research program aiming at decreasing coating thermal noise through doping, optimization of deposition parameters and post- deposition annealing. Finally, we propose sputtered Si3N4 as a candidate material for the mirrors of future detectors.

The Business Case for Systems Engineering Study: Results of the Systems Engineering Effectiveness Survey

DTIC Science & Technology

2012-11-01

reflecting the fact that project managers can often optimize the value of one of these parameters, but only at the expense of the other two. For example...which system developers can compare their SE capabilities to manage SE process improvements. As a reward for their participation, the companion...higher requirements development and management capability is strongly associat- ed with better program performance, particularly on challenging projects

Flight Test Validation of Optimal Input Design and Comparison to Conventional Inputs

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

1997-01-01

A technique for designing optimal inputs for aerodynamic parameter estimation was flight tested on the F-18 High Angle of Attack Research Vehicle (HARV). Model parameter accuracies calculated from flight test data were compared on an equal basis for optimal input designs and conventional inputs at the same flight condition. In spite of errors in the a priori input design models and distortions of the input form by the feedback control system, the optimal inputs increased estimated parameter accuracies compared to conventional 3-2-1-1 and doublet inputs. In addition, the tests using optimal input designs demonstrated enhanced design flexibility, allowing the optimal input design technique to use a larger input amplitude to achieve further increases in estimated parameter accuracy without departing from the desired flight test condition. This work validated the analysis used to develop the optimal input designs, and demonstrated the feasibility and practical utility of the optimal input design technique.

Integrated controls design optimization

DOEpatents

Lou, Xinsheng; Neuschaefer, Carl H.

2015-09-01

A control system (207) for optimizing a chemical looping process of a power plant includes an optimizer (420), an income algorithm (230) and a cost algorithm (225) and a chemical looping process models. The process models are used to predict the process outputs from process input variables. Some of the process in puts and output variables are related to the income of the plant; and some others are related to the cost of the plant operations. The income algorithm (230) provides an income input to the optimizer (420) based on a plurality of input parameters (215) of the power plant. The cost algorithm (225) provides a cost input to the optimizer (420) based on a plurality of output parameters (220) of the power plant. The optimizer (420) determines an optimized operating parameter solution based on at least one of the income input and the cost input, and supplies the optimized operating parameter solution to the power plant.

Long-term lifestyle intervention with optimized high-intensity interval training improves body composition, cardiometabolic risk, and exercise parameters in patients with abdominal obesity.

PubMed

Gremeaux, Vincent; Drigny, Joffrey; Nigam, Anil; Juneau, Martin; Guilbeault, Valérie; Latour, Elise; Gayda, Mathieu

2012-11-01

The aim of this study was to study the impact of a combined long-term lifestyle and high-intensity interval training intervention on body composition, cardiometabolic risk, and exercise tolerance in overweight and obese subjects. Sixty-two overweight and obese subjects (53.3 ± 9.7 yrs; mean body mass index, 35.8 ± 5 kg/m(2)) were retrospectively identified at their entry into a 9-mo program consisting of individualized nutritional counselling, optimized high-intensity interval exercise, and resistance training two to three times a week. Anthropometric measurements, cardiometabolic risk factors, and exercise tolerance were measured at baseline and program completion. Adherence rate was 97%, and no adverse events occurred with high-intensity interval exercise training. Exercise training was associated with a weekly energy expenditure of 1582 ± 284 kcal. Clinically and statistically significant improvements were observed for body mass (-5.3 ± 5.2 kg), body mass index (-1.9 ± 1.9 kg/m(2)), waist circumference (-5.8 ± 5.4 cm), and maximal exercise capacity (+1.26 ± 0.84 metabolic equivalents) (P < 0.0001 for all parameters). Total fat mass and trunk fat mass, lipid profile, and triglyceride/high-density lipoprotein ratio were also significantly improved (P < 0.0001). At program completion, the prevalence of metabolic syndrome was reduced by 32.5% (P < 0.05). Independent predictors of being a responder to body mass and waist circumference loss were baseline body mass index and resting metabolic rate; those for body mass index decrease were baseline waist circumference and triglyceride/high-density lipoprotein cholesterol ratio. A long-term lifestyle intervention with optimized high-intensity interval exercise improves body composition, cardiometabolic risk, and exercise tolerance in obese subjects. This intervention seems safe, efficient, and well tolerated and could improve adherence to exercise training in this population.

Optimal synthesis and design of the number of cycles in the leaching process for surimi production.

PubMed

Reinheimer, M Agustina; Scenna, Nicolás J; Mussati, Sergio F

2016-12-01

Water consumption required during the leaching stage in the surimi manufacturing process strongly depends on the design and the number and size of stages connected in series for the soluble protein extraction target, and it is considered as the main contributor to the operating costs. Therefore, the optimal synthesis and design of the leaching stage is essential to minimize the total annual cost. In this study, a mathematical optimization model for the optimal design of the leaching operation is presented. Precisely, a detailed Mixed Integer Nonlinear Programming (MINLP) model including operating and geometric constraints was developed based on our previous optimization model (NLP model). Aspects about quality, water consumption and main operating parameters were considered. The minimization of total annual costs, which considered a trade-off between investment and operating costs, led to an optimal solution with lesser number of stages (2 instead of 3 stages) and higher volumes of the leaching tanks comparing with previous results. An analysis was performed in order to investigate how the optimal solution was influenced by the variations of the unitary cost of fresh water, waste treatment and capital investment.

PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models.

PubMed

Dumont, Cyrielle; Lestini, Giulia; Le Nagard, Hervé; Mentré, France; Comets, Emmanuelle; Nguyen, Thu Thuy; Group, For The Pfim

2018-03-01

Nonlinear mixed-effect models (NLMEMs) are increasingly used for the analysis of longitudinal studies during drug development. When designing these studies, the expected Fisher information matrix (FIM) can be used instead of performing time-consuming clinical trial simulations. The function PFIM is the first tool for design evaluation and optimization that has been developed in R. In this article, we present an extended version, PFIM 4.0, which includes several new features. Compared with version 3.0, PFIM 4.0 includes a more complete pharmacokinetic/pharmacodynamic library of models and accommodates models including additional random effects for inter-occasion variability as well as discrete covariates. A new input method has been added to specify user-defined models through an R function. Optimization can be performed assuming some fixed parameters or some fixed sampling times. New outputs have been added regarding the FIM such as eigenvalues, conditional numbers, and the option of saving the matrix obtained after evaluation or optimization. Previously obtained results, which are summarized in a FIM, can be taken into account in evaluation or optimization of one-group protocols. This feature enables the use of PFIM for adaptive designs. The Bayesian individual FIM has been implemented, taking into account a priori distribution of random effects. Designs for maximum a posteriori Bayesian estimation of individual parameters can now be evaluated or optimized and the predicted shrinkage is also reported. It is also possible to visualize the graphs of the model and the sensitivity functions without performing evaluation or optimization. The usefulness of these approaches and the simplicity of use of PFIM 4.0 are illustrated by two examples: (i) an example of designing a population pharmacokinetic study accounting for previous results, which highlights the advantage of adaptive designs; (ii) an example of Bayesian individual design optimization for a pharmacodynamic study, showing that the Bayesian individual FIM can be a useful tool in therapeutic drug monitoring, allowing efficient prediction of estimation precision and shrinkage for individual parameters. PFIM 4.0 is a useful tool for design evaluation and optimization of longitudinal studies in pharmacometrics and is freely available at http://www.pfim.biostat.fr. Copyright © 2018 Elsevier B.V. All rights reserved.

CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field

DOE PAGES

Lee, Jumin; Cheng, Xi; Swails, Jason M.; ...

2015-11-12

Here we report that proper treatment of nonbonded interactions is essential for the accuracy of molecular dynamics (MD) simulations, especially in studies of lipid bilayers. The use of the CHARMM36 force field (C36 FF) in different MD simulation programs can result in disagreements with published simulations performed with CHARMM due to differences in the protocols used to treat the long-range and 1-4 nonbonded interactions. In this study, we systematically test the use of the C36 lipid FF in NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM. A wide range of Lennard-Jones (LJ) cutoff schemes and integrator algorithms were tested to find themore » optimal simulation protocol to best match bilayer properties of six lipids with varying acyl chain saturation and head groups. MD simulations of a 1,2-dipalmitoyl-sn-phosphatidylcholine (DPPC) bilayer were used to obtain the optimal protocol for each program. MD simulations with all programs were found to reasonably match the DPPC bilayer properties (surface area per lipid, chain order parameters, and area compressibility modulus) obtained using the standard protocol used in CHARMM as well as from experiments. The optimal simulation protocol was then applied to the other five lipid simulations and resulted in excellent agreement between results from most simulation programs as well as with experimental data. AMBER compared least favorably with the expected membrane properties, which appears to be due to its use of the hard-truncation in the LJ potential versus a force-based switching function used to smooth the LJ potential as it approaches the cutoff distance. The optimal simulation protocol for each program has been implemented in CHARMM-GUI. This protocol is expected to be applicable to the remainder of the additive C36 FF including the proteins, nucleic acids, carbohydrates, and small molecules.« less

CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field.

PubMed

Lee, Jumin; Cheng, Xi; Swails, Jason M; Yeom, Min Sun; Eastman, Peter K; Lemkul, Justin A; Wei, Shuai; Buckner, Joshua; Jeong, Jong Cheol; Qi, Yifei; Jo, Sunhwan; Pande, Vijay S; Case, David A; Brooks, Charles L; MacKerell, Alexander D; Klauda, Jeffery B; Im, Wonpil

2016-01-12

Proper treatment of nonbonded interactions is essential for the accuracy of molecular dynamics (MD) simulations, especially in studies of lipid bilayers. The use of the CHARMM36 force field (C36 FF) in different MD simulation programs can result in disagreements with published simulations performed with CHARMM due to differences in the protocols used to treat the long-range and 1-4 nonbonded interactions. In this study, we systematically test the use of the C36 lipid FF in NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM. A wide range of Lennard-Jones (LJ) cutoff schemes and integrator algorithms were tested to find the optimal simulation protocol to best match bilayer properties of six lipids with varying acyl chain saturation and head groups. MD simulations of a 1,2-dipalmitoyl-sn-phosphatidylcholine (DPPC) bilayer were used to obtain the optimal protocol for each program. MD simulations with all programs were found to reasonably match the DPPC bilayer properties (surface area per lipid, chain order parameters, and area compressibility modulus) obtained using the standard protocol used in CHARMM as well as from experiments. The optimal simulation protocol was then applied to the other five lipid simulations and resulted in excellent agreement between results from most simulation programs as well as with experimental data. AMBER compared least favorably with the expected membrane properties, which appears to be due to its use of the hard-truncation in the LJ potential versus a force-based switching function used to smooth the LJ potential as it approaches the cutoff distance. The optimal simulation protocol for each program has been implemented in CHARMM-GUI. This protocol is expected to be applicable to the remainder of the additive C36 FF including the proteins, nucleic acids, carbohydrates, and small molecules.

Hybrid computer optimization of systems with random parameters

NASA Technical Reports Server (NTRS)

White, R. C., Jr.

1972-01-01

A hybrid computer Monte Carlo technique for the simulation and optimization of systems with random parameters is presented. The method is applied to the simultaneous optimization of the means and variances of two parameters in the radar-homing missile problem treated by McGhee and Levine.

Design of underwater robot lines based on a hybrid automatic optimization strategy

NASA Astrophysics Data System (ADS)

Lyu, Wenjing; Luo, Weilin

2014-09-01

In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user programming and several commercial software including UG6.0, GAMBIT2.4.6 and FLUENT12.0. An intelligent parameter optimization method, the particle swarm optimization, is incorporated into the platform. To verify the strategy proposed, a simulation is conducted on the underwater robot model 5470, which originates from the DTRC SUBOFF project. With the automatic optimization platform, the minimal resistance is taken as the optimization goal; the wet surface area as the constraint condition; the length of the fore-body, maximum body radius and after-body's minimum radius as the design variables. With the CFD calculation, the RANS equations and the standard turbulence model are used for direct numerical simulation. By analyses of the simulation results, it is concluded that the platform is of high efficiency and feasibility. Through the platform, a variety of schemes for the design of the lines are generated and the optimal solution is achieved. The combination of the intelligent optimization algorithm and the numerical simulation ensures a global optimal solution and improves the efficiency of the searching solutions.

Optimization of HTS superconducting magnetic energy storage magnet volume

NASA Astrophysics Data System (ADS)

Korpela, Aki; Lehtonen, Jorma; Mikkonen, Risto

2003-08-01

Nonlinear optimization problems in the field of electromagnetics have been successfully solved by means of sequential quadratic programming (SQP) and the finite element method (FEM). For example, the combination of SQP and FEM has been proven to be an efficient tool in the optimization of low temperature superconductors (LTS) superconducting magnetic energy storage (SMES) magnets. The procedure can also be applied for the optimization of HTS magnets. However, due to a strongly anisotropic material and a slanted electric field, current density characteristic high temperature superconductors HTS optimization is quite different from that of the LTS. In this paper the volumes of solenoidal conduction-cooled Bi-2223/Ag SMES magnets have been optimized at the operation temperature of 20 K. In addition to the electromagnetic constraints the stress caused by the tape bending has also been taken into account. Several optimization runs with different initial geometries were performed in order to find the best possible solution for a certain energy requirement. The optimization constraints describe the steady-state operation, thus the presented coil geometries are designed for slow ramping rates. Different energy requirements were investigated in order to find the energy dependence of the design parameters of optimized solenoidal HTS coils. According to the results, these dependences can be described with polynomial expressions.

Optimization of a Thermodynamic Model Using a Dakota Toolbox Interface

NASA Astrophysics Data System (ADS)

Cyrus, J.; Jafarov, E. E.; Schaefer, K. M.; Wang, K.; Clow, G. D.; Piper, M.; Overeem, I.

2016-12-01

Scientific modeling of the Earth physical processes is an important driver of modern science. The behavior of these scientific models is governed by a set of input parameters. It is crucial to choose accurate input parameters that will also preserve the corresponding physics being simulated in the model. In order to effectively simulate real world processes the models output data must be close to the observed measurements. To achieve this optimal simulation, input parameters are tuned until we have minimized the objective function, which is the error between the simulation model outputs and the observed measurements. We developed an auxiliary package, which serves as a python interface between the user and DAKOTA. The package makes it easy for the user to conduct parameter space explorations, parameter optimizations, as well as sensitivity analysis while tracking and storing results in a database. The ability to perform these analyses via a Python library also allows the users to combine analysis techniques, for example finding an approximate equilibrium with optimization then immediately explore the space around it. We used the interface to calibrate input parameters for the heat flow model, which is commonly used in permafrost science. We performed optimization on the first three layers of the permafrost model, each with two thermal conductivity coefficients input parameters. Results of parameter space explorations indicate that the objective function not always has a unique minimal value. We found that gradient-based optimization works the best for the objective functions with one minimum. Otherwise, we employ more advanced Dakota methods such as genetic optimization and mesh based convergence in order to find the optimal input parameters. We were able to recover 6 initially unknown thermal conductivity parameters within 2% accuracy of their known values. Our initial tests indicate that the developed interface for the Dakota toolbox could be used to perform analysis and optimization on a `black box' scientific model more efficiently than using just Dakota.

Generation Expansion Planning With Large Amounts of Wind Power via Decision-Dependent Stochastic Programming

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

Zhan, Yiduo; Zheng, Qipeng P.; Wang, Jianhui

Power generation expansion planning needs to deal with future uncertainties carefully, given that the invested generation assets will be in operation for a long time. Many stochastic programming models have been proposed to tackle this challenge. However, most previous works assume predetermined future uncertainties (i.e., fixed random outcomes with given probabilities). In several recent studies of generation assets' planning (e.g., thermal versus renewable), new findings show that the investment decisions could affect the future uncertainties as well. To this end, this paper proposes a multistage decision-dependent stochastic optimization model for long-term large-scale generation expansion planning, where large amounts of windmore » power are involved. In the decision-dependent model, the future uncertainties are not only affecting but also affected by the current decisions. In particular, the probability distribution function is determined by not only input parameters but also decision variables. To deal with the nonlinear constraints in our model, a quasi-exact solution approach is then introduced to reformulate the multistage stochastic investment model to a mixed-integer linear programming model. The wind penetration, investment decisions, and the optimality of the decision-dependent model are evaluated in a series of multistage case studies. The results show that the proposed decision-dependent model provides effective optimization solutions for long-term generation expansion planning.« less

Stable sequential Kuhn-Tucker theorem in iterative form or a regularized Uzawa algorithm in a regular nonlinear programming problem

NASA Astrophysics Data System (ADS)

Sumin, M. I.

2015-06-01

A parametric nonlinear programming problem in a metric space with an operator equality constraint in a Hilbert space is studied assuming that its lower semicontinuous value function at a chosen individual parameter value has certain subdifferentiability properties in the sense of nonlinear (nonsmooth) analysis. Such subdifferentiability can be understood as the existence of a proximal subgradient or a Fréchet subdifferential. In other words, an individual problem has a corresponding generalized Kuhn-Tucker vector. Under this assumption, a stable sequential Kuhn-Tucker theorem in nondifferential iterative form is proved and discussed in terms of minimizing sequences on the basis of the dual regularization method. This theorem provides necessary and sufficient conditions for the stable construction of a minimizing approximate solution in the sense of Warga in the considered problem, whose initial data can be approximately specified. A substantial difference of the proved theorem from its classical same-named analogue is that the former takes into account the possible instability of the problem in the case of perturbed initial data and, as a consequence, allows for the inherited instability of classical optimality conditions. This theorem can be treated as a regularized generalization of the classical Uzawa algorithm to nonlinear programming problems. Finally, the theorem is applied to the "simplest" nonlinear optimal control problem, namely, to a time-optimal control problem.

COMSATCOM service technical baseline strategy development approach using PPBW concept

NASA Astrophysics Data System (ADS)

Nguyen, Tien M.; Guillen, Andy T.

2016-05-01

This paper presents an innovative approach to develop a Commercial Satellite Communications (COMSATCOM) service Technical Baseline (TB) and associated Program Baseline (PB) strategy using Portable Pool Bandwidth (PPBW) concept. The concept involves trading of the purchased commercial transponders' Bandwidths (BWs) with existing commercial satellites' bandwidths participated in a "designated pool bandwidth"3 according to agreed terms and conditions. Space Missile Systems Center (SMC) has been implementing the Better Buying Power (BBP 3.0) directive4 and recommending the System Program Offices (SPO) to own the Program and Technical Baseline (PTB) [1, 2] for the development of flexible acquisition strategy and achieving affordability and increased in competition. This paper defines and describes the critical PTB parameters and associated requirements that are important to the government SPO for "owning" an affordable COMSATCOM services contract using PPBW trading concept. The paper describes a step-by-step approach to optimally perform the PPBW trading to meet DoD and its stakeholders (i) affordability requirement, and (ii) fixed and variable bandwidth requirements by optimizing communications performance, cost and PPBW accessibility in terms of Quality of Services (QoS), Bandwidth Sharing Ratio (BSR), Committed Information Rate (CIR), Burstable Information Rate (BIR), Transponder equivalent bandwidth (TPE) and transponder Net Presence Value (NPV). The affordable optimal solution that meets variable bandwidth requirements will consider the operating and trading terms and conditions described in the Fair Access Policy (FAP).

Aggregation Pheromone System: A Real-parameter Optimization Algorithm using Aggregation Pheromones as the Base Metaphor

NASA Astrophysics Data System (ADS)

Tsutsui, Shigeyosi

This paper proposes an aggregation pheromone system (APS) for solving real-parameter optimization problems using the collective behavior of individuals which communicate using aggregation pheromones. APS was tested on several test functions used in evolutionary computation. The results showed APS could solve real-parameter optimization problems fairly well. The sensitivity analysis of control parameters of APS is also studied.

An optimal generic model for multi-parameters and big data optimizing: a laboratory experimental study

NASA Astrophysics Data System (ADS)

Utama, D. N.; Ani, N.; Iqbal, M. M.

2018-03-01

Optimization is a process for finding parameter (parameters) that is (are) able to deliver an optimal value for an objective function. Seeking an optimal generic model for optimizing is a computer science study that has been being practically conducted by numerous researchers. Generic model is a model that can be technically operated to solve any varieties of optimization problem. By using an object-oriented method, the generic model for optimizing was constructed. Moreover, two types of optimization method, simulated-annealing and hill-climbing, were functioned in constructing the model and compared to find the most optimal one then. The result said that both methods gave the same result for a value of objective function and the hill-climbing based model consumed the shortest running time.

Selecting and optimizing eco-physiological parameters of Biome-BGC to reproduce observed woody and leaf biomass growth of Eucommia ulmoides plantation in China using Dakota optimizer

NASA Astrophysics Data System (ADS)

Miyauchi, T.; Machimura, T.

2013-12-01

In the simulation using an ecosystem process model, the adjustment of parameters is indispensable for improving the accuracy of prediction. This procedure, however, requires much time and effort for approaching the simulation results to the measurements on models consisting of various ecosystem processes. In this study, we tried to apply a general purpose optimization tool in the parameter optimization of an ecosystem model, and examined its validity by comparing the simulated and measured biomass growth of a woody plantation. A biometric survey of tree biomass growth was performed in 2009 in an 11-year old Eucommia ulmoides plantation in Henan Province, China. Climate of the site was dry temperate. Leaf, above- and below-ground woody biomass were measured from three cut trees and converted into carbon mass per area by measured carbon contents and stem density. Yearly woody biomass growth of the plantation was calculated according to allometric relationships determined by tree ring analysis of seven cut trees. We used Biome-BGC (Thornton, 2002) to reproduce biomass growth of the plantation. Air temperature and humidity from 1981 to 2010 was used as input climate condition. The plant functional type was deciduous broadleaf, and non-optimizing parameters were left default. 11-year long normal simulations were performed following a spin-up run. In order to select optimizing parameters, we analyzed the sensitivity of leaf, above- and below-ground woody biomass to eco-physiological parameters. Following the selection, optimization of parameters was performed by using the Dakota optimizer. Dakota is an optimizer developed by Sandia National Laboratories for providing a systematic and rapid means to obtain optimal designs using simulation based models. As the object function, we calculated the sum of relative errors between simulated and measured leaf, above- and below-ground woody carbon at each of eleven years. In an alternative run, errors at the last year (at the field survey) were weighted for priority. We compared some gradient-based global optimization methods of Dakota starting with the default parameters of Biome-BGC. In the result of sensitive analysis, carbon allocation parameters between coarse root and leaf, between stem and leaf, and SLA had high contribution on both leaf and woody biomass changes. These parameters were selected to be optimized. The measured leaf, above- and below-ground woody biomass carbon density at the last year were 0.22, 1.81 and 0.86 kgC m-2, respectively, whereas those simulated in the non-optimized control case using all default parameters were 0.12, 2.26 and 0.52 kgC m-2, respectively. After optimizing the parameters, the simulated values were improved to 0.19, 1.81 and 0.86 kgC m-2, respectively. The coliny global optimization method gave the better fitness than efficient global and ncsu direct method. The optimized parameters showed the higher carbon allocation rates to coarse roots and leaves and the lower SLA than the default parameters, which were consistent to the general water physiological response in a dry climate. The simulation using the weighted object function resulted in the closer simulations to the measurements at the last year with the lower fitness during the previous years.

Oxide vapor distribution from a high-frequency sweep e-beam system

NASA Astrophysics Data System (ADS)

Chow, R.; Tassano, P. L.; Tsujimoto, N.

1995-03-01

Oxide vapor distributions have been determined as a function of operating parameters of a high frequency sweep e-beam source combined with a programmable sweep controller. We will show which parameters are significant, the parameters that yield the broadest oxide deposition distribution, and the procedure used to arrive at these conclusions. A design-of-experimental strategy was used with five operating parameters: evaporation rate, sweep speed, sweep pattern (pre-programmed), phase speed (azimuthal rotation of the pattern), profile (dwell time as a function of radial position). A design was chosen that would show which of the parameters and parameter pairs have a statistically significant effect on the vapor distribution. Witness flats were placed symmetrically across a 25 inches diameter platen. The stationary platen was centered 24 inches above the e-gun crucible. An oxide material was evaporated under 27 different conditions. Thickness measurements were made with a stylus profilometer. The information will enable users of the high frequency e-gun systems to optimally locate the source in a vacuum system and understand which parameters have a major effect on the vapor distribution.

Optimal control of hydroelectric facilities

NASA Astrophysics Data System (ADS)

Zhao, Guangzhi

This thesis considers a simple yet realistic model of pump-assisted hydroelectric facilities operating in a market with time-varying but deterministic power prices. Both deterministic and stochastic water inflows are considered. The fluid mechanical and engineering details of the facility are described by a model containing several parameters. We present a dynamic programming algorithm for optimizing either the total energy produced or the total cash generated by these plants. The algorithm allows us to give the optimal control strategy as a function of time and to see how this strategy, and the associated plant value, varies with water inflow and electricity price. We investigate various cases. For a single pumped storage facility experiencing deterministic power prices and water inflows, we investigate the varying behaviour for an oversimplified constant turbine- and pump-efficiency model with simple reservoir geometries. We then generalize this simple model to include more realistic turbine efficiencies, situations with more complicated reservoir geometry, and the introduction of dissipative switching costs between various control states. We find many results which reinforce our physical intuition about this complicated system as well as results which initially challenge, though later deepen, this intuition. One major lesson of this work is that the optimal control strategy does not differ much between two differing objectives of maximizing energy production and maximizing its cash value. We then turn our attention to the case of stochastic water inflows. We present a stochastic dynamic programming algorithm which can find an on-average optimal control in the face of this randomness. As the operator of a facility must be more cautious when inflows are random, the randomness destroys facility value. Following this insight we quantify exactly how much a perfect hydrological inflow forecast would be worth to a dam operator. In our final chapter we discuss the challenging problem of optimizing a sequence of two hydro dams sharing the same river system. The complexity of this problem is magnified and we just scratch its surface here. The thesis concludes with suggestions for future work in this fertile area. Keywords: dynamic programming, hydroelectric facility, optimization, optimal control, switching cost, turbine efficiency.

Three-level global resource allocation model for hiv control: A hierarchical decision system approach.

PubMed

Kassa, Semu Mitiku

2018-02-01

Funds from various global organizations, such as, The Global Fund, The World Bank, etc. are not directly distributed to the targeted risk groups. Especially in the so-called third-world-countries, the major part of the fund in HIV prevention programs comes from these global funding organizations. The allocations of these funds usually pass through several levels of decision making bodies that have their own specific parameters to control and specific objectives to achieve. However, these decisions are made mostly in a heuristic manner and this may lead to a non-optimal allocation of the scarce resources. In this paper, a hierarchical mathematical optimization model is proposed to solve such a problem. Combining existing epidemiological models with the kind of interventions being on practice, a 3-level hierarchical decision making model in optimally allocating such resources has been developed and analyzed. When the impact of antiretroviral therapy (ART) is included in the model, it has been shown that the objective function of the lower level decision making structure is a non-convex minimization problem in the allocation variables even if all the production functions for the intervention programs are assumed to be linear.

A class of stochastic optimization problems with one quadratic & several linear objective functions and extended portfolio selection model

NASA Astrophysics Data System (ADS)

Xu, Jiuping; Li, Jun

2002-09-01

In this paper a class of stochastic multiple-objective programming problems with one quadratic, several linear objective functions and linear constraints has been introduced. The former model is transformed into a deterministic multiple-objective nonlinear programming model by means of the introduction of random variables' expectation. The reference direction approach is used to deal with linear objectives and results in a linear parametric optimization formula with a single linear objective function. This objective function is combined with the quadratic function using the weighted sums. The quadratic problem is transformed into a linear (parametric) complementary problem, the basic formula for the proposed approach. The sufficient and necessary conditions for (properly, weakly) efficient solutions and some construction characteristics of (weakly) efficient solution sets are obtained. An interactive algorithm is proposed based on reference direction and weighted sums. Varying the parameter vector on the right-hand side of the model, the DM can freely search the efficient frontier with the model. An extended portfolio selection model is formed when liquidity is considered as another objective to be optimized besides expectation and risk. The interactive approach is illustrated with a practical example.

Human health risk constrained naphthalene-contaminated groundwater remediation management through an improved credibility method.

PubMed

Li, Jing; Lu, Hongwei; Fan, Xing; Chen, Yizhong

2017-07-01

In this study, a human health risk constrained groundwater remediation management program based on the improved credibility is developed for naphthalene contamination. The program integrates simulation, multivariate regression analysis, health risk assessment, uncertainty analysis, and nonlinear optimization into a general framework. The improved credibility-based optimization model for groundwater remediation management with consideration of human health risk (ICOM-HHR) is capable of not only effectively addressing parameter uncertainties and risk-exceeding possibility in human health risk but also providing a credibility level that indicates the satisfaction of the optimal groundwater remediation strategies with multiple contributions of possibility and necessity. The capabilities and effectiveness of ICOM-HHR are illustrated through a real-world case study in Anhui Province, China. Results indicate that the ICOM-HHR would generate double remediation cost yet reduce approximately 10 times of the naphthalene concentrations at monitoring wells, i.e., mostly less than 1 μg/L, which implies that the ICOM-HHR usually results in better environmental and health risk benefits. And it is acceptable to obtain a better environmental quality and a lower health risk level with sacrificing a certain economic benefit.

Method for Household Refrigerators Efficiency Increasing

NASA Astrophysics Data System (ADS)

Lebedev, V. V.; Sumzina, L. V.; Maksimov, A. V.

2017-11-01

The relevance of working processes parameters optimization in air conditioning systems is proved in the work. The research is performed with the use of the simulation modeling method. The parameters optimization criteria are considered, the analysis of target functions is given while the key factors of technical and economic optimization are considered in the article. The search for the optimal solution at multi-purpose optimization of the system is made by finding out the minimum of the dual-target vector created by the Pareto method of linear and weight compromises from target functions of the total capital costs and total operating costs. The tasks are solved in the MathCAD environment. The research results show that the values of technical and economic parameters of air conditioning systems in the areas relating to the optimum solutions’ areas manifest considerable deviations from the minimum values. At the same time, the tendencies for significant growth in deviations take place at removal of technical parameters from the optimal values of both the capital investments and operating costs. The production and operation of conditioners with the parameters which are considerably deviating from the optimal values will lead to the increase of material and power costs. The research allows one to establish the borders of the area of the optimal values for technical and economic parameters at air conditioning systems’ design.

A parameters optimization method for planar joint clearance model and its application for dynamics simulation of reciprocating compressor

NASA Astrophysics Data System (ADS)

Hai-yang, Zhao; Min-qiang, Xu; Jin-dong, Wang; Yong-bo, Li

2015-05-01

In order to improve the accuracy of dynamics response simulation for mechanism with joint clearance, a parameter optimization method for planar joint clearance contact force model was presented in this paper, and the optimized parameters were applied to the dynamics response simulation for mechanism with oversized joint clearance fault. By studying the effect of increased clearance on the parameters of joint clearance contact force model, the relation of model parameters between different clearances was concluded. Then the dynamic equation of a two-stage reciprocating compressor with four joint clearances was developed using Lagrange method, and a multi-body dynamic model built in ADAMS software was used to solve this equation. To obtain a simulated dynamic response much closer to that of experimental tests, the parameters of joint clearance model, instead of using the designed values, were optimized by genetic algorithms approach. Finally, the optimized parameters were applied to simulate the dynamics response of model with oversized joint clearance fault according to the concluded parameter relation. The dynamics response of experimental test verified the effectiveness of this application.

Visual tool for estimating the fractal dimension of images

NASA Astrophysics Data System (ADS)

Grossu, I. V.; Besliu, C.; Rusu, M. V.; Jipa, Al.; Bordeianu, C. C.; Felea, D.

2009-10-01

This work presents a new Visual Basic 6.0 application for estimating the fractal dimension of images, based on an optimized version of the box-counting algorithm. Following the attempt to separate the real information from "noise", we considered also the family of all band-pass filters with the same band-width (specified as parameter). The fractal dimension can be thus represented as a function of the pixel color code. The program was used for the study of paintings cracks, as an additional tool which can help the critic to decide if an artistic work is original or not. Program summaryProgram title: Fractal Analysis v01 Catalogue identifier: AEEG_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEEG_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 29 690 No. of bytes in distributed program, including test data, etc.: 4 967 319 Distribution format: tar.gz Programming language: MS Visual Basic 6.0 Computer: PC Operating system: MS Windows 98 or later RAM: 30M Classification: 14 Nature of problem: Estimating the fractal dimension of images. Solution method: Optimized implementation of the box-counting algorithm. Use of a band-pass filter for separating the real information from "noise". User friendly graphical interface. Restrictions: Although various file-types can be used, the application was mainly conceived for the 8-bit grayscale, windows bitmap file format. Running time: In a first approximation, the algorithm is linear.

Interval-parameter semi-infinite fuzzy-stochastic mixed-integer programming approach for environmental management under multiple uncertainties.

PubMed

Guo, P; Huang, G H

2010-03-01

In this study, an interval-parameter semi-infinite fuzzy-chance-constrained mixed-integer linear programming (ISIFCIP) approach is developed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing interval-parameter semi-infinite programming (ISIP) and fuzzy-chance-constrained programming (FCCP) by incorporating uncertainties expressed as dual uncertainties of functional intervals and multiple uncertainties of distributions with fuzzy-interval admissible probability of violating constraint within a general optimization framework. The binary-variable solutions represent the decisions of waste-management-facility expansion, and the continuous ones are related to decisions of waste-flow allocation. The interval solutions can help decision-makers to obtain multiple decision alternatives, as well as provide bases for further analyses of tradeoffs between waste-management cost and system-failure risk. In the application to the City of Regina, Canada, two scenarios are considered. In Scenario 1, the City's waste-management practices would be based on the existing policy over the next 25 years. The total diversion rate for the residential waste would be approximately 14%. Scenario 2 is associated with a policy for waste minimization and diversion, where 35% diversion of residential waste should be achieved within 15 years, and 50% diversion over 25 years. In this scenario, not only landfill would be expanded, but also CF and MRF would be expanded. Through the scenario analyses, useful decision support for the City's solid-waste managers and decision-makers has been generated. Three special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it is useful for tackling multiple uncertainties expressed as intervals, functional intervals, probability distributions, fuzzy sets, and their combinations; secondly, it has capability in addressing the temporal variations of the functional intervals; thirdly, it can facilitate dynamic analysis for decisions of facility-expansion planning and waste-flow allocation within a multi-facility, multi-period and multi-option context. Copyright 2009 Elsevier Ltd. All rights reserved.

Combined genetic algorithm and multiple linear regression (GA-MLR) optimizer: Application to multi-exponential fluorescence decay surface.

PubMed

Fisz, Jacek J

2006-12-07

The optimization approach based on the genetic algorithm (GA) combined with multiple linear regression (MLR) method, is discussed. The GA-MLR optimizer is designed for the nonlinear least-squares problems in which the model functions are linear combinations of nonlinear functions. GA optimizes the nonlinear parameters, and the linear parameters are calculated from MLR. GA-MLR is an intuitive optimization approach and it exploits all advantages of the genetic algorithm technique. This optimization method results from an appropriate combination of two well-known optimization methods. The MLR method is embedded in the GA optimizer and linear and nonlinear model parameters are optimized in parallel. The MLR method is the only one strictly mathematical "tool" involved in GA-MLR. The GA-MLR approach simplifies and accelerates considerably the optimization process because the linear parameters are not the fitted ones. Its properties are exemplified by the analysis of the kinetic biexponential fluorescence decay surface corresponding to a two-excited-state interconversion process. A short discussion of the variable projection (VP) algorithm, designed for the same class of the optimization problems, is presented. VP is a very advanced mathematical formalism that involves the methods of nonlinear functionals, algebra of linear projectors, and the formalism of Fréchet derivatives and pseudo-inverses. Additional explanatory comments are added on the application of recently introduced the GA-NR optimizer to simultaneous recovery of linear and weakly nonlinear parameters occurring in the same optimization problem together with nonlinear parameters. The GA-NR optimizer combines the GA method with the NR method, in which the minimum-value condition for the quadratic approximation to chi(2), obtained from the Taylor series expansion of chi(2), is recovered by means of the Newton-Raphson algorithm. The application of the GA-NR optimizer to model functions which are multi-linear combinations of nonlinear functions, is indicated. The VP algorithm does not distinguish the weakly nonlinear parameters from the nonlinear ones and it does not apply to the model functions which are multi-linear combinations of nonlinear functions.

Optimal wide-area monitoring and nonlinear adaptive coordinating neurocontrol of a power system with wind power integration and multiple FACTS devices.

PubMed

Qiao, Wei; Venayagamoorthy, Ganesh K; Harley, Ronald G

2008-01-01

Wide-area coordinating control is becoming an important issue and a challenging problem in the power industry. This paper proposes a novel optimal wide-area coordinating neurocontrol (WACNC), based on wide-area measurements, for a power system with power system stabilizers, a large wind farm and multiple flexible ac transmission system (FACTS) devices. An optimal wide-area monitor (OWAM), which is a radial basis function neural network (RBFNN), is designed to identify the input-output dynamics of the nonlinear power system. Its parameters are optimized through particle swarm optimization (PSO). Based on the OWAM, the WACNC is then designed by using the dual heuristic programming (DHP) method and RBFNNs, while considering the effect of signal transmission delays. The WACNC operates at a global level to coordinate the actions of local power system controllers. Each local controller communicates with the WACNC, receives remote control signals from the WACNC to enhance its dynamic performance and therefore helps improve system-wide dynamic and transient performance. The proposed control is verified by simulation studies on a multimachine power system.

Online adaptive optimal control for continuous-time nonlinear systems with completely unknown dynamics

NASA Astrophysics Data System (ADS)

Lv, Yongfeng; Na, Jing; Yang, Qinmin; Wu, Xing; Guo, Yu

2016-01-01

An online adaptive optimal control is proposed for continuous-time nonlinear systems with completely unknown dynamics, which is achieved by developing a novel identifier-critic-based approximate dynamic programming algorithm with a dual neural network (NN) approximation structure. First, an adaptive NN identifier is designed to obviate the requirement of complete knowledge of system dynamics, and a critic NN is employed to approximate the optimal value function. Then, the optimal control law is computed based on the information from the identifier NN and the critic NN, so that the actor NN is not needed. In particular, a novel adaptive law design method with the parameter estimation error is proposed to online update the weights of both identifier NN and critic NN simultaneously, which converge to small neighbourhoods around their ideal values. The closed-loop system stability and the convergence to small vicinity around the optimal solution are all proved by means of the Lyapunov theory. The proposed adaptation algorithm is also improved to achieve finite-time convergence of the NN weights. Finally, simulation results are provided to exemplify the efficacy of the proposed methods.

A new monitor set for the determination of neutron flux parameters in short-time k0-NAA

NASA Astrophysics Data System (ADS)

Kubešová, Marie; Kučera, Jan; Fikrle, Marek

2011-11-01

Multipurpose research reactors such as LVR-15 in Řež require monitoring of the neutron flux parameters (f, α) in each batch of samples analyzed when k0 standardization in NAA is to be used. The above parameters may change quite unpredictably, because experiments in channels adjacent to those used for NAA require an adjustment of the reactor operation parameters and/or active core configuration. For frequent monitoring of the neutron flux parameters the bare multi-monitor method is very convenient. The well-known Au-Zr tri-isotopic monitor set that provides a good tool for determining f and α after long-time irradiation is not optimal in case of short-time irradiation because only a low activity of the 95Zr radionuclide is formed. Therefore, several elements forming radionuclides with suitable half-lives and Q0 and Ēr parameters in a wide range of values were tested, namely 198Au, 56Mn, 88Rb, 128I, 139Ba, and 239U. As a result, an optimal mixture was selected consisting of Au, Mn, and Rb to form a well suited monitor set for irradiation at a thermal neutron fluence rate of 3×1017 m-2 s-1. The procedure of short-time INAA with the new monitor set for k0 standardization was successfully validated using the synthetic reference material SMELS 1 and several matrix reference materials (RMs) representing matrices of sample types frequently analyzed in our laboratory. The results were obtained using the Kayzero for Windows program.

A Bootstrap-Based Probabilistic Optimization Method to Explore and Efficiently Converge in Solution Spaces of Earthquake Source Parameter Estimation Problems: Application to Volcanic and Tectonic Earthquakes

NASA Astrophysics Data System (ADS)

Dahm, T.; Heimann, S.; Isken, M.; Vasyura-Bathke, H.; Kühn, D.; Sudhaus, H.; Kriegerowski, M.; Daout, S.; Steinberg, A.; Cesca, S.

2017-12-01

Seismic source and moment tensor waveform inversion is often ill-posed or non-unique if station coverage is poor or signals are weak. Therefore, the interpretation of moment tensors can become difficult, if not the full model space is explored, including all its trade-offs and uncertainties. This is especially true for non-double couple components of weak or shallow earthquakes, as for instance found in volcanic, geothermal or mining environments.We developed a bootstrap-based probabilistic optimization scheme (Grond), which is based on pre-calculated Greens function full waveform databases (e.g. fomosto tool, doi.org/10.5880/GFZ.2.1.2017.001). Grond is able to efficiently explore the full model space, the trade-offs and the uncertainties of source parameters. The program is highly flexible with respect to the adaption to specific problems, the design of objective functions, and the diversity of empirical datasets.It uses an integrated, robust waveform data processing based on a newly developed Python toolbox for seismology (Pyrocko, see Heimann et al., 2017, http://doi.org/10.5880/GFZ.2.1.2017.001), and allows for visual inspection of many aspects of the optimization problem. Grond has been applied to the CMT moment tensor inversion using W-phases, to nuclear explosions in Korea, to meteorite atmospheric explosions, to volcano-tectonic events during caldera collapse and to intra-plate volcanic and tectonic crustal events.Grond can be used to optimize simultaneously seismological waveforms, amplitude spectra and static displacements of geodetic data as InSAR and GPS (e.g. KITE, Isken et al., 2017, http://doi.org/10.5880/GFZ.2.1.2017.002). We present examples of Grond optimizations to demonstrate the advantage of a full exploration of source parameter uncertainties for interpretation.

Comparison of Optimal Design Methods in Inverse Problems

PubMed Central

Banks, H. T.; Holm, Kathleen; Kappel, Franz

2011-01-01

Typical optimal design methods for inverse or parameter estimation problems are designed to choose optimal sampling distributions through minimization of a specific cost function related to the resulting error in parameter estimates. It is hoped that the inverse problem will produce parameter estimates with increased accuracy using data collected according to the optimal sampling distribution. Here we formulate the classical optimal design problem in the context of general optimization problems over distributions of sampling times. We present a new Prohorov metric based theoretical framework that permits one to treat succinctly and rigorously any optimal design criteria based on the Fisher Information Matrix (FIM). A fundamental approximation theory is also included in this framework. A new optimal design, SE-optimal design (standard error optimal design), is then introduced in the context of this framework. We compare this new design criteria with the more traditional D-optimal and E-optimal designs. The optimal sampling distributions from each design are used to compute and compare standard errors; the standard errors for parameters are computed using asymptotic theory or bootstrapping and the optimal mesh. We use three examples to illustrate ideas: the Verhulst-Pearl logistic population model [13], the standard harmonic oscillator model [13] and a popular glucose regulation model [16, 19, 29]. PMID:21857762

Multiresponse Optimization of Process Parameters in Turning of GFRP Using TOPSIS Method

PubMed Central

Parida, Arun Kumar; Routara, Bharat Chandra

2014-01-01

Taguchi's design of experiment is utilized to optimize the process parameters in turning operation with dry environment. Three parameters, cutting speed (v), feed (f), and depth of cut (d), with three different levels are taken for the responses like material removal rate (MRR) and surface roughness (R a). The machining is conducted with Taguchi L9 orthogonal array, and based on the S/N analysis, the optimal process parameters for surface roughness and MRR are calculated separately. Considering the larger-the-better approach, optimal process parameters for material removal rate are cutting speed at level 3, feed at level 2, and depth of cut at level 3, that is, v 3-f 2-d 3. Similarly for surface roughness, considering smaller-the-better approach, the optimal process parameters are cutting speed at level 1, feed at level 1, and depth of cut at level 3, that is, v 1-f 1-d 3. Results of the main effects plot indicate that depth of cut is the most influencing parameter for MRR but cutting speed is the most influencing parameter for surface roughness and feed is found to be the least influencing parameter for both the responses. The confirmation test is conducted for both MRR and surface roughness separately. Finally, an attempt has been made to optimize the multiresponses using technique for order preference by similarity to ideal solution (TOPSIS) with Taguchi approach. PMID:27437503

[Cost-effectiveness and cost-benefit analysis on strategy for preventing mother-to-child transmission of hepatitis B virus].

PubMed

Cai, Y L; Zhang, S X; Yang, P C; Lin, Y

2016-06-01

Through cost-benefit analysis (CBA), cost-effectiveness analysis (CEA) and quantitative optimization analysis to understand the economic benefit and outcomes of strategy regarding preventing mother-to-child transmission (PMTCT) on hepatitis B virus. Based on the principle of Hepatitis B immunization decision analytic-Markov model, strategies on PMTCT and universal vaccination were compared. Related parameters of Shenzhen were introduced to the model, a birth cohort was set up as the study population in 2013. The net present value (NPV), benefit-cost ratio (BCR), incremental cost-effectiveness ratio (ICER) were calculated and the differences between CBA and CEA were compared. A decision tree was built as the decision analysis model for hepatitis B immunization. Three kinds of Markov models were used to simulate the outcomes after the implementation of vaccination program. The PMTCT strategy of Shenzhen showed a net-gain as 38 097.51 Yuan/per person in 2013, with BCR as 14.37. The universal vaccination strategy showed a net-gain as 37 083.03 Yuan/per person, with BCR as 12.07. Data showed that the PMTCT strategy was better than the universal vaccination one and would end with gaining more economic benefit. When comparing with the universal vaccination program, the PMTCT strategy would save 85 100.00 Yuan more on QALY gains for every person. The PMTCT strategy seemed more cost-effective compared with the one under universal vaccination program. In the CBA and CEA hepatitis B immunization programs, the immunization coverage rate and costs of hepatitis B related diseases were the most important influencing factors. Outcomes of joint-changes of all the parameters in CEA showed that PMTCT strategy was a more cost-effective. The PMTCT strategy gained more economic benefit and effects on health. However, the cost of PMTCT strategy was more than the universal vaccination program, thus it is important to pay attention to the process of PMTCT strategy and the universal vaccination program. CBA seemed suitable for strategy optimization while CEA was better for strategy evaluation. Hopefully, programs as combination of the above said two methods would facilitate the process of economic evaluation.

Improving flood forecasting capability of physically based distributed hydrological model by parameter optimization

NASA Astrophysics Data System (ADS)

Chen, Y.; Li, J.; Xu, H.

2015-10-01

Physically based distributed hydrological models discrete the terrain of the whole catchment into a number of grid cells at fine resolution, and assimilate different terrain data and precipitation to different cells, and are regarded to have the potential to improve the catchment hydrological processes simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters, but unfortunately, the uncertanties associated with this model parameter deriving is very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study, the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using PSO algorithm and to test its competence and to improve its performances, the second is to explore the possibility of improving physically based distributed hydrological models capability in cathcment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improverd Particle Swarm Optimization (PSO) algorithm is developed for the parameter optimization of Liuxihe model in catchment flood forecasting, the improvements include to adopt the linear decreasing inertia weight strategy to change the inertia weight, and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be used for Liuxihe model parameter optimization effectively, and could improve the model capability largely in catchment flood forecasting, thus proven that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological model. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for Liuxihe model catchment flood forcasting is 20 and 30, respectively.

A computational approach to animal breeding.

PubMed

Berger-Wolf, Tanya Y; Moore, Cristopher; Saia, Jared

2007-02-07

We propose a computational model of mating strategies for controlled animal breeding programs. A mating strategy in a controlled breeding program is a heuristic with some optimization criteria as a goal. Thus, it is appropriate to use the computational tools available for analysis of optimization heuristics. In this paper, we propose the first discrete model of the controlled animal breeding problem and analyse heuristics for two possible objectives: (1) breeding for maximum diversity and (2) breeding a target individual. These two goals are representative of conservation biology and agricultural livestock management, respectively. We evaluate several mating strategies and provide upper and lower bounds for the expected number of matings. While the population parameters may vary and can change the actual number of matings for a particular strategy, the order of magnitude of the number of expected matings and the relative competitiveness of the mating heuristics remains the same. Thus, our simple discrete model of the animal breeding problem provides a novel viable and robust approach to designing and comparing breeding strategies in captive populations.

ABCluster: the artificial bee colony algorithm for cluster global optimization.

PubMed

Zhang, Jun; Dolg, Michael

2015-10-07

Global optimization of cluster geometries is of fundamental importance in chemistry and an interesting problem in applied mathematics. In this work, we introduce a relatively new swarm intelligence algorithm, i.e. the artificial bee colony (ABC) algorithm proposed in 2005, to this field. It is inspired by the foraging behavior of a bee colony, and only three parameters are needed to control it. We applied it to several potential functions of quite different nature, i.e., the Coulomb-Born-Mayer, Lennard-Jones, Morse, Z and Gupta potentials. The benchmarks reveal that for long-ranged potentials the ABC algorithm is very efficient in locating the global minimum, while for short-ranged ones it is sometimes trapped into a local minimum funnel on a potential energy surface of large clusters. We have released an efficient, user-friendly, and free program "ABCluster" to realize the ABC algorithm. It is a black-box program for non-experts as well as experts and might become a useful tool for chemists to study clusters.

ATTDES: An Expert System for Satellite Attitude Determination and Control. 2

NASA Technical Reports Server (NTRS)

Mackison, Donald L.; Gifford, Kevin

1996-01-01

The design, analysis, and flight operations of satellite attitude determintion and attitude control systems require extensive mathematical formulations, optimization studies, and computer simulation. This is best done by an analyst with extensive education and experience. The development of programs such as ATTDES permit the use of advanced techniques by those with less experience. Typical tasks include the mission analysis to select stabilization and damping schemes, attitude determination sensors and algorithms, and control system designs to meet program requirements. ATTDES is a system that includes all of these activities, including high fidelity orbit environment models that can be used for preliminary analysis, parameter selection, stabilization schemes, the development of estimators covariance analyses, and optimization, and can support ongoing orbit activities. The modification of existing simulations to model new configurations for these purposes can be an expensive, time consuming activity that becomes a pacing item in the development and operation of such new systems. The use of an integrated tool such as ATTDES significantly reduces the effort and time required for these tasks.

State estimation bias induced by optimization under uncertainty and error cost asymmetry is likely reflected in perception.

PubMed

Shimansky, Y P

2011-05-01

It is well known from numerous studies that perception can be significantly affected by intended action in many everyday situations, indicating that perception and related decision-making is not a simple, one-way sequence, but a complex iterative cognitive process. However, the underlying functional mechanisms are yet unclear. Based on an optimality approach, a quantitative computational model of one such mechanism has been developed in this study. It is assumed in the model that significant uncertainty about task-related parameters of the environment results in parameter estimation errors and an optimal control system should minimize the cost of such errors in terms of the optimality criterion. It is demonstrated that, if the cost of a parameter estimation error is significantly asymmetrical with respect to error direction, the tendency to minimize error cost creates a systematic deviation of the optimal parameter estimate from its maximum likelihood value. Consequently, optimization of parameter estimate and optimization of control action cannot be performed separately from each other under parameter uncertainty combined with asymmetry of estimation error cost, thus making the certainty equivalence principle non-applicable under those conditions. A hypothesis that not only the action, but also perception itself is biased by the above deviation of parameter estimate is supported by ample experimental evidence. The results provide important insights into the cognitive mechanisms of interaction between sensory perception and planning an action under realistic conditions. Implications for understanding related functional mechanisms of optimal control in the CNS are discussed.

Presenting a new kinetic model for methanol to light olefins reactions over a hierarchical SAPO-34 catalyst using the Langmuir-Hinshelwood-Hougen-Watson mechanism

NASA Astrophysics Data System (ADS)

Javad Azarhoosh, Mohammad; Halladj, Rouein; Askari, Sima

2017-10-01

In this study, a new kinetic model for methanol to light olefins (MTO) reactions over a hierarchical SAPO-34 catalyst using the Langmuir-Hinshelwood-Hougen-Watson (LHHW) mechanism was presented and the kinetic parameters was obtained using a genetic algorithm (GA) and genetic programming (GP). Several kinetic models for the MTO reactions have been presented. However, due to the complexity of the reactions, most reactions are considered lumped and elementary, which cannot be deemed a completely accurate kinetic model of the process. Therefore, in this study, the LHHW mechanism is presented as kinetic models of MTO reactions. Because of the non-linearity of the kinetic models and existence of many local optimal points, evolutionary algorithms (GA and GP) are used in this study to estimate the kinetic parameters in the rate equations. Via the simultaneous connection of the code related to modelling the reactor and the GA and GP codes in the MATLAB R2013a software, optimization of the kinetic models parameters was performed such that the least difference between the results from the kinetic models and experiential results was obtained and the best kinetic parameters of MTO process reactions were achieved. A comparison of the results from the model with experiential results showed that the present model possesses good accuracy.

LOW-ENGINE-FRICTION TECHNOLOGY FOR ADVANCED NATURAL-GAS RECIPROCATING ENGINES

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

Victor Wong; Tian Tian; Luke Moughon

2005-09-30

This program aims at improving the efficiency of advanced natural-gas reciprocating engines (ANGRE) by reducing piston and piston ring assembly friction without major adverse effects on engine performance, such as increased oil consumption and wear. An iterative process of simulation, experimentation and analysis is being followed towards achieving the goal of demonstrating a complete optimized low-friction engine system. To date, a detailed set of piston and piston-ring dynamic and friction models have been developed and applied that illustrate the fundamental relationships between design parameters and friction losses. Low friction ring designs have already been recommended in a previous phase, withmore » full-scale engine validation partially completed. Current accomplishments include the addition of several additional power cylinder design areas to the overall system analysis. These include analyses of lubricant and cylinder surface finish and a parametric study of piston design. The Waukesha engine was found to be already well optimized in the areas of lubricant, surface skewness and honing cross-hatch angle, where friction reductions of 12% for lubricant, and 5% for surface characteristics, are projected. For the piston, a friction reduction of up to 50% may be possible by controlling waviness alone, while additional friction reductions are expected when other parameters are optimized. A total power cylinder friction reduction of 30-50% is expected, translating to an engine efficiency increase of two percentage points from its current baseline towards the goal of 50% efficiency. Key elements of the continuing work include further analysis and optimization of the engine piston design, in-engine testing of recommended lubricant and surface designs, design iteration and optimization of previously recommended technologies, and full-engine testing of a complete, optimized, low-friction power cylinder system.« less

National Academy of Sciences - National Research Council Resident Research Associateship Program (RRA)

DTIC Science & Technology

1992-03-11

calcim phosphate ceranuc- organic acid composites were prepared from hydroxyapatite , tricalcium phosphate, or zinc calcium phosphate, with malic acid...pressures during deposition. The film processing parameters and the basic mechanisms for the optimized conditions have been established for a possible...encountered laboratory influences detrimental to your Proposed research? Explain. Comments: JI - ve r " i O wt4 w 4 h0 - Ic hto cm pxtc 1/C Brie.f resume oa

Kernel-Based Approximate Dynamic Programming Using Bellman Residual Elimination

DTIC Science & Technology

2010-02-01

framework is the ability to utilize stochastic system models, thereby allowing the system to make sound decisions even if there is randomness in the system ...approximate policy when a system model is unavailable. We present theoretical analysis of all BRE algorithms proving convergence to the optimal policy in...policies based on MDPs is that there may be parameters of the system model that are poorly known and/or vary with time as the system operates. System

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

March-Leuba, S.; Jansen, J.F.; Kress, R.L.

A new program package, Symbolic Manipulator Laboratory (SML), for the automatic generation of both kinematic and static manipulator models in symbolic form is presented. Critical design parameters may be identified and optimized using symbolic models as shown in the sample application presented for the Future Armor Rearm System (FARS) arm. The computer-aided development of the symbolic models yields equations with reduced numerical complexity. Important considerations have been placed on the closed form solutions simplification and on the user friendly operation. The main emphasis of this research is the development of a methodology which is implemented in a computer program capablemore » of generating symbolic kinematic and static forces models of manipulators. The fact that the models are obtained trigonometrically reduced is among the most significant results of this work and the most difficult to implement. Mathematica, a commercial program that allows symbolic manipulation, is used to implement the program package. SML is written such that the user can change any of the subroutines or create new ones easily. To assist the user, an on-line help has been written to make of SML a user friendly package. Some sample applications are presented. The design and optimization of the 5-degrees-of-freedom (DOF) FARS manipulator using SML is discussed. Finally, the kinematic and static models of two different 7-DOF manipulators are calculated symbolically.« less

Interval Predictor Models with a Formal Characterization of Uncertainty and Reliability

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Giesy, Daniel P.; Kenny, Sean P.

2014-01-01

This paper develops techniques for constructing empirical predictor models based on observations. By contrast to standard models, which yield a single predicted output at each value of the model's inputs, Interval Predictors Models (IPM) yield an interval into which the unobserved output is predicted to fall. The IPMs proposed prescribe the output as an interval valued function of the model's inputs, render a formal description of both the uncertainty in the model's parameters and of the spread in the predicted output. Uncertainty is prescribed as a hyper-rectangular set in the space of model's parameters. The propagation of this set through the empirical model yields a range of outputs of minimal spread containing all (or, depending on the formulation, most) of the observations. Optimization-based strategies for calculating IPMs and eliminating the effects of outliers are proposed. Outliers are identified by evaluating the extent by which they degrade the tightness of the prediction. This evaluation can be carried out while the IPM is calculated. When the data satisfies mild stochastic assumptions, and the optimization program used for calculating the IPM is convex (or, when its solution coincides with the solution to an auxiliary convex program), the model's reliability (that is, the probability that a future observation would be within the predicted range of outputs) can be bounded rigorously by a non-asymptotic formula.

Automatic control of finite element models for temperature-controlled radiofrequency ablation

PubMed Central

Haemmerich, Dieter; Webster, John G

2005-01-01

Background The finite element method (FEM) has been used to simulate cardiac and hepatic radiofrequency (RF) ablation. The FEM allows modeling of complex geometries that cannot be solved by analytical methods or finite difference models. In both hepatic and cardiac RF ablation a common control mode is temperature-controlled mode. Commercial FEM packages don't support automating temperature control. Most researchers manually control the applied power by trial and error to keep the tip temperature of the electrodes constant. Methods We implemented a PI controller in a control program written in C++. The program checks the tip temperature after each step and controls the applied voltage to keep temperature constant. We created a closed loop system consisting of a FEM model and the software controlling the applied voltage. The control parameters for the controller were optimized using a closed loop system simulation. Results We present results of a temperature controlled 3-D FEM model of a RITA model 30 electrode. The control software effectively controlled applied voltage in the FEM model to obtain, and keep electrodes at target temperature of 100°C. The closed loop system simulation output closely correlated with the FEM model, and allowed us to optimize control parameters. Discussion The closed loop control of the FEM model allowed us to implement temperature controlled RF ablation with minimal user input. PMID:16018811

Detection analysis of surface hydroxyl active sites and simulation calculation of the surface dissociation constants of aqueous diatomite suspensions

NASA Astrophysics Data System (ADS)

Ma, Shu-Cui; Wang, Zhi-Gang; Zhang, Ji-Lin; Sun, De-Hui; Liu, Gui-Xia

2015-02-01

The surface properties of the diatomite were investigated using nitrogen adsorption/deadsorption isotherms, TG-DSC, FTIR, and XPS, and surface protonation-deprotonation behavior was determined by continuous acid-base potentiometric titration technique. The diatomite sample with porous honeycomb structure has a BET specific surface area of 10.21 m2/g and large numbers of surface hydroxyl functional groups (i.e. tbnd Si-OH, tbnd Fe-OH, and tbnd Al-OH). These surface hydroxyls can be protonated or deprotonated depending on the pH of the suspension. The experimental potentiometric data in two different ionic strength solutions (0.1 and 0.05 mol/L NaCl) were fitted using ProtoFit GUI V2.1 program by applying diffuse double layer model (DLM) with three amphoteric sites and minimizing the sum of squares between a dataset derivative function and a model derivative function. The optimized surface parameters (i.e. surface dissociation constants (log K1, log K2) and surface site concentrations (log C)) of the sample were obtained. Based on the optimized surface parameters, the surface species distribution was calculated using Program-free PHREEQC 3.1.2. Thus, this work reveals considerable new information about surface protonation-deprotonation processes and surface adsorptive behaviors of the diatomite, which helps us to effectively use the cheap and cheerful diatomite clay adsorbent.

A stochastic optimization model under modeling uncertainty and parameter certainty for groundwater remediation design--part I. Model development.

PubMed

He, L; Huang, G H; Lu, H W

2010-04-15

Solving groundwater remediation optimization problems based on proxy simulators can usually yield optimal solutions differing from the "true" ones of the problem. This study presents a new stochastic optimization model under modeling uncertainty and parameter certainty (SOMUM) and the associated solution method for simultaneously addressing modeling uncertainty associated with simulator residuals and optimizing groundwater remediation processes. This is a new attempt different from the previous modeling efforts. The previous ones focused on addressing uncertainty in physical parameters (i.e. soil porosity) while this one aims to deal with uncertainty in mathematical simulator (arising from model residuals). Compared to the existing modeling approaches (i.e. only parameter uncertainty is considered), the model has the advantages of providing mean-variance analysis for contaminant concentrations, mitigating the effects of modeling uncertainties on optimal remediation strategies, offering confidence level of optimal remediation strategies to system designers, and reducing computational cost in optimization processes. 2009 Elsevier B.V. All rights reserved.

Flight Control Development for the ARH-70 Armed Reconnaissance Helicopter Program

NASA Technical Reports Server (NTRS)

Christensen, Kevin T.; Campbell, Kip G.; Griffith, Carl D.; Ivler, Christina M.; Tischler, Mark B.; Harding, Jeffrey W.

2008-01-01

In July 2005, Bell Helicopter won the U.S. Army's Armed Reconnaissance Helicopter competition to produce a replacement for the OH-58 Kiowa Warrior capable of performing the armed reconnaissance mission. To meet the U.S. Army requirement that the ARH-70A have Level 1 handling qualities for the scout rotorcraft mission task elements defined by ADS-33E-PRF, Bell equipped the aircraft with their generic automatic flight control system (AFCS). Under the constraints of the tight ARH-70A schedule, the development team used modem parameter identification and control law optimization techniques to optimize the AFCS gains to simultaneously meet multiple handling qualities design criteria. This paper will show how linear modeling, control law optimization, and simulation have been used to produce a Level 1 scout rotorcraft for the U.S. Army, while minimizing the amount of flight testing required for AFCS development and handling qualities evaluation of the ARH-70A.

Advanced fast 3D DSA model development and calibration for design technology co-optimization

NASA Astrophysics Data System (ADS)

Lai, Kafai; Meliorisz, Balint; Muelders, Thomas; Welling, Ulrich; Stock, Hans-Jürgen; Marokkey, Sajan; Demmerle, Wolfgang; Liu, Chi-Chun; Chi, Cheng; Guo, Jing

2017-04-01

Direct Optimization (DO) of a 3D DSA model is a more optimal approach to a DTCO study in terms of accuracy and speed compared to a Cahn Hilliard Equation solver. DO's shorter run time (10X to 100X faster) and linear scaling makes it scalable to the area required for a DTCO study. However, the lack of temporal data output, as opposed to prior art, requires a new calibration method. The new method involves a specific set of calibration patterns. The calibration pattern's design is extremely important when temporal data is absent to obtain robust model parameters. A model calibrated to a Hybrid DSA system with a set of device-relevant constructs indicates the effectiveness of using nontemporal data. Preliminary model prediction using programmed defects on chemo-epitaxy shows encouraging results and agree qualitatively well with theoretical predictions from a strong segregation theory.

The fully actuated traffic control problem solved by global optimization and complementarity

NASA Astrophysics Data System (ADS)

Ribeiro, Isabel M.; de Lurdes de Oliveira Simões, Maria

2016-02-01

Global optimization and complementarity are used to determine the signal timing for fully actuated traffic control, regarding effective green and red times on each cycle. The average values of these parameters can be used to estimate the control delay of vehicles. In this article, a two-phase queuing system for a signalized intersection is outlined, based on the principle of minimization of the total waiting time for the vehicles. The underlying model results in a linear program with linear complementarity constraints, solved by a sequential complementarity algorithm. Departure rates of vehicles during green and yellow periods were treated as deterministic, while arrival rates of vehicles were assumed to follow a Poisson distribution. Several traffic scenarios were created and solved. The numerical results reveal that it is possible to use global optimization and complementarity over a reasonable number of cycles and determine with efficiency effective green and red times for a signalized intersection.

QSPIN: A High Level Java API for Quantum Computing Experimentation

NASA Technical Reports Server (NTRS)

Barth, Tim

2017-01-01

QSPIN is a high level Java language API for experimentation in QC models used in the calculation of Ising spin glass ground states and related quadratic unconstrained binary optimization (QUBO) problems. The Java API is intended to facilitate research in advanced QC algorithms such as hybrid quantum-classical solvers, automatic selection of constraint and optimization parameters, and techniques for the correction and mitigation of model and solution errors. QSPIN includes high level solver objects tailored to the D-Wave quantum annealing architecture that implement hybrid quantum-classical algorithms [Booth et al.] for solving large problems on small quantum devices, elimination of variables via roof duality, and classical computing optimization methods such as GPU accelerated simulated annealing and tabu search for comparison. A test suite of documented NP-complete applications ranging from graph coloring, covering, and partitioning to integer programming and scheduling are provided to demonstrate current capabilities.

QMCPACK: an open source ab initio quantum Monte Carlo package for the electronic structure of atoms, molecules and solids.

PubMed

Kim, Jeongnim; Baczewski, Andrew T; Beaudet, Todd D; Benali, Anouar; Bennett, M Chandler; Berrill, Mark A; Blunt, Nick S; Borda, Edgar Josué Landinez; Casula, Michele; Ceperley, David M; Chiesa, Simone; Clark, Bryan K; Clay, Raymond C; Delaney, Kris T; Dewing, Mark; Esler, Kenneth P; Hao, Hongxia; Heinonen, Olle; Kent, Paul R C; Krogel, Jaron T; Kylänpää, Ilkka; Li, Ying Wai; Lopez, M Graham; Luo, Ye; Malone, Fionn D; Martin, Richard M; Mathuriya, Amrita; McMinis, Jeremy; Melton, Cody A; Mitas, Lubos; Morales, Miguel A; Neuscamman, Eric; Parker, William D; Pineda Flores, Sergio D; Romero, Nichols A; Rubenstein, Brenda M; Shea, Jacqueline A R; Shin, Hyeondeok; Shulenburger, Luke; Tillack, Andreas F; Townsend, Joshua P; Tubman, Norm M; Van Der Goetz, Brett; Vincent, Jordan E; Yang, D ChangMo; Yang, Yubo; Zhang, Shuai; Zhao, Luning

2018-05-16

QMCPACK is an open source quantum Monte Carlo package for ab initio electronic structure calculations. It supports calculations of metallic and insulating solids, molecules, atoms, and some model Hamiltonians. Implemented real space quantum Monte Carlo algorithms include variational, diffusion, and reptation Monte Carlo. QMCPACK uses Slater-Jastrow type trial wavefunctions in conjunction with a sophisticated optimizer capable of optimizing tens of thousands of parameters. The orbital space auxiliary-field quantum Monte Carlo method is also implemented, enabling cross validation between different highly accurate methods. The code is specifically optimized for calculations with large numbers of electrons on the latest high performance computing architectures, including multicore central processing unit and graphical processing unit systems. We detail the program's capabilities, outline its structure, and give examples of its use in current research calculations. The package is available at http://qmcpack.org.

Genetic Algorithm Optimizes Q-LAW Control Parameters

NASA Technical Reports Server (NTRS)

Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard

2008-01-01

A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.

New generation photoelectric converter structure optimization using nano-structured materials

NASA Astrophysics Data System (ADS)

Dronov, A.; Gavrilin, I.; Zheleznyakova, A.

2014-12-01

In present work the influence of anodizing process parameters on PAOT geometric parameters for optimizing and increasing ETA-cell efficiency was studied. During the calculations optimal geometrical parameters were obtained. Parameters such as anodizing current density, electrolyte composition and temperature, as well as the anodic oxidation process time were selected for this investigation. Using the optimized TiO2 photoelectrode layer with 3,6 μm porous layer thickness and pore diameter more than 80 nm the ETA-cell efficiency has been increased by 3 times comparing to not nanostructured TiO2 photoelectrode.

Optimizing the physical conditioning of the NASCAR sprint cup pit crew athlete.

PubMed

Ferguson, David P; Davis, Adam M; Lightfoot, J Timothy

2015-03-01

Stock car racing is the largest spectator sport in the United States. As a result, National Association for Stock Car Automobile Racing (NASCAR) Sprint Cup teams have begun to invest in strength and conditioning programs for their pit crew athletes. However, there is limited knowledge regarding the physical characteristics of elite NASCAR pit crew athletes, how the NASCAR Sprint Cup season affects basic physiological parameters such as body composition, and what is the most appropriate physical training program that meets the needs of a pit crew athlete. We conducted 3 experiments involving Sprint Cup motorsport athletes to determine predictors of success at the elite level, seasonal physiological changes, and appropriate physical training programs. Our results showed that hamstring flexibility (p = 0.015) and the score on the 2-tire front run test (p = 0.012) were significant predictors of NASCAR Sprint Cup Pit Crew athlete performance. Additionally, during the off season, pit crew athletes lost lean body mass, which did not return until the middle of the season. Therefore, a strength and conditioning program was developed to optimize pit crew athlete performance throughout the season. Implementation of this strength and conditioning program in 1 NASCAR Sprint Cup team demonstrated that pit crew athletes were able to prevent lean body mass loss and have increased muscle power output from the start of the season to the end of the season.

Second derivative in the model of classical binary system

NASA Astrophysics Data System (ADS)

Abubekerov, M. K.; Gostev, N. Yu.

2016-06-01

We have obtained an analytical expression for the second derivatives of the light curve with respect to geometric parameters in the model of eclipsing classical binary systems. These expressions are essentially efficient algorithm to calculate the numerical values of these second derivatives for all physical values of geometric parameters. Knowledge of the values of second derivatives of the light curve at some point provides additional information about asymptotical behaviour of the function near this point and can significantly improve the search for the best-fitting light curve through the use of second-order optimization method. We write the expression for the second derivatives in a form which is most compact and uniform for all values of the geometric parameters and so make it easy to write a computer program to calculate the values of these derivatives.

Basic design considerations for free-electron lasers driven by electron beams from RF accelerators

NASA Astrophysics Data System (ADS)

Gover, A.; Freund, H.; Granatstein, V. L.; McAdoo, J. H.; Tang, C.-M.

A design procedure and design criteria are derived for free-electron lasers driven by electron beams from RF accelerators. The procedure and criteria permit an estimate of the oscillation-buildup time and the laser output power of various FEL schemes: with waveguide resonator or open resonator, with initial seed-radiation injection or with spontaneous-emission radiation source, with a linear wiggler or with a helical wiggler. Expressions are derived for computing the various FEL parameters, allowing for the design and optimization of the FEL operational characteristics under ideal conditions or with nonideal design parameters that may be limited by technological or practical constraints. The design procedure enables one to derive engineering curves and scaling laws for the FEL operating parameters. This can be done most conveniently with a computer program based on flowcharts given in the appendices.

Multi-objective design optimization and control of magnetorheological fluid brakes for automotive applications

NASA Astrophysics Data System (ADS)

Shamieh, Hadi; Sedaghati, Ramin

2017-12-01

The magnetorheological brake (MRB) is an electromechanical device that generates a retarding torque through employing magnetorheological (MR) fluids. The objective of this paper is to design, optimize and control an MRB for automotive applications considering. The dynamic range of a disk-type MRB expressing the ratio of generated toque at on and off states has been formulated as a function of the rotational speed, geometrical and material properties, and applied electrical current. Analytical magnetic circuit analysis has been conducted to derive the relation between magnetic field intensity and the applied electrical current as a function of the MRB geometrical and material properties. A multidisciplinary design optimization problem has then been formulated to identify the optimal brake geometrical parameters to maximize the dynamic range and minimize the response time and weight of the MRB under weight, size and magnetic flux density constraints. The optimization problem has been solved using combined genetic and sequential quadratic programming algorithms. Finally, the performance of the optimally designed MRB has been investigated in a quarter vehicle model. A PID controller has been designed to regulate the applied current required by the MRB in order to improve vehicle’s slipping on different road conditions.

Program Aids Analysis And Optimization Of Design

NASA Technical Reports Server (NTRS)

Rogers, James L., Jr.; Lamarsh, William J., II

1994-01-01

NETS/ PROSSS (NETS Coupled With Programming System for Structural Synthesis) computer program developed to provide system for combining NETS (MSC-21588), neural-network application program and CONMIN (Constrained Function Minimization, ARC-10836), optimization program. Enables user to reach nearly optimal design. Design then used as starting point in normal optimization process, possibly enabling user to converge to optimal solution in significantly fewer iterations. NEWT/PROSSS written in C language and FORTRAN 77.

Optimizing the passenger air bag of an adaptive restraint system for multiple size occupants.

PubMed

Bai, Zhonghao; Jiang, Binhui; Zhu, Feng; Cao, Libo

2014-01-01

The development of the adaptive occupant restraint system (AORS) has led to an innovative way to optimize such systems for multiple size occupants. An AORS consists of multiple units such as adaptive air bags, seat belts, etc. During a collision, as a supplemental protective device, air bags can provide constraint force and play a role in dissipating the crash energy of the occupants' head and thorax. This article presents an investigation into an adaptive passenger air bag (PAB). The purpose of this study is to develop a base shape of a PAB for different size occupants using an optimization method. Four typical base shapes of a PAB were designed based on geometric data on the passenger side. Then 4 PAB finite element (FE) models and a validated sled with different size dummy models were developed in MADYMO (TNO, Rijswijk, The Netherlands) to conduct the optimization to obtain the best baseline PAB that would be used in the AORS. The objective functions-that is, the minimum total probability of injuries (∑Pcomb) of the 5th percentile female and 50th and 95th percentile male dummies-were adopted to evaluate the optimal configurations. The injury probability (Pcomb) for each dummy was adopted from the U.S. New Car Assessment Program (US-NCAP). The parameters of the AORS were first optimized for different types of PAB base shapes in a frontal impact. Then, contact time duration and force between the PAB and dummy head/chest were optimized by adjusting the parameters of the PAB, such as the number and position of tethers, lower the Pcomb of the 95th percentile male dummy. According to the optimization results, 4 typical PABs could provide effective protection to 5th and 50th percentile dummies. However, due to the heavy and large torsos of the 95th percentile occupants, the current occupant restraint system does not demonstrate satisfactory protective function, particularly for the thorax.

Optimal Control Method of Robot End Position and Orientation Based on Dynamic Tracking Measurement

NASA Astrophysics Data System (ADS)

Liu, Dalong; Xu, Lijuan

2018-01-01

In order to improve the accuracy of robot pose positioning and control, this paper proposed a dynamic tracking measurement robot pose optimization control method based on the actual measurement of D-H parameters of the robot, the parameters is taken with feedback compensation of the robot, according to the geometrical parameters obtained by robot pose tracking measurement, improved multi sensor information fusion the extended Kalan filter method, with continuous self-optimal regression, using the geometric relationship between joint axes for kinematic parameters in the model, link model parameters obtained can timely feedback to the robot, the implementation of parameter correction and compensation, finally we can get the optimal attitude angle, realize the robot pose optimization control experiments were performed. 6R dynamic tracking control of robot joint robot with independent research and development is taken as experimental subject, the simulation results show that the control method improves robot positioning accuracy, and it has the advantages of versatility, simplicity, ease of operation and so on.

Design of Experiments for the Thermal Characterization of Metallic Foam

NASA Technical Reports Server (NTRS)

Crittenden, Paul E.; Cole, Kevin D.

2003-01-01

Metallic foams are being investigated for possible use in the thermal protection systems of reusable launch vehicles. As a result, the performance of these materials needs to be characterized over a wide range of temperatures and pressures. In this paper a radiation/conduction model is presented for heat transfer in metallic foams. Candidates for the optimal transient experiment to determine the intrinsic properties of the model are found by two methods. First, an optimality criterion is used to find an experiment to find all of the parameters using one heating event. Second, a pair of heating events is used to determine the parameters in which one heating event is optimal for finding the parameters related to conduction, while the other heating event is optimal for finding the parameters associated with radiation. Simulated data containing random noise was analyzed to determine the parameters using both methods. In all cases the parameter estimates could be improved by analyzing a larger data record than suggested by the optimality criterion.

Sequential ensemble-based optimal design for parameter estimation: SEQUENTIAL ENSEMBLE-BASED OPTIMAL DESIGN

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

Man, Jun; Zhang, Jiangjiang; Li, Weixuan

2016-10-01

The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees ofmore » freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.« less

A Short-Term and High-Resolution System Load Forecasting Approach Using Support Vector Regression with Hybrid Parameters Optimization

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

Jiang, Huaiguang

This work proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of themore » hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system.« less

MIRACAL: A mission radiation calculation program for analysis of lunar and interplanetary missions

NASA Technical Reports Server (NTRS)

Nealy, John E.; Striepe, Scott A.; Simonsen, Lisa C.

1992-01-01

A computational procedure and data base are developed for manned space exploration missions for which estimates are made for the energetic particle fluences encountered and the resulting dose equivalent incurred. The data base includes the following options: statistical or continuum model for ordinary solar proton events, selection of up to six large proton flare spectra, and galactic cosmic ray fluxes for elemental nuclei of charge numbers 1 through 92. The program requires an input trajectory definition information and specifications of optional parameters, which include desired spectral data and nominal shield thickness. The procedure may be implemented as an independent program or as a subroutine in trajectory codes. This code should be most useful in mission optimization and selection studies for which radiation exposure is of special importance.

Application of the advanced engineering environment for optimization energy consumption in designed vehicles

NASA Astrophysics Data System (ADS)

Monica, Z.; Sękala, A.; Gwiazda, A.; Banaś, W.

2016-08-01

Nowadays a key issue is to reduce the energy consumption of road vehicles. In particular solution one could find different strategies of energy optimization. The most popular but not sophisticated is so called eco-driving. In this strategy emphasized is particular behavior of drivers. In more sophisticated solution behavior of drivers is supported by control system measuring driving parameters and suggesting proper operation of the driver. The other strategy is concerned with application of different engineering solutions that aid optimization the process of energy consumption. Such systems take into consideration different parameters measured in real time and next take proper action according to procedures loaded to the control computer of a vehicle. The third strategy bases on optimization of the designed vehicle taking into account especially main sub-systems of a technical mean. In this approach the optimal level of energy consumption by a vehicle is obtained by synergetic results of individual optimization of particular constructional sub-systems of a vehicle. It is possible to distinguish three main sub-systems: the structural one the drive one and the control one. In the case of the structural sub-system optimization of the energy consumption level is related with the optimization or the weight parameter and optimization the aerodynamic parameter. The result is optimized body of a vehicle. Regarding the drive sub-system the optimization of the energy consumption level is related with the fuel or power consumption using the previously elaborated physical models. Finally the optimization of the control sub-system consists in determining optimal control parameters.

Parallel Optimization of Polynomials for Large-scale Problems in Stability and Control

NASA Astrophysics Data System (ADS)

Kamyar, Reza

In this thesis, we focus on some of the NP-hard problems in control theory. Thanks to the converse Lyapunov theory, these problems can often be modeled as optimization over polynomials. To avoid the problem of intractability, we establish a trade off between accuracy and complexity. In particular, we develop a sequence of tractable optimization problems --- in the form of Linear Programs (LPs) and/or Semi-Definite Programs (SDPs) --- whose solutions converge to the exact solution of the NP-hard problem. However, the computational and memory complexity of these LPs and SDPs grow exponentially with the progress of the sequence - meaning that improving the accuracy of the solutions requires solving SDPs with tens of thousands of decision variables and constraints. Setting up and solving such problems is a significant challenge. The existing optimization algorithms and software are only designed to use desktop computers or small cluster computers --- machines which do not have sufficient memory for solving such large SDPs. Moreover, the speed-up of these algorithms does not scale beyond dozens of processors. This in fact is the reason we seek parallel algorithms for setting-up and solving large SDPs on large cluster- and/or super-computers. We propose parallel algorithms for stability analysis of two classes of systems: 1) Linear systems with a large number of uncertain parameters; 2) Nonlinear systems defined by polynomial vector fields. First, we develop a distributed parallel algorithm which applies Polya's and/or Handelman's theorems to some variants of parameter-dependent Lyapunov inequalities with parameters defined over the standard simplex. The result is a sequence of SDPs which possess a block-diagonal structure. We then develop a parallel SDP solver which exploits this structure in order to map the computation, memory and communication to a distributed parallel environment. Numerical tests on a supercomputer demonstrate the ability of the algorithm to efficiently utilize hundreds and potentially thousands of processors, and analyze systems with 100+ dimensional state-space. Furthermore, we extend our algorithms to analyze robust stability over more complicated geometries such as hypercubes and arbitrary convex polytopes. Our algorithms can be readily extended to address a wide variety of problems in control such as Hinfinity synthesis for systems with parametric uncertainty and computing control Lyapunov functions.

Low-dose cone-beam CT via raw counts domain low-signal correction schemes: Performance assessment and task-based parameter optimization (Part II. Task-based parameter optimization).

PubMed

Gomez-Cardona, Daniel; Hayes, John W; Zhang, Ran; Li, Ke; Cruz-Bastida, Juan Pablo; Chen, Guang-Hong

2018-05-01

Different low-signal correction (LSC) methods have been shown to efficiently reduce noise streaks and noise level in CT to provide acceptable images at low-radiation dose levels. These methods usually result in CT images with highly shift-variant and anisotropic spatial resolution and noise, which makes the parameter optimization process highly nontrivial. The purpose of this work was to develop a local task-based parameter optimization framework for LSC methods. Two well-known LSC methods, the adaptive trimmed mean (ATM) filter and the anisotropic diffusion (AD) filter, were used as examples to demonstrate how to use the task-based framework to optimize filter parameter selection. Two parameters, denoted by the set P, for each LSC method were included in the optimization problem. For the ATM filter, these parameters are the low- and high-signal threshold levels p l and p h ; for the AD filter, the parameters are the exponents δ and γ in the brightness gradient function. The detectability index d' under the non-prewhitening (NPW) mathematical observer model was selected as the metric for parameter optimization. The optimization problem was formulated as an unconstrained optimization problem that consisted of maximizing an objective function d'(P), where i and j correspond to the i-th imaging task and j-th spatial location, respectively. Since there is no explicit mathematical function to describe the dependence of d' on the set of parameters P for each LSC method, the optimization problem was solved via an experimentally measured d' map over a densely sampled parameter space. In this work, three high-contrast-high-frequency discrimination imaging tasks were defined to explore the parameter space of each of the LSC methods: a vertical bar pattern (task I), a horizontal bar pattern (task II), and a multidirectional feature (task III). Two spatial locations were considered for the analysis, a posterior region-of-interest (ROI) located within the noise streaks region and an anterior ROI, located further from the noise streaks region. Optimal results derived from the task-based detectability index metric were compared to other operating points in the parameter space with different noise and spatial resolution trade-offs. The optimal operating points determined through the d' metric depended on the interplay between the major spatial frequency components of each imaging task and the highly shift-variant and anisotropic noise and spatial resolution properties associated with each operating point in the LSC parameter space. This interplay influenced imaging performance the most when the major spatial frequency component of a given imaging task coincided with the direction of spatial resolution loss or with the dominant noise spatial frequency component; this was the case of imaging task II. The performance of imaging tasks I and III was influenced by this interplay in a smaller scale than imaging task II, since the major frequency component of task I was perpendicular to imaging task II, and because imaging task III did not have strong directional dependence. For both LSC methods, there was a strong dependence of the overall d' magnitude and shape of the contours on the spatial location within the phantom, particularly for imaging tasks II and III. The d' value obtained at the optimal operating point for each spatial location and imaging task was similar when comparing the LSC methods studied in this work. A local task-based detectability framework to optimize the selection of parameters for LSC methods was developed. The framework takes into account the potential shift-variant and anisotropic spatial resolution and noise properties to maximize the imaging performance of the CT system. Optimal parameters for a given LSC method depend strongly on the spatial location within the image object. © 2018 American Association of Physicists in Medicine.

Space Shuttle propulsion parameter estimation using optimal estimation techniques, volume 1

NASA Technical Reports Server (NTRS)

1983-01-01

The mathematical developments and their computer program implementation for the Space Shuttle propulsion parameter estimation project are summarized. The estimation approach chosen is the extended Kalman filtering with a modified Bryson-Frazier smoother. Its use here is motivated by the objective of obtaining better estimates than those available from filtering and to eliminate the lag associated with filtering. The estimation technique uses as the dynamical process the six degree equations-of-motion resulting in twelve state vector elements. In addition to these are mass and solid propellant burn depth as the ""system'' state elements. The ""parameter'' state elements can include aerodynamic coefficient, inertia, center-of-gravity, atmospheric wind, etc. deviations from referenced values. Propulsion parameter state elements have been included not as options just discussed but as the main parameter states to be estimated. The mathematical developments were completed for all these parameters. Since the systems dynamics and measurement processes are non-linear functions of the states, the mathematical developments are taken up almost entirely by the linearization of these equations as required by the estimation algorithms.

Recursive Branching Simulated Annealing Algorithm

NASA Technical Reports Server (NTRS)

Bolcar, Matthew; Smith, J. Scott; Aronstein, David

2012-01-01

This innovation is a variation of a simulated-annealing optimization algorithm that uses a recursive-branching structure to parallelize the search of a parameter space for the globally optimal solution to an objective. The algorithm has been demonstrated to be more effective at searching a parameter space than traditional simulated-annealing methods for a particular problem of interest, and it can readily be applied to a wide variety of optimization problems, including those with a parameter space having both discrete-value parameters (combinatorial) and continuous-variable parameters. It can take the place of a conventional simulated- annealing, Monte-Carlo, or random- walk algorithm. In a conventional simulated-annealing (SA) algorithm, a starting configuration is randomly selected within the parameter space. The algorithm randomly selects another configuration from the parameter space and evaluates the objective function for that configuration. If the objective function value is better than the previous value, the new configuration is adopted as the new point of interest in the parameter space. If the objective function value is worse than the previous value, the new configuration may be adopted, with a probability determined by a temperature parameter, used in analogy to annealing in metals. As the optimization continues, the region of the parameter space from which new configurations can be selected shrinks, and in conjunction with lowering the annealing temperature (and thus lowering the probability for adopting configurations in parameter space with worse objective functions), the algorithm can converge on the globally optimal configuration. The Recursive Branching Simulated Annealing (RBSA) algorithm shares some features with the SA algorithm, notably including the basic principles that a starting configuration is randomly selected from within the parameter space, the algorithm tests other configurations with the goal of finding the globally optimal solution, and the region from which new configurations can be selected shrinks as the search continues. The key difference between these algorithms is that in the SA algorithm, a single path, or trajectory, is taken in parameter space, from the starting point to the globally optimal solution, while in the RBSA algorithm, many trajectories are taken; by exploring multiple regions of the parameter space simultaneously, the algorithm has been shown to converge on the globally optimal solution about an order of magnitude faster than when using conventional algorithms. Novel features of the RBSA algorithm include: 1. More efficient searching of the parameter space due to the branching structure, in which multiple random configurations are generated and multiple promising regions of the parameter space are explored; 2. The implementation of a trust region for each parameter in the parameter space, which provides a natural way of enforcing upper- and lower-bound constraints on the parameters; and 3. The optional use of a constrained gradient- search optimization, performed on the continuous variables around each branch s configuration in parameter space to improve search efficiency by allowing for fast fine-tuning of the continuous variables within the trust region at that configuration point.

Optimization of programmed-temperature vaporization injection preparative capillary GC for compound specific radiocarbon analysis.

PubMed

Zhang, Xinyu; Zhao, Liang; Wang, Yexin; Xu, Yunping; Zhou, Liping

2013-07-01

Preparative capillary GC (PCGC) is a powerful tool for the separation and purification of compounds from any complex matrix, which can be used for compound-specific radiocarbon analysis. However, the effect of PCGC parameters on the trapping efficiency is not well understood. Here, we present a comprehensive study on the optimization of parameters based on 11 reference compounds with different physicochemical properties. Under the optimum conditions, the trapping efficiencies of these 11 compounds (including high-boiling-point n-hentriacontane and methyl lignocerate) are about 80% (60-89%). The isolation of target compounds from standard solutions, plant and soil samples demonstrates that our optimized method is applicable for different classes of compounds including n-alkanes, fatty acid esters, long-chain fatty alcohol esters, polycyclic aromatic hydrocarbons (PAHs) and steranes. By injecting 25 μL in large volume injection mode, over 100 μg, high purity (>90%) target compounds are harvested within 24 h. The recovery ranges of two real samples are about 70% (59.9-83.8%) and about 83% (77.2-88.5%), respectively. Compared to previous studies, our study makes significant improvement in the recovery of PCGC, which is important for its wide application in biogeochemistry, environmental sciences, and archaeology. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Hybrid, experimental and computational, investigation of mechanical components

NASA Astrophysics Data System (ADS)

Furlong, Cosme; Pryputniewicz, Ryszard J.

1996-07-01

Computational and experimental methodologies have unique features for the analysis and solution of a wide variety of engineering problems. Computations provide results that depend on selection of input parameters such as geometry, material constants, and boundary conditions which, for correct modeling purposes, have to be appropriately chosen. In addition, it is relatively easy to modify the input parameters in order to computationally investigate different conditions. Experiments provide solutions which characterize the actual behavior of the object of interest subjected to specific operating conditions. However, it is impractical to experimentally perform parametric investigations. This paper discusses the use of a hybrid, computational and experimental, approach for study and optimization of mechanical components. Computational techniques are used for modeling the behavior of the object of interest while it is experimentally tested using noninvasive optical techniques. Comparisons are performed through a fringe predictor program used to facilitate the correlation between both techniques. In addition, experimentally obtained quantitative information, such as displacements and shape, can be applied in the computational model in order to improve this correlation. The result is a validated computational model that can be used for performing quantitative analyses and structural optimization. Practical application of the hybrid approach is illustrated with a representative example which demonstrates the viability of the approach as an engineering tool for structural analysis and optimization.

Intelligent reservoir operation system based on evolving artificial neural networks

NASA Astrophysics Data System (ADS)

Chaves, Paulo; Chang, Fi-John

2008-06-01

We propose a novel intelligent reservoir operation system based on an evolving artificial neural network (ANN). Evolving means the parameters of the ANN model are identified by the GA evolutionary optimization technique. Accordingly, the ANN model should represent the operational strategies of reservoir operation. The main advantages of the Evolving ANN Intelligent System (ENNIS) are as follows: (i) only a small number of parameters to be optimized even for long optimization horizons, (ii) easy to handle multiple decision variables, and (iii) the straightforward combination of the operation model with other prediction models. The developed intelligent system was applied to the operation of the Shihmen Reservoir in North Taiwan, to investigate its applicability and practicability. The proposed method is first built to a simple formulation for the operation of the Shihmen Reservoir, with single objective and single decision. Its results were compared to those obtained by dynamic programming. The constructed network proved to be a good operational strategy. The method was then built and applied to the reservoir with multiple (five) decision variables. The results demonstrated that the developed evolving neural networks improved the operation performance of the reservoir when compared to its current operational strategy. The system was capable of successfully simultaneously handling various decision variables and provided reasonable and suitable decisions.

Dynamic Optimization

NASA Technical Reports Server (NTRS)

Laird, Philip

1992-01-01

We distinguish static and dynamic optimization of programs: whereas static optimization modifies a program before runtime and is based only on its syntactical structure, dynamic optimization is based on the statistical properties of the input source and examples of program execution. Explanation-based generalization is a commonly used dynamic optimization method, but its effectiveness as a speedup-learning method is limited, in part because it fails to separate the learning process from the program transformation process. This paper describes a dynamic optimization technique called a learn-optimize cycle that first uses a learning element to uncover predictable patterns in the program execution and then uses an optimization algorithm to map these patterns into beneficial transformations. The technique has been used successfully for dynamic optimization of pure Prolog.

Optimizing Photosynthetic and Respiratory Parameters Based on the Seasonal Variation Pattern in Regional Net Ecosystem Productivity Obtained from Atmospheric Inversion

NASA Astrophysics Data System (ADS)

Chen, Z.; Chen, J.; Zheng, X.; Jiang, F.; Zhang, S.; Ju, W.; Yuan, W.; Mo, G.

2014-12-01

In this study, we explore the feasibility of optimizing ecosystem photosynthetic and respiratory parameters from the seasonal variation pattern of the net carbon flux. An optimization scheme is proposed to estimate two key parameters (Vcmax and Q10) by exploiting the seasonal variation in the net ecosystem carbon flux retrieved by an atmospheric inversion system. This scheme is implemented to estimate Vcmax and Q10 of the Boreal Ecosystem Productivity Simulator (BEPS) to improve its NEP simulation in the Boreal North America (BNA) region. Simultaneously, in-situ NEE observations at six eddy covariance sites are used to evaluate the NEE simulations. The results show that the performance of the optimized BEPS is superior to that of the BEPS with the default parameter values. These results have the implication on using atmospheric CO2 data for optimizing ecosystem parameters through atmospheric inversion or data assimilation techniques.

Gaussian mass optimization for kernel PCA parameters

NASA Astrophysics Data System (ADS)

Liu, Yong; Wang, Zulin

2011-10-01

This paper proposes a novel kernel parameter optimization method based on Gaussian mass, which aims to overcome the current brute force parameter optimization method in a heuristic way. Generally speaking, the choice of kernel parameter should be tightly related to the target objects while the variance between the samples, the most commonly used kernel parameter, doesn't possess much features of the target, which gives birth to Gaussian mass. Gaussian mass defined in this paper has the property of the invariance of rotation and translation and is capable of depicting the edge, topology and shape information. Simulation results show that Gaussian mass leads a promising heuristic optimization boost up for kernel method. In MNIST handwriting database, the recognition rate improves by 1.6% compared with common kernel method without Gaussian mass optimization. Several promising other directions which Gaussian mass might help are also proposed at the end of the paper.

A case study in programming a quantum annealer for hard operational planning problems

NASA Astrophysics Data System (ADS)

Rieffel, Eleanor G.; Venturelli, Davide; O'Gorman, Bryan; Do, Minh B.; Prystay, Elicia M.; Smelyanskiy, Vadim N.

2015-01-01

We report on a case study in programming an early quantum annealer to attack optimization problems related to operational planning. While a number of studies have looked at the performance of quantum annealers on problems native to their architecture, and others have examined performance of select problems stemming from an application area, ours is one of the first studies of a quantum annealer's performance on parametrized families of hard problems from a practical domain. We explore two different general mappings of planning problems to quadratic unconstrained binary optimization (QUBO) problems, and apply them to two parametrized families of planning problems, navigation-type and scheduling-type. We also examine two more compact, but problem-type specific, mappings to QUBO, one for the navigation-type planning problems and one for the scheduling-type planning problems. We study embedding properties and parameter setting and examine their effect on the efficiency with which the quantum annealer solves these problems. From these results, we derive insights useful for the programming and design of future quantum annealers: problem choice, the mapping used, the properties of the embedding, and the annealing profile all matter, each significantly affecting the performance.

Block matching sparsity regularization-based image reconstruction for incomplete projection data in computed tomography

NASA Astrophysics Data System (ADS)

Cai, Ailong; Li, Lei; Zheng, Zhizhong; Zhang, Hanming; Wang, Linyuan; Hu, Guoen; Yan, Bin

2018-02-01

In medical imaging many conventional regularization methods, such as total variation or total generalized variation, impose strong prior assumptions which can only account for very limited classes of images. A more reasonable sparse representation frame for images is still badly needed. Visually understandable images contain meaningful patterns, and combinations or collections of these patterns can be utilized to form some sparse and redundant representations which promise to facilitate image reconstructions. In this work, we propose and study block matching sparsity regularization (BMSR) and devise an optimization program using BMSR for computed tomography (CT) image reconstruction for an incomplete projection set. The program is built as a constrained optimization, minimizing the L1-norm of the coefficients of the image in the transformed domain subject to data observation and positivity of the image itself. To solve the program efficiently, a practical method based on the proximal point algorithm is developed and analyzed. In order to accelerate the convergence rate, a practical strategy for tuning the BMSR parameter is proposed and applied. The experimental results for various settings, including real CT scanning, have verified the proposed reconstruction method showing promising capabilities over conventional regularization.