Flexible operation strategy for environment control system in abnormal supply power condition

NASA Astrophysics Data System (ADS)

Liping, Pang; Guoxiang, Li; Hongquan, Qu; Yufeng, Fang

2017-04-01

This paper establishes an optimization method that can be applied to the flexible operation of the environment control system in an abnormal supply power condition. A proposed conception of lifespan is used to evaluate the depletion time of the non-regenerative substance. The optimization objective function is to maximize the lifespans. The optimization variables are the allocated powers of subsystems. The improved Non-dominated Sorting Genetic Algorithm is adopted to obtain the pareto optimization frontier with the constraints of the cabin environmental parameters and the adjustable operating parameters of the subsystems. Based on the same importance of objective functions, the preferred power allocation of subsystems can be optimized. Then the corresponding running parameters of subsystems can be determined to ensure the maximum lifespans. A long-duration space station with three astronauts is used to show the implementation of the proposed optimization method. Three different CO2 partial pressure levels are taken into consideration in this study. The optimization results show that the proposed optimization method can obtain the preferred power allocation for the subsystems when the supply power is at a less-than-nominal value. The method can be applied to the autonomous control for the emergency response of the environment control system.

Theoretic aspects of the identification of the parameters in the optimal control model

NASA Technical Reports Server (NTRS)

Vanwijk, R. A.; Kok, J. J.

1977-01-01

The identification of the parameters of the optimal control model from input-output data of the human operator is considered. Accepting the basic structure of the model as a cascade of a full-order observer and a feedback law, and suppressing the inherent optimality of the human controller, the parameters to be identified are the feedback matrix, the observer gain matrix, and the intensity matrices of the observation noise and the motor noise. The identification of the parameters is a statistical problem, because the system and output are corrupted by noise, and therefore the solution must be based on the statistics (probability density function) of the input and output data of the human operator. However, based on the statistics of the input-output data of the human operator, no distinction can be made between the observation and the motor noise, which shows that the model suffers from overparameterization.

Analytical design of an industrial two-term controller for optimal regulatory control of open-loop unstable processes under operational constraints.

PubMed

Tchamna, Rodrigue; Lee, Moonyong

2018-01-01

This paper proposes a novel optimization-based approach for the design of an industrial two-term proportional-integral (PI) controller for the optimal regulatory control of unstable processes subjected to three common operational constraints related to the process variable, manipulated variable and its rate of change. To derive analytical design relations, the constrained optimal control problem in the time domain was transformed into an unconstrained optimization problem in a new parameter space via an effective parameterization. The resulting optimal PI controller has been verified to yield optimal performance and stability of an open-loop unstable first-order process under operational constraints. The proposed analytical design method explicitly takes into account the operational constraints in the controller design stage and also provides useful insights into the optimal controller design. Practical procedures for designing optimal PI parameters and a feasible constraint set exclusive of complex optimization steps are also proposed. The proposed controller was compared with several other PI controllers to illustrate its performance. The robustness of the proposed controller against plant-model mismatch has also been investigated. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

A novel membrane-based process to isolate peroxidase from horseradish roots: optimization of operating parameters.

PubMed

Liu, Jianguo; Yang, Bo; Chen, Changzhen

2013-02-01

The optimization of operating parameters for the isolation of peroxidase from horseradish (Armoracia rusticana) roots with ultrafiltration (UF) technology was systemically studied. The effects of UF operating conditions on the transmission of proteins were quantified using the parameter scanning UF. These conditions included solution pH, ionic strength, stirring speed and permeate flux. Under optimized conditions, the purity of horseradish peroxidase (HRP) obtained was greater than 84 % after a two-stage UF process and the recovery of HRP from the feedstock was close to 90 %. The resulting peroxidase product was then analysed by isoelectric focusing, SDS-PAGE and circular dichroism, to confirm its isoelectric point, molecular weight and molecular secondary structure. The effects of calcium ion on HRP specific activities were also experimentally determined.

Optimal line drop compensation parameters under multi-operating conditions

NASA Astrophysics Data System (ADS)

Wan, Yuan; Li, Hang; Wang, Kai; He, Zhe

2017-01-01

Line Drop Compensation (LDC) is a main function of Reactive Current Compensation (RCC) which is developed to improve voltage stability. While LDC has benefit to voltage, it may deteriorate the small-disturbance rotor angle stability of power system. In present paper, an intelligent algorithm which is combined by Genetic Algorithm (GA) and Backpropagation Neural Network (BPNN) is proposed to optimize parameters of LDC. The objective function proposed in present paper takes consideration of voltage deviation and power system oscillation minimal damping ratio under multi-operating conditions. A simulation based on middle area of Jiangxi province power system is used to demonstrate the intelligent algorithm. The optimization result shows that coordinate optimized parameters can meet the multioperating conditions requirement and improve voltage stability as much as possible while guaranteeing enough damping ratio.

Defining a region of optimization based on engine usage data

DOEpatents

Jiang, Li; Lee, Donghoon; Yilmaz, Hakan; Stefanopoulou, Anna

2015-08-04

Methods and systems for engine control optimization are provided. One or more operating conditions of a vehicle engine are detected. A value for each of a plurality of engine control parameters is determined based on the detected one or more operating conditions of the vehicle engine. A range of the most commonly detected operating conditions of the vehicle engine is identified and a region of optimization is defined based on the range of the most commonly detected operating conditions of the vehicle engine. The engine control optimization routine is initiated when the one or more operating conditions of the vehicle engine are within the defined region of optimization.

Genetic Algorithm Application in Optimization of Wireless Sensor Networks

PubMed Central

Norouzi, Ali; Zaim, A. Halim

2014-01-01

There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs. PMID:24693235

Optimizing operating parameters of a honeycomb zeolite rotor concentrator for processing TFT-LCD volatile organic compounds with competitive adsorption characteristics.

PubMed

Lin, Yu-Chih; Chang, Feng-Tang

2009-05-30

In this study, we attempted to enhance the removal efficiency of a honeycomb zeolite rotor concentrator (HZRC), operated at optimal parameters, for processing TFT-LCD volatile organic compounds (VOCs) with competitive adsorption characteristics. The results indicated that when the HZRC processed a VOCs stream of mixed compounds, compounds with a high boiling point take precedence in the adsorption process. In addition, existing compounds with a low boiling point adsorbed onto the HZRC were also displaced by the high-boiling-point compounds. In order to achieve optimal operating parameters for high VOCs removal efficiency, results suggested controlling the inlet velocity to <1.5m/s, reducing the concentration ratio to 8 times, increasing the desorption temperature to 200-225 degrees C, and setting the rotation speed to 6.5rpm.

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.

Determining optimal operation parameters for reducing PCDD/F emissions (I-TEQ values) from the iron ore sintering process by using the Taguchi experimental design.

PubMed

Chen, Yu-Cheng; Tsai, Perng-Jy; Mou, Jin-Luh

2008-07-15

This study is the first one using the Taguchi experimental design to identify the optimal operating condition for reducing polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/ Fs) formations during the iron ore sintering process. Four operating parameters, including the water content (Wc; range = 6.0-7.0 wt %), suction pressure (Ps; range = 1000-1400 mmH2O), bed height (Hb; range = 500-600 mm), and type of hearth layer (including sinter, hematite, and limonite), were selected for conducting experiments in a pilot scale sinter pot to simulate various sintering operating conditions of a real-scale sinter plant We found that the resultant optimal combination (Wc = 6.5 wt%, Hb = 500 mm, Ps = 1000 mmH2O, and hearth layer = hematite) could decrease the emission factor of total PCDD/Fs (total EF(PCDD/Fs)) up to 62.8% by reference to the current operating condition of the real-scale sinter plant (Wc = 6.5 wt %, Hb = 550 mm, Ps = 1200 mmH2O, and hearth layer = sinter). Through the ANOVA analysis, we found that Wc was the most significant parameter in determining total EF(PCDD/Fs (accounting for 74.7% of the total contribution of the four selected parameters). The resultant optimal combination could also enhance slightly in both sinter productivity and sinter strength (30.3 t/m2/day and 72.4%, respectively) by reference to those obtained from the reference operating condition (29.9 t/m (2)/day and 72.2%, respectively). The above results further ensure the applicability of the obtained optimal combination for the real-scale sinter production without interfering its sinter productivity and sinter strength.

Battery Energy Storage State-of-Charge Forecasting: Models, Optimization, and Accuracy

DOE PAGES

Rosewater, David; Ferreira, Summer; Schoenwald, David; ...

2018-01-25

Battery energy storage systems (BESS) are a critical technology for integrating high penetration renewable power on an intelligent electrical grid. As limited energy restricts the steady-state operational state-of-charge (SoC) of storage systems, SoC forecasting models are used to determine feasible charge and discharge schedules that supply grid services. Smart grid controllers use SoC forecasts to optimize BESS schedules to make grid operation more efficient and resilient. This study presents three advances in BESS state-of-charge forecasting. First, two forecasting models are reformulated to be conducive to parameter optimization. Second, a new method for selecting optimal parameter values based on operational datamore » is presented. Last, a new framework for quantifying model accuracy is developed that enables a comparison between models, systems, and parameter selection methods. The accuracies achieved by both models, on two example battery systems, with each method of parameter selection are then compared in detail. The results of this analysis suggest variation in the suitability of these models for different battery types and applications. Finally, the proposed model formulations, optimization methods, and accuracy assessment framework can be used to improve the accuracy of SoC forecasts enabling better control over BESS charge/discharge schedules.« less

Battery Energy Storage State-of-Charge Forecasting: Models, Optimization, and Accuracy

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

Rosewater, David; Ferreira, Summer; Schoenwald, David

Battery energy storage systems (BESS) are a critical technology for integrating high penetration renewable power on an intelligent electrical grid. As limited energy restricts the steady-state operational state-of-charge (SoC) of storage systems, SoC forecasting models are used to determine feasible charge and discharge schedules that supply grid services. Smart grid controllers use SoC forecasts to optimize BESS schedules to make grid operation more efficient and resilient. This study presents three advances in BESS state-of-charge forecasting. First, two forecasting models are reformulated to be conducive to parameter optimization. Second, a new method for selecting optimal parameter values based on operational datamore » is presented. Last, a new framework for quantifying model accuracy is developed that enables a comparison between models, systems, and parameter selection methods. The accuracies achieved by both models, on two example battery systems, with each method of parameter selection are then compared in detail. The results of this analysis suggest variation in the suitability of these models for different battery types and applications. Finally, the proposed model formulations, optimization methods, and accuracy assessment framework can be used to improve the accuracy of SoC forecasts enabling better control over BESS charge/discharge schedules.« less

Systems and methods for optimal power flow on a radial network

DOEpatents

Low, Steven H.; Peng, Qiuyu

2018-04-24

Node controllers and power distribution networks in accordance with embodiments of the invention enable distributed power control. One embodiment includes a node controller including a distributed power control application; a plurality of node operating parameters describing the operating parameter of a node and a set of at least one node selected from the group consisting of an ancestor node and at least one child node; wherein send node operating parameters to nodes in the set of at least one node; receive operating parameters from the nodes in the set of at least one node; calculate a plurality of updated node operating parameters using an iterative process to determine the updated node operating parameters using the node operating parameters that describe the operating parameters of the node and the set of at least one node, where the iterative process involves evaluation of a closed form solution; and adjust node operating parameters.

Characterizing and Optimizing Photocathode Laser Distributions for Ultra-low Emittance Electron Beam Operations

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

Zhou, F.; Bohler, D.; Ding, Y.

2015-12-07

Photocathode RF gun has been widely used for generation of high-brightness electron beams for many different applications. We found that the drive laser distributions in such RF guns play important roles in minimizing the electron beam emittance. Characterizing the laser distributions with measurable parameters and optimizing beam emittance versus the laser distribution parameters in both spatial and temporal directions are highly desired for high-brightness electron beam operation. In this paper, we report systematic measurements and simulations of emittance dependence on the measurable parameters represented for spatial and temporal laser distributions at the photocathode RF gun systems of Linac Coherent Lightmore » Source. The tolerable parameter ranges for photocathode drive laser distributions in both directions are presented for ultra-low emittance beam operations.« less

Parameter optimization for transitions between memory states in small arrays of Josephson junctions

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

Rezac, Jacob D.; Imam, Neena; Braiman, Yehuda

Coupled arrays of Josephson junctions possess multiple stable zero voltage states. Such states can store information and consequently can be utilized for cryogenic memory applications. Basic memory operations can be implemented by sending a pulse to one of the junctions and studying transitions between the states. In order to be suitable for memory operations, such transitions between the states have to be fast and energy efficient. Here in this article we employed simulated annealing, a stochastic optimization algorithm, to study parameter optimization of array parameters which minimizes times and energies of transitions between specifically chosen states that can be utilizedmore » for memory operations (Read, Write, and Reset). Simulation results show that such transitions occur with access times on the order of 10–100 ps and access energies on the order of 10 -19–5×10 -18 J. Numerical simulations are validated with approximate analytical results.« less

Selection of operating parameters on the basis of hydrodynamics in centrifugal partition chromatography for the purification of nybomycin derivatives.

PubMed

Adelmann, S; Baldhoff, T; Koepcke, B; Schembecker, G

2013-01-25

The selection of solvent systems in centrifugal partition chromatography (CPC) is the most critical point in setting up a separation. Therefore, lots of research was done on the topic in the last decades. But the selection of suitable operating parameters (mobile phase flow rate, rotational speed and mode of operation) with respect to hydrodynamics and pressure drop limit in CPC is still mainly driven by experience of the chromatographer. In this work we used hydrodynamic analysis for the prediction of most suitable operating parameters. After selection of different solvent systems with respect to partition coefficients for the target compound the hydrodynamics were visualized. Based on flow pattern and retention the operating parameters were selected for the purification runs of nybomycin derivatives that were carried out with a 200 ml FCPC(®) rotor. The results have proven that the selection of optimized operating parameters by analysis of hydrodynamics only is possible. As the hydrodynamics are predictable by the physical properties of the solvent system the optimized operating parameters can be estimated, too. Additionally, we found that dispersion and especially retention are improved if the less viscous phase is mobile. Crown Copyright © 2012. Published by Elsevier B.V. All rights reserved.

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.

Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm

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

Sheng, Zheng, E-mail: 19994035@sina.com; Wang, Jun; Zhou, Bihua

2014-03-15

This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented tomore » tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.« less

Application study of evolutionary operation methods in optimization of process parameters for mosquito coils industry

NASA Astrophysics Data System (ADS)

Ginting, E.; Tambunanand, M. M.; Syahputri, K.

2018-02-01

Evolutionary Operation Methods (EVOP) is a method that is designed used in the process of running or operating routinely in the company to enables high productivity. Quality is one of the critical factors for a company to win the competition. Because of these conditions, the research for products quality has been done by gathering the production data of the company and make a direct observation to the factory floor especially the drying department to identify the problem which is the high water content in the mosquito incense coil. PT.X which is producing mosquito coils attempted to reduce product defects caused by the inaccuracy of operating conditions. One of the parameters of good quality insect repellent that is water content, that if the moisture content is too high then the product easy to mold and broken, and vice versa if it is too low the products are easily broken and burn shorter hours. Three factors that affect the value of the optimal water content, the stirring time, drying temperature and drying time. To obtain the required conditions Evolutionary Operation (EVOP) methods is used. Evolutionary Operation (EVOP) is used as an efficient technique for optimization of two or three variable experimental parameters using two-level factorial designs with center point. Optimal operating conditions in the experiment are stirring time performed for 20 minutes, drying temperature at 65°C, and drying time for 130 minutes. The results of the analysis based on the method of Evolutionary Operation (EVOP) value is the optimum water content of 6.90%, which indicates the value has approached the optimal in a production plant that is 7%.

Multi-parameter geometrical scaledown study for energy optimization of MTJ and related spintronics nanodevices

NASA Astrophysics Data System (ADS)

Farhat, I. A. H.; Alpha, C.; Gale, E.; Atia, D. Y.; Stein, A.; Isakovic, A. F.

The scaledown of magnetic tunnel junctions (MTJ) and related nanoscale spintronics devices poses unique challenges for energy optimization of their performance. We demonstrate the dependence of the switching current on the scaledown variable, while considering the influence of geometric parameters of MTJ, such as the free layer thickness, tfree, lateral size of the MTJ, w, and the anisotropy parameter of the MTJ. At the same time, we point out which values of the saturation magnetization, Ms, and anisotropy field, Hk, can lead to lowering the switching current and overall decrease of the energy needed to operate an MTJ. It is demonstrated that scaledown via decreasing the lateral size of the MTJ, while allowing some other parameters to be unconstrained, can improve energy performance by a measurable factor, shown to be the function of both geometric and physical parameters above. Given the complex interdependencies among both families of parameters, we developed a particle swarm optimization (PSO) algorithm that can simultaneously lower energy of operation and the switching current density. Results we obtained in scaledown study and via PSO optimization are compared to experimental results. Support by Mubadala-SRC 2012-VJ-2335 is acknowledged, as are staff at Cornell-CNF and BNL-CFN.

Parametric tests of a 40-Ah bipolar nickel-hydrogen battery

NASA Technical Reports Server (NTRS)

Cataldo, R. L.

1986-01-01

A series of tests were performed to characterize battery performance relating to certain operating parameters which include charge current, discharge current, temperature, and pressure. The parameters were varied to confirm battery design concepts and to determine optimal operating conditions.

Timing resolution and time walk in SLiK SPAD: measurement and optimization

NASA Astrophysics Data System (ADS)

Fong, Bernicy S.; Davies, Murray; Deschamps, Pierre

2017-08-01

Timing resolution (or timing jitter) and time walk are separate parameters associated with a detector's response time. Studies have been done mostly on the time resolution of various single photon detectors [1]. As the designer and manufacturer of the ultra-low noise (ƙ-factor) silicon avalanche photodiode the SLiK SPAD, which is used in many single photon counting applications, we often get inquiries from customers to better understand how this detector behaves under different operating conditions. Hence, here we will be focusing on the study of these time related parameters specifically for the SLiK SPAD, as a way to provide the most direct information for users of this detector to help with its use more efficiently and effectively. We will be providing the study data on how these parameters can be affected by temperature (both intrinsic to the detector chip and environmental input based on operating conditions), operating voltage, photon wavelength, as well as light spot size. How these parameters can be optimized and the trade-offs from optimization from the desired performance will be presented.

Reliability of system for precise cold forging

NASA Astrophysics Data System (ADS)

Krušič, Vid; Rodič, Tomaž

2017-07-01

The influence of scatter of principal input parameters of the forging system on the dimensional accuracy of product and on the tool life for closed-die forging process is presented in this paper. Scatter of the essential input parameters for the closed-die upsetting process was adjusted to the maximal values that enabled the reliable production of a dimensionally accurate product at optimal tool life. An operating window was created in which exists the maximal scatter of principal input parameters for the closed-die upsetting process that still ensures the desired dimensional accuracy of the product and the optimal tool life. Application of the adjustment of the process input parameters is shown on the example of making an inner race of homokinetic joint from mass production. High productivity in manufacture of elements by cold massive extrusion is often achieved by multiple forming operations that are performed simultaneously on the same press. By redesigning the time sequences of forming operations at multistage forming process of starter barrel during the working stroke the course of the resultant force is optimized.

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.

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.

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.

A flowsheet model of a well-mixed fluidized bed dryer: Applications in controllability assessment and optimization

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

Langrish, T.A.G.; Harvey, A.C.

2000-01-01

A model of a well-mixed fluidized-bed dryer within a process flowsheeting package (SPEEDUP{trademark}) has been developed and applied to a parameter sensitivity study, a steady-state controllability analysis and an optimization study. This approach is more general and would be more easily applied to a complex flowsheet than one which relied on stand-alone dryer modeling packages. The simulation has shown that industrial data may be fitted to the model outputs with sensible values of unknown parameters. For this case study, the parameter sensitivity study has found that the heat loss from the dryer and the critical moisture content of the materialmore » have the greatest impact on the dryer operation at the current operating point. An optimization study has demonstrated the dominant effect of the heat loss from the dryer on the current operating cost and the current operating conditions, and substantial cost savings (around 50%) could be achieved with a well-insulated and airtight dryer, for the specific case studied here.« less

Deriving adaptive operating rules of hydropower reservoirs using time-varying parameters generated by the EnKF

NASA Astrophysics Data System (ADS)

Feng, Maoyuan; Liu, Pan; Guo, Shenglian; Shi, Liangsheng; Deng, Chao; Ming, Bo

2017-08-01

Operating rules have been used widely to decide reservoir operations because of their capacity for coping with uncertain inflow. However, stationary operating rules lack adaptability; thus, under changing environmental conditions, they cause inefficient reservoir operation. This paper derives adaptive operating rules based on time-varying parameters generated using the ensemble Kalman filter (EnKF). A deterministic optimization model is established to obtain optimal water releases, which are further taken as observations of the reservoir simulation model. The EnKF is formulated to update the operating rules sequentially, providing a series of time-varying parameters. To identify the index that dominates the variations of the operating rules, three hydrologic factors are selected: the reservoir inflow, ratio of future inflow to current available water, and available water. Finally, adaptive operating rules are derived by fitting the time-varying parameters with the identified dominant hydrologic factor. China's Three Gorges Reservoir was selected as a case study. Results show that (1) the EnKF has the capability of capturing the variations of the operating rules, (2) reservoir inflow is the factor that dominates the variations of the operating rules, and (3) the derived adaptive operating rules are effective in improving hydropower benefits compared with stationary operating rules. The insightful findings of this study could be used to help adapt reservoir operations to mitigate the effects of changing environmental conditions.

Multi-Objective Optimization of Moving-magnet Linear Oscillatory Motor Using Response Surface Methodology with Quantum-Behaved PSO Operator

NASA Astrophysics Data System (ADS)

Lei, Meizhen; Wang, Liqiang

2018-01-01

To reduce the difficulty of manufacturing and increase the magnetic thrust density, a moving-magnet linear oscillatory motor (MMLOM) without inner-stators was Proposed. To get the optimal design of maximum electromagnetic thrust with minimal permanent magnetic material, firstly, the 3D finite element analysis (FEA) model of the MMLOM was built and verified by comparison with prototype experiment result. Then the influence of design parameters of permanent magnet (PM) on the electromagnetic thrust was systematically analyzed by the 3D FEA to get the design parameters. Secondly, response surface methodology (RSM) was employed to build the response surface model of the new MMLOM, which can obtain an analytical model of the PM volume and thrust. Then a multi-objective optimization methods for design parameters of PM, using response surface methodology (RSM) with a quantum-behaved PSO (QPSO) operator, was proposed. Then the way to choose the best design parameters of PM among the multi-objective optimization solution sets was proposed. Then the 3D FEA of the optimal design candidates was compared. The comparison results showed that the proposed method can obtain the best combination of the geometric parameters of reducing the PM volume and increasing the thrust.

On the physical operation and optimization of the p-GaN gate in normally-off GaN HEMT devices

NASA Astrophysics Data System (ADS)

Efthymiou, L.; Longobardi, G.; Camuso, G.; Chien, T.; Chen, M.; Udrea, F.

2017-03-01

In this study, an investigation is undertaken to determine the effect of gate design parameters on the on-state characteristics (threshold voltage and gate turn-on voltage) of pGaN/AlGaN/GaN high electron mobility transistors (HEMTs). Design parameters considered are pGaN doping and gate metal work function. The analysis considers the effects of variations on these parameters using a TCAD model matched with experimental results. A better understanding of the underlying physics governing the operation of these devices is achieved with a view to enable better optimization of such gate designs.

Optimal Parameter Design of Coarse Alignment for Fiber Optic Gyro Inertial Navigation System.

PubMed

Lu, Baofeng; Wang, Qiuying; Yu, Chunmei; Gao, Wei

2015-06-25

Two different coarse alignment algorithms for Fiber Optic Gyro (FOG) Inertial Navigation System (INS) based on inertial reference frame are discussed in this paper. Both of them are based on gravity vector integration, therefore, the performance of these algorithms is determined by integration time. In previous works, integration time is selected by experience. In order to give a criterion for the selection process, and make the selection of the integration time more accurate, optimal parameter design of these algorithms for FOG INS is performed in this paper. The design process is accomplished based on the analysis of the error characteristics of these two coarse alignment algorithms. Moreover, this analysis and optimal parameter design allow us to make an adequate selection of the most accurate algorithm for FOG INS according to the actual operational conditions. The analysis and simulation results show that the parameter provided by this work is the optimal value, and indicate that in different operational conditions, the coarse alignment algorithms adopted for FOG INS are different in order to achieve better performance. Lastly, the experiment results validate the effectiveness of the proposed algorithm.

The Effects of Operational Parameters on a Mono-wire Cutting System: Efficiency in Marble Processing

NASA Astrophysics Data System (ADS)

Yilmazkaya, Emre; Ozcelik, Yilmaz

2016-02-01

Mono-wire block cutting machines that cut with a diamond wire can be used for squaring natural stone blocks and the slab-cutting process. The efficient use of these machines reduces operating costs by ensuring less diamond wire wear and longer wire life at high speeds. The high investment costs of these machines will lead to their efficient use and reduce production costs by increasing plant efficiency. Therefore, there is a need to investigate the cutting performance parameters of mono-wire cutting machines in terms of rock properties and operating parameters. This study aims to investigate the effects of the wire rotational speed (peripheral speed) and wire descending speed (cutting speed), which are the operating parameters of a mono-wire cutting machine, on unit wear and unit energy, which are the performance parameters in mono-wire cutting. By using the obtained results, cuttability charts for each natural stone were created on the basis of unit wear and unit energy values, cutting optimizations were performed, and the relationships between some physical and mechanical properties of rocks and the optimum cutting parameters obtained as a result of the optimization were investigated.

Design of compact long-period gratings imprinted in optimized photonic crystal fibers

NASA Astrophysics Data System (ADS)

Seraji, F. E.; Chehreghani Anzabi, L.; Farsinezhad, S.

2009-10-01

To imprint a long-period grating (LPG) in a photonic crystal fiber (PCF) with an optimum response, first the parameters of the PCF should be optimized. In this paper, by using a semi-analytical enhanced improved vectorial effective index method, the optimized PCF parameters are determined by dividing the single-mode operation of the PCF into two regions in terms of air-hole spacing Λ ( Λ>3 μm and Λ≤3 μm). For each region appropriate expressions are suggested to evaluate the PCF parameters. By calculating the effective refractive index difference between the optimized core and cladding of the PCF under a phase-matching condition, the optimum grating period in terms of the PCF parameters is obtained.

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.

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.

Optimized Assistive Human-Robot Interaction Using Reinforcement Learning.

PubMed

Modares, Hamidreza; Ranatunga, Isura; Lewis, Frank L; Popa, Dan O

2016-03-01

An intelligent human-robot interaction (HRI) system with adjustable robot behavior is presented. The proposed HRI system assists the human operator to perform a given task with minimum workload demands and optimizes the overall human-robot system performance. Motivated by human factor studies, the presented control structure consists of two control loops. First, a robot-specific neuro-adaptive controller is designed in the inner loop to make the unknown nonlinear robot behave like a prescribed robot impedance model as perceived by a human operator. In contrast to existing neural network and adaptive impedance-based control methods, no information of the task performance or the prescribed robot impedance model parameters is required in the inner loop. Then, a task-specific outer-loop controller is designed to find the optimal parameters of the prescribed robot impedance model to adjust the robot's dynamics to the operator skills and minimize the tracking error. The outer loop includes the human operator, the robot, and the task performance details. The problem of finding the optimal parameters of the prescribed robot impedance model is transformed into a linear quadratic regulator (LQR) problem which minimizes the human effort and optimizes the closed-loop behavior of the HRI system for a given task. To obviate the requirement of the knowledge of the human model, integral reinforcement learning is used to solve the given LQR problem. Simulation results on an x - y table and a robot arm, and experimental implementation results on a PR2 robot confirm the suitability of the proposed method.

Chaos control in solar fed DC-DC boost converter by optimal parameters using nelder-mead algorithm powered enhanced BFOA

NASA Astrophysics Data System (ADS)

Sudhakar, N.; Rajasekar, N.; Akhil, Saya; Jyotheeswara Reddy, K.

2017-11-01

The boost converter is the most desirable DC-DC power converter for renewable energy applications for its favorable continuous input current characteristics. In other hand, these DC-DC converters known as practical nonlinear systems are prone to several types of nonlinear phenomena including bifurcation, quasiperiodicity, intermittency and chaos. These undesirable effects has to be controlled for maintaining normal periodic operation of the converter and to ensure the stability. This paper presents an effective solution to control the chaos in solar fed DC-DC boost converter since the converter experiences wide range of input power variation which leads to chaotic phenomena. Controlling of chaos is significantly achieved using optimal circuit parameters obtained through Nelder-Mead Enhanced Bacterial Foraging Optimization Algorithm. The optimization renders the suitable parameters in minimum computational time. The results are compared with the traditional methods. The obtained results of the proposed system ensures the operation of the converter within the controllable region.

Timing resolution and time walk in super low K factor single-photon avalanche diode-measurement and optimization

NASA Astrophysics Data System (ADS)

Fong, Bernicy S.; Davies, Murray; Deschamps, Pierre

2018-01-01

Timing resolution (or timing jitter) and time walk are separate parameters associated with a detector's response time. Studies have been done mostly on the time resolution of various single-photon detectors. As the designer and manufacturer of the ultra-low noise (ƙ-factor) silicon avalanche photodiode the super low K factor (SLiK) single-photon avalanche diode (SPAD), which is used in many single-photon counting applications, we often get inquiries from customers to better understand how this detector behaves under different operating conditions. Hence, here, we will be focusing on the study of these time-related parameters specifically for the SLiK SPAD, as a way to provide the most direct information for users of this detector to help with its use more efficiently and effectively. We will be providing the study data on how these parameters can be affected by temperature (both intrinsic to the detector chip and environmental input based on operating conditions), operating voltage, photon wavelength, as well as light spot size. How these parameters can be optimized and the trade-offs from optimization from the desired performance will be presented?

Application of an Optimal Tuner Selection Approach for On-Board Self-Tuning Engine Models

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Armstrong, Jeffrey B.; Garg, Sanjay

2012-01-01

An enhanced design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented in this paper. It specific-ally addresses the under-determined estimation problem, in which there are more unknown parameters than available sensor measurements. This work builds upon an existing technique for systematically selecting a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. While the existing technique was optimized for open-loop engine operation at a fixed design point, in this paper an alternative formulation is presented that enables the technique to be optimized for an engine operating under closed-loop control throughout the flight envelope. The theoretical Kalman filter mean squared estimation error at a steady-state closed-loop operating point is derived, and the tuner selection approach applied to minimize this error is discussed. A technique for constructing a globally optimal tuning parameter vector, which enables full-envelope application of the technology, is also presented, along with design steps for adjusting the dynamic response of the Kalman filter state estimates. Results from the application of the technique to linear and nonlinear aircraft engine simulations are presented and compared to the conventional approach of tuner selection. The new methodology is shown to yield a significant improvement in on-line Kalman filter estimation accuracy.

An improved parent-centric mutation with normalized neighborhoods for inducing niching behavior in differential evolution.

PubMed

Biswas, Subhodip; Kundu, Souvik; Das, Swagatam

2014-10-01

In real life, we often need to find multiple optimally sustainable solutions of an optimization problem. Evolutionary multimodal optimization algorithms can be very helpful in such cases. They detect and maintain multiple optimal solutions during the run by incorporating specialized niching operations in their actual framework. Differential evolution (DE) is a powerful evolutionary algorithm (EA) well-known for its ability and efficiency as a single peak global optimizer for continuous spaces. This article suggests a niching scheme integrated with DE for achieving a stable and efficient niching behavior by combining the newly proposed parent-centric mutation operator with synchronous crowding replacement rule. The proposed approach is designed by considering the difficulties associated with the problem dependent niching parameters (like niche radius) and does not make use of such control parameter. The mutation operator helps to maintain the population diversity at an optimum level by using well-defined local neighborhoods. Based on a comparative study involving 13 well-known state-of-the-art niching EAs tested on an extensive collection of benchmarks, we observe a consistent statistical superiority enjoyed by our proposed niching algorithm.

Stability assessment and operating parameter optimization on experimental results in very small plasma focus, using sensitivity analysis

NASA Astrophysics Data System (ADS)

Jafari, Hossein; Habibi, Morteza

2018-04-01

Regarding the importance of stability in small-scale plasma focus devices for producing the repeatable and strength pinching, a sensitivity analysis approach has been used for applicability in design parameters optimization of an actually very low energy device (84 nF, 48 nH, 8-9.5 kV, ∼2.7-3.7 J). To optimize the devices functional specification, four different coaxial electrode configurations have been studied, scanning an argon gas pressure range from 0.6 to 1.5 mbar via the charging voltage variation study from 8.3 to 9.3 kV. The strength and efficient pinching was observed for the tapered anode configuration, over an expanded operating pressure range of 0.6 to 1.5 mbar. The analysis results showed that the most sensitive of the pinch voltage was associated with 0.88 ± 0.8mbar argon gas pressure and 8.3-8.5 kV charging voltage, respectively, as the optimum operating parameters. From the viewpoint of stability assessment of the device, it was observed that the least variation in stable operation of the device was for a charging voltage range of 8.3 to 8.7 kV in an operating pressure range from 0.6 to 1.1 mbar.

Maximizing power generation from dark fermentation effluents in microbial fuel cell by selective enrichment of exoelectrogens and optimization of anodic operational parameters.

PubMed

Varanasi, Jhansi L; Sinha, Pallavi; Das, Debabrata

2017-05-01

To selectively enrich an electrogenic mixed consortium capable of utilizing dark fermentative effluents as substrates in microbial fuel cells and to further enhance the power outputs by optimization of influential anodic operational parameters. A maximum power density of 1.4 W/m 3 was obtained by an enriched mixed electrogenic consortium in microbial fuel cells using acetate as substrate. This was further increased to 5.43 W/m 3 by optimization of influential anodic parameters. By utilizing dark fermentative effluents as substrates, the maximum power densities ranged from 5.2 to 6.2 W/m 3 with an average COD removal efficiency of 75% and a columbic efficiency of 10.6%. A simple strategy is provided for selective enrichment of electrogenic bacteria that can be used in microbial fuel cells for generating power from various dark fermentative effluents.

An Integrated Framework for Parameter-based Optimization of Scientific Workflows.

PubMed

Kumar, Vijay S; Sadayappan, P; Mehta, Gaurang; Vahi, Karan; Deelman, Ewa; Ratnakar, Varun; Kim, Jihie; Gil, Yolanda; Hall, Mary; Kurc, Tahsin; Saltz, Joel

2009-01-01

Data analysis processes in scientific applications can be expressed as coarse-grain workflows of complex data processing operations with data flow dependencies between them. Performance optimization of these workflows can be viewed as a search for a set of optimal values in a multi-dimensional parameter space. While some performance parameters such as grouping of workflow components and their mapping to machines do not a ect the accuracy of the output, others may dictate trading the output quality of individual components (and of the whole workflow) for performance. This paper describes an integrated framework which is capable of supporting performance optimizations along multiple dimensions of the parameter space. Using two real-world applications in the spatial data analysis domain, we present an experimental evaluation of the proposed framework.

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

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.

Decision support for operations and maintenance (DSOM) system

DOEpatents

Jarrell, Donald B [Kennewick, WA; Meador, Richard J [Richland, WA; Sisk, Daniel R [Richland, WA; Hatley, Darrel D [Kennewick, WA; Brown, Daryl R [Richland, WA; Keibel, Gary R [Richland, WA; Gowri, Krishnan [Richland, WA; Reyes-Spindola, Jorge F [Richland, WA; Adams, Kevin J [San Bruno, CA; Yates, Kenneth R [Lake Oswego, OR; Eschbach, Elizabeth J [Fort Collins, CO; Stratton, Rex C [Richland, WA

2006-03-21

A method for minimizing the life cycle cost of processes such as heating a building. The method utilizes sensors to monitor various pieces of equipment used in the process, for example, boilers, turbines, and the like. The method then performs the steps of identifying a set optimal operating conditions for the process, identifying and measuring parameters necessary to characterize the actual operating condition of the process, validating data generated by measuring those parameters, characterizing the actual condition of the process, identifying an optimal condition corresponding to the actual condition, comparing said optimal condition with the actual condition and identifying variances between the two, and drawing from a set of pre-defined algorithms created using best engineering practices, an explanation of at least one likely source and at least one recommended remedial action for selected variances, and providing said explanation as an output to at least one user.

Metal-free virucidal effects induced by g-C3N4 under visible light irradiation: Statistical analysis and parameter optimization.

PubMed

Zhang, Chi; Li, Yi; Zhang, Wenlong; Wang, Peifang; Wang, Chao

2018-03-01

Waterborne viruses with a low infectious dose and a high pathogenic potential pose a serious risk for humans all over the world, calling for a cost-effective and environmentally-friendly inactivation method. Optimizing operational parameters during the disinfection process is a facile and efficient way to achieve the satisfactory viral inactivation efficiency. Here, the antiviral effects of a metal-free visible-light-driven graphitic carbon nitride (g-C 3 N 4 ) photocatalyst were optimized by varying operating parameters with response surface methodology (RSM). Twenty sets of viral inactivation experiments were performed by changing three operating parameters, namely light intensity, photocatalyst loading and reaction temperature, at five levels. According to the experimental data, a semi-empirical model was developed with a high accuracy (determination coefficient R 2  = 0.9908) and then applied to predict the final inactivation efficiency of MS2 (a model virus) after 180 min exposure to the photocatalyst and visible light illumination. The corresponding optimal values were found to be 199.80 mW/cm 2 , 135.40 mg/L and 24.05 °C for light intensity, photocatalyst loading and reaction temperature, respectively. Under the optimized conditions, 8 log PFU/mL of viruses could be completely inactivated by g-C 3 N 4 without regrowth within 240 min visible light irradiation. Our study provides not only an extended application of RSM in photocatalytic viral inactivation but also a green and effective method for water disinfection. Copyright © 2017 Elsevier Ltd. All rights reserved.

Ensemble of hybrid genetic algorithm for two-dimensional phase unwrapping

NASA Astrophysics Data System (ADS)

Balakrishnan, D.; Quan, C.; Tay, C. J.

2013-06-01

The phase unwrapping is the final and trickiest step in any phase retrieval technique. Phase unwrapping by artificial intelligence methods (optimization algorithms) such as hybrid genetic algorithm, reverse simulated annealing, particle swarm optimization, minimum cost matching showed better results than conventional phase unwrapping methods. In this paper, Ensemble of hybrid genetic algorithm with parallel populations is proposed to solve the branch-cut phase unwrapping problem. In a single populated hybrid genetic algorithm, the selection, cross-over and mutation operators are applied to obtain new population in every generation. The parameters and choice of operators will affect the performance of the hybrid genetic algorithm. The ensemble of hybrid genetic algorithm will facilitate to have different parameters set and different choice of operators simultaneously. Each population will use different set of parameters and the offspring of each population will compete against the offspring of all other populations, which use different set of parameters. The effectiveness of proposed algorithm is demonstrated by phase unwrapping examples and advantages of the proposed method are discussed.

Petroleum refinery operational planning using robust optimization

NASA Astrophysics Data System (ADS)

Leiras, A.; Hamacher, S.; Elkamel, A.

2010-12-01

In this article, the robust optimization methodology is applied to deal with uncertainties in the prices of saleable products, operating costs, product demand, and product yield in the context of refinery operational planning. A numerical study demonstrates the effectiveness of the proposed robust approach. The benefits of incorporating uncertainty in the different model parameters were evaluated in terms of the cost of ignoring uncertainty in the problem. The calculations suggest that this benefit is equivalent to 7.47% of the deterministic solution value, which indicates that the robust model may offer advantages to those involved with refinery operational planning. In addition, the probability bounds of constraint violation are calculated to help the decision-maker adopt a more appropriate parameter to control robustness and judge the tradeoff between conservatism and total profit.

Optimization of methylene blue removal by stable emulsified liquid membrane using Plackett–Burman and Box–Behnken designs of experiments

PubMed Central

Djenouhat, Meriem; Bendebane, Farida; Bahloul, Lynda; Samar, Mohamed E. H.

2018-01-01

The stability of an emulsified liquid membrane composed of Span80 as a surfactant, D2EHPA as an extractant and sulfuric acid as an internal phase was first studied according to different diluents and many operating parameters using the Plackett–Burman design of experiments. Then the removal of methylene blue from an aqueous solution has been carried out using this emulsified liquid membrane at its stability conditions. The effects of operating parameters were analysed from the Box–Behnken design of experiments. The optimization of the extraction has been realized applying the response surface methodology and the results showed that the dye extraction yielding 98.72% was achieved at optimized conditions. PMID:29515841

Aerodynamics as a subway design parameter

NASA Technical Reports Server (NTRS)

Kurtz, D. W.

1976-01-01

A parametric sensitivity study has been performed on the system operational energy requirement in order to guide subway design strategy. Aerodynamics can play a dominant or trivial role, depending upon the system characteristics. Optimization of the aerodynamic parameters may not minimize the total operational energy. Isolation of the station box from the tunnel and reduction of the inertial power requirements pay the largest dividends in terms of the operational energy requirement.

Memory-efficient decoding of LDPC codes

NASA Technical Reports Server (NTRS)

Kwok-San Lee, Jason; Thorpe, Jeremy; Hawkins, Jon

2005-01-01

We present a low-complexity quantization scheme for the implementation of regular (3,6) LDPC codes. The quantization parameters are optimized to maximize the mutual information between the source and the quantized messages. Using this non-uniform quantized belief propagation algorithm, we have simulated that an optimized 3-bit quantizer operates with 0.2dB implementation loss relative to a floating point decoder, and an optimized 4-bit quantizer operates less than 0.1dB quantization loss.

Study to investigate and evaluate means of optimizing the radar function. [systems engineering of pulse radar for the space shuttle

NASA Technical Reports Server (NTRS)

1975-01-01

The investigations for a rendezvous radar system design and an integrated radar/communication system design are presented. Based on these investigations, system block diagrams are given and system parameters are optimized for the noncoherent pulse and coherent pulse Doppler radar modulation types. Both cooperative (transponder) and passive radar operation are examined including the optimization of the corresponding transponder design for the cooperative mode of operation.

Optimizing Robinson Operator with Ant Colony Optimization As a Digital Image Edge Detection Method

NASA Astrophysics Data System (ADS)

Yanti Nasution, Tarida; Zarlis, Muhammad; K. M Nasution, Mahyuddin

2017-12-01

Edge detection serves to identify the boundaries of an object against a background of mutual overlap. One of the classic method for edge detection is operator Robinson. Operator Robinson produces a thin, not assertive and grey line edge. To overcome these deficiencies, the proposed improvements to edge detection method with the approach graph with Ant Colony Optimization algorithm. The repairs may be performed are thicken the edge and connect the edges cut off. Edge detection research aims to do optimization of operator Robinson with Ant Colony Optimization then compare the output and generated the inferred extent of Ant Colony Optimization can improve result of edge detection that has not been optimized and improve the accuracy of the results of Robinson edge detection. The parameters used in performance measurement of edge detection are morphology of the resulting edge line, MSE and PSNR. The result showed that Robinson and Ant Colony Optimization method produces images with a more assertive and thick edge. Ant Colony Optimization method is able to be used as a method for optimizing operator Robinson by improving the image result of Robinson detection average 16.77 % than classic Robinson result.

Quantum Parameter Estimation: From Experimental Design to Constructive Algorithm

NASA Astrophysics Data System (ADS)

Yang, Le; Chen, Xi; Zhang, Ming; Dai, Hong-Yi

2017-11-01

In this paper we design the following two-step scheme to estimate the model parameter ω 0 of the quantum system: first we utilize the Fisher information with respect to an intermediate variable v=\\cos ({ω }0t) to determine an optimal initial state and to seek optimal parameters of the POVM measurement operators; second we explore how to estimate ω 0 from v by choosing t when a priori information knowledge of ω 0 is available. Our optimal initial state can achieve the maximum quantum Fisher information. The formulation of the optimal time t is obtained and the complete algorithm for parameter estimation is presented. We further explore how the lower bound of the estimation deviation depends on the a priori information of the model. Supported by the National Natural Science Foundation of China under Grant Nos. 61273202, 61673389, and 61134008

Parameter optimization of electrochemical machining process using black hole algorithm

NASA Astrophysics Data System (ADS)

Singh, Dinesh; Shukla, Rajkamal

2017-12-01

Advanced machining processes are significant as higher accuracy in machined component is required in the manufacturing industries. Parameter optimization of machining processes gives optimum control to achieve the desired goals. In this paper, electrochemical machining (ECM) process is considered to evaluate the performance of the considered process using black hole algorithm (BHA). BHA considers the fundamental idea of a black hole theory and it has less operating parameters to tune. The two performance parameters, material removal rate (MRR) and overcut (OC) are considered separately to get optimum machining parameter settings using BHA. The variations of process parameters with respect to the performance parameters are reported for better and effective understanding of the considered process using single objective at a time. The results obtained using BHA are found better while compared with results of other metaheuristic algorithms, such as, genetic algorithm (GA), artificial bee colony (ABC) and bio-geography based optimization (BBO) attempted by previous researchers.

Definitive screening design enables optimization of LC-ESI-MS/MS parameters in proteomics.

PubMed

Aburaya, Shunsuke; Aoki, Wataru; Minakuchi, Hiroyoshi; Ueda, Mitsuyoshi

2017-12-01

In proteomics, more than 100,000 peptides are generated from the digestion of human cell lysates. Proteome samples have a broad dynamic range in protein abundance; therefore, it is critical to optimize various parameters of LC-ESI-MS/MS to comprehensively identify these peptides. However, there are many parameters for LC-ESI-MS/MS analysis. In this study, we applied definitive screening design to simultaneously optimize 14 parameters in the operation of monolithic capillary LC-ESI-MS/MS to increase the number of identified proteins and/or the average peak area of MS1. The simultaneous optimization enabled the determination of two-factor interactions between LC and MS. Finally, we found two parameter sets of monolithic capillary LC-ESI-MS/MS that increased the number of identified proteins by 8.1% or the average peak area of MS1 by 67%. The definitive screening design would be highly useful for high-throughput analysis of the best parameter set in LC-ESI-MS/MS systems.

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 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.

An Improved Cuckoo Search Optimization Algorithm for the Problem of Chaotic Systems Parameter Estimation

PubMed Central

Wang, Jun; Zhou, Bihua; Zhou, Shudao

2016-01-01

This paper proposes an improved cuckoo search (ICS) algorithm to establish the parameters of chaotic systems. In order to improve the optimization capability of the basic cuckoo search (CS) algorithm, the orthogonal design and simulated annealing operation are incorporated in the CS algorithm to enhance the exploitation search ability. Then the proposed algorithm is used to establish parameters of the Lorenz chaotic system and Chen chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the algorithm can estimate parameters with high accuracy and reliability. Finally, the results are compared with the CS algorithm, genetic algorithm, and particle swarm optimization algorithm, and the compared results demonstrate the method is energy-efficient and superior. PMID:26880874

Optimization of process parameters in CNC turning of aluminium alloy using hybrid RSM cum TLBO approach

NASA Astrophysics Data System (ADS)

Rudrapati, R.; Sahoo, P.; Bandyopadhyay, A.

2016-09-01

The main aim of the present work is to analyse the significance of turning parameters on surface roughness in computer numerically controlled (CNC) turning operation while machining of aluminium alloy material. Spindle speed, feed rate and depth of cut have been considered as machining parameters. Experimental runs have been conducted as per Box-Behnken design method. After experimentation, surface roughness is measured by using stylus profile meter. Factor effects have been studied through analysis of variance. Mathematical modelling has been done by response surface methodology, to made relationships between the input parameters and output response. Finally, process optimization has been made by teaching learning based optimization (TLBO) algorithm. Predicted turning condition has been validated through confirmatory experiment.

Control Parameters Optimization Based on Co-Simulation of a Mechatronic System for an UA-Based Two-Axis Inertially Stabilized Platform.

PubMed

Zhou, Xiangyang; Zhao, Beilei; Gong, Guohao

2015-08-14

This paper presents a method based on co-simulation of a mechatronic system to optimize the control parameters of a two-axis inertially stabilized platform system (ISP) applied in an unmanned airship (UA), by which high control performance and reliability of the ISP system are achieved. First, a three-dimensional structural model of the ISP is built by using the three-dimensional parametric CAD software SOLIDWORKS(®); then, to analyze the system's kinematic and dynamic characteristics under operating conditions, dynamics modeling is conducted by using the multi-body dynamics software ADAMS™, thus the main dynamic parameters such as displacement, velocity, acceleration and reaction curve are obtained, respectively, through simulation analysis. Then, those dynamic parameters were input into the established MATLAB(®) SIMULINK(®) controller to simulate and test the performance of the control system. By these means, the ISP control parameters are optimized. To verify the methods, experiments were carried out by applying the optimized parameters to the control system of a two-axis ISP. The results show that the co-simulation by using virtual prototyping (VP) is effective to obtain optimized ISP control parameters, eventually leading to high ISP control performance.

Control Parameters Optimization Based on Co-Simulation of a Mechatronic System for an UA-Based Two-Axis Inertially Stabilized Platform

PubMed Central

Zhou, Xiangyang; Zhao, Beilei; Gong, Guohao

2015-01-01

This paper presents a method based on co-simulation of a mechatronic system to optimize the control parameters of a two-axis inertially stabilized platform system (ISP) applied in an unmanned airship (UA), by which high control performance and reliability of the ISP system are achieved. First, a three-dimensional structural model of the ISP is built by using the three-dimensional parametric CAD software SOLIDWORKS®; then, to analyze the system’s kinematic and dynamic characteristics under operating conditions, dynamics modeling is conducted by using the multi-body dynamics software ADAMS™, thus the main dynamic parameters such as displacement, velocity, acceleration and reaction curve are obtained, respectively, through simulation analysis. Then, those dynamic parameters were input into the established MATLAB® SIMULINK® controller to simulate and test the performance of the control system. By these means, the ISP control parameters are optimized. To verify the methods, experiments were carried out by applying the optimized parameters to the control system of a two-axis ISP. The results show that the co-simulation by using virtual prototyping (VP) is effective to obtain optimized ISP control parameters, eventually leading to high ISP control performance. PMID:26287210

Evolution of Query Optimization Methods

NASA Astrophysics Data System (ADS)

Hameurlain, Abdelkader; Morvan, Franck

Query optimization is the most critical phase in query processing. In this paper, we try to describe synthetically the evolution of query optimization methods from uniprocessor relational database systems to data Grid systems through parallel, distributed and data integration systems. We point out a set of parameters to characterize and compare query optimization methods, mainly: (i) size of the search space, (ii) type of method (static or dynamic), (iii) modification types of execution plans (re-optimization or re-scheduling), (iv) level of modification (intra-operator and/or inter-operator), (v) type of event (estimation errors, delay, user preferences), and (vi) nature of decision-making (centralized or decentralized control).

Safety Ellipse Motion with Coarse Sun Angle Optimization

NASA Technical Reports Server (NTRS)

Naasz, Bo

2005-01-01

The Hubble Space Telescope Robotic Servicing and De-orbit Mission (HRSDM) was t o be performed by the unmanned Hubble Robotic Vehicle (HRV) consisting of a Deorbit Module (DM), responsible for the ultimate disposal of Hubble Space Telescope (HST) at the end of science operations, and an Ejection Module (EM), responsible for robotically servicing the HST to extend its useful operational lifetime. HRSDM consisted of eight distinct phases, including: launch, pursuit, proximity operations, capture, servicing, EM jettison and disposal, science operations, and deorbit. The scope of this paper is limited to the Proximity Operations phase of HRSDM. It introduces a relative motion strategy useful for Autonomous Rendezvous and Docking (AR&D) or Formation Flying missions where safe circumnavigation trajectories, or close proximity operations (tens or hundreds of meters) are required for extended periods of time. Parameters and algorithms used to model the relative motion of HRV with respect to HST during the Proximity Operations phase of the HRSDM are described. Specifically, the Safety Ellipse (SE) concept, convenient parameters for describing SE motion, and a concept for initializing SE motion around a target vehicle to coarsely optimize sun and relative navigation sensor angles are presented. The effects of solar incidence angle variations on sun angle optimization, and the effects of orbital perturbations and navigation uncertainty on long term SE motion are discussed.

Design considerations of high-performance InGaAs/InP single-photon avalanche diodes for quantum key distribution.

PubMed

Ma, Jian; Bai, Bing; Wang, Liu-Jun; Tong, Cun-Zhu; Jin, Ge; Zhang, Jun; Pan, Jian-Wei

2016-09-20

InGaAs/InP single-photon avalanche diodes (SPADs) are widely used in practical applications requiring near-infrared photon counting such as quantum key distribution (QKD). Photon detection efficiency and dark count rate are the intrinsic parameters of InGaAs/InP SPADs, due to the fact that their performances cannot be improved using different quenching electronics given the same operation conditions. After modeling these parameters and developing a simulation platform for InGaAs/InP SPADs, we investigate the semiconductor structure design and optimization. The parameters of photon detection efficiency and dark count rate highly depend on the variables of absorption layer thickness, multiplication layer thickness, excess bias voltage, and temperature. By evaluating the decoy-state QKD performance, the variables for SPAD design and operation can be globally optimized. Such optimization from the perspective of specific applications can provide an effective approach to design high-performance InGaAs/InP SPADs.

Perturbing engine performance measurements to determine optimal engine control settings

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

Jiang, Li; Lee, Donghoon; Yilmaz, Hakan

Methods and systems for optimizing a performance of a vehicle engine are provided. The method includes determining an initial value for a first engine control parameter based on one or more detected operating conditions of the vehicle engine, determining a value of an engine performance variable, and artificially perturbing the determined value of the engine performance variable. The initial value for the first engine control parameter is then adjusted based on the perturbed engine performance variable causing the engine performance variable to approach a target engine performance variable. Operation of the vehicle engine is controlled based on the adjusted initialmore » value for the first engine control parameter. These acts are repeated until the engine performance variable approaches the target engine performance variable.« less

Swarm size and iteration number effects to the performance of PSO algorithm in RFID tag coverage optimization

NASA Astrophysics Data System (ADS)

Prathabrao, M.; Nawawi, Azli; Sidek, Noor Azizah

2017-04-01

Radio Frequency Identification (RFID) system has multiple benefits which can improve the operational efficiency of the organization. The advantages are the ability to record data systematically and quickly, reducing human errors and system errors, update the database automatically and efficiently. It is often more readers (reader) is needed for the installation purposes in RFID system. Thus, it makes the system more complex. As a result, RFID network planning process is needed to ensure the RFID system works perfectly. The planning process is also considered as an optimization process and power adjustment because the coordinates of each RFID reader to be determined. Therefore, algorithms inspired by the environment (Algorithm Inspired by Nature) is often used. In the study, PSO algorithm is used because it has few number of parameters, the simulation time is fast, easy to use and also very practical. However, PSO parameters must be adjusted correctly, for robust and efficient usage of PSO. Failure to do so may result in disruption of performance and results of PSO optimization of the system will be less good. To ensure the efficiency of PSO, this study will examine the effects of two parameters on the performance of PSO Algorithm in RFID tag coverage optimization. The parameters to be studied are the swarm size and iteration number. In addition to that, the study will also recommend the most optimal adjustment for both parameters that is, 200 for the no. iterations and 800 for the no. of swarms. Finally, the results of this study will enable PSO to operate more efficiently in order to optimize RFID network planning system.

Review: Optimization methods for groundwater modeling and management

NASA Astrophysics Data System (ADS)

Yeh, William W.-G.

2015-09-01

Optimization methods have been used in groundwater modeling as well as for the planning and management of groundwater systems. This paper reviews and evaluates the various optimization methods that have been used for solving the inverse problem of parameter identification (estimation), experimental design, and groundwater planning and management. Various model selection criteria are discussed, as well as criteria used for model discrimination. The inverse problem of parameter identification concerns the optimal determination of model parameters using water-level observations. In general, the optimal experimental design seeks to find sampling strategies for the purpose of estimating the unknown model parameters. A typical objective of optimal conjunctive-use planning of surface water and groundwater is to minimize the operational costs of meeting water demand. The optimization methods include mathematical programming techniques such as linear programming, quadratic programming, dynamic programming, stochastic programming, nonlinear programming, and the global search algorithms such as genetic algorithms, simulated annealing, and tabu search. Emphasis is placed on groundwater flow problems as opposed to contaminant transport problems. A typical two-dimensional groundwater flow problem is used to explain the basic formulations and algorithms that have been used to solve the formulated optimization problems.

Environmental quality of the operating theaters in Campania Region: long lasting monitoring results.

PubMed

Triassi, M; Novi, C; Nardone, A; Russo, I; Montuori, P

2015-01-01

The health risk level in the operating theaters is directly correlated to the safety level offered by the healthcare facilities. This is the reason why the national Authorities released several regulations in order to monitor better environmental conditions of the operating theaters, to prevent occupational injuries and disease and to optimize working conditions. For the monitoring of environmental quality of the operating theaters following parameters are considered: quantity of supplied gases, anesthetics concentration, operating theatres volume measurement, air change rate, air conditioning system and air filtration. The objective is to minimize the risks in the operating theaters and to provide the optimal environmental working conditions. This paper reports the environmental conditions of operating rooms performed for several years in the public hospitals of the Campania Region. Investigation of environmental conditions of 162 operating theaters in Campania Region from January 2012 till July 2014 was conducted. Monitoring and analysis of physical and chemical parameters was done. The analysis of the results has been made considering specific standards suggested by national and international regulations. The study showed that 75% of the operating theaters presented normal values for microclimatic monitoring, while the 25% of the operating theaters had at least one parameter outside the limits. The monitoring of the anesthetics gases showed that in 9% of measurements of nitrous oxides and 4% of measurements of halogenated was not within the normal values.

Optimized positioning of autonomous surgical lamps

NASA Astrophysics Data System (ADS)

Teuber, Jörn; Weller, Rene; Kikinis, Ron; Oldhafer, Karl-Jürgen; Lipp, Michael J.; Zachmann, Gabriel

2017-03-01

We consider the problem of finding automatically optimal positions of surgical lamps throughout the whole surgical procedure, where we assume that future lamps could be robotized. We propose a two-tiered optimization technique for the real-time autonomous positioning of those robotized surgical lamps. Typically, finding optimal positions for surgical lamps is a multi-dimensional problem with several, in part conflicting, objectives, such as optimal lighting conditions at every point in time while minimizing the movement of the lamps in order to avoid distractions of the surgeon. Consequently, we use multi-objective optimization (MOO) to find optimal positions in real-time during the entire surgery. Due to the conflicting objectives, there is usually not a single optimal solution for such kinds of problems, but a set of solutions that realizes a Pareto-front. When our algorithm selects a solution from this set it additionally has to consider the individual preferences of the surgeon. This is a highly non-trivial task because the relationship between the solution and the parameters is not obvious. We have developed a novel meta-optimization that considers exactly this challenge. It delivers an easy to understand set of presets for the parameters and allows a balance between the lamp movement and lamp obstruction. This metaoptimization can be pre-computed for different kinds of operations and it then used by our online optimization for the selection of the appropriate Pareto solution. Both optimization approaches use data obtained by a depth camera that captures the surgical site but also the environment around the operating table. We have evaluated our algorithms with data recorded during a real open abdominal surgery. It is available for use for scientific purposes. The results show that our meta-optimization produces viable parameter sets for different parts of an intervention even when trained on a small portion of it.

[Numerical simulation and operation optimization of biological filter].

PubMed

Zou, Zong-Sen; Shi, Han-Chang; Chen, Xiang-Qiang; Xie, Xiao-Qing

2014-12-01

BioWin software and two sensitivity analysis methods were used to simulate the Denitrification Biological Filter (DNBF) + Biological Aerated Filter (BAF) process in Yuandang Wastewater Treatment Plant. Based on the BioWin model of DNBF + BAF process, the operation data of September 2013 were used for sensitivity analysis and model calibration, and the operation data of October 2013 were used for model validation. The results indicated that the calibrated model could accurately simulate practical DNBF + BAF processes, and the most sensitive parameters were the parameters related to biofilm, OHOs and aeration. After the validation and calibration of model, it was used for process optimization with simulating operation results under different conditions. The results showed that, the best operation condition for discharge standard B was: reflux ratio = 50%, ceasing methanol addition, influent C/N = 4.43; while the best operation condition for discharge standard A was: reflux ratio = 50%, influent COD = 155 mg x L(-1) after methanol addition, influent C/N = 5.10.

Optimization of biological sulfide removal in a CSTR bioreactor.

PubMed

Roosta, Aliakbar; Jahanmiri, Abdolhossein; Mowla, Dariush; Niazi, Ali; Sotoodeh, Hamidreza

2012-08-01

In this study, biological sulfide removal from natural gas in a continuous bioreactor is investigated for estimation of the optimal operational parameters. According to the carried out reactions, sulfide can be converted to elemental sulfur, sulfate, thiosulfate, and polysulfide, of which elemental sulfur is the desired product. A mathematical model is developed and was used for investigation of the effect of various parameters on elemental sulfur selectivity. The results of the simulation show that elemental sulfur selectivity is a function of dissolved oxygen, sulfide load, pH, and concentration of bacteria. Optimal parameter values are calculated for maximum elemental sulfur selectivity by using genetic algorithm as an adaptive heuristic search. In the optimal conditions, 87.76% of sulfide loaded to the bioreactor is converted to elemental sulfur.

Design Sensitivity for a Subsonic Aircraft Predicted by Neural Network and Regression Models

NASA Technical Reports Server (NTRS)

Hopkins, Dale A.; Patnaik, Surya N.

2005-01-01

A preliminary methodology was obtained for the design optimization of a subsonic aircraft by coupling NASA Langley Research Center s Flight Optimization System (FLOPS) with NASA Glenn Research Center s design optimization testbed (COMETBOARDS with regression and neural network analysis approximators). The aircraft modeled can carry 200 passengers at a cruise speed of Mach 0.85 over a range of 2500 n mi and can operate on standard 6000-ft takeoff and landing runways. The design simulation was extended to evaluate the optimal airframe and engine parameters for the subsonic aircraft to operate on nonstandard runways. Regression and neural network approximators were used to examine aircraft operation on runways ranging in length from 4500 to 7500 ft.

[Optimize dropping process of Ginkgo biloba dropping pills by using design space approach].

PubMed

Shen, Ji-Chen; Wang, Qing-Qing; Chen, An; Pan, Fang-Lai; Gong, Xing-Chu; Qu, Hai-Bin

2017-07-01

In this paper, a design space approach was applied to optimize the dropping process of Ginkgo biloba dropping pills. Firstly, potential critical process parameters and potential process critical quality attributes were determined through literature research and pre-experiments. Secondly, experiments were carried out according to Box-Behnken design. Then the critical process parameters and critical quality attributes were determined based on the experimental results. Thirdly, second-order polynomial models were used to describe the quantitative relationships between critical process parameters and critical quality attributes. Finally, a probability-based design space was calculated and verified. The verification results showed that efficient production of Ginkgo biloba dropping pills can be guaranteed by operating within the design space parameters. The recommended operation ranges for the critical dropping process parameters of Ginkgo biloba dropping pills were as follows: dropping distance of 5.5-6.7 cm, and dropping speed of 59-60 drops per minute, providing a reference for industrial production of Ginkgo biloba dropping pills. Copyright© by the Chinese Pharmaceutical Association.

Parameters Identification for Photovoltaic Module Based on an Improved Artificial Fish Swarm Algorithm

PubMed Central

Wang, Hong-Hua

2014-01-01

A precise mathematical model plays a pivotal role in the simulation, evaluation, and optimization of photovoltaic (PV) power systems. Different from the traditional linear model, the model of PV module has the features of nonlinearity and multiparameters. Since conventional methods are incapable of identifying the parameters of PV module, an excellent optimization algorithm is required. Artificial fish swarm algorithm (AFSA), originally inspired by the simulation of collective behavior of real fish swarms, is proposed to fast and accurately extract the parameters of PV module. In addition to the regular operation, a mutation operator (MO) is designed to enhance the searching performance of the algorithm. The feasibility of the proposed method is demonstrated by various parameters of PV module under different environmental conditions, and the testing results are compared with other studied methods in terms of final solutions and computational time. The simulation results show that the proposed method is capable of obtaining higher parameters identification precision. PMID:25243233

Optimization of automotive Rankine cycle waste heat recovery under various engine operating condition

NASA Astrophysics Data System (ADS)

Punov, Plamen; Milkov, Nikolay; Danel, Quentin; Perilhon, Christelle; Podevin, Pierre; Evtimov, Teodossi

2017-02-01

An optimization study of the Rankine cycle as a function of diesel engine operating mode is presented. The Rankine cycle here, is studied as a waste heat recovery system which uses the engine exhaust gases as heat source. The engine exhaust gases parameters (temperature, mass flow and composition) were defined by means of numerical simulation in advanced simulation software AVL Boost. Previously, the engine simulation model was validated and the Vibe function parameters were defined as a function of engine load. The Rankine cycle output power and efficiency was numerically estimated by means of a simulation code in Python(x,y). This code includes discretized heat exchanger model and simplified model of the pump and the expander based on their isentropic efficiency. The Rankine cycle simulation revealed the optimum value of working fluid mass flow and evaporation pressure according to the heat source. Thus, the optimal Rankine cycle performance was obtained over the engine operating map.

Modelling and multi objective optimization of WEDM of commercially Monel super alloy using evolutionary algorithms

NASA Astrophysics Data System (ADS)

Varun, Sajja; Reddy, Kalakada Bhargav Bal; Vardhan Reddy, R. R. Vishnu

2016-09-01

In this research work, development of a multi response optimization technique has been undertaken, using traditional desirability analysis and non-traditional particle swarm optimization techniques (for different customer's priorities) in wire electrical discharge machining (WEDM). Monel 400 has been selected as work material for experimentation. The effect of key process parameters such as pulse on time (TON), pulse off time (TOFF), peak current (IP), wire feed (WF) were on material removal rate (MRR) and surface roughness(SR) in WEDM operation were investigated. Further, the responses such as MRR and SR were modelled empirically through regression analysis. The developed models can be used by the machinists to predict the MRR and SR over a wide range of input parameters. The optimization of multiple responses has been done for satisfying the priorities of multiple users by using Taguchi-desirability function method and particle swarm optimization technique. The analysis of variance (ANOVA) is also applied to investigate the effect of influential parameters. Finally, the confirmation experiments were conducted for the optimal set of machining parameters, and the betterment has been proved.

Effective 2D-3D medical image registration using Support Vector Machine.

PubMed

Qi, Wenyuan; Gu, Lixu; Zhao, Qiang

2008-01-01

Registration of pre-operative 3D volume dataset and intra-operative 2D images gradually becomes an important technique to assist radiologists in diagnosing complicated diseases easily and quickly. In this paper, we proposed a novel 2D/3D registration framework based on Support Vector Machine (SVM) to compensate the disadvantages of generating large number of DRR images in the stage of intra-operation. Estimated similarity metric distribution could be built up from the relationship between parameters of transform and prior sparse target metric values by means of SVR method. Based on which, global optimal parameters of transform are finally searched out by an optimizer in order to guide 3D volume dataset to match intra-operative 2D image. Experiments reveal that our proposed registration method improved performance compared to conventional registration method and also provided a precise registration result efficiently.

Taguchi optimization of bismuth-telluride based thermoelectric cooler

NASA Astrophysics Data System (ADS)

Anant Kishore, Ravi; Kumar, Prashant; Sanghadasa, Mohan; Priya, Shashank

2017-07-01

In the last few decades, considerable effort has been made to enhance the figure-of-merit (ZT) of thermoelectric (TE) materials. However, the performance of commercial TE devices still remains low due to the fact that the module figure-of-merit not only depends on the material ZT, but also on the operating conditions and configuration of TE modules. This study takes into account comprehensive set of parameters to conduct the numerical performance analysis of the thermoelectric cooler (TEC) using a Taguchi optimization method. The Taguchi method is a statistical tool that predicts the optimal performance with a far less number of experimental runs than the conventional experimental techniques. Taguchi results are also compared with the optimized parameters obtained by a full factorial optimization method, which reveals that the Taguchi method provides optimum or near-optimum TEC configuration using only 25 experiments against 3125 experiments needed by the conventional optimization method. This study also shows that the environmental factors such as ambient temperature and cooling coefficient do not significantly affect the optimum geometry and optimum operating temperature of TECs. The optimum TEC configuration for simultaneous optimization of cooling capacity and coefficient of performance is also provided.

Extended RF shimming: Sequence‐level parallel transmission optimization applied to steady‐state free precession MRI of the heart

PubMed Central

Price, Anthony N.; Padormo, Francesco; Hajnal, Joseph V.; Malik, Shaihan J.

2017-01-01

Cardiac magnetic resonance imaging (MRI) at high field presents challenges because of the high specific absorption rate and significant transmit field (B 1 +) inhomogeneities. Parallel transmission MRI offers the ability to correct for both issues at the level of individual radiofrequency (RF) pulses, but must operate within strict hardware and safety constraints. The constraints are themselves affected by sequence parameters, such as the RF pulse duration and TR, meaning that an overall optimal operating point exists for a given sequence. This work seeks to obtain optimal performance by performing a ‘sequence‐level’ optimization in which pulse sequence parameters are included as part of an RF shimming calculation. The method is applied to balanced steady‐state free precession cardiac MRI with the objective of minimizing TR, hence reducing the imaging duration. Results are demonstrated using an eight‐channel parallel transmit system operating at 3 T, with an in vivo study carried out on seven male subjects of varying body mass index (BMI). Compared with single‐channel operation, a mean‐squared‐error shimming approach leads to reduced imaging durations of 32 ± 3% with simultaneous improvement in flip angle homogeneity of 32 ± 8% within the myocardium. PMID:28195684

Extended RF shimming: Sequence-level parallel transmission optimization applied to steady-state free precession MRI of the heart.

PubMed

Beqiri, Arian; Price, Anthony N; Padormo, Francesco; Hajnal, Joseph V; Malik, Shaihan J

2017-06-01

Cardiac magnetic resonance imaging (MRI) at high field presents challenges because of the high specific absorption rate and significant transmit field (B 1 + ) inhomogeneities. Parallel transmission MRI offers the ability to correct for both issues at the level of individual radiofrequency (RF) pulses, but must operate within strict hardware and safety constraints. The constraints are themselves affected by sequence parameters, such as the RF pulse duration and TR, meaning that an overall optimal operating point exists for a given sequence. This work seeks to obtain optimal performance by performing a 'sequence-level' optimization in which pulse sequence parameters are included as part of an RF shimming calculation. The method is applied to balanced steady-state free precession cardiac MRI with the objective of minimizing TR, hence reducing the imaging duration. Results are demonstrated using an eight-channel parallel transmit system operating at 3 T, with an in vivo study carried out on seven male subjects of varying body mass index (BMI). Compared with single-channel operation, a mean-squared-error shimming approach leads to reduced imaging durations of 32 ± 3% with simultaneous improvement in flip angle homogeneity of 32 ± 8% within the myocardium. © 2017 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.

Computational intelligence-based optimization of maximally stable extremal region segmentation for object detection

NASA Astrophysics Data System (ADS)

Davis, Jeremy E.; Bednar, Amy E.; Goodin, Christopher T.; Durst, Phillip J.; Anderson, Derek T.; Bethel, Cindy L.

2017-05-01

Particle swarm optimization (PSO) and genetic algorithms (GAs) are two optimization techniques from the field of computational intelligence (CI) for search problems where a direct solution can not easily be obtained. One such problem is finding an optimal set of parameters for the maximally stable extremal region (MSER) algorithm to detect areas of interest in imagery. Specifically, this paper describes the design of a GA and PSO for optimizing MSER parameters to detect stop signs in imagery produced via simulation for use in an autonomous vehicle navigation system. Several additions to the GA and PSO are required to successfully detect stop signs in simulated images. These additions are a primary focus of this paper and include: the identification of an appropriate fitness function, the creation of a variable mutation operator for the GA, an anytime algorithm modification to allow the GA to compute a solution quickly, the addition of an exponential velocity decay function to the PSO, the addition of an "execution best" omnipresent particle to the PSO, and the addition of an attractive force component to the PSO velocity update equation. Experimentation was performed with the GA using various combinations of selection, crossover, and mutation operators and experimentation was also performed with the PSO using various combinations of neighborhood topologies, swarm sizes, cognitive influence scalars, and social influence scalars. The results of both the GA and PSO optimized parameter sets are presented. This paper details the benefits and drawbacks of each algorithm in terms of detection accuracy, execution speed, and additions required to generate successful problem specific parameter sets.

Application of the gravity search algorithm to multi-reservoir operation optimization

NASA Astrophysics Data System (ADS)

Bozorg-Haddad, Omid; Janbaz, Mahdieh; Loáiciga, Hugo A.

2016-12-01

Complexities in river discharge, variable rainfall regime, and drought severity merit the use of advanced optimization tools in multi-reservoir operation. The gravity search algorithm (GSA) is an evolutionary optimization algorithm based on the law of gravity and mass interactions. This paper explores the GSA's efficacy for solving benchmark functions, single reservoir, and four-reservoir operation optimization problems. The GSA's solutions are compared with those of the well-known genetic algorithm (GA) in three optimization problems. The results show that the GSA's results are closer to the optimal solutions than the GA's results in minimizing the benchmark functions. The average values of the objective function equal 1.218 and 1.746 with the GSA and GA, respectively, in solving the single-reservoir hydropower operation problem. The global solution equals 1.213 for this same problem. The GSA converged to 99.97% of the global solution in its average-performing history, while the GA converged to 97% of the global solution of the four-reservoir problem. Requiring fewer parameters for algorithmic implementation and reaching the optimal solution in fewer number of functional evaluations are additional advantages of the GSA over the GA. The results of the three optimization problems demonstrate a superior performance of the GSA for optimizing general mathematical problems and the operation of reservoir systems.

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.

A pilot study for determining the optimal operation condition for simultaneously controlling the emissions of PCDD/Fs and PAHs from the iron ore sintering process.

PubMed

Chen, Yu-Cheng; Tsai, Perng-Jy; Mou, Jin-Luh; Kuo, Yu-Chieh; Wang, Shih-Min; Young, Li-Hao; Wang, Ya-Fen

2012-09-01

In this study, the cost-benefit analysis technique was developed and incorporated into the Taguchi experimental design to determine the optimal operation combination for the purpose of providing a technique solution for controlling both emissions of PCDD/Fs and PAHs, and increasing both the sinter productivity (SP) and sinter strength (SS) simultaneously. Four operating parameters, including the water content, suction pressure, bed height, and type of hearth layer, were selected and all experimental campaigns were conducted on a pilot-scale sinter pot to simulate various sintering operating conditions of a real-scale sinter plant. The resultant optimal combination could reduce the total carcinogenic emissions arising from both emissions of PCDD/Fs and PAHs by 49.8%, and increase the sinter benefit associated with the increase in both SP and SS by 10.1%, as in comparison with the operation condition currently used in the real plant. The ANOVA results indicate that the suction pressure was the most dominant parameter in determining the optimal operation combination. The above result was theoretically plausible since the higher suction pressure provided more oxygen contents leading to the decrease in both PCDD/F and PAH emissions. But it should be noted that the results obtained from the present study were based on pilot scale experiments, conducting confirmation tests in a real scale plant are still necessary in the future. Copyright © 2012 Elsevier Ltd. All rights reserved.

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.

Technique of optimization of minimum temperature driving forces in the heaters of regeneration system of a steam turbine unit

NASA Astrophysics Data System (ADS)

Shamarokov, A. S.; Zorin, V. M.; Dai, Fam Kuang

2016-03-01

At the current stage of development of nuclear power engineering, high demands on nuclear power plants (NPP), including on their economy, are made. In these conditions, improving the quality of NPP means, in particular, the need to reasonably choose the values of numerous managed parameters of technological (heat) scheme. Furthermore, the chosen values should correspond to the economic conditions of NPP operation, which are postponed usually a considerable time interval from the point of time of parameters' choice. The article presents the technique of optimization of controlled parameters of the heat circuit of a steam turbine plant for the future. Its particularity is to obtain the results depending on a complex parameter combining the external economic and operating parameters that are relatively stable under the changing economic environment. The article presents the results of optimization according to this technique of the minimum temperature driving forces in the surface heaters of the heat regeneration system of the steam turbine plant of a K-1200-6.8/50 type. For optimization, the collector-screen heaters of high and low pressure developed at the OAO All-Russia Research and Design Institute of Nuclear Power Machine Building, which, in the authors' opinion, have the certain advantages over other types of heaters, were chosen. The optimality criterion in the task was the change in annual reduced costs for NPP compared to the version accepted as the baseline one. The influence on the decision of the task of independent variables that are not included in the complex parameter was analyzed. An optimization task was decided using the alternating-variable descent method. The obtained values of minimum temperature driving forces can guide the design of new nuclear plants with a heat circuit, similar to that accepted in the considered task.

Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models.

PubMed

Allawi, Mohammed Falah; Jaafar, Othman; Mohamad Hamzah, Firdaus; Abdullah, Sharifah Mastura Syed; El-Shafie, Ahmed

2018-05-01

Efficacious operation for dam and reservoir system could guarantee not only a defenselessness policy against natural hazard but also identify rule to meet the water demand. Successful operation of dam and reservoir systems to ensure optimal use of water resources could be unattainable without accurate and reliable simulation models. According to the highly stochastic nature of hydrologic parameters, developing accurate predictive model that efficiently mimic such a complex pattern is an increasing domain of research. During the last two decades, artificial intelligence (AI) techniques have been significantly utilized for attaining a robust modeling to handle different stochastic hydrological parameters. AI techniques have also shown considerable progress in finding optimal rules for reservoir operation. This review research explores the history of developing AI in reservoir inflow forecasting and prediction of evaporation from a reservoir as the major components of the reservoir simulation. In addition, critical assessment of the advantages and disadvantages of integrated AI simulation methods with optimization methods has been reported. Future research on the potential of utilizing new innovative methods based AI techniques for reservoir simulation and optimization models have also been discussed. Finally, proposal for the new mathematical procedure to accomplish the realistic evaluation of the whole optimization model performance (reliability, resilience, and vulnerability indices) has been recommended.

Tuning the heat transfer medium and operating conditions in magnetic refrigeration

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

Ghahremani, Mohammadreza, E-mail: mghahrem@shepherd.edu; Dept. of Electrical and Computer Engineering, The George Washington University, Washington DC 20052; Aslani, Amir

A new experimental test bed has been designed, built, and tested to evaluate the effect of the system’s parameters on a reciprocating Active Magnetic Regenerator (AMR) near room temperature. Bulk gadolinium was used as the refrigerant, silicon oil as the heat transfer medium, and a magnetic field of 1.3 T was cycled. This study focuses on the methodology of single stage AMR operation conditions to get a high temperature span near room temperature. Herein, the main objective is not to report the absolute maximum attainable temperature span seen in an AMR system, but rather to find the system’s optimal operatingmore » conditions to reach that maximum span. The results of this research show that there is a optimal operating frequency, heat transfer fluid flow rate, flow duration, and displaced volume ratio in any AMR system. By optimizing these parameters in our AMR apparatus the temperature span between the hot and cold ends increased by 24%. The optimized values are system dependent and need to be determined and measured for any AMR system by following the procedures that are introduced in this research. It is expected that such optimization will permit the design of a more efficient magnetic refrigeration system.« less

Subthreshold SPICE Model Optimization

NASA Astrophysics Data System (ADS)

Lum, Gregory; Au, Henry; Neff, Joseph; Bozeman, Eric; Kamin, Nick; Shimabukuro, Randy

2011-04-01

The first step in integrated circuit design is the simulation of said design in software to verify proper functionally and design requirements. Properties of the process are provided by fabrication foundries in the form of SPICE models. These SPICE models contain the electrical data and physical properties of the basic circuit elements. A limitation of these models is that the data collected by the foundry only accurately model the saturation region. This is fine for most users, but when operating devices in the subthreshold region they are inadequate for accurate simulation results. This is why optimizing the current SPICE models to characterize the subthreshold region is so important. In order to accurately simulate this region of operation, MOSFETs of varying widths and lengths are fabricated and the electrical test data is collected. From the data collected the parameters of the model files are optimized through parameter extraction rather than curve fitting. With the completed optimized models the circuit designer is able to simulate circuit designs for the sub threshold region accurately.

Orthogonal optimization of a water hydraulic pilot-operated pressure-reducing valve

NASA Astrophysics Data System (ADS)

Mao, Xuyao; Wu, Chao; Li, Bin; Wu, Di

2017-12-01

In order to optimize the comprehensive characteristics of a water hydraulic pilot-operated pressure-reducing valve, numerical orthogonal experimental design was adopted. Six parameters of the valve, containing diameters of damping plugs, volume of spring chamber, half cone angle of main spool, half cone angle of pilot spool, mass of main spool and diameter of main spool, were selected as the orthogonal factors, and each factor has five different levels. An index of flowrate stability, pressure stability and pressure overstrike stability (iFPOS) was used to judge the merit of each orthogonal attempt. Embedded orthogonal process turned up and a final optimal combination of these parameters was obtained after totally 50 numerical orthogonal experiments. iFPOS could be low to a fairly low value which meant that the valve could have much better stabilities. During the optimization, it was also found the diameters of damping plugs and main spool played important roles in stability characteristics of the valve.

Extremal Optimization: Methods Derived from Co-Evolution

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

Boettcher, S.; Percus, A.G.

1999-07-13

We describe a general-purpose method for finding high-quality solutions to hard optimization problems, inspired by self-organized critical models of co-evolution such as the Bak-Sneppen model. The method, called Extremal Optimization, successively eliminates extremely undesirable components of sub-optimal solutions, rather than ''breeding'' better components. In contrast to Genetic Algorithms which operate on an entire ''gene-pool'' of possible solutions, Extremal Optimization improves on a single candidate solution by treating each of its components as species co-evolving according to Darwinian principles. Unlike Simulated Annealing, its non-equilibrium approach effects an algorithm requiring few parameters to tune. With only one adjustable parameter, its performance provesmore » competitive with, and often superior to, more elaborate stochastic optimization procedures. We demonstrate it here on two classic hard optimization problems: graph partitioning and the traveling salesman problem.« less

Modeling and optimization of proton-conducting solid oxide electrolysis cell: Conversion of CO2 into value-added products

NASA Astrophysics Data System (ADS)

Namwong, Lawit; Authayanun, Suthida; Saebea, Dang; Patcharavorachot, Yaneeporn; Arpornwichanop, Amornchai

2016-11-01

Proton-conducting solid oxide electrolysis cells (SOEC-H+) are a promising technology that can utilize carbon dioxide to produce syngas. In this work, a detailed electrochemical model was developed to predict the behavior of SOEC-H+ and to prove the assumption that the syngas is produced through a reversible water gas-shift (RWGS) reaction. The simulation results obtained from the model, which took into account all of the cell voltage losses (i.e., ohmic, activation, and concentration losses), were validated using experimental data to evaluate the unknown parameters. The developed model was employed to examine the structural and operational parameters. It is found that the cathode-supported SOEC-H+ is the best configuration because it requires the lowest cell potential. SOEC-H+ operated favorably at high temperatures and low pressures. Furthermore, the simulation results revealed that the optimal S/C molar ratio for syngas production, which can be used for methanol synthesis, is approximately 3.9 (at a constant temperature and pressure). The SOEC-H+ was optimized using a response surface methodology, which was used to determine the optimal operating conditions to minimize the cell potential and maximize the carbon dioxide flow rate.

[Simulation on remediation of benzene contaminated groundwater by air sparging].

PubMed

Fan, Yan-Ling; Jiang, Lin; Zhang, Dan; Zhong, Mao-Sheng; Jia, Xiao-Yang

2012-11-01

Air sparging (AS) is one of the in situ remedial technologies which are used in groundwater remediation for pollutions with volatile organic compounds (VOCs). At present, the field design of air sparging system was mainly based on experience due to the lack of field data. In order to obtain rational design parameters, the TMVOC module in the Petrasim software package, combined with field test results on a coking plant in Beijing, is used to optimize the design parameters and simulate the remediation process. The pilot test showed that the optimal injection rate was 23.2 m3 x h(-1), while the optimal radius of influence (ROI) was 5 m. The simulation results revealed that the pressure response simulated by the model matched well with the field test results, which indicated a good representation of the simulation. The optimization results indicated that the optimal injection location was at the bottom of the aquifer. Furthermore, simulated at the optimized injection location, the optimal injection rate was 20 m3 x h(-1), which was in accordance with the field test result. Besides, 3 m was the optimal ROI, less than the field test results, and the main reason was that field test reflected the flow behavior at the upper space of groundwater and unsaturated area, in which the width of flow increased rapidly, and became bigger than the actual one. With the above optimized operation parameters, in addition to the hydro-geological parameters measured on site, the model simulation result revealed that 90 days were needed to remediate the benzene from 371 000 microg x L(-1) to 1 microg x L(-1) for the site, and that the opeation model in which the injection wells were progressively turned off once the groundwater around them was "clean" was better than the one in which all the wells were kept operating throughout the remediation process.

Inversion for Refractivity Parameters Using a Dynamic Adaptive Cuckoo Search with Crossover Operator Algorithm

PubMed Central

Zhang, Zhihua; Sheng, Zheng; Shi, Hanqing; Fan, Zhiqiang

2016-01-01

Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg's 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter. PMID:27212938

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 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.

Optimal timing for performing hysterectomy according to different phase of menstrual cycle: Which is best?

PubMed

Kim, Jeong Jin; Kang, Jun Hyeok; Lee, Kyo Won; Kim, Kye Hyun; Song, Taejong

2017-05-01

The aim of this study was to determine whether the different phases of the menstrual cycle could affect operative bleeding in women undergoing laparoscopic hysterectomy. This was a retrospective comparative study. Based on the adjusted day of menstrual cycle, 212 women who underwent laparoscopic hysterectomy were classified into three groups: the follicular phase (n = 51), luteal phase group (n = 125), and menstruation group (n = 36). The primary outcome measure was the operative bleeding. There was no difference in the baseline characteristics of the patients belonging to the three groups. For the groups, there were no significant differences in operative bleeding (p = .469) and change in haemoglobin (p = .330), including operative time, length of hospital stay and complications. The menstrual cycle did not affect the operative bleeding and other parameters. Therefore, no phase of the menstrual cycle could be considered as an optimal timing for performing laparoscopic hysterectomy with minimal operative bleeding. Impact statement What is already known on this subject: the menstrual cycle results in periodic changes in haemostasis and blood flow in the reproductive organs. What the results of this study add: the menstrual cycle did not affect the operative bleeding and other operative parameters during laparoscopic hysterectomy. What the implications are of these findings for clinical practice and/or further research: no phase of the menstrual cycle could be considered as an optimal timing for performing laparoscopic hysterectomy with minimal operative bleeding.

Thermodynamic Analysis and Optimization of a High Temperature Triple Absorption Heat Transformer

PubMed Central

Khamooshi, Mehrdad; Yari, Mortaza; Egelioglu, Fuat; Salati, Hana

2014-01-01

First law of thermodynamics has been used to analyze and optimize inclusively the performance of a triple absorption heat transformer operating with LiBr/H2O as the working pair. A thermodynamic model was developed in EES (engineering equation solver) to estimate the performance of the system in terms of the most essential parameters. The assumed parameters are the temperature of the main components, weak and strong solutions, economizers' efficiencies, and bypass ratios. The whole cycle is optimized by EES software from the viewpoint of maximizing the COP via applying the direct search method. The optimization results showed that the COP of 0.2491 is reachable by the proposed cycle. PMID:25136702

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

Fang Baolong; Department of Mathematics and Physics, Hefei University, Hefei 230022; Yang Zhen

We propose a scheme for implementing a partial general quantum cloning machine with superconducting quantum-interference devices coupled to a nonresonant cavity. By regulating the time parameters, our system can perform optimal symmetric (asymmetric) universal quantum cloning, optimal symmetric (asymmetric) phase-covariant cloning, and optimal symmetric economical phase-covariant cloning. In the scheme the cavity is only virtually excited, thus, the cavity decay is suppressed during the cloning operations.

Performance optimization of an MHD generator with physical constraints

NASA Technical Reports Server (NTRS)

Pian, C. C. P.; Seikel, G. R.; Smith, J. M.

1979-01-01

A technique has been described which optimizes the power out of a Faraday MHD generator operating under a prescribed set of electrical and magnetic constraints. The method does not rely on complicated numerical optimization techniques. Instead the magnetic field and the electrical loading are adjusted at each streamwise location such that the resultant generator design operates at the most limiting of the cited stress levels. The simplicity of the procedure makes it ideal for optimizing generator designs for system analysis studies of power plants. The resultant locally optimum channel designs are, however, not necessarily the global optimum designs. The results of generator performance calculations are presented for an approximately 2000 MWe size plant. The difference between the maximum power generator design and the optimal design which maximizes net MHD power are described. The sensitivity of the generator performance to the various operational parameters are also presented.

Enhanced Engine Performance During Emergency Operation Using a Model-Based Engine Control Architecture

NASA Technical Reports Server (NTRS)

Csank, Jeffrey T.; Connolly, Joseph W.

2016-01-01

This paper discusses the design and application of model-based engine control (MBEC) for use during emergency operation of the aircraft. The MBEC methodology is applied to the Commercial Modular Aero-Propulsion System Simulation 40k (CMAPSS40k) and features an optimal tuner Kalman Filter (OTKF) to estimate unmeasured engine parameters, which can then be used for control. During an emergency scenario, normally-conservative engine operating limits may be relaxed to increase the performance of the engine and overall survivability of the aircraft; this comes at the cost of additional risk of an engine failure. The MBEC architecture offers the advantage of estimating key engine parameters that are not directly measureable. Estimating the unknown parameters allows for tighter control over these parameters, and on the level of risk the engine will operate at. This will allow the engine to achieve better performance than possible when operating to more conservative limits on a related, measurable parameter.

Enhanced Engine Performance During Emergency Operation Using a Model-Based Engine Control Architecture

NASA Technical Reports Server (NTRS)

Csank, Jeffrey T.; Connolly, Joseph W.

2015-01-01

This paper discusses the design and application of model-based engine control (MBEC) for use during emergency operation of the aircraft. The MBEC methodology is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (CMAPSS40,000) and features an optimal tuner Kalman Filter (OTKF) to estimate unmeasured engine parameters, which can then be used for control. During an emergency scenario, normally-conservative engine operating limits may be relaxed to increase the performance of the engine and overall survivability of the aircraft; this comes at the cost of additional risk of an engine failure. The MBEC architecture offers the advantage of estimating key engine parameters that are not directly measureable. Estimating the unknown parameters allows for tighter control over these parameters, and on the level of risk the engine will operate at. This will allow the engine to achieve better performance than possible when operating to more conservative limits on a related, measurable parameter.

Optimal design and control of an electromechanical transfemoral prosthesis with energy regeneration.

PubMed

Rohani, Farbod; Richter, Hanz; van den Bogert, Antonie J

2017-01-01

In this paper, we present the design of an electromechanical above-knee active prosthesis with energy storage and regeneration. The system consists of geared knee and ankle motors, parallel springs for each motor, an ultracapacitor, and controllable four-quadrant power converters. The goal is to maximize the performance of the system by finding optimal controls and design parameters. A model of the system dynamics was developed, and used to solve a combined trajectory and design optimization problem. The objectives of the optimization were to minimize tracking error relative to human joint motions, as well as energy use. The optimization problem was solved by the method of direct collocation, based on joint torque and joint angle data from ten subjects walking at three speeds. After optimization of controls and design parameters, the simulated system could operate at zero energy cost while still closely emulating able-bodied gait. This was achieved by controlled energy transfer between knee and ankle, and by controlled storage and release of energy throughout the gait cycle. Optimal gear ratios and spring parameters were similar across subjects and walking speeds.

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.

Optimal correction and design parameter search by modern methods of rigorous global optimization

NASA Astrophysics Data System (ADS)

Makino, K.; Berz, M.

2011-07-01

Frequently the design of schemes for correction of aberrations or the determination of possible operating ranges for beamlines and cells in synchrotrons exhibit multitudes of possibilities for their correction, usually appearing in disconnected regions of parameter space which cannot be directly qualified by analytical means. In such cases, frequently an abundance of optimization runs are carried out, each of which determines a local minimum depending on the specific chosen initial conditions. Practical solutions are then obtained through an often extended interplay of experienced manual adjustment of certain suitable parameters and local searches by varying other parameters. However, in a formal sense this problem can be viewed as a global optimization problem, i.e. the determination of all solutions within a certain range of parameters that lead to a specific optimum. For example, it may be of interest to find all possible settings of multiple quadrupoles that can achieve imaging; or to find ahead of time all possible settings that achieve a particular tune; or to find all possible manners to adjust nonlinear parameters to achieve correction of high order aberrations. These tasks can easily be phrased in terms of such an optimization problem; but while mathematically this formulation is often straightforward, it has been common belief that it is of limited practical value since the resulting optimization problem cannot usually be solved. However, recent significant advances in modern methods of rigorous global optimization make these methods feasible for optics design for the first time. The key ideas of the method lie in an interplay of rigorous local underestimators of the objective functions, and by using the underestimators to rigorously iteratively eliminate regions that lie above already known upper bounds of the minima, in what is commonly known as a branch-and-bound approach. Recent enhancements of the Differential Algebraic methods used in particle optics for the computation of aberrations allow the determination of particularly sharp underestimators for large regions. As a consequence, the subsequent progressive pruning of the allowed search space as part of the optimization progresses is carried out particularly effectively. The end result is the rigorous determination of the single or multiple optimal solutions of the parameter optimization, regardless of their location, their number, and the starting values of optimization. The methods are particularly powerful if executed in interplay with genetic optimizers generating their new populations within the currently active unpruned space. Their current best guess provides rigorous upper bounds of the minima, which can then beneficially be used for better pruning. Examples of the method and its performance will be presented, including the determination of all operating points of desired tunes or chromaticities, etc. in storage ring lattices.

An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.

PubMed

Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N V

2013-01-01

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.

An Evolutionary Firefly Algorithm for the Estimation of Nonlinear Biological Model Parameters

PubMed Central

Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N. V.

2013-01-01

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test. PMID:23469172

Optimisation d'analyses de grenat almandin realisees au microscope electronique a balayage

NASA Astrophysics Data System (ADS)

Larose, Miguel

The electron microprobe (EMP) is considered as the golden standard for the collection of precise and representative chemical composition of minerals in rocks, but data of similar quality should be obtainable with a scanning electron microscope (SEM). This thesis presents an analytical protocol aimed at optimizing operational parameters of an SEM paired with an EDS Si(Li) X-ray detector (JEOL JSM-840A) for the imaging, quantitative chemical analysis and compositional X-ray maps of almandine garnet found in pelitic schists from the Canadian Cordillera. Results are then compared to those obtained for the same samples on a JEOL JXA 8900 EMP. For imaging purposes, the secondary electrons and backscattered electrons signals have been used to obtain topographic and chemical contrast of the samples, respectively. The SEM allows the acquisition of images with higher resolution than the EMP when working at high magnifications. However, for millimetric size minerals requiring very low magnifications, the EMP can usually match the imaging capabilities of an SEM. When optimizing images for both signals, the optimal operational parameters to show similar contrasts are not restricted to a unique combination of values. Optimization of operational parameters for quantitative chemical analysis resulted in analytical data with a similar precision and showing good correlation to that obtained with an EMP. Optimization of operational parameters for compositional X-ray maps aimed at maximizing the collected intensity within a pixel as well as complying with the spatial resolution criterion in order to obtain a qualitative compositional map representative of the chemical variation within the grain. Even though various corrections were needed, such as the shadow effect and the background noise removal, as well as the impossibility to meet the spatial resolution criterion because of the limited pixel density available on the SEM, the compositional X-ray maps show a good correlation with those obtained with the EMP, even for concentrations as low as 0,5%. When paired with a rigorous analytical protocol, the use of an SEM equipped with an EDS Si (Li) X-ray detector allows the collection of qualitative and quantitative results similar to those obtained with an EMP for all three of the applications considered.

Parameter learning for performance adaptation

NASA Technical Reports Server (NTRS)

Peek, Mark D.; Antsaklis, Panos J.

1990-01-01

A parameter learning method is introduced and used to broaden the region of operability of the adaptive control system of a flexible space antenna. The learning system guides the selection of control parameters in a process leading to optimal system performance. A grid search procedure is used to estimate an initial set of parameter values. The optimization search procedure uses a variation of the Hooke and Jeeves multidimensional search algorithm. The method is applicable to any system where performance depends on a number of adjustable parameters. A mathematical model is not necessary, as the learning system can be used whenever the performance can be measured via simulation or experiment. The results of two experiments, the transient regulation and the command following experiment, are presented.

[Removal of toluene from waste gas by honeycomb adsorption rotor with modified 13X molecular sieves].

PubMed

Wang, Jia-De; Zheng, Liang-Wei; Zhu, Run-Ye; Yu, Yun-Feng

2013-12-01

The removal of toluene from waste gas by Honeycomb Adsorption Rotor with modified 13X molecular sieves was systematically investigated. The effects of the rotor operating parameters and the feed gas parameters on the adsorption efficiency were clarified. The experimental results indicated that the honeycomb adsorption rotor had a good humidity resistance. The removal efficiency of honeycomb adsorption rotor achieved the maximal value with optimal rotor speed and optimal generation air temperature. Moreover, for an appropriate flow rate ratio the removal efficiency and energy consumption should be taken into account. When the recommended operating parameters were regeneration air temperature of 180 degrees C, rotor speed of 2.8-5 r x h(-1), flow rate ratio of 8-12, the removal efficiency kept over 90% for the toluene gas with concentration of 100 mg x m(-3) and inlet velocity of 2 m x s(-1). The research provided design experience and operating parameters for industrial application of honeycomb adsorption rotor. It showed that lower empty bed velocity, faster rotor speed and higher temperature were necessary to purify organic waste gases of higher concentrations.

The technological raw material heating furnaces operation efficiency improving issue

NASA Astrophysics Data System (ADS)

Paramonov, A. M.

2017-08-01

The issue of fuel oil applying efficiency improving in the technological raw material heating furnaces by means of its combustion intensification is considered in the paper. The technical and economic optimization problem of the fuel oil heating before combustion is solved. The fuel oil heating optimal temperature defining method and algorithm analytically considering the correlation of thermal, operating parameters and discounted costs for the heating furnace were developed. The obtained optimization functionality provides the heating furnace appropriate thermal indices achievement at minimum discounted costs. The carried out research results prove the expediency of the proposed solutions using.

Viscoelastic material inversion using Sierra-SD and ROL

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

Walsh, Timothy; Aquino, Wilkins; Ridzal, Denis

2014-11-01

In this report we derive frequency-domain methods for inverse characterization of the constitutive parameters of viscoelastic materials. The inverse problem is cast in a PDE-constrained optimization framework with efficient computation of gradients and Hessian vector products through matrix free operations. The abstract optimization operators for first and second derivatives are derived from first principles. Various methods from the Rapid Optimization Library (ROL) are tested on the viscoelastic inversion problem. The methods described herein are applied to compute the viscoelastic bulk and shear moduli of a foam block model, which was recently used in experimental testing for viscoelastic property characterization.

Optimization on the impeller of a low-specific-speed centrifugal pump for hydraulic performance improvement

NASA Astrophysics Data System (ADS)

Pei, Ji; Wang, Wenjie; Yuan, Shouqi; Zhang, Jinfeng

2016-09-01

In order to widen the high-efficiency operating range of a low-specific-speed centrifugal pump, an optimization process for considering efficiencies under 1.0 Q d and 1.4 Q d is proposed. Three parameters, namely, the blade outlet width b 2, blade outlet angle β 2, and blade wrap angle φ, are selected as design variables. Impellers are generated using the optimal Latin hypercube sampling method. The pump efficiencies are calculated using the software CFX 14.5 at two operating points selected as objectives. Surrogate models are also constructed to analyze the relationship between the objectives and the design variables. Finally, the particle swarm optimization algorithm is applied to calculate the surrogate model to determine the best combination of the impeller parameters. The results show that the performance curve predicted by numerical simulation has a good agreement with the experimental results. Compared with the efficiencies of the original impeller, the hydraulic efficiencies of the optimized impeller are increased by 4.18% and 0.62% under 1.0 Q d and 1.4Qd, respectively. The comparison of inner flow between the original pump and optimized one illustrates the improvement of performance. The optimization process can provide a useful reference on performance improvement of other pumps, even on reduction of pressure fluctuations.

Application of Differential Evolutionary Optimization Methodology for Parameter Structure Identification in Groundwater Modeling

NASA Astrophysics Data System (ADS)

Chiu, Y.; Nishikawa, T.

2013-12-01

With the increasing complexity of parameter-structure identification (PSI) in groundwater modeling, there is a need for robust, fast, and accurate optimizers in the groundwater-hydrology field. For this work, PSI is defined as identifying parameter dimension, structure, and value. In this study, Voronoi tessellation and differential evolution (DE) are used to solve the optimal PSI problem. Voronoi tessellation is used for automatic parameterization, whereby stepwise regression and the error covariance matrix are used to determine the optimal parameter dimension. DE is a novel global optimizer that can be used to solve nonlinear, nondifferentiable, and multimodal optimization problems. It can be viewed as an improved version of genetic algorithms and employs a simple cycle of mutation, crossover, and selection operations. DE is used to estimate the optimal parameter structure and its associated values. A synthetic numerical experiment of continuous hydraulic conductivity distribution was conducted to demonstrate the proposed methodology. The results indicate that DE can identify the global optimum effectively and efficiently. A sensitivity analysis of the control parameters (i.e., the population size, mutation scaling factor, crossover rate, and mutation schemes) was performed to examine their influence on the objective function. The proposed DE was then applied to solve a complex parameter-estimation problem for a small desert groundwater basin in Southern California. Hydraulic conductivity, specific yield, specific storage, fault conductance, and recharge components were estimated simultaneously. Comparison of DE and a traditional gradient-based approach (PEST) shows DE to be more robust and efficient. The results of this work not only provide an alternative for PSI in groundwater models, but also extend DE applications towards solving complex, regional-scale water management optimization problems.

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.

Device Management and Flow Optimization on Left Ventricular Assist Device Support.

PubMed

Tchoukina, Inna; Smallfield, Melissa C; Shah, Keyur B

2018-07-01

The authors discuss principles of continuous flow left ventricular assist device (LVAD) operation, basic differences between the axial and centrifugal flow designs and hemodynamic performance, normal LVAD physiology, and device interaction with the heart. Systematic interpretation of LVAD parameters and recognition of abnormal patterns of flow and pulsatility on the device interrogation are necessary for clinical assessment of the patient. Optimization of pump flow using LVAD parameters and echocardiographic and hemodynamics guidance are reviewed. Copyright © 2018 Elsevier Inc. All rights reserved.

Simulation and optimization of ammonia removal at low temperature for a double channel oxidation ditch based on fully coupled activated sludge model (FCASM): a full-scale study.

PubMed

Yang, Min; Sun, Peide; Wang, Ruyi; Han, Jingyi; Wang, Jianqiao; Song, Yingqi; Cai, Jing; Tang, Xiudi

2013-09-01

An optimal operating condition for ammonia removal at low temperature, based on fully coupled activated sludge model (FCASM), was determined in a full-scale oxidation ditch process wastewater treatment plant (WWTP). The FCASM-based mechanisms model was calibrated and validated with the data measured on site. Several important kinetic parameters of the modified model were tested through respirometry experiment. Validated model was used to evaluate the relationship between ammonia removal and operating parameters, such as temperature (T), dissolved oxygen (DO), solid retention time (SRT) and hydraulic retention time of oxidation ditch (HRT). The simulated results showed that low temperature have a negative effect on the ammonia removal. Through orthogonal simulation tests of the last three factors and combination with the analysis of variance, the optimal operating mode acquired of DO, SRT, HRT for the WWTP at low temperature were 3.5 mg L(-1), 15 d and 14 h, respectively. Copyright © 2013 Elsevier Ltd. All rights reserved.

Two-Swim Operators in the Modified Bacterial Foraging Algorithm for the Optimal Synthesis of Four-Bar Mechanisms

PubMed Central

Hernández-Ocaña, Betania; Pozos-Parra, Ma. Del Pilar; Mezura-Montes, Efrén; Portilla-Flores, Edgar Alfredo; Vega-Alvarado, Eduardo; Calva-Yáñez, Maria Bárbara

2016-01-01

This paper presents two-swim operators to be added to the chemotaxis process of the modified bacterial foraging optimization algorithm to solve three instances of the synthesis of four-bar planar mechanisms. One swim favors exploration while the second one promotes fine movements in the neighborhood of each bacterium. The combined effect of the new operators looks to increase the production of better solutions during the search. As a consequence, the ability of the algorithm to escape from local optimum solutions is enhanced. The algorithm is tested through four experiments and its results are compared against two BFOA-based algorithms and also against a differential evolution algorithm designed for mechanical design problems. The overall results indicate that the proposed algorithm outperforms other BFOA-based approaches and finds highly competitive mechanisms, with a single set of parameter values and with less evaluations in the first synthesis problem, with respect to those mechanisms obtained by the differential evolution algorithm, which needed a parameter fine-tuning process for each optimization problem. PMID:27057156

Two-Swim Operators in the Modified Bacterial Foraging Algorithm for the Optimal Synthesis of Four-Bar Mechanisms.

PubMed

Hernández-Ocaña, Betania; Pozos-Parra, Ma Del Pilar; Mezura-Montes, Efrén; Portilla-Flores, Edgar Alfredo; Vega-Alvarado, Eduardo; Calva-Yáñez, Maria Bárbara

2016-01-01

This paper presents two-swim operators to be added to the chemotaxis process of the modified bacterial foraging optimization algorithm to solve three instances of the synthesis of four-bar planar mechanisms. One swim favors exploration while the second one promotes fine movements in the neighborhood of each bacterium. The combined effect of the new operators looks to increase the production of better solutions during the search. As a consequence, the ability of the algorithm to escape from local optimum solutions is enhanced. The algorithm is tested through four experiments and its results are compared against two BFOA-based algorithms and also against a differential evolution algorithm designed for mechanical design problems. The overall results indicate that the proposed algorithm outperforms other BFOA-based approaches and finds highly competitive mechanisms, with a single set of parameter values and with less evaluations in the first synthesis problem, with respect to those mechanisms obtained by the differential evolution algorithm, which needed a parameter fine-tuning process for each optimization problem.

Numerical Modeling and Optimization of Warm-water Heat Sinks

NASA Astrophysics Data System (ADS)

Hadad, Yaser; Chiarot, Paul

2015-11-01

For cooling in large data-centers and supercomputers, water is increasingly replacing air as the working fluid in heat sinks. Utilizing water provides unique capabilities; for example: higher heat capacity, Prandtl number, and convection heat transfer coefficient. The use of warm, rather than chilled, water has the potential to provide increased energy efficiency. The geometric and operating parameters of the heat sink govern its performance. Numerical modeling is used to examine the influence of geometry and operating conditions on key metrics such as thermal and flow resistance. This model also facilitates studies on cooling of electronic chip hot spots and failure scenarios. We report on the optimal parameters for a warm-water heat sink to achieve maximum cooling performance.

Efficiency Management in Spaceflight Systems

NASA Technical Reports Server (NTRS)

Murphy, Karen

2016-01-01

Efficiency in spaceflight is often approached as “faster, better, cheaper – pick two”. The high levels of performance and reliability required for each mission suggest that planners can only control for two of the three. True efficiency comes by optimizing a system across all three parameters. The functional processes of spaceflight become technical requirements on three operational groups during mission planning: payload, vehicle, and launch operations. Given the interrelationships among the functions performed by the operational groups, optimizing function resources from one operational group to the others affects the efficiency of those groups and therefore the mission overall. This paper helps outline this framework and creates a context in which to understand the effects of resource trades on the overall system, improving the efficiency of the operational groups and the mission as a whole. This allows insight into and optimization of the controlling factors earlier in the mission planning stage.

Estimating model parameters for an impact-produced shock-wave simulation: Optimal use of partial data with the extended Kalman filter

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

Kao, Jim; Flicker, Dawn; Ide, Kayo

2006-05-20

This paper builds upon our recent data assimilation work with the extended Kalman filter (EKF) method [J. Kao, D. Flicker, R. Henninger, S. Frey, M. Ghil, K. Ide, Data assimilation with an extended Kalman filter for an impact-produced shock-wave study, J. Comp. Phys. 196 (2004) 705-723.]. The purpose is to test the capability of EKF in optimizing a model's physical parameters. The problem is to simulate the evolution of a shock produced through a high-speed flyer plate. In the earlier work, we have showed that the EKF allows one to estimate the evolving state of the shock wave from amore » single pressure measurement, assuming that all model parameters are known. In the present paper, we show that imperfectly known model parameters can also be estimated accordingly, along with the evolving model state, from the same single measurement. The model parameter optimization using the EKF can be achieved through a simple modification of the original EKF formalism by including the model parameters into an augmented state variable vector. While the regular state variables are governed by both deterministic and stochastic forcing mechanisms, the parameters are only subject to the latter. The optimally estimated model parameters are thus obtained through a unified assimilation operation. We show that improving the accuracy of the model parameters also improves the state estimate. The time variation of the optimized model parameters results from blending the data and the corresponding values generated from the model and lies within a small range, of less than 2%, from the parameter values of the original model. The solution computed with the optimized parameters performs considerably better and has a smaller total variance than its counterpart using the original time-constant parameters. These results indicate that the model parameters play a dominant role in the performance of the shock-wave hydrodynamic code at hand.« less

Optimization of laminated stacking sequence for buckling load maximization by genetic algorithm

NASA Technical Reports Server (NTRS)

Le Riche, Rodolphe; Haftka, Raphael T.

1992-01-01

The use of a genetic algorithm to optimize the stacking sequence of a composite laminate for buckling load maximization is studied. Various genetic parameters including the population size, the probability of mutation, and the probability of crossover are optimized by numerical experiments. A new genetic operator - permutation - is proposed and shown to be effective in reducing the cost of the genetic search. Results are obtained for a graphite-epoxy plate, first when only the buckling load is considered, and then when constraints on ply contiguity and strain failure are added. The influence on the genetic search of the penalty parameter enforcing the contiguity constraint is studied. The advantage of the genetic algorithm in producing several near-optimal designs is discussed.

Optimization of operator and physical parameters for laser welding of dental materials.

PubMed

Bertrand, C; le Petitcorps, Y; Albingre, L; Dupuis, V

2004-04-10

Interactions between lasers and materials are very complex phenomena. The success of laser welding procedures in dental metals depends on the operator's control of many parameters. The aims of this study were to evaluate factors relating to the operator's dexterity and the choice of the welding parameters (power, pulse duration and therefore energy), which are recognized determinants of weld quality. In vitro laboratory study. FeNiCr dental drawn wires were chosen for these experiments because their properties are well known. Different diameters of wires were laser welded, then tested in tension and compared to the control material as extruded, in order to evaluate the quality of the welding. Scanning electron microscopy of the fractured zone and micrograph observations perpendicular and parallel to the wire axis were also conducted in order to analyse the depth penetration and the quality of the microstructure. Additionally, the micro-hardness (Vickers type) was measured both in the welded and the heat-affected zones and then compared to the non-welded alloy. Adequate combination of energy and pulse duration with the power set in the range between 0.8 to 1 kW appears to improve penetration depth of the laser beam and success of the welding procedure. Operator skill is also an important variable. The variation in laser weld quality in dental FeNiCr wires attributable to operator skill can be minimized by optimization of the physical welding parameters.

Autopilot for frequency-modulation atomic force microscopy.

PubMed

Kuchuk, Kfir; Schlesinger, Itai; Sivan, Uri

2015-10-01

One of the most challenging aspects of operating an atomic force microscope (AFM) is finding optimal feedback parameters. This statement applies particularly to frequency-modulation AFM (FM-AFM), which utilizes three feedback loops to control the cantilever excitation amplitude, cantilever excitation frequency, and z-piezo extension. These loops are regulated by a set of feedback parameters, tuned by the user to optimize stability, sensitivity, and noise in the imaging process. Optimization of these parameters is difficult due to the coupling between the frequency and z-piezo feedback loops by the non-linear tip-sample interaction. Four proportional-integral (PI) parameters and two lock-in parameters regulating these loops require simultaneous optimization in the presence of a varying unknown tip-sample coupling. Presently, this optimization is done manually in a tedious process of trial and error. Here, we report on the development and implementation of an algorithm that computes the control parameters automatically. The algorithm reads the unperturbed cantilever resonance frequency, its quality factor, and the z-piezo driving signal power spectral density. It analyzes the poles and zeros of the total closed loop transfer function, extracts the unknown tip-sample transfer function, and finds four PI parameters and two lock-in parameters for the frequency and z-piezo control loops that optimize the bandwidth and step response of the total system. Implementation of the algorithm in a home-built AFM shows that the calculated parameters are consistently excellent and rarely require further tweaking by the user. The new algorithm saves the precious time of experienced users, facilitates utilization of FM-AFM by casual users, and removes the main hurdle on the way to fully automated FM-AFM.

Autopilot for frequency-modulation atomic force microscopy

NASA Astrophysics Data System (ADS)

Kuchuk, Kfir; Schlesinger, Itai; Sivan, Uri

2015-10-01

One of the most challenging aspects of operating an atomic force microscope (AFM) is finding optimal feedback parameters. This statement applies particularly to frequency-modulation AFM (FM-AFM), which utilizes three feedback loops to control the cantilever excitation amplitude, cantilever excitation frequency, and z-piezo extension. These loops are regulated by a set of feedback parameters, tuned by the user to optimize stability, sensitivity, and noise in the imaging process. Optimization of these parameters is difficult due to the coupling between the frequency and z-piezo feedback loops by the non-linear tip-sample interaction. Four proportional-integral (PI) parameters and two lock-in parameters regulating these loops require simultaneous optimization in the presence of a varying unknown tip-sample coupling. Presently, this optimization is done manually in a tedious process of trial and error. Here, we report on the development and implementation of an algorithm that computes the control parameters automatically. The algorithm reads the unperturbed cantilever resonance frequency, its quality factor, and the z-piezo driving signal power spectral density. It analyzes the poles and zeros of the total closed loop transfer function, extracts the unknown tip-sample transfer function, and finds four PI parameters and two lock-in parameters for the frequency and z-piezo control loops that optimize the bandwidth and step response of the total system. Implementation of the algorithm in a home-built AFM shows that the calculated parameters are consistently excellent and rarely require further tweaking by the user. The new algorithm saves the precious time of experienced users, facilitates utilization of FM-AFM by casual users, and removes the main hurdle on the way to fully automated FM-AFM.

Autopilot for frequency-modulation atomic force microscopy

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

Kuchuk, Kfir; Schlesinger, Itai; Sivan, Uri, E-mail: phsivan@tx.technion.ac.il

2015-10-15

One of the most challenging aspects of operating an atomic force microscope (AFM) is finding optimal feedback parameters. This statement applies particularly to frequency-modulation AFM (FM-AFM), which utilizes three feedback loops to control the cantilever excitation amplitude, cantilever excitation frequency, and z-piezo extension. These loops are regulated by a set of feedback parameters, tuned by the user to optimize stability, sensitivity, and noise in the imaging process. Optimization of these parameters is difficult due to the coupling between the frequency and z-piezo feedback loops by the non-linear tip-sample interaction. Four proportional-integral (PI) parameters and two lock-in parameters regulating these loopsmore » require simultaneous optimization in the presence of a varying unknown tip-sample coupling. Presently, this optimization is done manually in a tedious process of trial and error. Here, we report on the development and implementation of an algorithm that computes the control parameters automatically. The algorithm reads the unperturbed cantilever resonance frequency, its quality factor, and the z-piezo driving signal power spectral density. It analyzes the poles and zeros of the total closed loop transfer function, extracts the unknown tip-sample transfer function, and finds four PI parameters and two lock-in parameters for the frequency and z-piezo control loops that optimize the bandwidth and step response of the total system. Implementation of the algorithm in a home-built AFM shows that the calculated parameters are consistently excellent and rarely require further tweaking by the user. The new algorithm saves the precious time of experienced users, facilitates utilization of FM-AFM by casual users, and removes the main hurdle on the way to fully automated FM-AFM.« less

Parameter Optimization for Turbulent Reacting Flows Using Adjoints

NASA Astrophysics Data System (ADS)

Lapointe, Caelan; Hamlington, Peter E.

2017-11-01

The formulation of a new adjoint solver for topology optimization of turbulent reacting flows is presented. This solver provides novel configurations (e.g., geometries and operating conditions) based on desired system outcomes (i.e., objective functions) for complex reacting flow problems of practical interest. For many such problems, it would be desirable to know optimal values of design parameters (e.g., physical dimensions, fuel-oxidizer ratios, and inflow-outflow conditions) prior to real-world manufacture and testing, which can be expensive, time-consuming, and dangerous. However, computational optimization of these problems is made difficult by the complexity of most reacting flows, necessitating the use of gradient-based optimization techniques in order to explore a wide design space at manageable computational cost. The adjoint method is an attractive way to obtain the required gradients, because the cost of the method is determined by the dimension of the objective function rather than the size of the design space. Here, the formulation of a novel solver is outlined that enables gradient-based parameter optimization of turbulent reacting flows using the discrete adjoint method. Initial results and an outlook for future research directions are provided.

Characterization and Optimization Design of the Polymer-Based Capacitive Micro-Arrayed Ultrasonic Transducer

NASA Astrophysics Data System (ADS)

Chiou, De-Yi; Chen, Mu-Yueh; Chang, Ming-Wei; Deng, Hsu-Cheng

2007-11-01

This study constructs an electromechanical finite element model of the polymer-based capacitive micro-arrayed ultrasonic transducer (P-CMUT). The electrostatic-structural coupled-field simulations are performed to investigate the operational characteristics, such as collapse voltage and resonant frequency. The numerical results are found to be in good agreement with experimental observations. The study of influence of each defined parameter on the collapse voltage and resonant frequency are also presented. To solve some conflict problems in diversely physical fields, an integrated design method is developed to optimize the geometric parameters of the P-CMUT. The optimization search routine conducted using the genetic algorithm (GA) is connected with the commercial FEM software ANSYS to obtain the best design variable using multi-objective functions. The results show that the optimal parameter values satisfy the conflicting objectives, namely to minimize the collapse voltage while simultaneously maintaining a customized frequency. Overall, the present result indicates that the combined FEM/GA optimization scheme provides an efficient and versatile approach of optimization design of the P-CMUT.

The value of compressed air energy storage in energy and reserve markets

DOE PAGES

Drury, Easan; Denholm, Paul; Sioshansi, Ramteen

2011-06-28

Storage devices can provide several grid services, however it is challenging to quantify the value of providing several services and to optimally allocate storage resources to maximize value. We develop a co-optimized Compressed Air Energy Storage (CAES) dispatch model to characterize the value of providing operating reserves in addition to energy arbitrage in several U.S. markets. We use the model to: (1) quantify the added value of providing operating reserves in addition to energy arbitrage; (2) evaluate the dynamic nature of optimally allocating storage resources into energy and reserve markets; and (3) quantify the sensitivity of CAES net revenues tomore » several design and performance parameters. We find that conventional CAES systems could earn an additional 23 ± 10/kW-yr by providing operating reserves, and adiabatic CAES systems could earn an additional 28 ± 13/kW-yr. We find that arbitrage-only revenues are unlikely to support a CAES investment in most market locations, but the addition of reserve revenues could support a conventional CAES investment in several markets. Adiabatic CAES revenues are not likely to support an investment in most regions studied. As a result, modifying CAES design and performance parameters primarily impacts arbitrage revenues, and optimizing CAES design will be nearly independent of dispatch strategy.« less

Optimizing conceptual aircraft designs for minimum life cycle cost

NASA Technical Reports Server (NTRS)

Johnson, Vicki S.

1989-01-01

A life cycle cost (LCC) module has been added to the FLight Optimization System (FLOPS), allowing the additional optimization variables of life cycle cost, direct operating cost, and acquisition cost. Extensive use of the methodology on short-, medium-, and medium-to-long range aircraft has demonstrated that the system works well. Results from the study show that optimization parameter has a definite effect on the aircraft, and that optimizing an aircraft for minimum LCC results in a different airplane than when optimizing for minimum take-off gross weight (TOGW), fuel burned, direct operation cost (DOC), or acquisition cost. Additionally, the economic assumptions can have a strong impact on the configurations optimized for minimum LCC or DOC. Also, results show that advanced technology can be worthwhile, even if it results in higher manufacturing and operating costs. Examining the number of engines a configuration should have demonstrated a real payoff of including life cycle cost in the conceptual design process: the minimum TOGW of fuel aircraft did not always have the lowest life cycle cost when considering the number of engines.

Finite burn maneuver modeling for a generalized spacecraft trajectory design and optimization system.

PubMed

Ocampo, Cesar

2004-05-01

The modeling, design, and optimization of finite burn maneuvers for a generalized trajectory design and optimization system is presented. A generalized trajectory design and optimization system is a system that uses a single unified framework that facilitates the modeling and optimization of complex spacecraft trajectories that may operate in complex gravitational force fields, use multiple propulsion systems, and involve multiple spacecraft. The modeling and optimization issues associated with the use of controlled engine burn maneuvers of finite thrust magnitude and duration are presented in the context of designing and optimizing a wide class of finite thrust trajectories. Optimal control theory is used examine the optimization of these maneuvers in arbitrary force fields that are generally position, velocity, mass, and are time dependent. The associated numerical methods used to obtain these solutions involve either, the solution to a system of nonlinear equations, an explicit parameter optimization method, or a hybrid parameter optimization that combines certain aspects of both. The theoretical and numerical methods presented here have been implemented in copernicus, a prototype trajectory design and optimization system under development at the University of Texas at Austin.

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.

Using Central Composite Experimental Design to Optimize the Degradation of Tylosin from Aqueous Solution by Photo-Fenton Reaction

PubMed Central

Sarrai, Abd Elaziz; Hanini, Salah; Merzouk, Nachida Kasbadji; Tassalit, Djilali; Szabó, Tibor; Hernádi, Klára; Nagy, László

2016-01-01

The feasibility of the application of the Photo-Fenton process in the treatment of aqueous solution contaminated by Tylosin antibiotic was evaluated. The Response Surface Methodology (RSM) based on Central Composite Design (CCD) was used to evaluate and optimize the effect of hydrogen peroxide, ferrous ion concentration and initial pH as independent variables on the total organic carbon (TOC) removal as the response function. The interaction effects and optimal parameters were obtained by using MODDE software. The significance of the independent variables and their interactions was tested by means of analysis of variance (ANOVA) with a 95% confidence level. Results show that the concentration of the ferrous ion and pH were the main parameters affecting TOC removal, while peroxide concentration had a slight effect on the reaction. The optimum operating conditions to achieve maximum TOC removal were determined. The model prediction for maximum TOC removal was compared to the experimental result at optimal operating conditions. A good agreement between the model prediction and experimental results confirms the soundness of the developed model. PMID:28773551

A CPS Based Optimal Operational Control System for Fused Magnesium Furnace

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

Chai, Tian-you; Wu, Zhi-wei; Wang, Hong

Fused magnesia smelting for fused magnesium furnace (FMF) is an energy intensive process with high temperature and comprehensive complexities. Its operational index namely energy consumption per ton (ECPT) is defined as the consumed electrical energy per ton of acceptable quality and is difficult to measure online. Moreover, the dynamics of ECPT cannot be precisely modelled mathematically. The model parameters of the three-phase currents of the electrodes such as the molten pool level, its variation rate and resistance are uncertain and nonlinear functions of the changes in both the smelting process and the raw materials composition. In this paper, an integratedmore » optimal operational control algorithm proposed is composed of a current set-point control, a current switching control and a self-optimized tuning mechanism. The tight conjoining of and coordination between the computational resources including the integrated optimal operational control, embedded software, industrial cloud, wireless communication and the physical resources of FMF constitutes a cyber-physical system (CPS) based embedded optimal operational control system. Successful application of this system has been made for a production line with ten fused magnesium furnaces in a factory in China, leading to a significant reduced ECPT.« less

Honing process optimization algorithms

NASA Astrophysics Data System (ADS)

Kadyrov, Ramil R.; Charikov, Pavel N.; Pryanichnikova, Valeria V.

2018-03-01

This article considers the relevance of honing processes for creating high-quality mechanical engineering products. The features of the honing process are revealed and such important concepts as the task for optimization of honing operations, the optimal structure of the honing working cycles, stepped and stepless honing cycles, simulation of processing and its purpose are emphasized. It is noted that the reliability of the mathematical model determines the quality parameters of the honing process control. An algorithm for continuous control of the honing process is proposed. The process model reliably describes the machining of a workpiece in a sufficiently wide area and can be used to operate the CNC machine CC743.

Enhanced Particle Swarm Optimization Algorithm: Efficient Training of ReaxFF Reactive Force Fields.

PubMed

Furman, David; Carmeli, Benny; Zeiri, Yehuda; Kosloff, Ronnie

2018-06-12

Particle swarm optimization (PSO) is a powerful metaheuristic population-based global optimization algorithm. However, when it is applied to nonseparable objective functions, its performance on multimodal landscapes is significantly degraded. Here we show that a significant improvement in the search quality and efficiency on multimodal functions can be achieved by enhancing the basic rotation-invariant PSO algorithm with isotropic Gaussian mutation operators. The new algorithm demonstrates superior performance across several nonlinear, multimodal benchmark functions compared with the rotation-invariant PSO algorithm and the well-established simulated annealing and sequential one-parameter parabolic interpolation methods. A search for the optimal set of parameters for the dispersion interaction model in the ReaxFF- lg reactive force field was carried out with respect to accurate DFT-TS calculations. The resulting optimized force field accurately describes the equations of state of several high-energy molecular crystals where such interactions are of crucial importance. The improved algorithm also presents better performance compared to a genetic algorithm optimization method in the optimization of the parameters of a ReaxFF- lg correction model. The computational framework is implemented in a stand-alone C++ code that allows the straightforward development of ReaxFF reactive force fields.

Optimal estimation and scheduling in aquifer management using the rapid feedback control method

NASA Astrophysics Data System (ADS)

Ghorbanidehno, Hojat; Kokkinaki, Amalia; Kitanidis, Peter K.; Darve, Eric

2017-12-01

Management of water resources systems often involves a large number of parameters, as in the case of large, spatially heterogeneous aquifers, and a large number of "noisy" observations, as in the case of pressure observation in wells. Optimizing the operation of such systems requires both searching among many possible solutions and utilizing new information as it becomes available. However, the computational cost of this task increases rapidly with the size of the problem to the extent that textbook optimization methods are practically impossible to apply. In this paper, we present a new computationally efficient technique as a practical alternative for optimally operating large-scale dynamical systems. The proposed method, which we term Rapid Feedback Controller (RFC), provides a practical approach for combined monitoring, parameter estimation, uncertainty quantification, and optimal control for linear and nonlinear systems with a quadratic cost function. For illustration, we consider the case of a weakly nonlinear uncertain dynamical system with a quadratic objective function, specifically a two-dimensional heterogeneous aquifer management problem. To validate our method, we compare our results with the linear quadratic Gaussian (LQG) method, which is the basic approach for feedback control. We show that the computational cost of the RFC scales only linearly with the number of unknowns, a great improvement compared to the basic LQG control with a computational cost that scales quadratically. We demonstrate that the RFC method can obtain the optimal control values at a greatly reduced computational cost compared to the conventional LQG algorithm with small and controllable losses in the accuracy of the state and parameter estimation.

Artificial Intelligence Based Selection of Optimal Cutting Tool and Process Parameters for Effective Turning and Milling Operations

NASA Astrophysics Data System (ADS)

Saranya, Kunaparaju; John Rozario Jegaraj, J.; Ramesh Kumar, Katta; Venkateshwara Rao, Ghanta

2016-06-01

With the increased trend in automation of modern manufacturing industry, the human intervention in routine, repetitive and data specific activities of manufacturing is greatly reduced. In this paper, an attempt has been made to reduce the human intervention in selection of optimal cutting tool and process parameters for metal cutting applications, using Artificial Intelligence techniques. Generally, the selection of appropriate cutting tool and parameters in metal cutting is carried out by experienced technician/cutting tool expert based on his knowledge base or extensive search from huge cutting tool database. The present proposed approach replaces the existing practice of physical search for tools from the databooks/tool catalogues with intelligent knowledge-based selection system. This system employs artificial intelligence based techniques such as artificial neural networks, fuzzy logic and genetic algorithm for decision making and optimization. This intelligence based optimal tool selection strategy is developed using Mathworks Matlab Version 7.11.0 and implemented. The cutting tool database was obtained from the tool catalogues of different tool manufacturers. This paper discusses in detail, the methodology and strategies employed for selection of appropriate cutting tool and optimization of process parameters based on multi-objective optimization criteria considering material removal rate, tool life and tool cost.

Designing Industrial Networks Using Ecological Food Web Metrics.

PubMed

Layton, Astrid; Bras, Bert; Weissburg, Marc

2016-10-18

Biologically Inspired Design (biomimicry) and Industrial Ecology both look to natural systems to enhance the sustainability and performance of engineered products, systems and industries. Bioinspired design (BID) traditionally has focused on a unit operation and single product level. In contrast, this paper describes how principles of network organization derived from analysis of ecosystem properties can be applied to industrial system networks. Specifically, this paper examines the applicability of particular food web matrix properties as design rules for economically and biologically sustainable industrial networks, using an optimization model developed for a carpet recycling network. Carpet recycling network designs based on traditional cost and emissions based optimization are compared to designs obtained using optimizations based solely on ecological food web metrics. The analysis suggests that networks optimized using food web metrics also were superior from a traditional cost and emissions perspective; correlations between optimization using ecological metrics and traditional optimization ranged generally from 0.70 to 0.96, with flow-based metrics being superior to structural parameters. Four structural food parameters provided correlations nearly the same as that obtained using all structural parameters, but individual structural parameters provided much less satisfactory correlations. The analysis indicates that bioinspired design principles from ecosystems can lead to both environmentally and economically sustainable industrial resource networks, and represent guidelines for designing sustainable industry networks.

Optimization of operation conditions for the startup of aerobic granular sludge reactors biologically removing carbon, nitrogen, and phosphorous.

PubMed

Lochmatter, Samuel; Holliger, Christof

2014-08-01

The transformation of conventional flocculent sludge to aerobic granular sludge (AGS) biologically removing carbon, nitrogen and phosphorus (COD, N, P) is still a main challenge in startup of AGS sequencing batch reactors (AGS-SBRs). On the one hand a rapid granulation is desired, on the other hand good biological nutrient removal capacities have to be maintained. So far, several operation parameters have been studied separately, which makes it difficult to compare their impacts. We investigated seven operation parameters in parallel by applying a Plackett-Burman experimental design approach with the aim to propose an optimized startup strategy. Five out of the seven tested parameters had a significant impact on the startup duration. The conditions identified to allow a rapid startup of AGS-SBRs with good nutrient removal performances were (i) alternation of high and low dissolved oxygen phases during aeration, (ii) a settling strategy avoiding too high biomass washout during the first weeks of reactor operation, (iii) adaptation of the contaminant load in the early stage of the startup in order to ensure that all soluble COD was consumed before the beginning of the aeration phase, (iv) a temperature of 20 °C, and (v) a neutral pH. Under such conditions, it took less than 30 days to produce granular sludge with high removal performances for COD, N, and P. A control run using this optimized startup strategy produced again AGS with good nutrient removal performances within four weeks and the system was stable during the additional operation period of more than 50 days. Copyright © 2014 Elsevier Ltd. All rights reserved.

Quantum dot ternary-valued full-adder: Logic synthesis by a multiobjective design optimization based on a genetic algorithm

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

Klymenko, M. V.; Remacle, F., E-mail: fremacle@ulg.ac.be

2014-10-28

A methodology is proposed for designing a low-energy consuming ternary-valued full adder based on a quantum dot (QD) electrostatically coupled with a single electron transistor operating as a charge sensor. The methodology is based on design optimization: the values of the physical parameters of the system required for implementing the logic operations are optimized using a multiobjective genetic algorithm. The searching space is determined by elements of the capacitance matrix describing the electrostatic couplings in the entire device. The objective functions are defined as the maximal absolute error over actual device logic outputs relative to the ideal truth tables formore » the sum and the carry-out in base 3. The logic units are implemented on the same device: a single dual-gate quantum dot and a charge sensor. Their physical parameters are optimized to compute either the sum or the carry out outputs and are compatible with current experimental capabilities. The outputs are encoded in the value of the electric current passing through the charge sensor, while the logic inputs are supplied by the voltage levels on the two gate electrodes attached to the QD. The complex logic ternary operations are directly implemented on an extremely simple device, characterized by small sizes and low-energy consumption compared to devices based on switching single-electron transistors. The design methodology is general and provides a rational approach for realizing non-switching logic operations on QD devices.« less

Optimizing the availability of a buffered industrial process

DOEpatents

Martz, Jr., Harry F.; Hamada, Michael S.; Koehler, Arthur J.; Berg, Eric C.

2004-08-24

A computer-implemented process determines optimum configuration parameters for a buffered industrial process. A population size is initialized by randomly selecting a first set of design and operation values associated with subsystems and buffers of the buffered industrial process to form a set of operating parameters for each member of the population. An availability discrete event simulation (ADES) is performed on each member of the population to determine the product-based availability of each member. A new population is formed having members with a second set of design and operation values related to the first set of design and operation values through a genetic algorithm and the product-based availability determined by the ADES. Subsequent population members are then determined by iterating the genetic algorithm with product-based availability determined by ADES to form improved design and operation values from which the configuration parameters are selected for the buffered industrial process.

Optimizing management of the condensing heat and cooling of gases compression in oxy block using of a genetic algorithm

NASA Astrophysics Data System (ADS)

Brzęczek, Mateusz; Bartela, Łukasz

2013-12-01

This paper presents the parameters of the reference oxy combustion block operating with supercritical steam parameters, equipped with an air separation unit and a carbon dioxide capture and compression installation. The possibility to recover the heat in the analyzed power plant is discussed. The decision variables and the thermodynamic functions for the optimization algorithm were identified. The principles of operation of genetic algorithm and methodology of conducted calculations are presented. The sensitivity analysis was performed for the best solutions to determine the effects of the selected variables on the power and efficiency of the unit. Optimization of the heat recovery from the air separation unit, flue gas condition and CO2 capture and compression installation using genetic algorithm was designed to replace the low-pressure section of the regenerative water heaters of steam cycle in analyzed unit. The result was to increase the power and efficiency of the entire power plant.

Optimization of the p-xylene oxidation process by a multi-objective differential evolution algorithm with adaptive parameters co-derived with the population-based incremental learning algorithm

NASA Astrophysics Data System (ADS)

Guo, Zhan; Yan, Xuefeng

2018-04-01

Different operating conditions of p-xylene oxidation have different influences on the product, purified terephthalic acid. It is necessary to obtain the optimal combination of reaction conditions to ensure the quality of the products, cut down on consumption and increase revenues. A multi-objective differential evolution (MODE) algorithm co-evolved with the population-based incremental learning (PBIL) algorithm, called PBMODE, is proposed. The PBMODE algorithm was designed as a co-evolutionary system. Each individual has its own parameter individual, which is co-evolved by PBIL. PBIL uses statistical analysis to build a model based on the corresponding symbiotic individuals of the superior original individuals during the main evolutionary process. The results of simulations and statistical analysis indicate that the overall performance of the PBMODE algorithm is better than that of the compared algorithms and it can be used to optimize the operating conditions of the p-xylene oxidation process effectively and efficiently.

Determination of optimal parameters for dual-layer cathode of polymer electrolyte fuel cell using computational intelligence-aided design.

PubMed

Chen, Yi; Huang, Weina; Peng, Bei

2014-01-01

Because of the demands for sustainable and renewable energy, fuel cells have become increasingly popular, particularly the polymer electrolyte fuel cell (PEFC). Among the various components, the cathode plays a key role in the operation of a PEFC. In this study, a quantitative dual-layer cathode model was proposed for determining the optimal parameters that minimize the over-potential difference η and improve the efficiency using a newly developed bat swarm algorithm with a variable population embedded in the computational intelligence-aided design. The simulation results were in agreement with previously reported results, suggesting that the proposed technique has potential applications for automating and optimizing the design of PEFCs.

On optimal infinite impulse response edge detection filters

NASA Technical Reports Server (NTRS)

Sarkar, Sudeep; Boyer, Kim L.

1991-01-01

The authors outline the design of an optimal, computationally efficient, infinite impulse response edge detection filter. The optimal filter is computed based on Canny's high signal to noise ratio, good localization criteria, and a criterion on the spurious response of the filter to noise. An expression for the width of the filter, which is appropriate for infinite-length filters, is incorporated directly in the expression for spurious responses. The three criteria are maximized using the variational method and nonlinear constrained optimization. The optimal filter parameters are tabulated for various values of the filter performance criteria. A complete methodology for implementing the optimal filter using approximating recursive digital filtering is presented. The approximating recursive digital filter is separable into two linear filters operating in two orthogonal directions. The implementation is very simple and computationally efficient, has a constant time of execution for different sizes of the operator, and is readily amenable to real-time hardware implementation.

Optimizing the Long-Term Operating Plan of Railway Marshalling Station for Capacity Utilization Analysis

PubMed Central

Zhou, Wenliang; Yang, Xia; Deng, Lianbo

2014-01-01

Not only is the operating plan the basis of organizing marshalling station's operation, but it is also used to analyze in detail the capacity utilization of each facility in marshalling station. In this paper, a long-term operating plan is optimized mainly for capacity utilization analysis. Firstly, a model is developed to minimize railcars' average staying time with the constraints of minimum time intervals, marshalling track capacity, and so forth. Secondly, an algorithm is designed to solve this model based on genetic algorithm (GA) and simulation method. It divides the plan of whole planning horizon into many subplans, and optimizes them with GA one by one in order to obtain a satisfactory plan with less computing time. Finally, some numeric examples are constructed to analyze (1) the convergence of the algorithm, (2) the effect of some algorithm parameters, and (3) the influence of arrival train flow on the algorithm. PMID:25525614

Aerobic composting of waste activated sludge: Kinetic analysis for microbiological reaction and oxygen consumption

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

Yamada, Y.; Kawase, Y.

2006-07-01

In order to examine the optimal design and operating parameters, kinetics for microbiological reaction and oxygen consumption in composting of waste activated sludge were quantitatively examined. A series of experiments was conducted to discuss the optimal operating parameters for aerobic composting of waste activated sludge obtained from Kawagoe City Wastewater Treatment Plant (Saitama, Japan) using 4 and 20 L laboratory scale bioreactors. Aeration rate, compositions of compost mixture and height of compost pile were investigated as main design and operating parameters. The optimal aerobic composting of waste activated sludge was found at the aeration rate of 2.0 L/min/kg (initial compostingmore » mixture dry weight). A compost pile up to 0.5 m could be operated effectively. A simple model for composting of waste activated sludge in a composting reactor was developed by assuming that a solid phase of compost mixture is well mixed and the kinetics for microbiological reaction is represented by a Monod-type equation. The model predictions could fit the experimental data for decomposition of waste activated sludge with an average deviation of 2.14%. Oxygen consumption during composting was also examined using a simplified model in which the oxygen consumption was represented by a Monod-type equation and the axial distribution of oxygen concentration in the composting pile was described by a plug-flow model. The predictions could satisfactorily simulate the experiment results for the average maximum oxygen consumption rate during aerobic composting with an average deviation of 7.4%.« less

Optimation of Operation System Integration between Main and Feeder Public Transport (Case Study: Trans Jakarta-Kopaja Bus Services)

NASA Astrophysics Data System (ADS)

Miharja, M.; Priadi, Y. N.

2018-05-01

Promoting a better public transport is a key strategy to cope with urban transport problems which are mostly caused by a huge private vehicle usage. A better public transport service quality not only focuses on one type of public transport mode, but also concerns on inter modes service integration. Fragmented inter mode public transport service leads to a longer trip chain as well as average travel time which would result in its failure to compete with a private vehicle. This paper examines the optimation process of operation system integration between Trans Jakarta Bus as the main public transport mode and Kopaja Bus as feeder public transport service in Jakarta. Using scoring-interview method combined with standard parameters in operation system integration, this paper identifies the key factors that determine the success of the two public transport operation system integrations. The study found that some key integration parameters, such as the cancellation of “system setoran”, passenger get in-get out at official stop points, and systematic payment, positively contribute to a better service integration. However, some parameters such as fine system, time and changing point reliability, and information system reliability are among those which need improvement. These findings are very useful for the authority to set the right strategy to improve operation system integration between Trans Jakarta and Kopaja Bus services.

Resolving the multiple sequence alignment problem using biogeography-based optimization with multiple populations.

PubMed

Zemali, El-Amine; Boukra, Abdelmadjid

2015-08-01

The multiple sequence alignment (MSA) is one of the most challenging problems in bioinformatics, it involves discovering similarity between a set of protein or DNA sequences. This paper introduces a new method for the MSA problem called biogeography-based optimization with multiple populations (BBOMP). It is based on a recent metaheuristic inspired from the mathematics of biogeography named biogeography-based optimization (BBO). To improve the exploration ability of BBO, we have introduced a new concept allowing better exploration of the search space. It consists of manipulating multiple populations having each one its own parameters. These parameters are used to build up progressive alignments allowing more diversity. At each iteration, the best found solution is injected in each population. Moreover, to improve solution quality, six operators are defined. These operators are selected with a dynamic probability which changes according to the operators efficiency. In order to test proposed approach performance, we have considered a set of datasets from Balibase 2.0 and compared it with many recent algorithms such as GAPAM, MSA-GA, QEAMSA and RBT-GA. The results show that the proposed approach achieves better average score than the previously cited methods.

SPECT System Optimization Against A Discrete Parameter Space

PubMed Central

Meng, L. J.; Li, N.

2013-01-01

In this paper, we present an analytical approach for optimizing the design of a static SPECT system or optimizing the sampling strategy with a variable/adaptive SPECT imaging hardware against an arbitrarily given set of system parameters. This approach has three key aspects. First, it is designed to operate over a discretized system parameter space. Second, we have introduced an artificial concept of virtual detector as the basic building block of an imaging system. With a SPECT system described as a collection of the virtual detectors, one can convert the task of system optimization into a process of finding the optimum imaging time distribution (ITD) across all virtual detectors. Thirdly, the optimization problem (finding the optimum ITD) could be solved with a block-iterative approach or other non-linear optimization algorithms. In essence, the resultant optimum ITD could provide a quantitative measure of the relative importance (or effectiveness) of the virtual detectors and help to identify the system configuration or sampling strategy that leads to an optimum imaging performance. Although we are using SPECT imaging as a platform to demonstrate the system optimization strategy, this development also provides a useful framework for system optimization problems in other modalities, such as positron emission tomography (PET) and X-ray computed tomography (CT) [1, 2]. PMID:23587609

Optimal Design of Calibration Signals in Space-Borne Gravitational Wave Detectors

NASA Technical Reports Server (NTRS)

Nofrarias, Miquel; Karnesis, Nikolaos; Gibert, Ferran; Armano, Michele; Audley, Heather; Danzmann, Karsten; Diepholz, Ingo; Dolesi, Rita; Ferraioli, Luigi; Ferroni, Valerio;

Optimal Design of Calibration Signals in Space Borne Gravitational Wave Detectors

NASA Technical Reports Server (NTRS)

Nofrarias, Miquel; Karnesis, Nikolaos; Gibert, Ferran; Armano, Michele; Audley, Heather; Danzmann, Karsten; Diepholz, Ingo; Dolesi, Rita; Ferraioli, Luigi; Thorpe, James I.

2014-01-01

Future space borne gravitational wave detectors will require a precise definition of calibration signals to ensure the achievement of their design sensitivity. The careful design of the test signals plays a key role in the correct understanding and characterization of these instruments. In that sense, methods achieving optimal experiment designs must be considered as complementary to the parameter estimation methods being used to determine the parameters describing the system. The relevance of experiment design is particularly significant for the LISA Pathfinder mission, which will spend most of its operation time performing experiments to characterize key technologies for future space borne gravitational wave observatories. Here we propose a framework to derive the optimal signals in terms of minimum parameter uncertainty to be injected to these instruments during its calibration phase. We compare our results with an alternative numerical algorithm which achieves an optimal input signal by iteratively improving an initial guess. We show agreement of both approaches when applied to the LISA Pathfinder case.

Architecture and settings optimization procedure of a TES frequency domain multiplexed readout firmware

NASA Astrophysics Data System (ADS)

Clenet, A.; Ravera, L.; Bertrand, B.; den Hartog, R.; Jackson, B.; van Leeuwen, B.-J.; van Loon, D.; Parot, Y.; Pointecouteau, E.; Sournac, A.

2014-11-01

IRAP is developing the readout electronics of the SPICA-SAFARI's TES bolometer arrays. Based on the frequency domain multiplexing technique the readout electronics provides the AC-signals to voltage-bias the detectors; it demodulates the data; and it computes a feedback to linearize the detection chain. The feedback is computed with a specific technique, so called baseband feedback (BBFB) which ensures that the loop is stable even with long propagation and processing delays (i.e. several μ s) and with fast signals (i.e. frequency carriers of the order of 5 MHz). To optimize the power consumption we took advantage of the reduced science signal bandwidth to decouple the signal sampling frequency and the data processing rate. This technique allowed a reduction of the power consumption of the circuit by a factor of 10. Beyond the firmware architecture the optimization of the instrument concerns the characterization routines and the definition of the optimal parameters. Indeed, to operate an array TES one has to properly define about 21000 parameters. We defined a set of procedures to automatically characterize these parameters and find out the optimal settings.

Optimization of wearable microwave antenna with simplified electromagnetic model of the human body

NASA Astrophysics Data System (ADS)

Januszkiewicz, Łukasz; Barba, Paolo Di; Hausman, Sławomir

2017-12-01

In this paper the problem of optimization design of a microwave wearable antenna is investigated. Reference is made to a specific antenna design that is a wideband Vee antenna the geometry of which is characterized by 6 parameters. These parameters were automatically adjusted with an evolution strategy based algorithm EStra to obtain the impedance matching of the antenna located in the proximity of the human body. The antenna was designed to operate in the ISM (industrial, scientific, medical) band which covers the frequency range of 2.4 GHz up to 2.5 GHz. The optimization procedure used the finite-difference time-domain method based full-wave simulator with a simplified human body model. In the optimization procedure small movements of antenna towards or away of the human body that are likely to happen during real use were considered. The stability of the antenna parameters irrespective of the movements of the user's body is an important factor in wearable antenna design. The optimization procedure allowed obtaining good impedance matching for a given range of antenna distances with respect to the human body.

Optimization of design and operating parameters of a space-based optical-electronic system with a distributed aperture.

PubMed

Tcherniavski, Iouri; Kahrizi, Mojtaba

2008-11-20

Using a gradient optimization method with objective functions formulated in terms of a signal-to-noise ratio (SNR) calculated at given values of the prescribed spatial ground resolution, optimization problems of geometrical parameters of a distributed optical system and a charge-coupled device of a space-based optical-electronic system are solved for samples of the optical systems consisting of two and three annular subapertures. The modulation transfer function (MTF) of the distributed aperture is expressed in terms of an average MTF taking residual image alignment (IA) and optical path difference (OPD) errors into account. The results show optimal solutions of the optimization problems depending on diverse variable parameters. The information on the magnitudes of the SNR can be used to determine the number of the subapertures and their sizes, while the information on the SNR decrease depending on the IA and OPD errors can be useful in design of a beam combination control system to produce the necessary requirements to its accuracy on the basis of the permissible deterioration in the image quality.

Optimization of bead milling parameters for the cell disruption of microalgae: process modeling and application to Porphyridium cruentum and Nannochloropsis oculata.

PubMed

Montalescot, V; Rinaldi, T; Touchard, R; Jubeau, S; Frappart, M; Jaouen, P; Bourseau, P; Marchal, L

2015-11-01

A study of cell disruption by bead milling for two microalgae, Nannochloropsis oculata and Porphyridium cruentum, was performed. Strains robustness was quantified by high-pressure disruption assays. The hydrodynamics in the bead mill grinding chamber was studied by Residence Time Distribution modeling. Operating parameters effects were analyzed and modeled in terms of stress intensities and stress number. RTD corresponded to a 2 CSTR in series model. First order kinetics cell disruption was modeled in consequence. Continuous bead milling was efficient for both strains disruption. SI-SN modeling was successfully adapted to microalgae. As predicted by high pressure assays, N. oculata was more resistant than P. cruentum. The critical stress intensity was twice more important for N. oculata than for P. cruentum. SI-SN modeling allows the determination of operating parameters minimizing energy consumption and gives a scalable approach to develop and optimize microalgal disruption by bead milling. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Optimization to the Culture Conditions for Phellinus Production with Regression Analysis and Gene-Set Based Genetic Algorithm

PubMed Central

Li, Zhongwei; Xin, Yuezhen; Wang, Xun; Sun, Beibei; Xia, Shengyu; Li, Hui

2016-01-01

Phellinus is a kind of fungus and is known as one of the elemental components in drugs to avoid cancers. With the purpose of finding optimized culture conditions for Phellinus production in the laboratory, plenty of experiments focusing on single factor were operated and large scale of experimental data were generated. In this work, we use the data collected from experiments for regression analysis, and then a mathematical model of predicting Phellinus production is achieved. Subsequently, a gene-set based genetic algorithm is developed to optimize the values of parameters involved in culture conditions, including inoculum size, PH value, initial liquid volume, temperature, seed age, fermentation time, and rotation speed. These optimized values of the parameters have accordance with biological experimental results, which indicate that our method has a good predictability for culture conditions optimization. PMID:27610365

Multi-response parametric optimization in drilling of bamboo/Kevlar fiber reinforced sandwich composite

NASA Astrophysics Data System (ADS)

Singh, Thingujam Jackson; Samanta, Sutanu

2016-09-01

In the present work an attempt was made towards parametric optimization of drilling bamboo/Kevlar K29 fiber reinforced sandwich composite to minimize the delamination occurred during the drilling process and also to maximize the tensile strength of the drilled composite. The spindle speed and the feed rate of the drilling operation are taken as the input parameters. The influence of these parameters on delamination and tensile strength of the drilled composite studied and analysed using Taguchi GRA and ANOVA technique. The results show that both the response parameters i.e. delamination and tensile strength are more influenced by feed rate than spindle speed. The percentage contribution of feed rate and spindle speed on response parameters are 13.88% and 81.74% respectively.

[Optimization of end-tool parameters based on robot hand-eye calibration].

PubMed

Zhang, Lilong; Cao, Tong; Liu, Da

2017-04-01

A new one-time registration method was developed in this research for hand-eye calibration of a surgical robot to simplify the operation process and reduce the preparation time. And a new and practical method is introduced in this research to optimize the end-tool parameters of the surgical robot based on analysis of the error sources in this registration method. In the process with one-time registration method, firstly a marker on the end-tool of the robot was recognized by a fixed binocular camera, and then the orientation and position of the marker were calculated based on the joint parameters of the robot. Secondly the relationship between the camera coordinate system and the robot base coordinate system could be established to complete the hand-eye calibration. Because of manufacturing and assembly errors of robot end-tool, an error equation was established with the transformation matrix between the robot end coordinate system and the robot end-tool coordinate system as the variable. Numerical optimization was employed to optimize end-tool parameters of the robot. The experimental results showed that the one-time registration method could significantly improve the efficiency of the robot hand-eye calibration compared with the existing methods. The parameter optimization method could significantly improve the absolute positioning accuracy of the one-time registration method. The absolute positioning accuracy of the one-time registration method can meet the requirements of the clinical surgery.

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.

LTI system order reduction approach based on asymptotical equivalence and the Co-operation of biology-related algorithms

NASA Astrophysics Data System (ADS)

Ryzhikov, I. S.; Semenkin, E. S.; Akhmedova, Sh A.

2017-02-01

A novel order reduction method for linear time invariant systems is described. The method is based on reducing the initial problem to an optimization one, using the proposed model representation, and solving the problem with an efficient optimization algorithm. The proposed method of determining the model allows all the parameters of the model with lower order to be identified and by definition, provides the model with the required steady-state. As a powerful optimization tool, the meta-heuristic Co-Operation of Biology-Related Algorithms was used. Experimental results proved that the proposed approach outperforms other approaches and that the reduced order model achieves a high level of accuracy.

Contrast research of CDMA and GSM network optimization

NASA Astrophysics Data System (ADS)

Wu, Yanwen; Liu, Zehong; Zhou, Guangyue

2004-03-01

With the development of mobile telecommunication network, users of CDMA advanced their request of network service quality. While the operators also change their network management object from signal coverage to performance improvement. In that case, reasonably layout & optimization of mobile telecommunication network, reasonably configuration of network resource, improvement of the service quality, and increase the enterprise's core competition ability, all those have been concerned by the operator companies. This paper firstly looked into the flow of CDMA network optimization. Then it dissertated to some keystones in the CDMA network optimization, like PN code assignment, calculation of soft handover, etc. As GSM is also the similar cellular mobile telecommunication system like CDMA, so this paper also made a contrast research of CDMA and GSM network optimization in details, including the similarity and the different. In conclusion, network optimization is a long time job; it will run through the whole process of network construct. By the adjustment of network hardware (like BTS equipments, RF systems, etc.) and network software (like parameter optimized, configuration optimized, capacity optimized, etc.), network optimization work can improve the performance and service quality of the network.

Realizing a partial general quantum cloning machine with superconducting quantum-interference devices in a cavity QED

NASA Astrophysics Data System (ADS)

Fang, Bao-Long; Yang, Zhen; Ye, Liu

2009-05-01

We propose a scheme for implementing a partial general quantum cloning machine with superconducting quantum-interference devices coupled to a nonresonant cavity. By regulating the time parameters, our system can perform optimal symmetric (asymmetric) universal quantum cloning, optimal symmetric (asymmetric) phase-covariant cloning, and optimal symmetric economical phase-covariant cloning. In the scheme the cavity is only virtually excited, thus, the cavity decay is suppressed during the cloning operations.

Modeling and optimization of a concentrated solar supercritical CO2 power plant

NASA Astrophysics Data System (ADS)

Osorio, Julian D.

Renewable energy sources are fundamental alternatives to supply the rising energy demand in the world and to reduce or replace fossil fuel technologies. In order to make renewable-based technologies suitable for commercial and industrial applications, two main challenges need to be solved: the design and manufacture of highly efficient devices and reliable systems to operate under intermittent energy supply conditions. In particular, power generation technologies based on solar energy are one of the most promising alternatives to supply the world energy demand and reduce the dependence on fossil fuel technologies. In this dissertation, the dynamic behavior of a Concentrated Solar Power (CSP) supercritical CO2 cycle is studied under different seasonal conditions. The system analyzed is composed of a central receiver, hot and cold thermal energy storage units, a heat exchanger, a recuperator, and multi-stage compression-expansion subsystems with intercoolers and reheaters between compressors and turbines respectively. The effects of operating and design parameters on the system performance are analyzed. Some of these parameters are the mass flow rate, intermediate pressures, number of compression-expansion stages, heat exchangers' effectiveness, multi-tank thermal energy storage, overall heat transfer coefficient between the solar receiver and the environment and the effective area of the recuperator. Energy and exergy models for each component of the system are developed to optimize operating parameters in order to lead to maximum efficiency. From the exergy analysis, the components with high contribution to exergy destruction were identified. These components, which represent an important potential of improvement, are the recuperator, the hot thermal energy storage tank and the solar receiver. Two complementary alternatives to improve the efficiency of concentrated solar thermal systems are proposed in this dissertation: the optimization of the system's operating parameters and optimization of less efficient components. The parametric optimization is developed for a 1MW reference CSP system with CO2 as the working fluid. The component optimization, focused on the less efficient components, comprises some design modifications to the traditional component configuration for the recuperator, the hot thermal energy storage tank and the solar receiver. The proposed optimization alternatives include the heat exchanger's effectiveness enhancement by optimizing fins shapes, multi-tank thermal energy storage configurations for the hot thermal energy storage tank and the incorporation of a transparent insulation material into the solar receiver. Some of the optimizations are conducted in a generalized way, using dimensionless models to be applicable no only to the CSP but also to other thermal systems. This project is therefore an effort to improve the efficiency of power generation systems based on solar energy in order to make them competitive with conventional fossil fuel power generation devices. The results show that the parametric optimization leads the system to an efficiency of about 21% and a maximum power output close to 1.5 MW. The process efficiencies obtained in this work, of more than 21%, are relatively good for a solar-thermal conversion system and are also comparable with efficiencies of conversion of high performance PV panels. The thermal energy storage allows the system to operate for several hours after sunset. This operating time is approximately increased from 220 to 480 minutes after optimization. The hot and cold thermal energy storage also lessens the temperature fluctuations by providing smooth changes of temperatures at the turbines' and compressors' inlets. Additional improvements in the overall system efficiency are possible by optimizing the less efficient components. In particular, the fin's effectiveness can be improved in more than 5% after its shape is optimized, increments in the efficiency of the thermal energy storage of about 5.7% are possible when the mass is divided into four tanks, and solar receiver efficiencies up to 70% can be maintained for high operating temperatures (~ 1200°C) when a transparent insulation material is incorporated to the receiver. The results obtained in this dissertation indicate that concentrated solar systems using supercritical CO2 could be a viable alternative to satisfying energy needs in desert areas with scarce water and fossil fuel resources.

Regenerator Operation at Very High Frequencies for Microcryocoolers

NASA Astrophysics Data System (ADS)

Radebaugh, Ray; O'Gallagher, Agnes

2006-04-01

The size of Stirling and Stirling-type pulse tube cryocoolers is dominated by the size of the pressure oscillator. Such cryocoolers typically operate at frequencies up to about 60 Hz for cold-end temperatures above about 60 K. Higher operating frequencies would allow the size and mass of the pressure oscillator to be reduced for a given power input. However, simply increasing the operating frequency leads to large losses in the regenerator. The simple analytical equations derived here show how the right combination of frequency and pressure, along with optimized regenerator geometry, can lead to successful regenerator operation at frequencies up to 1 kHz. Efficient regenerator operation at such high frequencies is possible only with pressures of about 5 to 8 MPa and with very small hydraulic diameters and lengths. Other geometrical parameters must also be optimized for such conditions. The analytical equations are used to provide guidance to the right combination of parameters. We give example numerical calculations with REGEN3.2 in the paper for 60 Hz, 400 Hz, and 1000 Hz operation of optimized screen regenerators and show that the coefficient of performance at 400 Hz and 1000 Hz is about 78 % and 68 %, respectively, of that for 60 Hz when an average pressure of 7 MPa is used with the higher frequency, compared with 2.5 MPa for 60 Hz operation. The 1000 Hz coefficient of performance for parallel tubes is about the same as that of the screen geometry at 60 Hz. The compressor and cold-end swept volumes are reduced by a factor of 47 at 1000 Hz, compared with the 60 Hz case for the same input acoustic power, which can enable the development of microcryocoolers for MEMS applications.

Multi-point optimization of recirculation flow type casing treatment in centrifugal compressors

NASA Astrophysics Data System (ADS)

Tun, Min Thaw; Sakaguchi, Daisaku

2016-06-01

High-pressure ratio and wide operating range are highly required for a turbocharger in diesel engines. A recirculation flow type casing treatment is effective for flow range enhancement of centrifugal compressors. Two ring grooves on a suction pipe and a shroud casing wall are connected by means of an annular passage and stable recirculation flow is formed at small flow rates from the downstream groove toward the upstream groove through the annular bypass. The shape of baseline recirculation flow type casing is modified and optimized by using a multi-point optimization code with a metamodel assisted evolutionary algorithm embedding a commercial CFD code CFX from ANSYS. The numerical optimization results give the optimized design of casing with improving adiabatic efficiency in wide operating flow rate range. Sensitivity analysis of design parameters as a function of efficiency has been performed. It is found that the optimized casing design provides optimized recirculation flow rate, in which an increment of entropy rise is minimized at grooves and passages of the rotating impeller.

Increase of Gas-Turbine Plant Efficiency by Optimizing Operation of Compressors

NASA Astrophysics Data System (ADS)

Matveev, V.; Goriachkin, E.; Volkov, A.

2018-01-01

The article presents optimization method for improving of the working process of axial compressors of gas turbine engines. Developed method allows to perform search for the best geometry of compressor blades automatically by using optimization software IOSO and CFD software NUMECA Fine/Turbo. The calculation of the compressor parameters was performed for work and stall point of its performance map on each optimization step. Study was carried out for seven-stage high-pressure compressor and three-stage low-pressure compressors. As a result of optimization, improvement of efficiency was achieved for all investigated compressors.

Optimal Operation of Energy Storage in Power Transmission and Distribution

NASA Astrophysics Data System (ADS)

Akhavan Hejazi, Seyed Hossein

In this thesis, we investigate optimal operation of energy storage units in power transmission and distribution grids. At transmission level, we investigate the problem where an investor-owned independently-operated energy storage system seeks to offer energy and ancillary services in the day-ahead and real-time markets. We specifically consider the case where a significant portion of the power generated in the grid is from renewable energy resources and there exists significant uncertainty in system operation. In this regard, we formulate a stochastic programming framework to choose optimal energy and reserve bids for the storage units that takes into account the fluctuating nature of the market prices due to the randomness in the renewable power generation availability. At distribution level, we develop a comprehensive data set to model various stochastic factors on power distribution networks, with focus on networks that have high penetration of electric vehicle charging load and distributed renewable generation. Furthermore, we develop a data-driven stochastic model for energy storage operation at distribution level, where the distribution of nodal voltage and line power flow are modelled as stochastic functions of the energy storage unit's charge and discharge schedules. In particular, we develop new closed-form stochastic models for such key operational parameters in the system. Our approach is analytical and allows formulating tractable optimization problems. Yet, it does not involve any restricting assumption on the distribution of random parameters, hence, it results in accurate modeling of uncertainties. By considering the specific characteristics of random variables, such as their statistical dependencies and often irregularly-shaped probability distributions, we propose a non-parametric chance-constrained optimization approach to operate and plan energy storage units in power distribution girds. In the proposed stochastic optimization, we consider uncertainty from various elements, such as solar photovoltaic , electric vehicle chargers, and residential baseloads, in the form of discrete probability functions. In the last part of this thesis we address some other resources and concepts for enhancing the operation of power distribution and transmission systems. In particular, we proposed a new framework to determine the best sites, sizes, and optimal payment incentives under special contracts for committed-type DG projects to offset distribution network investment costs. In this framework, the aim is to allocate DGs such that the profit gained by the distribution company is maximized while each DG unit's individual profit is also taken into account to assure that private DG investment remains economical.

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.

Investigation on gas medium parameters for an ArF excimer laser through orthogonal experimental design

NASA Astrophysics Data System (ADS)

Song, Xingliang; Sha, Pengfei; Fan, Yuanyuan; Jiang, R.; Zhao, Jiangshan; Zhou, Yi; Yang, Junhong; Xiong, Guangliang; Wang, Yu

2018-02-01

Due to complex kinetics of formation and loss mechanisms, such as ion-ion recombination reaction, neutral species harpoon reaction, excited state quenching and photon absorption, as well as their interactions, the performance behavior of different laser gas medium parameters for excimer laser varies greatly. Therefore, the effects of gas composition and total gas pressure on excimer laser performance attract continual research studies. In this work, orthogonal experimental design (OED) is used to investigate quantitative and qualitative correlations between output laser energy characteristics and gas medium parameters for an ArF excimer laser with plano-plano optical resonator operation. Optimized output laser energy with good pulse to pulse stability can be obtained effectively by proper selection of the gas medium parameters, which makes the most of the ArF excimer laser device. Simple and efficient method for gas medium optimization is proposed and demonstrated experimentally, which provides a global and systematic solution. By detailed statistical analysis, the significance sequence of relevant parameter factors and the optimized composition for gas medium parameters are obtained. Compared with conventional route of varying single gas parameter factor sequentially, this paper presents a more comprehensive way of considering multivariables simultaneously, which seems promising in striking an appropriate balance among various complicated parameters for power scaling study of an excimer laser.

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.

Method for Predicting and Optimizing System Parameters for Electrospinning System

NASA Technical Reports Server (NTRS)

Wincheski, Russell A. (Inventor)

2011-01-01

An electrospinning system using a spinneret and a counter electrode is first operated for a fixed amount of time at known system and operational parameters to generate a fiber mat having a measured fiber mat width associated therewith. Next, acceleration of the fiberizable material at the spinneret is modeled to determine values of mass, drag, and surface tension associated with the fiberizable material at the spinneret output. The model is then applied in an inversion process to generate predicted values of an electric charge at the spinneret output and an electric field between the spinneret and electrode required to fabricate a selected fiber mat design. The electric charge and electric field are indicative of design values for system and operational parameters needed to fabricate the selected fiber mat design.

Optimization of Design Parameters and Operating Conditions of Electrochemical Capacitors for High Energy and Power Performance

NASA Astrophysics Data System (ADS)

Ike, Innocent S.; Sigalas, Iakovos; Iyuke, Sunny E.

2017-03-01

Theoretical expressions for performance parameters of different electrochemical capacitors (ECs) have been optimized by solving them using MATLAB scripts as well as via the MATLAB R2014a optimization toolbox. The performance of the different kinds of ECs under given conditions was compared using theoretical equations and simulations of various models based on the conditions of device components, using optimal values for the coefficient associated with the battery-kind material ( K BMopt) and the constant associated with the electrolyte material ( K Eopt), as well as our symmetric electric double-layer capacitor (EDLC) experimental data. Estimation of performance parameters was possible based on values for the mass ratio of electrodes, operating potential range ratio, and specific capacitance of electrolyte. The performance of asymmetric ECs with suitable electrode mass and operating potential range ratios using aqueous or organic electrolyte at appropriate operating potential range and specific capacitance was 2.2 and 5.56 times greater, respectively, than for the symmetric EDLC and asymmetric EC using the same aqueous electrolyte, respectively. This enhancement was accompanied by reduced cell mass and volume. Also, the storable and deliverable energies of the asymmetric EC with suitable electrode mass and operating potential range ratios using the proper organic electrolyte were 12.9 times greater than those of the symmetric EDLC using aqueous electrolyte, again with reduced cell mass and volume. The storable energy, energy density, and power density of the asymmetric EDLC with suitable electrode mass and operating potential range ratios using the proper organic electrolyte were 5.56 times higher than for a similar symmetric EDLC using aqueous electrolyte, with cell mass and volume reduced by a factor of 1.77. Also, the asymmetric EDLC with the same type of electrode and suitable electrode mass ratio, working potential range ratio, and proper organic electrolyte showed enhanced performance compared with the conventional symmetric EDLC using aqueous electrolyte, with reduced cell mass and volume. These results can obviously reduce the number of experiments required to determine the optimum manufacturing design for ECs and also demonstrate that use of an asymmetric electrode and organic electrolyte was very successful for improving the performance of the EC, with reduced cell mass and volume. These results can also act as guidelines for design, fabrication, and operation of electrochemical capacitors with outstanding storable energy, energy density, and power density.

OPTIMIZING THROUGH CO-EVOLUTIONARY AVALANCHES

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

S. BOETTCHER; A. PERCUS

2000-08-01

We explore a new general-purpose heuristic for finding high-quality solutions to hard optimization problems. The method, called extremal optimization, is inspired by ''self-organized critically,'' a concept introduced to describe emergent complexity in many physical systems. In contrast to Genetic Algorithms which operate on an entire ''gene-pool'' of possible solutions, extremal optimization successively replaces extremely undesirable elements of a sub-optimal solution with new, random ones. Large fluctuations, called ''avalanches,'' ensue that efficiently explore many local optima. Drawing upon models used to simulate far-from-equilibrium dynamics, extremal optimization complements approximation methods inspired by equilibrium statistical physics, such as simulated annealing. With only onemore » adjustable parameter, its performance has proved competitive with more elaborate methods, especially near phase transitions. Those phase transitions are found in the parameter space of most optimization problems, and have recently been conjectured to be the origin of some of the hardest instances in computational complexity. We will demonstrate how extremal optimization can be implemented for a variety of combinatorial optimization problems. We believe that extremal optimization will be a useful tool in the investigation of phase transitions in combinatorial optimization problems, hence valuable in elucidating the origin of computational complexity.« less

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.

Near-Optimal Operation of Dual-Fuel Launch Vehicles

NASA Technical Reports Server (NTRS)

Ardema, M. D.; Chou, H. C.; Bowles, J. V.

1996-01-01

A near-optimal guidance law for the ascent trajectory from earth surface to earth orbit of a fully reusable single-stage-to-orbit pure rocket launch vehicle is derived. Of interest are both the optimal operation of the propulsion system and the optimal flight path. A methodology is developed to investigate the optimal throttle switching of dual-fuel engines. The method is based on selecting propulsion system modes and parameters that maximize a certain performance function. This function is derived from consideration of the energy-state model of the aircraft equations of motion. Because the density of liquid hydrogen is relatively low, the sensitivity of perturbations in volume need to be taken into consideration as well as weight sensitivity. The cost functional is a weighted sum of fuel mass and volume; the weighting factor is chosen to minimize vehicle empty weight for a given payload mass and volume in orbit.

Determination of Optimal Parameters for Dual-Layer Cathode of Polymer Electrolyte Fuel Cell Using Computational Intelligence-Aided Design

PubMed Central

Chen, Yi; Huang, Weina; Peng, Bei

2014-01-01

Because of the demands for sustainable and renewable energy, fuel cells have become increasingly popular, particularly the polymer electrolyte fuel cell (PEFC). Among the various components, the cathode plays a key role in the operation of a PEFC. In this study, a quantitative dual-layer cathode model was proposed for determining the optimal parameters that minimize the over-potential difference and improve the efficiency using a newly developed bat swarm algorithm with a variable population embedded in the computational intelligence-aided design. The simulation results were in agreement with previously reported results, suggesting that the proposed technique has potential applications for automating and optimizing the design of PEFCs. PMID:25490761

Design of 28 GHz, 200 kW Gyrotron for ECRH Applications

NASA Astrophysics Data System (ADS)

Yadav, Vivek; Singh, Udaybir; Kumar, Nitin; Kumar, Anil; Deorani, S. C.; Sinha, A. K.

2013-01-01

This paper presents the design of 28 GHz, 200 kW gyrotron for Indian TOKAMAK system. The paper reports the designs of interaction cavity, magnetron injection gun and RF window. EGUN code is used for the optimization of electron gun parameters. TE03 mode is selected as the operating mode by using the in-house developed code GCOMS. The simulation and optimization of the cavity parameters are carried out by using the Particle-in-cell, three dimensional (3-D)-electromagnetic simulation code MAGIC. The output power more than 250 kW is achieved.

A Simulation Modeling Approach Method Focused on the Refrigerated Warehouses Using Design of Experiment

NASA Astrophysics Data System (ADS)

Cho, G. S.

2017-09-01

For performance optimization of Refrigerated Warehouses, design parameters are selected based on the physical parameters such as number of equipment and aisles, speeds of forklift for ease of modification. This paper provides a comprehensive framework approach for the system design of Refrigerated Warehouses. We propose a modeling approach which aims at the simulation optimization so as to meet required design specifications using the Design of Experiment (DOE) and analyze a simulation model using integrated aspect-oriented modeling approach (i-AOMA). As a result, this suggested method can evaluate the performance of a variety of Refrigerated Warehouses operations.

Modelling and optimization of a wellhead gas flowmeter using concentric pipes

NASA Astrophysics Data System (ADS)

Nec, Yana; Huculak, Greg

2017-09-01

A novel configuration of a landfill wellhead was analysed to measure the flow rate of gas extracted from sanitary landfills. The device provides access points for pressure measurement integral to flow rate computation similarly to orifice and Venturi meters, and has the advantage of eliminating the problem of water condensation often impairing the accuracy thereof. It is proved that the proposed configuration entails comparable computational complexity and negligible sensitivity to geometric parameters. Calibration for the new device was attained using a custom optimization procedure, operating on a quadri-dimensional parameter surface evincing discontinuity and non-smoothness.

Mass Spectrometry Parameters Optimization for the 46 Multiclass Pesticides Determination in Strawberries with Gas Chromatography Ion-Trap Tandem Mass Spectrometry

NASA Astrophysics Data System (ADS)

Fernandes, Virgínia C.; Vera, Jose L.; Domingues, Valentina F.; Silva, Luís M. S.; Mateus, Nuno; Delerue-Matos, Cristina

2012-12-01

Multiclass analysis method was optimized in order to analyze pesticides traces by gas chromatography with ion-trap and tandem mass spectrometry (GC-MS/MS). The influence of some analytical parameters on pesticide signal response was explored. Five ion trap mass spectrometry (IT-MS) operating parameters, including isolation time (IT), excitation voltage (EV), excitation time (ET), maximum excitation energy or " q" value (q), and isolation mass window (IMW) were numerically tested in order to maximize the instrument analytical signal response. For this, multiple linear regression was used in data analysis to evaluate the influence of the five parameters on the analytical response in the ion trap mass spectrometer and to predict its response. The assessment of the five parameters based on the regression equations substantially increased the sensitivity of IT-MS/MS in the MS/MS mode. The results obtained show that for most of the pesticides, these parameters have a strong influence on both signal response and detection limit. Using the optimized method, a multiclass pesticide analysis was performed for 46 pesticides in a strawberry matrix. Levels higher than the limit established for strawberries by the European Union were found in some samples.

A multiple objective optimization approach to quality control

NASA Technical Reports Server (NTRS)

Seaman, Christopher Michael

1991-01-01

The use of product quality as the performance criteria for manufacturing system control is explored. The goal in manufacturing, for economic reasons, is to optimize product quality. The problem is that since quality is a rather nebulous product characteristic, there is seldom an analytic function that can be used as a measure. Therefore standard control approaches, such as optimal control, cannot readily be applied. A second problem with optimizing product quality is that it is typically measured along many dimensions: there are many apsects of quality which must be optimized simultaneously. Very often these different aspects are incommensurate and competing. The concept of optimality must now include accepting tradeoffs among the different quality characteristics. These problems are addressed using multiple objective optimization. It is shown that the quality control problem can be defined as a multiple objective optimization problem. A controller structure is defined using this as the basis. Then, an algorithm is presented which can be used by an operator to interactively find the best operating point. Essentially, the algorithm uses process data to provide the operator with two pieces of information: (1) if it is possible to simultaneously improve all quality criteria, then determine what changes to the process input or controller parameters should be made to do this; and (2) if it is not possible to improve all criteria, and the current operating point is not a desirable one, select a criteria in which a tradeoff should be made, and make input changes to improve all other criteria. The process is not operating at an optimal point in any sense if no tradeoff has to be made to move to a new operating point. This algorithm ensures that operating points are optimal in some sense and provides the operator with information about tradeoffs when seeking the best operating point. The multiobjective algorithm was implemented in two different injection molding scenarios: tuning of process controllers to meet specified performance objectives and tuning of process inputs to meet specified quality objectives. Five case studies are presented.

Wastewater treatment using hybrid treatment schemes based on cavitation and Fenton chemistry: a review.

PubMed

Bagal, Manisha V; Gogate, Parag R

2014-01-01

Advanced oxidation processes such as cavitation and Fenton chemistry have shown considerable promise for wastewater treatment applications due to the ease of operation and simple reactor design. In this review, hybrid methods based on cavitation coupled with Fenton process for the treatment of wastewater have been discussed. The basics of individual processes (Acoustic cavitation, Hydrodynamic cavitation, Fenton chemistry) have been discussed initially highlighting the need for combined processes. The different types of reactors used for the combined processes have been discussed with some recommendations for large scale operation. The effects of important operating parameters such as solution temperature, initial pH, initial pollutant concentration and Fenton's reagent dosage have been discussed with guidelines for selection of optimum parameters. The optimization of power density is necessary for ultrasonic processes (US) and combined processes (US/Fenton) whereas the inlet pressure needs to be optimized in the case of Hydrodynamic cavitation (HC) based processes. An overview of different pollutants degraded under optimized conditions using HC/Fenton and US/Fenton process with comparison with individual processes have been presented. It has been observed that the main mechanism for the synergy of the combined process depends on the generation of additional hydroxyl radicals and its proper utilization for the degradation of the pollutant, which is strongly dependent on the loading of hydrogen peroxide. Overall, efficient wastewater treatment with high degree of energy efficiency can be achieved using combined process operating under optimized conditions, as compared to the individual process. Copyright © 2013 Elsevier B.V. All rights reserved.

Numerical investigation of design and operational parameters on CHI spheromak performance

NASA Astrophysics Data System (ADS)

O'Bryan, J. B.; Romero-Talamas, C. A.; Woodruff, S.

2016-10-01

Nonlinear, extended-MHD computation with the NIMROD code is used to explore magnetic self-organization and performance with respect to externally controllable parameters in spheromaks formed with coaxial helicity injection. The goal of this study is to inform the design and operational parameters of proposed proof-of-principle spheromak experiment. The calculations explore multiple distinct phases of evolution (including adiabatic magnetic compression), which must be explored and optimized separately. Results indicate that modest changes to the design and operation of past experiments, e.g. SSPX [E.B. Hooper et al. PPCF 2012], could have significantly improved the plasma-current injector coupling efficiency and performance, particularly with respect to peak temperature and lifetime. Though we frequently characterize performance relative to SSPX, we are also exploring fundamentally different designs and modes of operation, e.g. flux compression. This work is supported by DAPRA under Grant No. N66001-14-1-4044.

Laser effects based optimal laser parameter identifications for paint removal from metal substrate at 1064 nm: a multi-pulse model

NASA Astrophysics Data System (ADS)

Han, Jinghua; Cui, Xudong; Wang, Sha; Feng, Guoying; Deng, Guoliang; Hu, Ruifeng

2017-10-01

Paint removal by laser ablation is favoured among cleaning techniques due to its high efficiency. How to predict the optimal laser parameters without producing damage to substrate still remains challenging for accurate paint stripping. On the basis of ablation morphologies and combining experiments with numerical modelling, the underlying mechanisms and the optimal conditions for paint removal by laser ablation are thoroughly investigated. Our studies suggest that laser paint removal is dominated by the laser vaporization effect, thermal stress effect and laser plasma effect, in which thermal stress effect is the most favoured while laser plasma effect should be avoided during removal operations. Based on the thermodynamic equations, we numerically evaluated the spatial distribution of the temperature as well as thermal stress in the paint and substrate under the irradiation of laser pulse at 1064 nm. The obtained curves of the paint thickness vs. threshold fluences can provide the reference standard of laser parameter selection in view of the paint layer with different thickness. A multi-pulse model is proposed and validated under a constant laser fluence to perfectly remove a thicker paint layer. The investigations and the methods proposed here might give hints to the efficient operations on the paint removal and lowering the risk of substrate damages.

Modeling human tracking error in several different anti-tank systems

NASA Technical Reports Server (NTRS)

Kleinman, D. L.

1981-01-01

An optimal control model for generating time histories of human tracking errors in antitank systems is outlined. Monte Carlo simulations of human operator responses for three Army antitank systems are compared. System/manipulator dependent data comparisons reflecting human operator limitations in perceiving displayed quantities and executing intended control motions are presented. Motor noise parameters are also discussed.

Gravity Thickening. Sludge Treatment and Disposal Course #166. Instructor's Guide [and] Student Workbook.

ERIC Educational Resources Information Center

Klopping, Paul H.

The basic operation of the gravity thickener is described in this lesson, focusing on the theory of operation, components found in a typical thickener, and the parameters which must be understood in optimizing the opeation of the thickener. Attention is given to mathematics concepts which are used in controlling hydraulic loading, detention time,…

Mystic: Implementation of the Static Dynamic Optimal Control Algorithm for High-Fidelity, Low-Thrust Trajectory Design

NASA Technical Reports Server (NTRS)

Whiffen, Gregory J.

2006-01-01

Mystic software is designed to compute, analyze, and visualize optimal high-fidelity, low-thrust trajectories, The software can be used to analyze inter-planetary, planetocentric, and combination trajectories, Mystic also provides utilities to assist in the operation and navigation of low-thrust spacecraft. Mystic will be used to design and navigate the NASA's Dawn Discovery mission to orbit the two largest asteroids, The underlying optimization algorithm used in the Mystic software is called Static/Dynamic Optimal Control (SDC). SDC is a nonlinear optimal control method designed to optimize both 'static variables' (parameters) and dynamic variables (functions of time) simultaneously. SDC is a general nonlinear optimal control algorithm based on Bellman's principal.

A Fast and Scalable Method for A-Optimal Design of Experiments for Infinite-dimensional Bayesian Nonlinear Inverse Problems with Application to Porous Medium Flow

NASA Astrophysics Data System (ADS)

Petra, N.; Alexanderian, A.; Stadler, G.; Ghattas, O.

2015-12-01

We address the problem of optimal experimental design (OED) for Bayesian nonlinear inverse problems governed by partial differential equations (PDEs). The inverse problem seeks to infer a parameter field (e.g., the log permeability field in a porous medium flow model problem) from synthetic observations at a set of sensor locations and from the governing PDEs. The goal of the OED problem is to find an optimal placement of sensors so as to minimize the uncertainty in the inferred parameter field. We formulate the OED objective function by generalizing the classical A-optimal experimental design criterion using the expected value of the trace of the posterior covariance. This expected value is computed through sample averaging over the set of likely experimental data. Due to the infinite-dimensional character of the parameter field, we seek an optimization method that solves the OED problem at a cost (measured in the number of forward PDE solves) that is independent of both the parameter and the sensor dimension. To facilitate this goal, we construct a Gaussian approximation to the posterior at the maximum a posteriori probability (MAP) point, and use the resulting covariance operator to define the OED objective function. We use randomized trace estimation to compute the trace of this covariance operator. The resulting OED problem includes as constraints the system of PDEs characterizing the MAP point, and the PDEs describing the action of the covariance (of the Gaussian approximation to the posterior) to vectors. We control the sparsity of the sensor configurations using sparsifying penalty functions, and solve the resulting penalized bilevel optimization problem via an interior-point quasi-Newton method, where gradient information is computed via adjoints. We elaborate our OED method for the problem of determining the optimal sensor configuration to best infer the log permeability field in a porous medium flow problem. Numerical results show that the number of PDE solves required for the evaluation of the OED objective function and its gradient is essentially independent of both the parameter dimension and the sensor dimension (i.e., the number of candidate sensor locations). The number of quasi-Newton iterations for computing an OED also exhibits the same dimension invariance properties.

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.

Asymptotic Linearity of Optimal Control Modification Adaptive Law with Analytical Stability Margins

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.

2010-01-01

Optimal control modification has been developed to improve robustness to model-reference adaptive control. For systems with linear matched uncertainty, optimal control modification adaptive law can be shown by a singular perturbation argument to possess an outer solution that exhibits a linear asymptotic property. Analytical expressions of phase and time delay margins for the outer solution can be obtained. Using the gradient projection operator, a free design parameter of the adaptive law can be selected to satisfy stability margins.

Chandrasekhar equations and computational algorithms for distributed parameter systems

NASA Technical Reports Server (NTRS)

Burns, J. A.; Ito, K.; Powers, R. K.

1984-01-01

The Chandrasekhar equations arising in optimal control problems for linear distributed parameter systems are considered. The equations are derived via approximation theory. This approach is used to obtain existence, uniqueness, and strong differentiability of the solutions and provides the basis for a convergent computation scheme for approximating feedback gain operators. A numerical example is presented to illustrate these ideas.

Optimization of Acid Orange 7 Degradation in Heterogeneous Fenton-like Reaction Using Fe3-xCoxO4 Catalyst

NASA Astrophysics Data System (ADS)

Ibrahim, M. Z.; Alrozi, R.; Zubir, N. A.; Bashah, N. A.; Ali, S. A. Md; Ibrahim, N.

2018-05-01

The oxidation process such as heterogeneous Fenton and/or Fenton-like reactions is considered as an effective and efficient method for treatment of dye degradation. In this study, the degradation of Acid Orange 7 (AO7) was investigated by using Fe3-xCoxO4 as a heterogeneous Fenton-like catalyst. Response surface methodology (RSM) was used to optimize the operational parameters condition and the interaction of two or more parameters. The parameter studies were catalyst dosage (X1 ), pH (X2 ) and H2O2 concentration (X3 ) towards AO7 degradation. Based on analysis of variance (ANOVA), the derived quadratic polynomial model was significant whereby the predicted values matched the experimental values with regression coefficient of R2 = 0.9399. The optimum condition for AO7 degradation was obtained at catalyst dosage of 0.84 g/L, pH of 3 and H2O2 concentration of 46.70 mM which resulted in 86.30% removal of AO7 dye. These findings present new insights into the influence of operational parameters in the heterogeneous Fenton-like oxidation of AO7 using Fe3-xCoxO4 catalyst.

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

The relationships between field length and cruise speed and aircraft direct operating cost were determined. A gradient optimizing computer program was developed to minimize direct operating cost (DOC) as a function of airplane geometry. In this way, the best airplane operating under one set of constraints can be compared with the best operating under another. A constant 30-passenger fuselage and rubberized engines based on the General Electric CT-7 were used as a baseline. All aircraft had to have a 600 nautical mile maximum range and were designed to FAR part 25 structural integrity and climb gradient regulations. Direct operating cost was minimized for a typical design mission of 150 nautical miles. For purposes of C sub L sub max calculation, all aircraft had double-slotted flaps but with no Fowler action.

Online total organic carbon (TOC) monitoring for water and wastewater treatment plants processes and operations optimization

NASA Astrophysics Data System (ADS)

Assmann, Céline; Scott, Amanda; Biller, Dondra

2017-08-01

Organic measurements, such as biological oxygen demand (BOD) and chemical oxygen demand (COD) were developed decades ago in order to measure organics in water. Today, these time-consuming measurements are still used as parameters to check the water treatment quality; however, the time required to generate a result, ranging from hours to days, does not allow COD or BOD to be useful process control parameters - see (1) Standard Method 5210 B; 5-day BOD Test, 1997, and (2) ASTM D1252; COD Test, 2012. Online organic carbon monitoring allows for effective process control because results are generated every few minutes. Though it does not replace BOD or COD measurements still required for compliance reporting, it allows for smart, data-driven and rapid decision-making to improve process control and optimization or meet compliances. Thanks to the smart interpretation of generated data and the capability to now take real-time actions, municipal drinking water and wastewater treatment facility operators can positively impact their OPEX (operational expenditure) efficiencies and their capabilities to meet regulatory requirements. This paper describes how three municipal wastewater and drinking water plants gained process insights, and determined optimization opportunities thanks to the implementation of online total organic carbon (TOC) monitoring.

Neural network based optimal control of HVAC&R systems

NASA Astrophysics Data System (ADS)

Ning, Min

Heating, Ventilation, Air-Conditioning and Refrigeration (HVAC&R) systems have wide applications in providing a desired indoor environment for different types of buildings. It is well acknowledged that 30%-40% of the total energy generated is consumed by buildings and HVAC&R systems alone account for more than 50% of the building energy consumption. Low operational efficiency especially under partial load conditions and poor control are part of reasons for such high energy consumption. To improve energy efficiency, HVAC&R systems should be properly operated to maintain a comfortable and healthy indoor environment under dynamic ambient and indoor conditions with the least energy consumption. This research focuses on the optimal operation of HVAC&R systems. The optimization problem is formulated and solved to find the optimal set points for the chilled water supply temperature, discharge air temperature and AHU (air handling unit) fan static pressure such that the indoor environment is maintained with the least chiller and fan energy consumption. To achieve this objective, a dynamic system model is developed first to simulate the system behavior under different control schemes and operating conditions. The system model is modular in structure, which includes a water-cooled vapor compression chiller model and a two-zone VAV system model. A fuzzy-set based extended transformation approach is then applied to investigate the uncertainties of this model caused by uncertain parameters and the sensitivities of the control inputs with respect to the interested model outputs. A multi-layer feed forward neural network is constructed and trained in unsupervised mode to minimize the cost function which is comprised of overall energy cost and penalty cost when one or more constraints are violated. After training, the network is implemented as a supervisory controller to compute the optimal settings for the system. In order to implement the optimal set points predicted by the supervisory controller, a set of five adaptive PI (proportional-integral) controllers are designed for each of the five local control loops of the HVAC&R system. The five controllers are used to track optimal set points and zone air temperature set points. Parameters of these PI controllers are tuned online to reduce tracking errors. The updating rules are derived from Lyapunov stability analysis. Simulation results show that compared to the conventional night reset operation scheme, the optimal operation scheme saves around 10% energy under full load condition and 19% energy under partial load conditions.

Multicompare tests of the performance of different metaheuristics in EEG dipole source localization.

PubMed

Escalona-Vargas, Diana Irazú; Lopez-Arevalo, Ivan; Gutiérrez, David

2014-01-01

We study the use of nonparametric multicompare statistical tests on the performance of simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), when used for electroencephalographic (EEG) source localization. Such task can be posed as an optimization problem for which the referred metaheuristic methods are well suited. Hence, we evaluate the localization's performance in terms of metaheuristics' operational parameters and for a fixed number of evaluations of the objective function. In this way, we are able to link the efficiency of the metaheuristics with a common measure of computational cost. Our results did not show significant differences in the metaheuristics' performance for the case of single source localization. In case of localizing two correlated sources, we found that PSO (ring and tree topologies) and DE performed the worst, then they should not be considered in large-scale EEG source localization problems. Overall, the multicompare tests allowed to demonstrate the little effect that the selection of a particular metaheuristic and the variations in their operational parameters have in this optimization problem.

Using of material-technological modelling for designing production of closed die forgings

NASA Astrophysics Data System (ADS)

Ibrahim, K.; Vorel, I.; Jeníček, Š.; Káňa, J.; Aišman, D.; Kotěšovec, V.

2017-02-01

Production of forgings is a complex and demanding process which consists of a number of forging operations and, in many cases, includes post-forge heat treatment. An optimized manufacturing line is a prerequisite for obtaining prime-quality products which in turn are essential to profitable operation of a forging company. Problems may, however, arise from modifications to the manufacturing route due to changing customer needs. As a result, the production may have to be suspended temporarily to enable changeover and optimization. Using material-technological modelling, the required modifications can be tested and optimized under laboratory conditions outside the plant without disrupting the production. Thanks to material-technological modelling, the process parameters can be varied rapidly in response to changes in market requirements. Outcomes of the modelling runs include optimum parameters for the forging part’s manufacturing route, values of mechanical properties, and results of microstructure analysis. This article describes the use of material-technological modelling for exploring the impact of the amount of deformation and the rate of cooling of a particular forged part from the finish-forging temperature on its microstructure and related mechanical properties.

A rapid and low energy consumption method to decolorize the high concentration triphenylmethane dye wastewater: operational parameters optimization for the ultrasonic-assisted ozone oxidation process.

PubMed

Zhou, Xian-Jiao; Guo, Wan-Qian; Yang, Shan-Shan; Ren, Nan-Qi

2012-02-01

This research set up an ultrasonic-assisted ozone oxidation process (UAOOP) to decolorize the triphenylmethane dyes wastewater. Five factors - temperature, initial pH, reaction time, ultrasonic power (low frequency 20 kHz), and ozone concentration - were investigated. Response surface methodology was used to find out the major factors influencing color removal rate and the interactions between these factors, and optimized the operating parameters as well. Under the experimental conditions: reaction temperature 39.81 °C, initial pH 5.29, ultrasonic power 60 W and ozone concentration 0.17 g/L, the highest color removals were achieved with 10 min reaction time and the initial concentration of the MG solution was 1000 mg/L. The optimal results indicated that the UAOOP was a rapid, efficient and low energy consumption technique to decolorize the high concentration MG wastewater. The predicted model was approximately in accordance with the experimental cases with correlation coefficients R(2) and R(adj)(2) of 0.9103 and 0.8386. Crown Copyright © 2011. Published by Elsevier Ltd. All rights reserved.

Simulation and optimization of a coking wastewater biological treatment process by activated sludge models (ASM).

PubMed

Wu, Xiaohui; Yang, Yang; Wu, Gaoming; Mao, Juan; Zhou, Tao

2016-01-01

Applications of activated sludge models (ASM) in simulating industrial biological wastewater treatment plants (WWTPs) are still difficult due to refractory and complex components in influents as well as diversity in activated sludges. In this study, an ASM3 modeling study was conducted to simulate and optimize a practical coking wastewater treatment plant (CWTP). First, respirometric characterizations of the coking wastewater and CWTP biomasses were conducted to determine the specific kinetic and stoichiometric model parameters for the consecutive aeration-anoxic-aeration (O-A/O) biological process. All ASM3 parameters have been further estimated and calibrated, through cross validation by the model dynamic simulation procedure. Consequently, an ASM3 model was successfully established to accurately simulate the CWTP performances in removing COD and NH4-N. An optimized CWTP operation condition could be proposed reducing the operation cost from 6.2 to 5.5 €/m(3) wastewater. This study is expected to provide a useful reference for mathematic simulations of practical industrial WWTPs. Copyright © 2015 Elsevier Ltd. All rights reserved.

Optimization of operating parameters for gas-phase photocatalytic splitting of H2S by novel vermiculate packed tubular reactor.

PubMed

Preethi, V; Kanmani, S

2016-10-01

Hydrogen production by gas-phase photocatalytic splitting of Hydrogen Sulphide (H2S) was investigated on four semiconductor photocatalysts including CuGa1.6Fe0.4O2, ZnFe2O3, (CdS + ZnS)/Fe2O3 and Ce/TiO2. The CdS and ZnS coated core shell particles (CdS + ZnS)/Fe2O3 shows the highest rate of hydrogen (H2) production under optimized conditions. Packed bed tubular reactor was used to study the performance of prepared photocatalysts. Selection of the best packing material is a key for maximum removal efficiency. Cheap, lightweight and easily adsorbing vermiculate materials were used as a novel packing material and were found to be effective in splitting H2S. Effect of various operating parameters like flow rate, sulphide concentration, catalyst dosage, light irradiation were tested and optimized for maximum H2 conversion of 92% from industrial waste H2S. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

An operation support expert system based on on-line dynamics simulation and fuzzy reasoning for startup schedule optimization in fossil power plants

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

Matsumoto, H.; Eki, Y.; Kaji, A.

1993-12-01

An expert system which can support operators of fossil power plants in creating the optimum startup schedule and executing it accurately is described. The optimum turbine speed-up and load-up pattern is obtained through an iterative manner which is based on fuzzy resonating using quantitative calculations as plant dynamics models and qualitative knowledge as schedule optimization rules with fuzziness. The rules represent relationships between stress margins and modification rates of the schedule parameters. Simulations analysis proves that the system provides quick and accurate plant startups.

Analysis of an operator-differential model for magnetostrictive energy harvesting

NASA Astrophysics Data System (ADS)

Davino, D.; Krejčí, P.; Pimenov, A.; Rachinskii, D.; Visone, C.

2016-10-01

We present a model of, and analysis of an optimization problem for, a magnetostrictive harvesting device which converts mechanical energy of the repetitive process such as vibrations of the smart material to electrical energy that is then supplied to an electric load. The model combines a lumped differential equation for a simple electronic circuit with an operator model for the complex constitutive law of the magnetostrictive material. The operator based on the formalism of the phenomenological Preisach model describes nonlinear saturation effects and hysteresis losses typical of magnetostrictive materials in a thermodynamically consistent fashion. We prove well-posedness of the full operator-differential system and establish global asymptotic stability of the periodic regime under periodic mechanical forcing that represents mechanical vibrations due to varying environmental conditions. Then we show the existence of an optimal solution for the problem of maximization of the output power with respect to a set of controllable parameters (for the periodically forced system). Analytical results are illustrated with numerical examples of an optimal solution.

Thermal control systems for low-temperature heat rejection on a lunar base

NASA Technical Reports Server (NTRS)

Sridhar, K. R.; Gottmann, Matthias

1992-01-01

One of the important issues in the lunar base architecture is the design of a Thermal Control System (TCS) to reject the low temperature heat from the base. The TCS ensures that the base and all components inside are maintained within the operating temperature range. A significant portion of the total mass of the TCS is due to the radiator. Shading the radiation from the sun and the hot lunar soil could decrease the radiator operating temperature significantly. Heat pumps have been in use for terrestrial applications. To optimize the mass of the heat pump augmented TCS, all promising options have to be evaluated and compared. Careful attention is given to optimizing system operating parameters, working fluids, and component masses. The systems are modeled for full load operation.

Braking System Integration in Dual Mode Systems

DOT National Transportation Integrated Search

1974-05-01

An optimal braking system for Dual Mode is a complex product of vast number of multivariate, interdependent parameters that encompass on-guideway and off-guideway operation as well as normal and emergency braking. : Details of, and interralations amo...

Mathematical model of optical signals emitted by electrical discharges occuring in electroinsulating oil

NASA Astrophysics Data System (ADS)

Kozioł, Michał

2017-10-01

The article presents a parametric model describing the registered distributions spectrum of optical radiation emitted by electrical discharges generated in the systems: the needle- needle, the needleplate and in the system for surface discharges. Generation of electrical discharges and registration of the emitted radiation was carried out in three different electrical insulating oils: fabric new, operated (used) and operated with air bubbles. For registration of optical spectra in the range of ultraviolet, visible and near infrared a high resolution spectrophotometer was. The proposed mathematical model was developed in a regression procedure using gauss-sigmoid type function. The dependent variable was the intensity of the recorded optical signals. In order to estimate the optimal parameters of the model an evolutionary algorithm was used. The optimization procedure was performed in Matlab environment. For determination of the matching quality of theoretical parameters of the regression function to the empirical data determination coefficient R2 was applied.

Optimal Energy Consumption Analysis of Natural Gas Pipeline

PubMed Central

Liu, Enbin; Li, Changjun; Yang, Yi

2014-01-01

There are many compressor stations along long-distance natural gas pipelines. Natural gas can be transported using different boot programs and import pressures, combined with temperature control parameters. Moreover, different transport methods have correspondingly different energy consumptions. At present, the operating parameters of many pipelines are determined empirically by dispatchers, resulting in high energy consumption. This practice does not abide by energy reduction policies. Therefore, based on a full understanding of the actual needs of pipeline companies, we introduce production unit consumption indicators to establish an objective function for achieving the goal of lowering energy consumption. By using a dynamic programming method for solving the model and preparing calculation software, we can ensure that the solution process is quick and efficient. Using established optimization methods, we analyzed the energy savings for the XQ gas pipeline. By optimizing the boot program, the import station pressure, and the temperature parameters, we achieved the optimal energy consumption. By comparison with the measured energy consumption, the pipeline now has the potential to reduce energy consumption by 11 to 16 percent. PMID:24955410

Distributed Bees Algorithm Parameters Optimization for a Cost Efficient Target Allocation in Swarms of Robots

PubMed Central

Jevtić, Aleksandar; Gutiérrez, Álvaro

2011-01-01

Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the Distributed Bees Algorithm (DBA), previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA’s control parameters by means of a Genetic Algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots’ distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce. PMID:22346677

Hybrid simulated annealing and its application to optimization of hidden Markov models for visual speech recognition.

PubMed

Lee, Jong-Seok; Park, Cheol Hoon

2010-08-01

We propose a novel stochastic optimization algorithm, hybrid simulated annealing (SA), to train hidden Markov models (HMMs) for visual speech recognition. In our algorithm, SA is combined with a local optimization operator that substitutes a better solution for the current one to improve the convergence speed and the quality of solutions. We mathematically prove that the sequence of the objective values converges in probability to the global optimum in the algorithm. The algorithm is applied to train HMMs that are used as visual speech recognizers. While the popular training method of HMMs, the expectation-maximization algorithm, achieves only local optima in the parameter space, the proposed method can perform global optimization of the parameters of HMMs and thereby obtain solutions yielding improved recognition performance. The superiority of the proposed algorithm to the conventional ones is demonstrated via isolated word recognition experiments.

Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems.

PubMed

Cho, Ming-Yuan; Hoang, Thi Thom

2017-01-01

Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

Apparatus and Methods for Manipulation and Optimization of Biological Systems

NASA Technical Reports Server (NTRS)

Sun, Ren (Inventor); Ho, Chih-Ming (Inventor); Wong, Pak Kin (Inventor); Yu, Fuqu (Inventor)

2014-01-01

The invention provides systems and methods for manipulating biological systems, for example to elicit a more desired biological response from a biological sample, such as a tissue, organ, and/or a cell. In one aspect, the invention operates by efficiently searching through a large parametric space of stimuli and system parameters to manipulate, control, and optimize the response of biological samples sustained in the system. In one aspect, the systems and methods of the invention use at least one optimization algorithm to modify the actuator's control inputs for stimulation, responsive to the sensor's output of response signals. The invention can be used, e.g., to optimize any biological system, e.g., bioreactors for proteins, and the like, small molecules, polysaccharides, lipids, and the like. Another use of the apparatus and methods includes is for the discovery of key parameters in complex biological systems.

Optimization of Pulsed-DEER Measurements for Gd-Based Labels: Choice of Operational Frequencies, Pulse Durations and Positions, and Temperature

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

Raitsimring, A.; Astashkin, A. V.; Enemark, J. H.

2012-12-29

In this work, the experimental conditions and parameters necessary to optimize the long-distance (≥ 60 Å) Double Electron-Electron Resonance (DEER) measurements of biomacromolecules labeled with Gd(III) tags are analyzed. The specific parameters discussed are the temperature, microwave band, the separation between the pumping and observation frequencies, pulse train repetition rate, pulse durations and pulse positioning in the electron paramagnetic resonance spectrum. It was found that: (i) in optimized DEER measurements, the observation pulses have to be applied at the maximum of the EPR spectrum; (ii) the optimal temperature range for Ka-band measurements is 14-17 K, while in W-band the optimalmore » temperatures are between 6-9 K; (iii) W-band is preferable to Ka-band for DEER measurements. Recent achievements and the conditions necessary for short-distance measurements (<15 Å) are also briefly discussed.« less

Combining local search with co-evolution in a remarkably simple way

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

Boettcher, S.; Percus, A.

2000-05-01

The authors explore a new general-purpose heuristic for finding high-quality solutions to hard optimization problem. The method, called extremal optimization, is inspired by self-organized criticality, a concept introduced to describe emergent complexity in physical systems. In contrast to genetic algorithms, which operate on an entire gene-pool of possible solutions, extremal optimization successively replaces extremely undesirable elements of a single sub-optimal solution with new, random ones. Large fluctuations, or avalanches, ensue that efficiently explore many local optima. Drawing upon models used to simulate far-from-equilibrium dynamics, extremal optimization complements heuristics inspired by equilibrium statistical physics, such as simulated annealing. With only onemore » adjustable parameter, its performance has proved competitive with more elaborate methods, especially near phase transitions. Phase transitions are found in many combinatorial optimization problems, and have been conjectured to occur in the region of parameter space containing the hardest instances. We demonstrate how extremal optimization can be implemented for a variety of hard optimization problems. We believe that this will be a useful tool in the investigation of phase transitions in combinatorial optimization, thereby helping to elucidate the origin of computational complexity.« less

Optimal design and operation of solid oxide fuel cell systems for small-scale stationary applications

NASA Astrophysics Data System (ADS)

Braun, Robert Joseph

The advent of maturing fuel cell technologies presents an opportunity to achieve significant improvements in energy conversion efficiencies at many scales; thereby, simultaneously extending our finite resources and reducing "harmful" energy-related emissions to levels well below that of near-future regulatory standards. However, before realization of the advantages of fuel cells can take place, systems-level design issues regarding their application must be addressed. Using modeling and simulation, the present work offers optimal system design and operation strategies for stationary solid oxide fuel cell systems applied to single-family detached dwellings. A one-dimensional, steady-state finite-difference model of a solid oxide fuel cell (SOFC) is generated and verified against other mathematical SOFC models in the literature. Fuel cell system balance-of-plant components and costs are also modeled and used to provide an estimate of system capital and life cycle costs. The models are used to evaluate optimal cell-stack power output, the impact of cell operating and design parameters, fuel type, thermal energy recovery, system process design, and operating strategy on overall system energetic and economic performance. Optimal cell design voltage, fuel utilization, and operating temperature parameters are found using minimization of the life cycle costs. System design evaluations reveal that hydrogen-fueled SOFC systems demonstrate lower system efficiencies than methane-fueled systems. The use of recycled cell exhaust gases in process design in the stack periphery are found to produce the highest system electric and cogeneration efficiencies while achieving the lowest capital costs. Annual simulations reveal that efficiencies of 45% electric (LHV basis), 85% cogenerative, and simple economic paybacks of 5--8 years are feasible for 1--2 kW SOFC systems in residential-scale applications. Design guidelines that offer additional suggestions related to fuel cell-stack sizing and operating strategy (base-load or load-following and cogeneration or electric-only) are also presented.

Optimization of operation conditions and configurations for solid-propellant ducted rocket combustors

NASA Astrophysics Data System (ADS)

Onn, Shing-Chung; Chiang, Hau-Jei; Hwang, Hang-Che; Wei, Jen-Ko; Cherng, Dao-Lien

1993-06-01

The dynamic behavior of a 2D turbulent mixing and combustion process has been studied numerically in the main combustion chamber of a solid-propellant ducted rocket (SDR). The mathematical model is based on the Favre-averaged conservation equations developed by Cherng (1990). Combustion efficiency, rather than specific impulse from earlier studies, is applied successfully to optimize the effects of two parameters by a multiple linear regression model. Specifically, the fuel-air equivalence ratio of the operating conditions and the air inlet location of configurations for the SDR combustor have been studied. For a equivalence ratio near the stoichiometric condition, the use of specific impulse or combustion efficiency will show similar trend in characterizing the reacting flow field in the combustor. For the overall fuel lean operating conditions, the change of combustion efficiency is much more sensitive to that of air inlet location than specific impulse does, suggesting combustion efficiency a better property than specific impulse in representing the condition toward flammability limits. In addition, the air inlet for maximum efficiency, in general, appears to be located at downstream of that for highest specific impulse. The optimal case for the effects of two parameters occurs at fuel lean condition, which shows a larger recirculation zone in front, deeper penetration of ram air into the combustor and much larger high temperature zone near the centerline of the combustor exit than those shown in the optimal case for overall equivalence ratio close to stoichiometric.

Optimal integration strategies for a syngas fuelled SOFC and gas turbine hybrid

NASA Astrophysics Data System (ADS)

Zhao, Yingru; Sadhukhan, Jhuma; Lanzini, Andrea; Brandon, Nigel; Shah, Nilay

This article aims to develop a thermodynamic modelling and optimization framework for a thorough understanding of the optimal integration of fuel cell, gas turbine and other components in an ambient pressure SOFC-GT hybrid power plant. This method is based on the coupling of a syngas-fed SOFC model and an associated irreversible GT model, with an optimization algorithm developed using MATLAB to efficiently explore the range of possible operating conditions. Energy and entropy balance analysis has been carried out for the entire system to observe the irreversibility distribution within the plant and the contribution of different components. Based on the methodology developed, a comprehensive parametric analysis has been performed to explore the optimum system behavior, and predict the sensitivity of system performance to the variations in major design and operating parameters. The current density, operating temperature, fuel utilization and temperature gradient of the fuel cell, as well as the isentropic efficiencies and temperature ratio of the gas turbine cycle, together with three parameters related to the heat transfer between subsystems are all set to be controllable variables. Other factors affecting the hybrid efficiency have been further simulated and analysed. The model developed is able to predict the performance characteristics of a wide range of hybrid systems potentially sizing from 2000 to 2500 W m -2 with efficiencies varying between 50% and 60%. The analysis enables us to identify the system design tradeoffs, and therefore to determine better integration strategies for advanced SOFC-GT systems.

Optimal Reservoir Operation using Stochastic Model Predictive Control

NASA Astrophysics Data System (ADS)

Sahu, R.; McLaughlin, D.

2016-12-01

Hydropower operations are typically designed to fulfill contracts negotiated with consumers who need reliable energy supplies, despite uncertainties in reservoir inflows. In addition to providing reliable power the reservoir operator needs to take into account environmental factors such as downstream flooding or compliance with minimum flow requirements. From a dynamical systems perspective, the reservoir operating strategy must cope with conflicting objectives in the presence of random disturbances. In order to achieve optimal performance, the reservoir system needs to continually adapt to disturbances in real time. Model Predictive Control (MPC) is a real-time control technique that adapts by deriving the reservoir release at each decision time from the current state of the system. Here an ensemble-based version of MPC (SMPC) is applied to a generic reservoir to determine both the optimal power contract, considering future inflow uncertainty, and a real-time operating strategy that attempts to satisfy the contract. Contract selection and real-time operation are coupled in an optimization framework that also defines a Pareto trade off between the revenue generated from energy production and the environmental damage resulting from uncontrolled reservoir spills. Further insight is provided by a sensitivity analysis of key parameters specified in the SMPC technique. The results demonstrate that SMPC is suitable for multi-objective planning and associated real-time operation of a wide range of hydropower reservoir systems.

A VVWBO-BVO-based GM (1,1) and its parameter optimization by GRA-IGSA integration algorithm for annual power load forecasting

PubMed Central

Wang, Hongguang

2018-01-01

Annual power load forecasting is not only the premise of formulating reasonable macro power planning, but also an important guarantee for the safety and economic operation of power system. In view of the characteristics of annual power load forecasting, the grey model of GM (1,1) are widely applied. Introducing buffer operator into GM (1,1) to pre-process the historical annual power load data is an approach to improve the forecasting accuracy. To solve the problem of nonadjustable action intensity of traditional weakening buffer operator, variable-weight weakening buffer operator (VWWBO) and background value optimization (BVO) are used to dynamically pre-process the historical annual power load data and a VWWBO-BVO-based GM (1,1) is proposed. To find the optimal value of variable-weight buffer coefficient and background value weight generating coefficient of the proposed model, grey relational analysis (GRA) and improved gravitational search algorithm (IGSA) are integrated and a GRA-IGSA integration algorithm is constructed aiming to maximize the grey relativity between simulating value sequence and actual value sequence. By the adjustable action intensity of buffer operator, the proposed model optimized by GRA-IGSA integration algorithm can obtain a better forecasting accuracy which is demonstrated by the case studies and can provide an optimized solution for annual power load forecasting. PMID:29768450

Optimization of Machining Parameters of Milling Operation by Application of Semi-synthetic oil based Nano cutting Fluids

NASA Astrophysics Data System (ADS)

Giri Prasad, M. J.; Abhishek Raaj, A. S.; Rishi Kumar, R.; Gladson, Frank; M, Gautham

2016-09-01

The present study is concerned with resolving the problems pertaining to the conventional cutting fluids. Two samples of nano cutting fluids were prepared by dispersing 0.01 vol% of MWCNTs and a mixture of 0.01 vol% of MWCNTs and 0.01 vol% of nano ZnO in the soluble oil. The thermophysical properties such as the kinematic viscosity, density, flash point and the tribological properties of the prepared nano cutting fluid samples were experimentally investigated and were compared with those of plain soluble oil. In addition to this, a milling process was carried by varying the process parameters and by application of different samples of cutting fluids and an attempt was made to determine optimal cutting condition using the Taguchi optimization technique.

Optimization of A(2)O BNR processes using ASM and EAWAG Bio-P models: model performance.

PubMed

El Shorbagy, Walid E; Radif, Nawras N; Droste, Ronald L

2013-12-01

This paper presents the performance of an optimization model for a biological nutrient removal (BNR) system using the anaerobic-anoxic-oxic (A(2)O) process. The formulated model simulates removal of organics, nitrogen, and phosphorus using a reduced International Water Association (IWA) Activated Sludge Model #3 (ASM3) model and a Swiss Federal Institute for Environmental Science and Technology (EAWAG) Bio-P module. Optimal sizing is attained considering capital and operational costs. Process performance is evaluated against the effect of influent conditions, effluent limits, and selected parameters of various optimal solutions with the following results: an increase of influent temperature from 10 degrees C to 25 degrees C decreases the annual cost by about 8.5%, an increase of influent flow from 500 to 2500 m(3)/h triples the annual cost, the A(2)O BNR system is more sensitive to variations in influent ammonia than phosphorus concentration and the maximum growth rate of autotrophic biomass was the most sensitive kinetic parameter in the optimization model.

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.

Metal Matrix Superconductor Composites for SMES-Driven, Ultra High Power BEP Applications: Part 2

NASA Astrophysics Data System (ADS)

Gross, Dan A.; Myrabo, Leik N.

2006-05-01

A 2.5 TJ superconducting magnetic energy storage (SMES) design presentation is continued from the preceding paper (Part 1) with electromagnetic and associated stress analysis. The application of interest is a rechargeable power-beaming infrastructure for manned microwave Lightcraft operations. It is demonstrated that while operational performance is within manageable parameter bounds, quench (loss of superconducting state) imposes enormous electrical stresses. Therefore, alternative multiple toroid modular configurations are identified, alleviating simultaneously all excessive stress conditions, operational and quench, in the structural, thermal and electromagnetic sense — at some reduction in specific energy, but presenting programmatic advantages for a lengthy technology development, demonstration and operation schedule. To this end several natural units, based on material properties and operating parameters are developed, in order to identify functional relationships and optimization paths more effectively.

Influences of operational parameters on phosphorus removal in batch and continuous electrocoagulation process performance.

PubMed

Nguyen, Dinh Duc; Yoon, Yong Soo; Bui, Xuan Thanh; Kim, Sung Su; Chang, Soon Woong; Guo, Wenshan; Ngo, Huu Hao

2017-11-01

Performance of an electrocoagulation (EC) process in batch and continuous operating modes was thoroughly investigated and evaluated for enhancing wastewater phosphorus removal under various operating conditions, individually or combined with initial phosphorus concentration, wastewater conductivity, current density, and electrolysis times. The results revealed excellent phosphorus removal (72.7-100%) for both processes within 3-6 min of electrolysis, with relatively low energy requirements, i.e., less than 0.5 kWh/m 3 for treated wastewater. However, the removal efficiency of phosphorus in the continuous EC operation mode was better than that in batch mode within the scope of the study. Additionally, the rate and efficiency of phosphorus removal strongly depended on operational parameters, including wastewater conductivity, initial phosphorus concentration, current density, and electrolysis time. Based on experimental data, statistical model verification of the response surface methodology (RSM) (multiple factor optimization) was also established to provide further insights and accurately describe the interactive relationship between the process variables, thus optimizing the EC process performance. The EC process using iron electrodes is promising for improving wastewater phosphorus removal efficiency, and RSM can be a sustainable tool for predicting the performance of the EC process and explaining the influence of the process variables.

A thermodynamic approach for selecting operating conditions in the design of reversible solid oxide cell energy systems

NASA Astrophysics Data System (ADS)

Wendel, Christopher H.; Kazempoor, Pejman; Braun, Robert J.

2016-01-01

Reversible solid oxide cell (ReSOC) systems are being increasingly considered for electrical energy storage, although much work remains before they can be realized, including cell materials development and system design optimization. These systems store electricity by generating a synthetic fuel in electrolysis mode and subsequently recover electricity by electrochemically oxidizing the stored fuel in fuel cell mode. System thermal management is improved by promoting methane synthesis internal to the ReSOC stack. Within this strategy, the cell-stack operating conditions are highly impactful on system performance and optimizing these parameters to suit both operating modes is critical to achieving high roundtrip efficiency. Preliminary analysis shows the thermoneutral voltage to be a useful parameter for analyzing ReSOC systems and the focus of this study is to quantitatively examine how it is affected by ReSOC operating conditions. The results reveal that the thermoneutral voltage is generally reduced by increased pressure, and reductions in temperature, fuel utilization, and hydrogen-to-carbon ratio. Based on the thermodynamic analysis, many different combinations of these operating conditions are expected to promote efficient energy storage. Pressurized systems can achieve high efficiency at higher temperature and fuel utilization, while non-pressurized systems may require lower stack temperature and suffer from reduced energy density.

Periodic Application of Stochastic Cost Optimization Methodology to Achieve Remediation Objectives with Minimized Life Cycle Cost

NASA Astrophysics Data System (ADS)

Kim, U.; Parker, J.

2016-12-01

Many dense non-aqueous phase liquid (DNAPL) contaminated sites in the U.S. are reported as "remediation in progress" (RIP). However, the cost to complete (CTC) remediation at these sites is highly uncertain and in many cases, the current remediation plan may need to be modified or replaced to achieve remediation objectives. This study evaluates the effectiveness of iterative stochastic cost optimization that incorporates new field data for periodic parameter recalibration to incrementally reduce prediction uncertainty and implement remediation design modifications as needed to minimize the life cycle cost (i.e., CTC). This systematic approach, using the Stochastic Cost Optimization Toolkit (SCOToolkit), enables early identification and correction of problems to stay on track for completion while minimizing the expected (i.e., probability-weighted average) CTC. This study considers a hypothetical site involving multiple DNAPL sources in an unconfined aquifer using thermal treatment for source reduction and electron donor injection for dissolved plume control. The initial design is based on stochastic optimization using model parameters and their joint uncertainty based on calibration to site characterization data. The model is periodically recalibrated using new monitoring data and performance data for the operating remediation systems. Projected future performance using the current remediation plan is assessed and reoptimization of operational variables for the current system or consideration of alternative designs are considered depending on the assessment results. We compare remediation duration and cost for the stepwise re-optimization approach with single stage optimization as well as with a non-optimized design based on typical engineering practice.

The 12-foot pressure wind tunnel restoration project model support systems

NASA Technical Reports Server (NTRS)

Sasaki, Glen E.

1992-01-01

The 12 Foot Pressure Wind Tunnel is a variable density, low turbulence wind tunnel that operates at subsonic speeds, and up to six atmospheres total pressure. The restoration of this facility is of critical importance to the future of the U.S. aerospace industry. As part of this project, several state of the art model support systems are furnished to provide an optimal balance between aerodynamic and operational efficiency parameters. Two model support systems, the Rear Strut Model Support, and the High Angle of Attack Model Support are discussed. This paper covers design parameters, constraints, development, description, and component selection.

Inferential Framework for Autonomous Cryogenic Loading Operations

NASA Technical Reports Server (NTRS)

Luchinsky, Dmitry G.; Khasin, Michael; Timucin, Dogan; Sass, Jared; Perotti, Jose; Brown, Barbara

2017-01-01

We address problem of autonomous cryogenic management of loading operations on the ground and in space. As a step towards solution of this problem we develop a probabilistic framework for inferring correlations parameters of two-fluid cryogenic flow. The simulation of two-phase cryogenic flow is performed using nearly-implicit scheme. A concise set of cryogenic correlations is introduced. The proposed approach is applied to an analysis of the cryogenic flow in experimental Propellant Loading System built at NASA KSC. An efficient simultaneous optimization of a large number of model parameters is demonstrated and a good agreement with the experimental data is obtained.

Impact of longitudinal flying qualities upon the design of a transport with active controls

NASA Technical Reports Server (NTRS)

Sliwa, S. M.

1980-01-01

Direct constrained parameter optimization was used to optimally size a medium range transport for minimum direct operating cost. Several stability and control constraints were varied to study the sensitivity of the configuration to specifying the unaugmented flying qualities of transports designed with relaxed static stability. Additionally, a number of handling quality related design constants were studied with respect to their impact to the design.

OPTIMIZING SYNTHESIS GAS YIELD FROM THE CROSS ...

EPA Pesticide Factsheets

Symposium Paper Biomass can be gasified to yield synthesis gas, tars, and ash. The process is governed by a number of parameters such as the temperature of the gasifying medium (in this case), and the moisture content of the feedstock. Synthesis gas from gasifying wood pellets was collected and analyzed as a function of inlet air temperature and feedstock moisture content. The air was introduced at temperatures ranging from 630 to 730 °C and the moisture content of the feedstock ranged from 8 to 20%. The data collected was used to establish the relationship between the outcome of gasification and these two parameters, and then to determine optimal operating parameters for maximizing the fuel value (maximizing the concentrations of flammable gases in the synthesis gas) while minimizing the production of gasification tars.

Construction and Setup of a Bench-scale Algal Photosynthetic Bioreactor with Temperature, Light, and pH Monitoring for Kinetic Growth Tests.

PubMed

Karam, Amanda L; McMillan, Catherine C; Lai, Yi-Chun; de Los Reyes, Francis L; Sederoff, Heike W; Grunden, Amy M; Ranjithan, Ranji S; Levis, James W; Ducoste, Joel J

2017-06-14

The optimal design and operation of photosynthetic bioreactors (PBRs) for microalgal cultivation is essential for improving the environmental and economic performance of microalgae-based biofuel production. Models that estimate microalgal growth under different conditions can help to optimize PBR design and operation. To be effective, the growth parameters used in these models must be accurately determined. Algal growth experiments are often constrained by the dynamic nature of the culture environment, and control systems are needed to accurately determine the kinetic parameters. The first step in setting up a controlled batch experiment is live data acquisition and monitoring. This protocol outlines a process for the assembly and operation of a bench-scale photosynthetic bioreactor that can be used to conduct microalgal growth experiments. This protocol describes how to size and assemble a flat-plate, bench-scale PBR from acrylic. It also details how to configure a PBR with continuous pH, light, and temperature monitoring using a data acquisition and control unit, analog sensors, and open-source data acquisition software.

Construction and Setup of a Bench-scale Algal Photosynthetic Bioreactor with Temperature, Light, and pH Monitoring for Kinetic Growth Tests

PubMed Central

Karam, Amanda L.; McMillan, Catherine C.; Lai, Yi-Chun; de los Reyes, Francis L.; Sederoff, Heike W.; Grunden, Amy M.; Ranjithan, Ranji S.; Levis, James W.; Ducoste, Joel J.

2017-01-01

The optimal design and operation of photosynthetic bioreactors (PBRs) for microalgal cultivation is essential for improving the environmental and economic performance of microalgae-based biofuel production. Models that estimate microalgal growth under different conditions can help to optimize PBR design and operation. To be effective, the growth parameters used in these models must be accurately determined. Algal growth experiments are often constrained by the dynamic nature of the culture environment, and control systems are needed to accurately determine the kinetic parameters. The first step in setting up a controlled batch experiment is live data acquisition and monitoring. This protocol outlines a process for the assembly and operation of a bench-scale photosynthetic bioreactor that can be used to conduct microalgal growth experiments. This protocol describes how to size and assemble a flat-plate, bench-scale PBR from acrylic. It also details how to configure a PBR with continuous pH, light, and temperature monitoring using a data acquisition and control unit, analog sensors, and open-source data acquisition software. PMID:28654054

Mathematical model for the analysis of structure and optimal operational parameters of a solid oxide fuel cell generator

NASA Astrophysics Data System (ADS)

Coralli, Alberto; Villela de Miranda, Hugo; Espiúca Monteiro, Carlos Felipe; Resende da Silva, José Francisco; Valadão de Miranda, Paulo Emílio

2014-12-01

Solid oxide fuel cells are globally recognized as a very promising technology in the area of highly efficient electricity generation with a low environmental impact. This technology can be advantageously implemented in many situations in Brazil and it is well suited to the use of ethanol as a primary energy source, an important feature given the highly developed Brazilian ethanol industry. In this perspective, a simplified mathematical model is developed for a fuel cell and its balance of plant, in order to identify the optimal system structure and the most convenient values for the operational parameters, with the aim of maximizing the global electric efficiency. In this way it is discovered the best operational configuration for the desired application, which is the distributed generation in the concession area of the electricity distribution company Elektro. The data regarding this configuration are required for the continuation of the research project, i.e. the development of a prototype, a cost analysis of the developed system and a detailed perspective of the market opportunities in Brazil.

Operators manual for the magnetograph program (section 2)

NASA Technical Reports Server (NTRS)

November, L.; Title, A. M.

1974-01-01

This manual for use of the magnetograph program describes: (1) black box use of the programs; (2) the magtape data formats used; (3) the adjustable control parameters in the program; and (4) the algorithms. With no adjustments on the control parameters this program may be used purely as a black box. For optimal use, however, the control parameters may be varied. The magtape data formats are of use in adopting other programs to look at raw data or final magnetograph data.

Multiple-objective optimization in precision laser cutting of different thermoplastics

NASA Astrophysics Data System (ADS)

Tamrin, K. F.; Nukman, Y.; Choudhury, I. A.; Shirley, S.

2015-04-01

Thermoplastics are increasingly being used in biomedical, automotive and electronics industries due to their excellent physical and chemical properties. Due to the localized and non-contact process, use of lasers for cutting could result in precise cut with small heat-affected zone (HAZ). Precision laser cutting involving various materials is important in high-volume manufacturing processes to minimize operational cost, error reduction and improve product quality. This study uses grey relational analysis to determine a single optimized set of cutting parameters for three different thermoplastics. The set of the optimized processing parameters is determined based on the highest relational grade and was found at low laser power (200 W), high cutting speed (0.4 m/min) and low compressed air pressure (2.5 bar). The result matches with the objective set in the present study. Analysis of variance (ANOVA) is then carried out to ascertain the relative influence of process parameters on the cutting characteristics. It was found that the laser power has dominant effect on HAZ for all thermoplastics.

Mixture optimization for mixed gas Joule-Thomson cycle

NASA Astrophysics Data System (ADS)

Detlor, J.; Pfotenhauer, J.; Nellis, G.

2017-12-01

An appropriate gas mixture can provide lower temperatures and higher cooling power when used in a Joule-Thomson (JT) cycle than is possible with a pure fluid. However, selecting gas mixtures to meet specific cooling loads and cycle parameters is a challenging design problem. This study focuses on the development of a computational tool to optimize gas mixture compositions for specific operating parameters. This study expands on prior research by exploring higher heat rejection temperatures and lower pressure ratios. A mixture optimization model has been developed which determines an optimal three-component mixture based on the analysis of the maximum value of the minimum value of isothermal enthalpy change, ΔhT , that occurs over the temperature range. This allows optimal mixture compositions to be determined for a mixed gas JT system with load temperatures down to 110 K and supply temperatures above room temperature for pressure ratios as small as 3:1. The mixture optimization model has been paired with a separate evaluation of the percent of the heat exchanger that exists in a two-phase range in order to begin the process of selecting a mixture for experimental investigation.

Global optimization framework for solar building design

NASA Astrophysics Data System (ADS)

Silva, N.; Alves, N.; Pascoal-Faria, P.

2017-07-01

The generative modeling paradigm is a shift from static models to flexible models. It describes a modeling process using functions, methods and operators. The result is an algorithmic description of the construction process. Each evaluation of such an algorithm creates a model instance, which depends on its input parameters (width, height, volume, roof angle, orientation, location). These values are normally chosen according to aesthetic aspects and style. In this study, the model's parameters are automatically generated according to an objective function. A generative model can be optimized according to its parameters, in this way, the best solution for a constrained problem is determined. Besides the establishment of an overall framework design, this work consists on the identification of different building shapes and their main parameters, the creation of an algorithmic description for these main shapes and the formulation of the objective function, respecting a building's energy consumption (solar energy, heating and insulation). Additionally, the conception of an optimization pipeline, combining an energy calculation tool with a geometric scripting engine is presented. The methods developed leads to an automated and optimized 3D shape generation for the projected building (based on the desired conditions and according to specific constrains). The approach proposed will help in the construction of real buildings that account for less energy consumption and for a more sustainable world.

Drilling force and temperature of bone under dry and physiological drilling conditions

NASA Astrophysics Data System (ADS)

Xu, Linlin; Wang, Chengyong; Jiang, Min; He, Huiyu; Song, Yuexian; Chen, Hanyuan; Shen, Jingnan; Zhang, Jiayong

2014-11-01

Many researches on drilling force and temperature have been done with the aim to reduce the labour intensiveness of surgery, avoid unnecessary damage and improve drilling quality. However, there has not been a systematic study of mid- and high-speed drilling under dry and physiological conditions(injection of saline). Furthermore, there is no consensus on optimal drilling parameters. To study these parameters under dry and physiological drilling conditions, pig humerus bones are drilled with medical twist drills operated using a wide range of drilling speeds and feed rates. Drilling force and temperature are measured using a YDZ-II01W dynamometer and a NEC TVS-500EX thermal infrared imager, respectively, to evaluate internal bone damage. To evaluate drilling quality, bone debris and hole morphology are observed by SEM(scanning electron microscopy). Changes in drilling force and temperature give similar results during drilling such that the value of each parameter peaks just before the drill penetrates through the osteon of the compact bone into the trabeculae of the spongy bone. Drilling temperatures under physiological conditions are much lower than those observed under dry conditions, while a larger drilling force occurs under physiological conditions than dry conditions. Drilling speed and feed rate have a significant influence on drilling force, temperature, bone debris and hole morphology. The investigation of the effect of drilling force and temperature on internal bone damage reveals that a drilling speed of 4500 r/min and a feed rate of 50 mm/min are recommended for bone drilling under physiological conditions. Drilling quality peaks under these optimal parameter conditions. This paper proposes the optimal drilling parameters under mid- and high-speed surgical drilling, considering internal bone damage and drilling quality, which can be looked as a reference for surgeons performing orthopedic operations.

Systematic parameter estimation and sensitivity analysis using a multidimensional PEMFC model coupled with DAKOTA.

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

Wang, Chao Yang; Luo, Gang; Jiang, Fangming

2010-05-01

Current computational models for proton exchange membrane fuel cells (PEMFCs) include a large number of parameters such as boundary conditions, material properties, and numerous parameters used in sub-models for membrane transport, two-phase flow and electrochemistry. In order to successfully use a computational PEMFC model in design and optimization, it is important to identify critical parameters under a wide variety of operating conditions, such as relative humidity, current load, temperature, etc. Moreover, when experimental data is available in the form of polarization curves or local distribution of current and reactant/product species (e.g., O2, H2O concentrations), critical parameters can be estimated inmore » order to enable the model to better fit the data. Sensitivity analysis and parameter estimation are typically performed using manual adjustment of parameters, which is also common in parameter studies. We present work to demonstrate a systematic approach based on using a widely available toolkit developed at Sandia called DAKOTA that supports many kinds of design studies, such as sensitivity analysis as well as optimization and uncertainty quantification. In the present work, we couple a multidimensional PEMFC model (which is being developed, tested and later validated in a joint effort by a team from Penn State Univ. and Sandia National Laboratories) with DAKOTA through the mapping of model parameters to system responses. Using this interface, we demonstrate the efficiency of performing simple parameter studies as well as identifying critical parameters using sensitivity analysis. Finally, we show examples of optimization and parameter estimation using the automated capability in DAKOTA.« less

IMPROVED ALGORITHMS FOR RADAR-BASED RECONSTRUCTION OF ASTEROID SHAPES

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

Greenberg, Adam H.; Margot, Jean-Luc

We describe our implementation of a global-parameter optimizer and Square Root Information Filter into the asteroid-modeling software shape. We compare the performance of our new optimizer with that of the existing sequential optimizer when operating on various forms of simulated data and actual asteroid radar data. In all cases, the new implementation performs substantially better than its predecessor: it converges faster, produces shape models that are more accurate, and solves for spin axis orientations more reliably. We discuss potential future changes to improve shape's fitting speed and accuracy.

Thermodynamic metrics and optimal paths.

PubMed

Sivak, David A; Crooks, Gavin E

2012-05-11

A fundamental problem in modern thermodynamics is how a molecular-scale machine performs useful work, while operating away from thermal equilibrium without excessive dissipation. To this end, we derive a friction tensor that induces a Riemannian manifold on the space of thermodynamic states. Within the linear-response regime, this metric structure controls the dissipation of finite-time transformations, and bestows optimal protocols with many useful properties. We discuss the connection to the existing thermodynamic length formalism, and demonstrate the utility of this metric by solving for optimal control parameter protocols in a simple nonequilibrium model.

General Methodology for Designing Spacecraft Trajectories

NASA Technical Reports Server (NTRS)

Condon, Gerald; Ocampo, Cesar; Mathur, Ravishankar; Morcos, Fady; Senent, Juan; Williams, Jacob; Davis, Elizabeth C.

2012-01-01

A methodology for designing spacecraft trajectories in any gravitational environment within the solar system has been developed. The methodology facilitates modeling and optimization for problems ranging from that of a single spacecraft orbiting a single celestial body to that of a mission involving multiple spacecraft and multiple propulsion systems operating in gravitational fields of multiple celestial bodies. The methodology consolidates almost all spacecraft trajectory design and optimization problems into a single conceptual framework requiring solution of either a system of nonlinear equations or a parameter-optimization problem with equality and/or inequality constraints.

Optimization of a thermal hydrolysis process for sludge pre-treatment.

PubMed

Sapkaite, I; Barrado, E; Fdz-Polanco, F; Pérez-Elvira, S I

2017-05-01

At industrial scale, thermal hydrolysis is the most used process to enhance biodegradability of the sludge produced in wastewater treatment plants. Through statistically guided Box-Behnken experimental design, the present study analyses the effect of TH as pre-treatment applied to activated sludge. The selected process variables were temperature (130-180 °C), time (5-50 min) and decompression mode (slow or steam-explosion effect), and the parameters evaluated were sludge solubilisation and methane production by anaerobic digestion. A quadratic polynomial model was generated to compare the process performance for the 15 different combinations of operation conditions by modifying the process variables evaluated. The statistical analysis performed exhibited that methane production and solubility were significantly affected by pre-treatment time and temperature. During high intensity pre-treatment (high temperature and long times), the solubility increased sharply while the methane production exhibited the opposite behaviour, indicating the formation of some soluble but non-biodegradable materials. Therefore, solubilisation is not a reliable parameter to quantify the efficiency of a thermal hydrolysis pre-treatment, since it is not directly related to methane production. Based on the operational parameters optimization, the estimated optimal thermal hydrolysis conditions to enhance of sewage sludge digestion were: 140-170 °C heating temperature, 5-35min residence time, and one sudden decompression. Copyright © 2017 Elsevier Ltd. All rights reserved.

Deterministic Computer-Controlled Polishing Process for High-Energy X-Ray Optics

NASA Technical Reports Server (NTRS)

Khan, Gufran S.; Gubarev, Mikhail; Speegle, Chet; Ramsey, Brian

2010-01-01

A deterministic computer-controlled polishing process for large X-ray mirror mandrels is presented. Using tool s influence function and material removal rate extracted from polishing experiments, design considerations of polishing laps and optimized operating parameters are discussed

Grey Wolf based control for speed ripple reduction at low speed operation of PMSM drives.

PubMed

Djerioui, Ali; Houari, Azeddine; Ait-Ahmed, Mourad; Benkhoris, Mohamed-Fouad; Chouder, Aissa; Machmoum, Mohamed

2018-03-01

Speed ripple at low speed-high torque operation of Permanent Magnet Synchronous Machine (PMSM) drives is considered as one of the major issues to be treated. The presented work proposes an efficient PMSM speed controller based on Grey Wolf (GW) algorithm to ensure a high-performance control for speed ripple reduction at low speed operation. The main idea of the proposed control algorithm is to propose a specific objective function in order to incorporate the advantage of fast optimization process of the GW optimizer. The role of GW optimizer is to find the optimal input controls that satisfy the speed tracking requirements. The synthesis methodology of the proposed control algorithm is detailed and the feasibility and performances of the proposed speed controller is confirmed by simulation and experimental results. The GW algorithm is a model-free controller and the parameters of its objective function are easy to be tuned. The GW controller is compared to PI one on real test bench. Then, the superiority of the first algorithm is highlighted. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Optimal fixed-finite-dimensional compensator for Burgers' equation with unbounded input/output operators

NASA Technical Reports Server (NTRS)

Burns, John A.; Marrekchi, Hamadi

1993-01-01

The problem of using reduced order dynamic compensators to control a class of nonlinear parabolic distributed parameter systems was considered. Concentration was on a system with unbounded input and output operators governed by Burgers' equation. A linearized model was used to compute low-order-finite-dimensional control laws by minimizing certain energy functionals. Then these laws were applied to the nonlinear model. Standard approaches to this problem employ model/controller reduction techniques in conjunction with linear quadratic Gaussian (LQG) theory. The approach used is based on the finite dimensional Bernstein/Hyland optimal projection theory which yields a fixed-finite-order controller.

Method of multi-dimensional moment analysis for the characterization of signal peaks

DOEpatents

Pfeifer, Kent B; Yelton, William G; Kerr, Dayle R; Bouchier, Francis A

2012-10-23

A method of multi-dimensional moment analysis for the characterization of signal peaks can be used to optimize the operation of an analytical system. With a two-dimensional Peclet analysis, the quality and signal fidelity of peaks in a two-dimensional experimental space can be analyzed and scored. This method is particularly useful in determining optimum operational parameters for an analytical system which requires the automated analysis of large numbers of analyte data peaks. For example, the method can be used to optimize analytical systems including an ion mobility spectrometer that uses a temperature stepped desorption technique for the detection of explosive mixtures.

Co-operation of digital nonlinear equalizers and soft-decision LDPC FEC in nonlinear transmission.

PubMed

Tanimura, Takahito; Oda, Shoichiro; Hoshida, Takeshi; Aoki, Yasuhiko; Tao, Zhenning; Rasmussen, Jens C

2013-12-30

We experimentally and numerically investigated the characteristics of 128 Gb/s dual polarization - quadrature phase shift keying signals received with two types of nonlinear equalizers (NLEs) followed by soft-decision (SD) low-density parity-check (LDPC) forward error correction (FEC). Successful co-operation among SD-FEC and NLEs over various nonlinear transmissions were demonstrated by optimization of parameters for NLEs.

Towards adjoint-based inversion for rheological parameters in nonlinear viscous mantle flow

NASA Astrophysics Data System (ADS)

Worthen, Jennifer; Stadler, Georg; Petra, Noemi; Gurnis, Michael; Ghattas, Omar

2014-09-01

We address the problem of inferring mantle rheological parameter fields from surface velocity observations and instantaneous nonlinear mantle flow models. We formulate this inverse problem as an infinite-dimensional nonlinear least squares optimization problem governed by nonlinear Stokes equations. We provide expressions for the gradient of the cost functional of this optimization problem with respect to two spatially-varying rheological parameter fields: the viscosity prefactor and the exponent of the second invariant of the strain rate tensor. Adjoint (linearized) Stokes equations, which are characterized by a 4th order anisotropic viscosity tensor, facilitates efficient computation of the gradient. A quasi-Newton method for the solution of this optimization problem is presented, which requires the repeated solution of both nonlinear forward Stokes and linearized adjoint Stokes equations. For the solution of the nonlinear Stokes equations, we find that Newton’s method is significantly more efficient than a Picard fixed point method. Spectral analysis of the inverse operator given by the Hessian of the optimization problem reveals that the numerical eigenvalues collapse rapidly to zero, suggesting a high degree of ill-posedness of the inverse problem. To overcome this ill-posedness, we employ Tikhonov regularization (favoring smooth parameter fields) or total variation (TV) regularization (favoring piecewise-smooth parameter fields). Solution of two- and three-dimensional finite element-based model inverse problems show that a constant parameter in the constitutive law can be recovered well from surface velocity observations. Inverting for a spatially-varying parameter field leads to its reasonable recovery, in particular close to the surface. When inferring two spatially varying parameter fields, only an effective viscosity field and the total viscous dissipation are recoverable. Finally, a model of a subducting plate shows that a localized weak zone at the plate boundary can be partially recovered, especially with TV regularization.

Optimal input shaping for Fisher identifiability of control-oriented lithium-ion battery models

NASA Astrophysics Data System (ADS)

Rothenberger, Michael J.

This dissertation examines the fundamental challenge of optimally shaping input trajectories to maximize parameter identifiability of control-oriented lithium-ion battery models. Identifiability is a property from information theory that determines the solvability of parameter estimation for mathematical models using input-output measurements. This dissertation creates a framework that exploits the Fisher information metric to quantify the level of battery parameter identifiability, optimizes this metric through input shaping, and facilitates faster and more accurate estimation. The popularity of lithium-ion batteries is growing significantly in the energy storage domain, especially for stationary and transportation applications. While these cells have excellent power and energy densities, they are plagued with safety and lifespan concerns. These concerns are often resolved in the industry through conservative current and voltage operating limits, which reduce the overall performance and still lack robustness in detecting catastrophic failure modes. New advances in automotive battery management systems mitigate these challenges through the incorporation of model-based control to increase performance, safety, and lifespan. To achieve these goals, model-based control requires accurate parameterization of the battery model. While many groups in the literature study a variety of methods to perform battery parameter estimation, a fundamental issue of poor parameter identifiability remains apparent for lithium-ion battery models. This fundamental challenge of battery identifiability is studied extensively in the literature, and some groups are even approaching the problem of improving the ability to estimate the model parameters. The first approach is to add additional sensors to the battery to gain more information that is used for estimation. The other main approach is to shape the input trajectories to increase the amount of information that can be gained from input-output measurements, and is the approach used in this dissertation. Research in the literature studies optimal current input shaping for high-order electrochemical battery models and focuses on offline laboratory cycling. While this body of research highlights improvements in identifiability through optimal input shaping, each optimal input is a function of nominal parameters, which creates a tautology. The parameter values must be known a priori to determine the optimal input for maximizing estimation speed and accuracy. The system identification literature presents multiple studies containing methods that avoid the challenges of this tautology, but these methods are absent from the battery parameter estimation domain. The gaps in the above literature are addressed in this dissertation through the following five novel and unique contributions. First, this dissertation optimizes the parameter identifiability of a thermal battery model, which Sergio Mendoza experimentally validates through a close collaboration with this dissertation's author. Second, this dissertation extends input-shaping optimization to a linear and nonlinear equivalent-circuit battery model and illustrates the substantial improvements in Fisher identifiability for a periodic optimal signal when compared against automotive benchmark cycles. Third, this dissertation presents an experimental validation study of the simulation work in the previous contribution. The estimation study shows that the automotive benchmark cycles either converge slower than the optimized cycle, or not at all for certain parameters. Fourth, this dissertation examines how automotive battery packs with additional power electronic components that dynamically route current to individual cells/modules can be used for parameter identifiability optimization. While the user and vehicle supervisory controller dictate the current demand for these packs, the optimized internal allocation of current still improves identifiability. Finally, this dissertation presents a robust Bayesian sequential input shaping optimization study to maximize the conditional Fisher information of the battery model parameters without prior knowledge of the nominal parameter set. This iterative algorithm only requires knowledge of the prior parameter distributions to converge to the optimal input trajectory.

Techniques for shuttle trajectory optimization

NASA Technical Reports Server (NTRS)

Edge, E. R.; Shieh, C. J.; Powers, W. F.

1973-01-01

The application of recently developed function-space Davidon-type techniques to the shuttle ascent trajectory optimization problem is discussed along with an investigation of the recently developed PRAXIS algorithm for parameter optimization. At the outset of this analysis, the major deficiency of the function-space algorithms was their potential storage problems. Since most previous analyses of the methods were with relatively low-dimension problems, no storage problems were encountered. However, in shuttle trajectory optimization, storage is a problem, and this problem was handled efficiently. Topics discussed include: the shuttle ascent model and the development of the particular optimization equations; the function-space algorithms; the operation of the algorithm and typical simulations; variable final-time problem considerations; and a modification of Powell's algorithm.

Optimal observables for multiparameter seismic tomography

NASA Astrophysics Data System (ADS)

Bernauer, Moritz; Fichtner, Andreas; Igel, Heiner

2014-08-01

We propose a method for the design of seismic observables with maximum sensitivity to a target model parameter class, and minimum sensitivity to all remaining parameter classes. The resulting optimal observables thereby minimize interparameter trade-offs in multiparameter inverse problems. Our method is based on the linear combination of fundamental observables that can be any scalar measurement extracted from seismic waveforms. Optimal weights of the fundamental observables are determined with an efficient global search algorithm. While most optimal design methods assume variable source and/or receiver positions, our method has the flexibility to operate with a fixed source-receiver geometry, making it particularly attractive in studies where the mobility of sources and receivers is limited. In a series of examples we illustrate the construction of optimal observables, and assess the potentials and limitations of the method. The combination of Rayleigh-wave traveltimes in four frequency bands yields an observable with strongly enhanced sensitivity to 3-D density structure. Simultaneously, sensitivity to S velocity is reduced, and sensitivity to P velocity is eliminated. The original three-parameter problem thereby collapses into a simpler two-parameter problem with one dominant parameter. By defining parameter classes to equal earth model properties within specific regions, our approach mimics the Backus-Gilbert method where data are combined to focus sensitivity in a target region. This concept is illustrated using rotational ground motion measurements as fundamental observables. Forcing dominant sensitivity in the near-receiver region produces an observable that is insensitive to the Earth structure at more than a few wavelengths' distance from the receiver. This observable may be used for local tomography with teleseismic data. While our test examples use a small number of well-understood fundamental observables, few parameter classes and a radially symmetric earth model, the method itself does not impose such restrictions. It can easily be applied to large numbers of fundamental observables and parameters classes, as well as to 3-D heterogeneous earth models.

Continued research on selected parameters to minimize community annoyance from airplane noise

NASA Technical Reports Server (NTRS)

Frair, L.

1981-01-01

Results from continued research on selected parameters to minimize community annoyance from airport noise are reported. First, a review of the initial work on this problem is presented. Then the research focus is expanded by considering multiobjective optimization approaches for this problem. A multiobjective optimization algorithm review from the open literature is presented. This is followed by the multiobjective mathematical formulation for the problem of interest. A discussion of the appropriate solution algorithm for the multiobjective formulation is conducted. Alternate formulations and associated solution algorithms are discussed and evaluated for this airport noise problem. Selected solution algorithms that have been implemented are then used to produce computational results for example airports. These computations involved finding the optimal operating scenario for a moderate size airport and a series of sensitivity analyses for a smaller example airport.

Optimization of properties and operating parameters of a passive DMFC mini-stack at ambient temperature

NASA Astrophysics Data System (ADS)

Baglio, V.; Stassi, A.; Matera, F. V.; Di Blasi, A.; Antonucci, V.; Aricò, A. S.

An investigation of properties and operating parameters of a passive DMFC monopolar mini-stack, such as catalyst loading and methanol concentration, was carried out. From this analysis, it was derived that a proper Pt loading is necessary to achieve the best compromise between electrode thickness and number of catalytic sites for the anode and cathode reactions to occur at suitable rates. Methanol concentrations ranging from 1 M up to 10 M and an air-breathing operation mode were investigated. A maximum power of 225 mW was obtained at ambient conditions for a three-cell stack, with an active single cell area of 4 cm 2, corresponding to a power density of about 20 mW cm -2.

An optimized ensemble local mean decomposition method for fault detection of mechanical components

NASA Astrophysics Data System (ADS)

Zhang, Chao; Li, Zhixiong; Hu, Chao; Chen, Shuai; Wang, Jianguo; Zhang, Xiaogang

2017-03-01

Mechanical transmission systems have been widely adopted in most of industrial applications, and issues related to the maintenance of these systems have attracted considerable attention in the past few decades. The recently developed ensemble local mean decomposition (ELMD) method shows satisfactory performance in fault detection of mechanical components for preventing catastrophic failures and reducing maintenance costs. However, the performance of ELMD often heavily depends on proper selection of its model parameters. To this end, this paper proposes an optimized ensemble local mean decomposition (OELMD) method to determinate an optimum set of ELMD parameters for vibration signal analysis. In OELMD, an error index termed the relative root-mean-square error (Relative RMSE) is used to evaluate the decomposition performance of ELMD with a certain amplitude of the added white noise. Once a maximum Relative RMSE, corresponding to an optimal noise amplitude, is determined, OELMD then identifies optimal noise bandwidth and ensemble number based on the Relative RMSE and signal-to-noise ratio (SNR), respectively. Thus, all three critical parameters of ELMD (i.e. noise amplitude and bandwidth, and ensemble number) are optimized by OELMD. The effectiveness of OELMD was evaluated using experimental vibration signals measured from three different mechanical components (i.e. the rolling bearing, gear and diesel engine) under faulty operation conditions.

4E analysis and multi objective optimization of a micro gas turbine and solid oxide fuel cell hybrid combined heat and power system

NASA Astrophysics Data System (ADS)

Sanaye, Sepehr; Katebi, Arash

2014-02-01

Energy, exergy, economic and environmental (4E) analysis and optimization of a hybrid solid oxide fuel cell and micro gas turbine (SOFC-MGT) system for use as combined generation of heat and power (CHP) is investigated in this paper. The hybrid system is modeled and performance related results are validated using available data in literature. Then a multi-objective optimization approach based on genetic algorithm is incorporated. Eight system design parameters are selected for the optimization procedure. System exergy efficiency and total cost rate (including capital or investment cost, operational cost and penalty cost of environmental emissions) are the two objectives. The effects of fuel unit cost, capital investment and system power output on optimum design parameters are also investigated. It is observed that the most sensitive and important design parameter in the hybrid system is fuel cell current density which has a significant effect on the balance between system cost and efficiency. The selected design point from the Pareto distribution of optimization results indicates a total system exergy efficiency of 60.7%, with estimated electrical energy cost 0.057 kW-1 h-1, and payback period of about 6.3 years for the investment.

Modeling and optimization of a typical fuel cell-heat engine hybrid system and its parametric design criteria

NASA Astrophysics Data System (ADS)

Zhao, Yingru; Chen, Jincan

A theoretical modeling approach is presented, which describes the behavior of a typical fuel cell-heat engine hybrid system in steady-state operating condition based on an existing solid oxide fuel cell model, to provide useful fundamental design characteristics as well as potential critical problems. The different sources of irreversible losses, such as the electrochemical reaction, electric resistances, finite-rate heat transfer between the fuel cell and the heat engine, and heat-leak from the fuel cell to the environment are specified and investigated. Energy and entropy analyses are used to indicate the multi-irreversible losses and to assess the work potentials of the hybrid system. Expressions for the power output and efficiency of the hybrid system are derived and the performance characteristics of the system are presented and discussed in detail. The effects of the design parameters and operating conditions on the system performance are studied numerically. It is found that there exist certain optimum criteria for some important parameters. The results obtained here may provide a theoretical basis for both the optimal design and operation of real fuel cell-heat engine hybrid systems. This new approach can be easily extended to other fuel cell hybrid systems to develop irreversible models suitable for the investigation and optimization of similar energy conversion settings and electrochemistry systems.

Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization.

PubMed

Nishio, Mizuho; Nishizawa, Mitsuo; Sugiyama, Osamu; Kojima, Ryosuke; Yakami, Masahiro; Kuroda, Tomohiro; Togashi, Kaori

2018-01-01

We aimed to evaluate a computer-aided diagnosis (CADx) system for lung nodule classification focussing on (i) usefulness of the conventional CADx system (hand-crafted imaging feature + machine learning algorithm), (ii) comparison between support vector machine (SVM) and gradient tree boosting (XGBoost) as machine learning algorithms, and (iii) effectiveness of parameter optimization using Bayesian optimization and random search. Data on 99 lung nodules (62 lung cancers and 37 benign lung nodules) were included from public databases of CT images. A variant of the local binary pattern was used for calculating a feature vector. SVM or XGBoost was trained using the feature vector and its corresponding label. Tree Parzen Estimator (TPE) was used as Bayesian optimization for parameters of SVM and XGBoost. Random search was done for comparison with TPE. Leave-one-out cross-validation was used for optimizing and evaluating the performance of our CADx system. Performance was evaluated using area under the curve (AUC) of receiver operating characteristic analysis. AUC was calculated 10 times, and its average was obtained. The best averaged AUC of SVM and XGBoost was 0.850 and 0.896, respectively; both were obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters for achieving high AUC were obtained with fewer numbers of trials when using TPE, compared with random search. Bayesian optimization of SVM and XGBoost parameters was more efficient than random search. Based on observer study, AUC values of two board-certified radiologists were 0.898 and 0.822. The results show that diagnostic accuracy of our CADx system was comparable to that of radiologists with respect to classifying lung nodules.

Optimization of Operation Parameters for Helical Flow Cleanout with Supercritical CO2 in Horizontal Wells Using Back-Propagation Artificial Neural Network.

PubMed

Song, Xianzhi; Peng, Chi; Li, Gensheng; He, Zhenguo; Wang, Haizhu

2016-01-01

Sand production and blockage are common during the drilling and production of horizontal oil and gas wells as a result of formation breakdown. The use of high-pressure rotating jets and annular helical flow is an effective way to enhance horizontal wellbore cleanout. In this paper, we propose the idea of using supercritical CO2 (SC-CO2) as washing fluid in water-sensitive formation. SC-CO2 is manifested to be effective in preventing formation damage and enhancing production rate as drilling fluid, which justifies tis potential in wellbore cleanout. In order to investigate the effectiveness of SC-CO2 helical flow cleanout, we perform the numerical study on the annular flow field, which significantly affects sand cleanout efficiency, of SC-CO2 jets in horizontal wellbore. Based on the field data, the geometry model and mathematical models were built. Then a numerical simulation of the annular helical flow field by SC-CO2 jets was accomplished. The influences of several key parameters were investigated, and SC-CO2 jets were compared to conventional water jets. The results show that flow rate, ambient temperature, jet temperature, and nozzle assemblies play the most important roles on wellbore flow field. Once the difference between ambient temperatures and jet temperatures is kept constant, the wellbore velocity distributions will not change. With increasing lateral nozzle size or decreasing rear/forward nozzle size, suspending ability of SC-CO2 flow improves obviously. A back-propagation artificial neural network (BP-ANN) was successfully employed to match the operation parameters and SC-CO2 flow velocities. A comprehensive model was achieved to optimize the operation parameters according to two strategies: cost-saving strategy and local optimal strategy. This paper can help to understand the distinct characteristics of SC-CO2 flow. And it is the first time that the BP-ANN is introduced to analyze the flow field during wellbore cleanout in horizontal wells.

Optimization of Operation Parameters for Helical Flow Cleanout with Supercritical CO2 in Horizontal Wells Using Back-Propagation Artificial Neural Network

PubMed Central

Song, Xianzhi; Peng, Chi; Li, Gensheng

2016-01-01

Sand production and blockage are common during the drilling and production of horizontal oil and gas wells as a result of formation breakdown. The use of high-pressure rotating jets and annular helical flow is an effective way to enhance horizontal wellbore cleanout. In this paper, we propose the idea of using supercritical CO2 (SC-CO2) as washing fluid in water-sensitive formation. SC-CO2 is manifested to be effective in preventing formation damage and enhancing production rate as drilling fluid, which justifies tis potential in wellbore cleanout. In order to investigate the effectiveness of SC-CO2 helical flow cleanout, we perform the numerical study on the annular flow field, which significantly affects sand cleanout efficiency, of SC-CO2 jets in horizontal wellbore. Based on the field data, the geometry model and mathematical models were built. Then a numerical simulation of the annular helical flow field by SC-CO2 jets was accomplished. The influences of several key parameters were investigated, and SC-CO2 jets were compared to conventional water jets. The results show that flow rate, ambient temperature, jet temperature, and nozzle assemblies play the most important roles on wellbore flow field. Once the difference between ambient temperatures and jet temperatures is kept constant, the wellbore velocity distributions will not change. With increasing lateral nozzle size or decreasing rear/forward nozzle size, suspending ability of SC-CO2 flow improves obviously. A back-propagation artificial neural network (BP-ANN) was successfully employed to match the operation parameters and SC-CO2 flow velocities. A comprehensive model was achieved to optimize the operation parameters according to two strategies: cost-saving strategy and local optimal strategy. This paper can help to understand the distinct characteristics of SC-CO2 flow. And it is the first time that the BP-ANN is introduced to analyze the flow field during wellbore cleanout in horizontal wells. PMID:27249026

QPSO-Based Adaptive DNA Computing Algorithm

PubMed Central

Karakose, Mehmet; Cigdem, Ugur

2013-01-01

DNA (deoxyribonucleic acid) computing that is a new computation model based on DNA molecules for information storage has been increasingly used for optimization and data analysis in recent years. However, DNA computing algorithm has some limitations in terms of convergence speed, adaptability, and effectiveness. In this paper, a new approach for improvement of DNA computing is proposed. This new approach aims to perform DNA computing algorithm with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO). Some contributions provided by the proposed QPSO based on adaptive DNA computing algorithm are as follows: (1) parameters of population size, crossover rate, maximum number of operations, enzyme and virus mutation rate, and fitness function of DNA computing algorithm are simultaneously tuned for adaptive process, (2) adaptive algorithm is performed using QPSO algorithm for goal-driven progress, faster operation, and flexibility in data, and (3) numerical realization of DNA computing algorithm with proposed approach is implemented in system identification. Two experiments with different systems were carried out to evaluate the performance of the proposed approach with comparative results. Experimental results obtained with Matlab and FPGA demonstrate ability to provide effective optimization, considerable convergence speed, and high accuracy according to DNA computing algorithm. PMID:23935409

The impact of the condenser on cytogenetic image quality in digital microscope system.

PubMed

Ren, Liqiang; Li, Zheng; Li, Yuhua; Zheng, Bin; Li, Shibo; Chen, Xiaodong; Liu, Hong

2013-01-01

Optimizing operational parameters of the digital microscope system is an important technique to acquire high quality cytogenetic images and facilitate the process of karyotyping so that the efficiency and accuracy of diagnosis can be improved. This study investigated the impact of the condenser on cytogenetic image quality and system working performance using a prototype digital microscope image scanning system. Both theoretical analysis and experimental validations through objectively evaluating a resolution test chart and subjectively observing large numbers of specimen were conducted. The results show that the optimal image quality and large depth of field (DOF) are simultaneously obtained when the numerical aperture of condenser is set as 60%-70% of the corresponding objective. Under this condition, more analyzable chromosomes and diagnostic information are obtained. As a result, the system shows higher working stability and less restriction for the implementation of algorithms such as autofocusing especially when the system is designed to achieve high throughput continuous image scanning. Although the above quantitative results were obtained using a specific prototype system under the experimental conditions reported in this paper, the presented evaluation methodologies can provide valuable guidelines for optimizing operational parameters in cytogenetic imaging using the high throughput continuous scanning microscopes in clinical practice.

An optimal output feedback gain variation scheme for the control of plants exhibiting gross parameter changes

NASA Technical Reports Server (NTRS)

Moerder, Daniel D.

1987-01-01

A concept for optimally designing output feedback controllers for plants whose dynamics exhibit gross changes over their operating regimes was developed. This was to formulate the design problem in such a way that the implemented feedback gains vary as the output of a dynamical system whose independent variable is a scalar parameterization of the plant operating point. The results of this effort include derivation of necessary conditions for optimality for the general problem formulation, and for several simplified cases. The question of existence of a solution to the design problem was also examined, and it was shown that the class of gain variation schemes developed are capable of achieving gain variation histories which are arbitrarily close to the unconstrained gain solution for each point in the plant operating range. The theory was implemented in a feedback design algorithm, which was exercised in a numerical example. The results are applicable to the design of practical high-performance feedback controllers for plants whose dynamics vary significanly during operation. Many aerospace systems fall into this category.

Hybrid protection algorithms based on game theory in multi-domain optical networks

NASA Astrophysics Data System (ADS)

Guo, Lei; Wu, Jingjing; Hou, Weigang; Liu, Yejun; Zhang, Lincong; Li, Hongming

2011-12-01

With the network size increasing, the optical backbone is divided into multiple domains and each domain has its own network operator and management policy. At the same time, the failures in optical network may lead to a huge data loss since each wavelength carries a lot of traffic. Therefore, the survivability in multi-domain optical network is very important. However, existing survivable algorithms can achieve only the unilateral optimization for profit of either users or network operators. Then, they cannot well find the double-win optimal solution with considering economic factors for both users and network operators. Thus, in this paper we develop the multi-domain network model with involving multiple Quality of Service (QoS) parameters. After presenting the link evaluation approach based on fuzzy mathematics, we propose the game model to find the optimal solution to maximize the user's utility, the network operator's utility, and the joint utility of user and network operator. Since the problem of finding double-win optimal solution is NP-complete, we propose two new hybrid protection algorithms, Intra-domain Sub-path Protection (ISP) algorithm and Inter-domain End-to-end Protection (IEP) algorithm. In ISP and IEP, the hybrid protection means that the intelligent algorithm based on Bacterial Colony Optimization (BCO) and the heuristic algorithm are used to solve the survivability in intra-domain routing and inter-domain routing, respectively. Simulation results show that ISP and IEP have the similar comprehensive utility. In addition, ISP has better resource utilization efficiency, lower blocking probability, and higher network operator's utility, while IEP has better user's utility.

Manufacture of conical springs with elastic medium technology improvement

NASA Astrophysics Data System (ADS)

Kurguzov, S. A.; Mikhailova, U. V.; Kalugina, O. B.

2018-01-01

This article considers the manufacturing technology improvement by using an elastic medium in the stamping tool forming space to improve the conical springs performance characteristics and reduce the costs of their production. Estimation technique of disk spring operational properties is developed by mathematical modeling of the compression process during the operation of a spring. A technique for optimizing the design parameters of a conical spring is developed, which ensures a minimum voltage value when operated in the edge of the spring opening.

Nitrogen removal performance and microbial community of an enhanced multistage A/O biofilm reactor treating low-strength domestic wastewater.

PubMed

Chen, Han; Li, Ang; Wang, Qiao; Cui, Di; Cui, Chongwei; Ma, Fang

2018-06-01

The low-strength domestic wastewater (LSDW) treatment with low chemical oxygen demand (COD) has drawn extensive attention for the poor total nitrogen (TN) removal performance. In the present study, an enhanced multistage anoxic/oxic (A/O) biofilm reactor was designed to improve the TN removal performance of the LSDW treatment. Efficient nitrifying and denitrifying biofilm carriers were cultivated and then filled into the enhanced biofilm reactor as the sole microbial source. Step-feed strategy and internal recycle were adopted to optimize the substrate distribution and the organics utilization. Key operational parameters were optimized to obtain the best nitrogen and organics removal efficiencies. A hydraulic retention time of 8 h, an influent distribution ratio of 2:1 and an internal recycle ratio of 200% were tested as the optimum parameters. The ammonium, TN and COD removal efficiencies under the optimal operational parameters separately achieved 99.75 ± 0.21, 59.51 ± 1.95 and 85.06 ± 0.79% with an organic loading rate at around 0.36 kg COD/m 3  d. The high-throughput sequencing technology confirmed that nitrifying and denitrifying biofilm could maintain functional bacteria in the system during long-period operation. Proteobacteria and Bacteroidetes were the dominant phyla in all the nitrifying and denitrifying biofilm samples. Nitrosomonadaceae_uncultured and Nitrospira sp. stably existed in nitrifying biofilm as the main nitrifiers, while several heterotrophic genera, such as Thauera sp. and Flavobacterium sp., acted as potential genera responsible for TN removal in denitrifying biofilm. These findings suggested that the enhanced biofilm reactor could be a promising route for the treatment of LSDW with a low COD level.

Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities

PubMed Central

Li, Jun-qing; Pan, Quan-ke; Mao, Kun

2014-01-01

A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm. PMID:24883414

Utilization of Supercapacitors in Adaptive Protection Applications for Resiliency against Communication Failures: A Size and Cost Optimization Case Study

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

Habib, Hany F; El Hariri, Mohamad; Elsayed, Ahmed

Microgrids’ adaptive protection techniques rely on communication signals from the point of common coupling to ad- just the corresponding relays’ settings for either grid-connected or islanded modes of operation. However, during communication out- ages or in the event of a cyberattack, relays settings are not changed. Thus adaptive protection schemes are rendered unsuc- cessful. Due to their fast response, supercapacitors, which are pre- sent in the microgrid to feed pulse loads, could also be utilized to enhance the resiliency of adaptive protection schemes to communi- cation outages. Proper sizing of the supercapacitors is therefore im- portant in order to maintainmore » a stable system operation and also reg- ulate the protection scheme’s cost. This paper presents a two-level optimization scheme for minimizing the supercapacitor size along with optimizing its controllers’ parameters. The latter will lead to a reduction of the supercapacitor fault current contribution and an increase in that of other AC resources in the microgrid in the ex- treme case of having a fault occurring simultaneously with a pulse load. It was also shown that the size of the supercapacitor can be reduced if the pulse load is temporary disconnected during the transient fault period. Simulations showed that the resulting super- capacitor size and the optimized controller parameters from the proposed two-level optimization scheme were feeding enough fault currents for different types of faults and minimizing the cost of the protection scheme.« less

Optimization of Reflux Conditions for Total Flavonoid and Total Phenolic Extraction and Enhanced Antioxidant Capacity in Pandan (Pandanus amaryllifolius Roxb.) Using Response Surface Methodology

PubMed Central

Ghasemzadeh, Ali; Jaafar, Hawa Z. E.

2014-01-01

Response surface methodology was applied to optimization of the conditions for reflux extraction of Pandan (Pandanus amaryllifolius Roxb.) in order to achieve a high content of total flavonoids (TF), total phenolics (TP), and high antioxidant capacity (AC) in the extracts. Central composite experimental design with three factors and three levels was employed to consider the effects of the operation parameters, including the methanol concentration (MC, 40%–80%), extraction temperature (ET, 40–70°C), and liquid-to-solid ratio (LS ratio, 20–40 mL/g) on the properties of the extracts. Response surface plots showed that increasing these operation parameters induced the responses significantly. The TF content and AC could be maximized when the extraction conditions (MC, ET, and LS ratio) were 78.8%, 69.5°C, and 32.4 mL/g, respectively, whereas the TP content was optimal when these variables were 75.1%, 70°C, and 31.8 mL/g, respectively. Under these optimum conditions, the experimental TF and TP content and AC were 1.78, 6.601 mg/g DW, and 87.38%, respectively. The optimized model was validated by a comparison of the predicted and experimental values. The experimental values were found to be in agreement with the predicted values, indicating the suitability of the model for optimizing the conditions for the reflux extraction of Pandan. PMID:25147852

Application of grey system theory on the influencing parameters of aerobic granulation in SBR.

PubMed

Bindhu, B K; Madhu, G

2017-09-01

Aerobic granulation is a promising technology for wastewater treatment. Four operational parameters were selected as influencing factors for this study. Aerobic granulation was experimented with three different values of organic loading rate (3, 6 and 9 kg COD m -3  d -1 ), superficial upflow air velocity (SUAV) (2, 3 and 4 cm s -1 ), settling time (3, 5 and 10 min) and volume exchange ratio (25%, 50% and 75%) in sequencing batch reactor in nine trials for the optimal performance of aerobic granulation. The influence of compared parameters on five reference parameters (sludge volume index (SVI), time taken for the appearance of granules, size and specific gravity of granules and chemical oxygen demand (COD) removal) was analyzed using grey system theory. The grey relational coefficients and grey entropy relational grade of each parameter were calculated. Hydrodynamic shear force in terms of SUAV was found to have the greatest influence on granule appearance, specific gravity of granules and COD removal efficiency. SVI is greatly affected by settling time. The optimal scopes of all the compared parameters were found.

Hybrid Self-Adaptive Evolution Strategies Guided by Neighborhood Structures for Combinatorial Optimization Problems.

PubMed

Coelho, V N; Coelho, I M; Souza, M J F; Oliveira, T A; Cota, L P; Haddad, M N; Mladenovic, N; Silva, R C P; Guimarães, F G

2016-01-01

This article presents an Evolution Strategy (ES)--based algorithm, designed to self-adapt its mutation operators, guiding the search into the solution space using a Self-Adaptive Reduced Variable Neighborhood Search procedure. In view of the specific local search operators for each individual, the proposed population-based approach also fits into the context of the Memetic Algorithms. The proposed variant uses the Greedy Randomized Adaptive Search Procedure with different greedy parameters for generating its initial population, providing an interesting exploration-exploitation balance. To validate the proposal, this framework is applied to solve three different [Formula: see text]-Hard combinatorial optimization problems: an Open-Pit-Mining Operational Planning Problem with dynamic allocation of trucks, an Unrelated Parallel Machine Scheduling Problem with Setup Times, and the calibration of a hybrid fuzzy model for Short-Term Load Forecasting. Computational results point out the convergence of the proposed model and highlight its ability in combining the application of move operations from distinct neighborhood structures along the optimization. The results gathered and reported in this article represent a collective evidence of the performance of the method in challenging combinatorial optimization problems from different application domains. The proposed evolution strategy demonstrates an ability of adapting the strength of the mutation disturbance during the generations of its evolution process. The effectiveness of the proposal motivates the application of this novel evolutionary framework for solving other combinatorial optimization problems.

A one dimensional moving bed biofilm reactor model for nitrification of municipal wastewaters.

PubMed

Barry, Ugo; Choubert, Jean-Marc; Canler, Jean-Pierre; Pétrimaux, Olivier; Héduit, Alain; Lessard, Paul

2017-08-01

This work presents a one-dimensional model of a moving bed bioreactor (MBBR) process designed for the removal of nitrogen from raw wastewaters. A comprehensive experimental strategy was deployed at a semi-industrial pilot-scale plant fed with a municipal wastewater operated at 10-12 °C, and surface loading rates of 1-2 g filtered COD/m 2 d and 0.4-0.55 g NH 4 -N/m 2 d. Data were collected on influent/effluent composition, and on measurement of key variables or parameters (biofilm mass and maximal thickness, thickness of the limit liquid layer, maximal nitrification rate, oxygen mass transfer coefficient). Based on time-course variations in these variables, the MBBR model was calibrated at two time-scales and magnitudes of dynamic conditions, i.e., short-term (4 days) calibration under dynamic conditions and long-term (33 days) calibration, and for three types of carriers. A set of parameters suitable for the conditions was proposed, and the calibrated parameter set is able to simulate the time-course change of nitrogen forms in the effluent of the MBBR tanks, under the tested operated conditions. Parameters linked to diffusion had a strong influence on how robustly the model is able to accurately reproduce time-course changes in effluent quality. Then the model was used to optimize the operations of MBBR layout. It was shown that the main optimization track consists of the limitation of the aeration supply without changing the overall performance of the process. Further work would investigate the influence of the hydrodynamic conditions onto the thickness of the limit liquid layer and the "apparent" diffusion coefficient in the biofilm parameters.

Analog design optimization methodology for ultralow-power circuits using intuitive inversion-level and saturation-level parameters

NASA Astrophysics Data System (ADS)

Eimori, Takahisa; Anami, Kenji; Yoshimatsu, Norifumi; Hasebe, Tetsuya; Murakami, Kazuaki

2014-01-01

A comprehensive design optimization methodology using intuitive nondimensional parameters of inversion-level and saturation-level is proposed, especially for ultralow-power, low-voltage, and high-performance analog circuits with mixed strong, moderate, and weak inversion metal-oxide-semiconductor transistor (MOST) operations. This methodology is based on the synthesized charge-based MOST model composed of Enz-Krummenacher-Vittoz (EKV) basic concepts and advanced-compact-model (ACM) physics-based equations. The key concept of this methodology is that all circuit and system characteristics are described as some multivariate functions of inversion-level parameters, where the inversion level is used as an independent variable representative of each MOST. The analog circuit design starts from the first step of inversion-level design using universal characteristics expressed by circuit currents and inversion-level parameters without process-dependent parameters, followed by the second step of foundry-process-dependent design and the last step of verification using saturation-level criteria. This methodology also paves the way to an intuitive and comprehensive design approach for many kinds of analog circuit specifications by optimization using inversion-level log-scale diagrams and saturation-level criteria. In this paper, we introduce an example of our design methodology for a two-stage Miller amplifier.

Effect of fermentation parameters on bio-alcohols production from glycerol using immobilized Clostridium pasteurianum: an optimization study.

PubMed

Khanna, Swati; Goyal, Arun; Moholkar, Vijayanand S

2013-01-01

This article addresses the issue of effect of fermentation parameters for conversion of glycerol (in both pure and crude form) into three value-added products, namely, ethanol, butanol, and 1,3-propanediol (1,3-PDO), by immobilized Clostridium pasteurianum and thereby addresses the statistical optimization of this process. The analysis of effect of different process parameters such as agitation rate, fermentation temperature, medium pH, and initial glycerol concentration indicated that medium pH was the most critical factor for total alcohols production in case of pure glycerol as fermentation substrate. On the other hand, initial glycerol concentration was the most significant factor for fermentation with crude glycerol. An interesting observation was that the optimized set of fermentation parameters was found to be independent of the type of glycerol (either pure or crude) used. At optimum conditions of agitation rate (200 rpm), initial glycerol concentration (25 g/L), fermentation temperature (30°C), and medium pH (7.0), the total alcohols production was almost equal in anaerobic shake flasks and 2-L bioreactor. This essentially means that at optimum process parameters, the scale of operation does not affect the output of the process. The immobilized cells could be reused for multiple cycles for both pure and crude glycerol fermentation.

Advanced interactive display formats for terminal area traffic control

NASA Technical Reports Server (NTRS)

Grunwald, Arthur J.

1995-01-01

The basic design considerations for perspective Air Traffic Control displays are described. A software framework has been developed for manual viewing parameter setting (MVPS) in preparation for continued, ongoing developments on automated viewing parameter setting (AVPS) schemes. The MVPS system is based on indirect manipulation of the viewing parameters. Requests for changes in viewing parameter setting are entered manually by the operator by moving viewing parameter manipulation pointers on the screen. The motion of these pointers, which are an integral part of the 3-D scene, is limited to the boundaries of screen. This arrangement has been chosen, in order to preserve the correspondence between the new and the old viewing parameter setting, a feature which contributes to preventing spatial disorientation of the operator. For all viewing operations, e.g. rotation, translation and ranging, the actual change is executed automatically by the system, through gradual transitions with an exponentially damped, sinusoidal velocity profile, in this work referred to as 'slewing' motions. The slewing functions, which eliminate discontinuities in the viewing parameter changes, are designed primarily for enhancing the operator's impression that he, or she, is dealing with an actually existing physical system, rather than an abstract computer generated scene. Current, ongoing efforts deal with the development of automated viewing parameter setting schemes. These schemes employ an optimization strategy, aimed at identifying the best possible vantage point, from which the Air Traffic Control scene can be viewed, for a given traffic situation.

Treatment of cyanide containing wastewater using cavitation based approach.

PubMed

Jawale, Rajashree H; Gogate, Parag R; Pandit, Aniruddha B

2014-07-01

Industrial wastewater streams containing high concentrations of biorefractory materials like cyanides should ideally be treated at source. In the present work, degradation of potassium ferrocyanide (K4Fe(CN)6) as a model pollutant has been investigated using cavitational reactors with possible intensification studies using different approaches. Effect of different operating parameters such as initial concentration, temperature and pH on the extent of degradation using acoustic cavitation has been investigated. For the case of hydrodynamic cavitation, flow characteristics of cavitating device (venturi) have been established initially followed by the effect of inlet pressure and pH on the extent of degradation. Under the optimized set of operating parameters, the addition of hydrogen peroxide (ratio of K4Fe(CN)6:H2O2 varied from 1:1 to 1:30 mol basis) as process intensifying approach has been investigated. The present work has conclusively established that under the set of optimized operating parameters, cavitation can be effectively used for degradation of potassium ferrocyanide. The comparative study of hydrodynamic cavitation and acoustic cavitation suggested that hydrodynamic cavitation is more energy efficient and gives higher degradation as compared to acoustic cavitation for equivalent power/energy dissipation. The present work is the first one to report comparison of cavitation based treatment schemes for degradation of cyanide containing wastewaters. Copyright © 2014 Elsevier B.V. All rights reserved.

Optimization of solid content, carbon/nitrogen ratio and food/inoculum ratio for biogas production from food waste.

PubMed

Dadaser-Celik, Filiz; Azgin, Sukru Taner; Yildiz, Yalcin Sevki

2016-12-01

Biogas production from food waste has been used as an efficient waste treatment option for years. The methane yields from decomposition of waste are, however, highly variable under different operating conditions. In this study, a statistical experimental design method (Taguchi OA 9 ) was implemented to investigate the effects of simultaneous variations of three parameters on methane production. The parameters investigated were solid content (SC), carbon/nitrogen ratio (C/N) and food/inoculum ratio (F/I). Two sets of experiments were conducted with nine anaerobic reactors operating under different conditions. Optimum conditions were determined using statistical analysis, such as analysis of variance (ANOVA). A confirmation experiment was carried out at optimum conditions to investigate the validity of the results. Statistical analysis showed that SC was the most important parameter for methane production with a 45% contribution, followed by F/I ratio with a 35% contribution. The optimum methane yield of 151 l kg -1 volatile solids (VS) was achieved after 24 days of digestion when SC was 4%, C/N was 28 and F/I were 0.3. The confirmation experiment provided a methane yield of 167 l kg -1 VS after 24 days. The analysis showed biogas production from food waste may be increased by optimization of operating conditions. © The Author(s) 2016.

Multi-objective optimization of hole characteristics during pulsed Nd:YAG laser microdrilling of gamma-titanium aluminide alloy sheet

NASA Astrophysics Data System (ADS)

Biswas, R.; Kuar, A. S.; Mitra, S.

2014-09-01

Nd:YAG laser microdrilled holes on gamma-titanium aluminide, a newly developed alloy having wide applications in turbine blades, engine valves, cases, metal cutting tools, missile components, nuclear fuel and biomedical engineering, are important from the dimensional accuracy and quality of hole point of view. Keeping this in mind, a central composite design (CCD) based on response surface methodology (RSM) is employed for multi-objective optimization of pulsed Nd:YAG laser microdrilling operation on gamma-titanium aluminide alloy sheet to achieve optimum hole characteristics within existing resources. The three characteristics such as hole diameter at entry, hole diameter at exit and hole taper have been considered for simultaneous optimization. The individual optimization of all three responses has also been carried out. The input parameters considered are lamp current, pulse frequency, assist air pressure and thickness of the job. The responses at predicted optimum parameter level are in good agreement with the results of confirmation experiments conducted for verification tests.

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.

Optimal design of earth-moving machine elements with cusp catastrophe theory application

NASA Astrophysics Data System (ADS)

Pitukhin, A. V.; Skobtsov, I. G.

2017-10-01

This paper deals with the optimal design problem solution for the operator of an earth-moving machine with a roll-over protective structure (ROPS) in terms of the catastrophe theory. A brief description of the catastrophe theory is presented, the cusp catastrophe is considered, control parameters are viewed as Gaussian stochastic quantities in the first part of the paper. The statement of optimal design problem is given in the second part of the paper. It includes the choice of the objective function and independent design variables, establishment of system limits. The objective function is determined as mean total cost that includes initial cost and cost of failure according to the cusp catastrophe probability. Algorithm of random search method with an interval reduction subject to side and functional constraints is given in the last part of the paper. The way of optimal design problem solution can be applied to choose rational ROPS parameters, which will increase safety and reduce production and exploitation expenses.

Optimization Strategies for Single-Stage, Multi-Stage and Continuous ADRs

NASA Technical Reports Server (NTRS)

Shirron, Peter J.

2014-01-01

Adiabatic Demagnetization Refrigerators (ADR) have many advantages that are prompting a resurgence in their use in spaceflight and laboratory applications. They are solid-state coolers capable of very high efficiency and very wide operating range. However, their low energy storage density translates to larger mass for a given cooling capacity than is possible with other refrigeration techniques. The interplay between refrigerant mass and other parameters such as magnetic field and heat transfer points in multi-stage ADRs gives rise to a wide parameter space for optimization. This paper first presents optimization strategies for single ADR stages, focusing primarily on obtaining the largest cooling capacity per stage mass, then discusses the optimization of multi-stage and continuous ADRs in the context of the coordinated heat transfer that must occur between stages. The goal for the latter is usually to obtain the largest cooling power per mass or volume, but there can also be many secondary objectives, such as limiting instantaneous heat rejection rates and producing intermediate temperatures for cooling of other instrument components.

Harmonic component detection: Optimized Spectral Kurtosis for operational modal analysis

NASA Astrophysics Data System (ADS)

Dion, J.-L.; Tawfiq, I.; Chevallier, G.

2012-01-01

This work is a contribution in the field of Operational Modal Analysis to identify the modal parameters of mechanical structures using only measured responses. The study deals with structural responses coupled with harmonic components amplitude and frequency modulated in a short range, a common combination for mechanical systems with engines and other rotating machines in operation. These harmonic components generate misleading data interpreted erroneously by the classical methods used in OMA. The present work attempts to differentiate maxima in spectra stemming from harmonic components and structural modes. The detection method proposed is based on the so-called Optimized Spectral Kurtosis and compared with others definitions of Spectral Kurtosis described in the literature. After a parametric study of the method, a critical study is performed on numerical simulations and then on an experimental structure in operation in order to assess the method's performance.

Modeling Total Dissolved Gas for Optimal Operation of Multireservoir Systems

DOE PAGES

Politano, Marcela; Castro, Alejandro; Hadjerioua, Boualem

2017-02-09

One important environmental issue of hydropower in the Columbia and Snake River Basins (Pacific Northwest region of United States) is elevated total dissolved gas (TDG) downstream of a dam, which has the potential to cause gas bubble disease in affected fish. Gas supersaturation in the Columbia River Basin primarily occurs due to dissolution of bubbles entrained during spill events. This paper presents a physically based TDG model that can be used to optimize spill operations in multireservoir hydropower systems. Independent variables of the model are forebay TDG, tailwater elevation, spillway and powerhouse discharges, project head, and environmental parameters such asmore » temperature and atmospheric pressure. The model contains seven physically meaningful experimental parameters, which were calibrated and validated against TDG data collected downstream of Rock Island Dam (Washington) from 2008 to 2012. In conclusion, a sensitivity analysis was performed to increase the understanding of the relationships between TDG downstream of the dam and processes such as air entrainment, lateral powerhouse flow, and dissolution.« less

Guidelines for Developing Spacecraft Structural Requirements: A Thermal and Environmental Perspective

NASA Technical Reports Server (NTRS)

Holladay, Jon; Day, Greg; Gill, Larry

2004-01-01

Spacecraft are typically designed with a primary focus on weight in order to meet launch vehicle performance parameters. However, for pressurized and/or man-rated spacecraft, it is also necessary to have an understanding of the vehicle operating environments to properly size the pressure vessel. Proper sizing of the pressure vessel requires an understanding of the space vehicle's life cycle and compares the physical design optimization (weight and launch "cost") to downstream operational complexity and total life cycle cost. This paper will provide an overview of some major environmental design drivers and provide examples for calculating the optimal design pressure versus a selected set of design parameters related to thermal and environmental perspectives. In addition, this paper will provide a generic set of cracking pressures for both positive and negative pressure relief valves that encompasses worst case environmental effects for a variety of launch / landing sites. Finally, several examples are included to highlight pressure relief set points and vehicle weight impacts for a selected set of orbital missions.

Optimization of PET instrumentation for brain activation studies

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

Dahlbom, M.; Cherry, S.R.; Hoffman, E.J.

By performing cerebral blood flow studies with positron emission tomography (PET), and comparing blood flow images of different states of activation, functional mapping of the brain is possible. The ability of current commercial instruments to perform such studies is investigated in this work, based on a comparison of noise equivalent count (NEC) rates. Differences in the NEC performance of the different scanners in conjunction with scanner design parameters, provide insights into the importance of block design (size, dead time, crystal thickness) and overall scanner design (sensitivity and scatter fraction) for optimizing data from activation studies. The newer scanners with removablemore » septa, operating with 3-D acquisition, have much higher sensitivity, but require new methodology for optimized operation. Only by administering multiple low doses (fractionation) of the flow tracer can the high sensitivity be utilized.« less

Design optimization of RF lines in vacuum environment for the MITICA experiment

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

De Muri, Michela, E-mail: michela.demuri@igi.cnr.it; Consorzio RFX, Corso Stati Uniti, 4, I-35127 Padova; Pavei, Mauro

This contribution regards the Radio Frequency (RF) transmission line of the Megavolt ITER Injector and Concept Advancement (MITICA) experiment. The original design considered copper coaxial lines of 1″ 5/8, but thermal simulations under operating conditions showed maximum temperatures of the lines at regime not compatible with the prescription of the component manufacturer. Hence, an optimization of the design was necessary. Enhancing thermal radiation and increasing the conductor size were considered for design optimization: thermal analyses were carried out to calculate the temperature of MITICA RF lines during operation, as a function of the emissivity value and of other geometrical parameters.more » Five coating products to increase the conductor surface emissivity were tested, measuring the outgassing behavior of the selected products and the obtained emissivity values.« less

Properties and parameters of the electron beam injected into the mirror magnetic trap of a plasma accelerator

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

Andreev, V. V., E-mail: temple18@mail.ru; Novitsky, A. A.; Vinnichenko, L. A.

2016-03-15

The parameters of the injector of an axial plasma beam injected into a plasma accelerator operating on the basis of gyroresonance acceleration of electrons in the reverse magnetic field are determined. The trapping of the beam electrons into the regime of gyroresonance acceleration is numerically simulated by the particle- in-cell method. The optimal time of axial injection of the beam into a magnetic mirror trap is determined. The beam parameters satisfying the condition of efficient particle trapping into the gyromagnetic autoresonance regime are found.

A Study of Al-Mn Transition Edge Sensor Engineering for Stability

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

George, E. M.; et al.

2013-11-10

The stability of Al-Mn transition edge sensor (TES) bolometers is studied as we vary the engineered TES transition, heat capacity, and/or coupling between the heat capacity and TES. We present thermal structure measurements of each of the 39 designs tested. The data is accurately fit by a two-body bolometer model, which allows us to extract the basic TES parameters that affect device stability. We conclude that parameters affecting device stability can be engineered for optimal device operation, and present the model parameters extracted for the different TES designs.

Estimation of the laser cutting operating cost by support vector regression methodology

NASA Astrophysics Data System (ADS)

Jović, Srđan; Radović, Aleksandar; Šarkoćević, Živče; Petković, Dalibor; Alizamir, Meysam

2016-09-01

Laser cutting is a popular manufacturing process utilized to cut various types of materials economically. The operating cost is affected by laser power, cutting speed, assist gas pressure, nozzle diameter and focus point position as well as the workpiece material. In this article, the process factors investigated were: laser power, cutting speed, air pressure and focal point position. The aim of this work is to relate the operating cost to the process parameters mentioned above. CO2 laser cutting of stainless steel of medical grade AISI316L has been investigated. The main goal was to analyze the operating cost through the laser power, cutting speed, air pressure, focal point position and material thickness. Since the laser operating cost is a complex, non-linear task, soft computing optimization algorithms can be used. Intelligent soft computing scheme support vector regression (SVR) was implemented. The performance of the proposed estimator was confirmed with the simulation results. The SVR results are then compared with artificial neural network and genetic programing. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR compared to other soft computing methodologies. The new optimization methods benefit from the soft computing capabilities of global optimization and multiobjective optimization rather than choosing a starting point by trial and error and combining multiple criteria into a single criterion.

RANS computations for identification of 1-D cavitation model parameters: application to full load cavitation vortex rope

NASA Astrophysics Data System (ADS)

Alligné, S.; Decaix, J.; Müller, A.; Nicolet, C.; Avellan, F.; Münch, C.

2017-04-01

Due to the massive penetration of alternative renewable energies, hydropower is a key energy conversion technology for stabilizing the electrical power network by using hydraulic machines at off design operating conditions. At full load, the axisymmetric cavitation vortex rope developing in Francis turbines acts as an internal source of energy, leading to an instability commonly referred to as self-excited surge. 1-D models are developed to predict this phenomenon and to define the range of safe operating points for a hydropower plant. These models require a calibration of several parameters. The present work aims at identifying these parameters by using CFD results as objective functions for an optimization process. A 2-D Venturi and 3-D Francis turbine are considered.

Economic evaluation of flying-qualities design criteria for a transport configured with relaxed static stability

NASA Technical Reports Server (NTRS)

Sliwa, S. M.

1980-01-01

Direct constrained parameter optimization was used to optimally size a medium range transport for minimum direct operating cost. Several stability and control constraints were varied to study the sensitivity of the configuration to specifying the unaugmented flying qualities of transports designed to take maximum advantage of relaxed static stability augmentation systems. Additionally, a number of handling qualities related design constants were studied with respect to their impact on the design.

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.

Adjusting the specificity of an engine map based on the sensitivity of an engine control parameter relative to a performance variable

DOEpatents

Jiang, Li; Lee, Donghoon; Yilmaz, Hakan; Stefanopoulou, Anna

2014-10-28

Methods and systems for engine control optimization are provided. A first and a second operating condition of a vehicle engine are detected. An initial value is identified for a first and a second engine control parameter corresponding to a combination of the detected operating conditions according to a first and a second engine map look-up table. The initial values for the engine control parameters are adjusted based on a detected engine performance variable to cause the engine performance variable to approach a target value. A first and a second sensitivity of the engine performance variable are determined in response to changes in the engine control parameters. The first engine map look-up table is adjusted when the first sensitivity is greater than a threshold, and the second engine map look-up table is adjusted when the second sensitivity is greater than a threshold.

Impact of Capital and Current Costs Changes of the Incineration Process of the Medical Waste on System Management Cost

NASA Astrophysics Data System (ADS)

Jolanta Walery, Maria

2017-12-01

The article describes optimization studies aimed at analysing the impact of capital and current costs changes of medical waste incineration on the cost of the system management and its structure. The study was conducted on the example of an analysis of the system of medical waste management in the Podlaskie Province, in north-eastern Poland. The scope of operational research carried out under the optimization study was divided into two stages of optimization calculations with assumed technical and economic parameters of the system. In the first stage, the lowest cost of functioning of the analysed system was generated, whereas in the second one the influence of the input parameter of the system, i.e. capital and current costs of medical waste incineration on economic efficiency index (E) and the spatial structure of the system was determined. Optimization studies were conducted for the following cases: with a 25% increase in capital and current costs of incineration process, followed by 50%, 75% and 100% increase. As a result of the calculations, the highest cost of system operation was achieved at the level of 3143.70 PLN/t with the assumption of 100% increase in capital and current costs of incineration process. There was an increase in the economic efficiency index (E) by about 97% in relation to run 1.

Optimization study on structural analyses for the J-PARC mercury target vessel

NASA Astrophysics Data System (ADS)

Guan, Wenhai; Wakai, Eiichi; Naoe, Takashi; Kogawa, Hiroyuki; Wakui, Takashi; Haga, Katsuhiro; Takada, Hiroshi; Futakawa, Masatoshi

2018-06-01

The spallation neutron source at the Japan Proton Accelerator Research Complex (J-PARC) mercury target vessel is used for various materials science studies, work is underway to achieve stable operation at 1 MW. This is very important for enhancing the structural integrity and durability of the target vessel, which is being developed for 1 MW operation. In the present study, to reduce thermal stress and relax stress concentrations more effectively in the existing target vessel in J-PARC, an optimization approach called the Taguchi method (TM) is applied to thermo-mechanical analysis. The ribs and their relative parameters, as well as the thickness of the mercury vessel and shrouds, were selected as important design parameters for this investigation. According to the analytical results of 18 model types designed using the TM, the optimal design was determined. It is characterized by discrete ribs and a thicker vessel wall than the current design. The maximum thermal stresses in the mercury vessel and the outer shroud were reduced by 14% and 15%, respectively. Furthermore, it was indicated that variations in rib width, left/right rib intervals, and shroud thickness could influence the maximum thermal stress performance. It is therefore concluded that the TM was useful for optimizing the structure of the target vessel and to reduce the thermal stress in a small number of calculation cases.

Exponential Modelling for Mutual-Cohering of Subband Radar Data

NASA Astrophysics Data System (ADS)

Siart, U.; Tejero, S.; Detlefsen, J.

2005-05-01

Increasing resolution and accuracy is an important issue in almost any type of radar sensor application. However, both resolution and accuracy are strongly related to the available signal bandwidth and energy that can be used. Nowadays, often several sensors operating in different frequency bands become available on a sensor platform. It is an attractive goal to use the potential of advanced signal modelling and optimization procedures by making proper use of information stemming from different frequency bands at the RF signal level. An important prerequisite for optimal use of signal energy is coherence between all contributing sensors. Coherent multi-sensor platforms are greatly expensive and are thus not available in general. This paper presents an approach for accurately estimating object radar responses using subband measurements at different RF frequencies. An exponential model approach allows to compensate for the lack of mutual coherence between independently operating sensors. Mutual coherence is recovered from the a-priori information that both sensors have common scattering centers in view. Minimizing the total squared deviation between measured data and a full-range exponential signal model leads to more accurate pole angles and pole magnitudes compared to single-band optimization. The model parameters (range and magnitude of point scatterers) after this full-range optimization process are also more accurate than the parameters obtained from a commonly used super-resolution procedure (root-MUSIC) applied to the non-coherent subband data.

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.

Assessment of parameter regionalization methods for modeling flash floods in China

NASA Astrophysics Data System (ADS)

Ragettli, Silvan; Zhou, Jian; Wang, Haijing

2017-04-01

Rainstorm flash floods are a common and serious phenomenon during the summer months in many hilly and mountainous regions of China. For this study, we develop a modeling strategy for simulating flood events in small river basins of four Chinese provinces (Shanxi, Henan, Beijing, Fujian). The presented research is part of preliminary investigations for the development of a national operational model for predicting and forecasting hydrological extremes in basins of size 10 - 2000 km2, whereas most of these basins are ungauged or poorly gauged. The project is supported by the China Institute of Water Resources and Hydropower Research within the framework of the national initiative for flood prediction and early warning system for mountainous regions in China (research project SHZH-IWHR-73). We use the USGS Precipitation-Runoff Modeling System (PRMS) as implemented in the Java modeling framework Object Modeling System (OMS). PRMS can operate at both daily and storm timescales, switching between the two using a precipitation threshold. This functionality allows the model to perform continuous simulations over several years and to switch to the storm mode to simulate storm response in greater detail. The model was set up for fifteen watersheds for which hourly precipitation and runoff data were available. First, automatic calibration based on the Shuffled Complex Evolution method was applied to different hydrological response unit (HRU) configurations. The Nash-Sutcliffe efficiency (NSE) was used as assessment criteria, whereas only runoff data from storm events were considered. HRU configurations reflect the drainage-basin characteristics and depend on assumptions regarding drainage density and minimum HRU size. We then assessed the sensitivity of optimal parameters to different HRU configurations. Finally, the transferability to other watersheds of optimal model parameters that were not sensitive to HRU configurations was evaluated. Model calibration for the 15 catchments resulted in good model performance (NSE > 0.5) in 10 and medium performance (NSE > 0.2) in 3 catchments. Optimal model parameters proofed to be relatively insensitive to different HRU configurations. This suggests that dominant controls on hydrologic parameter transfer can potentially be identified based on catchment attributes describing meteorological, geological or landscape characteristics. Parameter regionalization based on a principal component analysis (PCA) nearest neighbor search (using all available catchment attributes) resulted in a 54% success rate in transferring optimal parameter sets and still yielding acceptable model performance. Data from more catchments are required to further increase the parameter transferability success rate or to develop regionalization strategies for individual parameters.

Introduction of organic/hydro-organic matrices in inductively coupled plasma optical emission spectrometry and mass spectrometry: a tutorial review. Part II. Practical considerations.

PubMed

Leclercq, Amélie; Nonell, Anthony; Todolí Torró, José Luis; Bresson, Carole; Vio, Laurent; Vercouter, Thomas; Chartier, Frédéric

2015-07-23

Inductively coupled plasma optical emission spectrometry (ICP-OES) and mass spectrometry (ICP-MS) are increasingly used to carry out analyses in organic/hydro-organic matrices. The introduction of such matrices into ICP sources is particularly challenging and can be the cause of numerous drawbacks. This tutorial review, divided in two parts, explores the rich literature related to the introduction of organic/hydro-organic matrices in ICP sources. Part I provided theoretical considerations associated with the physico-chemical properties of such matrices, in an attempt to understand the induced phenomena. Part II of this tutorial review is dedicated to more practical considerations on instrumentation, instrumental and operating parameters, as well as analytical strategies for elemental quantification in such matrices. Two important issues are addressed in this part: the first concerns the instrumentation and optimization of instrumental and operating parameters, pointing out (i) the description, benefits and drawbacks of different kinds of nebulization and desolvation devices and the impact of more specific instrumental parameters such as the injector characteristics and the material used for the cone; and, (ii) the optimization of operating parameters, for both ICP-OES and ICP-MS. Even if it is at the margin of this tutorial review, Electrothermal Vaporization and Laser Ablation will also be shortly described. The second issue is devoted to the analytical strategies for elemental quantification in such matrices, with particular insight into the isotope dilution technique, particularly used in speciation analysis by ICP-coupled separation techniques. Copyright © 2015 Elsevier B.V. All rights reserved.

A novel optimized hybrid fuzzy logic intelligent PID controller for an interconnected multi-area power system with physical constraints and boiler dynamics.

PubMed

Gomaa Haroun, A H; Li, Yin-Ya

2017-11-01

In the fast developing world nowadays, load frequency control (LFC) is considered to be a most significant role for providing the power supply with good quality in the power system. To deliver a reliable power, LFC system requires highly competent and intelligent control technique. Hence, in this article, a novel hybrid fuzzy logic intelligent proportional-integral-derivative (FLiPID) controller has been proposed for LFC of interconnected multi-area power systems. A four-area interconnected thermal power system incorporated with physical constraints and boiler dynamics is considered and the adjustable parameters of the FLiPID controller are optimized using particle swarm optimization (PSO) scheme employing an integral square error (ISE) criterion. The proposed method has been established to enhance the power system performances as well as to reduce the oscillations of uncertainties due to variations in the system parameters and load perturbations. The supremacy of the suggested method is demonstrated by comparing the simulation results with some recently reported heuristic methods such as fuzzy logic proportional-integral (FLPI) and intelligent proportional-integral-derivative (PID) controllers for the same electrical power system. the investigations showed that the FLiPID controller provides a better dynamic performance and outperform compared to the other approaches in terms of the settling time, and minimum undershoots of the frequency as well as tie-line power flow deviations following a perturbation, in addition to perform appropriate settlement of integral absolute error (IAE). Finally, the sensitivity analysis of the plant is inspected by varying the system parameters and operating load conditions from their nominal values. It is observed that the suggested controller based optimization algorithm is robust and perform satisfactorily with the variations in operating load condition, system parameters and load pattern. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Image quality, threshold contrast and mean glandular dose in CR mammography

NASA Astrophysics Data System (ADS)

Jakubiak, R. R.; Gamba, H. R.; Neves, E. B.; Peixoto, J. E.

2013-09-01

In many countries, computed radiography (CR) systems represent the majority of equipment used in digital mammography. This study presents a method for optimizing image quality and dose in CR mammography of patients with breast thicknesses between 45 and 75 mm. Initially, clinical images of 67 patients (group 1) were analyzed by three experienced radiologists, reporting about anatomical structures, noise and contrast in low and high pixel value areas, and image sharpness and contrast. Exposure parameters (kV, mAs and target/filter combination) used in the examinations of these patients were reproduced to determine the contrast-to-noise ratio (CNR) and mean glandular dose (MGD). The parameters were also used to radiograph a CDMAM (version 3.4) phantom (Artinis Medical Systems, The Netherlands) for image threshold contrast evaluation. After that, different breast thicknesses were simulated with polymethylmethacrylate layers and various sets of exposure parameters were used in order to determine optimal radiographic parameters. For each simulated breast thickness, optimal beam quality was defined as giving a target CNR to reach the threshold contrast of CDMAM images for acceptable MGD. These results were used for adjustments in the automatic exposure control (AEC) by the maintenance team. Using optimized exposure parameters, clinical images of 63 patients (group 2) were evaluated as described above. Threshold contrast, CNR and MGD for such exposure parameters were also determined. Results showed that the proposed optimization method was effective for all breast thicknesses studied in phantoms. The best result was found for breasts of 75 mm. While in group 1 there was no detection of the 0.1 mm critical diameter detail with threshold contrast below 23%, after the optimization, detection occurred in 47.6% of the images. There was also an average MGD reduction of 7.5%. The clinical image quality criteria were attended in 91.7% for all breast thicknesses evaluated in both patient groups. Finally, this study also concluded that the use of the AEC of the x-ray unit based on the constant dose to the detector may bring some difficulties to CR systems to operate under optimal conditions. More studies must be performed, so that the compatibility between systems and optimization methodologies can be evaluated, as well as this optimization method. Most methods are developed for phantoms, so comparative studies including clinical images must be developed.

Optimization of Wireless Power Transfer Systems Enhanced by Passive Elements and Metasurfaces

NASA Astrophysics Data System (ADS)

Lang, Hans-Dieter; Sarris, Costas D.

2017-10-01

This paper presents a rigorous optimization technique for wireless power transfer (WPT) systems enhanced by passive elements, ranging from simple reflectors and intermedi- ate relays all the way to general electromagnetic guiding and focusing structures, such as metasurfaces and metamaterials. At its core is a convex semidefinite relaxation formulation of the otherwise nonconvex optimization problem, of which tightness and optimality can be confirmed by a simple test of its solutions. The resulting method is rigorous, versatile, and general -- it does not rely on any assumptions. As shown in various examples, it is able to efficiently and reliably optimize such WPT systems in order to find their physical limitations on performance, optimal operating parameters and inspect their working principles, even for a large number of active transmitters and passive elements.

Biological hydrogen production by dark fermentation: challenges and prospects towards scaled-up production.

PubMed

RenNanqi; GuoWanqian; LiuBingfeng; CaoGuangli; DingJie

2011-06-01

Among different technologies of hydrogen production, bio-hydrogen production exhibits perhaps the greatest potential to replace fossil fuels. Based on recent research on dark fermentative hydrogen production, this article reviews the following aspects towards scaled-up application of this technology: bioreactor development and parameter optimization, process modeling and simulation, exploitation of cheaper raw materials and combining dark-fermentation with photo-fermentation. Bioreactors are necessary for dark-fermentation hydrogen production, so the design of reactor type and optimization of parameters are essential. Process modeling and simulation can help engineers design and optimize large-scale systems and operations. Use of cheaper raw materials will surely accelerate the pace of scaled-up production of biological hydrogen. And finally, combining dark-fermentation with photo-fermentation holds considerable promise, and has successfully achieved maximum overall hydrogen yield from a single substrate. Future development of bio-hydrogen production will also be discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.

A model for the value of a business, some optimization problems in its operating procedures and the valuation of its debt

NASA Astrophysics Data System (ADS)

1997-12-01

In this paper we present a model for the value of a firm based on observable variables and parameters: the annual turnover, the expenses, interest rates. This value is the solution of a parabolic partial differential equation. We show how the value of the company depends on its legal status such as its liability (that is, whether it is a Limited Company or a sole trader/partnership). We give examples of how the operating procedures can be optimized (for example, whether the firm should close down, relocate etc.). Finally, we show how the model can be used to value the debt issued by the firm.

Computer package for the design and optimization of absorption air conditioning system operated by solar energy

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

Sofrata, H.; Khoshaim, B.; Megahed, M.

1980-12-01

In this paper a computer package for the design and optimization of the simple Li-Br absorption air conditioning system, operated by solar energy, is developed in order to study its performance. This was necessary, as a first step, before carrying out any computations regarding the dual system (1-3). The computer package has the facilities of examining any parameter which may control the system; namely generator, evaporator, condenser, absorber temperatures and pumping factor. The output may be tabulated and also fed to the graph plotter. The flow chart of the programme is explained in an easy way and a typical examplemore » is included.« less

Effects of Sampling and Spatio/Temporal Granularity in Traffic Monitoring on Anomaly Detectability

NASA Astrophysics Data System (ADS)

Ishibashi, Keisuke; Kawahara, Ryoichi; Mori, Tatsuya; Kondoh, Tsuyoshi; Asano, Shoichiro

We quantitatively evaluate how sampling and spatio/temporal granularity in traffic monitoring affect the detectability of anomalous traffic. Those parameters also affect the monitoring burden, so network operators face a trade-off between the monitoring burden and detectability and need to know which are the optimal paramter values. We derive equations to calculate the false positive ratio and false negative ratio for given values of the sampling rate, granularity, statistics of normal traffic, and volume of anomalies to be detected. Specifically, assuming that the normal traffic has a Gaussian distribution, which is parameterized by its mean and standard deviation, we analyze how sampling and monitoring granularity change these distribution parameters. This analysis is based on observation of the backbone traffic, which exhibits spatially uncorrelated and temporally long-range dependence. Then we derive the equations for detectability. With those equations, we can answer the practical questions that arise in actual network operations: what sampling rate to set to find the given volume of anomaly, or, if the sampling is too high for actual operation, what granularity is optimal to find the anomaly for a given lower limit of sampling rate.

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.

Study of array plasma antenna parameters

NASA Astrophysics Data System (ADS)

Kumar, Rajneesh; Kumar, Prince

2018-04-01

This paper is aimed to investigate the array plasma antenna parameters to help the optimization of an array plasma antenna. Single plasma antenna is transformed into array plasma antenna by changing the operating parameters. The re-configurability arises in the form of striations, due to transverse bifurcation of plasma column by changing the operating parameters. Each striation can be treated as an antenna element and system performs like an array plasma antenna. In order to achieve the goal of this paper, three different configurations of array plasma antenna (namely Array 1, Array 2 and Array 3) are simulated. The observations are made on variation in antenna parameters like resonance frequency, radiation pattern, directivity and gain with variation in length and number of antenna elements for each array plasma antenna. Moreover experiments are also performed and results are compared with simulation. Further array plasma antenna parameters are also compared with monopole plasma antenna parameters. The study of present paper invoke the array plasma antenna can be applied for steering and controlling the strength of Wi-Fi signals as per requirement.

Enhanced genetic algorithm optimization model for a single reservoir operation based on hydropower generation: case study of Mosul reservoir, northern Iraq.

PubMed

Al-Aqeeli, Yousif H; Lee, T S; Abd Aziz, S

2016-01-01

Achievement of the optimal hydropower generation from operation of water reservoirs, is a complex problems. The purpose of this study was to formulate and improve an approach of a genetic algorithm optimization model (GAOM) in order to increase the maximization of annual hydropower generation for a single reservoir. For this purpose, two simulation algorithms were drafted and applied independently in that GAOM during 20 scenarios (years) for operation of Mosul reservoir, northern Iraq. The first algorithm was based on the traditional simulation of reservoir operation, whilst the second algorithm (Salg) enhanced the GAOM by changing the population values of GA through a new simulation process of reservoir operation. The performances of these two algorithms were evaluated through the comparison of their optimal values of annual hydropower generation during the 20 scenarios of operating. The GAOM achieved an increase in hydropower generation in 17 scenarios using these two algorithms, with the Salg being superior in all scenarios. All of these were done prior adding the evaporation (Ev) and precipitation (Pr) to the water balance equation. Next, the GAOM using the Salg was applied by taking into consideration the volumes of these two parameters. In this case, the optimal values obtained from the GAOM were compared, firstly with their counterpart that found using the same algorithm without taking into consideration of Ev and Pr, secondly with the observed values. The first comparison showed that the optimal values obtained in this case decreased in all scenarios, whilst maintaining the good results compared with the observed in the second comparison. The results proved the effectiveness of the Salg in increasing the hydropower generation through the enhanced approach of the GAOM. In addition, the results indicated to the importance of taking into account the Ev and Pr in the modelling of reservoirs operation.

Image data-processing system for solar astronomy

NASA Technical Reports Server (NTRS)

Wilson, R. M.; Teuber, D. L.; Watkins, J. R.; Thomas, D. T.; Cooper, C. M.

1977-01-01

The paper describes an image data processing system (IDAPS), its hardware/software configuration, and interactive and batch modes of operation for the analysis of the Skylab/Apollo Telescope Mount S056 X-Ray Telescope experiment data. Interactive IDAPS is primarily designed to provide on-line interactive user control of image processing operations for image familiarization, sequence and parameter optimization, and selective feature extraction and analysis. Batch IDAPS follows the normal conventions of card control and data input and output, and is best suited where the desired parameters and sequence of operations are known and when long image-processing times are required. Particular attention is given to the way in which this system has been used in solar astronomy and other investigations. Some recent results obtained by means of IDAPS are presented.

Assessment and Reduction of Model Parametric Uncertainties: A Case Study with A Distributed Hydrological Model

NASA Astrophysics Data System (ADS)

Gan, Y.; Liang, X. Z.; Duan, Q.; Xu, J.; Zhao, P.; Hong, Y.

2017-12-01

The uncertainties associated with the parameters of a hydrological model need to be quantified and reduced for it to be useful for operational hydrological forecasting and decision support. An uncertainty quantification framework is presented to facilitate practical assessment and reduction of model parametric uncertainties. A case study, using the distributed hydrological model CREST for daily streamflow simulation during the period 2008-2010 over ten watershed, was used to demonstrate the performance of this new framework. Model behaviors across watersheds were analyzed by a two-stage stepwise sensitivity analysis procedure, using LH-OAT method for screening out insensitive parameters, followed by MARS-based Sobol' sensitivity indices for quantifying each parameter's contribution to the response variance due to its first-order and higher-order effects. Pareto optimal sets of the influential parameters were then found by the adaptive surrogate-based multi-objective optimization procedure, using MARS model for approximating the parameter-response relationship and SCE-UA algorithm for searching the optimal parameter sets of the adaptively updated surrogate model. The final optimal parameter sets were validated against the daily streamflow simulation of the same watersheds during the period 2011-2012. The stepwise sensitivity analysis procedure efficiently reduced the number of parameters that need to be calibrated from twelve to seven, which helps to limit the dimensionality of calibration problem and serves to enhance the efficiency of parameter calibration. The adaptive MARS-based multi-objective calibration exercise provided satisfactory solutions to the reproduction of the observed streamflow for all watersheds. The final optimal solutions showed significant improvement when compared to the default solutions, with about 65-90% reduction in 1-NSE and 60-95% reduction in |RB|. The validation exercise indicated a large improvement in model performance with about 40-85% reduction in 1-NSE, and 35-90% reduction in |RB|. Overall, this uncertainty quantification framework is robust, effective and efficient for parametric uncertainty analysis, the results of which provide useful information that helps to understand the model behaviors and improve the model simulations.

A Novel Protocol for Model Calibration in Biological Wastewater Treatment

PubMed Central

Zhu, Ao; Guo, Jianhua; Ni, Bing-Jie; Wang, Shuying; Yang, Qing; Peng, Yongzhen

2015-01-01

Activated sludge models (ASMs) have been widely used for process design, operation and optimization in wastewater treatment plants. However, it is still a challenge to achieve an efficient calibration for reliable application by using the conventional approaches. Hereby, we propose a novel calibration protocol, i.e. Numerical Optimal Approaching Procedure (NOAP), for the systematic calibration of ASMs. The NOAP consists of three key steps in an iterative scheme flow: i) global factors sensitivity analysis for factors fixing; ii) pseudo-global parameter correlation analysis for non-identifiable factors detection; and iii) formation of a parameter subset through an estimation by using genetic algorithm. The validity and applicability are confirmed using experimental data obtained from two independent wastewater treatment systems, including a sequencing batch reactor and a continuous stirred-tank reactor. The results indicate that the NOAP can effectively determine the optimal parameter subset and successfully perform model calibration and validation for these two different systems. The proposed NOAP is expected to use for automatic calibration of ASMs and be applied potentially to other ordinary differential equations models. PMID:25682959

The effect of inflation rate on the cost of medical waste management system

NASA Astrophysics Data System (ADS)

Jolanta Walery, Maria

2017-11-01

This paper describes the optimization study aimed to analyse the impact of the parameter describing the inflation rate on the cost of the system and its structure. The study was conducted on the example of the analysis of medical waste management system in north-eastern Poland, in the Podlaskie Province. The scope of operational research carried out under the optimization study was divided into two stages of optimization calculations with assumed technical and economic parameters of the system. In the first stage, the lowest cost of functioning of the analysed system was generated, whereas in the second one the influence of the input parameter of the system, i.e. the inflation rate on the economic efficiency index (E) and the spatial structure of the system was determined. With the assumed inflation rate in the range of 1.00 to 1.12, the highest cost of the system was achieved at the level of PLN 2022.20/t (increase of economic efficiency index E by ca. 27% in comparison with run 1, with inflation rate = 1.12).

Serial robot for the trajectory optimization and error compensation of TMT mask exchange system

NASA Astrophysics Data System (ADS)

Wang, Jianping; Zhang, Feifan; Zhou, Zengxiang; Zhai, Chao

2015-10-01

Mask exchange system is the main part of Multi-Object Broadband Imaging Echellette (MOBIE) on the Thirty Meter Telescope (TMT). According to the conception of the TMT mask exchange system, the pre-design was introduced in the paper which was based on IRB 140 robot. The stiffness model of IRB 140 in SolidWorks was analyzed under different gravity vectors for further error compensation. In order to find the right location and path planning, the robot and the mask cassette model was imported into MOBIE model to perform different schemes simulation. And obtained the initial installation position and routing. Based on these initial parameters, IRB 140 robot was operated to simulate the path and estimate the mask exchange time. Meanwhile, MATLAB and ADAMS software were used to perform simulation analysis and optimize the route to acquire the kinematics parameters and compare with the experiment results. After simulation and experimental research mentioned in the paper, the theoretical reference was acquired which could high efficient improve the structure of the mask exchange system parameters optimization of the path and precision of the robot position.

Information theoretic methods for image processing algorithm optimization

NASA Astrophysics Data System (ADS)

Prokushkin, Sergey F.; Galil, Erez

2015-01-01

Modern image processing pipelines (e.g., those used in digital cameras) are full of advanced, highly adaptive filters that often have a large number of tunable parameters (sometimes > 100). This makes the calibration procedure for these filters very complex, and the optimal results barely achievable in the manual calibration; thus an automated approach is a must. We will discuss an information theory based metric for evaluation of algorithm adaptive characteristics ("adaptivity criterion") using noise reduction algorithms as an example. The method allows finding an "orthogonal decomposition" of the filter parameter space into the "filter adaptivity" and "filter strength" directions. This metric can be used as a cost function in automatic filter optimization. Since it is a measure of a physical "information restoration" rather than perceived image quality, it helps to reduce the set of the filter parameters to a smaller subset that is easier for a human operator to tune and achieve a better subjective image quality. With appropriate adjustments, the criterion can be used for assessment of the whole imaging system (sensor plus post-processing).

Imaging diagnostics in ovarian cancer: magnetic resonance imaging and a scoring system guiding choice of primary treatment.

PubMed

Kasper, Sigrid M; Dueholm, Margit; Marinovskij, Edvard; Blaakær, Jan

2017-03-01

To analyze the ability of magnetic resonance imaging (MRI) and systematic evaluation at surgery to predict optimal cytoreduction in primary advanced ovarian cancer and to develop a preoperative scoring system for cancer staging. Preoperative MRI and standard laparotomy were performed in 99 women with either ovarian or primary peritoneal cancer. Using univariate and multivariate logistic regression analysis of a systematic description of the tumor in nine abdominal compartments obtained by MRI and during surgery plus clinical parameters, a scoring system was designed that predicted non-optimal cytoreduction. Non-optimal cytoreduction at operation was predicted by the following: (A) presence of comorbidities group 3 or 4 (ASA); (B) tumor presence in multiple numbers of different compartments, and (C) numbers of specified sites of organ involvement. The score includes: number of compartments involved (1-9 points), >1 subdiaphragmal location with presence of tumor (1 point); deep organ involvement of liver (1 point), porta hepatis (1 point), spleen (1 point), mesentery/vessel (1 point), cecum/ileocecal (1 point), rectum/vessels (1 point): ASA groups 3 and 4 (2 points). Use of the scoring system based on operative findings gave an area under the curve (AUC) of 91% (85-98%) for patients in whom optimal cytoreduction could not be achieved. The score AUC obtained by MRI was 84% (76-92%), and 43% of non-optimal cytoreduction patients were identified, with only 8% of potentially operable patients being falsely evaluated as suitable for non-optimal cytoreduction at the most optimal cut-off value. Tumor in individual locations did not predict operability. This systematic scoring system based on operative findings and MRI may predict non-optimal cytoreduction. MRI is able to assess ovarian cancer with peritoneal carcinomatosis with satisfactory concordance with laparotomic findings. This scoring system could be useful as a clinical guideline and should be evaluated and developed further in larger studies. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Primary gamma ray selection in a hybrid timing/imaging Cherenkov array

NASA Astrophysics Data System (ADS)

Postnikov, E. B.; Grinyuk, A. A.; Kuzmichev, L. A.; Sveshnikova, L. G.

2017-06-01

This work is a methodical study on hybrid reconstruction techniques for hybrid imaging/timing Cherenkov observations. This type of hybrid array is to be realized at the gamma-observatory TAIGA intended for very high energy gamma-ray astronomy (> 30 TeV). It aims at combining the cost-effective timing-array technique with imaging telescopes. Hybrid operation of both of these techniques can lead to a relatively cheap way of development of a large area array. The joint approach of gamma event selection was investigated on both types of simulated data: the image parameters from the telescopes, and the shower parameters reconstructed from the timing array. The optimal set of imaging parameters and shower parameters to be combined is revealed. The cosmic ray background suppression factor depending on distance and energy is calculated. The optimal selection technique leads to cosmic ray background suppression of about 2 orders of magnitude on distances up to 450 m for energies greater than 50 TeV.

Zener Diode Compact Model Parameter Extraction Using Xyce-Dakota Optimization.

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

Buchheit, Thomas E.; Wilcox, Ian Zachary; Sandoval, Andrew J

This report presents a detailed process for compact model parameter extraction for DC circuit Zener diodes. Following the traditional approach of Zener diode parameter extraction, circuit model representation is defined and then used to capture the different operational regions of a real diode's electrical behavior. The circuit model contains 9 parameters represented by resistors and characteristic diodes as circuit model elements. The process of initial parameter extraction, the identification of parameter values for the circuit model elements, is presented in a way that isolates the dependencies between certain electrical parameters and highlights both the empirical nature of the extraction andmore » portions of the real diode physical behavior which of the parameters are intended to represent. Optimization of the parameters, a necessary part of a robost parameter extraction process, is demonstrated using a 'Xyce-Dakota' workflow, discussed in more detail in the report. Among other realizations during this systematic approach of electrical model parameter extraction, non-physical solutions are possible and can be difficult to avoid because of the interdependencies between the different parameters. The process steps described are fairly general and can be leveraged for other types of semiconductor device model extractions. Also included in the report are recommendations for experiment setups for generating optimum dataset for model extraction and the Parameter Identification and Ranking Table (PIRT) for Zener diodes.« less

Beyond Euler angles: exploiting the angle-axis parametrization in a multipole expansion of the rotation operator.

PubMed

Siemens, Mark; Hancock, Jason; Siminovitch, David

2007-02-01

Euler angles (alpha,beta,gamma) are cumbersome from a computational point of view, and their link to experimental parameters is oblique. The angle-axis {Phi, n} parametrization, especially in the form of quaternions (or Euler-Rodrigues parameters), has served as the most promising alternative, and they have enjoyed considerable success in rf pulse design and optimization. We focus on the benefits of angle-axis parameters by considering a multipole operator expansion of the rotation operator D(Phi, n), and a Clebsch-Gordan expansion of the rotation matrices D(MM')(J)(Phi, n). Each of the coefficients in the Clebsch-Gordan expansion is proportional to the product of a spherical harmonic of the vector n specifying the axis of rotation, Y(lambdamu)(n), with a fixed function of the rotation angle Phi, a Gegenbauer polynomial C(2J-lambda)(lambda+1)(cosPhi/2). Several application examples demonstrate that this Clebsch-Gordan expansion gives easy and direct access to many of the parameters of experimental interest, including coherence order changes (isolated in the Clebsch-Gordan coefficients), and rotation angle (isolated in the Gegenbauer polynomials).

Rendering of HDR content on LDR displays: an objective approach

NASA Astrophysics Data System (ADS)

Krasula, Lukáš; Narwaria, Manish; Fliegel, Karel; Le Callet, Patrick

2015-09-01

Dynamic range compression (or tone mapping) of HDR content is an essential step towards rendering it on traditional LDR displays in a meaningful way. This is however non-trivial and one of the reasons is that tone mapping operators (TMOs) usually need content-specific parameters to achieve the said goal. While subjective TMO parameter adjustment is the most accurate, it may not be easily deployable in many practical applications. Its subjective nature can also influence the comparison of different operators. Thus, there is a need for objective TMO parameter selection to automate the rendering process. To that end, we investigate into a new objective method for TMO parameters optimization. Our method is based on quantification of contrast reversal and naturalness. As an important advantage, it does not require any prior knowledge about the input HDR image and works independently on the used TMO. Experimental results using a variety of HDR images and several popular TMOs demonstrate the value of our method in comparison to default TMO parameter settings.

Construction of Pancreatic Cancer Classifier Based on SVM Optimized by Improved FOA

PubMed Central

Ma, Xiaoqi

2015-01-01

A novel method is proposed to establish the pancreatic cancer classifier. Firstly, the concept of quantum and fruit fly optimal algorithm (FOA) are introduced, respectively. Then FOA is improved by quantum coding and quantum operation, and a new smell concentration determination function is defined. Finally, the improved FOA is used to optimize the parameters of support vector machine (SVM) and the classifier is established by optimized SVM. In order to verify the effectiveness of the proposed method, SVM and other classification methods have been chosen as the comparing methods. The experimental results show that the proposed method can improve the classifier performance and cost less time. PMID:26543867

Exact solution for an optimal impermeable parachute problem

NASA Astrophysics Data System (ADS)

Lupu, Mircea; Scheiber, Ernest

2002-10-01

In the paper there are solved direct and inverse boundary problems and analytical solutions are obtained for optimization problems in the case of some nonlinear integral operators. It is modeled the plane potential flow of an inviscid, incompressible and nonlimited fluid jet, witch encounters a symmetrical, curvilinear obstacle--the deflector of maximal drag. There are derived integral singular equations, for direct and inverse problems and the movement in the auxiliary canonical half-plane is obtained. Next, the optimization problem is solved in an analytical manner. The design of the optimal airfoil is performed and finally, numerical computations concerning the drag coefficient and other geometrical and aerodynamical parameters are carried out. This model corresponds to the Helmholtz impermeable parachute problem.

Program manual for ASTOP, an Arbitrary space trajectory optimization program

NASA Technical Reports Server (NTRS)

Horsewood, J. L.

1974-01-01

The ASTOP program (an Arbitrary Space Trajectory Optimization Program) designed to generate optimum low-thrust trajectories in an N-body field while satisfying selected hardware and operational constraints is presented. The trajectory is divided into a number of segments or arcs over which the control is held constant. This constant control over each arc is optimized using a parameter optimization scheme based on gradient techniques. A modified Encke formulation of the equations of motion is employed. The program provides a wide range of constraint, end conditions, and performance index options. The basic approach is conducive to future expansion of features such as the incorporation of new constraints and the addition of new end conditions.

Optimization Strategies for Bruch's Membrane Opening Minimum Rim Area Calculation: Sequential versus Simultaneous Minimization.

PubMed

Enders, Philip; Adler, Werner; Schaub, Friederike; Hermann, Manuel M; Diestelhorst, Michael; Dietlein, Thomas; Cursiefen, Claus; Heindl, Ludwig M

2017-10-24

To compare a simultaneously optimized continuous minimum rim surface parameter between Bruch's membrane opening (BMO) and the internal limiting membrane to the standard sequential minimization used for calculating the BMO minimum rim area in spectral domain optical coherence tomography (SD-OCT). In this case-control, cross-sectional study, 704 eyes of 445 participants underwent SD-OCT of the optic nerve head (ONH), visual field testing, and clinical examination. Globally and clock-hour sector-wise optimized BMO-based minimum rim area was calculated independently. Outcome parameters included BMO-globally optimized minimum rim area (BMO-gMRA) and sector-wise optimized BMO-minimum rim area (BMO-MRA). BMO area was 1.89 ± 0.05 mm 2 . Mean global BMO-MRA was 0.97 ± 0.34 mm 2 , mean global BMO-gMRA was 1.01 ± 0.36 mm 2 . Both parameters correlated with r = 0.995 (P < 0.001); mean difference was 0.04 mm 2 (P < 0.001). In all sectors, parameters differed by 3.0-4.2%. In receiver operating characteristics, the calculated area under the curve (AUC) to differentiate glaucoma was 0.873 for BMO-MRA, compared to 0.866 for BMO-gMRA (P = 0.004). Among ONH sectors, the temporal inferior location showed the highest AUC. Optimization strategies to calculate BMO-based minimum rim area led to significantly different results. Imposing an additional adjacency constraint within calculation of BMO-MRA does not improve diagnostic power. Global and temporal inferior BMO-MRA performed best in differentiating glaucoma patients.

Evolutionary design optimization of traffic signals applied to Quito city.

PubMed

Armas, Rolando; Aguirre, Hernán; Daolio, Fabio; Tanaka, Kiyoshi

2017-01-01

This work applies evolutionary computation and machine learning methods to study the transportation system of Quito from a design optimization perspective. It couples an evolutionary algorithm with a microscopic transport simulator and uses the outcome of the optimization process to deepen our understanding of the problem and gain knowledge about the system. The work focuses on the optimization of a large number of traffic lights deployed on a wide area of the city and studies their impact on travel time, emissions and fuel consumption. An evolutionary algorithm with specialized mutation operators is proposed to search effectively in large decision spaces, evolving small populations for a short number of generations. The effects of the operators combined with a varying mutation schedule are studied, and an analysis of the parameters of the algorithm is also included. In addition, hierarchical clustering is performed on the best solutions found in several runs of the algorithm. An analysis of signal clusters and their geolocation, estimation of fuel consumption, spatial analysis of emissions, and an analysis of signal coordination provide an overall picture of the systemic effects of the optimization process.

Evolutionary design optimization of traffic signals applied to Quito city

PubMed Central

2017-01-01

This work applies evolutionary computation and machine learning methods to study the transportation system of Quito from a design optimization perspective. It couples an evolutionary algorithm with a microscopic transport simulator and uses the outcome of the optimization process to deepen our understanding of the problem and gain knowledge about the system. The work focuses on the optimization of a large number of traffic lights deployed on a wide area of the city and studies their impact on travel time, emissions and fuel consumption. An evolutionary algorithm with specialized mutation operators is proposed to search effectively in large decision spaces, evolving small populations for a short number of generations. The effects of the operators combined with a varying mutation schedule are studied, and an analysis of the parameters of the algorithm is also included. In addition, hierarchical clustering is performed on the best solutions found in several runs of the algorithm. An analysis of signal clusters and their geolocation, estimation of fuel consumption, spatial analysis of emissions, and an analysis of signal coordination provide an overall picture of the systemic effects of the optimization process. PMID:29236733

Nonlinear Burn Control and Operating Point Optimization in ITER

NASA Astrophysics Data System (ADS)

Boyer, Mark; Schuster, Eugenio

2013-10-01

Control of the fusion power through regulation of the plasma density and temperature will be essential for achieving and maintaining desired operating points in fusion reactors and burning plasma experiments like ITER. In this work, a volume averaged model for the evolution of the density of energy, deuterium and tritium fuel ions, alpha-particles, and impurity ions is used to synthesize a multi-input multi-output nonlinear feedback controller for stabilizing and modulating the burn condition. Adaptive control techniques are used to account for uncertainty in model parameters, including particle confinement times and recycling rates. The control approach makes use of the different possible methods for altering the fusion power, including adjusting the temperature through auxiliary heating, modulating the density and isotopic mix through fueling, and altering the impurity density through impurity injection. Furthermore, a model-based optimization scheme is proposed to drive the system as close as possible to desired fusion power and temperature references. Constraints are considered in the optimization scheme to ensure that, for example, density and beta limits are avoided, and that optimal operation is achieved even when actuators reach saturation. Supported by the NSF CAREER award program (ECCS-0645086).

Optimal architectures for long distance quantum communication.

PubMed

Muralidharan, Sreraman; Li, Linshu; Kim, Jungsang; Lütkenhaus, Norbert; Lukin, Mikhail D; Jiang, Liang

2016-02-15

Despite the tremendous progress of quantum cryptography, efficient quantum communication over long distances (≥ 1000 km) remains an outstanding challenge due to fiber attenuation and operation errors accumulated over the entire communication distance. Quantum repeaters (QRs), as a promising approach, can overcome both photon loss and operation errors, and hence significantly speedup the communication rate. Depending on the methods used to correct loss and operation errors, all the proposed QR schemes can be classified into three categories (generations). Here we present the first systematic comparison of three generations of quantum repeaters by evaluating the cost of both temporal and physical resources, and identify the optimized quantum repeater architecture for a given set of experimental parameters for use in quantum key distribution. Our work provides a roadmap for the experimental realizations of highly efficient quantum networks over transcontinental distances.

Optimal architectures for long distance quantum communication

PubMed Central

Muralidharan, Sreraman; Li, Linshu; Kim, Jungsang; Lütkenhaus, Norbert; Lukin, Mikhail D.; Jiang, Liang

2016-01-01

Despite the tremendous progress of quantum cryptography, efficient quantum communication over long distances (≥1000 km) remains an outstanding challenge due to fiber attenuation and operation errors accumulated over the entire communication distance. Quantum repeaters (QRs), as a promising approach, can overcome both photon loss and operation errors, and hence significantly speedup the communication rate. Depending on the methods used to correct loss and operation errors, all the proposed QR schemes can be classified into three categories (generations). Here we present the first systematic comparison of three generations of quantum repeaters by evaluating the cost of both temporal and physical resources, and identify the optimized quantum repeater architecture for a given set of experimental parameters for use in quantum key distribution. Our work provides a roadmap for the experimental realizations of highly efficient quantum networks over transcontinental distances. PMID:26876670

Optimal architectures for long distance quantum communication

NASA Astrophysics Data System (ADS)

Muralidharan, Sreraman; Li, Linshu; Kim, Jungsang; Lütkenhaus, Norbert; Lukin, Mikhail D.; Jiang, Liang

2016-02-01

Despite the tremendous progress of quantum cryptography, efficient quantum communication over long distances (≥1000 km) remains an outstanding challenge due to fiber attenuation and operation errors accumulated over the entire communication distance. Quantum repeaters (QRs), as a promising approach, can overcome both photon loss and operation errors, and hence significantly speedup the communication rate. Depending on the methods used to correct loss and operation errors, all the proposed QR schemes can be classified into three categories (generations). Here we present the first systematic comparison of three generations of quantum repeaters by evaluating the cost of both temporal and physical resources, and identify the optimized quantum repeater architecture for a given set of experimental parameters for use in quantum key distribution. Our work provides a roadmap for the experimental realizations of highly efficient quantum networks over transcontinental distances.

Combinatorial optimization problem solution based on improved genetic algorithm

NASA Astrophysics Data System (ADS)

Zhang, Peng

2017-08-01

Traveling salesman problem (TSP) is a classic combinatorial optimization problem. It is a simplified form of many complex problems. In the process of study and research, it is understood that the parameters that affect the performance of genetic algorithm mainly include the quality of initial population, the population size, and crossover probability and mutation probability values. As a result, an improved genetic algorithm for solving TSP problems is put forward. The population is graded according to individual similarity, and different operations are performed to different levels of individuals. In addition, elitist retention strategy is adopted at each level, and the crossover operator and mutation operator are improved. Several experiments are designed to verify the feasibility of the algorithm. Through the experimental results analysis, it is proved that the improved algorithm can improve the accuracy and efficiency of the solution.

Optimization of a pressure control valve for high power automatic transmission considering stability

NASA Astrophysics Data System (ADS)

Jian, Hongchao; Wei, Wei; Li, Hongcai; Yan, Qingdong

2018-02-01

The pilot-operated electrohydraulic clutch-actuator system is widely utilized by high power automatic transmission because of the demand of large flowrate and the excellent pressure regulating capability. However, a self-excited vibration induced by the inherent non-linear characteristics of valve spool motion coupled with the fluid dynamics can be generated during the working state of hydraulic systems due to inappropriate system parameters, which causes sustaining instability in the system and leads to unexpected performance deterioration and hardware damage. To ensure a stable and fast response performance of the clutch actuator system, an optimal design method for the pressure control valve considering stability is proposed in this paper. A non-linear dynamic model of the clutch actuator system is established based on the motion of the valve spool and coupling fluid dynamics in the system. The stability boundary in the parameter space is obtained by numerical stability analysis. Sensitivity of the stability boundary and output pressure response time corresponding to the valve parameters are identified using design of experiment (DOE) approach. The pressure control valve is optimized using particle swarm optimization (PSO) algorithm with the stability boundary as constraint. The simulation and experimental results reveal that the optimization method proposed in this paper helps in improving the response characteristics while ensuring the stability of the clutch actuator system during the entire gear shift process.

Effect of parameters in moving average method for event detection enhancement using phase sensitive OTDR

NASA Astrophysics Data System (ADS)

Kwon, Yong-Seok; Naeem, Khurram; Jeon, Min Yong; Kwon, Il-bum

2017-04-01

We analyze the relations of parameters in moving average method to enhance the event detectability of phase sensitive optical time domain reflectometer (OTDR). If the external events have unique frequency of vibration, then the control parameters of moving average method should be optimized in order to detect these events efficiently. A phase sensitive OTDR was implemented by a pulsed light source, which is composed of a laser diode, a semiconductor optical amplifier, an erbium-doped fiber amplifier, a fiber Bragg grating filter, and a light receiving part, which has a photo-detector and high speed data acquisition system. The moving average method is operated with the control parameters: total number of raw traces, M, number of averaged traces, N, and step size of moving, n. The raw traces are obtained by the phase sensitive OTDR with sound signals generated by a speaker. Using these trace data, the relation of the control parameters is analyzed. In the result, if the event signal has one frequency, then the optimal values of N, n are existed to detect the event efficiently.

Design of experiments to optimize an in vitro cast to predict human nasal drug deposition.

PubMed

Shah, Samir A; Dickens, Colin J; Ward, David J; Banaszek, Anna A; George, Chris; Horodnik, Walter

2014-02-01

Previous studies showed nasal spray in vitro tests cannot predict in vivo deposition, pharmacokinetics, or pharmacodynamics. This challenge makes it difficult to assess deposition achieved with new technologies delivering to the therapeutically beneficial posterior nasal cavity. In this study, we determined best parameters for using a regionally divided nasal cast to predict deposition. Our study used a model suspension and a design of experiments to produce repeatable deposition results that mimic nasal deposition patterns of nasal suspensions from the literature. The seven-section (the nozzle locator, nasal vestibule, front turbinate, rear turbinate, olfactory region, nasopharynx, and throat filter) nylon nasal cast was based on computed tomography images of healthy humans. It was coated with a glycerol/Brij-35 solution to mimic mucus. After assembling and orienting, airflow was applied and nasal spray containing a model suspension was sprayed. After disassembling the cast, drug depositing in each section was assayed by HPLC. The success criteria for optimal settings were based on nine in vivo studies in the literature. The design of experiments included exploratory and half factorial screening experiments to identify variables affecting deposition (angles, airflow, and airflow time), optimization experiments, and then repeatability and reproducibility experiments. We found tilt angle and airflow time after actuation affected deposition the most. The optimized settings were flow rate of 16 L/min, postactuation flow time of 12 sec, a tilt angle of 23°, nozzle angles of 0°, and actuation speed of 5 cm/sec. Neither cast nor operator caused significant variation of results. We determined cast parameters to produce results resembling suspension nasal sprays in the literature. The results were repeatable and unaffected by operator or cast. These nasal spray parameters could be used to assess deposition from new devices or formulations. For human deposition studies using radiolabeled formulations, this cast could show that radiolabel deposition represents drug deposition. Our methods could also be used to optimize settings for other casts.

Optimized Biasing of Pump Laser Diodes in a Highly Reliable Metrology Source for Long-Duration Space Missions

NASA Technical Reports Server (NTRS)

Poberezhskiy, Ilya Y; Chang, Daniel H.; Erlig, Herman

2011-01-01

Optical metrology system reliability during a prolonged space mission is often limited by the reliability of pump laser diodes. We developed a metrology laser pump module architecture that meets NASA SIM Lite instrument optical power and reliability requirements by combining the outputs of multiple single-mode pump diodes in a low-loss, high port count fiber coupler. We describe Monte-Carlo simulations used to calculate the reliability of the laser pump module and introduce a combined laser farm aging parameter that serves as a load-sharing optimization metric. Employing these tools, we select pump module architecture, operating conditions, biasing approach and perform parameter sensitivity studies to investigate the robustness of the obtained solution.

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.

Analysis of noise emitted from diesel engines

NASA Astrophysics Data System (ADS)

Narayan, S.

2015-12-01

In this work combustion noise produced in diesel engines has been investigated. In order to reduce the exhaust emissions various injection parameters need to be studied and optimized. The noise has been investigated by mean of data obtained from cylinder pressure measurements using piezo electric transducers and microphones on a dual cylinder diesel engine test rig. The engine was run under various operating conditions varying various injection parameters to investigate the effects of noise emissions under various testing conditions.

Approaches in highly parameterized inversion—PEST++ Version 3, a Parameter ESTimation and uncertainty analysis software suite optimized for large environmental models

USGS Publications Warehouse

Welter, David E.; White, Jeremy T.; Hunt, Randall J.; Doherty, John E.

2015-09-18

The PEST++ Version 3 software suite can be compiled for Microsoft Windows®4 and Linux®5 operating systems; the source code is available in a Microsoft Visual Studio®6 2013 solution; Linux Makefiles are also provided. PEST++ Version 3 continues to build a foundation for an open-source framework capable of producing robust and efficient parameter estimation tools for large environmental models.

Statistically Optimized Inversion Algorithm for Enhanced Retrieval of Aerosol Properties from Spectral Multi-Angle Polarimetric Satellite Observations

NASA Technical Reports Server (NTRS)

Dubovik, O; Herman, M.; Holdak, A.; Lapyonok, T.; Taure, D.; Deuze, J. L.; Ducos, F.; Sinyuk, A.

2011-01-01

The proposed development is an attempt to enhance aerosol retrieval by emphasizing statistical optimization in inversion of advanced satellite observations. This optimization concept improves retrieval accuracy relying on the knowledge of measurement error distribution. Efficient application of such optimization requires pronounced data redundancy (excess of the measurements number over number of unknowns) that is not common in satellite observations. The POLDER imager on board the PARASOL microsatellite registers spectral polarimetric characteristics of the reflected atmospheric radiation at up to 16 viewing directions over each observed pixel. The completeness of such observations is notably higher than for most currently operating passive satellite aerosol sensors. This provides an opportunity for profound utilization of statistical optimization principles in satellite data inversion. The proposed retrieval scheme is designed as statistically optimized multi-variable fitting of all available angular observations obtained by the POLDER sensor in the window spectral channels where absorption by gas is minimal. The total number of such observations by PARASOL always exceeds a hundred over each pixel and the statistical optimization concept promises to be efficient even if the algorithm retrieves several tens of aerosol parameters. Based on this idea, the proposed algorithm uses a large number of unknowns and is aimed at retrieval of extended set of parameters affecting measured radiation.

Machine Learning Approach to Optimizing Combined Stimulation and Medication Therapies for Parkinson's Disease.

PubMed

Shamir, Reuben R; Dolber, Trygve; Noecker, Angela M; Walter, Benjamin L; McIntyre, Cameron C

2015-01-01

Deep brain stimulation (DBS) of the subthalamic region is an established therapy for advanced Parkinson's disease (PD). However, patients often require time-intensive post-operative management to balance their coupled stimulation and medication treatments. Given the large and complex parameter space associated with this task, we propose that clinical decision support systems (CDSS) based on machine learning algorithms could assist in treatment optimization. Develop a proof-of-concept implementation of a CDSS that incorporates patient-specific details on both stimulation and medication. Clinical data from 10 patients, and 89 post-DBS surgery visits, were used to create a prototype CDSS. The system was designed to provide three key functions: (1) information retrieval; (2) visualization of treatment, and; (3) recommendation on expected effective stimulation and drug dosages, based on three machine learning methods that included support vector machines, Naïve Bayes, and random forest. Measures of medication dosages, time factors, and symptom-specific pre-operative response to levodopa were significantly correlated with post-operative outcomes (P < 0.05) and their effect on outcomes was of similar magnitude to that of DBS. Using those results, the combined machine learning algorithms were able to accurately predict 86% (12/14) of the motor improvement scores at one year after surgery. Using patient-specific details, an appropriately parameterized CDSS could help select theoretically optimal DBS parameter settings and medication dosages that have potential to improve the clinical management of PD patients. Copyright © 2015 Elsevier Inc. All rights reserved.

Analysis of material particle motion and optimizing parameters of vibration of two-mass GZS vibratory feeder

NASA Astrophysics Data System (ADS)

Nguyen, Van Xo; Golikov, N. S.

2018-05-01

The structure and kinematics of the two-mass GZS vibratory feeder operation are considered. It is established that the movement of the material's particles on the feeder surface determines its capacity. The development and analysis of the mathematical model of material's particle movement on the two-mass GZS vibratory feeder surface are shown. The results of Matlab optimization of material particles velocity function are given that allows setting rational kinematics of the feeder.

Optimal Synthesis of the Joint Unitary Evolutions

NASA Astrophysics Data System (ADS)

Wei, Hai-Rui; Alsaedi, Ahmed; Hobiny, Aatef; Deng, Fu-Guo; Hu, Hui; Zhang, Dun

2018-07-01

Joint unitary operations play a central role in quantum communication and computation. We give a quantum circuit for implementing a type of unconstructed useful joint unitary evolutions in terms of controlled-NOT (CNOT) gates and single-qubit rotations. Our synthesis is optimal and possible in experiment. Two CNOT gates and seven R x , R y or R z rotations are required for our synthesis, and the arbitrary parameter contained in the evolutions can be controlled by local Hamiltonian or external fields.

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

Sivak, David; Crooks, Gavin

A fundamental problem in modern thermodynamics is how a molecular-scale machine performs useful work, while operating away from thermal equilibrium without excessive dissipation. To this end, we derive a friction tensor that induces a Riemannian manifold on the space of thermodynamic states. Within the linear-response regime, this metric structure controls the dissipation of finite-time transformations, and bestows optimal protocols with many useful properties. We discuss the connection to the existing thermodynamic length formalism, and demonstrate the utility of this metric by solving for optimal control parameter protocols in a simple nonequilibrium model.

Optimal Synthesis of the Joint Unitary Evolutions

NASA Astrophysics Data System (ADS)

Wei, Hai-Rui; Alsaedi, Ahmed; Hobiny, Aatef; Deng, Fu-Guo; Hu, Hui; Zhang, Dun

2018-03-01

Joint unitary operations play a central role in quantum communication and computation. We give a quantum circuit for implementing a type of unconstructed useful joint unitary evolutions in terms of controlled-NOT (CNOT) gates and single-qubit rotations. Our synthesis is optimal and possible in experiment. Two CNOT gates and seven R x , R y or R z rotations are required for our synthesis, and the arbitrary parameter contained in the evolutions can be controlled by local Hamiltonian or external fields.

Multivariate modelling of prostate cancer combining magnetic resonance derived T2, diffusion, dynamic contrast-enhanced and spectroscopic parameters.

PubMed

Riches, S F; Payne, G S; Morgan, V A; Dearnaley, D; Morgan, S; Partridge, M; Livni, N; Ogden, C; deSouza, N M

2015-05-01

The objectives are determine the optimal combination of MR parameters for discriminating tumour within the prostate using linear discriminant analysis (LDA) and to compare model accuracy with that of an experienced radiologist. Multiparameter MRIs in 24 patients before prostatectomy were acquired. Tumour outlines from whole-mount histology, T2-defined peripheral zone (PZ), and central gland (CG) were superimposed onto slice-matched parametric maps. T2, Apparent Diffusion Coefficient, initial area under the gadolinium curve, vascular parameters (K(trans),Kep,Ve), and (choline+polyamines+creatine)/citrate were compared between tumour and non-tumour tissues. Receiver operating characteristic (ROC) curves determined sensitivity and specificity at spectroscopic voxel resolution and per lesion, and LDA determined the optimal multiparametric model for identifying tumours. Accuracy was compared with an expert observer. Tumours were significantly different from PZ and CG for all parameters (all p < 0.001). Area under the ROC curve for discriminating tumour from non-tumour was significantly greater (p < 0.001) for the multiparametric model than for individual parameters; at 90 % specificity, sensitivity was 41 % (MRSI voxel resolution) and 59 % per lesion. At this specificity, an expert observer achieved 28 % and 49 % sensitivity, respectively. The model was more accurate when parameters from all techniques were included and performed better than an expert observer evaluating these data. • The combined model increases diagnostic accuracy in prostate cancer compared with individual parameters • The optimal combined model includes parameters from diffusion, spectroscopy, perfusion, and anatominal MRI • The computed model improves tumour detection compared to an expert viewing parametric maps.

Optimized dispatch in a first-principles concentrating solar power production model

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

Wagner, Michael J.; Newman, Alexandra M.; Hamilton, William T.

Concentrating solar power towers, which include a steam-Rankine cycle with molten salt thermal energy storage, is an emerging technology whose maximum effectiveness relies on an optimal operational and dispatch policy. Given parameters such as start-up and shut-down penalties, expected electricity price profiles, solar availability, and system interoperability requirements, this paper seeks a profit-maximizing solution that determines start-up and shut-down times for the power cycle and solar receiver, and the times at which to dispatch stored and instantaneous quantities of energy over a 48-h horizon at hourly fidelity. The mixed-integer linear program (MIP) is subject to constraints including: (i) minimum andmore » maximum rates of start-up and shut-down, (ii) energy balance, including energetic state of the system as a whole and its components, (iii) logical rules governing the operational modes of the power cycle and solar receiver, and (iv) operational consistency between time periods. The novelty in this work lies in the successful integration of a dispatch optimization model into a detailed techno-economic analysis tool, specifically, the National Renewable Energy Laboratory's System Advisor Model (SAM). The MIP produces an optimized operating strategy, historically determined via a heuristic. Using several market electricity pricing profiles, we present comparative results for a system with and without dispatch optimization, indicating that dispatch optimization can improve plant profitability by 5-20% and thereby alter the economics of concentrating solar power technology. While we examine a molten salt power tower system, this analysis is equally applicable to the more mature concentrating solar parabolic trough system with thermal energy storage.« less

D-Optimal Experimental Design for Contaminant Source Identification

NASA Astrophysics Data System (ADS)

Sai Baba, A. K.; Alexanderian, A.

2016-12-01

Contaminant source identification seeks to estimate the release history of a conservative solute given point concentration measurements at some time after the release. This can be mathematically expressed as an inverse problem, with a linear observation operator or a parameter-to-observation map, which we tackle using a Bayesian approach. Acquisition of experimental data can be laborious and expensive. The goal is to control the experimental parameters - in our case, the sparsity of the sensors, to maximize the information gain subject to some physical or budget constraints. This is known as optimal experimental design (OED). D-optimal experimental design seeks to maximize the expected information gain, and has long been considered the gold standard in the statistics community. Our goal is to develop scalable methods for D-optimal experimental designs involving large-scale PDE constrained problems with high-dimensional parameter fields. A major challenge for the OED, is that a nonlinear optimization algorithm for the D-optimality criterion requires repeated evaluation of objective function and gradient involving the determinant of large and dense matrices - this cost can be prohibitively expensive for applications of interest. We propose novel randomized matrix techniques that bring down the computational costs of the objective function and gradient evaluations by several orders of magnitude compared to the naive approach. The effect of randomized estimators on the accuracy and the convergence of the optimization solver will be discussed. The features and benefits of our new approach will be demonstrated on a challenging model problem from contaminant source identification involving the inference of the initial condition from spatio-temporal observations in a time-dependent advection-diffusion problem.

MASTOS: Mammography Simulation Tool for design Optimization Studies.

PubMed

Spyrou, G; Panayiotakis, G; Tzanakos, G

2000-01-01

Mammography is a high quality imaging technique for the detection of breast lesions, which requires dedicated equipment and optimum operation. The design parameters of a mammography unit have to be decided and evaluated before the construction of such a high cost of apparatus. The optimum operational parameters also must be defined well before the real breast examination. MASTOS is a software package, based on Monte Carlo methods, that is designed to be used as a simulation tool in mammography. The input consists of the parameters that have to be specified when using a mammography unit, and also the parameters specifying the shape and composition of the breast phantom. In addition, the input may specify parameters needed in the design of a new mammographic apparatus. The main output of the simulation is a mammographic image and calculations of various factors that describe the image quality. The Monte Carlo simulation code is PC-based and is driven by an outer shell of a graphical user interface. The entire software package is a simulation tool for mammography and can be applied in basic research and/or in training in the fields of medical physics and biomedical engineering as well as in the performance evaluation of new designs of mammography units and in the determination of optimum standards for the operational parameters of a mammography unit.

Experimental Results of the EU ITER Prototype Gyrotrons

NASA Astrophysics Data System (ADS)

Gantenbein, G.; Albajar, F.; Alberti, S.; Avramidis, K.; Bin, W.; Bonicelli, T.; Bruschi, A.; Chelis, J.; Fanale, F.; Legrand, F.; Hermann, V.; Hogge, J.-P.; Illy, S.; Ioannidis, Z. C.; Jin, J.; Jelonnek, J.; Kasparek, W.; Latsas, G. P.; Lechte, C.; Lontano, M.; Pagonakis, I. G.; Rzesnicki, T.; Schlatter, C.; Schmid, M.; Tigelis, I. G.; Thumm, M.; Tran, M. Q.; Vomvoridis, J. L.; Zein, A.; Zisis, A.

2017-10-01

The European 1 MW, 170 GHz CW industrial prototype gyrotron for ECRH&CD on ITER was under test at the KIT test facility during 2016. In order to optimize the gyrotron operation, the tube was thoroughly tested in the short-pulse regime, with pulse lengths below 10 ms, for a wide range of operational parameters. The operation was extended to longer pulses with a duration of up to 180 s. In this work we present in detail the achievements and the challenges that were faced during the long-pulse experimental campaign.

Coupling strategies for coherent operation of quantum cascade ring laser arrays

NASA Astrophysics Data System (ADS)

Schwarzer, Clemens; Yao, Y.; Mujagić, E.; Ahn, S.; Schrenk, W.; Chen, J.; Gmachl, C.; Strasser, G.

2011-12-01

We report the design, fabrication and operation of coherently coupled ring cavity surface emitting quantum cascade lasers, emitting at wavelength around 8 μm. Special emphasis is placed on the evaluation of optimal coupling approaches and corresponding parameters. Evanescent field coupling as well as direct coupling where both devices are physically connected is presented. Furthermore, exploiting the Vernier-effect was used to obtain enhanced mode selectivity and robust coherent coupling of two ring-type quantum cascade lasers. Investigations were performed at pulsed room-temperature operation.

Characterization of a Raman spectroscopy probe system for intraoperative brain tissue classification

PubMed Central

Desroches, Joannie; Jermyn, Michael; Mok, Kelvin; Lemieux-Leduc, Cédric; Mercier, Jeanne; St-Arnaud, Karl; Urmey, Kirk; Guiot, Marie-Christine; Marple, Eric; Petrecca, Kevin; Leblond, Frédéric

2015-01-01

A detailed characterization study is presented of a Raman spectroscopy system designed to maximize the volume of resected cancer tissue in glioma surgery based on in vivo molecular tissue characterization. It consists of a hand-held probe system measuring spectrally resolved inelastically scattered light interacting with tissue, designed and optimized for in vivo measurements. Factors such as linearity of the signal with integration time and laser power, and their impact on signal to noise ratio, are studied leading to optimal data acquisition parameters. The impact of ambient light sources in the operating room is assessed and recommendations made for optimal operating conditions. In vivo Raman spectra of normal brain, cancer and necrotic tissue were measured in 10 patients, demonstrating that real-time inelastic scattering measurements can distinguish necrosis from vital tissue (including tumor and normal brain tissue) with an accuracy of 87%, a sensitivity of 84% and a specificity of 89%. PMID:26203368

Maximization of fructose esters synthesis by response surface methodology.

PubMed

Neta, Nair Sampaio; Peres, António M; Teixeira, José A; Rodrigues, Ligia R

2011-07-01

Enzymatic synthesis of fructose fatty acid ester was performed in organic solvent media, using a purified lipase from Candida antartica B immobilized in acrylic resin. Response surface methodology with a central composite rotatable design based on five levels was implemented to optimize three experimental operating conditions (temperature, agitation and reaction time). A statistical significant cubic model was established. Temperature and reaction time were found to be the most significant parameters. The optimum operational conditions for maximizing the synthesis of fructose esters were 57.1°C, 100 rpm and 37.8 h. The model was validated in the identified optimal conditions to check its adequacy and accuracy, and an experimental esterification percentage of 88.4% (±0.3%) was obtained. These results showed that an improvement of the enzymatic synthesis of fructose esters was obtained under the optimized conditions. Copyright © 2011 Elsevier B.V. All rights reserved.

Optimizing the wireless power transfer over MIMO Channels

NASA Astrophysics Data System (ADS)

Wiedmann, Karsten; Weber, Tobias

2017-09-01

In this paper, the optimization of the power transfer over wireless channels having multiple-inputs and multiple-outputs (MIMO) is studied. Therefore, the transmitter, the receiver and the MIMO channel are modeled as multiports. The power transfer efficiency is described by a Rayleigh quotient, which is a function of the channel's scattering parameters and the incident waves from both transmitter and receiver side. This way, the power transfer efficiency can be maximized analytically by solving a generalized eigenvalue problem, which is deduced from the Rayleigh quotient. As a result, the maximum power transfer efficiency achievable over a given MIMO channel is obtained. This maximum can be used as a performance bound in order to benchmark wireless power transfer systems. Furthermore, the optimal operating point which achieves this maximum will be obtained. The optimal operating point will be described by the complex amplitudes of the optimal incident and reflected waves of the MIMO channel. This supports the design of the optimal transmitter and receiver multiports. The proposed method applies for arbitrary MIMO channels, taking transmitter-side and/or receiver-side cross-couplings in both near- and farfield scenarios into consideration. Special cases are briefly discussed in this paper in order to illustrate the method.

Optimizing a reconfigurable material via evolutionary computation

NASA Astrophysics Data System (ADS)

Wilken, Sam; Miskin, Marc Z.; Jaeger, Heinrich M.

2015-08-01

Rapid prototyping by combining evolutionary computation with simulations is becoming a powerful tool for solving complex design problems in materials science. This method of optimization operates in a virtual design space that simulates potential material behaviors and after completion needs to be validated by experiment. However, in principle an evolutionary optimizer can also operate on an actual physical structure or laboratory experiment directly, provided the relevant material parameters can be accessed by the optimizer and information about the material's performance can be updated by direct measurements. Here we provide a proof of concept of such direct, physical optimization by showing how a reconfigurable, highly nonlinear material can be tuned to respond to impact. We report on an entirely computer controlled laboratory experiment in which a 6 ×6 grid of electromagnets creates a magnetic field pattern that tunes the local rigidity of a concentrated suspension of ferrofluid and iron filings. A genetic algorithm is implemented and tasked to find field patterns that minimize the force transmitted through the suspension. Searching within a space of roughly 1010 possible configurations, after testing only 1500 independent trials the algorithm identifies an optimized configuration of layered rigid and compliant regions.

Uncertainty Analysis of Simulated Hydraulic Fracturing

NASA Astrophysics Data System (ADS)

Chen, M.; Sun, Y.; Fu, P.; Carrigan, C. R.; Lu, Z.

2012-12-01

Artificial hydraulic fracturing is being used widely to stimulate production of oil, natural gas, and geothermal reservoirs with low natural permeability. Optimization of field design and operation is limited by the incomplete characterization of the reservoir, as well as the complexity of hydrological and geomechanical processes that control the fracturing. Thus, there are a variety of uncertainties associated with the pre-existing fracture distribution, rock mechanics, and hydraulic-fracture engineering that require evaluation of their impact on the optimized design. In this study, a multiple-stage scheme was employed to evaluate the uncertainty. We first define the ranges and distributions of 11 input parameters that characterize the natural fracture topology, in situ stress, geomechanical behavior of the rock matrix and joint interfaces, and pumping operation, to cover a wide spectrum of potential conditions expected for a natural reservoir. These parameters were then sampled 1,000 times in an 11-dimensional parameter space constrained by the specified ranges using the Latin-hypercube method. These 1,000 parameter sets were fed into the fracture simulators, and the outputs were used to construct three designed objective functions, i.e. fracture density, opened fracture length and area density. Using PSUADE, three response surfaces (11-dimensional) of the objective functions were developed and global sensitivity was analyzed to identify the most sensitive parameters for the objective functions representing fracture connectivity, which are critical for sweep efficiency of the recovery process. The second-stage high resolution response surfaces were constructed with dimension reduced to the number of the most sensitive parameters. An additional response surface with respect to the objective function of the fractal dimension for fracture distributions was constructed in this stage. Based on these response surfaces, comprehensive uncertainty analyses were conducted among input parameters and objective functions. In addition, reduced-order emulation models resulting from this analysis can be used for optimal control of hydraulic fracturing. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Direct modeling parameter signature analysis and failure mode prediction of physical systems using hybrid computer optimization

NASA Technical Reports Server (NTRS)

Drake, R. L.; Duvoisin, P. F.; Asthana, A.; Mather, T. W.

1971-01-01

High speed automated identification and design of dynamic systems, both linear and nonlinear, are discussed. Special emphasis is placed on developing hardware and techniques which are applicable to practical problems. The basic modeling experiment and new results are described. Using the improvements developed successful identification of several systems, including a physical example as well as simulated systems, was obtained. The advantages of parameter signature analysis over signal signature analysis in go-no go testing of operational systems were demonstrated. The feasibility of using these ideas in failure mode prediction in operating systems was also investigated. An improved digital controlled nonlinear function generator was developed, de-bugged, and completely documented.

Piston Bowl Optimization for RCCI Combustion in a Light-Duty Multi-Cylinder Engine

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

Hanson, Reed M; Curran, Scott; Wagner, Robert M

2012-01-01

Reactivity Controlled Compression Ignition (RCCI) is an engine combustion strategy that that produces low NO{sub x} and PM emissions with high thermal efficiency. Previous RCCI research has been investigated in single-cylinder heavy-duty engines. The current study investigates RCCI operation in a light-duty multi-cylinder engine at 3 operating points. These operating points were chosen to cover a range of conditions seen in the US EPA light-duty FTP test. The operating points were chosen by the Ad Hoc working group to simulate operation in the FTP test. The fueling strategy for the engine experiments consisted of in-cylinder fuel blending using port fuel-injectionmore » (PFI) of gasoline and early-cycle, direct-injection (DI) of diesel fuel. At these 3 points, the stock engine configuration is compared to operation with both the original equipment manufacturer (OEM) and custom machined pistons designed for RCCI operation. The pistons were designed with assistance from the KIVA 3V computational fluid dynamics (CFD) code. By using a genetic algorithm optimization, in conjunction with KIVA, the piston bowl profile was optimized for dedicated RCCI operation to reduce unburned fuel emissions and piston bowl surface area. By reducing these parameters, the thermal efficiency of the engine was improved while maintaining low NOx and PM emissions. Results show that with the new piston bowl profile and an optimized injection schedule, RCCI brake thermal efficiency was increased from 37%, with the stock EURO IV configuration, to 40% at the 2,600 rev/min, 6.9 bar BMEP condition, and NOx and PM emissions targets were met without the need for exhaust after-treatment.« less

Constrained multi-objective optimization of storage ring lattices

NASA Astrophysics Data System (ADS)

Husain, Riyasat; Ghodke, A. D.

2018-03-01

The storage ring lattice optimization is a class of constrained multi-objective optimization problem, where in addition to low beam emittance, a large dynamic aperture for good injection efficiency and improved beam lifetime are also desirable. The convergence and computation times are of great concern for the optimization algorithms, as various objectives are to be optimized and a number of accelerator parameters to be varied over a large span with several constraints. In this paper, a study of storage ring lattice optimization using differential evolution is presented. The optimization results are compared with two most widely used optimization techniques in accelerators-genetic algorithm and particle swarm optimization. It is found that the differential evolution produces a better Pareto optimal front in reasonable computation time between two conflicting objectives-beam emittance and dispersion function in the straight section. The differential evolution was used, extensively, for the optimization of linear and nonlinear lattices of Indus-2 for exploring various operational modes within the magnet power supply capabilities.

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 shape optimization of runner blade for Kaplan turbine

NASA Astrophysics Data System (ADS)

Semenova, A.; Chirkov, D.; Lyutov, A.; Chemy, S.; Skorospelov, V.; Pylev, I.

2014-03-01

Automatic runner shape optimization based on extensive CFD analysis proved to be a useful design tool in hydraulic turbomachinery. Previously the authors developed an efficient method for Francis runner optimization. It was successfully applied to the design of several runners with different specific speeds. In present work this method is extended to the task of a Kaplan runner optimization. Despite of relatively simpler blade shape, Kaplan turbines have several features, complicating the optimization problem. First, Kaplan turbines normally operate in a wide range of discharges, thus CFD analysis of each variant of the runner should be carried out for several operation points. Next, due to a high specific speed, draft tube losses have a great impact on the overall turbine efficiency, and thus should be accurately evaluated. Then, the flow in blade tip and hub clearances significantly affects the velocity profile behind the runner and draft tube behavior. All these features are accounted in the present optimization technique. Parameterization of runner blade surface using 24 geometrical parameters is described in details. For each variant of runner geometry steady state three-dimensional turbulent flow computations are carried out in the domain, including wicket gate, runner, draft tube, blade tip and hub clearances. The objectives are maximization of efficiency in best efficiency and high discharge operation points, with simultaneous minimization of cavitation area on the suction side of the blade. Multiobjective genetic algorithm is used for the solution of optimization problem, requiring the analysis of several thousands of runner variants. The method is applied to optimization of runner shape for several Kaplan turbines with different heads.

Learning the manifold of quality ultrasound acquisition.

PubMed

El-Zehiry, Noha; Yan, Michelle; Good, Sara; Fang, Tong; Zhou, S Kevin; Grady, Leo

2013-01-01

Ultrasound acquisition is a challenging task that requires simultaneous adjustment of several acquisition parameters (the depth, the focus, the frequency and its operation mode). If the acquisition parameters are not properly chosen, the resulting image will have a poor quality and will degrade the patient diagnosis and treatment workflow. Several hardware-based systems for autotuning the acquisition parameters have been previously proposed, but these solutions were largely abandoned because they failed to properly account for tissue inhomogeneity and other patient-specific characteristics. Consequently, in routine practice the clinician either uses population-based parameter presets or manually adjusts the acquisition parameters for each patient during the scan. In this paper, we revisit the problem of autotuning the acquisition parameters by taking a completely novel approach and producing a solution based on image analytics. Our solution is inspired by the autofocus capability of conventional digital cameras, but is significantly more challenging because the number of acquisition parameters is large and the determination of "good quality" images is more difficult to assess. Surprisingly, we show that the set of acquisition parameters which produce images that are favored by clinicians comprise a 1D manifold, allowing for a real-time optimization to maximize image quality. We demonstrate our method for acquisition parameter autotuning on several live patients, showing that our system can start with a poor initial set of parameters and automatically optimize the parameters to produce high quality images.

Investigation, sensitivity analysis, and multi-objective optimization of effective parameters on temperature and force in robotic drilling cortical bone.

PubMed

Tahmasbi, Vahid; Ghoreishi, Majid; Zolfaghari, Mojtaba

2017-11-01

The bone drilling process is very prominent in orthopedic surgeries and in the repair of bone fractures. It is also very common in dentistry and bone sampling operations. Due to the complexity of bone and the sensitivity of the process, bone drilling is one of the most important and sensitive processes in biomedical engineering. Orthopedic surgeries can be improved using robotic systems and mechatronic tools. The most crucial problem during drilling is an unwanted increase in process temperature (higher than 47 °C), which causes thermal osteonecrosis or cell death and local burning of the bone tissue. Moreover, imposing higher forces to the bone may lead to breaking or cracking and consequently cause serious damage. In this study, a mathematical second-order linear regression model as a function of tool drilling speed, feed rate, tool diameter, and their effective interactions is introduced to predict temperature and force during the bone drilling process. This model can determine the maximum speed of surgery that remains within an acceptable temperature range. Moreover, for the first time, using designed experiments, the bone drilling process was modeled, and the drilling speed, feed rate, and tool diameter were optimized. Then, using response surface methodology and applying a multi-objective optimization, drilling force was minimized to sustain an acceptable temperature range without damaging the bone or the surrounding tissue. In addition, for the first time, Sobol statistical sensitivity analysis is used to ascertain the effect of process input parameters on process temperature and force. The results show that among all effective input parameters, tool rotational speed, feed rate, and tool diameter have the highest influence on process temperature and force, respectively. The behavior of each output parameters with variation in each input parameter is further investigated. Finally, a multi-objective optimization has been performed considering all the aforementioned parameters. This optimization yielded a set of data that can considerably improve orthopedic osteosynthesis outcomes.

A Computational Framework for Quantifying and Optimizing the Performance of Observational Networks in 4D-Var Data Assimilation

NASA Astrophysics Data System (ADS)

Cioaca, Alexandru

A deep scientific understanding of complex physical systems, such as the atmosphere, can be achieved neither by direct measurements nor by numerical simulations alone. Data assimila- tion is a rigorous procedure to fuse information from a priori knowledge of the system state, the physical laws governing the evolution of the system, and real measurements, all with associated error statistics. Data assimilation produces best (a posteriori) estimates of model states and parameter values, and results in considerably improved computer simulations. The acquisition and use of observations in data assimilation raises several important scientific questions related to optimal sensor network design, quantification of data impact, pruning redundant data, and identifying the most beneficial additional observations. These questions originate in operational data assimilation practice, and have started to attract considerable interest in the recent past. This dissertation advances the state of knowledge in four dimensional variational (4D-Var) data assimilation by developing, implementing, and validating a novel computational framework for estimating observation impact and for optimizing sensor networks. The framework builds on the powerful methodologies of second-order adjoint modeling and the 4D-Var sensitivity equations. Efficient computational approaches for quantifying the observation impact include matrix free linear algebra algorithms and low-rank approximations of the sensitivities to observations. The sensor network configuration problem is formulated as a meta-optimization problem. Best values for parameters such as sensor location are obtained by optimizing a performance criterion, subject to the constraint posed by the 4D-Var optimization. Tractable computational solutions to this "optimization-constrained" optimization problem are provided. The results of this work can be directly applied to the deployment of intelligent sensors and adaptive observations, as well as to reducing the operating costs of measuring networks, while preserving their ability to capture the essential features of the system under consideration.

Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO

PubMed Central

Zhu, Zhichuan; Zhao, Qingdong; Liu, Liwei; Zhang, Lijuan

2018-01-01

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified. PMID:29853983

Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO.

PubMed

Li, Yang; Zhu, Zhichuan; Hou, Alin; Zhao, Qingdong; Liu, Liwei; Zhang, Lijuan

2018-01-01

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified.

A fast Chebyshev method for simulating flexible-wing propulsion

NASA Astrophysics Data System (ADS)

Moore, M. Nicholas J.

2017-09-01

We develop a highly efficient numerical method to simulate small-amplitude flapping propulsion by a flexible wing in a nearly inviscid fluid. We allow the wing's elastic modulus and mass density to vary arbitrarily, with an eye towards optimizing these distributions for propulsive performance. The method to determine the wing kinematics is based on Chebyshev collocation of the 1D beam equation as coupled to the surrounding 2D fluid flow. Through small-amplitude analysis of the Euler equations (with trailing-edge vortex shedding), the complete hydrodynamics can be represented by a nonlocal operator that acts on the 1D wing kinematics. A class of semi-analytical solutions permits fast evaluation of this operator with O (Nlog ⁡ N) operations, where N is the number of collocation points on the wing. This is in contrast to the minimum O (N2) cost of a direct 2D fluid solver. The coupled wing-fluid problem is thus recast as a PDE with nonlocal operator, which we solve using a preconditioned iterative method. These techniques yield a solver of near-optimal complexity, O (Nlog ⁡ N) , allowing one to rapidly search the infinite-dimensional parameter space of all possible material distributions and even perform optimization over this space.

Performance analysis and optimization of an advanced pharmaceutical wastewater treatment plant through a visual basic software tool (PWWT.VB).

PubMed

Pal, Parimal; Thakura, Ritwik; Chakrabortty, Sankha

2016-05-01

A user-friendly, menu-driven simulation software tool has been developed for the first time to optimize and analyze the system performance of an advanced continuous membrane-integrated pharmaceutical wastewater treatment plant. The software allows pre-analysis and manipulation of input data which helps in optimization and shows the software performance visually on a graphical platform. Moreover, the software helps the user to "visualize" the effects of the operating parameters through its model-predicted output profiles. The software is based on a dynamic mathematical model, developed for a systematically integrated forward osmosis-nanofiltration process for removal of toxic organic compounds from pharmaceutical wastewater. The model-predicted values have been observed to corroborate well with the extensive experimental investigations which were found to be consistent under varying operating conditions like operating pressure, operating flow rate, and draw solute concentration. Low values of the relative error (RE = 0.09) and high values of Willmott-d-index (d will = 0.981) reflected a high degree of accuracy and reliability of the software. This software is likely to be a very efficient tool for system design or simulation of an advanced membrane-integrated treatment plant for hazardous wastewater.

Decolorization of Acid Orange 7 by an electric field-assisted modified orifice plate hydrodynamic cavitation system: Optimization of operational parameters.

PubMed

Jung, Kyung-Won; Park, Dae-Seon; Hwang, Min-Jin; Ahn, Kyu-Hong

2015-09-01

In this study, the decolorization of Acid Orange 7 (AO-7) with intensified performance was obtained using hydrodynamic cavitation (HC) combined with an electric field (graphite electrodes). As a preliminary step, various HC systems were compared in terms of decolorization, and, among them, the electric field-assisted modified orifice plate HC (EFM-HC) system exhibited perfect decolorization performance within 40 min of reaction time. Interestingly, when H2O2 was injected into the EFM-HC system as an additional oxidant, the reactor performance gradually decreased as the dosing ratio increased; thus, the remaining experiments were performed without H2O2. Subsequently, an optimization process was conducted using response surface methodology with a Box-Behnken design. The inlet pressure, initial pH, applied voltage, and reaction time were chosen as operational key factors, while decolorization was selected as the response variable. The overall performance revealed that the selected parameters were either slightly interdependent, or had significant interactive effects on the decolorization. In the verification test, complete decolorization was observed under statistically optimized conditions. This study suggests that EFM-HC is a useful method for pretreatment of dye wastewater with positive economic and commercial benefits. Copyright © 2015 Elsevier B.V. All rights reserved.

The Impact of the Condenser on Cytogenetic Image Quality in Digital Microscope System

PubMed Central

Ren, Liqiang; Li, Zheng; Li, Yuhua; Zheng, Bin; Li, Shibo; Chen, Xiaodong; Liu, Hong

2013-01-01

Background: Optimizing operational parameters of the digital microscope system is an important technique to acquire high quality cytogenetic images and facilitate the process of karyotyping so that the efficiency and accuracy of diagnosis can be improved. OBJECTIVE: This study investigated the impact of the condenser on cytogenetic image quality and system working performance using a prototype digital microscope image scanning system. Methods: Both theoretical analysis and experimental validations through objectively evaluating a resolution test chart and subjectively observing large numbers of specimen were conducted. Results: The results show that the optimal image quality and large depth of field (DOF) are simultaneously obtained when the numerical aperture of condenser is set as 60%–70% of the corresponding objective. Under this condition, more analyzable chromosomes and diagnostic information are obtained. As a result, the system shows higher working stability and less restriction for the implementation of algorithms such as autofocusing especially when the system is designed to achieve high throughput continuous image scanning. Conclusions: Although the above quantitative results were obtained using a specific prototype system under the experimental conditions reported in this paper, the presented evaluation methodologies can provide valuable guidelines for optimizing operational parameters in cytogenetic imaging using the high throughput continuous scanning microscopes in clinical practice. PMID:23676284

Sensor-Based Optimized Control of the Full Load Instability in Large Hydraulic Turbines

PubMed Central

Presas, Alexandre; Valero, Carme; Egusquiza, Eduard

2018-01-01

Hydropower plants are of paramount importance for the integration of intermittent renewable energy sources in the power grid. In order to match the energy generated and consumed, Large hydraulic turbines have to work under off-design conditions, which may lead to dangerous unstable operating points involving the hydraulic, mechanical and electrical system. Under these conditions, the stability of the grid and the safety of the power plant itself can be compromised. For many Francis Turbines one of these critical points, that usually limits the maximum output power, is the full load instability. Therefore, these machines usually work far away from this unstable point, reducing the effective operating range of the unit. In order to extend the operating range of the machine, working closer to this point with a reasonable safety margin, it is of paramount importance to monitor and to control relevant parameters of the unit, which have to be obtained with an accurate sensor acquisition strategy. Within the framework of a large EU project, field tests in a large Francis Turbine located in Canada (rated power of 444 MW) have been performed. Many different sensors were used to monitor several working parameters of the unit for all its operating range. Particularly for these tests, more than 80 signals, including ten type of different sensors and several operating signals that define the operating point of the unit, were simultaneously acquired. The present study, focuses on the optimization of the acquisition strategy, which includes type, number, location, acquisition frequency of the sensors and corresponding signal analysis to detect the full load instability and to prevent the unit from reaching this point. A systematic approach to determine this strategy has been followed. It has been found that some indicators obtained with different types of sensors are linearly correlated with the oscillating power. The optimized strategy has been determined based on the correlation characteristics (linearity, sensitivity and reactivity), the simplicity of the installation and the acquisition frequency necessary. Finally, an economic and easy implementable protection system based on the resulting optimized acquisition strategy is proposed. This system, which can be used in a generic Francis turbine with a similar full load instability, permits one to extend the operating range of the unit by working close to the instability with a reasonable safety margin. PMID:29601512

Sensor-Based Optimized Control of the Full Load Instability in Large Hydraulic Turbines.

PubMed

Presas, Alexandre; Valentin, David; Egusquiza, Mònica; Valero, Carme; Egusquiza, Eduard

2018-03-30

Hydropower plants are of paramount importance for the integration of intermittent renewable energy sources in the power grid. In order to match the energy generated and consumed, Large hydraulic turbines have to work under off-design conditions, which may lead to dangerous unstable operating points involving the hydraulic, mechanical and electrical system. Under these conditions, the stability of the grid and the safety of the power plant itself can be compromised. For many Francis Turbines one of these critical points, that usually limits the maximum output power, is the full load instability. Therefore, these machines usually work far away from this unstable point, reducing the effective operating range of the unit. In order to extend the operating range of the machine, working closer to this point with a reasonable safety margin, it is of paramount importance to monitor and to control relevant parameters of the unit, which have to be obtained with an accurate sensor acquisition strategy. Within the framework of a large EU project, field tests in a large Francis Turbine located in Canada (rated power of 444 MW) have been performed. Many different sensors were used to monitor several working parameters of the unit for all its operating range. Particularly for these tests, more than 80 signals, including ten type of different sensors and several operating signals that define the operating point of the unit, were simultaneously acquired. The present study, focuses on the optimization of the acquisition strategy, which includes type, number, location, acquisition frequency of the sensors and corresponding signal analysis to detect the full load instability and to prevent the unit from reaching this point. A systematic approach to determine this strategy has been followed. It has been found that some indicators obtained with different types of sensors are linearly correlated with the oscillating power. The optimized strategy has been determined based on the correlation characteristics (linearity, sensitivity and reactivity), the simplicity of the installation and the acquisition frequency necessary. Finally, an economic and easy implementable protection system based on the resulting optimized acquisition strategy is proposed. This system, which can be used in a generic Francis turbine with a similar full load instability, permits one to extend the operating range of the unit by working close to the instability with a reasonable safety margin.

Advanced interactive display formats for terminal area traffic control

NASA Technical Reports Server (NTRS)

Grunwald, Arthur J.

1996-01-01

This report describes the basic design considerations for perspective air traffic control displays. A software framework has been developed for manual viewing parameter setting (MVPS) in preparation for continued, ongoing developments on automated viewing parameter setting (AVPS) schemes. Two distinct modes of MVPS operations are considered, both of which utilize manipulation pointers imbedded in the three-dimensional scene: (1) direct manipulation of the viewing parameters -- in this mode the manipulation pointers act like the control-input device, through which the viewing parameter changes are made. Part of the parameters are rate controlled, and part of them position controlled. This mode is intended for making fast, iterative small changes in the parameters. (2) Indirect manipulation of the viewing parameters -- this mode is intended primarily for introducing large, predetermined changes in the parameters. Requests for changes in viewing parameter setting are entered manually by the operator by moving viewing parameter manipulation pointers on the screen. The motion of these pointers, which are an integral part of the 3-D scene, is limited to the boundaries of the screen. This arrangement has been chosen in order to preserve the correspondence between the spatial lay-outs of the new and the old viewing parameter setting, a feature which contributes to preventing spatial disorientation of the operator. For all viewing operations, e.g. rotation, translation and ranging, the actual change is executed automatically by the system, through gradual transitions with an exponentially damped, sinusoidal velocity profile, in this work referred to as 'slewing' motions. The slewing functions, which eliminate discontinuities in the viewing parameter changes, are designed primarily for enhancing the operator's impression that he, or she, is dealing with an actually existing physical system, rather than an abstract computer-generated scene. The proposed, continued research efforts will deal with the development of automated viewing parameter setting schemes. These schemes employ an optimization strategy, aimed at identifying the best possible vantage point, from which the air traffic control scene can be viewed for a given traffic situation. They determine whether a change in viewing parameter setting is required and determine the dynamic path along which the change to the new viewing parameter setting should take place.

Development of a web based monitoring system for safety and activity analysis in operating theatres.

PubMed

Frosini, Francesco; Miniati, Roberto; Avezzano, Paolo; Cecconi, Giulio; Dori, Fabrizio; Gentili, Guido Biffi; Belardinelli, Andrea

2016-01-01

The management and the monitoring of the operating rooms on the part of the general management have the objective of optimizing their use and maximizing the internal safety. The expenses owed to their safe use represent, besides reimbursements coming from the surgical activity, important factors for the analysis of the medical facility. Given that it is not possible to reduce the safety, it is necessary to develop supporting systems with the aim to enhance and optimize the use of the rooms. The developed analysis model of the operating rooms in this study is based on the specific performance indicators and allows the effective monitoring of both the parameters that influence the safety (environmental, microbiological parameters) and those that influence the efficiency of the usage (employment rate, delays, necessary formalities, etc.). This allows you to have a systematic dashboard on hand for all of the OTs and, thus, organize the intervention schedules and more appropriate improvements. A monitoring dashboard has been achieved, accessible from any platform and any device, capable of aggregating hospital information. The undertaken organizational modifications, through the use of the dashboard, have allowed for an average annual savings of 29.52 minutes per intervention and increase the use of the ORs of 5%. The increment of the employment rate and the optimization of the operating room have allowed for savings of around $299,88 for every intervention carried out in 2013, corresponding to an annual savings of $343,362,60. Integration dashboards, as the one proposed in this study as a prototype, represent a governance model of economically sustainable healthcare systems capable of guiding the hospital management in the choices and in the implementation of the most efficient organizational modifications.

Automatic Calibration of a Semi-Distributed Hydrologic Model Using Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Bekele, E. G.; Nicklow, J. W.

2005-12-01

Hydrologic simulation models need to be calibrated and validated before using them for operational predictions. Spatially-distributed hydrologic models generally have a large number of parameters to capture the various physical characteristics of a hydrologic system. Manual calibration of such models is a very tedious and daunting task, and its success depends on the subjective assessment of a particular modeler, which includes knowledge of the basic approaches and interactions in the model. In order to alleviate these shortcomings, an automatic calibration model, which employs an evolutionary optimization technique known as Particle Swarm Optimizer (PSO) for parameter estimation, is developed. PSO is a heuristic search algorithm that is inspired by social behavior of bird flocking or fish schooling. The newly-developed calibration model is integrated to the U.S. Department of Agriculture's Soil and Water Assessment Tool (SWAT). SWAT is a physically-based, semi-distributed hydrologic model that was developed to predict the long term impacts of land management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, land use, and management conditions. SWAT was calibrated for streamflow and sediment concentration. The calibration process involves parameter specification, whereby sensitive model parameters are identified, and parameter estimation. In order to reduce the number of parameters to be calibrated, parameterization was performed. The methodology is applied to a demonstration watershed known as Big Creek, which is located in southern Illinois. Application results show the effectiveness of the approach and model predictions are significantly improved.

Algorithm and Software for Calculating Optimal Regimes of the Process Water Supply System at the Kalininskaya NPP{sup 1}

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

Murav’ev, V. P., E-mail: murval@mail.ru; Kochetkov, A. V.; Glazova, E. G.

An algorithm and software for calculating the optimal operating regimes of the process water supply system at the Kalininskaya NPP are described. The parameters of the optimal regimes are determined for time varying meteorological conditions and condensation loads of the NPP. The optimal flow of the cooling water in the turbines is determined computationally; a regime map with the data on the optimal water consumption distribution between the coolers and displaying the regimes with an admissible heat load on the natural cooling lakes is composed. Optimizing the cooling system for a 4000-MW NPP will make it possible to conserve atmore » least 155,000 MW · h of electricity per year. The procedure developed can be used to optimize the process water supply systems of nuclear and thermal power plants.« less

Biogas Production: Microbiology and Technology.

PubMed

Schnürer, Anna

Biogas, containing energy-rich methane, is produced by microbial decomposition of organic material under anaerobic conditions. Under controlled conditions, this process can be used for the production of energy and a nutrient-rich residue suitable for use as a fertilising agent. The biogas can be used for production of heat, electricity or vehicle fuel. Different substrates can be used in the process and, depending on substrate character, various reactor technologies are available. The microbiological process leading to methane production is complex and involves many different types of microorganisms, often operating in close relationships because of the limited amount of energy available for growth. The microbial community structure is shaped by the incoming material, but also by operating parameters such as process temperature. Factors leading to an imbalance in the microbial community can result in process instability or even complete process failure. To ensure stable operation, different key parameters, such as levels of degradation intermediates and gas quality, are often monitored. Despite the fact that the anaerobic digestion process has long been used for industrial production of biogas, many questions need still to be resolved to achieve optimal management and gas yields and to exploit the great energy and nutrient potential available in waste material. This chapter discusses the different aspects that need to be taken into consideration to achieve optimal degradation and gas production, with particular focus on operation management and microbiology.

Parametric optimization of optical signal detectors employing the direct photodetection scheme

NASA Astrophysics Data System (ADS)

Kirakosiants, V. E.; Loginov, V. A.

1984-08-01

The problem of optimization of the optical signal detection scheme parameters is addressed using the concept of a receiver with direct photodetection. An expression is derived which accurately approximates the field of view (FOV) values obtained by a direct computer minimization of the probability of missing a signal; optimum values of the receiver FOV were found for different atmospheric conditions characterized by the number of coherence spots and the intensity fluctuations of a plane wave. It is further pointed out that the criterion presented can be possibly used for parametric optimization of detectors operating in accordance with the Neumann-Pearson criterion.

Restaurant emissions removal by a biofilter with immobilized bacteria*

PubMed Central

Miao, Jian-yu; Zheng, Lian-ying; Guo, Xiao-fen

2005-01-01

Pseudomonas sp. ZD8 isolated from contaminated soil was immobilized with platane wood chips to produce packing materials for a novel biofilter system utilized to control restaurant emissions. The effects of operational parameters including retention time, temperature, and inlet gas concentration on the removal efficiency and elimination capacity were evaluated. Criteria necessary for a scale-up design of the biofilter was established. High and satisfactory level of rapeseed oil smoke removal efficiency was maintained during operation and the optimal retention time was found to be 18 s corresponding to smoke removal efficiency greater than 97%. The optimal inlet rapeseed oil smoke loading was 120 mg/(m3·h) at the upper end of the linear correlation between inlet loading and elimination capacity. PMID:15822160

Restaurant emissions removal by a biofilter with immobilized bacteria.

PubMed

Miao, Jian-Yu; Zheng, Lian-Ying; Guo, Xiao-Fen

2005-05-01

Pseudomonas sp. ZD8 isolated from contaminated soil was immobilized with platane wood chips to produce packing materials for a novel biofilter system utilized to control restaurant emissions. The effects of operational parameters including retention time, temperature, and inlet gas concentration on the removal efficiency and elimination capacity were evaluated. Criteria necessary for a scale-up design of the biofilter was established. High and satisfactory level of rapeseed oil smoke removal efficiency was maintained during operation and the optimal retention time was found to be 18 s corresponding to smoke removal efficiency greater than 97%. The optimal inlet rapeseed oil smoke loading was 120 mg/(m(3) x h) at the upper end of the linear correlation between inlet loading and elimination capacity.

Remote Control and Monitoring of VLBI Experiments by Smartphones

NASA Astrophysics Data System (ADS)

Ruztort, C. H.; Hase, H.; Zapata, O.; Pedreros, F.

2012-12-01

For the remote control and monitoring of VLBI operations, we developed a software optimized for smartphones. This is a new tool based on a client-server architecture with a Web interface optimized for smartphone screens and cellphone networks. The server uses variables of the Field System and its station specific parameters stored in the shared memory. The client running on the smartphone by a Web interface analyzes and visualizes the current status of the radio telescope, receiver, schedule, and recorder. In addition, it allows commands to be sent remotely to the Field System computer and displays the log entries. The user has full access to the entire operation process, which is important in emergency cases. The software also integrates a webcam interface.

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.

Analysis of automated quantification of motor activity in REM sleep behaviour disorder.

PubMed

Frandsen, Rune; Nikolic, Miki; Zoetmulder, Marielle; Kempfner, Lykke; Jennum, Poul

2015-10-01

Rapid eye movement (REM) sleep behaviour disorder (RBD) is characterized by dream enactment and REM sleep without atonia. Atonia is evaluated on the basis of visual criteria, but there is a need for more objective, quantitative measurements. We aimed to define and optimize a method for establishing baseline and all other parameters in automatic quantifying submental motor activity during REM sleep. We analysed the electromyographic activity of the submental muscle in polysomnographs of 29 patients with idiopathic RBD (iRBD), 29 controls and 43 Parkinson's (PD) patients. Six adjustable parameters for motor activity were defined. Motor activity was detected and quantified automatically. The optimal parameters for separating RBD patients from controls were investigated by identifying the greatest area under the receiver operating curve from a total of 648 possible combinations. The optimal parameters were validated on PD patients. Automatic baseline estimation improved characterization of atonia during REM sleep, as it eliminates inter/intra-observer variability and can be standardized across diagnostic centres. We found an optimized method for quantifying motor activity during REM sleep. The method was stable and can be used to differentiate RBD from controls and to quantify motor activity during REM sleep in patients with neurodegeneration. No control had more than 30% of REM sleep with increased motor activity; patients with known RBD had as low activity as 4.5%. We developed and applied a sensitive, quantitative, automatic algorithm to evaluate loss of atonia in RBD patients. © 2015 European Sleep Research Society.

Performance comparison of extracellular spike sorting algorithms for single-channel recordings.

PubMed

Wild, Jiri; Prekopcsak, Zoltan; Sieger, Tomas; Novak, Daniel; Jech, Robert

2012-01-30

Proper classification of action potentials from extracellular recordings is essential for making an accurate study of neuronal behavior. Many spike sorting algorithms have been presented in the technical literature. However, no comparative analysis has hitherto been performed. In our study, three widely-used publicly-available spike sorting algorithms (WaveClus, KlustaKwik, OSort) were compared with regard to their parameter settings. The algorithms were evaluated using 112 artificial signals (publicly available online) with 2-9 different neurons and varying noise levels between 0.00 and 0.60. An optimization technique based on Adjusted Mutual Information was employed to find near-optimal parameter settings for a given artificial signal and algorithm. All three algorithms performed significantly better (p<0.01) with optimized parameters than with the default ones. WaveClus was the most accurate spike sorting algorithm, receiving the best evaluation score for 60% of all signals. OSort operated at almost five times the speed of the other algorithms. In terms of accuracy, OSort performed significantly less well (p<0.01) than WaveClus for signals with a noise level in the range 0.15-0.30. KlustaKwik achieved similar scores to WaveClus for signals with low noise level 0.00-0.15 and was worse otherwise. In conclusion, none of the three compared algorithms was optimal in general. The accuracy of the algorithms depended on proper choice of the algorithm parameters and also on specific properties of the examined signal. Copyright © 2011 Elsevier B.V. All rights reserved.

Recovery of water and minerals from shale gas produced water by membrane distillation crystallization.

PubMed

Kim, Junghyun; Kim, Jungwon; Hong, Seungkwan

2018-02-01

Shale gas produced water (SGPW) treatment imposes greater technical challenges because of its high concentration of various contaminants. Membrane distillation crystallization (MDC) has a great potential to manage SGPW since it is capable of recovering both water and minerals at high rates, up to near a zero liquid discharge (ZLD) condition. To evaluate the feasibility of MDC for SGPW treatment, MDC performance indicators, such as water recovery rate, solid production rate (SPR) and specific energy consumption (SEC), were systematically investigated, to our knowledge for the first time, by using actual SGPW from Eagle Ford Shale (USA). The main operating parameters including feed cross-flow velocity (CFV) and crystallization temperature (T Cr ) were optimized by performing a series of MDC experiments. The results reported that water and minerals were effectively recovered with 84% of recovery rate and 2.72 kg/m 2 day of SPR under respective optimal operating conditions. Furthermore, the scale mechanism was firstly identified as limiting factor for MDC performance degradation. Lastly, SEC of MDC was estimated to be as low as 28.2 kWh/m 3 under ideal optimal operating conditions. Our experimental observations demonstrated that MDC could sustainably and effectively recover water and mineral with low energy consumption from SGPW by optimizing operating condition. Copyright © 2017 Elsevier Ltd. All rights reserved.

Optimization of a chemical identification algorithm

NASA Astrophysics Data System (ADS)

Chyba, Thomas H.; Fisk, Brian; Gunning, Christin; Farley, Kevin; Polizzi, Amber; Baughman, David; Simpson, Steven; Slamani, Mohamed-Adel; Almassy, Robert; Da Re, Ryan; Li, Eunice; MacDonald, Steve; Slamani, Ahmed; Mitchell, Scott A.; Pendell-Jones, Jay; Reed, Timothy L.; Emge, Darren

2010-04-01

A procedure to evaluate and optimize the performance of a chemical identification algorithm is presented. The Joint Contaminated Surface Detector (JCSD) employs Raman spectroscopy to detect and identify surface chemical contamination. JCSD measurements of chemical warfare agents, simulants, toxic industrial chemicals, interferents and bare surface backgrounds were made in the laboratory and under realistic field conditions. A test data suite, developed from these measurements, is used to benchmark algorithm performance throughout the improvement process. In any one measurement, one of many possible targets can be present along with interferents and surfaces. The detection results are expressed as a 2-category classification problem so that Receiver Operating Characteristic (ROC) techniques can be applied. The limitations of applying this framework to chemical detection problems are discussed along with means to mitigate them. Algorithmic performance is optimized globally using robust Design of Experiments and Taguchi techniques. These methods require figures of merit to trade off between false alarms and detection probability. Several figures of merit, including the Matthews Correlation Coefficient and the Taguchi Signal-to-Noise Ratio are compared. Following the optimization of global parameters which govern the algorithm behavior across all target chemicals, ROC techniques are employed to optimize chemical-specific parameters to further improve performance.

Computer Program for Analysis, Design and Optimization of Propulsion, Dynamics, and Kinematics of Multistage Rockets

NASA Astrophysics Data System (ADS)

Lali, Mehdi

2009-03-01

A comprehensive computer program is designed in MATLAB to analyze, design and optimize the propulsion, dynamics, thermodynamics, and kinematics of any serial multi-staging rocket for a set of given data. The program is quite user-friendly. It comprises two main sections: "analysis and design" and "optimization." Each section has a GUI (Graphical User Interface) in which the rocket's data are entered by the user and by which the program is run. The first section analyzes the performance of the rocket that is previously devised by the user. Numerous plots and subplots are provided to display the performance of the rocket. The second section of the program finds the "optimum trajectory" via billions of iterations and computations which are done through sophisticated algorithms using numerical methods and incremental integrations. Innovative techniques are applied to calculate the optimal parameters for the engine and designing the "optimal pitch program." This computer program is stand-alone in such a way that it calculates almost every design parameter in regards to rocket propulsion and dynamics. It is meant to be used for actual launch operations as well as educational and research purposes.

Learning Multisensory Integration and Coordinate Transformation via Density Estimation

PubMed Central

Sabes, Philip N.

2013-01-01

Sensory processing in the brain includes three key operations: multisensory integration—the task of combining cues into a single estimate of a common underlying stimulus; coordinate transformations—the change of reference frame for a stimulus (e.g., retinotopic to body-centered) effected through knowledge about an intervening variable (e.g., gaze position); and the incorporation of prior information. Statistically optimal sensory processing requires that each of these operations maintains the correct posterior distribution over the stimulus. Elements of this optimality have been demonstrated in many behavioral contexts in humans and other animals, suggesting that the neural computations are indeed optimal. That the relationships between sensory modalities are complex and plastic further suggests that these computations are learned—but how? We provide a principled answer, by treating the acquisition of these mappings as a case of density estimation, a well-studied problem in machine learning and statistics, in which the distribution of observed data is modeled in terms of a set of fixed parameters and a set of latent variables. In our case, the observed data are unisensory-population activities, the fixed parameters are synaptic connections, and the latent variables are multisensory-population activities. In particular, we train a restricted Boltzmann machine with the biologically plausible contrastive-divergence rule to learn a range of neural computations not previously demonstrated under a single approach: optimal integration; encoding of priors; hierarchical integration of cues; learning when not to integrate; and coordinate transformation. The model makes testable predictions about the nature of multisensory representations. PMID:23637588

Optimization of the operation of packed bed bioreactor to improve the sulfate and metal removal from acid mine drainage.

PubMed

Dev, Subhabrata; Roy, Shantonu; Bhattacharya, Jayanta

2017-09-15

The present study discusses the potentiality of using anaerobic Packed Bed Bioreactor (PBR) for the treatment of acid mine drainage (AMD). The multiple process parameters such as pH, hydraulic retention time (HRT), concentration of marine waste extract (MWE), total organic carbon (TOC) and sulfate were optimized together using Taguchi design. The order of influence of the parameters towards biological sulfate reduction was found to be pH > MWE > sulfate > HRT > TOC. At optimized conditions (pH - 7, 20% (v/v) MWE, 1500 mg/L sulfate, 48 h HRT and 2300 mg/L TOC), 98.3% and 95% sulfate at a rate of 769.7 mg/L/d. and 732.1 mg/L/d. was removed from the AMD collected from coal and metal mine, respectively. Efficiency of metal removal (Fe, Cu, Zn, Mg and Ni) was in the range of 94-98%. The levels of contaminants in the treated effluent were below the minimum permissible limits of industrial discharge as proposed by Bureau of Indian Standards (IS 2490:1981). The present study establishes the optimized conditions for PBR operation to completely remove sulfate and metal removal from AMD at high rate. The study also creates the future scope to develop an efficient treatment process for sulfate and metal-rich mine wastewater in a large scale. Copyright © 2017 Elsevier Ltd. All rights reserved.

Unsteady Adjoint Approach for Design Optimization of Flapping Airfoils

NASA Technical Reports Server (NTRS)

Lee, Byung Joon; Liou, Meng-Sing

2012-01-01

This paper describes the work for optimizing the propulsive efficiency of flapping airfoils, i.e., improving the thrust under constraining aerodynamic work during the flapping flights by changing their shape and trajectory of motion with the unsteady discrete adjoint approach. For unsteady problems, it is essential to properly resolving time scales of motion under consideration and it must be compatible with the objective sought after. We include both the instantaneous and time-averaged (periodic) formulations in this study. For the design optimization with shape parameters or motion parameters, the time-averaged objective function is found to be more useful, while the instantaneous one is more suitable for flow control. The instantaneous objective function is operationally straightforward. On the other hand, the time-averaged objective function requires additional steps in the adjoint approach; the unsteady discrete adjoint equations for a periodic flow must be reformulated and the corresponding system of equations solved iteratively. We compare the design results from shape and trajectory optimizations and investigate the physical relevance of design variables to the flapping motion at on- and off-design conditions.

CNV detection method optimized for high-resolution arrayCGH by normality test.

PubMed

Ahn, Jaegyoon; Yoon, Youngmi; Park, Chihyun; Park, Sanghyun

2012-04-01

High-resolution arrayCGH platform makes it possible to detect small gains and losses which previously could not be measured. However, current CNV detection tools fitted to early low-resolution data are not applicable to larger high-resolution data. When CNV detection tools are applied to high-resolution data, they suffer from high false-positives, which increases validation cost. Existing CNV detection tools also require optimal parameter values. In most cases, obtaining these values is a difficult task. This study developed a CNV detection algorithm that is optimized for high-resolution arrayCGH data. This tool operates up to 1500 times faster than existing tools on a high-resolution arrayCGH of whole human chromosomes which has 42 million probes whose average length is 50 bases, while preserving false positive/negative rates. The algorithm also uses a normality test, thereby removing the need for optimal parameters. To our knowledge, this is the first formulation for CNV detecting problems that results in a near-linear empirical overall complexity for real high-resolution data. Copyright © 2012 Elsevier Ltd. All rights reserved.

Optimizing the operating parameters of corona electrostatic separation for recycling waste scraped printed circuit boards by computer simulation of electric field.

PubMed

Li, Jia; Lu, Hongzhou; Liu, Shushu; Xu, Zhenming

2008-05-01

The printed circuit board (PCB) has a metal content of nearly 28% metal, including an abundance of nonferrous metals such as copper, lead, and tin. The purity of precious metals in PCBs is more than 10 times that of rich-content minerals. Therefore, the recycling of PCBs is an important subject, not only from the viewpoint of waste treatment, but also with respect to the recovery of valuable materials. Compared with traditional process the corona electrostatic separation (CES) had no waste water or gas during the process and it had high productivity with a low-energy cost. In this paper, the roll-type corona electrostatic separator was used to separate metals and nonmetals from scraped waste PCBs. The software MATLAB was used to simulate the distribution of electric field in separating space. It was found that, the variations of parameters of electrodes and applied voltages directly influenced the distribution of electric field. Through the correlation of simulated and experimental results, the good separation results were got under the optimized operating parameter: U=20-30 kV, L=L(1)=L(2)=0.21 m, R(1)=0.114, R(2)=0.019 m, theta(1)=20 degrees and theta(2)=60 degrees .

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.

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.

Interpreting cost of ownership for mix-and-match lithography

NASA Astrophysics Data System (ADS)

Levine, Alan L.; Bergendahl, Albert S.

1994-05-01

Cost of ownership modeling is a critical and emerging tool that provides significant insight into the ways to optimize device manufacturing costs. The development of a model to deal with a particular application, mix-and-match lithography, was performed in order to determine the level of cost savings and the optimum ways to create these savings. The use of sensitivity analysis with cost of ownership allows the user to make accurate trade-offs between technology and cost. The use and interpretation of the model results are described in this paper. Parameters analyzed include several manufacturing considerations -- depreciation, maintenance, engineering and operator labor, floorspace, resist, consumables and reticles. Inherent in this study is the ability to customize this analysis for a particular operating environment. Results demonstrate the clear advantages of a mix-and-match approach for three different operating environments. These case studies also demonstrate various methods to efficiently optimize cost savings strategies.

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.

The Set of Diagnostics for the First Operation Campaign of the Wendelstein 7-X Stellarator

NASA Astrophysics Data System (ADS)

König, Ralf; Baldzuhn, J.; Biel, W.; Biedermann, C.; Bosch, H. S.; Bozhenkov, S.; Bräuer, T.; Brotas de Carvalho, B.; Burhenn, R.; Buttenschön, B.; Cseh, G.; Czarnecka, A.; Endler, M.; Erckmann, V.; Estrada, T.; Geiger, J.; Grulke, O.; Hartmann, D.; Hathiramani, D.; Hirsch, M.; Jabłonski, S.; Jakubowski, M.; Kaczmarczyk, J.; Klinger, T.; Klose, S.; Kocsis, G.; Kornejew, P.; Krämer-Flecken, A.; Kremeyer, T.; Krychowiak, M.; Kubkowska, M.; Langenberg, A.; Laqua, H. P.; Laux, M.; Liang, Y.; Lorenz, A.; Marchuk, A. O.; Moncada, V.; Neubauer, O.; Neuner, U.; Oosterbeek, J. W.; Otte, M.; Pablant, N.; Pasch, E.; Pedersen, T. S.; Rahbarnia, K.; Ryc, L.; Schmitz, O.; Schneider, W.; Schuhmacher, H.; Schweer, B.; Stange, T.; Thomsen, H.; Travere, J.-M.; Szepesi, T.; Wenzel, U.; Werner, A.; Wiegel, B.; Windisch, T.; Wolf, R.; Wurden, G. A.; Zhang, D.; Zimbal, A.; Zoletnik, S.; the W7-X Team

2015-10-01

Wendelstein 7-X (W7-X) is a large optimized stellarator (B=2.5T, V=30m3) aiming at demonstrating the reactor relevance of the optimized stellarators. In 2015 W7-X will begin its first operation phase (OP1.1) with five inertially cooled inboard limiters made of graphite. Assuming the heat loads can be spread out evenly between the limiters, 1 second discharges at 2 MW of ECRH heating power could be run in OP1.1. The expected plasma parameters will be sufficient to demonstrate the readiness of the installed diagnostics and even to run a first physics program. The diagnostics available for this first operation phase, including some special limiter diagnostics, and their capabilities are being presented. A shorter version of this contribution is due to be published in PoS at: 1st EPS conference on Plasma Diagnostics

Effect of diatomic molecular properties on binary laser pulse optimizations of quantum gate operations.

PubMed

Zaari, Ryan R; Brown, Alex

2011-07-28

The importance of the ro-vibrational state energies on the ability to produce high fidelity binary shaped laser pulses for quantum logic gates is investigated. The single frequency 2-qubit ACNOT(1) and double frequency 2-qubit NOT(2) quantum gates are used as test cases to examine this behaviour. A range of diatomics is sampled. The laser pulses are optimized using a genetic algorithm for binary (two amplitude and two phase parameter) variation on a discretized frequency spectrum. The resulting trends in the fidelities were attributed to the intrinsic molecular properties and not the choice of method: a discretized frequency spectrum with genetic algorithm optimization. This is verified by using other common laser pulse optimization methods (including iterative optimal control theory), which result in the same qualitative trends in fidelity. The results differ from other studies that used vibrational state energies only. Moreover, appropriate choice of diatomic (relative ro-vibrational state arrangement) is critical for producing high fidelity optimized quantum logic gates. It is also suggested that global phase alignment imposes a significant restriction on obtaining high fidelity regions within the parameter search space. Overall, this indicates a complexity in the ability to provide appropriate binary laser pulse control of diatomics for molecular quantum computing. © 2011 American Institute of Physics

An Evolutionary Optimization of the Refueling Simulation for a CANDU Reactor

NASA Astrophysics Data System (ADS)

Do, Q. B.; Choi, H.; Roh, G. H.

2006-10-01

This paper presents a multi-cycle and multi-objective optimization method for the refueling simulation of a 713 MWe Canada deuterium uranium (CANDU-6) reactor based on a genetic algorithm, an elitism strategy and a heuristic rule. The proposed algorithm searches for the optimal refueling patterns for a single cycle that maximizes the average discharge burnup, minimizes the maximum channel power and minimizes the change in the zone controller unit water fills while satisfying the most important safety-related neutronic parameters of the reactor core. The heuristic rule generates an initial population of individuals very close to a feasible solution and it reduces the computing time of the optimization process. The multi-cycle optimization is carried out based on a single cycle refueling simulation. The proposed approach was verified by a refueling simulation of a natural uranium CANDU-6 reactor for an operation period of 6 months at an equilibrium state and compared with the experience-based automatic refueling simulation and the generalized perturbation theory. The comparison has shown that the simulation results are consistent from each other and the proposed approach is a reasonable optimization method of the refueling simulation that controls all the safety-related parameters of the reactor core during the simulation

Design and analysis of tilt integral derivative controller with filter for load frequency control of multi-area interconnected power systems.

PubMed

Kumar Sahu, Rabindra; Panda, Sidhartha; Biswal, Ashutosh; Chandra Sekhar, G T

2016-03-01

In this paper, a novel Tilt Integral Derivative controller with Filter (TIDF) is proposed for Load Frequency Control (LFC) of multi-area power systems. Initially, a two-area power system is considered and the parameters of the TIDF controller are optimized using Differential Evolution (DE) algorithm employing an Integral of Time multiplied Absolute Error (ITAE) criterion. The superiority of the proposed approach is demonstrated by comparing the results with some recently published heuristic approaches such as Firefly Algorithm (FA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) optimized PID controllers for the same interconnected power system. Investigations reveal that proposed TIDF controllers provide better dynamic response compared to PID controller in terms of minimum undershoots and settling times of frequency as well as tie-line power deviations following a disturbance. The proposed approach is also extended to two widely used three area test systems considering nonlinearities such as Generation Rate Constraint (GRC) and Governor Dead Band (GDB). To improve the performance of the system, a Thyristor Controlled Series Compensator (TCSC) is also considered and the performance of TIDF controller in presence of TCSC is investigated. It is observed that system performance improves with the inclusion of TCSC. Finally, sensitivity analysis is carried out to test the robustness of the proposed controller by varying the system parameters, operating condition and load pattern. It is observed that the proposed controllers are robust and perform satisfactorily with variations in operating condition, system parameters and load pattern. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

DESIGN AND PERFORMANCE CHARACTERISTICS OF A TURBULENT MIXING CONDENSATION NUCLEI COUNTER. (R826654)

EPA Science Inventory

The design and optimization of operation parameters of a Turbulent Mixing Condensation Nuclei Counter (TMCNC) are discussed as well as its performance using dibutylphthalate (DBP) as the working fluid. A detection limit of 3 nm has been achieved at a flow rate of 2.8 lmin^-1<...

First Report on Non-Thermal Plasma Reactor Scaling Criteria and Optimization Models

DTIC Science & Technology

1998-01-13

decomposition chemistry of nitric oxide and two representative VOCs, trichloroethylene and carbon tetrachloride, and the connection between the basic plasma ... chemistry , the target species properties, and the reactor operating parameters. System architecture, that is how NTP reactors can be combined or ganged to achieve higher capacity, will also be briefly discussed.

Synthesis of Trigeneration Systems: Sensitivity Analyses and Resilience

PubMed Central

Carvalho, Monica; Lozano, Miguel A.; Ramos, José; Serra, Luis M.

2013-01-01

This paper presents sensitivity and resilience analyses for a trigeneration system designed for a hospital. The following information is utilized to formulate an integer linear programming model: (1) energy service demands of the hospital, (2) technical and economical characteristics of the potential technologies for installation, (3) prices of the available utilities interchanged, and (4) financial parameters of the project. The solution of the model, minimizing the annual total cost, provides the optimal configuration of the system (technologies installed and number of pieces of equipment) and the optimal operation mode (operational load of equipment, interchange of utilities with the environment, convenience of wasting cogenerated heat, etc.) at each temporal interval defining the demand. The broad range of technical, economic, and institutional uncertainties throughout the life cycle of energy supply systems for buildings makes it necessary to delve more deeply into the fundamental properties of resilient systems: feasibility, flexibility and robustness. The resilience of the obtained solution is tested by varying, within reasonable limits, selected parameters: energy demand, amortization and maintenance factor, natural gas price, self-consumption of electricity, and time-of-delivery feed-in tariffs. PMID:24453881

Synthesis of trigeneration systems: sensitivity analyses and resilience.

PubMed

Carvalho, Monica; Lozano, Miguel A; Ramos, José; Serra, Luis M

2013-01-01

This paper presents sensitivity and resilience analyses for a trigeneration system designed for a hospital. The following information is utilized to formulate an integer linear programming model: (1) energy service demands of the hospital, (2) technical and economical characteristics of the potential technologies for installation, (3) prices of the available utilities interchanged, and (4) financial parameters of the project. The solution of the model, minimizing the annual total cost, provides the optimal configuration of the system (technologies installed and number of pieces of equipment) and the optimal operation mode (operational load of equipment, interchange of utilities with the environment, convenience of wasting cogenerated heat, etc.) at each temporal interval defining the demand. The broad range of technical, economic, and institutional uncertainties throughout the life cycle of energy supply systems for buildings makes it necessary to delve more deeply into the fundamental properties of resilient systems: feasibility, flexibility and robustness. The resilience of the obtained solution is tested by varying, within reasonable limits, selected parameters: energy demand, amortization and maintenance factor, natural gas price, self-consumption of electricity, and time-of-delivery feed-in tariffs.

Quantification of Gear Tooth Damage by Optimal Tracking of Vibration Signatures

NASA Technical Reports Server (NTRS)

Choy, F. K.; Veillette, R. J.; Polyshchuk, V.; Braun, M. J.; Hendricks, R. C.

1996-01-01

This paper presents a technique for quantifying the wear or damage of gear teeth in a transmission system. The procedure developed in this study can be applied as a part of either an onboard machine health-monitoring system or a health diagnostic system used during regular maintenance. As the developed methodology is based on analysis of gearbox vibration under normal operating conditions, no shutdown or special modification of operating parameters is required during the diagnostic process. The process of quantifying the wear or damage of gear teeth requires a set of measured vibration data and a model of the gear mesh dynamics. An optimization problem is formulated to determine the profile of a time-varying mesh stiffness parameter for which the model output approximates the measured data. The resulting stiffness profile is then related to the level of gear tooth wear or damage. The procedure was applied to a data set generated artificially and to another obtained experimentally from a spiral bevel gear test rig. The results demonstrate the utility of the procedure as part of an overall health-monitoring system.

Subcritical n-hexane/isopropanol extraction of lipid from wet microalgal pastes of Scenedesmus obliquus.

PubMed

Bian, Xiaoyu; Jin, Wenbiao; Gu, Qiong; Zhou, Xu; Xi, Yuhe; Tu, Renjie; Han, Song-Fang; Xie, Guo-Jun; Gao, Shu-Hong; Wang, Qilin

2018-02-19

Subcritical co-solvents of n-hexane/isopropanol were primarily utilized to extract lipid from wet microalgal pastes of Scenedesmus obliquus. The effects of key operational parameters were investigated, and the optimal parameters were obtained: solvent ratio of n-hexane to isopropanol was 3:2 (V:V), phase ratio of co-solvents to microalgal biomass was 35:1 (mL:g), reactor stirring speed was 900 rpm, extraction time was 60 min. Additional pretreatment with acid, ultrasonic and microwave as well as enhanced subcritical pressure/heating treatments were also applied to further study their effects on lipid extraction. The results showed that the lipid recovery rate with acid pretreatment was 8.6 and 6.2% higher than ultrasonic and microwave pretreatment; the optimum enhanced subcritical condition was 55 °C with atmospheric pressure. Under optimal operating conditions, the lipid and FAME yield were 13.5 and 7.2%, which was 82.6 and 135.1% higher than the traditional method. The results indicated that the subcritical n-hexane/isopropanol extraction process had promising application potential.

Effect of Process Parameters on Catalytic Incineration of Solvent Emissions

PubMed Central

Ojala, Satu; Lassi, Ulla; Perämäki, Paavo; Keiski, Riitta L.

2008-01-01

Catalytic oxidation is a feasible and affordable technology for solvent emission abatement. However, finding optimal operation conditions is important, since they are strongly dependent on the application area of VOC incineration. This paper presents the results of the laboratory experiments concerning four most central parameters, that is, effects of concentration, gas hourly space velocity (GHSV), temperature, and moisture on the oxidation of n-butyl acetate. Both fresh and industrially aged commercial Pt/Al2O3 catalysts were tested to determine optimal process conditions and the significance order and level of selected parameters. The effects of these parameters were evaluated by computer-aided statistical experimental design. According to the results, GHSV was the most dominant parameter in the oxidation of n-butyl acetate. Decreasing GHSV and increasing temperature increased the conversion of n-butyl acetate. The interaction effect of GHSV and temperature was more significant than the effect of concentration. Both of these affected the reaction by increasing the conversion of n-butyl acetate. Moisture had only a minor decreasing effect on the conversion, but it also decreased slightly the formation of by products. Ageing did not change the significance order of the above-mentioned parameters, however, the effects of individual parameters increased slightly as a function of ageing. PMID:18584032

The concept of a plasma centrifuge with a high frequency rotating magnetic field and axial circulation

NASA Astrophysics Data System (ADS)

Borisevich, V. D.; Potanin, E. P.

2017-07-01

The possibility of using a rotating magnetic field (RMF) in a plasma centrifuge (PC), with axial circulation to multiply the radial separation effect in an axial direction, is considered. For the first time, a traveling magnetic field (TMF) is proposed to drive an axial circulation flow in a PC. The longitudinal separation effect is calculated for a notional model, using specified operational parameters and the properties of a plasma, comprising an isotopic mixture of 20Ne-22Ne and generated by a high frequency discharge. The optimal intensity of a circulation flow, in which the longitudinal separation effect reaches its maximum value, is studied. The optimal parameters of the RMF and TMF for effective separation, as well as the centrifuge performance, are calculated.

Contract portfolio optimization for a gasoline supply chain

NASA Astrophysics Data System (ADS)

Wang, Shanshan

Major oil companies sell gasoline through three channels of trade: branded (associated with long-term contracts), unbranded (associated with short-term contracts), and spot market. The branded channel provides them with a long-term secured and sustainable demand source, but requires an inflexible long-term commitment with demand and price risks. The unbranded channel provides a medium level of allocation flexibility. The spot market provides them with the greatest allocation flexibility to the changing market conditions, but the spot market's illiquidity mitigates this benefit. In order to sell the product in a profitable and sustainable way, they need an optimal contract portfolio. This dissertation addresses the contract portfolio optimization problem from different perspectives (retrospective view and forward-looking view) at different levels (strategic level, tactical level and operational level). The objective of the retrospective operational model is to develop a financial case to estimate the business value of having a dynamic optimization model and quantify the opportunity values missed in the past. This model proves the financial significance of the problem and provides top management valuable insights into the business. BP has applied the insights and principles gained from this work and implemented the model to the entire Midwest gasoline supply chain to retrospectively review optimization opportunities. The strategic model is the most parsimonious model that captures the essential economic tradeoffs among different contract types, to demonstrate the need for a contract portfolio and what drives the portfolio. We examine the properties of the optimal contract portfolio and provide a comparative statics analysis by changing the model parameters. As the strategic model encapsulates the business problem at the macroscopic level, the tactical model resolves lower level issues. It considers the time dynamics, the information flow and contracting flow. Using this model, we characterize a simple and easily implementable dynamic contract portfolio policy that would enable the company to dynamically rebalance its supply contract portfolio over time in anticipation of the future market conditions in each individual channel while satisfying the contractual obligations. The optimal policy is a state-dependent base-share contract portfolio policy characterized by a branded base-share level and an unbranded contract commitment combination, given as a function of the initial information state. Using real-world market data, we estimate the model parameters. We also apply an efficient modified policy iteration method to compute the optimal contract portfolio strategies and corresponding profit value. We present computational results in order to obtain insights into the structure of optimal policies, capture the value of the dynamic contract portfolio policy by comparing it with static policies, and illustrate the sensitivity of the optimal contract portfolio and corresponding profit value in terms of the different parameters. Considering the geographic dispersion of different market areas and the pipeline network together with the dynamic contract portfolio optimization problem, we formulate a forward-looking operational model, which could be used by gasoline suppliers for lower-level planning. Finally, we discuss the generalization of the framework to other problems and applications, as well as further research.

Early warning indicators for monitoring the process failure of anaerobic digestion system of food waste.

PubMed

Li, Lei; He, Qingming; Wei, Yunmei; He, Qin; Peng, Xuya

2014-11-01

To determine reliable state parameters which could be used as early warning indicators of process failure due to the acidification of anaerobic digestion of food waste, three mesophilic anaerobic digesters of food waste with different operation conditions were investigated. Such parameters as gas production, methane content, pH, concentrations of volatile fatty acid (VFA), alkalinity and their combined indicators were evaluated. Results revealed that operation conditions significantly affect the responses of parameters and thus the optimal early warning indicators of each reactor differ from each other. None of the single indicators was universally valid for all the systems. The universally valid indicators should combine several parameters to supply complementary information. A combination of total VFA, the ratio of VFA to total alkalinity (VFA/TA) and the ratio of bicarbonate alkalinity to total alkalinity (BA/TA) can reflect the metabolism of the digesting system and realize rapid and effective early warning. Copyright © 2014 Elsevier Ltd. All rights reserved.

Strategie de commande optimale de la production electrique dans un site isole

NASA Astrophysics Data System (ADS)

Barris, Nicolas

Hydro-Quebec manages more than 20 isolated power grids all over the province. The grids are located in small villages where the electricity demand is rather small. Those villages being far away from each other and from the main electricity production facilities, energy is produced locally using diesel generators. Electricity production costs at the isolated power grids are very important due to elevated diesel prices and transportation costs. However, the price of electricity is the same for the entire province, with no regards to the production costs of the electricity consumed. These two factors combined result in yearly exploitation losses for Hydro-Quebec. For any given village, several diesel generators are required to satisfy the demand. When the load increases, it becomes necessary to increase the capacity either by adding a generator to the production or by switching to a more powerful generator. The same thing happens when the load decreases. Every decision regarding changes in the production is included in the control strategy, which is based on predetermined parameters. These parameters were specified according to empirical studies and the knowledge base of the engineers managing the isolated power grids, but without any optimisation approach. The objective of the presented work is to minimize the diesel consumption by optimizing the parameters included in the control strategy. Its impact would be to limit the exploitation losses generated by the isolated power grids and the CO2 equivalent emissions without adding new equipment or completely changing the nature of the strategy. To satisfy this objective, the isolated power grid simulator OPERA is used along with the optimization library NOMAD and the data of three villages in northern Quebec. The preliminary optimization instance for the first village showed that some modifications to the existing control strategy must be done to better achieve the minimization objective. The main optimization processes consist of three different optimization approaches: the optimization of one set of parameters for all the villages, the optimization of one set of parameters per village, and the optimization of one set of parameters per diesel generator configuration per village. In the first scenario, the optimization of one set of parameters for all the villages leads to compromises for all three villages without allowing a full potential reduction for any village. Therefore, it is proven that applying one set of parameters to all the villages is not suitable for finding an optimal solution. In the second scenario, the optimization of one set of parameters per village allows an improvement over the previous results. At this point, it is shown that it is crucial to remove from the production the less efficient configurations when they are next to more efficient configurations. In the third scenario, the optimization of one set of parameters per configuration per village requires a very large number of function evaluations but does not result in any satisfying solution. In order to improve the performance of the optimization, it has been decided that the problem structure would be used. Two different approaches are considered: optimizing one set of parameters at a time and optimizing different rules included in the control strategy one at a time. In both cases, results are similar but calculation costs differ, the second method being much more cost efficient. The optimal values of the ultimate rules parameters can be directly linked to the efficient transition points that favor an efficient operation of the isolated power grids. Indeed, these transition points are defined in such a way that the high efficiency zone of every configuration is used. Therefore, it seems possible to directly identify on the graphs these optimal transition points and define the parameters in the control strategy without even having to run any optimization process. The diesel consumption reduction for all three villages is about 1.9%. Considering elevated diesel costs and the existence of about 20 other isolated power grids, the use of the developed methods together with a calibration of OPERA would allow a substantial reduction of Hydro-Quebec's annual deficit. Also, since one of the developed methods is very cost effective and produces equivalent results, it could be possible to use it during other processes; for example, when buying new equipment for the grid it could be possible to assess its full potential, under an optimized control strategy, and improve the net present value.

A strategy to determine operating parameters in tissue engineering hollow fiber bioreactors

PubMed Central

Shipley, RJ; Davidson, AJ; Chan, K; Chaudhuri, JB; Waters, SL; Ellis, MJ

2011-01-01

The development of tissue engineering hollow fiber bioreactors (HFB) requires the optimal design of the geometry and operation parameters of the system. This article provides a strategy for specifying operating conditions for the system based on mathematical models of oxygen delivery to the cell population. Analytical and numerical solutions of these models are developed based on Michaelis–Menten kinetics. Depending on the minimum oxygen concentration required to culture a functional cell population, together with the oxygen uptake kinetics, the strategy dictates the model needed to describe mass transport so that the operating conditions can be defined. If cmin ≫ Km we capture oxygen uptake using zero-order kinetics and proceed analytically. This enables operating equations to be developed that allow the user to choose the medium flow rate, lumen length, and ECS depth to provide a prescribed value of cmin. When , we use numerical techniques to solve full Michaelis–Menten kinetics and present operating data for the bioreactor. The strategy presented utilizes both analytical and numerical approaches and can be applied to any cell type with known oxygen transport properties and uptake kinetics. PMID:21370228

ISOT_Calc: A versatile tool for parameter estimation in sorption isotherms

NASA Astrophysics Data System (ADS)

Beltrán, José L.; Pignatello, Joseph J.; Teixidó, Marc

2016-09-01

Geochemists and soil chemists commonly use parametrized sorption data to assess transport and impact of pollutants in the environment. However, this evaluation is often hampered by a lack of detailed sorption data analysis, which implies further non-accurate transport modeling. To this end, we present a novel software tool to precisely analyze and interpret sorption isotherm data. Our developed tool, coded in Visual Basic for Applications (VBA), operates embedded within the Microsoft Excel™ environment. It consists of a user-defined function named ISOT_Calc, followed by a supplementary optimization Excel macro (Ref_GN_LM). The ISOT_Calc function estimates the solute equilibrium concentration in the aqueous and solid phases (Ce and q, respectively). Hence, it represents a very flexible way in the optimization of the sorption isotherm parameters, as it can be carried out over the residuals of q, Ce, or both simultaneously (i.e., orthogonal distance regression). The developed function includes the most usual sorption isotherm models, as predefined equations, as well as the possibility to easily introduce custom-defined ones. Regarding the Ref_GN_LM macro, it allows the parameter optimization by using a Levenberg-Marquardt modified Gauss-Newton iterative procedure. In order to evaluate the performance of the presented tool, both function and optimization macro have been applied to different sorption data examples described in the literature. Results showed that the optimization of the isotherm parameters was successfully achieved in all cases, indicating the robustness and reliability of the developed tool. Thus, the presented software tool, available to researchers and students for free, has proven to be a user-friendly and an interesting alternative to conventional fitting tools used in sorption data analysis.

Optimization of Injection Molding Parameters for HDPE/TiO₂ Nanocomposites Fabrication with Multiple Performance Characteristics Using the Taguchi Method and Grey Relational Analysis.

PubMed

Pervez, Hifsa; Mozumder, Mohammad S; Mourad, Abdel-Hamid I

2016-08-22

The current study presents an investigation on the optimization of injection molding parameters of HDPE/TiO₂ nanocomposites using grey relational analysis with the Taguchi method. Four control factors, including filler concentration (i.e., TiO₂), barrel temperature, residence time and holding time, were chosen at three different levels of each. Mechanical properties, such as yield strength, Young's modulus and elongation, were selected as the performance targets. Nine experimental runs were carried out based on the Taguchi L₉ orthogonal array, and the data were processed according to the grey relational steps. The optimal process parameters were found based on the average responses of the grey relational grades, and the ideal operating conditions were found to be a filler concentration of 5 wt % TiO₂, a barrel temperature of 225 °C, a residence time of 30 min and a holding time of 20 s. Moreover, analysis of variance (ANOVA) has also been applied to identify the most significant factor, and the percentage of TiO₂ nanoparticles was found to have the most significant effect on the properties of the HDPE/TiO₂ nanocomposites fabricated through the injection molding process.

Flow range enhancement by secondary flow effect in low solidity circular cascade diffusers

NASA Astrophysics Data System (ADS)

Sakaguchi, Daisaku; Tun, Min Thaw; Mizokoshi, Kanata; Kishikawa, Daiki

2014-08-01

High-pressure ratio and wide operating range are highly required for compressors and blowers. The technical issue of the design is achievement of suppression of flow separation at small flow rate without deteriorating the efficiency at design flow rate. A numerical simulation is very effective in design procedure, however, cost of the numerical simulation is generally high during the practical design process, and it is difficult to confirm the optimal design which is combined with many parameters. A multi-objective optimization technique is the idea that has been proposed for solving the problem in practical design process. In this study, a Low Solidity circular cascade Diffuser (LSD) in a centrifugal blower is successfully designed by means of multi-objective optimization technique. An optimization code with a meta-model assisted evolutionary algorithm is used with a commercial CFD code ANSYS-CFX. The optimization is aiming at improving the static pressure coefficient at design point and at low flow rate condition while constraining the slope of the lift coefficient curve. Moreover, a small tip clearance of the LSD blade was applied in order to activate and to stabilize the secondary flow effect at small flow rate condition. The optimized LSD blade has an extended operating range of 114 % towards smaller flow rate as compared to the baseline design without deteriorating the diffuser pressure recovery at design point. The diffuser pressure rise and operating flow range of the optimized LSD blade are experimentally verified by overall performance test. The detailed flow in the diffuser is also confirmed by means of a Particle Image Velocimeter. Secondary flow is clearly captured by PIV and it spreads to the whole area of LSD blade pitch. It is found that the optimized LSD blade shows good improvement of the blade loading in the whole operating range, while at small flow rate the flow separation on the LSD blade has been successfully suppressed by the secondary flow effect.

A new algorithm for design, operation and cost assessment of struvite (MgNH4PO4) precipitation processes.

PubMed

Birnhack, Liat; Nir, Oded; Telzhenski, Marina; Lahav, Ori

2015-01-01

Deliberate struvite (MgNH4PO4) precipitation from wastewater streams has been the topic of extensive research in the last two decades and is expected to gather worldwide momentum in the near future as a P-reuse technique. A wide range of operational alternatives has been reported for struvite precipitation, including the application of various Mg(II) sources, two pH elevation techniques and several Mg:P ratios and pH values. The choice of each operational parameter within the struvite precipitation process affects process efficiency, the overall cost and also the choice of other operational parameters. Thus, a comprehensive simulation program that takes all these parameters into account is essential for process design. This paper introduces a systematic decision-supporting tool which accepts a wide range of possible operational parameters, including unconventional Mg(II) sources (i.e. seawater and seawater nanofiltration brines). The study is supplied with a free-of-charge computerized tool (http://tx.technion.ac.il/~agrengn/agr/Struvite_Program.zip) which links two computer platforms (Python and PHREEQC) for executing thermodynamic calculations according to predefined kinetic considerations. The model can be (inter alia) used for optimizing the struvite-fluidized bed reactor process operation with respect to P removal efficiency, struvite purity and economic feasibility of the chosen alternative. The paper describes the algorithm and its underlying assumptions, and shows results (i.e. effluent water quality, cost breakdown and P removal efficiency) of several case studies consisting of typical wastewaters treated at various operational conditions.

Comparison of the fractional power motor with cores made of various magnetic materials

NASA Astrophysics Data System (ADS)

Gmyrek, Zbigniew; Lefik, Marcin; Cavagnino, Andrea; Ferraris, Luca

2017-12-01

The optimization of the motor cores, coupled with new core shapes as well as powering the motor at high frequency are the primary reasons for the use of new materials. The utilization of new materials, like SMC (soft magnetic composite), reduce the core loss and/or provide quasi-isotropic core's properties in any magnetization direction. Moreover, the use of SMC materials allows for avoiding degradation of the material portions, resulting from punching process, thereby preventing the deterioration of operating parameters of the motor. The authors examine the impact of technological parameters on the properties of a new type of SMC material and analyze the possibility of its use as the core of the fractional power motor. The result of the work is an indication of the shape of the rotor core made of a new SMC material to achieve operational parameters similar to those that have a motor with a core made of laminations.

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.

A Model Parameter Extraction Method for Dielectric Barrier Discharge Ozone Chamber using Differential Evolution

NASA Astrophysics Data System (ADS)

Amjad, M.; Salam, Z.; Ishaque, K.

2014-04-01

In order to design an efficient resonant power supply for ozone gas generator, it is necessary to accurately determine the parameters of the ozone chamber. In the conventional method, the information from Lissajous plot is used to estimate the values of these parameters. However, the experimental setup for this purpose can only predict the parameters at one operating frequency and there is no guarantee that it results in the highest ozone gas yield. This paper proposes a new approach to determine the parameters using a search and optimization technique known as Differential Evolution (DE). The desired objective function of DE is set at the resonance condition and the chamber parameter values can be searched regardless of experimental constraints. The chamber parameters obtained from the DE technique are validated by experiment.

Data Transfer Advisor with Transport Profiling Optimization

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

Rao, Nageswara S.; Liu, Qiang; Yun, Daqing

The network infrastructures have been rapidly upgraded in many high-performance networks (HPNs). However, such infrastructure investment has not led to corresponding performance improvement in big data transfer, especially at the application layer, largely due to the complexity of optimizing transport control on end hosts. We design and implement ProbData, a PRofiling Optimization Based DAta Transfer Advisor, to help users determine the most effective data transfer method with the most appropriate control parameter values to achieve the best data transfer performance. ProbData employs a profiling optimization based approach to exploit the optimal operational zone of various data transfer methods in supportmore » of big data transfer in extreme scale scientific applications. We present a theoretical framework of the optimized profiling approach employed in ProbData as wellas its detailed design and implementation. The advising procedure and performance benefits of ProbData are illustrated and evaluated by proof-of-concept experiments in real-life networks.« less

Numerical investigation of optimized CZTSSe based solar cell in Wx-Amps environment

NASA Astrophysics Data System (ADS)

Mohanty, Soumya Priyadarshini; Padhy, Srinibasa; Chowdhury, Joy; Sing, Udai P.

2018-05-01

The CZTSSe is the modified version of CZTS with selenium infusion. It shows maximum efficiency in the band gap from 1 to 1.4 eV. In our present work CZTSSe based solar cell is investigated using Wx-Amps tool. The Mo layer, absorber layer, CdS layer, i-ZnO [4]and Al-ZnO layers with their electrical, optical and material parameters are fitted in the tool. The vital parameters such as carrier density, thickness of the CZTSSe absorber layer, operating temperature, CdS buffer layer thickness and its carrier density on the cell interpretation are calculated. From[4] the simulation results it is apparent that the optimal absorber layer varies from 2.9 µm to 3.7 µm. The temperature variation has a strong influence on the efficiency of the cell. An optimal efficiency of 22% (With Jsc=33 mA/cm2, Voc=0.98 V, and fill factor= 68%) are attained. These results will give some insight for makeing higher efficiency CZTSSe based solar cell.

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.

Multiple optimization of chemical components and texture of purple maize expanded by IVDV treatment using the response surface methodology.

PubMed

Mrad, Rachelle; Debs, Espérance; Maroun, Richard G; Louka, Nicolas

2014-12-15

A new process, Intensification of Vaporization by Decompression to the Vacuum (IVDV), is proposed for texturizing purple maize. It consists in exposing humid kernels to high steam pressure followed by a decompression to the vacuum. Response surface methodology with three operating parameters (initial water content (W), steam pressure (P) and processing time (T)) was used to study the response parameters: Total Anthocyanins Content, Total Polyphenols Content, Free Radical Scavenging Activity, Expansion Ratio, Hardness and Work Done. P was the most important variable, followed by T. Pressure drop helped the release of bound phenolics arriving to their expulsion outside the cell. Combined with convenient T and W, it caused kernels expansion. Multiple optimization of expansion and chemical content showed that IVDV resulted in good texturization of maize while preserving the antioxidant compounds and activity. Optimal conditions were: W=29%, P=5 bar and T=37s. Copyright © 2014 Elsevier Ltd. All rights reserved.

A conceptual demonstration of freeze desalination-membrane distillation (FD-MD) hybrid desalination process utilizing liquefied natural gas (LNG) cold energy.

PubMed

Wang, Peng; Chung, Tai-Shung

2012-09-01

The severe global water scarcity and record-high fossil oil price have greatly stimulated the research interests on new desalination technologies which can be driven by renewable energy or waste energy. In this study, a hybrid desalination process comprising freeze desalination and membrane distillation (FD-MD) processes was developed and explored in an attempt to utilize the waste cold energy released from re-gasification of liquefied natural gas (LNG). The concept of this technology was demonstrated using indirect-contact freeze desalination (ICFD) and direct-contact membrane distillation (DCMD) configurations. By optimizing the ICFD operation parameters, namely, the usage of nucleate seeds, operation duration and feed concentration, high quality drinkable water with a low salinity ∼0.144 g/L was produced in the ICFD process. At the same time, using the optimized hollow fiber module length and packing density in the DCMD process, ultra pure water with a low salinity of 0.062 g/L was attained at a condition of high energy efficiency (EE). Overall, by combining FD and MD processes and adopting the optimized operation parameters, the hybrid FD-MD system has been successfully demonstrated. A high total water recovery of 71.5% was achieved, and the water quality obtained met the standard for drinkable water. In addition, with results from specific energy calculation, it was proven that the hybrid process is an energy-saving process and utilization of LNG cold energy could greatly reduce the total energy consumption. Copyright © 2012 Elsevier Ltd. All rights reserved.

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

Anderson, Katherine H.; Cutler, Dylan S.; Olis, Daniel R.

REopt is a techno-economic decision support model used to optimize energy systems for buildings, campuses, communities, and microgrids. The primary application of the model is for optimizing the integration and operation of behind-the-meter energy assets. This report provides an overview of the model, including its capabilities and typical applications; inputs and outputs; economic calculations; technology descriptions; and model parameters, variables, and equations. The model is highly flexible, and is continually evolving to meet the needs of each analysis. Therefore, this report is not an exhaustive description of all capabilities, but rather a summary of the core components of the model.

A Formal Approach to Empirical Dynamic Model Optimization and Validation

NASA Technical Reports Server (NTRS)

Crespo, Luis G; Morelli, Eugene A.; Kenny, Sean P.; Giesy, Daniel P.

2014-01-01

A framework was developed for the optimization and validation of empirical dynamic models subject to an arbitrary set of validation criteria. The validation requirements imposed upon the model, which may involve several sets of input-output data and arbitrary specifications in time and frequency domains, are used to determine if model predictions are within admissible error limits. The parameters of the empirical model are estimated by finding the parameter realization for which the smallest of the margins of requirement compliance is as large as possible. The uncertainty in the value of this estimate is characterized by studying the set of model parameters yielding predictions that comply with all the requirements. Strategies are presented for bounding this set, studying its dependence on admissible prediction error set by the analyst, and evaluating the sensitivity of the model predictions to parameter variations. This information is instrumental in characterizing uncertainty models used for evaluating the dynamic model at operating conditions differing from those used for its identification and validation. A practical example based on the short period dynamics of the F-16 is used for illustration.

A Two-Stage Method to Determine Optimal Product Sampling considering Dynamic Potential Market

PubMed Central

Hu, Zhineng; Lu, Wei; Han, Bing

2015-01-01

This paper develops an optimization model for the diffusion effects of free samples under dynamic changes in potential market based on the characteristics of independent product and presents a two-stage method to figure out the sampling level. The impact analysis of the key factors on the sampling level shows that the increase of the external coefficient or internal coefficient has a negative influence on the sampling level. And the changing rate of the potential market has no significant influence on the sampling level whereas the repeat purchase has a positive one. Using logistic analysis and regression analysis, the global sensitivity analysis gives a whole analysis of the interaction of all parameters, which provides a two-stage method to estimate the impact of the relevant parameters in the case of inaccuracy of the parameters and to be able to construct a 95% confidence interval for the predicted sampling level. Finally, the paper provides the operational steps to improve the accuracy of the parameter estimation and an innovational way to estimate the sampling level. PMID:25821847

Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint

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

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

In the traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of load forecasting technique can provide accurate prediction of load power that will happen in future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution systemmore » operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.« less

The optimization of design parameters for surge relief valve for pipeline systems

NASA Astrophysics Data System (ADS)

Kim, Hyunjun; Hur, Jisung; Kim, Sanghyun

2017-06-01

Surge is an abnormal pressure which induced by rapid changes of flow rate in pipeline systems. In order to protect pipeline system from the surge pressure, various hydraulic devices have been developed. Surge-relief valve(SRV) is one of the widely applied devices to control surge due to its feasibility in application, efficiency and cost-effectiveness. SRV is designed to automatically open under abnormal pressure and discharge the flow and makes pressure of the system drop to the allowable level. The performance of the SRV is influenced by hydraulics. According to previous studies, there are several affecting factors which determine performance of the PRV such as design parameters (e.g. size of the valve), system parameters (e.g. number of the valves and location of the valve), and operation parameters (e.g. set point and operation time). Therefore, the systematic consideration for factors affecting performance of SRV is required for the proper installation of SRV in the system. In this study, methodology for finding optimum parameters of the SRV is explored through the integration of Genetic Algorithm(GA) into surge analysis.

Using natural biomass microorganisms for drinking water denitrification.

PubMed

Costa, Darleila Damasceno; Gomes, Anderson Albino; Fernandes, Mylena; Lopes da Costa Bortoluzzi, Roseli; Magalhães, Maria de Lourdes Borba; Skoronski, Everton

2018-07-01

Among the methods that are studied to eliminate nitrate from drinking water, biological denitrification is an attractive strategy. Although several studies report the use of denitrifying bacteria for nitrate removal, they usually involve the use of sewage sludge as biomass to obtain the microbiota. In the present study, denitrifying bacteria was isolated from bamboo, and variable parameters were controlled focusing on optimal bacterial performance followed by physicochemical analysis of water adequacy. In this way, bamboo was used as a source of denitrifying microorganisms, using either Immobilized Microorganisms (IM) or Suspended Microorganisms (SM) for nitrate removal. Denitrification parameters optimization was carried out by analysis of denitrification at different pH values, temperature, nitrate concentrations, carbon sources as well as different C/N ratios. In addition, operational stability and denitrification kinetics were evaluated. Microorganisms present in the biomass responsible for denitrification were identified as Proteus mirabilis. The denitrified water was submitted to physicochemical treatment such as coagulation and flocculation to adjust to the parameters of color and turbidity to drinking water standards. Denitrification using IM occurred with 73% efficiency in the absence of an external carbon source. The use of SM provided superior denitrification efficiency using ethanol (96.46%), glucose (98.58%) or glycerol (98.5%) as carbon source. The evaluation of the operational stability allowed 12 cycles of biomass reuse using the IM and 9 cycles using the SM. After physical-chemical treatment, only SM denitrified water remained within drinking water standards parameters of color and turbidity. Copyright © 2018 Elsevier Ltd. All rights reserved.

Particle swarm optimization with recombination and dynamic linkage discovery.

PubMed

Chen, Ying-Ping; Peng, Wen-Chih; Jian, Ming-Chung

2007-12-01

In this paper, we try to improve the performance of the particle swarm optimizer by incorporating the linkage concept, which is an essential mechanism in genetic algorithms, and design a new linkage identification technique called dynamic linkage discovery to address the linkage problem in real-parameter optimization problems. Dynamic linkage discovery is a costless and effective linkage recognition technique that adapts the linkage configuration by employing only the selection operator without extra judging criteria irrelevant to the objective function. Moreover, a recombination operator that utilizes the discovered linkage configuration to promote the cooperation of particle swarm optimizer and dynamic linkage discovery is accordingly developed. By integrating the particle swarm optimizer, dynamic linkage discovery, and recombination operator, we propose a new hybridization of optimization methodologies called particle swarm optimization with recombination and dynamic linkage discovery (PSO-RDL). In order to study the capability of PSO-RDL, numerical experiments were conducted on a set of benchmark functions as well as on an important real-world application. The benchmark functions used in this paper were proposed in the 2005 Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation. The experimental results on the benchmark functions indicate that PSO-RDL can provide a level of performance comparable to that given by other advanced optimization techniques. In addition to the benchmark, PSO-RDL was also used to solve the economic dispatch (ED) problem for power systems, which is a real-world problem and highly constrained. The results indicate that PSO-RDL can successfully solve the ED problem for the three-unit power system and obtain the currently known best solution for the 40-unit system.

The cost of noise reduction for departure and arrival operations of commercial tilt rotor aircraft

NASA Technical Reports Server (NTRS)

Faulkner, H. B.; Swan, W. M.

1976-01-01

The relationship between direct operating cost (DOC) and noise annoyance due to a departure and an arrival operation was developed for commercial tilt rotor aircraft. This was accomplished by generating a series of tilt rotor aircraft designs to meet various noise goals at minimum DOC. These vehicles ranged across the spectrum of possible noise levels from completely unconstrained to the quietest vehicles that could be designed within the study ground rules. Optimization parameters were varied to find the minimum DOC. This basic variation was then extended to different aircraft sizes and technology time frames.

Switching and optimizing control for coal flotation process based on a hybrid model

PubMed Central

Dong, Zhiyong; Wang, Ranfeng; Fan, Minqiang; Fu, Xiang

2017-01-01

Flotation is an important part of coal preparation, and the flotation column is widely applied as efficient flotation equipment. This process is complex and affected by many factors, with the froth depth and reagent dosage being two of the most important and frequently manipulated variables. This paper proposes a new method of switching and optimizing control for the coal flotation process. A hybrid model is built and evaluated using industrial data. First, wavelet analysis and principal component analysis (PCA) are applied for signal pre-processing. Second, a control model for optimizing the set point of the froth depth is constructed based on fuzzy control, and a control model is designed to optimize the reagent dosages based on expert system. Finally, the least squares-support vector machine (LS-SVM) is used to identify the operating conditions of the flotation process and to select one of the two models (froth depth or reagent dosage) for subsequent operation according to the condition parameters. The hybrid model is developed and evaluated on an industrial coal flotation column and exhibits satisfactory performance. PMID:29040305

Optimization of the Electrochemical Extraction and Recovery of Metals from Electronic Waste Using Response Surface Methodology

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

Diaz, Luis A.; Clark, Gemma G.; Lister, Tedd E.

The rapid growth of the electronic waste can be viewed both as an environmental threat and as an attractive source of minerals that can reduce the mining of natural resources, and stabilize the market of critical materials, such as rare earths. Here in this article surface response methodology was used to optimize a previously developed electrochemical recovery process for base metals from electronic waste using a mild oxidant (Fe 3+). Through this process an effective extraction of base metals can be achieved enriching the concentration of precious metals and significantly reducing environmental impacts and operational costs associated with the wastemore » generation and chemical consumption. The optimization was performed using a bench-scale system specifically designed for this process. Operational parameters such as flow rate, applied current density and iron concentration were optimized to reduce the specific energy consumption of the electrochemical recovery process to 1.94 kWh per kg of metal recovered at a processing rate of 3.3 g of electronic waste per hour.« less

Adaptive model-based control systems and methods for controlling a gas turbine

NASA Technical Reports Server (NTRS)

Brunell, Brent Jerome (Inventor); Mathews, Jr., Harry Kirk (Inventor); Kumar, Aditya (Inventor)

2004-01-01

Adaptive model-based control systems and methods are described so that performance and/or operability of a gas turbine in an aircraft engine, power plant, marine propulsion, or industrial application can be optimized under normal, deteriorated, faulted, failed and/or damaged operation. First, a model of each relevant system or component is created, and the model is adapted to the engine. Then, if/when deterioration, a fault, a failure or some kind of damage to an engine component or system is detected, that information is input to the model-based control as changes to the model, constraints, objective function, or other control parameters. With all the information about the engine condition, and state and directives on the control goals in terms of an objective function and constraints, the control then solves an optimization so the optimal control action can be determined and taken. This model and control may be updated in real-time to account for engine-to-engine variation, deterioration, damage, faults and/or failures using optimal corrective control action command(s).

Optimization of the Electrochemical Extraction and Recovery of Metals from Electronic Waste Using Response Surface Methodology

DOE PAGES

Diaz, Luis A.; Clark, Gemma G.; Lister, Tedd E.

2017-06-08

The rapid growth of the electronic waste can be viewed both as an environmental threat and as an attractive source of minerals that can reduce the mining of natural resources, and stabilize the market of critical materials, such as rare earths. Here in this article surface response methodology was used to optimize a previously developed electrochemical recovery process for base metals from electronic waste using a mild oxidant (Fe 3+). Through this process an effective extraction of base metals can be achieved enriching the concentration of precious metals and significantly reducing environmental impacts and operational costs associated with the wastemore » generation and chemical consumption. The optimization was performed using a bench-scale system specifically designed for this process. Operational parameters such as flow rate, applied current density and iron concentration were optimized to reduce the specific energy consumption of the electrochemical recovery process to 1.94 kWh per kg of metal recovered at a processing rate of 3.3 g of electronic waste per hour.« less

Electrochemical oxidation of ampicillin antibiotic at boron-doped diamond electrodes and process optimization using response surface methodology.

PubMed

Körbahti, Bahadır K; Taşyürek, Selin

2015-03-01

Electrochemical oxidation and process optimization of ampicillin antibiotic at boron-doped diamond electrodes (BDD) were investigated in a batch electrochemical reactor. The influence of operating parameters, such as ampicillin concentration, electrolyte concentration, current density, and reaction temperature, on ampicillin removal, COD removal, and energy consumption was analyzed in order to optimize the electrochemical oxidation process under specified cost-driven constraints using response surface methodology. Quadratic models for the responses satisfied the assumptions of the analysis of variance well according to normal probability, studentized residuals, and outlier t residual plots. Residual plots followed a normal distribution, and outlier t values indicated that the approximations of the fitted models to the quadratic response surfaces were very good. Optimum operating conditions were determined at 618 mg/L ampicillin concentration, 3.6 g/L electrolyte concentration, 13.4 mA/cm(2) current density, and 36 °C reaction temperature. Under response surface optimized conditions, ampicillin removal, COD removal, and energy consumption were obtained as 97.1 %, 92.5 %, and 71.7 kWh/kg CODr, respectively.

[Application of optimized parameters SVM based on photoacoustic spectroscopy method in fault diagnosis of power transformer].

PubMed

Zhang, Yu-xin; Cheng, Zhi-feng; Xu, Zheng-ping; Bai, Jing

2015-01-01

In order to solve the problems such as complex operation, consumption for the carrier gas and long test period in traditional power transformer fault diagnosis approach based on dissolved gas analysis (DGA), this paper proposes a new method which is detecting 5 types of characteristic gas content in transformer oil such as CH4, C2H2, C2H4, C2H6 and H2 based on photoacoustic Spectroscopy and C2H2/C2H4, CH4/H2, C2H4/C2H6 three-ratios data are calculated. The support vector machine model was constructed using cross validation method under five support vector machine functions and four kernel functions, heuristic algorithms were used in parameter optimization for penalty factor c and g, which to establish the best SVM model for the highest fault diagnosis accuracy and the fast computing speed. Particles swarm optimization and genetic algorithm two types of heuristic algorithms were comparative studied in this paper for accuracy and speed in optimization. The simulation result shows that SVM model composed of C-SVC, RBF kernel functions and genetic algorithm obtain 97. 5% accuracy in test sample set and 98. 333 3% accuracy in train sample set, and genetic algorithm was about two times faster than particles swarm optimization in computing speed. The methods described in this paper has many advantages such as simple operation, non-contact measurement, no consumption for the carrier gas, long test period, high stability and sensitivity, the result shows that the methods described in this paper can instead of the traditional transformer fault diagnosis by gas chromatography and meets the actual project needs in transformer fault diagnosis.

Tailored parameter optimization methods for ordinary differential equation models with steady-state constraints.

PubMed

Fiedler, Anna; Raeth, Sebastian; Theis, Fabian J; Hausser, Angelika; Hasenauer, Jan

2016-08-22

Ordinary differential equation (ODE) models are widely used to describe (bio-)chemical and biological processes. To enhance the predictive power of these models, their unknown parameters are estimated from experimental data. These experimental data are mostly collected in perturbation experiments, in which the processes are pushed out of steady state by applying a stimulus. The information that the initial condition is a steady state of the unperturbed process provides valuable information, as it restricts the dynamics of the process and thereby the parameters. However, implementing steady-state constraints in the optimization often results in convergence problems. In this manuscript, we propose two new methods for solving optimization problems with steady-state constraints. The first method exploits ideas from optimization algorithms on manifolds and introduces a retraction operator, essentially reducing the dimension of the optimization problem. The second method is based on the continuous analogue of the optimization problem. This continuous analogue is an ODE whose equilibrium points are the optima of the constrained optimization problem. This equivalence enables the use of adaptive numerical methods for solving optimization problems with steady-state constraints. Both methods are tailored to the problem structure and exploit the local geometry of the steady-state manifold and its stability properties. A parameterization of the steady-state manifold is not required. The efficiency and reliability of the proposed methods is evaluated using one toy example and two applications. The first application example uses published data while the second uses a novel dataset for Raf/MEK/ERK signaling. The proposed methods demonstrated better convergence properties than state-of-the-art methods employed in systems and computational biology. Furthermore, the average computation time per converged start is significantly lower. In addition to the theoretical results, the analysis of the dataset for Raf/MEK/ERK signaling provides novel biological insights regarding the existence of feedback regulation. Many optimization problems considered in systems and computational biology are subject to steady-state constraints. While most optimization methods have convergence problems if these steady-state constraints are highly nonlinear, the methods presented recover the convergence properties of optimizers which can exploit an analytical expression for the parameter-dependent steady state. This renders them an excellent alternative to methods which are currently employed in systems and computational biology.

Mammalian cell culture process for monoclonal antibody production: nonlinear modelling and parameter estimation.

PubMed

Selişteanu, Dan; Șendrescu, Dorin; Georgeanu, Vlad; Roman, Monica

2015-01-01

Monoclonal antibodies (mAbs) are at present one of the fastest growing products of pharmaceutical industry, with widespread applications in biochemistry, biology, and medicine. The operation of mAbs production processes is predominantly based on empirical knowledge, the improvements being achieved by using trial-and-error experiments and precedent practices. The nonlinearity of these processes and the absence of suitable instrumentation require an enhanced modelling effort and modern kinetic parameter estimation strategies. The present work is dedicated to nonlinear dynamic modelling and parameter estimation for a mammalian cell culture process used for mAb production. By using a dynamical model of such kind of processes, an optimization-based technique for estimation of kinetic parameters in the model of mammalian cell culture process is developed. The estimation is achieved as a result of minimizing an error function by a particle swarm optimization (PSO) algorithm. The proposed estimation approach is analyzed in this work by using a particular model of mammalian cell culture, as a case study, but is generic for this class of bioprocesses. The presented case study shows that the proposed parameter estimation technique provides a more accurate simulation of the experimentally observed process behaviour than reported in previous studies.

Mammalian Cell Culture Process for Monoclonal Antibody Production: Nonlinear Modelling and Parameter Estimation

PubMed Central

Selişteanu, Dan; Șendrescu, Dorin; Georgeanu, Vlad

2015-01-01

Monoclonal antibodies (mAbs) are at present one of the fastest growing products of pharmaceutical industry, with widespread applications in biochemistry, biology, and medicine. The operation of mAbs production processes is predominantly based on empirical knowledge, the improvements being achieved by using trial-and-error experiments and precedent practices. The nonlinearity of these processes and the absence of suitable instrumentation require an enhanced modelling effort and modern kinetic parameter estimation strategies. The present work is dedicated to nonlinear dynamic modelling and parameter estimation for a mammalian cell culture process used for mAb production. By using a dynamical model of such kind of processes, an optimization-based technique for estimation of kinetic parameters in the model of mammalian cell culture process is developed. The estimation is achieved as a result of minimizing an error function by a particle swarm optimization (PSO) algorithm. The proposed estimation approach is analyzed in this work by using a particular model of mammalian cell culture, as a case study, but is generic for this class of bioprocesses. The presented case study shows that the proposed parameter estimation technique provides a more accurate simulation of the experimentally observed process behaviour than reported in previous studies. PMID:25685797

Optimization of life support systems and their systems reliability

NASA Technical Reports Server (NTRS)

Fan, L. T.; Hwang, C. L.; Erickson, L. E.

1971-01-01

The identification, analysis, and optimization of life support systems and subsystems have been investigated. For each system or subsystem that has been considered, the procedure involves the establishment of a set of system equations (or mathematical model) based on theory and experimental evidences; the analysis and simulation of the model; the optimization of the operation, control, and reliability; analysis of sensitivity of the system based on the model; and, if possible, experimental verification of the theoretical and computational results. Research activities include: (1) modeling of air flow in a confined space; (2) review of several different gas-liquid contactors utilizing centrifugal force: (3) review of carbon dioxide reduction contactors in space vehicles and other enclosed structures: (4) application of modern optimal control theory to environmental control of confined spaces; (5) optimal control of class of nonlinear diffusional distributed parameter systems: (6) optimization of system reliability of life support systems and sub-systems: (7) modeling, simulation and optimal control of the human thermal system: and (8) analysis and optimization of the water-vapor eletrolysis cell.

Genetic algorithms and their use in Geophysical Problems

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

Parker, Paul B.

1999-04-01

Genetic algorithms (GAs), global optimization methods that mimic Darwinian evolution are well suited to the nonlinear inverse problems of geophysics. A standard genetic algorithm selects the best or ''fittest'' models from a ''population'' and then applies operators such as crossover and mutation in order to combine the most successful characteristics of each model and produce fitter models. More sophisticated operators have been developed, but the standard GA usually provides a robust and efficient search. Although the choice of parameter settings such as crossover and mutation rate may depend largely on the type of problem being solved, numerous results show thatmore » certain parameter settings produce optimal performance for a wide range of problems and difficulties. In particular, a low (about half of the inverse of the population size) mutation rate is crucial for optimal results, but the choice of crossover method and rate do not seem to affect performance appreciably. Optimal efficiency is usually achieved with smaller (< 50) populations. Lastly, tournament selection appears to be the best choice of selection methods due to its simplicity and its autoscaling properties. However, if a proportional selection method is used such as roulette wheel selection, fitness scaling is a necessity, and a high scaling factor (> 2.0) should be used for the best performance. Three case studies are presented in which genetic algorithms are used to invert for crustal parameters. The first is an inversion for basement depth at Yucca mountain using gravity data, the second an inversion for velocity structure in the crust of the south island of New Zealand using receiver functions derived from teleseismic events, and the third is a similar receiver function inversion for crustal velocities beneath the Mendocino Triple Junction region of Northern California. The inversions demonstrate that genetic algorithms are effective in solving problems with reasonably large numbers of free parameters and with computationally expensive objective function calculations. More sophisticated techniques are presented for special problems. Niching and island model algorithms are introduced as methods to find multiple, distinct solutions to the nonunique problems that are typically seen in geophysics. Finally, hybrid algorithms are investigated as a way to improve the efficiency of the standard genetic algorithm.« less

Genetic algorithms and their use in geophysical problems

NASA Astrophysics Data System (ADS)

Parker, Paul Bradley

Genetic algorithms (GAs), global optimization methods that mimic Darwinian evolution are well suited to the nonlinear inverse problems of geophysics. A standard genetic algorithm selects the best or "fittest" models from a "population" and then applies operators such as crossover and mutation in order to combine the most successful characteristics of each model and produce fitter models. More sophisticated operators have been developed, but the standard GA usually provides a robust and efficient search. Although the choice of parameter settings such as crossover and mutation rate may depend largely on the type of problem being solved, numerous results show that certain parameter settings produce optimal performance for a wide range of problems and difficulties. In particular, a low (about half of the inverse of the population size) mutation rate is crucial for optimal results, but the choice of crossover method and rate do not seem to affect performance appreciably. Also, optimal efficiency is usually achieved with smaller (<50) populations. Lastly, tournament selection appears to be the best choice of selection methods due to its simplicity and its autoscaling properties. However, if a proportional selection method is used such as roulette wheel selection, fitness scaling is a necessity, and a high scaling factor (>2.0) should be used for the best performance. Three case studies are presented in which genetic algorithms are used to invert for crustal parameters. The first is an inversion for basement depth at Yucca mountain using gravity data, the second an inversion for velocity structure in the crust of the south island of New Zealand using receiver functions derived from teleseismic events, and the third is a similar receiver function inversion for crustal velocities beneath the Mendocino Triple Junction region of Northern California. The inversions demonstrate that genetic algorithms are effective in solving problems with reasonably large numbers of free parameters and with computationally expensive objective function calculations. More sophisticated techniques are presented for special problems. Niching and island model algorithms are introduced as methods to find multiple, distinct solutions to the nonunique problems that are typically seen in geophysics. Finally, hybrid algorithms are investigated as a way to improve the efficiency of the standard genetic algorithm.

Ancient numerical daemons of conceptual hydrological modeling: 2. Impact of time stepping schemes on model analysis and prediction

NASA Astrophysics Data System (ADS)

Kavetski, Dmitri; Clark, Martyn P.

2010-10-01

Despite the widespread use of conceptual hydrological models in environmental research and operations, they remain frequently implemented using numerically unreliable methods. This paper considers the impact of the time stepping scheme on model analysis (sensitivity analysis, parameter optimization, and Markov chain Monte Carlo-based uncertainty estimation) and prediction. It builds on the companion paper (Clark and Kavetski, 2010), which focused on numerical accuracy, fidelity, and computational efficiency. Empirical and theoretical analysis of eight distinct time stepping schemes for six different hydrological models in 13 diverse basins demonstrates several critical conclusions. (1) Unreliable time stepping schemes, in particular, fixed-step explicit methods, suffer from troublesome numerical artifacts that severely deform the objective function of the model. These deformations are not rare isolated instances but can arise in any model structure, in any catchment, and under common hydroclimatic conditions. (2) Sensitivity analysis can be severely contaminated by numerical errors, often to the extent that it becomes dominated by the sensitivity of truncation errors rather than the model equations. (3) Robust time stepping schemes generally produce "better behaved" objective functions, free of spurious local optima, and with sufficient numerical continuity to permit parameter optimization using efficient quasi Newton methods. When implemented within a multistart framework, modern Newton-type optimizers are robust even when started far from the optima and provide valuable diagnostic insights not directly available from evolutionary global optimizers. (4) Unreliable time stepping schemes lead to inconsistent and biased inferences of the model parameters and internal states. (5) Even when interactions between hydrological parameters and numerical errors provide "the right result for the wrong reason" and the calibrated model performance appears adequate, unreliable time stepping schemes make the model unnecessarily fragile in predictive mode, undermining validation assessments and operational use. Erroneous or misleading conclusions of model analysis and prediction arising from numerical artifacts in hydrological models are intolerable, especially given that robust numerics are accepted as mainstream in other areas of science and engineering. We hope that the vivid empirical findings will encourage the conceptual hydrological community to close its Pandora's box of numerical problems, paving the way for more meaningful model application and interpretation.

Multicriteria optimization approach to design and operation of district heating supply system over its life cycle

NASA Astrophysics Data System (ADS)

Hirsch, Piotr; Duzinkiewicz, Kazimierz; Grochowski, Michał

2017-11-01

District Heating (DH) systems are commonly supplied using local heat sources. Nowadays, modern insulation materials allow for effective and economically viable heat transportation over long distances (over 20 km). In the paper a method for optimized selection of design and operating parameters of long distance Heat Transportation System (HTS) is proposed. The method allows for evaluation of feasibility and effectivity of heat transportation from the considered heat sources. The optimized selection is formulated as multicriteria decision-making problem. The constraints for this problem include a static HTS model, allowing considerations of system life cycle, time variability and spatial topology. Thereby, variation of heat demand and ground temperature within the DH area, insulation and pipe aging and/or terrain elevation profile are taken into account in the decision-making process. The HTS construction costs, pumping power, and heat losses are considered as objective functions. Inner pipe diameter, insulation thickness, temperatures and pumping stations locations are optimized during the decision-making process. Moreover, the variants of pipe-laying e.g. one pipeline with the larger diameter or two with the smaller might be considered during the optimization. The analyzed optimization problem is multicriteria, hybrid and nonlinear. Because of such problem properties, the genetic solver was applied.

First results from negative ion beam extraction in ROBIN in surface mode

NASA Astrophysics Data System (ADS)

Pandya, Kaushal; Gahlaut, Agrajit; Yadav, Ratnakar K.; Bhuyan, Manas; Bandyopadhyay, Mainak; Das, B. K.; Bharathi, P.; Vupugalla, Mahesh; Parmar, K. G.; Tyagi, Himanshu; Patel, Kartik; Bhagora, Jignesh; Mistri, Hiren; Prajapati, Bhavesh; Pandey, Ravi; Chakraborty, Arun. K.

2017-08-01

ROBIN, the first step in the Indian R&D program on negative ion beams has reached an important milestone, with the production of negative ions in the surface conversion mode through Cesium (Cs) vapor injection into the source. In the present set-up, negative hydrogen ion beam extraction is effected through an extraction area of ˜73.38 cm2 (146 apertures of 8mm diameter). The three grid electrostatic accelerator system of ROBIN is fed by high voltage DC power supplies (Extraction Power Supply System: 11kV, 35A and Acceleration Power Supply System: 35kV, 15A). Though, a considerable reduction of co-extracted electron current is usually observed during surface mode operation, in order to increase the negative ion current, various other parameters such as plasma grid temperature, plasma grid bias, extraction to acceleration voltage ratio, impurity control and Cs recycling need to be optimized. In the present experiments, to control and to understand the impurity behavior, a Cryopump (14,000 l/s for Hydrogen) is installed along with a Residual Gas Analyzer (RGA). To characterize the source plasma, two sets of Langmuir probes are inserted through the diagnostic flange ports available at the extraction plane. To characterize the beam properties, thermal differential calorimeter, Doppler Shift Spectroscopy and electrical current measurements are implemented in ROBIN. In the present set up, all the negative ion beam extraction experiments have been performed by varying different experimental parameters e.g. RF power (30-70 kW), source operational pressure (0.3 - 0.6Pa), plasma grid bias voltage, extraction & acceleration voltage combination etc. The experiments in surface mode operation is resulted a reduction of co-extracted electron current having electron to ion ratio (e/i) ˜2 whereas the extracted negative ion current density was increased. However, further increase in negative ion current density is expected to be improved after a systematic optimization of the operational parameters and Cs conditioning of the source. It was also found out that a better performance of ROBIN is achieved in the pressure range: 0.5-0.6 Pa. In this paper, the preliminary results on parametric study of ROBIN operation and beam optimization in surface mode are discussed.

Dynamic Response during PEM Fuel Cell Loading-up

PubMed Central

Pei, Pucheng; Yuan, Xing; Gou, Jun; Li, Pengcheng

2009-01-01

A study on the effects of controlling and operating parameters for a Proton Exchange Membrane (PEM) fuel cell on the dynamic phenomena during the loading-up process is presented. The effect of the four parameters of load-up amplitudes and rates, operating pressures and current levels on gas supply or even starvation in the flow field is analyzed based accordingly on the transient characteristics of current output and voltage. Experiments are carried out in a single fuel cell with an active area of 285 cm2. The results show that increasing the loading-up amplitude can inevitably increase the possibility of gas starvation in channels when a constant flow rate has been set for the cathode; With a higher operating pressure, the dynamic performance will be improved and gas starvations can be relieved. The transient gas supply in the flow channel during two loading-up mode has also been discussed. The experimental results will be helpful for optimizing the control and operation strategies for PEM fuel cells in vehicles.

Operational modal analysis applied to the concert harp

NASA Astrophysics Data System (ADS)

Chomette, B.; Le Carrou, J.-L.

2015-05-01

Operational modal analysis (OMA) methods are useful to extract modal parameters of operating systems. These methods seem to be particularly interesting to investigate the modal basis of string instruments during operation to avoid certain disadvantages due to conventional methods. However, the excitation in the case of string instruments is not optimal for OMA due to the presence of damped harmonic components and low noise in the disturbance signal. Therefore, the present study investigates the least-square complex exponential (LSCE) and the modified least-square complex exponential methods in the case of a string instrument to identify modal parameters of the instrument when it is played. The efficiency of the approach is experimentally demonstrated on a concert harp excited by some of its strings and the two methods are compared to a conventional modal analysis. The results show that OMA allows us to identify modes particularly present in the instrument's response with a good estimation especially if they are close to the excitation frequency with the modified LSCE method.

Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA

PubMed Central

Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari

2014-01-01

A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. PMID:24982962

Dynamic PID loop control

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

Pei, L.; Klebaner, A.; Theilacker, J.

2011-06-01

The Horizontal Test Stand (HTS) SRF Cavity and Cryomodule 1 (CM1) of eight 9-cell, 1.3GHz SRF cavities are operating at Fermilab. For the cryogenic control system, how to hold liquid level constant in the cryostat by regulation of its Joule-Thompson JT-valve is very important after cryostat cool down to 2.0 K. The 72-cell cryostat liquid level response generally takes a long time delay after regulating its JT-valve; therefore, typical PID control loop should result in some cryostat parameter oscillations. This paper presents a type of PID parameter self-optimal and Time-Delay control method used to reduce cryogenic system parameters oscillation.

Optimization of the microcable and detector parameters towards low noise in the STS readout system

NASA Astrophysics Data System (ADS)

Kasinski, Krzysztof; Kleczek, Rafal; Schmidt, Christian J.

2015-09-01

Successful operation of the Silicon Tracking System requires charge measurement of each hit with equivalent noise charge lower than 1000 e- rms. Detector channels will not be identical, they will be constructed accordingly to the estimated occupancy, therefore for the readout electronics, detector system will exhibit various parameters. This paper presents the simulation-based study on the required microcable (trace width, dielectric material), detector (aluminum strip resistance) and external passives' (decoupling capacitors) parameters in the Silicon Tracking System. Studies will be performed using a front-end electronics (charge sensitive amplifier with shaper) designed for the power budget of 10 mA/channel.

Optimization and Performance parameters for adsorption of Cr6+ by microwave assisted carbon from Sterculia foetida shells

NASA Astrophysics Data System (ADS)

Gnanasundaram, N.; Loganathan, M.; Singh, A.

2017-06-01

Modeling of adsorption of Cr6+ on to activated carbon prepared from Sterculia foetida dried seed shells under different drying techniques namely sun, oven, and microwave drying (450W, 600W, 900W power). Optimization of process parameters such as pH, adsorbent dosage (g/ml), temperature (°C), contact time (min) were evaluated using Central Composite Rotatable Design (CCRD) of Response Surface Methodology (RSM). For batch adsorption studies at pH 3, adsorbent dosage of 1.5 g/ml, temperature 35°C and contact time 90 min were found to be optimum for the system under consideration and Microwave Activated Carbonized Sterculia foetida (MACSF) at 450W was found to be best suited for the adsorption of Cr+6 ions. The system was found to follow Langmuir type monolayer adsorption for the given operational parameters. SEM analysis was used to study the surface morphology of the carbon samples and the effect of pretreatment on carbonization.

Profiling optimization for big data transfer over dedicated channels

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

Yun, D.; Wu, Qishi; Rao, Nageswara S

The transfer of big data is increasingly supported by dedicated channels in high-performance networks, where transport protocols play an important role in maximizing applicationlevel throughput and link utilization. The performance of transport protocols largely depend on their control parameter settings, but it is prohibitively time consuming to conduct an exhaustive search in a large parameter space to find the best set of parameter values. We propose FastProf, a stochastic approximation-based transport profiler, to quickly determine the optimal operational zone of a given data transfer protocol/method over dedicated channels. We implement and test the proposed method using both emulations based onmore » real-life performance measurements and experiments over physical connections with short (2 ms) and long (380 ms) delays. Both the emulation and experimental results show that FastProf significantly reduces the profiling overhead while achieving a comparable level of end-to-end throughput performance with the exhaustive search-based approach.« less

Design and implementation of fuzzy-PD controller based on relation models: A cross-entropy optimization approach

NASA Astrophysics Data System (ADS)

Anisimov, D. N.; Dang, Thai Son; Banerjee, Santo; Mai, The Anh

2017-07-01

In this paper, an intelligent system use fuzzy-PD controller based on relation models is developed for a two-wheeled self-balancing robot. Scaling factors of the fuzzy-PD controller are optimized by a Cross-Entropy optimization method. A linear Quadratic Regulator is designed to bring a comparison with the fuzzy-PD controller by control quality parameters. The controllers are ported and run on STM32F4 Discovery Kit based on the real-time operating system. The experimental results indicate that the proposed fuzzy-PD controller runs exactly on embedded system and has desired performance in term of fast response, good balance and stabilize.

Research on charging and discharging control strategy for electric vehicles as distributed energy storage devices

NASA Astrophysics Data System (ADS)

Zhang, Min; Yang, Feng; Zhang, Dongqing; Tang, Pengcheng

2018-02-01

A large number of electric vehicles are connected to the family micro grid will affect the operation safety of the power grid and the quality of power. Considering the factors of family micro grid price and electric vehicle as a distributed energy storage device, a two stage optimization model is established, and the improved discrete binary particle swarm optimization algorithm is used to optimize the parameters in the model. The proposed control strategy of electric vehicle charging and discharging is of practical significance for the rational control of electric vehicle as a distributed energy storage device and electric vehicle participating in the peak load regulation of power consumption.

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.

Optimal plane change during constant altitude hypersonic flight

NASA Technical Reports Server (NTRS)

Mease, K. D.; Vinh, N. X.; Kuo, S. H.

1988-01-01

Future spacecraft operating in the vicinity of the earth may have resort to the atmosphere as an aid in effecting orbital change. While a previous treatment of this technique chose constant altitude, speed, and angle-of-attack values in order to maximize the plane change for a fixed amount of propellant consumption during hypersonic flight, the former two parameters are presently released from the constraint of constancy. The general characteristics of the optimal controls are described on the basis of the domain of maneuverability, and numerical solutions are obtained for several specific cases. Under the condition of constant-altitude flight, it is generally not optimal to fly at constant angle-of-attack.

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.

Optimizing coagulation-adsorption for haloform and TOC (Total Organic Carbon) reduction

NASA Astrophysics Data System (ADS)

Semmens, M. J.; Hohenstein, G.; Staples, A.; Norgaard, G.; Ayers, K.; Tyson, M. P.

1983-05-01

The removal of organic matter from Mississippi River water by coagulation and softening processes and the influence of operating parameters upon the removal process are examined. Furthermore, since activated carbon is typically employed to reduce organic concentrations, the effectiveness of various pretreatments are evaluated for their impact upon carbon bed life and the product water quality.

Mass-based design and optimization of wave rotors for gas turbine engine enhancement

NASA Astrophysics Data System (ADS)

Chan, S.; Liu, H.

2017-03-01

An analytic method aiming at mass properties was developed for the preliminary design and optimization of wave rotors. In the present method, we introduce the mass balance principle into the design and thus can predict and optimize the mass qualities as well as the performance of wave rotors. A dedicated least-square method with artificial weighting coefficients was developed to solve the over-constrained system in the mass-based design. This method and the adoption of the coefficients were validated by numerical simulation. Moreover, the problem of fresh air exhaustion (FAE) was put forward and analyzed, and exhaust gas recirculation (EGR) was investigated. Parameter analyses and optimization elucidated which designs would not only achieve the best performance, but also operate with minimum EGR and no FAE.

Design optimization of a prescribed vibration system using conjoint value analysis

NASA Astrophysics Data System (ADS)

Malinga, Bongani; Buckner, Gregory D.

2016-12-01

This article details a novel design optimization strategy for a prescribed vibration system (PVS) used to mechanically filter solids from fluids in oil and gas drilling operations. A dynamic model of the PVS is developed, and the effects of disturbance torques are detailed. This model is used to predict the effects of design parameters on system performance and efficiency, as quantified by system attributes. Conjoint value analysis, a statistical technique commonly used in marketing science, is utilized to incorporate designer preferences. This approach effectively quantifies and optimizes preference-based trade-offs in the design process. The effects of designer preferences on system performance and efficiency are simulated. This novel optimization strategy yields improvements in all system attributes across all simulated vibration profiles, and is applicable to other industrial electromechanical systems.

Optimal design of an electro-hydraulic valve for heavy-duty vehicle clutch actuator with certain constraints

NASA Astrophysics Data System (ADS)

Meng, Fei; Shi, Peng; Karimi, Hamid Reza; Zhang, Hui

2016-02-01

The main objective of this paper is to investigate the sensitivity analysis and optimal design of a proportional solenoid valve (PSV) operated pressure reducing valve (PRV) for heavy-duty automatic transmission clutch actuators. The nonlinear electro-hydraulic valve model is developed based on fluid dynamics. In order to implement the sensitivity analysis and optimization for the PRV, the PSV model is validated by comparing the results with data obtained from a real test-bench. The sensitivity of the PSV pressure response with regard to the structural parameters is investigated by using Sobol's method. Finally, simulations and experimental investigations are performed on the optimized prototype and the results reveal that the dynamical characteristics of the valve have been improved in comparison with the original valve.

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.

Application of Fourier transform near-infrared spectroscopy to optimization of green tea steaming process conditions.

PubMed

Ono, Daiki; Bamba, Takeshi; Oku, Yuichi; Yonetani, Tsutomu; Fukusaki, Eiichiro

2011-09-01

In this study, we constructed prediction models by metabolic fingerprinting of fresh green tea leaves using Fourier transform near-infrared (FT-NIR) spectroscopy and partial least squares (PLS) regression analysis to objectively optimize of the steaming process conditions in green tea manufacture. The steaming process is the most important step for manufacturing high quality green tea products. However, the parameter setting of the steamer is currently determined subjectively by the manufacturer. Therefore, a simple and robust system that can be used to objectively set the steaming process parameters is necessary. We focused on FT-NIR spectroscopy because of its simple operation, quick measurement, and low running costs. After removal of noise in the spectral data by principal component analysis (PCA), PLS regression analysis was performed using spectral information as independent variables, and the steaming parameters set by experienced manufacturers as dependent variables. The prediction models were successfully constructed with satisfactory accuracy. Moreover, the results of the demonstrated experiment suggested that the green tea steaming process parameters could be predicted on a larger manufacturing scale. This technique will contribute to improvement of the quality and productivity of green tea because it can objectively optimize the complicated green tea steaming process and will be suitable for practical use in green tea manufacture. Copyright © 2011 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Strategy Developed for Selecting Optimal Sensors for Monitoring Engine Health

NASA Technical Reports Server (NTRS)

2004-01-01

Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate expected sensor values for targeted fault scenarios. Taken together, this information provides an efficient condensation of the engineering experience and engine flow physics needed for sensor selection. The systematic sensor selection strategy is composed of three primary algorithms. The core of the selection process is a genetic algorithm that iteratively improves a defined quality measure of selected sensor suites. A merit algorithm is employed to compute the quality measure for each test sensor suite presented by the selection process. The quality measure is based on the fidelity of fault detection and the level of fault source discrimination provided by the test sensor suite. An inverse engine model, whose function is to derive hardware performance parameters from sensor data, is an integral part of the merit algorithm. The final component is a statistical evaluation algorithm that characterizes the impact of interference effects, such as control-induced sensor variation and sensor noise, on the probability of fault detection and isolation for optimal and near-optimal sensor suites.

A new effective operator for the hybrid algorithm for solving global optimisation problems

NASA Astrophysics Data System (ADS)

Duc, Le Anh; Li, Kenli; Nguyen, Tien Trong; Yen, Vu Minh; Truong, Tung Khac

2018-04-01

Hybrid algorithms have been recently used to solve complex single-objective optimisation problems. The ultimate goal is to find an optimised global solution by using these algorithms. Based on the existing algorithms (HP_CRO, PSO, RCCRO), this study proposes a new hybrid algorithm called MPC (Mean-PSO-CRO), which utilises a new Mean-Search Operator. By employing this new operator, the proposed algorithm improves the search ability on areas of the solution space that the other operators of previous algorithms do not explore. Specifically, the Mean-Search Operator helps find the better solutions in comparison with other algorithms. Moreover, the authors have proposed two parameters for balancing local and global search and between various types of local search, as well. In addition, three versions of this operator, which use different constraints, are introduced. The experimental results on 23 benchmark functions, which are used in previous works, show that our framework can find better optimal or close-to-optimal solutions with faster convergence speed for most of the benchmark functions, especially the high-dimensional functions. Thus, the proposed algorithm is more effective in solving single-objective optimisation problems than the other existing algorithms.

Coordinated Control of Cross-Flow Turbines

NASA Astrophysics Data System (ADS)

Strom, Benjamin; Brunton, Steven; Polagye, Brian

2016-11-01

Cross-flow turbines, also known as vertical-axis turbines, have several advantages over axial-flow turbines for a number of applications including urban wind power, high-density arrays, and marine or fluvial currents. By controlling the angular velocity applied to the turbine as a function of angular blade position, we have demonstrated a 79 percent increase in cross-flow turbine efficiency over constant-velocity control. This strategy uses the downhill simplex method to optimize control parameter profiles during operation of a model turbine in a recirculating water flume. This optimization method is extended to a set of two turbines, where the blade motions and position of the downstream turbine are optimized to beneficially interact with the coherent structures in the wake of the upstream turbine. This control scheme has the potential to enable high-density arrays of cross-flow turbines to operate at cost-effective efficiency. Turbine wake and force measurements are analyzed for insight into the effect of a coordinated control strategy.

An improved self-adaptive ant colony algorithm based on genetic strategy for the traveling salesman problem

NASA Astrophysics Data System (ADS)

Wang, Pan; Zhang, Yi; Yan, Dong

2018-05-01

Ant Colony Algorithm (ACA) is a powerful and effective algorithm for solving the combination optimization problem. Moreover, it was successfully used in traveling salesman problem (TSP). But it is easy to prematurely converge to the non-global optimal solution and the calculation time is too long. To overcome those shortcomings, a new method is presented-An improved self-adaptive Ant Colony Algorithm based on genetic strategy. The proposed method adopts adaptive strategy to adjust the parameters dynamically. And new crossover operation and inversion operation in genetic strategy was used in this method. We also make an experiment using the well-known data in TSPLIB. The experiment results show that the performance of the proposed method is better than the basic Ant Colony Algorithm and some improved ACA in both the result and the convergence time. The numerical results obtained also show that the proposed optimization method can achieve results close to the theoretical best known solutions at present.

Green synthesis of isopropyl myristate in novel single phase medium Part I: Batch optimization studies.

PubMed

Vadgama, Rajeshkumar N; Odaneth, Annamma A; Lali, Arvind M

2015-12-01

Isopropyl myristate finds many applications in food, cosmetic and pharmaceutical industries as an emollient, thickening agent, or lubricant. Using a homogeneous reaction phase, non-specific lipase derived from Candida antartica, marketed as Novozym 435, was determined to be most suitable for the enzymatic synthesis of isopropyl myristate. The high molar ratio of alcohol to acid creates novel single phase medium which overcomes mass transfer effects and facilitates downstream processing. The effect of various reaction parameters was optimized to obtain a high yield of isopropyl myristate. Effect of temperature, agitation speed, organic solvent, biocatalyst loading and batch operational stability of the enzyme was systematically studied. The conversion of 87.65% was obtained when the molar ratio of isopropyl alcohol to myristic acid (15:1) was used with 4% (w/w) catalyst loading and agitation speed of 150 rpm at 60 °C. The enzyme has also shown good batch operational stability under optimized conditions.

Ultra compact spectrometer apparatus and method using photonic crystals

NASA Technical Reports Server (NTRS)

Ting, David Z. (Inventor); Hill, Cory J. (Inventor); Bandara, Sumith V. (Inventor); Gunapala, Sarath D. (Inventor)

2009-01-01

The present invention is directed to methods of photonic crystal formation, and to methods and apparatus for using such photonic crystals, particularly in conjunction with detector arrays. Photonic crystal parameters and detector array parameters are compared to optimize the selection and orientation of a photonic crystal shape. A photonic crystal is operatively positioned relative to a plurality of light sensors. The light sensors can be separated by a pitch distance and positioned within one half of the pitch distance of an exit surface of the photonic crystals.

Wavelet decomposition and radial basis function networks for system monitoring

NASA Astrophysics Data System (ADS)

Ikonomopoulos, A.; Endou, A.

1998-10-01

Two approaches are coupled to develop a novel collection of black box models for monitoring operational parameters in a complex system. The idea springs from the intention of obtaining multiple predictions for each system variable and fusing them before they are used to validate the actual measurement. The proposed architecture pairs the analytical abilities of the discrete wavelet decomposition with the computational power of radial basis function networks. Members of a wavelet family are constructed in a systematic way and chosen through a statistical selection criterion that optimizes the structure of the network. Network parameters are further optimized through a quasi-Newton algorithm. The methodology is demonstrated utilizing data obtained during two transients of the Monju fast breeder reactor. The models developed are benchmarked with respect to similar regressors based on Gaussian basis functions.

Need for Cost Optimization of Space Life Support Systems

NASA Technical Reports Server (NTRS)

Jones, Harry W.; Anderson, Grant

2017-01-01

As the nation plans manned missions that go far beyond Earth orbit to Mars, there is an urgent need for a robust, disciplined systems engineering methodology that can identify an optimized Environmental Control and Life Support (ECLSS) architecture for long duration deep space missions. But unlike the previously used Equivalent System Mass (ESM), the method must be inclusive of all driving parameters and emphasize the economic analysis of life support system design. The key parameter for this analysis is Life Cycle Cost (LCC). LCC takes into account the cost for development and qualification of the system, launch costs, operational costs, maintenance costs and all other relevant and associated costs. Additionally, an effective methodology must consider system technical performance, safety, reliability, maintainability, crew time, and other factors that could affect the overall merit of the life support system.

Real option valuation of a decremental regulation service provided by electricity storage.

PubMed

Szabó, Dávid Zoltán; Martyr, Randall

2017-08-13

This paper is a quantitative study of a reserve contract for real-time balancing of a power system. Under this contract, the owner of a storage device, such as a battery, helps smooth fluctuations in electricity demand and supply by using the device to increase electricity consumption. The battery owner must be able to provide immediate physical cover, and should therefore have sufficient storage available in the battery before entering the contract. Accordingly, the following problem can be formulated for the battery owner: determine the optimal time to enter the contract and, if necessary, the optimal time to discharge electricity before entering the contract. This problem is formulated as one of optimal stopping, and is solved explicitly in terms of the model parameters and instantaneous values of the power system imbalance. The optimal operational strategies thus obtained ensure that the battery owner has positive expected economic profit from the contract. Furthermore, they provide explicit conditions under which the optimal discharge time is consistent with the overall objective of power system balancing. This paper also carries out a preliminary investigation of the 'lifetime value' aggregated from an infinite sequence of these balancing reserve contracts. This lifetime value, which can be viewed as a single project valuation of the battery, is shown to be positive and bounded. Therefore, in the long run such reserve contracts can be beneficial to commercial operators of electricity storage, while reducing some of the financial and operational risks in power system balancing.This article is part of the themed issue 'Energy management: flexibility, risk and optimization'. © 2017 The Author(s).