A stochastic optimal control problem and its applications.
Stochastic optimal control problem solution by dynamic programming and relation to interplanetary guidance
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Experimental Validation of Approximate Dynamic Programming Based Optimization and Convergence on Microgrid Applications
Stochastic optimization can better address uncertainties in power system problems. However, when state space and action space become large, many existing approaches become computationally expensive and even infeasible. Approximate dynamic programming (ADP) attracts researchers’ attention as a powerful tool for solving power system optimization problems with reduced computational cost. In this paper, in light of the existing literature, we investigate how the ADP approach with post-decision value function approximation converges to the nearly optimal solution with improved computational speed and experimentally validate the performance of the approach for a microgrid energy optimization problem. The approximation error versus the number of iteration is studied for convergence analysis of the post-decision ADP. A flowchart is provided to illustrate the proposed ADP algorithm for a microgrid energy optimization problem. The performance of ADP and dynamic programming (DP) is compared in terms of optimization error and computational time. It has found that the post-decision ADP approach can achieve competitive optimality with improved computational speed compared to the traditional DP.
Das, Avijit↗
Global Optimization of Chemical Cluster Structures: Methods, Applications, and Challenges
Chemical clusters are relevant to many applications in catalysis, separations, materials, and energy sciences. Experimentally, the structure of clusters is difficult to determine, but it is very important in understanding their chemistry and properties. Computational methods can be used to examine cluster structure, however finding the most stable structure is not simple, particularly as the cluster size increases. Global optimization techniques have long been used to tackle the problem of the most stable structure, but such approaches would have to look for a global minimum, while sampling local minima over the whole potential energy surface as well. In this review, the state-of-the-art theory of global optimization theory is summarized. First, the definition, significance, relation to experiments, and a brief history of global optimization is presented. We then discuss, in more detail, three versatile global optimization methods: the basin hopping, the artificial bee colony algorithm, and the genetic algorithm. We close with some representative application examples of global optimization of clusters since 2016 and the challenges, open questions and opportunities in this field.
Global optimization, Chemical clusters, Artificial↗
An Initial Assessment of Variable Depth Liner Optimization for Ducted Proprotor Applications
The rise of the Urban Air Mobility market has spurred the design of a new generation of novel aircraft. To aid industry and researchers interested in these types of aircraft, the Revolutionary Vertical Lift Technology project at NASA has developed a fleet of reference vehicles for system studies. A new six-passenger reference vehicle has recently been added to the research portfolio that has ducted proprotors for propulsors. The ducts present an opportunity to apply acoustic treatment to the interior of the duct that could target both tonal and broadband noise, representative of the sound produced by this type of propulsor. In this paper, design methodologies are presented to design a variable depth liner for this application. An optimizer is used to design multiple liners with variable chamber depths. Experimental results from normal impedance testing are compared to numerical predictions using the optimizer model and a finite element model. Results show that an optimizer can be used to design an acoustic liner with favorable performance for a broad range of frequencies, which could be appropriate for a ducted proprotor application.
Matthew B Galles↗
Application of quadratic optimization to supersonic inlet control
The application of linear stochastic optimal control theory to the design of the control system for the air intake (inlet) of a supersonic air-breathing propulsion system is discussed. The controls must maintain a stable inlet shock position in the presence of random airflow disturbances and prevent inlet unstart. Two different linear time invariant control systems are developed. One is designed to minimize a nonquadratic index, the expected frequency of inlet unstart, and the other is designed to minimize the mean square value of inlet shock motion. The quadratic equivalence principle is used to obtain the best linear controller that minimizes the nonquadratic performance index. The two systems are compared on the basis of unstart prevention, control effort requirements, and sensitivity to parameter variations.
Lehtinen, B.↗
or-topas: Operations Research Toolkit for Pyomo Alternative Solutions
SAND2026-16702O OR-TOPAS: Operations Research Toolkit for Pyomo Alternative Solutions is a tool that enhances optimization applications defined by the Pyomo modeling library. It offers functions to generate optimal or near-optimal solutions, operating independently of Pyomo’s solver interface. Users can configure these functions with specific solver names and options, resulting in a custom solution object that returns a list of solutions. The OR-TOPAS library includes methods tailored to the properties of the model, such as binary integer programs versus linear programs, and specific solver interfaces like Gurobi. It does not provide models for specific applications, but it is applicable to a wide range of Pyomo optimization models. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Siirola, John [Sandia National Lab. (SNL-CA), Live↗
AN APPLICATION OF TIME OPTIMAL CONTROL TO A FLEXIBLE LAUNCH VEHICLE
Application of time optimal control to a flexible launch vehicle - pitch controller
TRAJECTORY CONTROL↗
An engineering optimization method with application to STOL-aircraft approach and landing trajectories
An optimization method has been developed that computes the optimal open loop inputs for a dynamical system by observing only its output. The method reduces to static optimization by expressing the inputs as series of functions with parameters to be optimized. Since the method is not concerned with the details of the dynamical system to be optimized, it works for both linear and nonlinear systems. The method and the application to optimizing longitudinal landing paths for a STOL aircraft with an augmented wing are discussed. Noise, fuel, time, and path deviation minimizations are considered with and without angle of attack, acceleration excursion, flight path, endpoint, and other constraints.
Jacob, H. G.↗
An application of linear programming to minimum fuel optimal control
Application of linear programming to minimum fuel optimal control
OPTIMAL CONTROL↗
A decomposition-based design optimization method with applications
A two-level design optimization metholology is described. A progress report of its application to Printed Wiring Board (PWB) assembly examples is given. The design of PWB assemblies is a complex task which is generally conducted as a sequential process. Individual PWBs are usually designed first, followed by the composition of the PWBs into an assembly. As a result, optimizing design considerations such as assembly reliability cannot be accomplished. This study showed that a two-level decomposition method can be employed to optimize for reliability at both the PWB- and the assembly-level in a coupled manner. The two-level decomposition method also resolved the mixed-integer nonlinear programming nature of the problem rather easily.
Azarm, Shapour↗
ParaOpt: Automated Application Parameterization and Optimization for the Cloud
The variety of instance types available on cloud platforms offers enormous flexibility to match the requirements of applications with available resources. However, selecting the most suitable instance type and configuring an application to optimally execute on that instance type can be complicated and time-consuming. For example, application parallelism flags must match available cores and problem sizes must be tuned to match available memory. As the search space of application configurations can be enormous, we propose an automated approach, called ParaOpt, to automatically explore and tune application configurations on arbitrary cloud instances. ParaOpt supports arbitrary applications, enables use of custom optimization methods, and can be configured with different optimization targets such as runtime and cost. We evaluate ParaOpt by optimizing genomics, molecular dynamics, and machine learning applications with four types of optimizers. We show with as few as 15 parameterized executions of an application, representing between 1.2%-26.7% of the search space, that ParaOpt is able to identify the optimal configuration in 32.7% of experiments and a near-optimal configuration in 83.2% of cases. As a result of using near-optimal configurations, ParaOpt reduces overall execution time by up to 85.8% when compared with using the default configuration.
Wu, Chaofeng↗
Development of Building Design Optimization Methodology: Residential Building Applications
Building design optimization is a highly complex problem, requiring long computational running processes because of the many options that exist when a building is being designed. This paper introduces an integrated approach through which to perform this optimization within an acceptable time frame. The approach includes the methods of variable selection, model simplification, and a sequential optimization process. Using singular value decomposition, a large number of design variables is reduced to a smaller subset that can be solved more quickly through the optimization algorithm. To expedite the variable selection process, a modeling approach that quickly simulates annual energy consumption was developed to replace full annual energy simulations. The developed methodology was applied to two residential buildings in the US, and the results are discussed herein. To assess the accuracy of the integrated optimization methodology, the optimized life cycle costs are compaa variables demonstrating the strongest contributions in the optimization study were identified. The proposed methodology significantly shortened the time requirements for the optimization processes of the two case studies by 74% and 84%; the optimized life cycle costs were within 0.05% and 0.06%, respectively, of the optimum point.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Self-organization in neural networks - Applications in structural optimization
The present paper discusses the applicability of ART (Adaptive Resonance Theory) networks, and the Hopfield and Elastic networks, in problems of structural analysis and design. A characteristic of these network architectures is the ability to classify patterns presented as inputs into specific categories. The categories may themselves represent distinct procedural solution strategies. The paper shows how this property can be adapted in the structural analysis and design problem. A second application is the use of Hopfield and Elastic networks in optimization problems. Of particular interest are problems characterized by the presence of discrete and integer design variables. The parallel computing architecture that is typical of neural networks is shown to be effective in such problems. Results of preliminary implementations in structural design problems are also included in the paper.
Hajela, Prabhat↗
Complex optimization and its application to distributed-lumped-active networks
Complex optimization and its application to distributed-lumped-active networks
Huelsman, L. P.↗
Application of an optimization method to high performance propeller designs
The application of an optimization method to determine the propeller blade twist distribution which maximizes propeller efficiency is presented. The optimization employs a previously developed method which has been improved to include the effects of blade drag, camber and thickness. Before the optimization portion of the computer code is used, comparisons of calculated propeller efficiencies and power coefficients are made with experimental data for one NACA propeller at Mach numbers in the range of 0.24 to 0.50 and another NACA propeller at a Mach number of 0.71 to validate the propeller aerodynamic analysis portion of the computer code. Then comparisons of calculated propeller efficiencies for the optimized and the original propellers show the benefits of the optimization method in improving propeller performance. This method can be applied to the aerodynamic design of propellers having straight, swept, or nonplanar propeller blades.
Li, K. C.↗
Application of an optimization method to high performance propeller designs
The application of an optimization method to determine the propeller blade twist distribution which maximizes propeller efficiency is presented. The optimization employs a previously developed method which has been improved to include the effects of blade drag, camber and thickness. Before the optimization portion of the computer code is used, comparisons of calculated propeller efficiencies and power coefficients are made with experimental data for one NACA propeller at Mach numbers in the range of 0.24 to 0.50 and another NACA propeller at a Mach number of 0.71 to validate the propeller aerodynamic analysis portion of the computer code. Then comparisons of calculated propeller efficiencies for the optimized and the original propellers show the benefits of the optimization method in improving propeller performance. This method can be applied to the aerodynamic design of propellers having straight, swept, or nonplanar propeller blades.
Li, K. C.↗
Quadratic optimal cooperative control synthesis with flight control application
An optimal control-law synthesis approach is presented that involves simultaneous solution for two cooperating controllers operating in parallel. One controller's structure includes stochastic state estimation and linear feedback of the state estimates, while the other controller involves direct linear feedback of selected system output measurements. This structure is shown to be optimal under the constraint of linear feedback of system outputs in one controller. Furthermore, it is appropriate for flight control synthesis where the full-state optimal stochastic controller can be adjusted to be representative of an optimal control model of the human pilot in a stochastic regulation task. The method is experimentally verified in the case of the selection of pitch-damper gain for optimum pitch tracking, where optimum implies the best subjective pilot rating in the task. Finally, results from application of the method to synthesize a controller for a multivariable fighter aircraft are presented, and implications of the results of this method regarding the optimal plant dynamics for tracking are discussed.
Schmidt, D. K.↗
Sensitivity Studies, Gap Analysis, and Benchmark Experiment Optimization for Reactor Applications
In regards to nuclear data, some reactor applications may lack validation experiments, which reduces confidence in predicted results. This is especially true for emerging advanced reactor, micro reactor, and Accelerator Driven System (ADS) designs. This work presents an approach to design new criticality experiments that have similar k eff cross section sensitivities to an application of interest. This process involves simulations to generate cross-section sensitivities to a parameter of interest (such as k eff ), a gap analysis to determine which existing benchmarks are most similar to the application, and an experiment optimization. This work focuses on cross-section sensitives and gap analysis for three examples relevant to the reactor physics community including a Travelling Wave Reactor (TWR) type-design, Kilopower (a space reactor design), and a lead-bismuth eutectic cooled accelerator-driven system (ADS) to transmute minor actinides.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗