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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,333 records · Page 74

All sources NASA NTRS DOE OSTI

NASA NTRS1992

Approximation, abstraction and decomposition in search and optimization

In this paper, I discuss four different areas of my research. One portion of my research has focused on automatic synthesis of search control heuristics for constraint satisfaction problems (CSPs). I have developed techniques for automatically synthesizing two types of heuristics for CSPs: Filtering functions are used to remove portions of a search space from consideration. Another portion of my research is focused on automatic synthesis of hierarchic algorithms for solving constraint satisfaction problems (CSPs). I have developed a technique for constructing hierarchic problem solvers based on numeric interval algebra. Another portion of my research is focused on automatic decomposition of design optimization problems. We are using the design of racing yacht hulls as a testbed domain for this research. Decomposition is especially important in the design of complex physical shapes such as yacht hulls. Another portion of my research is focused on intelligent model selection in design optimization. The model selection problem results from the difficulty of using exact models to analyze the performance of candidate designs.

Ellman, Thomas↗

DOE OSTI2022

Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor

With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO 2 ) has received increasing attention as an alternative source of carbon-based fuels. As CO 2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO 2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO 2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO 2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Finally, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO 2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO 2 .

42 ENGINEERING↗

NASA NTRS1990

State-space design of low-order stabilizers

An algorithm is presented for stabilizing a linear multivariable system with a controller of fixed dynamic order. This is an output feedback stabilization problem. The algorithm attempts to solve this by a sequence of approximate pole assignment problems. The approximation is driven by the optimization of an objective function consisting of a weighted sum of the condition number of the closed-loop eigenvectors and the norm of the difference between the computed and actual equivalent state-feedback controls.

Keel, L. H.↗

NASA NTRS1974

Control system design using frequency domain models and parameter optimization, with application to supersonic inlet controls

A technique is described for designing feedback control systems using frequency domain models, a quadratic cost function, and a parameter optimization computer program. FORTRAN listings for the computer program are included. The approach is applied to the design of shock position controllers for a supersonic inlet. Deterministic or random system disturbances, and the presence of random measurement noise are considered. The cost function minimization is formulated in the time domain, but the problem solution is obtained using a frequency domain system description. A scaled and constrained conjugate gradient algorithm is used for the minimization. The approach to a supersonic inlet included the calculations of the optimal proportional-plus integral (PI) and proportional-plus-integral-plus-derivative controllers. A single-loop PI controller was the most desirable of the designs considered.

Seidel, R. C.↗

NASA NTRS1987

Simultaneous structure/control optimization of large flexible spacecraft

This paper presents an approach to the simultaneous optimal design of a structure and control system for large flexible spacecrafts based on realistic objective function and constraints. The weight or total cost of structure and control system is minimized subject to constraints on the magnitude of response to a given disturbance involving both rigid-body and elastic modes. A nested optimization technique is developed to solve the combined problem. As an example, simple beam-like spacecraft under a steady-state white-noise disturbance force is investigated and some results of optimization are presented. In the numerical examples, the stiffness distribution, the location of controller and the control gains are optimized. Direct feedback control and linear quadratic optimal control laws are used with both inertial and non-inertial disturbing force. It is shown that the total cost is sensitive to the overall structural stiffness so that a simultaneous optimization of the structure and control system is indeed useful.

Onoda, Junjiro↗

NASA NTRS1979

Towards fault-tolerant optimal control

The paper considers the design of fault-tolerant controllers that may endow systems with dynamic reliability. Results for jump linear quadratic Gaussian control problems are extended to include random jump costs, trajectory discontinuities, and a simple case of non-Markovian mode transitions.

Chizeck, H. J.↗

NASA NTRS1984

Applications of numerical optimization methods to helicopter design problems: A survey

A survey of applications of mathematical programming methods is used to improve the design of helicopters and their components. Applications of multivariable search techniques in the finite dimensional space are considered. Five categories of helicopter design problems are considered: (1) conceptual and preliminary design, (2) rotor-system design, (3) airframe structures design, (4) control system design, and (5) flight trajectory planning. Key technical progress in numerical optimization methods relevant to rotorcraft applications are summarized.

Miura, H.↗

NASA NTRS1985

Applications of numerical optimization methods to helicopter design problems - A survey

Miura, H.↗

NASA NTRS1984

Applications of numerical optimization methods to helicopter design problems - A survey

Miura, H.↗

NASA NTRS2003

Optimal Feedback Control of Thermal Networks

An improved approach to the mathematical modeling of feedback control of thermal networks has been devised. Heretofore software for feedback control of thermal networks has been developed by time-consuming trial-and-error methods that depend on engineers expertise. In contrast, the present approach is a systematic means of developing algorithms for feedback control that is optimal in the sense that it combines performance with low cost of implementation. An additional advantage of the present approach is that a thermal engineer need not be expert in control theory. Thermal networks are lumped-parameter approximations used to represent complex thermal systems. Thermal networks are closely related to electrical networks commonly represented by lumped-parameter circuit diagrams. Like such electrical circuits, thermal networks are mathematically modeled by systems of differential-algebraic equations (DAEs) that is, ordinary differential equations subject to a set of algebraic constraints. In the present approach, emphasis is placed on applications in which thermal networks are subject to constant disturbances and, therefore, integral control action is necessary to obtain steady-state responses. The mathematical development of the present approach begins with the derivation of optimal integral-control laws via minimization of an appropriate cost functional that involves augmented state vectors. Subsequently, classical variational arguments provide optimality conditions in the form of the Hamiltonian equations for the standard linear-quadratic-regulator (LQR) problem. These equations are reduced to an algebraic Riccati equation (ARE) with respect to the augmented state vector. The solution of the ARE leads to the direct computation of the optimal proportional- and integral-feedback control gains. In cases of very complex networks, large numbers of state variables make it difficult to implement optimal controllers in the manner described in the preceding paragraph.

Papalexandris, Miltiadis↗

NASA NTRS2007

Application of Semi Active Control Techniques to the Damping Suppression Problem of Solar Sail Booms

Solar sails provide a propellant free form for space propulsion. These are large flat surfaces that generate thrust when they are impacted by light. When attached to a space vehicle, the thrust generated can propel the space vehicle to great distances at significant speeds. For optimal performance the sail must be kept from excessive vibration. Active control techniques can provide the best performance. However, they require an external power-source that may create significant parasitic mass to the solar sail. However, solar sails require low mass for optimal performance. Secondly, active control techniques typically require a good system model to ensure stability and performance. However, the accuracy of solar sail models validated on earth for a space environment is questionable. An alternative approach is passive vibration techniques. These do not require an external power supply, and do not destabilize the system. A third alternative is referred to as semi-active control. This approach tries to get the best of both active and passive control, while avoiding their pitfalls. In semi-active control, an active control law is designed for the system, and passive control techniques are used to implement it. As a result, no external power supply is needed so the system is not destabilize-able. Though it typically underperforms active control techniques, it has been shown to out-perform passive control approaches and can be unobtrusively installed on a solar sail boom. Motivated by this, the objective of this research is to study the suitability of a Piezoelectric (PZT) patch actuator/sensor based semi-active control system for the vibration suppression problem of solar sail booms. Accordingly, we develop a suitable mathematical and computer model for such studies and demonstrate the capabilities of the proposed approach with computer simulations.

Adetona, O.↗

NASA NTRS1993

Non-oscillatory and non-diffusive solution of convection problems by the iteratively reweighted least-squares finite element method

A comparative description is presented for the least-squares FEM (LSFEM) for 2D steady-state pure convection problems. In addition to exhibiting better control of the streamline derivative than the streamline upwinding Petrov-Galerkin method, numerical convergence rates are obtained which show the LSFEM to be virtually optimal. The LSFEM is used as a framework for an iteratively reweighted LSFEM yielding nonoscillatory and nondiffusive solutions for problems with contact discontinuities; this method is shown to convect contact discontinuities without error when using triangular and bilinear elements.

Jiang, Bo-Nan↗

NASA NTRS1971

On stochastic control and optimal measurement strategies

The control of stochastic dynamic systems is studied with particular emphasis on those which influence the quality or nature of the measurements which are made to effect control. Four main areas are discussed: (1) the meaning of stochastic optimality and the means by which dynamic programming may be applied to solve a combined control/measurement problem; (2) a technique by which it is possible to apply deterministic methods, specifically the minimum principle, to the study of stochastic problems; (3) the methods described are applied to linear systems with Gaussian disturbances to study the structure of the resulting control system; and (4) several applications are considered.

Kramer, L. C.↗

DOE OSTI2019

A biomimetic approach to fast selection of optimal controlled variables using multiagent algorithms and a decomposition approach

Selection of primary controlled variables (CVs) with due consideration of economic loss and ease of control is an important step for achieving optimal plant operation under design and off-design conditions. However, the computational expense of the combinatorial optimization step that is undertaken for CV selection renders the existing approaches unaffordable for fast selection of optimal CVs. Two approaches are proposed in this paper for improving the computational efficiency of the optimization step. Typically, the CV selection process is typically executed by considering the entire plant together. Opposed to that, the first approach proposed here seeks to decompose the process plant into multiple partitions based on the connectivity strength. Then the CV selection algorithm can be executed on each partition independently. The decomposition algorithm is biomimetic and has been developed, in analogy to neuroscience, mimicking how the information about the connectivity information is extracted using a dynamic casual model. This approach can significantly decrease the number of combinatorial optimization problems that needs to be solved and facilitates parallelization of the solution. In the second approach, a multiagent optimization framework is proposed that utilizes metaheuristic optimization strategies such as the efficient ant colony algorithm, simulated annealing and the genetic algorithm. These two approaches can be used by themselves or in combination. This framework enables cooperative search by a group of algorithmic agents facilitated through an information sharing protocol. These two approaches are applied to a toy problem and an acid gas removal unit as part of an integrated gasification combined cycle. This study shows how the computational time and economic and control performance of the optimal CV sets vary as the number of partitions are changed.

42 ENGINEERING↗

NASA NTRS1966

Theory of minimum effort control

Optimum control theory formulations for solving problems in optimum guidance for interplanetary manned space flight missions

MANNED SPACE FLIGHT↗

NASA NTRS2015

DUKSUP: A Computer Program for High Thrust Launch Vehicle Trajectory Design and Optimization

From the late 1960s through 1997, the leadership of NASAs Intermediate and Large class unmanned expendable launch vehicle projects resided at the NASA Lewis (now Glenn) Research Center (LeRC). One of LeRCs primary responsibilities --- trajectory design and performance analysis --- was accomplished by an internally-developed analytic three dimensional computer program called DUKSUP. Because of its Calculus of Variations-based optimization routine, this code was generally more capable of finding optimal solutions than its contemporaries. A derivation of optimal control using the Calculus of Variations is summarized including transversality, intermediate, and final conditions. The two point boundary value problem is explained. A brief summary of the codes operation is provided, including iteration via the Newton-Raphson scheme and integration of variational and motion equations via a 4th order Runge-Kutta scheme. Main subroutines are discussed. The history of the LeRC trajectory design efforts in the early 1960s is explained within the context of supporting the Centaur upper stage program. How the code was constructed based on the operation of the AtlasCentaur launch vehicle, the limits of the computers of that era, the limits of the computer programming languages, and the missions it supported are discussed. The vehicles DUKSUP supported (AtlasCentaur, TitanCentaur, and ShuttleCentaur) are briefly described. The types of missions, including Earth orbital and interplanetary, are described. The roles of flight constraints and their impact on launch operations are detailed (such as jettisoning hardware on heating, Range Safety, ground station tracking, and elliptical parking orbits). The computer main frames on which the code was hosted are described. The applications of the code are detailed, including independent check of contractor analysis, benchmarking, leading edge analysis, and vehicle performance improvement assessments. Several of DUKSUPs many major impacts on launches are discussed including Intelsat, Voyager, Pioneer Venus, HEAO, Galileo, and Cassini.

Launch vehicle performance optimization↗

NASA NTRS2014

DUKSUP: A Computer Program for High Thrust Launch Vehicle Trajectory Design and Optimization

From the late 1960's through 1997, the leadership of NASA's Intermediate and Large class unmanned expendable launch vehicle projects resided at the NASA Lewis (now Glenn) Research Center (LeRC). One of LeRC's primary responsibilities --- trajectory design and performance analysis --- was accomplished by an internally-developed analytic three dimensional computer program called DUKSUP. Because of its Calculus of Variations-based optimization routine, this code was generally more capable of finding optimal solutions than its contemporaries. A derivation of optimal control using the Calculus of Variations is summarized including transversality, intermediate, and final conditions. The two point boundary value problem is explained. A brief summary of the code's operation is provided, including iteration via the Newton-Raphson scheme and integration of variational and motion equations via a 4th order Runge-Kutta scheme. Main subroutines are discussed. The history of the LeRC trajectory design efforts in the early 1960's is explained within the context of supporting the Centaur upper stage program. How the code was constructed based on the operation of the Atlas/Centaur launch vehicle, the limits of the computers of that era, the limits of the computer programming languages, and the missions it supported are discussed. The vehicles DUKSUP supported (Atlas/Centaur, Titan/Centaur, and Shuttle/Centaur) are briefly described. The types of missions, including Earth orbital and interplanetary, are described. The roles of flight constraints and their impact on launch operations are detailed (such as jettisoning hardware on heating, Range Safety, ground station tracking, and elliptical parking orbits). The computer main frames on which the code was hosted are described. The applications of the code are detailed, including independent check of contractor analysis, benchmarking, leading edge analysis, and vehicle performance improvement assessments. Several of DUKSUP's many major impacts on launches are discussed including Intelsat, Voyager, Pioneer Venus, HEAO, Galileo, and Cassini.

high thrust trajectory design↗

NASA NTRS2022

Model Predictive Control in the Three-body Problem Using Invariant Funnels As Terminal Sets

This paper describes a method for augmenting Model Predictive Control techniques using invariant funnels computed in the Circular Restricted Three-Body Problem. We use ellipsoids that roughly approximate the boundary of the invariant funnel as convex terminal sets for a short look-ahead optimization problem at each time step. We apply this method to a hypothetical low-thrust mission to land on Jupiter’s mooon Europa and show that including the invariant funnels as terminal sets reduces the amount of control effort required by almost an order of magnitude.

Close, Sigrid↗