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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Proper Orthogonal Decomposition in Optimal Control of Fluids

In this article, we present a reduced order modeling approach suitable for active control of fluid dynamical systems based on proper orthogonal decomposition (POD). The rationale behind the reduced order modeling is that numerical simulation of Navier-Stokes equations is still too costly for the purpose of optimization and control of unsteady flows. We examine the possibility of obtaining reduced order models that reduce computational complexity associated with the Navier-Stokes equations while capturing the essential dynamics by using the POD. The POD allows extraction of certain optimal set of basis functions, perhaps few, from a computational or experimental data-base through an eigenvalue analysis. The solution is then obtained as a linear combination of these optimal set of basis functions by means of Galerkin projection. This makes it attractive for optimal control and estimation of systems governed by partial differential equations. We here use it in active control of fluid flows governed by the Navier-Stokes equations. We show that the resulting reduced order model can be very efficient for the computations of optimization and control problems in unsteady flows. Finally, implementational issues and numerical experiments are presented for simulations and optimal control of fluid flow through channels.

Ravindran, S. S.↗
NASA NTRS1999

Integration of a Decentralized Linear-Quadratic-Gaussian Control into GSFC's Universal 3-D Autonomous Formation Flying Algorithm

A decentralized control is investigated for applicability to the autonomous formation flying control algorithm developed by GSFC for the New Millenium Program Earth Observer-1 (EO-1) mission. This decentralized framework has the following characteristics: The approach is non-hierarchical, and coordination by a central supervisor is not required; Detected failures degrade the system performance gracefully; Each node in the decentralized network processes only its own measurement data, in parallel with the other nodes; Although the total computational burden over the entire network is greater than it would be for a single, centralized controller, fewer computations are required locally at each node; Requirements for data transmission between nodes are limited to only the dimension of the control vector, at the cost of maintaining a local additional data vector. The data vector compresses all past measurement history from all the nodes into a single vector of the dimension of the state; and The approach is optimal with respect to standard cost functions. The current approach is valid for linear time-invariant systems only. Similar to the GSFC formation flying algorithm, the extension to linear LQG time-varying systems requires that each node propagate its filter covariance forward (navigation) and controller Riccati matrix backward (guidance) at each time step. Extension of the GSFC algorithm to non-linear systems can also be accomplished via linearization about a reference trajectory in the standard fashion, or linearization about the current state estimate as with the extended Kalman filter. To investigate the feasibility of the decentralized integration with the GSFC algorithm, an existing centralized LQG design for a single spacecraft orbit control problem is adapted to the decentralized framework while using the GSFC algorithm's state transition matrices and framework. The existing GSFC design uses both reference trajectories of each spacecraft in formation and by appropriate choice of coordinates and simplified measurement modeling is formulated as a linear time-invariant system. Results for improvements to the GSFC algorithm and a multiple satellite formation will be addressed. The goal of this investigation is to progressively relax the assumptions that result in linear time-invariance, ultimately to the point of linearization of the non-linear dynamics about the current state estimate as in the extended Kalman filter. An assessment will then be made about the feasibility of the decentralized approach to the realistic formation flying application of the EO-1/Landsat 7 formation flying experiment.

Folta, David C.↗
NASA NTRSDate not supplied

Trajectory Optimization for the Virtual Telescope for X-Ray Observations

The Virtual Telescope for X-Ray Observations (VTXO) is a long focal length telescope which promises to provide orders of magnitude improvement in angular resolution in the X-ray band. VTXO will include a Phased Fresnel Lens (PFL), which provides nearly diffraction-limited imaging, with a 1 km focal length. The PFL is carried by the Optics Spacecraft, which flies in a formation with the Detector Spacecraft, approximating a rigid telescope body. In order to maintain the formation requirements, while pointing the telescope axis at the desired astronomical targets, one spacecraft will be traveling on a non-natural trajectory, requiring the vehicle to maneuver regularly to maintain the telescope pointing. If care is not taken in the trajectory design, these paths result in large propellant consumption. However, there is an opportunity to optimize trajectories when re-arranging the formation between different astronomical targets. This paper presents an optimization scheme for re-pointing the telescope, utilizing a non-traditional path-based cost function to solve the propellant optimal trajectory. The resulting trajectories show a factor of four improvement in propellant consumption compared to the baseline. The optimization techniques developed for VTXO are applicable to orbits ranging from low-Earth orbit, to highly eccentric Earth orbits, and Lagrange point orbits.

Kyle Rankin↗
NASA NTRS1998

Using Approximations to Accelerate Engineering Design Optimization

Optimization problems that arise in engineering design are often characterized by several features that hinder the use of standard nonlinear optimization techniques. Foremost among these features is that the functions used to define the engineering optimization problem often are computationally intensive. Within a standard nonlinear optimization algorithm, the computational expense of evaluating the functions that define the problem would necessarily be incurred for each iteration of the optimization algorithm. Faced with such prohibitive computational costs, an attractive alternative is to make use of surrogates within an optimization context since surrogates can be chosen or constructed so that they are typically much less expensive to compute. For the purposes of this paper, we will focus on the use of algebraic approximations as surrogates for the objective. In this paper we introduce the use of so-called merit functions that explicitly recognize the desirability of improving the current approximation to the objective during the course of the optimization. We define and experiment with the use of merit functions chosen to simultaneously improve both the solution to the optimization problem (the objective) and the quality of the approximation. Our goal is to further improve the effectiveness of our general approach without sacrificing any of its rigor.

Torczon, Virginia↗
NASA NTRS1990

An averaging approach to optimal adaptive control of large space structures

Averaging methods are applied to analyzing the transient response associated with adaptive control of large space structures. Using a dominant mode approximation to the plant, an analytical bound is found on the envelope of the adaptive response, characterizing many of the features of the response useful for control design (e.g., peak values, quadratic costs, settling times, etc.). An optimal adaptive design problem is formulated based on minimizing the product of the settling time and peak torque requirement. The resulting nonlinear constrained optimization problem is solved in closed form, and several properties of the optimal adaptive design are discussed.

Bayard, D. S.↗
NASA NTRSDate not supplied

Machine Learning for Dynamic Test Sensor Placement

There are multiple different algorithms to perform modal test sensor placement optimization: effective independence, residual kinetic energy, iterative Guyan reduction, genetic algorithms, or a brute-force methodology. However, any of these methods may be computationally expensive, especially for structural models with a large number of degrees of freedom. Given the high-cost and the need to optimize the solution, modal sensor placement is a great application for machine learning (ML) algorithms. In this paper, we will apply ML algorithms to determine the optimal sensor locations for simple and complex structures. We will also discuss the benefits and drawbacks of using machine learning over other sensor placement algorithms.

Kelsey Buckles↗
NASA NTRS2001

Aircraft Optimization for Minimum Environmental Impact

The objective of this research is to investigate the tradeoff between operating cost and environmental acceptability of commercial aircraft. This involves optimizing the aircraft design and mission to minimize operating cost while constraining exterior noise and emissions. Growth in air traffic and airport neighboring communities has resulted in increased pressure to severely penalize airlines that do not meet strict local noise and emissions requirements. As a result, environmental concerns have become potent driving forces in commercial aviation. Traditionally, aircraft have been first designed to meet performance and cost goals, and adjusted to satisfy the environmental requirements at given airports. The focus of the present study is to determine the feasibility of including noise and emissions constraints in the early design of the aircraft and mission. This paper introduces the design tool and results from a case study involving a 250-passenger airliner.

Antoine, Nicolas↗
NASA NTRS1987

Iterative-deepening heuristic search for optimal and semi-optimal resource allocation

It is demonstrated that when iterative-deepening A asterisk (IDA asterisk) is applied to one type of resource allocation problem, it uses far less storage than A asterisk, but opens far more nodes and thus has unacceptable time complexity. This is shown to be due, at least in part, to the low-valued effective branching factor that is a characteristic of problems with real-valued cost functions. The semi-optimal, epsilon-admissible IDA asterisk sub epsilon search algorithm that the authors described was shown to open fewer nodes than both A asterisk and IDA asterisk with storage complexity proportional to the depth of the search tree.

Bridges, Susan M.↗
NASA NTRS2001

Determination of an Optimal Commercial Data Bus Architecture for a Flight Data System

NASA/Marshall Space Flight Center (MSFC) is continually looking for methods to reduce cost and schedule while keeping the quality of work high. MSFC is NASA's lead center for space transportation and microgravity research. When supporting NASA's programs several decisions concerning the avionics system must be made. Usually many trade studies must be conducted to determine the best ways to meet the customer's requirements. When deciding the flight data system, one of the first trade studies normally conducted is the determination of the data bus architecture. The schedule, cost, reliability, and environments are some of the factors that are reviewed in the determination of the data bus architecture. Based on the studies, the data bus architecture could result in a proprietary data bus or a commercial data bus. The cost factor usually removes the proprietary data bus from consideration. The commercial data bus's range from Versa Module Eurocard (VME) to Compact PCI to STD 32 to PC 104. If cost, schedule and size are prime factors, VME is usually not considered. If the prime factors are cost, schedule, and size then Compact PCI, STD 32 and PC104 are the choices for the data bus architecture. MSFC's center director has funded a study from his discretionary fund to determine an optimal low cost commercial data bus architecture. The goal of the study is to functionally and environmentally test Compact PCI, STD 32 and PC 104 data bus architectures. This paper will summarize the results of the data bus architecture study.

Crawford, Kevin↗
NASA NTRS1985

Finite element engine blade structural optimization

The Structural Tailoring of Engine Blades (STAEBL) computer program was developed to perform engine fan blade numerical optimizations. These blade optimizations seek a minimum weight or cost design that satisfies realistic blade design constraints, by tuning one to twenty design variables. The STAEBL system has been generalized to include both fan and compressor blade numerical optimizations. The system analyses have been significantly improved through the inclusion of an efficient plate finite element analysis for blade stress and frequency determinations. Additionally, a finite element based approximate severe foreign object damage (FOD) analysis has been included. The new FOD analysis gives very accurate estimates of the full nonlinear bird ingestion solution. Optimizations of fan and compressor blades have been performed using the system, showing significant cost and weight reductions, while comparing very favorably with refined design validation procedures.

Brown, K. W.↗
NASA NTRS1991

User's guide for NETS/PROSSS

Expensive analysis programs are often combined with optimization procedures to solve engineering problems. To obtain an optimal solution requires numerous iterations between the analysis program and the optimizer. This often becomes prohibitive due to the cost and amount of computer time needed to converge to an optimal solution. NETS/PROSSS was developed to address this problem. The purpose of this paper is to serve as a user's guide for NETS/PROSSS. The key features include the neural network, determining the training pairs for the neural network, and the approximated analysis/optimization process. A small problem is given to serve as an example of how to apply the system.

Rogers, James L.↗
NASA NTRS1983

Optimal short range trajectories for helicopters

An optimal flight path algorithm using a simplified altitude state model and an apriori climb-cruise-descent flight profile has been developed and applied to determine minimum fuel and minimum cost trajectories for a helicopter flying a fixed range trajectory. The performance model is based on standard flight manual data and is such that on-line trajectory optimization is feasible with a relatively small computer. The results show that the optimal flight path and optimal cruise altitude can represent a 10 percent fuel saving on a minimum fuel trajectory. The optimal trajectories show considerable variability due to helicopter weight, ambient winds and the relative cost trade-off between time and fuel. In general, 'reasonable' variations from the optimal velocities and cruise altitudes do not significantly degrade the optimal cost.

Slater, G. L.↗
NASA NTRS2015

Extension of an Object-Oriented Optimization Tool: User's Reference Manual

The National Aeronautics and Space Administration Armstrong Flight Research Center has developed a cost-effective and flexible object-oriented optimization (O (sup 3)) tool that leverages existing tools and practices and allows easy integration and adoption of new state-of-the-art software. This object-oriented framework can integrate the analysis codes for multiple disciplines, as opposed to relying on one code to perform analysis for all disciplines. Optimization can thus take place within each discipline module, or in a loop between the O (sup 3) tool and the discipline modules, or both. Six different sample mathematical problems are presented to demonstrate the performance of the O (sup 3) tool. Instructions for preparing input data for the O (sup 3) tool are detailed in this user's manual.

Multidisciplinary design optimization↗
NASA NTRS1978

Design of transonic airfoil sections using a similarity theory

A study of the available methods for transonic airfoil and wing design indicates that the most powerful technique is the numerical optimization procedure. However, the computer time for this method is relatively large because of the amount of computation required in the searches during optimization. The optimization method requires that base and calibration solutions be computed to determine a minimum drag direction. The design space is then computationally searched in this direction; it is these searches that dominate the computation time. A recent similarity theory allows certain transonic flows to be calculated rapidly from the base and calibration solutions. In this paper the application of the similarity theory to design problems is examined with the object of at least partially eliminating the costly searches of the design optimization method. An example of an airfoil design is presented.

Nixon, D.↗
NASA NTRS2019

Optical Array Versus Monolithic Telescope Ground Station Cost Assessment

The purpose of this study is to outline the design of an optimal array of optical telescopes to emulate performance of a monolithic 12 m telescope in support of deep-space communications. In this case, optimal means minimizing the initial capital investment and operational cost while maintaining performance requirements of the deep-space link. The design is approached from a practical, engineering perspective. Pulse position modulation (PPM) signal formatting and photon counting detectors are assumed at each telescope in the array. That is, the telescopes function as so-called light buckets, so direct detection (as opposed to coherent reception) of the received signals is assumed, and there is no intention to consider active compensation for atmospheric turbulence-induced phase fluctuations. A parametric analysis among aperture size, detector size, and primary mirror surface quality, in the context of field-of-view expansion, is presented to minimize the cost function.

Romanofsky, Robert R.↗
NASA NTRS2013

Risk-Constrained Dynamic Programming for Optimal Mars Entry, Descent, and Landing

A chance-constrained dynamic programming algorithm was developed that is capable of making optimal sequential decisions within a user-specified risk bound. This work handles stochastic uncertainties over multiple stages in the CEMAT (Combined EDL-Mobility Analyses Tool) framework. It was demonstrated by a simulation of Mars entry, descent, and landing (EDL) using real landscape data obtained from the Mars Reconnaissance Orbiter. Although standard dynamic programming (DP) provides a general framework for optimal sequential decisionmaking under uncertainty, it typically achieves risk aversion by imposing an arbitrary penalty on failure states. Such a penalty-based approach cannot explicitly bound the probability of mission failure. A key idea behind the new approach is called risk allocation, which decomposes a joint chance constraint into a set of individual chance constraints and distributes risk over them. The joint chance constraint was reformulated into a constraint on an expectation over a sum of an indicator function, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the chance-constraint optimization problem can be turned into an unconstrained optimization over a Lagrangian, which can be solved efficiently using a standard DP approach.

Ono, Masahiro↗
NASA NTRS1990

Optimal dynamic remapping of data parallel computations

A large class of data parallel computations is characterized by a sequence of phases, with phase changes occurring unpredictably. Dynamic remapping of the workload to processors may be required to maintain good performance. The problem considered, for which the utility of remapping and the future behavior of the workload are uncertain, arises when phases exhibit stable execution requirements during a given phase, but requirements change radically between phases. For these situations, a workload assignment generated for one phase may hinder performance during the next phase. This problem is treated formally for a probabilistic model of computation with at most two phases. The authors address the fundamental problem of balancing the expected remapping performance gain against the delay cost, and they derive the optimal remapping decision policy. The promise of the approach is shown by application to multiprocessor implementations of an adaptive gridding fluid dynamics program and to a battlefield simulation program.

Nicol, David M.↗
NASA NTRS1997

Automation for Air Traffic Control: The Rise of a New Discipline

The current debate over the concept of Free Flight has renewed interest in automated conflict detection and resolution in the enroute airspace. An essential requirement for effective conflict detection is accurate prediction of trajectories. Trajectory prediction is, however, an inexact process which accumulates errors that grow in proportion to the length of the prediction time interval. Using a model of prediction errors for the trajectory predictor incorporated in the Center-TRACON Automation System (CTAS), a computationally fast algorithm for computing conflict probability has been derived. Furthermore, a method of conflict resolution has been formulated that minimizes the average cost of resolution, when cost is defined as the increment in airline operating costs incurred in flying the resolution maneuver. The method optimizes the trade off between early resolution at lower maneuver costs but higher prediction error on the one hand and late resolution with higher maneuver costs but lower prediction errors on the other. The method determines both the time to initiate the resolution maneuver as well as the characteristics of the resolution trajectory so as to minimize the cost of the resolution. Several computational examples relevant to the design of a conflict probe that can support user-preferred trajectories in the enroute airspace will be presented.

Erzberger, Heinz↗