Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “optimization algorithms”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,585 records · Page 88

Advanced 3-D viscous SSME turbine rotor stator CFD algorithms

Current Space Shuttle Main Engine (SSME) high pressure fuel turbopump problems have generated a desire to analyze the flow field of rotating machinery. The status of Computational Fluid Dynamics (CFD) has reached the point that soon the capability to solve unsteady three-dimensional viscous flow fields will be at hand. The work presented involves upgrading the computational efficiency of an operational three-dimensional algorithm. The modifications include algorithm development, algorithm approximation and acceleration, and special coding optimizations. The overall result of these modifications has reduced processing time by nearly 80%.

Janus, J. Mark↗

Optimal reentry guidance for aeroassisted orbit transfer vehicles

A three-state model is presented for analyzing the problem of optimal changes in heading with minimum energy loss for a hypersonic gliding vehicle. A further model order reduction to a single state model is examined using singular perturbation theory. The optimal solution for the reduced problem defines an optimal altitude profile dependent on the current energy of the vehicle. A separate boundary-layer analysis is used to account for altitude and flight path angle dynamics, and to obtain lift and bank angle control solutions. By considering alternative approximations to solve the boundary-layer problem, three guidance laws are obtained, each having a feedback form. The guidance laws are evaluated for a hypothetical vehicle, and compared to an optimal solution obtained using a multiple shooting algorithm.

Calise, Anthony J.↗

An improved algorithm for optimum structural design with multiple frequency constraints

An optimality criterion (OC) method for minimum-weight design of structures having multiple constraints on natural frequencies is presented. In this work a new resizing strategy is developed based on relaxation techniques. A computationally adaptive control parameter is used in conjunction with existing OC recursive formulae to promote convergence of optimum structural designs. Some considerations regarding the coupling of the modified Aitken accelerator with the OC method are discussed. Improved and rapidly converged minimum-weight designs are obtained when using an under-relaxed recursive scheme combined with the modified Aitken accelerator.

Mcgee, Oliver G.↗

Conceptual designs and computer models of imaging radiometers for earth observations

An analytical model is developed based on engineering principles which can be used to predict the performance of spaceborne electro-optical remote sensors. Computer simulations and trade-off studies can be performed with this model to determine optimal sensor performance and design parameters. Computer algorithms were developed from which LOTUS spreadsheet macros were written. Computer simulations were performed to determine the performance characteristics and design parameters for the Moderate Resolution Imaging Spectrometer Nadir (MODIS-N) currently under development at NASA/Goddard Space Flight Center.

Montgomery, Harry E.↗

Maximum likelihood techniques in QELS

A framework for the analysis of Quasi Elastic Light Scattering (QELS) experiments designed to be used in microgravity environment is derived. Example calculations of the type to be used to design the QELS system are given. The framework for the analysis is based on the concepts of parameter estimation typified by Maximum Likelihood Estimation methods. These methods not only serve as the template for parameter estimation algorithms, but can also be used for optimal design of the experiments. Optimal design of experiments is facilitated by the fact that these methods not only give procedures for parameter estimation, but also estimates of the errors associated with the parameter estimation.

Edwards, Robert V.↗

Relative-Error-Covariance Algorithms

Two algorithms compute error covariance of difference between optimal estimates, based on data acquired during overlapping or disjoint intervals, of state of discrete linear system. Provides quantitative measure of mutual consistency or inconsistency of estimates of states. Relative-error-covariance concept applied, to determine degree of correlation between trajectories calculated from two overlapping sets of measurements and construct real-time test of consistency of state estimates based upon recently acquired data.

Bierman, Gerald J.↗

Two alternative ways for solving the coordination problem in multilevel optimization

Two techniques for formulating the coupling between levels in multilevel optimization by linear decomposition, proposed as improvements over the original formulation, now several years old, that relied on explicit equality constraints which were shown by application experience as occasionally causing numerical difficulties. The two new techniques represent the coupling without using explicit equality constraints, thus avoiding the above diffuculties and also reducing computational cost of the procedure. The old and new formulations are presented in detail and illustrated by an example of a structural optimization. A generic version of the improved algorithm is also developed for applications to multidisciplinary systems not limited to structures.

Sobieszczanski-Sobieski, Jaroslaw↗

Autonomous landing on Mars

Long communication times between earth and Mars demand autonomous landing capabilities. If high-resolution imagery acquired from an orbiter is available to select and certify a specific safe landing site or sites, navigational updates relative to the surface can be used to achieve the necessary accuracy to land within these certified sites. Autonomous registrations of the orbiter's imagery with photographs of the landing area taken by the lander during descent can provide the necessary accuracy and robustness. If orbital imagery is not available, autonomous hazard recognition and avoidance will be required to guide the lander to a hazard-free site. Feature extraction and matching algorithms, applied to visible light imagery and optimized to the terrain discovered by the Viking landers, can provide both an accurate surface-relative navigational update capability and a hazard recognition capability.

Stevenson, John↗

Optimizing Reduced-Order Transfer Functions

Transfer-function approximations made optimal in special least-squares sense. Algorithm computes reduced-order rational-fraction approximates to single-input/single-output transfer functions. Reduces amount of computation needed for such purposes as numerical simulation of dynamics and design of control subsystems.

Spanos, John T.↗

Error control techniques for satellite and space communications

Worked performed during the reporting period is summarized. Construction of robustly good trellis codes for use with sequential decoding was developed. The robustly good trellis codes provide a much better trade off between free distance and distance profile. The unequal error protection capabilities of convolutional codes was studied. The problem of finding good large constraint length, low rate convolutional codes for deep space applications is investigated. A formula for computing the free distance of 1/n convolutional codes was discovered. Double memory (DM) codes, codes with two memory units per unit bit position, were studied; a search for optimal DM codes is being conducted. An algorithm for constructing convolutional codes from a given quasi-cyclic code was developed. Papers based on the above work are included in the appendix.

Costello, Daniel J., Jr.↗

Electronic Neural Networks

Viewgraphs on electronic neural networks for space station are presented. Topics covered include: electronic neural networks; electronic implementations; VLSI/thin film hybrid hardware for neurocomputing; computations with analog parallel processing; features of neuroprocessors; applications of neuroprocessors; neural network hardware for terrain trafficability determination; a dedicated processor for path planning; neural network system interface; neural network for robotic control; error backpropagation algorithm for learning; resource allocation matrix; global optimization neuroprocessor; and electrically programmable read only thin-film synaptic array.

Thakoor, Anil↗

Algorithmic problems of nontransitive (SSB) utilities

The standard utility theory is based on several natural axioms including transitivity of preference; however, real preference is often not transitive. To describe such preferences, Fishburn (1988) introduced a new formalism (SSB-utilities), in which preference is described by a skew-symmetric function F:M x M - R, where M is the set of all alternatives. He also showed that it is in principle possible to reconstruct this function F by asking the person to compare different alternatives and lotteries. In the present paper we propose a new algorithm for reconstructing F that is asymptotically optimal in the sense that the number of binary (yes-no) questions that one has to ask to determine the values of F with given precision is of minimal possible order.

Kosheleva, O. M.↗

Implementation of ADI: Schemes on MIMD parallel computers

In order to simulate the effects of the impingement of hot exhaust jets of High Performance Aircraft on landing surfaces a multi-disciplinary computation coupling flow dynamics to heat conduction in the runway needs to be carried out. Such simulations, which are essentially unsteady, require very large computational power in order to be completed within a reasonable time frame of the order of an hour. Such power can be furnished by the latest generation of massively parallel computers. These remove the bottleneck of ever more congested data paths to one or a few highly specialized central processing units (CPU's) by having many off-the-shelf CPU's work independently on their own data, and exchange information only when needed. During the past year the first phase of this project was completed, in which the optimal strategy for mapping an ADI-algorithm for the three dimensional unsteady heat equation to a MIMD parallel computer was identified. This was done by implementing and comparing three different domain decomposition techniques that define the tasks for the CPU's in the parallel machine. These implementations were done for a Cartesian grid and Dirichlet boundary conditions. The most promising technique was then used to implement the heat equation solver on a general curvilinear grid with a suite of nontrivial boundary conditions. Finally, this technique was also used to implement the Scalar Penta-diagonal (SP) benchmark, which was taken from the NAS Parallel Benchmarks report. All implementations were done in the programming language C on the Intel iPSC/860 computer.

Vanderwijngaart, Rob F.↗

First MEM task IRMEO in IRAF

The first task for image restoration using the Maximum Entropy Method (MEM) in IRAF, called IRMEO, is described. The underlining algorithm is the approximate Newton method for optimization. The basic input images and parameters for deconvolution are described in some detail. Results of preliminary tests, including the number of iterations, required CPU time on a variety of computers, and deconvolved images are reported and compared with those from other deconvolution methods. The merits and limitations of this task are pointed out. The possible development of better MEM tasks on the basis of IRMEO is also discussed.

Wu, Nailong↗

Reliability enhancement of Navier-Stokes codes through convergence enhancement

Reduction of total computing time required by an iterative algorithm for solving Navier-Stokes equations is an important aspect of making the existing and future analysis codes more cost effective. Several attempts have been made to accelerate the convergence of an explicit Runge-Kutta time-stepping algorithm. These acceleration methods are based on local time stepping, implicit residual smoothing, enthalpy damping, and multigrid techniques. Also, an extrapolation procedure based on the power method and the Minimal Residual Method (MRM) were applied to the Jameson's multigrid algorithm. The MRM uses same values of optimal weights for the corrections to every equation in a system and has not been shown to accelerate the scheme without multigriding. Our Distributed Minimal Residual (DMR) method based on our General Nonlinear Minimal Residual (GNLMR) method allows each component of the solution vector in a system of equations to have its own convergence speed. The DMR method was found capable of reducing the computation time by 10-75 percent depending on the test case and grid used. Recently, we have developed and tested a new method termed Sensitivity Based DMR or SBMR method that is easier to implement in different codes and is even more robust and computationally efficient than our DMR method.

Choi, K.-Y.↗

On the use of controls for subsonic transport performance improvement: Overview and future directions

Increasing competition among airline manufacturers and operators has highlighted the issue of aircraft efficiency. Fewer aircraft orders have led to an all-out efficiency improvement effort among the manufacturers to maintain if not increase their share of the shrinking number of aircraft sales. Aircraft efficiency is important in airline profitability and is key if fuel prices increase from their current low. In a continuing effort to improve aircraft efficiency and develop an optimal performance technology base, NASA Dryden Flight Research Center developed and flight tested an adaptive performance seeking control system to optimize the quasi-steady-state performance of the F-15 aircraft. The demonstrated technology is equally applicable to transport aircraft although with less improvement. NASA Dryden, in transitioning this technology to transport aircraft, is specifically exploring the feasibility of applying adaptive optimal control techniques to performance optimization of redundant control effectors. A simulation evaluation of a preliminary control law optimizes wing-aileron camber for minimum net aircraft drag. Two submodes are evaluated: one to minimize fuel and the other to maximize velocity. This paper covers the status of performance optimization of the current fleet of subsonic transports. Available integrated controls technologies are reviewed to define approaches using active controls. A candidate control law for adaptive performance optimization is presented along with examples of algorithm operation.

Gilyard, Glenn↗

Dominant takeover regimes for genetic algorithms

The genetic algorithm (GA) is a machine-based optimization routine which connects evolutionary learning to natural genetic laws. The present work addresses the problem of obtaining the dominant takeover regimes in the GA dynamics. Estimated GA run times are computed for slow and fast convergence in the limits of high and low fitness ratios. Using Euler's device for obtaining partial sums in closed forms, the result relaxes the previously held requirements for long time limits. Analytical solution reveal that appropriately accelerated regimes can mark the ascendancy of the most fit solution. In virtually all cases, the weak (logarithmic) dependence of convergence time on problem size demonstrates the potential for the GA to solve large N-P complete problems.

Noever, David↗

A Comparison of Techniques for Scheduling Fleets of Earth-Observing Satellites

Earth observing satellite (EOS) scheduling is a complex real-world domain representative of a broad class of over-subscription scheduling problems. Over-subscription problems are those where requests for a facility exceed its capacity. These problems arise in a wide variety of NASA and terrestrial domains and are .XI important class of scheduling problems because such facilities often represent large capital investments. We have run experiments comparing multiple variants of the genetic algorithm, hill climbing, simulated annealing, squeaky wheel optimization and iterated sampling on two variants of a realistically-sized model of the EOS scheduling problem. These are implemented as permutation-based methods; methods that search in the space of priority orderings of observation requests and evaluate each permutation by using it to drive a greedy scheduler. Simulated annealing performs best and random mutation operators outperform our squeaky (more intelligent) operator. Furthermore, taking smaller steps towards the end of the search improves performance.

Globus, Al↗