SEARCH · Search NASA
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.
Techniques for solving the feature selection problem are presented. Topics discussed include the reduction of the number of variables in "best b", and the iterative selection of H sub i.
Analytical Mechanics Associates, Inc. (AMA), of Hampton, Virginia, created the EZopt software application through Small Business Innovation Research (SBIR) funding from NASA's Langley Research Center. The new software is a user-friendly tool kit that provides quick and logical solutions to complex optimal control problems. In its most basic form, EZopt converts process data into math equations and then proceeds to utilize those equations to solve problems within control systems. EZopt successfully proved its advantage when applied to short-term mission planning and onboard flight computer implementation. The technology has also solved multiple real-life engineering problems faced in numerous commercial operations. For instance, mechanical engineers use EZopt to solve control problems with robots, while chemical plants implement the application to overcome situations with batch reactors and temperature control. In the emerging field of commercial aerospace, EZopt is able to optimize trajectories for launch vehicles and perform potential space station- keeping tasks. Furthermore, the software also helps control electromagnetic devices in the automotive industry.
The theory of elasto-plastic flow is developed so that it may accommodate features such as work-hardening, anisotropy, plastic compressibility, non-continuous loading including local or global unloading, and others. A complete theory is given in quasi-linear form; as a result, many useful attributes are accessible. Several integral theorems may be written, finite deformations may be incorporated, and efficient methods for solving problems may be developed; these and other aspects are described in some detail. The theory is reduced to special forms for 2-space, and extensive experience in solving such problems is cited.
The Automated Scheduling and Planning Environment (ASPEN) software system, aspects of which have been reported in several previous NASA Tech Briefs articles, includes a subsystem that utilizes a portfolio of heuristic algorithms that work synergistically to solve problems. The nature of the synergy of the specific algorithms is that their likelihoods of success are negatively correlated: that is, when a combination of them is used to solve a problem, the probability that at least one of them will succeed is greater than the sum of probabilities of success of the individual algorithms operating independently of each other. In ASPEN, the portfolio of algorithms is used in a planning process of the iterative repair type, in which conflicts are detected and addressed one at a time until either no conflicts exist or a user-defined time limit has been exceeded. At each choice point (e.g., selection of conflict; selection of method of resolution of conflict; or choice of move, addition, or deletion) ASPEN makes a stochastic choice of a combination of algorithms from the portfolio. This approach makes it possible for the search to escape from looping and from solutions that are locally but not globally optimum.
Solving problems of controllability, observability, and motion stability of dynamic control systems pertaining to theory of optimal processes
Our goal has been to investigate the influence of both dilution and radiation on the extinction process of nonpremixed flames at low strain rates. Simulations have been performed by using a counterflow code and three radiation models have been included in it, namely, the optically thin, the narrowband, and discrete ordinate models. The counterflow flame code OPPDIFF was modified to account for heat transfer losses by radiation from the hot gases. The discrete ordinate method (DOM) approximation was first suggested by Chandrasekhar for solving problems in interstellar atmospheres. Carlson and Lathrop developed the method for solving multi-dimensional problem in neutron transport. Only recently has the method received attention in the field of heat transfer. Due to the applicability of the discrete ordinate method for thermal radiation problems involving flames, the narrowband code RADCAL was modified to calculate the radiative properties of the gases. A non-premixed counterflow flame was simulated with the discrete ordinate method for radiative emissions. In comparison with two other models, it was found that the heat losses were comparable with the optically thin and simple narrowband model. The optically thin model had the highest heat losses followed by the DOM model and the narrow-band model.
The discipline of computational fluid dynamics (CFD) is at a crossroad. Most of the significant advances related to computational methods have taken place. The emphasis is now shifting from methods to results. Significant efforts are made in applying CFD to solve design problems. The value of CFD results in design depends on the credibility of computed results for the intended use. The process of establishing credibility requires a standard so that there is a consistency and uniformity in this process and in the interpretation of its outcome. The key element for establishing the credibility is the quantification of uncertainty. This paper presents salient features of a proposed standard and a procedure for determining the uncertainty. A customer of CFD products - computer codes and computed results - expects the following: A computer code in terms of its logic, numerics, and fluid dynamics and the results generated by this code are in compliance with specified requirements. This expectation is fulfilling by verification and validation of these requirements. The verification process assesses whether the problem is solved correctly and the validation process determines whether the right problem is solved. Standards for these processes are recommended. There is always some uncertainty, even if one uses validated models and verified computed results. The value of this uncertainty is important in the design process. This value is obtained by conducting a sensitivity-uncertainty analysis. Sensitivity analysis is generally defined as the procedure for determining the sensitivities of output parameters to input parameters. This analysis is a necessary step in the uncertainty analysis, and the results of this analysis highlight which computed quantities and integrated quantities in computations need to be determined accurately and which quantities do not require such attention. Uncertainty analysis is generally defined as the analysis of the effect of the uncertainties involved in all stages of a process on the final responses. There are two approaches for conducting the uncertainty analysis: experimental and computational. These analyses and approaches are briefly described.
Spacecraft design optimization is a difficult problem, due to the complexity of optimization cost surfaces, and human expertise in optimization that is necessary in order to achieve good results. In this paper, we propose the use of a set of generic, metaheuristic optimization algorithms (e.g., generic algorithms, simulated annealing), which is configured for a particular optimization problem by an adaptive problem solver based on artificial intelligence and machine learning techniques. We describe work in progress on OASIS, a system for adaptive problem solving based on these principles.
The properties of spectral methods are surveyed and their extension to solve problems in complex geometries is developed. A new iteration procedure is introduced to solve efficiently the full matrix equations resulting from spectral approximations to nonconstant coefficient boundary-value problems in complex geometries. It is shown that the work required to solve these spectral equations exceeds that of solving the lowest-order finite-difference approximation to the same problem by only O(N log N).
Thermal Synthesizer System (TSS) is integrated set of thermal-analysis application programs designed to solve problems encountered by thermal engineers. Combines functionality of Systems Improved Numerical Differencing Analyzer/Fluid Integrator (SINDA/FLUINT) and radiation analysis with friendly and easily understood user-interface environment coupled with powerful interactive color graphics and geometric modeling capability. Enables thermal engineers to spend more time solving engineering problems instead of laboriously constructing and verifying math models. Written in FORTRAN and C language.
A theoretical basis is established for analysis of finite deformation of metals. The observation that finite deformation of such elastoplastic materials may be viewed as a process rather than an event leads to derivation of a complete initial and boundary value problem distinguished by its quasilinear nature. This feature of the formulation motivates adoption of an incremental approach to numerical problem solving. Numerical solution capability is established for problems of plane stress and plane strain. The validity of the theory and numerical analysis is demonstrated by consideration of a number of problems of homogeneous finite deformation for which analytic solutions are available. Subsequently the analysis is employed for the investigation of necking in flat metal tensile bars. The results of this investigation provide the first full numerical solutions for tensile necking in plane stress and plane strain. In addition a basis is provided for assessment of the validity of stress-strain relations inferred from tensile test data.
Since the late 1950's, the National Aeronautics and Space Administration's Dryden Flight Research Facility has found in-flight simulation to be an invaluable tool. In-flight simulation has been used to address a wide variety of flying qualities questions, including low-lift-to-drag ratio approach characteristics for vehicles like the X-15, the lifting bodies, and the Space Shuttle; the effects of time delays on controllability of aircraft with digital flight-control systems, the causes and cures of pilot-induced oscillation in a variety of aircraft, and flight-control systems for such diverse aircraft as the X-15 and the X-29. In-flight simulation has also been used to anticipate problems and to avoid them and to solve problems once they appear. Presented here is an account of the in-flight simulation at the Dryden Flight Research Facility and some discussion. An extensive bibliography is included.
Few human endeavors can be viewed both as extremely successful and unsuccessful at the same time. This is typically the case when goals have not been well defined or have been shifting in time. This has certainly been true of Artificial Intelligence (AI). The nature of intelligence has been the object of much thought and speculation throughout the history of philosophy. It is in the nature of philosophy that real headway is sometimes made only when appropriate tools become available. Similarly the computer, coupled with the ability to program (at least in principle) any function, appeared to be the tool that could tackle the notion of intelligence. To suit the tool, the problem of the nature of intelligence was soon sidestepped in favor of this notion: If a probing conversation with a computer could not be distinguished from a conversation with a human, then AI had been achieved. This notion became known as the Turing test, after the mathematician Alan Turing who proposed it in 1950. Conceptually rich and interesting, these early efforts gave rise to a large portion of the field's framework. Key to AI, rather than the 'number crunching' typical of computers until then, was viewed as the ability to manipulate symbols and make logical inferences. To facilitate these tasks, AI languages such as LISP and Prolog were invented and used widely in the field. One idea that emerged and enabled some success with real world problems was the notion that 'most intelligence' really resided in knowledge. A phrase attributed to Feigenbaum, one of the pioneers, was 'knowledge is the power.' With this premise, the problem is shifted from 'how do we solve problems' to 'how do we represent knowledge.' A good knowledge representation scheme could allow one to draw conclusions from given premises. Such schemes took forms such as rules,frames and scripts. It allowed the building of what became known as expert systems or knowledge based systems (KBS).
We are using Web (Grid) service technology to demonstrate the assimilation of multiple distributed data sources (a typical data grid problem) into a major parallel high-performance computing earthquake forecasting code. Such a linkage of Geoinformatics with Geocomplexity demonstrates the value of the Solid Earth Research Virtual Observatory (SERVO) Grid concept, and advance Grid technology by building the first real-time large-scale data assimilation grid Here we develop the next steps for both the SERVO concept and the identified need for a Solid Earth problem-solving environment. We use a challenging motivating problem of importance to NASA namely integrating NASA space geodetic observations with numerical simulations of a changing earth.
The quantum approximate optimization algorithm (QAOA) has the potential to approximately solve complex combinatorial optimization problems in polynomial time. However, current noisy quantum devices cannot solve large problems due to hardware constraints. In this work, we develop an algorithm that decomposes the QAOA input problem graph into a smaller problem and solves MaxCut using QAOA on the reduced graph. The algorithm requires a subroutine that can be classical or quantum—in this work, we implement the algorithm twice on each graph. One implementation uses the classical solver Gurobi in the subroutine and the other uses QAOA. We solve these reduced problems with QAOA. On average, the reduced problems require only approximately 1/10 of the number of vertices than the original MaxCut instances. Furthermore, the average approximation ratio of the original MaxCut problems is 0.75, while the approximation ratios of the decomposed graphs are on average of 0.96 for both Gurobi and QAOA. With this decomposition, we are able to measure optimal solutions for ten 100-vertex graphs by running single-layer QAOA circuits on the Quantinuum trapped-ion quantum computer H1-1, sampling each circuit only 500 times. This approach is best suited for sparse, particularly k-regular graphs, as k-regular graphs on n vertices can be decomposed into a graph with at most $\frac{nk}{k+1}$ vertices in polynomial time. Further reductions can be obtained with a potential trade-off in computational time. In conclusion, while this paper applies the decomposition method to the MaxCut problem, it can be applied to more general classes of combinatorial optimization problems.
Failure by foreign students to perceive the rhetorical structure or overall organization of an engineering lecture, rendering them unable to understand it, is discussed. Equally serious failure by such students to perceive the organizing role of theory in structuring the activities in their field is reported. Failure to identify the role of theory in the problem-solving process that underlies engineering is emphasized. Engineering was not seen as a series of on-going problems where each stage of solution exposed new problems to be solved. Implications for course planners and material designers are discussed.
The H(sub z)-polarized inverse problem has rarely been studied before due to the complicated way in which the unknown permittivity appears in the wave equation. This problem is equivalent to the acoustic inverse problem with variable density. We have recently reported the solution to the nonlinear variable-permittivity H(sub z)-polarized inverse problem using the Born iterative method. Here, the linear inverse problem is solved for permittivity (epsilon) and permeability (mu) using a different approach which is an extension of the basic ideas of diffraction tomography (DT). The key to solving this problem is to utilize frequency diversity to obtain the required independent measurements. The receivers are assumed to be in the far field of the object, and plane wave incidence is also assumed. It is assumed that the scatterer is weak, so that the Born approximation can be used to arrive at a relationship between the measured pressure field and two terms related to the spatial Fourier transform of the two unknowns, epsilon and mu. The term involving permeability corresponds to monopole scattering and that for permittivity to dipole scattering. Measurements at several frequencies are used and a least squares problem is solved to reconstruct epsilon and mu. It is observed that the low spatial frequencies in the spectra of epsilon and mu produce inaccuracies in the results. Hence, a regularization method is devised to remove this problem. Several results are shown. Low contrast objects for which the above analysis holds are used to show that good reconstructions are obtained for both permittivity and permeability after regularization is applied.
In this paper we study the difficulty of solving problems in economics. For this purpose, we adopt the notion of undecidability from recursion theory. We show that certain problems in economics are undecidable, i.e., cannot be solved by a Turing Machine, a device that is at least as powerful as any computational device that can be constructed. In particular, we prove that even in finite closed economies subject to a variable initial condition, in which a social planner knows the behavior of every agent in the economy, certain important social planning problems are undecidable. Thus, it may be impossible to make effective policy decisions. Philosophically, this result formally brings into question the Rational Expectations Hypothesis which assumes that each agent is able to determine what it should do if it wishes to maximize its utility. We show that even when an optimal rational forecast exists for each agency (based on the information currently available to it), agents may lack the ability to make these forecasts. For example, Lucas describes economic models as 'mechanical, artificial world(s), populated by ... interacting robots'. Since any mechanical robot can be at most as computationally powerful as a Turing Machine, such economies are vulnerable to the phenomenon of undecidability.