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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 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

State-Space System Realization with Input- and Output-Data Correlation

This paper introduces a general version of the information matrix consisting of the autocorrelation and cross-correlation matrices of the shifted input and output data. Based on the concept of data correlation, a new system realization algorithm is developed to create a model directly from input and output data. The algorithm starts by computing a special type of correlation matrix derived from the information matrix. The special correlation matrix provides information on the system-observability matrix and the state-vector correlation. A system model is then developed from the observability matrix in conjunction with other algebraic manipulations. This approach leads to several different algorithms for computing system matrices for use in representing the system model. The relationship of the new algorithms with other realization algorithms in the time and frequency domains is established with matrix factorization of the information matrix. Several examples are given to illustrate the validity and usefulness of these new algorithms.

Juang, Jer-Nan↗
NASA NTRS1983

A chimera grid scheme

A mesh system composed of multiple overset body-conforming grids is described for adapting finite-difference procedures to complex aircraft configurations. In this so-called 'chimera mesh,' a major grid is generated about a main component of the configuration and overset minor grids are used to resolve all other features. Methods for connecting overset multiple grids and modifications of flow-simulation algorithms are discussed. Computational tests in two dimensions indicate that the use of multiple overset grids can simplify the task of grid generation without an adverse effect on flow-field algorithms and computer code complexity.

Steger, J. L.↗
DOE OSTI2024

Reduced-order modeling on a near-term quantum computer

Quantum computing is an advancing area of research in which computer hardware and algorithms are developed to take advantage of quantum mechanical phenomena. In recent studies, quantum algorithms have shown promise in solving linear systems of equations as well as systems of linear ordinary differential equations (ODEs) and partial differential equations (PDEs). Reducedorder modeling (ROM) algorithms for studying fluid dynamics have shown success in identifying linear operators that can describe flowfields, where dynamic mode decomposition (DMD) is a particularly useful method in which a linear operator is identified from data. In this work, DMD is reformulated as an optimization problem to propagate the state of the linearized dynamical system on a quantum computer. This reformulation was chosen as a means of facilitating implementation on a near-term quantum computer. Quadratic unconstrained binary optimization (QUBO), a technique for optimizing quadratic polynomials in binary variables, allows for quantum annealing algorithms to be applied. A quantum circuit model (quantum approximation optimization algorithm, QAOA) is utilized to obtain predictions of the state trajectories. Results are shown for the quantum-ROM predictions for flow over a 2D cylinder at Re = 220 and flow over a NACA0009 airfoil at Re = 500 and α = 15°. The quantum-ROM predictions are found to depend on the number of bits utilized for a fixed point representation and the truncation level of the DMD model. Comparisons with DMD predictions from a classical computer algorithm are made, as well as an analysis of the computational complexity and prospects for future, more fault-tolerant quantum computers.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1988

I-BIEM, an iterative boundary integral equation method for computer solutions of current distribution problems with complex boundaries: A new algorithm. I - Theoretical

A new algorithm for an iterative computation of solutions of Laplace's or Poisson's equations in two dimensions, using Green's second identity, is presented. This algorithm converges strongly and geometrically and can be applied to curved, irregular, or moving boundaries with nonlinear and/or discontinuous boundary conditions. It has been implemented in Pascal on a number of micro- and minicomputers and applied to several geometries. Cases with known analytic solutions have been tested. Convergence to within 0.1 percent to 0.01 percent of the theoretical values are obtained in a few minutes on a microcomputer.

Cahan, B. D.↗
NASA NTRS1986

A diagonal algorithm for the method of pseudocompressibility

The method of pseudocompressibility has been shown to be an efficient method for obtaining a steady-state solution to the incompressible Navier-Stokes equations. Recent improvements to this method include the use of a diagonal scheme for the inversion of the equations at each iteration. The necessary transformations have been derived for the pseudocompressibility equations in generalized coordinates. The diagonal algorithm reduces the computing time necessary to obtain a steady-state solution by a factor of nearly three. Implicit viscous terms are maintained in the equations, and it has become possible to use fourth-order implicit dissipation. The steady-state solution is unchanged by the approximations resulting from the diagonalization of the equations. Computed results for flow over a two-dimensional backward-facing step and a three-dimensional cylinder mounted normal to a flat plate are presented for both the old and new algorithms. The accuracy and computing efficiency of these algorithms are compared.

Rogers, S. E.↗
DOE OSTI2019

Simulation of Motion of H{sub 2} and D{sub 2} Molecules in Sextupole Magnets

We simulate the motion of hydrogen and deuterium molecules in the magnetic system of a setup intended for obtaining nuclear-spin-polarized molecules. Spatial separation of molecules with different magnetic moment projections by the spin filtration method in a nonuniform magnetic field is performed using superconducting sextupole magnets. Calculations are carried out for a magnetic field induction of 3.7 T at the poles and a nozzle temperature of 7 K. Simulation show that the ratio of polarized flux of hydrogen molecules to the detector to the total flux from the source nozzle is 2.3 × 10{sup –6}, nuclear polarization being close to 100%. Calculations performed for deuterium reveal that this ratio is 7 times smaller due to the smallness of the magnetic moment relative to hydrogen molecules. Trajectories of molecules in the magnetic system and their spatial distribution are represented graphically. Mathematical aspects of the algorithm of the computer program developed for this purpose are considered.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS1991

The fractional Fourier transform and applications

This paper describes the 'fractional Fourier transform', which admits computation by an algorithm that has complexity proportional to the fast Fourier transform algorithm. Whereas the discrete Fourier transform (DFT) is based on integral roots of unity e exp -2(pi)i/n, the fractional Fourier transform is based on fractional roots of unity e exp -2(pi)i(alpha), where alpha is arbitrary. The fractional Fourier transform and the corresponding fast algorithm are useful for such applications as computing DFTs of sequences with prime lengths, computing DFTs of sparse sequences, analyzing sequences with noninteger periodicities, performing high-resolution trigonometric interpolation, detecting lines in noisy images, and detecting signals with linearly drifting frequencies. In many cases, the resulting algorithms are faster by arbitrarily large factors than conventional techniques.

Bailey, David H.↗
NASA NTRS2006

Stochastic Evolutionary Algorithms for Planning Robot Paths

A computer program implements stochastic evolutionary algorithms for planning and optimizing collision-free paths for robots and their jointed limbs. Stochastic evolutionary algorithms can be made to produce acceptably close approximations to exact, optimal solutions for path-planning problems while often demanding much less computation than do exhaustive-search and deterministic inverse-kinematics algorithms that have been used previously for this purpose. Hence, the present software is better suited for application aboard robots having limited computing capabilities (see figure). The stochastic aspect lies in the use of simulated annealing to (1) prevent trapping of an optimization algorithm in local minima of an energy-like error measure by which the fitness of a trial solution is evaluated while (2) ensuring that the entire multidimensional configuration and parameter space of the path-planning problem is sampled efficiently with respect to both robot joint angles and computation time. Simulated annealing is an established technique for avoiding local minima in multidimensional optimization problems, but has not, until now, been applied to planning collision-free robot paths by use of low-power computers.

Fink, Wolfgang↗
NASA NTRS1987

Solving the Cauchy-Riemann equations on parallel computers

Discussed is the implementation of a single algorithm on three parallel-vector computers. The algorithm is a relaxation scheme for the solution of the Cauchy-Riemann equations; a set of coupled first order partial differential equations. The computers were chosen so as to encompass a variety of architectures. They are: the MPP, and SIMD machine with 16K bit serial processors; FLEX/32, an MIMD machine with 20 processors; and CRAY/2, an MIMD machine with four vector processors. The machine architectures are briefly described. The implementation of the algorithm is discussed in relation to these architectures and measures of the performance on each machine are given. Simple performance models are used to describe the performance. These models highlight the bottlenecks and limiting factors for this algorithm on these architectures. Conclusions are presented.

Fatoohi, Raad A.↗
DOE OSTI2018

Simulating 0+1 Dimensional Quantum Gravity on Quantum Computers: Mini-Superspace Quantum Cosmology and the World Line Approach in Quantum Field Theory

Quantum computers are promising candidates to radically expand the domain of computational science by their increased computing power and more effective algorithms. In particular, quantum computing could have a tremendous impact on the field of quantum cosmology. The goal of quantum cosmology is to describe the evolution of the universe through the Wheeler-DeWitt equation or path integral methods without having to first formulate a full theory of quantum gravity. The quantum computer provides an advantage in this endeavor because it can perform path integrals directly in Lorentzian space and does not require constructing contour integrations in Euclidean gravity. Also, quantum computers can provide advantages in systems with fermions which are difficult to simulate on classical computers. In this study, we first employed classical computational methods to analyze a Friedmann-Robertson-Walker mini-superspace with a scalar field and visualize the calculated wavefunction of the universe for a variety of different values of the curvature of the universe and the cosmological constant. We then used IBM’s Quantum Information Science Kit Python library and the variational quantum eigensolver algorithm to study the same systems on a quantum computer. The framework was extended to the world line approach to quantum field theory.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

On Feedback-Principle Control for Systems with Aftereffect Under Incomplete Phase-Coordinate Data

For a nonlinear system of differential equations with aftereffect, two mutually complement game minimax (maximin) problems for the quality functional are considered. Assuming that a part of phase coordinates of the system is measured (with error) sufficiently frequently, we provide solving algorithms that are stable with respect to the information noise and computational errors. The proposed algorithms are based on the Krasovskii extremal translation principle.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

Maximum likelihood algorithm using an efficient scheme for computing sensitivities and parameter confidence intervals

Improved techniques for estimating airplane stability and control derivatives and their standard errors are presented. A maximum likelihood estimation algorithm is developed which relies on an optimization scheme referred to as a modified Newton-Raphson scheme with estimated sensitivities (MNRES). MNRES determines sensitivities by using slope information from local surface approximations of each output variable in parameter space. The fitted surface allows sensitivity information to be updated at each iteration with a significant reduction in computational effort compared to integrating the analytically-determined sensitivity equations or using a finite difference scheme. An aircraft estimation problem is solved using real flight data to compare MNRES with the commonly used modified Newton-Raphson technique; MNRES is found to be faster and more generally applicable. Parameter standard errors are determined using a random search technique. The confidence intervals obtained are compared with Cramer-Rao lower bounds at the same confidence level. It is observed that the nonlinearity of the cost function is an important factor in the relationship between Cramer-Rao bounds and the error bounds determined by the search technique.

Murphy, P. C.↗
NASA NTRS1993

A comparison of two incompressible Navier-Stokes algorithms for unsteady internal flow

A comparative study of two different incompressible Navier-Stokes algorithms for solving an unsteady, incompressible, internal flow problem is performed. The first algorithm uses an artificial compressibility method coupled with upwind differencing and a line relaxation scheme. The second algorithm uses a fractional step method with a staggered grid, finite volume approach. Unsteady, viscous, incompressible, internal flow through a channel with a constriction is computed using the first algorithm. A grid resolution study and parameter studies on the artificial compressibility coefficient and the maximum allowable residual of the continuity equation are performed. The periodicity of the solution is examined and several periodic data sets are generated using the first algorithm. These computational results are compared with previously published results computed using the second algorithm and experimental data.

Wiltberger, N. Lyn↗
NASA NTRS1983

Computer Control of a Seven Degree of Freedom Manipulator Arm

An algorithm for the computer control of a seven degree of freedom manipulator arm, termed the Hawk Control Mode, is described. The algorithm is segmented into two distinct parts. One part controls the extension, azimuth, and elevation of the wrist joint. The second part controls the attitude of the terminal device. The two segments interact to achieve decoupling of wrist joint translation and terminal device rotation.

Stensby, J.↗
DOE OSTI2021

Using Grover's search algorithm to characterize the Rigetti Quantum Computing Platform

Performance of the Rigetti quantum computing platform was tested during the period of time from 12/16/2019 to 05/18/2020. The Grover's search (GS) algorithm was used as a testing tool, in particular, 3- and 4-level versions of the algorithm. As a result, a number of hardware issues were revealed, so the algorithm was split to smaller blocks and individual gates, and all of them were tested separately. The fidelity decay was found to be due to both decoherent processes and coherent errors of the native hardware gates. These errors were estimated from RX gate benchmarks and were in a form of extra rotation of the quantum state. As a consequence of that, performance of the algorithm was shown to be strongly dependent on the native gate decomposition of the program. Suggestions and possible improvements of the future Rigetti runs are made based on the obtained observations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A Roadmap for Reaching the Potential of Brain-Derived Computing

Neuromorphic computing is a critical future technology for the computing industry, but it has yet to achieve its promise and has struggled to establish a cohesive research community. A large part of the challenge is that full realization of the potential of brain inspiration requires advances in both device hardware, computing architectures, and algorithms. This simultaneous development across technology scales is unprecedented in the computing field. This article presents a strategy, framed by market and policy pressures, for moving past these current technological and cultural hurdles to realize its full impact across technology. Achieving the full potential of brain-derived algorithms as well as post-complementary metal-oxide-semiconductor (CMOS) scaling neuromorphic hardware requires appropriately balancing the near-term opportunities of deep learning applications with the long-term potential of less understood opportunities in neural computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

NOvA Reconstruction using Deep Learning [Slides]

The NOvA experiment has made measurements of the disappearance of νμ and the appearance of νe in the NuMI beam at Fermilab. Key to these measurements is the identification of the neutrino flavor and measurement of the neutrino energy, for which NOvA has implemented deep learning algorithms utilizing tools from the field of computer vision. These algorithms, first applied to NOvA's 2016 results, showed significant improvement over previous reconstruction methods. I will present NOvA's implementation of deep learning algorithms using convolutional neural networks for identification of event neutrino flavor and single particles used in the 2018 analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Quantum Information Science in High Energy Physics at the Large Hadron Collider (Final Report-QuantISED)

We pursue scientific research at the interface of High Energy Physics and Quantum Information Science. This includes studies of thermal radiation and quantum entanglement in high-energy collisions at the Large Hadron Collider (LHC), with special emphasis on entanglement entropy and the Higgs boson. This project has also been extended to include quantum entanglement and charged current weak interactions using Fermilab results. And most recently, we have begun tests of the temporal entanglement using LHC data. Collider experiments such as proton-proton collisions at the LHC yield hadrons that exhibit an exponential behavior at low transverse momenta. This surprising behavior is seen in data from both the ATLAS and CMS collaborations. We attribute this phenomenon to quantum entanglement between the regions in the nucleon wave function. The exponential component to the transverse momentum distribution is a result of thermal radiation that is akin to Hawking or Unruh radiation that should exist at the event horizon of astrophysical black holes and neutron stars. The Principal Investigator, in collaboration with a theoretical physicist at Stony Brook University and Brookhaven National Laboratory, and with Yale University students, has shown evidence for this thermal radiation in several production and decay processes in the ATLAS and CMS data, and its connection to entanglement entropy (O.K. Baker and D.E Kharzeev, Phys. Rev. D 98, 054007 (2018)), including even the Higgs boson sector. Interestingly, this thermal behavior is also seen in momentum distributions of charged current weak interactions according to our studies. These findings suggest a deep connection between quantum entanglement (entanglement entropy) and thermalization in both hadron collisions at the energy frontier and neutrino scattering at the intensity frontier. We have confirmed the proposed relation between the effective temperature and the hard-scattering scale at lower energies using the most recent LHC data for the following systems: Higgs bosons, top quarks, and charged hadrons. Additionally, we have results for hadron production in neutrino scattering from nuclei using Fermilab weak interaction data. This study is carried out using data from the MINERvA collaboration. In those cases where entanglement is expected, there is an exponential component to the momentum distribution, while this component is absent in those processes where no entanglement is expected. This research thus tests the hypothesis about a link between quantum entanglement and thermalization in strong and weak interactions. See Phys Lett B 811, 135948 (2020). We also initiated research applying a quantum search algorithm (Grover's Algorithm) to LHC data. This quantum algorithm was used to show how rare events in LHC data can be searched for in large, unsorted databases, with quadratic speedup compared to classical search algorithms on classical computers. See "Application of a Quantum Search Algorithm to High- Energy Physics Data at the Large Hadron Collider", arXiv:2010.00649 [quant-ph].

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗