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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Towards a Mass-Consistent Methodology for Realistic Melting Hydrometeor Retrieval

To address the acute challenge posed by the melting layer to accurate surface precipitation retrievals from space, we en-sure the compositional consistency in ice, liquid, and total masses of synthetic melting hydrometeors with a method of stochastic compensation. The method is applied to simulated melting hydrometeors prior to calculating their scattering properties using the discrete dipole approximation (DDA). We investigate the impact of this stochastic compensation to calculated scattering properties by contrasting it with a naïve approach and report our findings.

precipitation remote sensing↗
NASA NTRS2010

Navier-Stokes Dynamics by a Discrete Boltzmann Model

This work investigates the possibility of particle-based algorithms for the Navier-Stokes equations and higher order continuum approximations of the Boltzmann equation; such algorithms would generalize the well-known Pullin scheme for the Euler equations. One such method is proposed in the context of a discrete velocity model of the Boltzmann equation. Preliminary results on shock structure are consistent with the expectation that the shock should be much broader than the near discontinuity predicted by the Pullin scheme, yet narrower than the prediction of the Boltzmann equation. We discuss the extension of this essentially deterministic method to a stochastic particle method that, like DSMC, samples the distribution function rather than resolving it completely.

Rubinstein, Robet↗
NASA NTRS1990

Forward Stochastic Nonlinear Adaptive Control Method

New method of computation for optimal stochastic nonlinear and adaptive control undergoing development. Solves systematically stochastic dynamic programming equations forward in time, using nested-stochastic-approximation technique. Main advantage, simplicity of programming and reduced complexity with clear performance/computation trade-offs.

Bayard, David S.↗
NASA NTRS1993

Successful combination of the stochastic linearization and Monte Carlo methods

A combination of a stochastic linearization and Monte Carlo techniques is presented for the first time in literature. A system with separable nonlinear damping and nonlinear restoring force is considered. The proposed combination of the energy-wise linearization with the Monte Carlo method yields an error under 5 percent, which corresponds to the error reduction associated with the conventional stochastic linearization by a factor of 4.6.

Elishakoff, I.↗
NASA NTRS2017

Thermal Expansion from Stochastic Nonlinear Acoustic Fields

The methods of stochastic mechanics are applied to nonlinear acoustic fields generated by random acoustic radiation sources associated with oscillating lattice sites in nonlinear lattices. The assumption of stochastically independent zero-point and temperature-dependent acoustic fields leads to expressions of the thermodynamic internal and Helmholtz free energies per unit mass in terms of modal energies per unit mass that account for the nonlinearity of the propagation modes. The thermodynamic state functions canonically transform to the familiar results of a system of quantized, simple harmonic oscillators in the linear field limit. The thermal expansion coefficient, derived from the Helmholtz free energy per unit mass, is obtained as a sum of the zero-point modal nonlinearity parameters, weighted by temperature-dependent, modal heat capacities. The relationship is fully anharmonic, in contrast to commonly used quasi-harmonic models, and predicts with excellent agreement the experimentally observed null thermal expansion of vitreous silica at the ‘cross-over’ temperature corresponding to the balance between long wavelength modes that contribute negative nonlinearity parameters to the modal sum and short wavelength modes that contribute positive nonlinearity parameters.

Cantrell, John H., Jr.↗
NASA NTRS2010

Vestibular Stochastic Resonance as a Method to Improve Balance Function: Optimization of Stimulus Characteristics

Stochastic resonance (SR) is a mechanism by which noise can assist and enhance the response of neural systems to relevant sensory signals. Application of imperceptible SR noise coupled with sensory input through the proprioceptive, visual, or vestibular sensory systems has been shown to improve motor function. Specifically, studies have shown that that vestibular electrical stimulation by imperceptible stochastic noise, when applied to normal young and elderly subjects, significantly improved their ocular stabilization reflexes in response to whole-body tilt as well as balance performance during postural disturbances. The goal of this study was to optimize the characteristics of the stochastic vestibular signals for balance performance during standing on an unstable surface. Subjects performed a standardized balance task of standing on a block of 10 cm thick medium density foam with their eyes closed for a total of 40 seconds. Stochastic electrical stimulation was applied to the vestibular system through electrodes placed over the mastoid process behind the ears during the last 20 seconds of the test period. A custom built constant current stimulator with subject isolation delivered the stimulus. Stimulation signals were generated with frequencies in the bandwidth of 1-2 Hz and 0.01-30 Hz. Amplitude of the signals were varied in the range of 0- +/-700 micro amperes with the RMS of the signal increased by 30 micro amperes for each 100 micro amperes increase in the current range. Balance performance was measured using a force plate under the foam block and inertial motion sensors placed on the torso and head segments. Preliminary results indicate that balance performance is improved in the range of 10-25% compared to no stimulation conditions. Subjects improved their performance consistently across the blocks of stimulation. Further the signal amplitude at which the performance was maximized was different in the two frequency ranges. Optimization of the frequency and amplitude of the signal characteristics of the stochastic noise signals on maximizing balance performance will have a significant impact in its development as a unique system to aid recovery of function in astronauts after long duration space flight or for people with balance disorders.

Mulavara, Ajitkumar↗
NASA NTRS2003

A Stochastic Collocation Algorithm for Uncertainty Analysis

This report describes a stochastic collocation method to adequately handle a physically intrinsic uncertainty in the variables of a numerical simulation. For instance, while the standard Galerkin approach to Polynomial Chaos requires multi-dimensional summations over the stochastic basis functions, the stochastic collocation method enables to collapse those summations to a one-dimensional summation only. This report furnishes the essential algorithmic details of the new stochastic collocation method and provides as a numerical example the solution of the Riemann problem with the stochastic collocation method used for the discretization of the stochastic parameters.

Mathelin, Lionel↗
DOE OSTI2024

Advanced Computing is at the Forefront of a New “Moonshot” Revolutionizing the North American Power Grid

In the 50+ years since the first humans landed on the moon, computing has grown at breakneck speed. We are faced with another challenge that is just as daunting, and just as important to overcome-modernizing the North American electric power grid-and high-performance computing (HPC) systems with specialized software will be an important element in rising to this challenge. We describe at a high level how software developed in the ExaSGD project addresses this "moonshot" goal by utilizing exascale computing and a novel high performance solver software stack to support the mission of decarbonizing power grid operations in an environment of uncertain weather and climate. To reach the exascale benchmark the team has made a number of first-of-their-kind innovations, including novel method for stochastic optimization, fine grained parallel methods for modeling power systems, and GPU resident sparse numerical linear solvers.

17 WIND ENERGY↗
NASA NTRS1972

On stochastic control system design methods for weakly coupled large scale linear systems.

This paper considers the problem of decentralized control of two weakly coupled linear stochastic systems, using quadratic performance indices. The basic idea is to have each controller control independently his own system, based upon noisy measurements of his own output. To compensate for the effects of weak coupling upon the resultant performance, fake white plant noise is introduced to each system. The appropriate intensity of the fake plant noise is obtained through the solution of an off-line deterministic matrix optimal control problem. The effects of this design method upon the overall coupled system performance are analyzed as a function of the degree of intersystem coupling.

Kwong, R.↗
NASA NTRS1999

A New Stochastic Equivalent Linearization Implementation for Prediction of Geometrically Nonlinear Vibrations

In this paper, the problem of random vibration of geometrically nonlinear MDOF structures is considered. The solutions obtained by application of two different versions of a stochastic linearization method are compared with exact (F-P-K) solutions. The formulation of a relatively new version of the stochastic linearization method (energy-based version) is generalized to the MDOF system case. Also, a new method for determination of nonlinear sti ness coefficients for MDOF structures is demonstrated. This method in combination with the equivalent linearization technique is implemented in a new computer program. Results in terms of root-mean-square (RMS) displacements obtained by using the new program and an existing in-house code are compared for two examples of beam-like structures.

Muravyov, Alexander A.↗
NASA NTRS1992

On the use of stochastic process-based methods for the analysis of hyperspectral data

Further development in remote sensing technology requires refinement of information system design aspects, i.e., the ability to specify precisely the data to collect and the means to extract increasing amounts of information from the increasingly rich and complex data stream created. One of the principal directions of advance is that data from much larger numbers of spectral bands can be collected, but with significantly increased signal-to-noise ratio. The theory of stochastic or random processes may be applied to the modeling of second-order variations. A multispectral data set with a large number of spectral bands is analyzed using standard pattern recognition techniques. The data were classified using first a single spectral feature, then two, and continuing on with greater and greater numbers of features. Three different classification schemes are used: a standard maximum likelihood Gaussian scheme; the same approach with the mean values of all classes adjusted to be the same; and the use of a minimum distance to means scheme such that mean differences are used.

Landgrebe, David A.↗
DOE OSTI2024

Exploring Interferometry Diagnostics for Optical Stochastic Cooling at FAST/IOTA

Optical Stochastic Cooling (OSC) is an advanced beam-cooling technique that will precede the traditional stochastic cooling. This method leverages optical radiation and high-precision feedback to cool the particles more efficiently than traditional stochastic methods by more than three orders of magnitude. As such, it is an enabler for the development of next generation discovery science machines at the frontiers of energy and intensity. This paper focuses on the development of the second phase of OSC and improving extreme beam cooling technique in accelerators. Along with the assembly and use of an interferometer to provide diagnostics and optimization to OSC systems. The interferometer can allow for the observation and analysis of fringe patterns from a recreated simpler version in the laser room. By leveraging interferometric techniques, OSC can achieve higher efficiency, verification, stability and performance.

Teriba, Folashade↗
DOE OSTI2024

EXPLORING INTERFEROMETRY DIAGNOSTICS FOR OPTICAL STOCHASTIC COOLING AT FAST/IOTA

Optical Stochastic Cooling (OSC) is an advanced beam-cooling technique that will advance the traditional stochastic cooling. This method leverages optical radiation and high-precision feedback to cool the particles more efficiently than traditional stochastic methods by more than three orders of magnitude. As such, it is an enabler for the development of next generation discovery science machines at the frontiers of energy and intensity. This paper focuses on the development of the second phase of OSC and improving extreme beam cooling techniques in accelerators. The goal was to build and characterize a Mach-Zehnder Interferometer (MZI) in the FAST laser lab using known glass plates thickness which will allow future measurements of unknown phase change due to nonlinear amplification processes.

Teriba, Folashade↗
DOE OSTI2024

Stochastic Optimization to Find Optimum Beginning-of-Life Core Configuration of Stable Salt Reactor with Online Refueling

A stochastic optimization method has been developed to find an optimum equilibrium cycle core configuration of the waste-burning stable salt reactor, which is a fast-spectrum molten salt reactor with frequent online refueling. An optimum core configuration was determined with the goal of minimizing radial power peaking. Because of the vast number of potential candidate core configurations, stochastic optimization was applied based on simulated annealing and an additional acceleration method, which screened out unpromising core configurations. It has been demonstrated that the developed stochastic optimization method successfully finds the optimal core configuration regardless of the initial guess and outperforms the gradient descent approach. In addition, it has been observed that the use of a so-called out-in core configuration as the initial guess speeds up convergence of the iterative solution more than five times. Based on the searched optimum equilibrium cycle core configuration, new beginning-of-life (BOL) core configurations have been developed. In conclusion, the new BOL core configurations will be used in developing optimum refueling strategies.

Moltex static salt reactor↗
DOE OSTI2024

S-OPT: A Points Selection Algorithm for Hyper-Reduction in Reduced Order Models

While projection-based reduced order models can reduce the dimension of full order solutions, the resulting reduced models may still contain terms that scale with the full order dimension. Hyper-reduction techniques are sampling-based methods that further reduce this computational complexity by approximating such terms with a much smaller dimension. The goal of this work is to introduce the points selection algorithm developed by Shin and Xiu as a hyper-reduction method. The selection algorithm was originally proposed as a stochastic collocation method for uncertainty quantification. Since the algorithm aims at maximizing a quantity $\mathcal{S}$ that measures both the column orthogonality and the determinant, we refer to the algorithm as S-OPT. Numerical examples are provided to demonstrate the performance of S-OPT and to compare its performance with a gappy proper orthogonal decomposition (POD) algorithm. Here, we found that using the S-OPT algorithm is shown to predict the full order solutions with higher accuracy than gappy POD especially when the number of sampling points is small, although we note that S-OPT shows slow asymptotic convergence with respect to the number of samples for some applications, e.g., Lagrangian hydrodynamics.

97 MATHEMATICS AND COMPUTING↗