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Results for “Monte Carlo Methods”

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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Low-Density Nozzle Flow by the Direct Simulation Monte Carlo and Continuum Methods

Two different approaches, the direct simulation Monte Carlo (DSMC) method based on molecular gasdynamics, and a finite-volume approximation of the Navier-Stokes equations, which are based on continuum gasdynamics, are employed in the analysis of a low-density gas flow in a small converging-diverging nozzle. The fluid experiences various kinds of flow regimes including continuum, slip, transition, and free-molecular. Results from the two numerical methods are compared with Rothe's experimental data, in which density and rotational temperature variations along the centerline and at various locations inside a low-density nozzle were measured by the electron-beam fluorescence technique. The continuum approach showed good agreement with the experimental data as far as density is concerned. The results from the DSMC method showed good agreement with the experimental data, both in the density and the rotational temperature. It is also shown that the simulation parameters, such as the gas/surface interaction model, the energy exchange model between rotational and translational modes, and the viscosity-temperature exponent, have substantial effects on the results of the DSMC method.

Chung, Chang-Hong↗
NASA NTRS1973

LP-search and its use in analysis of the accuracy of control systems with acoustical models

The LP-search is proposed as an analog of the Monte Carlo method for finding values in nonlinear statistical systems. It is concluded that: To attain the required accuracy in solution to the problem of control for a statistical system in the LP-search, a considerably smaller number of tests is required than in the Monte Carlo method. The LP-search allows the possibility of multiple repetitions of tests under identical conditions and observability of the output variables of the system.

Sergeyev, V. I.↗
NASA NTRS1994

A Monte Carlo study of photochemical model uncertainties affecting the calculation of subsonic perturbations in the upper troposphere

Monte Carlo methods are frequently applied to the evaluation of uncertainties in models with multiple inputs that themselves have associated imprecisions. In the case of photochemical models used to evaluate changes in O3 or OH, inputs analyzed include rate coefficients measured in the laboratory, and chemical and physical constituents measured in the atmosphere. The Monte Carlo method was used with the 1-dimensional GSFC tropospheric photochemical model to examine uncertainty propagation to calculation of NO(x) and the major oxidants (O3, OH) in the upper troposphere. In all cases chemical kinetics inputs are varied and the NO(x) perturbation of a subsonic fleet is simulated. A series of model runs is used to explore sensitivities of model-computed parameters to other parameters: heterogeneous processes, the uncertainty in upper tropospheric H2O vapor measurements, aircraft emissions at different latitudes.

Thompson, Anne M.↗
DOE OSTI2025

Investigating the crust of neutron stars with neural-network quantum states

An accurate description of low-density nuclear matter is crucial for explaining the physics of neutron star crusts. In the density range between approximately 0.01 fm −3 and 0.1 fm −3 , matter transitions from neutron-rich nuclei to various higher-density pasta shapes, before ultimately reaching a uniform liquid. In this work, we introduce a variational Monte Carlo method based on a neural Pfaffian-Jastrow quantum state, which allows us to model the transition from the liquid phase to neutron-rich nuclei microscopically. At low densities, nuclear clusters dynamically emerge from the microscopic interactions among protons and neutrons, which we model based on pionless effective field theory. Our variational Monte Carlo approach represents a significant improvement over the state-of-the-art auxiliary-field diffusion Monte Carlo method, which is severely hindered by the fermion-sign problem in this low-density regime and cannot capture the onset of clusters. In addition to computing the energy per particle of symmetric nuclear matter and pure neutron matter, we analyze an intermediate isospin-asymmetry configuration to elucidate the formation of nuclear clusters. We also provide evidence that the presence of such nuclear clusters influences the amount of protons in the crust compared to protons in beta-equilibrated, neutrino-transparent matter.

Nuclear astrophysics↗
NASA NTRS1978

Three-dimensional radiative exchange factors for nongray, nondiffuse surfaces

A computer program using a variation of the Monte Carlo method was developed to predict radiative exchange factors in three-dimensional configurations with curved surfaces. The model allows for arbitrary emission, absorption, and reflection characteristics and for radiation exchange between surfaces as well as external irradiation through openings. It was shown that exchange factors can be determined efficiently by using the normal Monte Carlo method for closed configurations and/or high surface emissivities, while energy partitioning is vastly superior in the case of open configurations and/or low surface emissivities. Comparison with some experiments performed on the Space Shuttle heat rejection system demonstrates the model's accuracy as well as its superiority over a simplified diffuse-surface analysis, which requires similar amounts of computer time.

Modest, M. F.↗
DOE OSTI2025

Emulators for Scarce and Noisy Data: Application to Auxiliary-Field Diffusion Monte Carlo for Neutron Matter

Understanding the equation of state (EOS) of pure neutron matter is necessary for interpreting multimessenger observations of neutron stars. Reliable data analyses of these observations require well-quantified uncertainties for the EOS input, ideally propagating uncertainties from nuclear interactions directly to the EOS. This, however, requires calculations of the EOS for a prohibitively larger number of nuclear Hamiltonians, solving the nuclear many-body problem for each one. Quantum Monte Carlo methods, such as auxiliary-field diffusion Monte Carlo (AFDMC), provide precise and accurate results for the neutron matter EOS, but they are very computationally expensive, making them unsuitable for the fast evaluations necessary for uncertainty propagation. Here, we employ parametric matrix models to develop fast emulators for AFDMC calculations of neutron matter and use them to directly propagate uncertainties of coupling constants in the Hamiltonian to the EOS. As these uncertainties include estimates of the effective field theory truncation uncertainty, this approach provides robust uncertainty estimates for use in astrophysical data analyses. In conclusion, this Letter will enable novel applications such as using astrophysical observations to put constraints on coupling constants for nuclear interactions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1992

Solution of the Burnett equations for hypersonic flows near the continuum limit

The INCA code, a three-dimensional Navier-Stokes code for analysis of hypersonic flowfields, was modified to analyze the lower reaches of the continuum transition regime, where the Navier-Stokes equations become inaccurate and Monte Carlo methods become too computationally expensive. The two-dimensional Burnett equations and the three-dimensional rotational energy transport equation were added to the code and one- and two-dimensional calculations were performed. For the structure of normal shock waves, the Burnett equations give consistently better results than Navier-Stokes equations and compare reasonably well with Monte Carlo methods. For two-dimensional flow of Nitrogen past a circular cylinder the Burnett equations predict the total drag reasonably well. Care must be taken, however, not to exceed the range of validity of the Burnett equations.

Imlay, Scott T.↗
NASA NTRS1982

Transport of energetic electrons in a fully ionized hydrogen plasma

A Monte Carlo method for calculating energetic electron transport in a plasma is presented. The energy loss and angular deflection due to Coulomb collisions as a function of travel distance and energy are derived for energetic electrons in a fully ionized plasma with a uniform magnetic field. Formulas which include the effect of nonuniform B fields on the angular deflection are derived. The Monte Carlo method is applied to the thick-target beam model in which the energetic electrons are injected vertically downward.

Bai, T.↗
NASA NTRS1986

Monte Carlo study of a model of diffusion-controlled reactions

The Monte Carlo method is used to perform averages over sink configurations in the present study of diffusion-controlled reactions occurring between solute particles and immobile spherical sinks. In order to determine the average steady state solute concentration profile in a locally perturbed solution for sink volume fractions phi of less than 0.3, the diffusion equation in the monopolar plus dipolar approximation of diffusive couplings between the sinks is solved numerically. The Monte Carlo method is shown to be the most accurate and efficient in the phi = 0.001-0.1 region, where a system of only 25 sinks suffices and the monopolar approximation alone is sufficiently accurate.

Beenakker, C. W. J.↗
NASA NTRS1972

Monte Carlo investigation of transient acoustic fields in partially or completely bounded medium

A simple repetitive calculation was used to investigate what happens to the field in terms of the signal paths of disturbances originating from the energy source. The computation allowed the field to be reconstructed as a function of space and time on a statistical basis. The suggested Monte Carlo method is in response to the need for a numerical method to supplement analytical methods of solution which are only valid when the boundaries have simple shapes, rather than for a medium that is bounded. For the analysis, a suitable model was created from which was developed an algorithm for the estimation of acoustic pressure variations in the region under investigation. The validity of the technique was demonstrated by analysis of simple physical models with the aid of a digital computer. The Monte Carlo method is applicable to a medium which is homogeneous and is enclosed by either rectangular or curved boundaries.

Thanedar, B. D.↗
DOE OSTI2024

Modern chemical graph theory

Abstract Graph theory has a long history in chemistry. Yet as the breadth and variety of chemical data is rapidly changing, so too do graph encoding methods and analyses that yield qualitative and quantitative insights. Using illustrative cases within a basic mathematical framework, we showcase modern chemical graph theory's utility in Chemists' analysis and model development toolkit. The encoding of both experimental and simulation data is discussed at various levels of granularity of information. This is followed by a discussion of the two major classes of graph theoretical analyses: identifying connectivity patterns and partitioning methods. Measures, metrics, descriptors, and topological indices are then introduced with an emphasis upon enhancing interpretability and incorporation into physical models. Challenging data cases are described that include strategies for studying time dependence. Throughout, we incorporate recent advancements in computer science and applied mathematics that are propelling chemical graph theory into new domains of chemical study. This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Structure and Mechanism > Computational Materials Science Structure and Mechanism > Molecular Structures

Leite, Leonardo S. G.↗
NASA NTRS1989

Monte Carlo simulation of an expanding gas

By application of simple computer graphics techniques, the statistical performance of two Monte Carlo methods used in the simulation of rarefied gas flows are assessed. Specifically, two direct simulation Monte Carlo (DSMC) methods developed by Bird and Nanbu are considered. The graphics techniques are found to be of great benefit in the reduction and interpretation of the large volume of data generated, thus enabling important conclusions to be drawn about the simulation results. Hence, it is discovered that the method of Nanbu suffers from increased statistical fluctuations, thereby prohibiting its use in the solution of practical problems.

Boyd, Iain D.↗
NASA NTRS1977

Methods of shield analysis for protection against electrons in space

It is anticipated that many future manned space operations will be radiation limited and that laminated wall structures and the use of new materials will be required to reduce radiation exposure. Methods for electron shield analysis are reviewed in light of anticipated needs in the space program. The most general method is still the Monte Carlo method, which is of limited usefulness for shield analysis due to excessive computer requirements. Methods based on energy deposition coefficients or energy transmission and reflection factors are quite accurate, but are currently limited to aluminum shield material. Analytical methods based on Mar's approximation for the electron transmission factor are relatively general and computer efficient but seriously underestimate shield requirements. A correction to methods using Mar's approximate transmission factor is derived herein and results in a slightly conservative estimate of shield requirements. Techniques for laminated shield design are still lacking.

Wilson, J. W.↗
NASA NTRS2009

A New Monte Carlo Filtering Method for the Diagnosis of Mission-Critical Failures

Testing large-scale systems is expensive in terms of both time and money. Running simulations early in the process is a proven method of finding the design faults likely to lead to critical system failures, but determining the exact cause of those errors is still time-consuming and requires access to a limited number of domain experts. It is desirable to find an automated method that explores the large number of combinations and is able to isolate likely fault points. Treatment learning is a subset of minimal contrast-set learning that, rather than classifying data into distinct categories, focuses on finding the unique factors that lead to a particular classification. That is, they find the smallest change to the data that causes the largest change in the class distribution. These treatments, when imposed, are able to identify the settings most likely to cause a mission-critical failure. This research benchmarks two treatment learning methods against standard optimization techniques across three complex systems, including two projects from the Robust Software Engineering (RSE) group within the National Aeronautics and Space Administration (NASA) Ames Research Center. It is shown that these treatment learners are both faster than traditional methods and show demonstrably better results.

Gay, Gregory↗