Search NASA⌕ Search

SEARCH · Search NASA

Results for “Monte Carlo sampling”

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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Materials Degradation in the Jovian Radiation Environment

The radiation environment of Jupiter represents a significant hazard for Europa Lander deorbit stage components, and presents a significant potential mission risk. The radiolytic degradation of ammonium perchlorate (AP) oxidizer in solid propellants may affect its properties and performance. The Monte Carlo code MONSOL was used for modeling of laboratory experiments on the electron irradiation of propellant samples. An approach for flattening dose profiles along the depth of irradiated samples is proposed. Depth-dose distributions produced by Jovian electrons in multi-layer slabs of materials are calculated. It is found that the absorbed dose in a particular slab is significantly affected by backscattered electrons and photons from neighboring slabs. The dose and radiolytic decomposition of AP crystals are investigated and radiation-induced chemical yields and weight percent of radical products are reported.

Miloshevsky, Gennady↗
DOE OSTI2026

Uncertainty quantification for Joule heating processes in fibrous pore-resolved media

Joule heating (JH) is an energy-efficient and sustainable technique for heating materials. Its application for industrial heating, particularly, has been gaining attention due to its potential for increasing the yield of various chemical products. The process involves the use of heating elements (materials that are highly conductive electrically and thermally) to heat up other materials or substances. These conductors, however, can exhbit varying degrees of uncertainty due to non-linearities in their temperature-dependent properties, which could result in variable material behavior. In this work, we carry out uncertainty quantification (UQ) at the pore scale to describe the uncertainty of such materials. In so doing, we applied the non-intrusive polynomial chaos expansion (PCE) technique to quantify the uncertainty within the system. The steady state Joule heating equation was solved numerically at the pore scale mimicking conditions within a heating chamber for propane dehydrogenation, and various electro-thermal profiles were obtained. We also examined the effect of the number of sampling points (20 – 100) and order of the PCE coefficients (2 – 5) on the accuracy of the temperature evaluations. The results were then benchmarked with the standard Monte Carlo (MC) method. The average temperature of the 4th-order global PCE showed good agreement with the MC results (which were positively skewed). Orders greater than 4 gave an underestimation of the temperatures while predictions for the peak temperature improved as the number of sampling points increased.

Fagbemi, Samuel [ORNL] (ORCID:0000000236995025)↗
DOE OSTI2025

Uncertainty Quantification for Neutron Shield Using Convolutional Neural Networks

Uncertainty quantification from radiation transport calculations was conducted using a Bayesian inference approach. A surrogate model, using a convolutional neural network, was employed to emulate the neutron fluence, which was simulated with a Monte Carlo radiation transport model. This allowed for a computationally cheap approach to evaluate input parameters and to sample their corresponding posterior probability distributions. Experimental data from the literature were employed to perform uncertainty quantification studies for concrete shields. As a result, the method is a nonintrusive approach that enables studies with multiple input parameters and can be applied to any radiation transport model.

Bayesian inference↗
DOE OSTI2026

Determination of proton PDF uncertainties with Markov chain Monte Carlo

We present an analysis of parton distribution functions (PDFs) of the proton using Markov chain Monte Carlo (MCMC) methods. The MCMC approach naturally implements Bayes’ theorem and, thus, provides a means to directly sample the underlying probability distribution—in this case, the probability distribution of the PDF parameters. This allows for a straightforward propagation of the resulting uncertainties into any PDF-dependent observable, preserving their simple probabilistic interpretation. In our analysis we include a broad set of deep inelastic scattering data from HERA, BCDMS and NMC experiments along with the Drell-Yan, 𝑊 and 𝑍 boson data from LHC and Tevatron experiments, which combined with theoretical calculations at next-to-next-to-leading order in QCD allow for realistic determination of PDFs. The main focus of this analysis is to explore alternative methods for PDF uncertainty estimation that are more firmly grounded in statistical principles. We show that the flexibility of the Bayes framework, allowing one, e.g., to account for non-Gaussianity or inconsistencies of datasets, is crucial to extract realistic uncertainties when such assumptions are not fulfilled. We also demonstrate that MCMC allows one to determine the Δ⁢𝜒 2 value corresponding to a given confidence level in the sample, which can, in turn, be used as a statistically well-founded tolerance criterion used in the Hessian method, thus addressing one of its main long-standing drawbacks.

Risse, Peter Clemens [Universität Münster (Germany↗
DOE OSTI2025

Discrete generative diffusion models without stochastic differential equations: A tensor network approach

Diffusion models (DMs) are a class of generative machine learning methods that sample a target distribution by transforming samples of a trivial (often Gaussian) distribution using a learned stochastic differential equation. In standard DMs, this is done by learning a “score function” that reverses the effect of adding diffusive noise to the distribution of interest. Here we consider the generalisation of DMs to lattice systems with discrete degrees of freedom, and where noise is added via Markov chain jump dynamics. We show how to use tensor networks (TNs) to efficiently define and sample such “discrete diffusion models” (DDMs) without explicitly having to solve a stochastic differential equation. We show the following: (i) by parametrising the data and evolution operators as TNs, the denoising dynamics can be represented exactly; (ii) the auto-regressive nature of TNs allows to generate samples efficiently and without bias; (iii) for sampling Boltzmann-like distributions, TNs allow to construct an efficient learning scheme that integrates well with Monte Carlo. We illustrate this approach to study the equilibrium of two models with non-trivial thermodynamics, the d = 1 constrained Fredkin chain and the d = 2 Ising model. Published by the American Physical Society 2025

Causer, Luke (ORCID:0000000194243473)↗
NASA NTRS1976

A Monte-Carlo simulation of galactic cosmic ray effects in the lunar regolith

A Monte-Carlo soil mixing computer code was used to evaluate isotopic effects induced by galactic cosmic rays in samples on the top surface of the lunar regolith and in lunar core tubes. For surface samples, good agreement was found between theoretical predictions of Ne-21 concentrations, neutron fluences, and Xe-131/Xe-126 ratios, and the corresponding observations.

Langevin, Y.↗
NASA NTRS1993

Cosmogenic-radionuclide profiles in Knyahinya: New measurements and models

Cosmic-ray-produced nuclides measured in samples taken from known locations on a big slab of the large (R approximately equals to 45 cm) L5-chondrite Knyahinya provide good depth-vs-concentration profiles to develop and test models for the production of cosmogenic nuclides in meteorites. We report new profiles for Be-10, Al-26, Cl-36 in metallic and non-magnetic phases of 8 documented samples from Knyahinya and for C-14 in bulk samples from 7 Knyahinya samples. These new measured profiles are very similar to profiles calculated with particle fluxes from the LAHET Monte Carlo production and transport code system and with cross sections for major reactions.

Reedy, R. C.↗
NASA NTRS1993

The cosmic Mach number - Direct comparisons of observations and models

A robust method is used to derive the cosmic Mach number, M, from existing peculiar velocity data sets. For the IR Tully-Fisher distances of spirals in the Local Supercluster of Aaronson et al. (1979, 1982), M of 1.03 and a characteristic distance from the Local Group of 1464 km/s are found. For the elliptical galaxy sample of Faber et al. (1987, 1988), M of 0.57, with a characteristic distance of 2572 km/s is found. These results are compared with Monte Carlo simulations of the observational realizations drawn from numerical simulations of the universe based on various scenarios, including the standard cold dark matter (CDM) scenario. The effect of velocity bias on the derived Mach number is found to be small. Only 5 percent of the CDM simulations have M as large as or larger than that derived for the Aaronson et al. sample.

Strauss, Michael A.↗
DOE OSTI2024

General guide concepts for compact, high-brilliance neutron moderators

The trend in neutron sciences is toward integrating compact, high-brightness moderators into new or upgraded facilities. Transporting neutrons from the source to the sample position with a phase-space distribution tailored to specific requirements is crucial to leverage high source brilliance. Here we have investigated four guide concepts using Monte Carlo ray tracing simulations: Montel beamline with nested Kirkpatrick–Baez mirrors, curved-tapered beamline with a bender and straight sections, straight-elliptical beamline, and curved-elliptical beamline. The straight-elliptical (curved-elliptical) beamline features two half-ellipse guides connected by a straight (non-straight) guide section. The neutron transport efficiency and phase space homogeneity have been quantitatively compared. Our results show that the straight-elliptical beamline performs best because of few neutron bounces on the guide surface with small reflection angles, minimizing flux loss. The Montel beamline provides the best spatial confinement of neutrons within the desired region; however, there is a high thermal-neutron loss due to large reflection angles. The curved-tapered beamline suffers from significant flux loss due to high bounces, and it shows a non-uniform angular distribution related to broad ranges of bounces and reflection angles. The non-straight guide section of the curved-elliptical beamline increases the phase space inhomogeneity, leading to a spatially non-uniform beam profile. The results apply to general neutron instruments that require transporting thermal and cold neutrons from a compact, high-brilliance moderator to the sample location with a moderate phase-space volume.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

scANN

SAND2025-00656O scANN, also known as sampling by coinflips artificial neural networks, is a software tool that estimates uncertainty in artificial intelligence by performing Monte Carlo analysis on the weight matrices of feedforward neural networks. This computationally intensive process aims to explore the potential value added by future probabilistic hardware. The program’s output helps researchers gain insights into how probabilistic neural networks work. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗
NASA NTRS1994

Coherent-phase or random-phase acceleration of electron beams in solar flares

Time structures of electron beam signatures at radio wavelengths are investigated to probe correlated versus random behavior in solar flares. In particular we address the issue whether acceleration and injection of electron beams is coherently modulated by a single source, or whether the injection is driven by a stochastic (possibly spatially fragmented) process. We analyze a total of approximately = 6000 type III bursts observed by Ikarus (Zurich) in the frequency range of 100-500 MHz, during 359 solar flares with simultaneous greater than or = 25 keV hard X-ray emission, in the years 1890-1983. In 155 flares we find a total of 260 continuous type III groups, with an average number of 13 +/- 9 bursts per group, a mean duration of D = 12 +/- 14 s, a mean period of P = 2.0 +/- 1.2 s, with the highest burst rate at a frequency of nu = 310 +/- 120 MHz. Pulse periods have been measured between 0.5 and 10 s, and can be described by an exponential distribution, i.e., N(P) varies as e (exp -P/1.0s). The period shows a frequency dependence of P(nu)=46(exp-0.6)(sub MHz)s for different flares, but is invariant during a particular flare. We measure the mean period P and its standard deviation sigma (sub p) in each type III group, and quantify the degree of periodicity (or phase-coherence) by the dimensionless parameter sigma (sub p)P. The representative sample of 260 type III burst groups shows a mean periodicity of sigma (sub p/P) = 0.37 +/- 0.12, while Monte Carlo simulations of an equivalent set of truly random time series show a distinctly different value of sigma (sub p)P = 0.93 +/- 0.26. This result indicates that the injection of electron beams is coherently modulated by a particle acceleration source which is either compact or has a global organization on a timescale of seconds, in contrast to an incoherent acceleration source, which is stochastic either in time or space. We discuss the constraints on the size of the acceleration region resulting from electron beam propagation delays and from Alfvenic synchronization during a pulse period. We discuss two periodic preocesses in flares, which potentially control quasi-periodic particle acceleration: (1) MHD oscillations, and (2) current sheets with oscillatory dynamics.

Aschwanden, Markus J.↗
NASA NTRS1997

Low-Density Aerodynamics of the Stardust Sample Return Capsule

The aerodynamics of the Stardust Sample Return Capsule are analyzed in the low- density, transitional flow regime using free-molecular, Direct Simulation Monte Carlo, Navier-Stokes, and Newtonian methods to provide inputs for constructing a transitional flow bridging relation. The accuracy of this bridging relation in reconstructing the aero- dynamic coefficients given by the more exact methods is presented for a range of flight conditions and vehicle attitudes. There is good agreement between the various prediction methods, and a simple sine-squared bridging relation is shown to provide a reasonably good description of the axial force, normal force, and pitching moment over a range of Knudsen numbers from 0.001 to 10. The predictions show a static instability of the Star- dust capsule in the free-molecular regime that persists well into the transitional flow. The addition of a thin disk to the base of the capsule is shown to remove this static instability. However, the extremely high entry velocity of 12.6 km/s for the proposed trajectory introduces difficult design issues for incorporating this disk caused by the high aerothermal loads that occur even under relatively rarefied conditions.

Wilmoth, Richard G.↗
NASA NTRS2015

Optimization of Microphone Locations for Acoustic Liner Impedance Eduction

Two impedance eduction methods are explored for use with data acquired in the NASA Langley Grazing Flow Impedance Tube. The first is an indirect method based on the convected Helmholtz equation, and the second is a direct method based on the Kumaresan and Tufts algorithm. Synthesized no-flow data, with random jitter to represent measurement error, are used to evaluate a number of possible microphone locations. Statistical approaches are used to evaluate the suitability of each set of microphone locations. Given the computational resources required, small sample statistics are employed for the indirect method. Since the direct method is much less computationally intensive, a Monte Carlo approach is employed to gather its statistics. A comparison of results achieved with full and reduced sets of microphone locations is used to determine which sets of microphone locations are acceptable. For the indirect method, each array that includes microphones in all three regions (upstream and downstream hard wall sections, and liner test section) provides acceptable results, even when as few as eight microphones are employed. The best arrays employ microphones well away from the leading and trailing edges of the liner. The direct method is constrained to use microphones opposite the liner. Although a number of arrays are acceptable, the optimum set employs 14 microphones positioned well away from the leading and trailing edges of the liner. The selected sets of microphone locations are also evaluated with data measured for ceramic tubular and perforate-over-honeycomb liners at three flow conditions (Mach 0.0, 0.3, and 0.5). They compare favorably with results attained using all 53 microphone locations. Although different optimum microphone locations are selected for the two impedance eduction methods, there is significant overlap. Thus, the union of these two microphone arrays is preferred, as it supports usage of both methods. This array contains 3 microphones in the upstream hard wall section, 14 microphones opposite the liner, and 3 microphones in the downstream hard wall section.

Jones, M. G.↗
NASA NTRS2005

Cosmological Constraints from Sunyaev-Zeldovich Effect and X-ray data for 37 Galaxy Clusters

We present a sample of 37 clusters of galaxies (0.14<=z<=0.89) for which we have Sunyaev-Zeldovich Effect data and Chandra X-ray imaging and spectroscopy. We use a Markov chain Monte Carlo technique to determine the spatial properties and surface brightness of the X-ray emitting gas, the X-ray temperature and abundance, and the SZE decrement for each cluster. From these observables, we infer each cluster's total mass, gas mass fraction, and angular diameter distance. The sample properties are used to constrain the Hubble parameter and Omega Matter, and scaling relations between integrated SZE flux and cluster mass and temperature are constructed. We also investigate the cosmological constraints that could be obtained if the X-ray/SZE sample were extended to a redshift of z approximately 2.

Bonamente, Massimiliano↗
DOE OSTI2024

Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models

Purpose To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET)'s SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally validate the model-predicted outcomes to calibration targets.Methods We used Latin hypercube sampling to sample up to 50,000 parameter sets for each CISNET-CRC model and generated the corresponding outputs. We trained multilayer perceptron artificial neural networks (ANNs) as emulators using the input and output samples for each CISNET-CRC model. We selected ANN structures with corresponding hyperparameters (i.e., number of hidden layers, nodes, activation functions, epochs, and optimizer) that minimize the predicted mean square error on the validation sample. We implemented the ANN emulators in a probabilistic programming language and calibrated the input parameters with Hamiltonian Monte Carlo-based algorithms to obtain the joint posterior distributions of the CISNET-CRC models' parameters. We internally validated each calibrated emulator by comparing the model-predicted posterior outputs against the calibration targets.Results The optimal ANN for SimCRC had 4 hidden layers and 360 hidden nodes, MISCAN-Colon had 4 hidden layers and 114 hidden nodes, and CRC-SPIN had 1 hidden layer and 140 hidden nodes. The total time for training and calibrating the emulators was 7.3, 4.0, and 0.66 h for SimCRC, MISCAN-Colon, and CRC-SPIN, respectively. The mean of the model-predicted outputs fell within the 95% confidence intervals of the calibration targets in 98 of 110 for SimCRC, 65 of 93 for MISCAN, and 31 of 41 targets for CRC-SPIN.Conclusions Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis, such as the CISNET CRC models. In this work, we present a step-by-step guide to constructing emulators for calibrating 3 realistic CRC individual-level models using a Bayesian approach.

artificial neural networks↗
NASA NTRS1991

Do clusters of galaxies affect the spectrum of the microwave background?

The distortion, averaged over the sky, due to the Compton scattering of background photons with electrons in the hot gas in clusters of galaxies is calculated. Using an existing sample of X-ray clusters, various values of the density parameter Omega, and plausible models for cluster evolution, Monte Carlo realizations of the microwave sky are generated. The spatial structure of these simulations shows a network of discrete sources whose properties can be a strong function of both evolution and Omega. The amount of spectral distortion in the models is greatest for models characterized by self-similar cluster evolution in an open universe and is within an order of magnitude of the current upper limits. Thus, improved observational sensitivity must inevitably detect some deviation from a blackbody spectrum.

Markevitch, M.↗