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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Monte Carlo Perturbation Analysis of Fuel Temperature Variations in the MCNP Model of the Annular Core Research Reactor

The Annular Core Research Reactor (ACRR) Monte Carlo N-Particle (MCNP) model is used by ACRR reactor operators and experiment designers at Sandia National Laboratories for a variety of computational calculations ranging from reactor kinetics parameter estimates and safety analyses to experimental planning. To understand the dominant source of uncertainty within the MCNP model, perturbations in temperature were applied to individual ACRR MCNP fuel rods. Fuel rod temperatures were randomly sampled from a uniform distribution from operational temperatures to quantify temperature-related uncertainty effects. Stochastic mixing was used to blend the cross sections of the desired temperatures using the MCNP continuous and Thermal Neutron Scattering Treatment [S(α,β)] libraries in ENDF/B-VII.1. Furthermore, this uncertainty analysis produced a 640 row × 640 column correlation and covariance matrix of the neutron energy spectra. Positive covariance was produced around the 1-MeV region and the 0.2-eV region. Correlation was found in the thermal and fast energy regions, but no correlation was observed in the slowing-down energy region because interactions in this region are not dominated by fuel.

ACRR↗
NASA NTRS2016

Orbit Determination Sensitivity Analysis for the Europa Multiple Flyby Mission Concept

This paper details the results of parametric variations on the notional Europa Mission Concept tour navigation strategy on orbit determination delivery and knowledge errors and the associated statistical ∆V consumption. Approach maneuver placement at encounter minus two and a half days and at encounter minus three and a half days is compared to the reference encounter minus three days location as well as variations to the data cutoff for approach maneuver design, which is baselined one day prior to maneuver execution. An execution error model with fixed and proportional components assigned to the magnitude and pointing directions is applied to each maneuver in a Monte Carlo simulation. Variations on the fixed and proportional contributions to both component errors are simulated and analyzed to show the effect of varying thruster characterization levels. The effects of varying levels of a priori satellite ephemeris errors are also characterized in this study.

Boone, Dylan R.↗
DOE OSTI2025

Exclusive $J/ψ$ photoproduction on nuclei

Motivated by the recent experimental developments, the Pom-CQM model of the γ+N → J/ψ+N reaction of Lee et al. [Eur. Phys. J. A. 58, 252 (2022)] and Sakinah et al. [Phys. Rev. C. 109, 065204 (2024)] has been applied to predict the exclusive J/ψ photo-production on nuclei (A). Within the multiple scattering theory, the calculations have been performed by including the impulse amplitude $T^{\textrm{IMP}}_{J/ψA,γA}$ and the J/ψ-nucleus final state interaction (FSI) amplitude $T^{\textrm{FSI}}_{J/ψA,γA}$. For the deuteron target, $T^{\textrm{IMP}}_{J/ψd,γd}$ is calculated exactly using the wave function generated from the realistic nucleon-nucleon potentials. Here, it is found that, near the threshold region, the J/ψ photoproduction cross sections depend sensitively on the d-state of the deuteron wave function. The FSI amplitude $T^{\textrm{FSI}}_{J/ψA,γA}$ is calculated using the first-order optical potential constructed from the J/ψ-N scattering amplitude generated from the employed Pom-CQM model. It turns out that the FSI has significant effects in the large momentum-transfer region. By using the conventional fixed scatter approximation (FSA) and the nuclear form factors from the variational Monte-Carlo (VMC) calculations of Lonardoni et al. [Phys. Rev.C. 96, 024326 (2017)], the cross sections of the J/ψ photo-production on 4 He, 16 O, and 40 Ca are also predicted for future experimental investigations at JLab and EIC.

Kim, Sang-Ho [Soongsil University, Seoul (Korea, R↗
DOE OSTI2025

Autoregressive neural quantum states of Fermi Hubbard models

Neural quantum states (NQSs) have emerged as a powerful ansatz for variational quantum Monte Carlo studies of strongly correlated systems. Here, we apply recurrent neural networks (RNNs) and autoregressive transformer neural networks to the Fermi-Hubbard and the (non-Hermitian) Hatano-Nelson-Hubbard models in one and two dimensions. In both cases, we observe that the convergence of the RNN ansatz is challenged when increasing the interaction strength. We present a physically motivated and easy-to-implement strategy for improving the optimization, namely, by ramping of the model parameters. Furthermore, we investigate the advantages and disadvantages of the autoregressive sampling property of both network architectures. For the Hatano-Nelson-Hubbard model, we identify convergence issues that stem from the autoregressive sampling scheme in combination with the non-Hermitian nature of the model. Our findings provide insights into the challenges of the NQS approach and make the first step towards exploring strongly correlated electrons using this ansatz. Published by the American Physical Society 2025

Ibarra-García-Padilla, Eduardo (ORCID:000000019165↗
NASA NTRS1989

Monte Carlo simulations of the diurnal variation in seismic detection rate of sporadic meteoroid impacts on the moon

The rate of detection of meteoroid impacts on the moon by the lunar seismic network shows a characteristic diurnal variation. Assuming that these meteoroids have a flux and a preimpact orbital distribution similar to that of fireballs observed by terrestrial camera networks, one can compute the expected diurnal variation for a given set of parameters that describe the seismic wave generation and transmission on the moon. An iterative process to match the theoretical variation with the observed one has led us to the following results: (1) the majority of the detected impact events occur within a closer range of the network than was believed earlier. This results in higher meteoroid flux estimates from lunar seismic data that agree with the terrestrially measured flux. (2) For meteoroid masses smaller than 1000 g, seismic amplitude is approximately proportional to the one-fifth power of the impact speed; for larger masses it is approximately proportional to the eight-fifth power, provided that the terrestrial meteor data used for analysis are not biased. (3) Seismic efficiency of meteoroids smaller than 1000 g is significantly less than that of large meteoroids. (4) Using orbits of fireballs that represent meteorites, we predict that the share of meteorites among the detected impacts is approximately 15 percent assuming that seismic efficiency of the high-density meteorites is the same as that of average meteoroids. A greatly increased seismic efficiency for these high-density objects is not likely.

Oberst, J.↗
NASA NTRS1983

Monte Carlo simulation of the effect of soil moisture variation on the microwave emission from soils

In this paper, results of a Monte Carlo simulation of the effect of noise on the relationship between the microwave emissivity of soil and its moisture content are presented. It is found that whenever the magnitude of the noise for the independent variable, in this case the soil moisture, is increased, both the slope of the regression and the correlation coefficient decrease. In particular, when the noise has magnitude equivalent to a coefficient of variation of 0.25, the slope and correlation coefficient are in good agreement with those obtained from the data of a 21-cm airborne microwave radiometer which was flown over a test site in hand county, South Dakota. The comparison was made using a linear relationship to determine the estimated emissivity from the ground measurements of soil moisture. The linear relationship was derived from a radiative transfer model calculation of the microwave emissivities using realistic soil-moisture profiles. The effect of surface roughness was included in the relationship, and the variability of the surface roughness was also simulated by a Monte Carlo technique.

Mo, T.↗
DOE OSTI2024

Variational inference of effective range parameters for 3 He− 4 He scattering

We use two different methods, Monte Carlo sampling and variational inference (VI), to perform a Bayesian calibration of the effective-range parameters in 3 He– 4 He elastic scattering. The parameters are calibrated to data from a recent set of 3 He– 4 He elastic scattering differential cross section measurements. Analysis of these data for E lab ≤ 4.3 MeV yields a unimodal posterior for which both methods obtain the same structure. However, the effective-range expansion amplitude does not account for the 7/2 − state of 7 Be so, even after calibration, the description of data at the upper end of this energy range is poor. The data up to E lab = 2.6 MeV can be well described, but calibration to this lower-energy subset of the data yields a bimodal posterior. After adapting VI to treat such a multi-modal posterior we find good agreement between the VI results and those obtained with parallel-tempered Monte Carlo sampling.

effective field theory↗
NASA NTRS1990

Planetary atmosphere representation for Monte-Carlo simulation of aerospace vehicle trajectories

Methods of simulating large scale random atmospheric variations are examined for use in Monte Carlo simulations of hypervelocity vehicles. Random density, temperature, and pressure variations have a significant impact on the performance of such vehicles. Methods for generating random atmosphere profiles are described and compared in the context of a Monte Carlo simulation of the Aeroassist Flight Experiment (AFE). It is seen that these techniques result in statistically significant differences in key simulation point statistics.

Dutton, K.↗
DOE OSTI2020

Convergence acceleration of Monte Carlo many-body perturbation methods by using many control variates

Herein, the use of many control variates is proposed as a method to accelerate the second- and third-order Monte Carlo (MC) many-body perturbation (MC-MP2 and MC-MP3) calculations. A control variate is an exactly integrable function that is strongly correlated or anti-correlated with the target function to be integrated by the MC method. Evaluating both integrals as well as their covariances in the same MC run, one can effect a mutual cancellation of the statistical uncertainties and biases in the MC integrations, thereby accelerating its convergence considerably. Six and thirty-six control variates, whose integrals are known a priori, are generated for MC-MP2 and MC-MP3, respectively, by systematically replacing one or more two-electron-integral vertexes of certain configurations by zero-valued overlap-integral vertexes in their Goldstone diagrams. The variances and co- variances of these control variates are computed at a marginal cost, enhancing the overall efficiency of the MC-MP2 and MC-MP3 calculations by a factor of up to 14 and 20, respectively.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

B-PINNs: Bayesian Physics-informal Neural Networks for Forward and Inverse PDE Problems with Noisy Data

We propose a Bayesian physics-informed neural network (B-PINN) to solve both forward and inverse nonlinear problems described by partial differential equations (PDEs) and noisy data. In this Bayesian framework, the Bayesian neural network (BNN) combined with a PINN for PDEs serves as the prior while the Hamiltonian Monte Carlo (HMC) or the variational inference (VI) could serve as an estimator of the posterior. B-PINNs make use of both physical laws and scattered noisy measurements to provide predictions and quantify the aleatoric uncertainty arising from the noisy data in the Bayesian framework. Compared with PINNs, in addition to uncertainty quantification, B-PINNs obtain more accurate predictions in scenarios with large noise due to their capability of avoiding overfitting. We conduct a systematic comparison between the two different approaches for the B-PINNs posterior estimation (i.e., HMC or VI), along with dropout used for quantifying uncertainty in deep neural networks. Our experiments show that HMC is more suitable than VI with mean field Gaussian approximation for the B-PINNs posterior estimation, while dropout employed in PINNs can hardly provide accurate predictions with reasonable uncertainty. Finally, we replace the BNN in the prior with a truncated Karhunen-Loève (KL) expansion combined with HMC or a deep normalizing flow (DNF) model as posterior estimators. The KL is as accurate as BNN and much faster but this framework cannot be easily extended to high-dimensional problems unlike the BNN based framework.

Non linear PDEs, Noisy data, Bysian physics inform↗
DOE OSTI2021

Toward quantum Monte Carlo forces on heavier ions: Scaling properties

Quantum Monte Carlo (QMC) forces have been studied extensively in recent decades because of their importance with spectroscopic observables and geometry optimization. Here, we benchmark the accuracy and computational cost of QMC forces. The zero-variance zero-bias (ZVZB) force estimator is used in standard variational and diffusion Monte Carlo simulations with mean-field based trial wavefunctions and atomic pseudopotentials. Statistical force uncertainties are obtained with a recently developed regression technique for heavy tailed QMC data [P. Lopez Rios and G. J. Conduit, Phys. Rev. E 99, 063312 (2019)]. By considering selected atoms and dimers with elements ranging from H to Zn (1 ≤ Z eff ≤ 20), we assess the accuracy and the computational cost of ZVZB forces as the effective pseudopotential valence charge, Z eff , increases. We find that the costs of QMC energies and forces approximately follow simple power laws in Zeff. The force uncertainty grows more rapidly, leading to a best case cost scaling relationship of approximately Z e ff 6.5 ( 3 ) for diffusion Monte Carlo. We find that the accessible system size at fixed computational cost scales as Z e ff − 2 , insensitive to model assumptions or the use of the “space warp” variance-reduction technique. Our results predict the practical cost of obtaining forces for a range of materials, such as transition metal oxides where QMC forces have yet to be applied, and underscore the importance of further developing force variance-reduction techniques, particularly for atoms with high Z eff .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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.↗
NASA NTRS1980

Simulation studies of a model of high-density metallic hydrogen

Upper bounds for the ground-state energies of liquid and solid phases of metallic hydrogen and metallic deuterium have been calculated with variational methods and Monte Carlo techniques. At four densities (0.8, 1.2, 1.36, and 1.488) crystalline phases are clearly preferred in the sense that the energy difference, when compared to the liquid, is in excess of the errors inherent in the numerical procedures. At a fifth density (1.6), the energy differences between solid and liquid phases are smaller than these errors.

Mon, K. K.↗
DOE OSTI2024

Variance reduction via simultaneous importance sampling and control variates techniques using vegas

Monte Carlo (MC) integration is an important calculational technique in the physical sciences. Practical considerations require that the calculations are performed as accurately as possible for a given set of computational resources. To improve the accuracy of MC integration, a number of useful variance reduction algorithms have been developed, including importance sampling and control variates. In this work, we demonstrate how these two methods can be applied simultaneously, thus combining their benefits. We provide a python wrapper, named CoVVVR, which implements our approach in the VEGAS program. The improvements are quantified with several benchmark examples from the literature.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗