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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine learning force field model for kinetic Monte Carlo simulations of itinerant Ising magnets

Here, we present a scalable machine learning (ML) framework for large-scale kinetic Monte Carlo (kMC) simulations of itinerant electron Ising systems. As the effective interactions between Ising spins in such itinerant magnets are mediated by conducting electrons, the calculation of energy change due to a local spin update requires solving an electronic structure problem. Such repeated electronic structure calculations could be overwhelmingly prohibitive for large systems. Assuming the locality principle, a convolutional neural network (CNN) model is developed to directly predict the effective local field and the corresponding energy change associated with a given spin update based on Ising configuration in a finite neighborhood. As the kernel size of the CNN is fixed at a constant, the model can be directly scalable to kMC simulations of large lattices. Our approach is reminiscent of the ML force field models widely used in first-principles molecular dynamics simulations. Applying our ML framework to a square-lattice double-exchange Ising model, we uncover unusual coarsening of ferromagnetic domains at low temperatures. Our work highlights the potential of ML methods for large-scale modeling of similar itinerant systems with discrete dynamical variables.

machine learning↗
DOE OSTI2024

Magnetic structure of A ≤ 10 nuclei using the Norfolk nuclear models with quantum Monte Carlo methods

Here we present quantum Monte Carlo calculations of magnetic moments, form factors, and densities of A ≤ 10 nuclei within a chiral effective field theory approach. We use the Norfolk two- and three-body chiral potentials and their consistent electromagnetic one- and two-nucleon current operators. We find that two-body contributions to the magnetic moment can be large (up to ≈ 33% in A = 9 systems). We study the model dependence of these observables and place particular emphasis on investigating their sensitivity to using different cutoffs to regulate the many-nucleon operators. Calculations of elastic magnetic form factors for A ≤ 10 nuclei show excellent agreement with the data out to momentum transfers q ≈ 3 fm -1 .

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Accelerating multilevel Markov Chain Monte Carlo using machine learning models

Here, this work presents an efficient approach for accelerating multilevel Markov Chain Monte Carlo (MCMC) sampling for large-scale problems using low-fidelity machine learning models. While conventional techniques for large-scale Bayesian inference often substitute computationally expensive high-fidelity models with machine learning models, thereby introducing approximation errors, our approach offers a computationally efficient alternative by augmenting high-fidelity models with low-fidelity ones within a hierarchical framework. The multilevel approach utilizes the low-fidelity machine learning model (MLM) for inexpensive evaluation of proposed samples thereby improving the acceptance of samples by the high-fidelity model. The hierarchy in our multilevel algorithm is derived from geometric multigrid hierarchy. We utilize an MLM to accelerate the coarse level sampling. Training machine learning model for the coarsest level significantly reduces the computational cost associated with generating training data and training the model. We present an MCMC algorithm to accelerate the coarsest level sampling using MLM and account for the approximation error introduced. We provide theoretical proofs of detailed balance and demonstrate that our multilevel approach constitutes a consistent MCMC algorithm. Additionally, we derive the expression for cost reduction due to machine learning model to facilitate cost analysis of the hierarchical sampling algorithm. Our technique is demonstrated on a standard benchmark inference problem in groundwater flow, where we estimate the probability density of a quantity of interest using a four-level MCMC algorithm. Our proposed algorithm accelerates multilevel sampling by a factor of two while achieving similar accuracy compared to sampling using the standard multilevel algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

GPU-friendly surface model for Monte-Carlo detector simulations

The demands for Monte-Carlo simulation are drastically increasing with the Large Hadron Collider’s high-luminosity upgrade, and are expected to exceed the currently available compute resources. At the same time, modern high-performance computing has adopted powerful hardware accelerators, particularly GPUs. The AdePT and Celeritas projects aim to address the demanding computational needs by leveraging these heterogeneous computing architectures. While both have successfully ported realistic detector simulations to GPUs using the VecGeom library, the complexity of geometry modeling emerged as a bottleneck. Thread divergence and high register usage were degrading the GPU performance. Therefore, a new, GPU-friendly surface-based model has been introduced in the VecGeom library that decomposes the divergent code of the 3D primitive solids into simpler and more balanced surface algorithms. In this work, we present the latest developments, focusing on the additions required to efficiently model complex setups like the CMS Phase-2 geometry. This includes memory reduction techniques, and adding accelerating structures for faster traversal.

Diederichs, Severin [CERN]↗
NASA NTRS2001

Radiation dosimetry using three-dimensional optical random access memories

Three-dimensional optical random access memories (3D ORAMs) are a new generation of high-density data storage devices. Binary information is stored and retrieved via a light induced reversible transformation of an ensemble of bistable photochromic molecules embedded in a polymer matrix. This paper describes the application of 3D ORAM materials to radiation dosimetry. It is shown both theoretically and experimentally, that ionizing radiation in the form of heavy charged particles is capable of changing the information originally stored on the ORAM material. The magnitude and spatial distribution of these changes are used as a measure of the absorbed dose, particle type and energy. The effects of exposure on 3D ORAM materials have been investigated for a variety of particle types and energies, including protons, alpha particles and 12C ions. The exposed materials are observed to fluoresce when exposed to laser light. The intensity and the depth of the fluorescence is dependent on the type and energy of the particle to which the materials were exposed. It is shown that these effects can be modeled using Monte Carlo calculations. The model provides a better understanding of the properties of these materials. which should prove useful for developing systems for charged particle and neutron dosimetry/detector applications. c2001 Published by Elsevier Science B.V.

Non-NASA Center↗
DOE OSTI2022

Fast and scalable quantum Monte Carlo simulations of electron-phonon models

We introduce methodologies for highly scalable quantum Monte Carlo simulations of electron-phonon models, and report benchmark results for the Holstein model on the square lattice. The determinant quantum Monte Carlo (DQMC) method is a widely used tool for simulating simple electron-phonon models at finite temperatures, but incurs a computational cost that scales cubically with system size. Alternatively, near-linear scaling with system size can be achieved with the hybrid Monte Carlo (HMC) method and an integral representation of the Fermion determinant. Here, we introduce a collection of methodologies that make such simulations even faster. To combat "stiffness" arising from the bosonic action, we review how Fourier acceleration can be combined with time-step splitting. To overcome phonon sampling barriers associated with strongly-bound bipolaron formation, we design global Monte Carlo updates that approximately respect particle-hole symmetry. To accelerate the iterative linear solver, we introduce a preconditioner that becomes exact in the adiabatic limit of infinite atomic mass. Finally, we demonstrate how stochastic measurements can be accelerated using fast Fourier transforms. Here, these methods are all complementary and, combined, may produce multiple orders of magnitude speedup, depending on model details.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1998

A Monte Carlo Uncertainty Analysis of Ozone Trend Predictions in a Two Dimensional Model

We use Monte Carlo analysis to estimate the uncertainty in predictions of total O3 trends between 1979 and 1995 made by the Goddard Space Flight Center (GSFC) two-dimensional (2D) model of stratospheric photochemistry and dynamics. The uncertainty is caused by gas-phase chemical reaction rates, photolysis coefficients, and heterogeneous reaction parameters which are model inputs. The uncertainty represents a lower bound to the total model uncertainty assuming the input parameter uncertainties are characterized correctly. Each of the Monte Carlo runs was initialized in 1970 and integrated for 26 model years through the end of 1995. This was repeated 419 times using input parameter sets generated by Latin Hypercube Sampling. The standard deviation (a) of the Monte Carlo ensemble of total 03 trend predictions is used to quantify the model uncertainty. The 34% difference between the model trend in globally and annually averaged total O3 using nominal inputs and atmospheric trends calculated from Nimbus 7 and Meteor 3 total ozone mapping spectrometer (TOMS) version 7 data is less than the 46% calculated 1 (sigma), model uncertainty, so there is no significant difference between the modeled and observed trends. In the northern hemisphere midlatitude spring the modeled and observed total 03 trends differ by more than 1(sigma) but less than 2(sigma), which we refer to as marginal significance. We perform a multiple linear regression analysis of the runs which suggests that only a few of the model reactions contribute significantly to the variance in the model predictions. The lack of significance in these comparisons suggests that they are of questionable use as guides for continuing model development. Large model/measurement differences which are many multiples of the input parameter uncertainty are seen in the meridional gradients of the trend and the peak-to-peak variations in the trends over an annual cycle. These discrepancies unambiguously indicate model formulation problems and provide a measure of model performance which can be used in attempts to improve such models.

Considine, D. B.↗
NASA NTRS2004

Infrared Spectroscopy of Star Formation in Galactic and Extragalactic Regions

Last year we submitted and had accepted a paper entitled "The Far-Infrared Emission Line and Continuum Spectrum of the Seyfert Galaxy NGC 1068," by Spinoglio, L., Malkan, M., Smith. HA, Gonzalez-Alfonso, E., and Fischer, J. This analysis was based on the SWAS Monte Carlo code modeling of the OH lines in galaxies observed by ISO. Since that meeting last spring considerable effort has been put into improving the Monte Carlo code. A group of European astronomers, including Prof. Eduardo Gonzalez-Alfonso, had been performing Monte Carlo modeling of other molecules seen in ISO galaxies. We used portions of this grant to bring Prof. Gonzalez-Alfonso to Cambridge for an intensive working visit. A second major paper on the ISO IR spectroscopy of galaxies, "The Far Infrared Spectrum of Arp 220," Gonzalez-Alfonso, E., Smith. H., Fischer, J., and Cernicharo, J., is in press. Spitzer science development was the major component of this past year;s research. This program supported the development of five Early Release Objects for Spitzer observations on which Dr. Smith was Principal Investigator or Co-Investigator, and another five proposals for GO time. The early release program is designed to rapidly present to the public and the scientific community some exciting results from Spitzer in the first months of its operation. The Spitzer instrument and science teams submitted proposals for ERO objects, and a competitive selection process narrowed these down to a small group with exciting science and realistic observational parameters. This grant supported Dr. Smith's participation in the ERO process, including developing science goals, identifying key objects for observation, and developing the detailed AOR (observing formulae) to be use by the instruments for mapping, integrating, etc.). During this year Dr. Smith worked on writing up and publishing these early results. The attached bibliography includes six of Dr. Smith's articles. During this past year Dr. Smith also led or helped to develop proposals for ten Spitzer GO Programs, and three others. Appendix B lists the programs involved.

Smith, Howard A.↗
DOE OSTI2025

Model Calibration with Markov Chain Monte Carlo Tutorial

The purpose of this tutorial is to demonstrate how to use Markov chain Monte Carlo (MCMC) to calibrate a model. By calibration, we mean the selection of model parameters (and, when relevant, structures). A common goal in model development and diagnostics is calibration, or the identification of model structures and parameters which are consistent with data. While models can be calibrated through hand-tuning parameters or minimizing simple error metrics such as root-mean-square-error (RMSE), these approaches can underrepresent the probabilistic nature of the data-generating process, as well as the potential for multiple model configurations to be consistent with the data. Probabilistic uncertainty quantification, which is the topic of this notebook, can address these concerns. This tutorial is presented as an appendix to the e-book: Addressing Uncertainty in MultiSector Dynamics Research.

Markov chain Monte Carlo↗
DOE OSTI2023

Single-photon absorption and emission from a natural photosynthetic complex

Photosynthesis is generally assumed to be initiated by a single photon from the Sun, which, as a weak light source, delivers at most a few tens of photons per nanometre squared per second within a chlorophyll absorption band1. Yet much experimental and theoretical work over the past 40 years has explored the events during photosynthesis subsequent to absorption of light from intense, ultrashort laser pulses. Here, we use single photons to excite under ambient conditions the light-harvesting 2 (LH2) complex of the purple bacterium Rhodobacter sphaeroides, comprising B800 and B850 rings that contain 9 and 18 bacteriochlorophyll molecules, respectively. Excitation of the B800 ring leads to electronic energy transfer to the B850 ring in approximately 0.7 ps, followed by rapid B850-to-B850 energy transfer on an approximately 100-fs timescale and light emission at 850–875 nm. Using a heralded single photon source20,21 along with coincidence counting, we establish time correlation functions for B800 excitation and B850 fluorescence emission and demonstrate that both events involve single photons. We also find that the probability distribution of the number of heralds per detected fluorescence photon supports the view that a single photon can upon absorption drive the subsequent energy transfer and fluorescence emission and hence, by extension, the primary charge separation of photosynthesis. An analytical stochastic model and a Monte Carlo numerical model capture the data, further confirming that absorption of single photons is correlated with emission of single photons in a natural light-harvesting complex.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Dynamic Binary Complexes (DBC) as Super-Adjustable Viscosity Modifiers for Hydraulic Fracturing Fluids

In the preceding project year two, we refined three DBC formulations from a selection of over 50 different chemistries. The optimization study primarily encompassed testing for (i) reversibility extent, (ii) performance in the presence of chemical additives, (iii) adhesion and friction behavior during displacement in wellbores and pipelines, (iv) corrosion protection performance, and (v) injection performance with model fracture systems at the laboratory scale. Highly promising results obtained from all these tests signify the significant potential of DBCs in enhancing hydrocarbon recovery from unconventional reservoirs. The primary activities in the third project year included publishing experimental findings across multiple articles and conducting outreach initiatives. Throughout the year, we undertook tasks such as replicating experimental results, further optimizing various formulations and their associated experimental sets, and conducting additional tests to address missing components based on reviewer feedback and suggestions. We also explored the surfactant and friction-reduction aspects of selected formulations through drag reduction tests. In addition, we constructed an improved fracturing performance setup and performed flow injection tests. The specific DBC formulations focused on during this project period were A8/B1, A12/B5, and A10/B12. We also obtained results for additional DBC formulations and a select few commercial fracturing fluids for the purpose of comparison. Within the project's scope, we aim to enhance the experimental findings with the development of various models. The first two years focused on two key aspects: (i) the creation of a high-fidelity hydraulic fracturing model for non-Newtonian fluids to gain insights into the implementation of DBC fluids in fracking environments, and (ii) the development of a multiphase flow simulator for estimating total production, fluid saturation in the reservoir, and the creation of a fracture propagation model and kinetic Monte Carlo (kMC) models for diverse applications. In the third year, we delved into the fundamental nanostructural properties of DBCs, exploring aspects such as material chemistry, pH tunability, and control of DBC formation and stability. Subsequently, in the extension year, we conducted a systematic investigation of various building blocks containing primary, secondary, and tertiary amine functional groups to understand their impact on rheological and viscoelastic properties. Furthermore, we explored a Dissipative Particle Dynamics (DPD) model to simulate self-assembly processes with precision, creating a high-fidelity representation of relevant nanostructures. The tasks performed this year with the significant results obtained have been discussed in Section 2.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1984

Theoretical development of Monte Carlo codes for modeling cumulus cloud fields

Reflected fluxes are calculated for stratocumulus cloud fields as a function of skycover, cloud aspect ratio, and cloud shape. Cloud liquid water volume is held invariant as cloud shape is varied so that the results can be utilized more effectively by general circulation models and climate models. On the basis of required accuracy in the Earth Radiation Budget Experiment program, an order of magnitude value of 10 W/sq m is used to estimate significant differences between plane parallel and broken cloudiness. This limit is exceeded for cloud covers between 10% and 90%, indicating that plane paralel calculations are not satisfactory at most values of cloud cover. The choice of cloud shape also leads to large differences in reflected fluxes. These differences are traced to the anisotropic intensity pattern out the cloud sides, to the size and shape of the holes between clouds, and to variations in cloud area as viewed from the solar direction. An empirical relationship for effective cloud cover is given at solar zenith angle of theta = 60 deg. This relationship allows for the relatively accurate (delta F = 10 - 15 W/sq m.) computation of broken cloud field reflected fluxes from plane parallel calculations.

Welch, R. M.↗
NASA NTRS2010

Radiation Beamline Testbeds for the Simulation of Planetary and Spacecraft Environments for Human and Robotic Mission Risk Assessment

The Center for Radiation Engineering and Science for Space Exploration (CRESSE) at Prairie View A&M University, Prairie View, Texas, USA, is establishing an integrated, multi-disciplinary research program on the scientific and engineering challenges faced by NASA and the international space community caused by space radiation. CRESSE focuses on space radiation research directly applicable to astronaut health and safety during future long term, deep space missions, including Martian, lunar, and other planetary body missions beyond low earth orbit. The research approach will consist of experimental and theoretical radiation modeling studies utilizing particle accelerator facilities including: 1. NASA Space Radiation Laboratory (NSRL) at Brookhaven National Laboratory; 2. Proton Synchrotron at Loma Linda University Medical Center; and 3. Los Alamos Neutron Science Center (LANSCE) at Los Alamos National Laboratory. Specifically, CRESSE investigators are designing, developing, and building experimental test beds that simulate the lunar and Martian radiation environments for experiments focused on risk assessment for astronauts and instrumentation. The testbeds have been designated the Bioastronautics Experimental Research Testbeds for Environmental Radiation Nostrum Investigations and Education (BERT and ERNIE). The designs of BERT and ERNIE will allow for a high degree of flexibility and adaptability to modify experimental configurations to simulate planetary surface environments, planetary habitats, and spacecraft interiors. In the nominal configuration, BERT and ERIE will consist of a set of experimental zones that will simulate the planetary atmosphere (Solid CO2 in the case of the Martian surface.), the planetary surface, and sub-surface regions. These experimental zones can be used for dosimetry, shielding, biological, and electronic effects radiation studies in support of space exploration missions. BERT and ERNIE are designed to be compatible with the experimental areas associated with the above facilities. CRESSE has broad expertise in space radiation in the areas of space radiation environment modeling, Monte-Carlo radiation transport modeling, space radiation instrumentation and dosimetry, radiation effects on electronics, and multi-functional composite shielding materials. The BERT and ERNIE testbeds will be utilized in individual and collaborative research incorporating this expertise. The research goal is to maximize the technical readiness level (TRL) of radiation instrumentation for human and robotic missions, optimizing the return value of CRESSE for NASA exploration and international co-operative missions. Outcomes and knowledge from research utilizing BERT and ERNIE will be applied to a variety of scientific and engineering disciplines vital for safe and reliable execution of future space exploration missions, which can be negatively impacted by the space radiation environment. The testbeds will be central to a variety of university educational activities and educational goals of NASA. Specifically, BERT and ERNIE will enhance educational opportunities in science, technology, engineering and mathematics (STEM) disciplines for engineering and science students at PVAMU, a historically black college/university. Preliminary data on prototype testbed configurations, including simulated lunar regolith (JSC-1A stimulant based on Apollo 11 samples), regolith/polyethylene composites, and dry ice, will be presented to demonstrate the usefulness of BERT and ERNIE in radiation beam line experiments.

Wilkins, Richard↗
DOE OSTI2026

Renovating Monte Carlo Methods and Codebases with Generative Models

The code will implement a standardized interface for Monte Carlo sampling methods, including conventional techniques, and going beyond current available packages to also incorporate generative model-enabled Monte Carlo sampling to provide a unified framewor

Garcia-Cardona, Cristina↗