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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Simulating Operation of a Planetary Rover

Simulating Operation of a Planetary Rover Rover Analysis, Modeling, and Simulations (ROAMS) is a computer program that simulates the operation of a robotic vehicle (rover) engaged in exploration of a remote planet. ROAMS is a roverspecific extension of the DARTS and Dshell programs, described in prior NASA Tech Briefs articles, which afford capabilities for mathematical modeling of the dynamics of a spacecraft as a whole and of its instruments, actuators, and other subsystems. ROAMS incorporates mathematical models of kinematics and dynamics of rover mechanical subsystems, sensors, interactions with terrain, solar panels and batteries, and onboard navigation and locomotion-control software. ROAMS provides a modular simulation framework that can be used for analysis, design, development, testing, and operation of rovers. ROAMS can be used alone for system performance and trade studies. Alternatively, ROAMS can be used in an operator-in-the-loop or flight-software closed-loop environment. ROAMS can also be embedded within other software for use in analysis and development of algorithms, or for Monte Carlo studies, using a variety of terrain models, to generate performance statistics. Moreover, taking advantage of realtime features of the underlying DARTS/Dshell simulation software, ROAMS can also be used for real-time simulations.

Jain, Abhinandan↗
DOE OSTI2022

Phase diagram of a distorted kagome antiferromagnet and application to Y-kapellasite

Abstract We investigate the magnetism of a previously unexplored distorted spin-1/2 kagome model consisting of three symmetry-inequivalent nearest-neighbor antiferromagnetic Heisenberg couplings J ⬡ , J , and $$J^{\prime}$$ J ′ , and uncover a rich ground state phase diagram even at the classical level. Using analytical arguments and numerical techniques we identify a collinear $$\overrightarrow{Q}=0$$ Q → = 0 magnetic phase, two unusual non-collinear coplanar $$\overrightarrow{Q}=(1/3,1/3)$$ Q → = ( 1 / 3 , 1 / 3 ) phases and a classical spin liquid phase with a degenerate manifold of non-coplanar ground states, resembling the jammed spin liquid phase found in the context of a bond-disordered kagome antiferromagnet. We further show with density functional theory calculations that the recently synthesized Y-kapellasite Y 3 Cu 9 (OH) 19 Cl 8 is a realization of this model and predict its ground state to lie in the region of $$\overrightarrow{Q}=(1/3,1/3)$$ Q → = ( 1 / 3 , 1 / 3 ) order, which remains stable even after the inclusion of quantum fluctuation effects within variational Monte Carlo and pseudofermion functional renormalization group. The presented model opens a new direction in the study of kagome antiferromagnets.

36 MATERIALS SCIENCE↗
NASA NTRS2014

An Integrated Reliability and Physics-Based Risk Modeling Approach for Assessing Human Spaceflight Systems

This paper presents an integrated reliability and physics-based risk modeling approach for assessing human spaceflight systems. The approach is demonstrated using an example, end-to-end risk assessment of a generic-crewed space transportation system during a reference mission to the International Space Station. The behavior of the system is modeled using analysis techniques from multiple disciplines in order to properly capture the dynamic time- and state- dependent consequences of failures encountered in different mission phases. We discuss how to combine traditional reliability analyses with Monte Carlo simulation methods and physics-based engineering models to produce loss-of- mission and loss-of-crew risk estimates supporting risk-based decision-making and requirement verification. This approach facilitates risk-informed design by providing more realistic representation of system failures and interactions; identifying key risk-driving sensitivities, dependencies, and assumptions; and tracking multiple figures of merit within a single, responsive assessment framework that can readily incorporate evolving design information throughout system development.

Risk assessment↗
DOE OSTI2025

Emulator-based Bayesian calibration of a subglacial drainage model

Subglacial drainage models, often motivated by the relationship between hydrology and ice flow, sensitively depend on numerous unconstrained parameters. We explore using borehole water-pressure time series to calibrate the uncertain parameters of a popular subglacial drainage model, taking a Bayesian perspective to quantify the uncertainty in parameter estimates and in the calibrated model predictions. To reduce the computation time associated with Markov Chain Monte Carlo sampling, we construct a fast Gaussian process emulator to stand in for the subglacial drainage model. We first carry out a calibration experiment using synthetic observations consisting of model simulations with hidden parameter values as a demonstration of the method. Using real borehole water pressures measured in western Greenland, we find meaningful constraints on four of the eight model parameters and a factor-of-three reduction in uncertainty of the calibrated model predictions. These experiments illustrate Gaussian process-based Bayesian inference as a useful tool for calibration and uncertainty quantification of complex glaciological models using field data. However, significant differences between the calibrated model and the borehole data suggest that structural limitations of the model, rather than poorly constrained parameters or computational cost, remain the most important constraint on subglacial drainage modelling.

58 GEOSCIENCES↗
DOE OSTI2025

LandScan Mosaic

The LandScan program at Oak Ridge National Laboratory (ORNL), in collaboration with the National Geospatial-Intelligence Agency (NGA), continues to deliver the most accurate and up to date global, high resolution gridded population data. Additionally, the latest advancements in the LandScan HD methodology led to reduced latency in development of rapid updates for geopolitical events. With momentum towards reporting more up to date population estimates, feedback from the user community expressed interest in reporting population estimates in ranges - whether to express a level of uncertainty or confirm to leadership and stakeholders the modeled data are estimates. Building upon the need to understand uncertainty or confidence in the modeled data and report ranges at the global scale, LandScan Mosaic was developed. LandScan Mosaic represents the next generation of high-resolution population modeling, building upon the established success of previous LandScan HD iterations. While LandScan HD employed a deterministic big data fusion approach, LandScan Mosaic enhances this methodology by integrating advanced machine learning techniques to impute missing, yet crucial, population model parameters. This advancement allows for probabilistic modeling of building occupancy and population distribution, incorporating uncertainty quantification through Monte Carlo sampling methods. By combining big data fusion with machine learning-driven imputation and stochastic modeling, LandScan Mosaic provides a more comprehensive and robust representation of population dynamics. LandScan Mosaic will be following the in the footsteps of its longstanding counterpart LandScan Global and releasing a global gridded population raster, at the 3-arcsecond resolution. This technical report documents the current stage of development of LandScan Mosaic, detailing the methodologies and data sources behind the modeling. Stakeholders are encouraged to use this document as an authoritative reference for insight into Mosaic’s data development processes. However, readers should note that LandScan Mosaic remains in a late-stage research and development phase, and methodologies and data presented here are subject to refinements ahead of the anticipated global release in Summer 2025. Feedback and inquiries from users and stakeholders are welcomed as we continue to refine and enhance this important population resource.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Bulk and surface diffusion of neodymium in alpha-uranium: Ab initio calculations and kinetic Monte Carlo simulations

A fundamental understanding of lanthanide transport in metallic fuels is critical for high fidelity modeling of the fuel-cladding chemical interaction (FCCI) phenomenon, which can lead to the formation of brittle intermetallic compounds and premature failure of the cladding. Here we report a combined ab initio density functional theory (DFT) and kinetic Monte Carlo (KMC) study of the bulk diffusivity of Nd in α-U, fully taking into account the effect of radiation enhanced diffusion. In this work, the vacancy mechanism is considered to be the dominant mechanism for the bulk diffusion of Nd since a Nd interstitial is found to be intrinsically unstable in α-U. The surface diffusivity of a Nd adatom on α-U (001) surface has been further predicted using KMC simulations parameterized by DFT calculations. The present study suggests that Nd transport via the surface diffusion mechanism can be many orders of magnitude faster than bulk diffusion. Furthermore, the results from the present lower length scale study can be used to inform mesoscale phase-field simulations to determine the effective diffusion coefficient of Nd through α-U with a porous microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Maximum likelihood estimation of difference scaling functions for suprathreshold judgments

Maximum likelihood estimation (MLE) has been used to produce perceptual scales from binary judgments of triads and quadruples. This method relies on Thurstone’s theory of a stochastic perceptual process where the perceived difference of two stimuli is the difference in their perceived strengths. It is possible that the perception of a suprathreshold difference is overestimated when adding smaller differences, a phenomenon referred to as diminishing returns. The current approach to construct a perceptual scale using MLE does not account for this phenomenon. We present a way to model the perception of differences using MLE and Thurstone’s theory, adapted to allow the possibility of diminishing returns. This method is validated using Monte Carlo simulated responses to experimental triads and can correctly model diminishing returns, the absence of diminishing returns, and the opposite of diminishing returns both in the cases when a perceptual scale is known and when the true perceived strengths of the stimuli are unknown. Additionally, this method was applied to empirical data sets to determine its feasibility in investigations of perception. Ultimately, it was found that this analysis allows for more accurate modeling of suprathreshold difference judgments, a more complete understanding of the perceptual processes underlying comparisons, and the evaluation of Thurstone’s theory of difference judgments.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2010

Modeling Martian Dust Using Mars-GRAM

Engineering-level atmospheric model widely used for diverse mission applications. Mars-GRAM s perturbation modeling capability is commonly used, in a Monte-Carlo mode, to perform high fidelity engineering end-to-end simulations for entry, descent, and landing (EDL). From the surface to 80 km altitude, Mars-GRAM is based on NASA Ames Mars General Circulation Model (MGCM). Mars-GRAM and MGCM use surface topography from Mars Global Surveyor Mars Orbiter Laser Altimeter (MOLA), with altitudes referenced to the MOLA areoid, or constant potential surface. Traditional Mars-GRAM options for representing the mean atmosphere along entry corridors include: TES Mapping Years 1 and 2, with Mars-GRAM data coming from MGCM model results driven by observed TES dust optical depth TES Mapping Year 0, with user-controlled dust optical depth and Mars-GRAM data interpolated from MGCM model results driven by selected values of globally-uniform dust optical depth. Mars-GRAM 2005 has been validated against Radio Science data, and both nadir and limb data from the Thermal Emission Spectrometer (TES).

Justh, Hilary L.↗
DOE OSTI2022

Adversarial autoencoder ensemble for fast and probabilistic reconstructions of few-shot photon correlation functions for solid-state quantum emitters

Second-order photon correlation measurements [g (2) (τ) functions] are widely used to classify single-photon emission purity in quantum emitters or to measure the multiexciton quantum yield of emitters that can simultaneously host multiple excitations – such as quantum dots – by evaluating the value of g (2) (τ = 0). Accumulating enough photons to accurately calculate this value is time consuming and could be accelerated by fitting of few-shot photon correlations. Here, we develop an uncertainty-aware, deep adversarial autoencoder ensemble (AAE) that reconstructs noise-free g (2) (τ) functions from noise-dominated, few-shot inputs. The model is trained with simulated g (2) (τ) functions that are facilely generated by Poisson sampling time bins. The AAE reconstructions are performed orders-of-magnitude faster, with reconstruction errors and estimates of g (2) (τ = 0) that are lower in variance and similar in accuracy compared to Maximum likelihood estimation and Levenberg-Marquardt least-squares fitting approaches, for simulated and experimentally measured few-shot g (2) (τ) functions (~100 two-photon events) of InP/ZnS/ZnSe and CdS/CdSe/CdS quantum dots. The deep-ensemble model comprises eight individual autoencoders, allowing for probabilistic reconstructions of noise-free g (2) (τ) functions, and we show that the predicted variance scales inversely with number of shots, with comparable uncertainties to computationally intensive Markov chain Monte Carlo sampling. Furthermore, this work demonstrates the advantage of machine learning models to perform uncertainty-aware, fast, and accurate reconstructions of simple Poisson-distributed photon correlation functions, allowing for on-the-fly reconstructions and accelerated materials characterization of solid-state quantum emitters.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Generalized Bayesian Monte Carlo Evaluation [Slides]

Bayesian Monte Carlo is a tool to address imperfect data & models, non-linear models, non-normal PDFs, and integration of differential and integral data. Simple propagation to criticality shows effects of new $\mathcal{L}$($β|z,γ$).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Electrifying Airport GSE: Monte Carlo Grid Impacts

Airports globally are shifting from ICE-powered to electric Ground Support Equipment (eGSE) to enhance efficiency, reduce operational costs, and improve operator health. Leveraging predictable routes, flat terrain, and low operational speeds, airports provide ideal conditions for electrification. This study evaluates freight GSE electrification at Dallas-Fort Worth International Airport (DFW), USA, using the Agile@ platform, which integrates three analytical methods: Freight Facility Model (FFM), Activity-Structure-Intensity-Fuel (ASIF), and Monte Carlo simulations. Results from 10,000 simulations indicate modest but critical increases in electricity demand and significant variability in GSE energy consumption. These insights emphasize the importance of data-driven scheduling, targeted maintenance, and strategic infrastructure planning. For high-uncertainty scenarios, airports are advised to deploy buffer energy storage systems (battery banks), implement demand-response charging strategies, schedule flexible workforce shifts, and prioritize proactive maintenance-particularly for equipment with higher operational uncertainty, such as tug tractors with trailers. Agile@ thus offers a robust, scalable, and data-driven framework to optimize long-term GSE planning and enhance reliability across diverse airport environments.

Bose, Ranjan [ORNL] (ORCID:0009000791026327)↗
NASA NTRS1997

Modelling two dust features in comet Hale-Bopp (1995 O1)

A Monte Carlo computer simulation code for generating synthetic images of dust features in comets is applied to comet Hale-Bopp to model the diurnal evolution of a bright jet and the appearance of nearly concentric halos.

comet-Hale-Bopp diurnal evolution of a dust jet du↗
NASA NTRS1979

Monte Carlo simulation of single accident airport risk profile

A computer simulation model was developed for estimating the potential economic impacts of a carbon fiber release upon facilities within an 80 kilometer radius of a major airport. The model simulated the possible range of release conditions and the resulting dispersion of the carbon fibers. Each iteration of the model generated a specific release scenario, which would cause a specific amount of dollar loss to the surrounding community. By repeated iterations, a risk profile was generated, showing the probability distribution of losses from one accident. Using accident probability estimates, the risks profile for annual losses was derived. The mechanics are described of the simulation model, the required input data, and the risk profiles generated for the 26 large hub airports.

Source record↗
NASA NTRS1980

Analytical simulation of nonlinear satellite links

It is noted that performance predictions for nonlinear digital satellite channels are usually obtained by either purely analytical methods or Monte Carlo simulations. When used with other than very simple channel models, however, they become extremely costly. It is shown that, through analytical modeling of the effect of the nonlinearity on the uplink waveform, it is possible to obtain approximate performance predictions with a minimum of computer time. It is also demonstrated that the results agree quite well with Monte Carlo simulations and that significant trade-offs can be performed with this tool. An analytical simulation based on Blachman's (1968) definition of signal x signal, signal x noise, and noise x noise terms is shown to provide the required flexibility for modeling the effect of signal distortions on the link performance and to produce good results over a wide range of nonlinearities and channel conditions.

Braun, W. R.↗
NASA NTRS2008

Evaluating Mars Science Laboratory Landing Sites with the Mars Global Reference Atmospheric Model (Mars-GRAM 2005)

The Mars Global Reference Atmospheric Model (Mars-GRAM) is an engineering-level atmospheric model widely used for diverse mission applications. Mars-GRAM s perturbation modeling capability is commonly used, in a Monte-Carlo mode, to perform high fidelity engineering end-to-end simulations for entry, descent, and landing (EDL) [1]. From the surface to 80 km altitude, Mars-GRAM is based on the NASA Ames Mars General Circulation Model (MGCM). Mars-GRAM and MGCM use surface topography from Mars Global Surveyor Mars Orbiter Laser Altimeter (MOLA), with altitudes referenced to the MOLA areoid, or constant potential surface. Traditional Mars-GRAM options for representing the mean atmosphere along entry corridors include: (1) Thermal Emission Spectrometer (TES) mapping years 1 and 2, with Mars-GRAM data coming from NASA Ames Mars General Circulation Model (MGCM) results driven by observed TES dust optical depth or (2) TES mapping year 0, with user-controlled dust optical depth and Mars-GRAM data interpolated from MGCM model results driven by selected values of globally-uniform dust optical depth. Mars-GRAM 2005 has been validated [2] against Radio Science data, and both nadir and limb data from TES [3]. There are several new features included in Mars-GRAM 2005. The first is the option to use input data sets from MGCM model runs that were designed to closely simulate conditions observed during the first two years of TES observations at Mars. The TES Year 1 option includes values from April 1999 through January 2001. The TES Year 2 option includes values from February 2001 through December 2002. The second new feature is the option to read and use any auxiliary profile of temperature and density versus altitude. In exercising the auxiliary profile Mars-GRAM option, values from the auxiliary profile replace data from the original MGCM databases. Some examples of auxiliary profiles include data from TES nadir or limb observations and Mars mesoscale model output at a particular location and time. The final new feature is the addition of two Mars-GRAM parameters that allow standard deviations of Mars-GRAM perturbations to be adjusted. The parameter rpscale can be used to scale density perturbations up or down while rwscale can be used to scale wind perturbations.

Justh, H. L.↗
NASA NTRS2020

A Digital Twin Feasibility Study (Part II):Non-Deterministic Predictions of Fatigue Life Using In-Situ Diagnostics and Prognostics

The Digital Twin (DT) concept has the potential to revolutionize the way systems and their components are designed, managed, maintained, and operated across a vast number of fields from engineering to healthcare. The focus of this work is the implementation of DT for the health management of fatigue critical structures. This paper is the second part of a two-part series. The first of the series demonstrated the use of multi-scale, initiation-to-failure crack growth modeling to form non-deterministic predictions of fatigue life. In this second part, a general method for reducing uncertainty in fatigue life predictions is presented that couples in-situ diagnostics and prognostics in a probabilistic framework. Monte Carlo methods and high-fidelity finite element models are used to (i) generate probabilistic estimates of crack state throughout the life of the same geometrically complex test specimen and (ii) predict fatigue life with decreasing uncertainty as more of these diagnoses are obtained. The ability to predict accurately and in the presence of uncertainty is demonstrated, suggesting that the proposed DT method is feasible for fatigue life prognosis and should be pursued further with a focus on increasing application realism.

Patrick E Leser↗
DOE OSTI2022

A Laboratory-driven Multiscale Investigation of X-Ray Induced Mass Loss and Photochemical Evolution in Cosmic Carbon and Silicate Dust

We present the results of an integrated laboratory and modeling investigation into the impact of stellar X-rays on cosmic dust. Carbonaceous grains were prepared in a cooled (< 200 K) supersonic expansion from aromatic molecular precursors, and were later irradiated with 970 eV X-rays. Silicate (enstatite) grains were prepared via laser ablation, thermally annealed, and later irradiated with 500 eV X-rays. Infrared spectra of the 3.4 μm band of the carbon sample prepared with benzene revealed 84% ± 5% band area loss for an X-ray dose of 5.2 x 10 23 eV.cm -2 . Infrared spectra of the 8 -12 μm Si-O band of the silicate sample revealed band area loss up to 63% ± 5% for doses of 2.3 x 10 23 eV.cm -2 . A hybrid Monte Carlo particle trajectory approach was used to model the impact of X-rays and ensuing photoelectrons, Auger and collisionally ionized electrons through the bulk. As a result of x-ray ionization and ensuing Coulomb explosions on surface molecules, the calculated mass loss is 60% for the carbonaceous sample and 46% for the silicate sample, within a factor of 2 of the IR band loss, supporting an X-ray induced mass-loss mechanism. We apply the laboratory X-ray destruction rates to estimate the lifetimes of dust grains in protoplanetary disks surrounding 1 M ⊙ and 0.1 M ⊙ G and M stars. In both cases, X-ray destruction timescales are short (a few million years) at the disk surface, but are found to be much longer than typical disk lifetimes (≳10 Myr) over the disk bulk.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1997

Assay of the Martian Regolith with Neutrons

The purpose of the research is to combine experiments and Monte Carlo transport of neutrons through volume of soil in an attempt to model neutron leakage from planetary surfaces. Emphasis is given to the change of neutron spectra as a function of water content and location. During the first stage of effort, two experiments were conducted in which leakage of neutrons from a Pu-Be source through about 30 g/cm(exp 2) of soil were measured with several counters. A Monte Carlo code, MCNP, has been used to model many of the 100 individual runs of the experiment. Hydrogen is the element that has the most dramatic effect on the neutron spectrum and its effect on the neutron spectrum is almost the same whether it is in the form of water or polyethylene. In order to simulate various water configurations, sheets of polyethylene have been used between layers of soil as well as water in several concentrations up to 18%. Comparison of experimental results to theoretical predictions made with the MCNP code were disappointing for low concentrations of water. We have made extensive calculations to see if room return could be the cause of the discrepancies. Water concentrations of the 'dry' soil were measured by two different laboratories and differed only by 0.5%. We have made calculations to optimize the next experiment and are investigating other methods of determining the water content of 'dry' soil.

Drake, Darrell M.↗