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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 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

High energy x-ray and neutron studies of disordered energy-related materials at extreme conditions

The fundamental scientific accomplishments are: (1) advances in a general description of the liquid state by employing structural models constrained by measurements to interpret experimental results and extend them to liquids in general, with special emphasis on (2) The structure of the high-temperature crystal and molten UO 2 and 3) water. Specifically, samples of UO 2 and water were probed using high-energy x-rays at the Advanced Photon Source. The high Z of UO 2 , and the 2-3mm diameter droplet shape of the molten sample, means that >100keV X-rays are required to minimize absorption and multiple scattering, which can distort the measured structure factor. A high flux of x-rays is also required to obtain sufficient statistical accuracy in short (a few seconds) measurement times. The scattered x-ray data were analyzed and pair distribution functions, extracted that characterize the local and long-range atomic structure of the material. The measurements of the hot UO 2 solid show a substantial increase in oxygen disorder and, upon melting, the average U-O coordination was found to decrease from 8 to 6.7±0.5. The research incorporated development of diffraction techniques, sample environment optimization and state-of-the-art simulation techniques. The symbiotic nature of the advances in simulation and experiment allowed for a more focused and informed development of future experiments, effective use of expensive beam time and generated new research agendas for the growing number of research groups, within the US and internationally, that focus on the structure of liquids. Molecular dynamics (MD) provided detailed information when combined with high-quality XN data including addressing key issues in liquids; the relationship between cooling path, structure and fictive temperature, and the trade-offs between network over connectedness in liquids containing low-coordination cations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Elucidation of complexity and prediction of interactions in microbial communities

Summary Microorganisms engage in complex interactions with other members of the microbial community, higher organisms as well as their environment. However, determining the exact nature of these interactions can be challenging due to the large number of members in these communities and the manifold of interactions they can engage in. Various omic data, such as 16S rRNA gene sequencing, shotgun metagenomics, metatranscriptomics, metaproteomics and metabolomics, have been deployed to unravel the community structure, interactions and resulting community dynamics in situ . Interpretation of these multi‐omic data often requires advanced computational methods. Modelling approaches are powerful tools to integrate, contextualize and interpret experimental data, thus shedding light on the underlying processes shaping the microbiome. Here, we review current methods and approaches, both experimental and computational, to elucidate interactions in microbial communities and to predict their responses to perturbations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

PyOECP: A flexible open-source software for the measurement and modeling of the dielectric spectrum from the reflection coefficient

We present PyOECP, a Python-based flexible open-source software for estimating and modeling the complex permittivity obtained from the open-ended coaxial probe (OECP) technique. The transformation of the measured reflection coefficient to complex permittivity is performed based on two different models, including the capacitance model and the antenna model. The software library contains dielectric spectra of common reference liquids, which can be used to transform the reflection coefficient into the dielectric spectra. Several Python routines that are commonly employed (e.g., SciPy and NumPy) are only required so that the users can alter the software structure depending on their needs. The modeling algorithm exploits the Markov chain Monte Carlo method for the data regression. The discrete relaxation models can be built by a proper combination of well-known relaxation models. In addition to these models, the electrode polarization, which is a common measurement artifact for interpreting the dielectric spectra, can be incorporated in the modeling algorithm. A continuous relaxation model, which solves the Fredholm integral equation of the first kind (a mathematically ill-posed problem) is also included.

Yoon, Tae-Jun↗
DOE OSTI2019

Data-driven discovery of coordinates and governing equations

The discovery of governing equations from scientific data has the potential to transform data-rich fields that lack well-characterized quantitative descriptions. Advances in sparse regression are currently enabling the tractable identification of both the structure and parameters of a nonlinear dynamical system from data. The resulting models have the fewest terms necessary to describe the dynamics, balancing model complexity with descriptive ability, and thus promoting interpretability and generalizability. This provides an algorithmic approach to Occam’s razor for model discovery. However, this approach fundamentally relies on an effective coordinate system in which the dynamics have a simple representation. In this work, we design a custom deep autoencoder network to discover a coordinate transformation into a reduced space where the dynamics may be sparsely represented. Thus, we simultaneously learn the governing equations and the associated coordinate system. We demonstrate this approach on several example high-dimensional systems with low-dimensional behavior. The resulting modeling framework combines the strengths of deep neural networks for flexible representation and sparse identification of nonlinear dynamics (SINDy) for parsimonious models. This method places the discovery of coordinates and models on an equal footing.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2002

2-D Magnetohydrodynamic Modeling of A Pulsed Plasma Thruster

Experiments are being performed on the NASA Marshall Space Flight Center (MSFC) MK-1 pulsed plasma thruster. Data produced from the experiments provide an opportunity to further understand the plasma dynamics in these thrusters via detailed computational modeling. The detailed and accurate understanding of the plasma dynamics in these devices holds the key towards extending their capabilities in a number of applications, including their applications as high power (greater than 1 MW) thrusters, and their use for producing high-velocity, uniform plasma jets for experimental purposes. For this study, the 2-D MHD modeling code, MACH2, is used to provide detailed interpretation of the experimental data. At the same time, a 0-D physics model of the plasma initial phase is developed to guide our 2-D modeling studies.

Thio, Y. C. Francis↗
NASA NTRS2002

An Experimental Study of a Pulsed Electromagnetic Plasma Accelerator

Experiments are being performed on the NASA Marshall Space Flight Center (MSFC) pulsed electromagnetic plasma accelerator (PEPA-0). Data produced from the experiments provide an opportunity to further understand the plasma dynamics in these thrusters via detailed computational modeling. The detailed and accurate understanding of the plasma dynamics in these devices holds the key towards extending their capabilities in a number of applications, including their applications as high power (greater than 1 MW) thrusters, and their use for producing high-velocity, uniform plasma jets for experimental purposes. For this study, the 2-D MHD modeling code, MACH2, is used to provide detailed interpretation of the experimental data. At the same time, a 0-D physics model of the plasma initial phase is developed to guide our 2-D modeling studies.

Thio, Y. C. Francis↗
DOE OSTI2024

Advancing electrochemical impedance analysis through innovations in the distribution of relaxation times method

Electrochemical impedance spectroscopy (EIS) is a key tool across various scientific disciplines, including energy sciences, chemistry, and biology, enabling the analysis of electrochemical systems. However, conventional methods for interpreting EIS data are often complex and model dependent. The distribution of relaxation times (DRT) offers a non-parametric approach that simplifies the interpretation process by providing a timescale interpretation of EIS data. This article provides a comprehensive review of current methods for DRT inversion. Additionally, a survey of practitioners highlights key challenges in the field. Here, the findings underscore the need for standardized DRT analysis and benchmarks, as well as the development of automated analysis tools. These advancements would improve the usability and interpretability of EIS data. Ultimately, implementing these improvements could not only propel the field forward but also expand the application of DRT in scientific research by making it accessible to a broader range of researchers, including those without specialized expertise in programming or statistics.

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

Posterior comparison of model dynamics in several hybrid turbulence model forms

Hybrid turbulence models that can accurately reproduce unsteady three-dimensional flow physics across the entire range of grid scales and turbulence dynamics from Reynolds-averaged Navier–Stokes (RANS), through large-eddy simulation (LES), down to direct numerical simulations (DNS) are of increasing interest to the turbulence modeling community. However, despite decades of research and development, the basic tasks of eliminating poor-performing hybrid RANS-LES models and accelerating adoption of superior models through well-designed validation and verification have yet to occur. As a step in this direction, in this work we evaluate thirteen different hybrid RANS-LES models via systematic grid refinement of decaying homogeneous isotropic turbulence. We further derive a novel mathematical framework for assessing the energy partitioning dynamics of each Hybrid RANS-LES model, wherein model-to-model variations in energy partitioning can be interpreted as different feedback mechanisms operating on a low-dimensional nonlinear dynamical system. We found that model forms similar to the flow simulation methodology—also often termed very-large eddy simulation—are dynamically inconsistent with DNS at all resolutions. Additionally, we found a strong dynamical similarity in the feedback mechanisms of all models related to detached eddy simulation and partially averaged Navier–Stokes that is inherent to their general model forms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

AIF for Vis (Active Inference for simulating human interpretation of data visualization) [SWR-26-084]

AIF for Vis contains the Active Inference models and analysis scripts used to study a simple visualization-interpretation task: estimating the average value of two bars in a bar chart. The work is a proof of concept for translating hypothesized cognitive strategies into executable, inspectable process models. We implement two idealized strategies inspired by dual-process accounts of visualization-aided decision making: *Fast model: a compressed, heuristic strategy that estimates the visual midpoint of the two bars and maintains a single belief over their average. *Slow model: a sequential, analytic strategy that estimates the two bar heights separately and maintains them in working memory before computing an average. Both models use a common Active-Inference-inspired framework for sequential perception, belief updating, action selection, and reporting. Their different internal representations produce distinct predicted vulnerabilities: *the Fast model is more susceptible to tick-salience bias; *the Slow model is more susceptible to working-memory decay. The repository includes the model implementations, scripts used for the experiments reported in the paper, precomputed trial-level results, and plotting scripts.

Goldwyn, Harrison [National Laboratory of the Rock↗
DOE OSTI2025

Data-Enabled Fusion Technology (Final Scientific/Technical Report)

Advancing Scientific Understanding in Fusion Energy and Machine Learning This research represented a significant step forward in machine learning (ML) applications for fusion energy experiments. The project integrated advanced data-driven modeling, optimization techniques, and artificial intelligence to enhance the predictive capabilities and operational efficiency of plasma-based fusion systems. Specifically, tasks focused on ML-enhanced diagnostics, operator guidance tools, and predictive modeling helped improve the ability to interpret complex fusion experiments. Key areas of advancement included: 1) data-driven plasma control, i.e., using ML algorithms to optimize experimental conditions and classify plasma behaviors based on historical data; 2) spectroscopy and diagnostics, i.e., applying AI models to extract previously inaccessible insights from experimental spectroscopy data; and 3) configuration mapping and operator guidance, i.e., developing a predictive framework to assist scientists in identifying the most effective experimental parameters, reducing reliance on manual adjustments. By refining these ML-driven techniques, the project contributed to the broader scientific community’s understanding of plasma dynamics and fusion energy viability. Technical Effectiveness and Economic Feasibility The methods investigated demonstrated high technical effectiveness, as reflected in milestones assessing the predictive accuracy, performance, and optimization of fusion configurations. The development of an Operator Guidance Tool (OGT), for example, led to more precise control of plasma conditions by learning from experimental data and offering real-time adjustments. From an economic standpoint, DeFT provided: 1) the ability to reduce trial-and-error experimentation, which lowered operational costs; 2) improved data interpretation methods, which enabled more efficient resource allocation in large-scale fusion research projects; and 3) the automation of key diagnostic tasks, which reduced manual labor and human error, increasing overall efficiency. 13 The final assessments of predictive models and optimization strategies demonstrated that these approaches were scalable and could be implemented across multiple fusion energy research programs. Public Benefit and Societal Impact This project contributed directly to the broader goal of achieving sustainable and commercially viable fusion energy, which had profound implications for clean energy production and climate change mitigation. The integration of AI-driven solutions into fusion research: 1) sped up scientific discovery, accelerating progress towards achieving energy breakthroughs; 2) reduced the cost of experimentation, making fusion research more accessible; and 3) provided a framework for future AI applications in high-energy physics, benefiting adjacent fields like space exploration, material science, and renewable energy. Additionally, by fostering collaborations between AI researchers and plasma physicists, this project promoted interdisciplinary innovation that could lead to broader applications beyond fusion research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

A mathematical approach to survey electrochemical impedance spectroscopy for aging in lithium-ion batteries

Electrochemical impedance spectroscopy (EIS) is a valuable technique to detect the health status and aging phenomena in lithium-ion batteries (LiB). Equivalent circuit modeling (ECM) is conventionally used when interpreting EIS data and gaining physical insights into the aging mechanisms. However, performing ECM is resource intensive and expert-level of knowledge is usually required to select suitable models and fitting parameters. This article presents a quick and user-friendly data analysis algorithm as an alternative to ECM by mathematically fitting geometric features in Nyquist plots and obtaining the growth trends of the features. The evolving trends in the Nyquist plots, such as chord lengths of the arcs and interception points, are consistent with the growth of resistance components obtained using ECM with R 2 values from 0.67 to 0.99, and therefore can be used as indicators of battery aging. Our results show that the quick-fitting approach is suitable for analyzing a series of EIS data acquired during battery cycling and identifying the underlying aging mechanisms.

Chen, Bor-Rong↗
DOE OSTI2023

Polarimetric Geometric Modeling for mm-VLBI Observations of Black Holes

The Event Horizon Telescope (EHT) is a millimeter very long baseline interferometry (VLBI) array that has imaged the apparent shadows of the supermassive black holes M87* and Sagittarius A*. Polarimetric data from these observations contain a wealth of information on the black hole and accretion flow properties. In this work, we develop polarimetric geometric modeling methods for mm-VLBI data, focusing on approaches that fit data products with differing degrees of invariance to broad classes of calibration errors. We establish a fitting procedure using a polarimetric “m-ring” model to approximate the image structure near a black hole. By fitting this model to synthetic EHT data from general relativistic magnetohydrodynamic models, we show that the linear and circular polarization structure can be successfully approximated with relatively few model parameters. We then fit this model to EHT observations of M87* taken in 2017. In total intensity and linear polarization, the m-ring fits are consistent with previous results from imaging methods. In circular polarization, the m-ring fits indicate the presence of event-horizon-scale circular polarization structure, with a persistent dipolar asymmetry and orientation across several days. The same structure was recovered independently of observing band, used data products, and model assumptions. Despite this broad agreement, imaging methods do not produce similarly consistent results. Our circular polarization results, which imposed additional assumptions on the source structure, should thus be interpreted with some caution. Polarimetric geometric modeling provides a useful and powerful method to constrain the properties of horizon-scale polarized emission, particularly for sparse arrays like the EHT.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1977

An investigation of the temporal character of mesoscale perturbations in the troposphere and stratosphere

The effectiveness of mesoscale models in explaining perturbations observed in vertical detailed wind profile measurements in the troposphere and lower stratosphere is assessed. The structure and persistence of the data were analyzed and interpreted in terms of several physical models with the goal of establishing explanations for the observed persistent features of the mesoscale flow patterns. The experimental data used in the investigation were obtained by a unique detailed wind profile measurement system. This system is capable of providing resolution of 50 to 100 m wavelengths for the altitude region from approximately 200 m to 18 km. The system consists of a high-resolution tracking radar and special super-pressure balloon configuration known as a Jimsphere.

Vaughan, W. W.↗
NASA NTRS2018

Active and Passive Radiative Transfer Modeling of the Olympic Mountains Experiment

Sensor forward models are an important tool for interpreting remote sensing observations of geophysical phenomena. By implementing a three-dimensional framework, we can simulate and analyze observations from various sensors on disparate platforms. To demonstrate our model framework, we simulate observations from the Olympic Mountains Experiment (OLYMPEX). The use of cloud model simulations allows us to understand sensor response to cloud ice, falling snow, and other processes and features, and the application of model tools to observations allows us to quantify precipitation.MIIST 3D Forward ModelThe Multi-Instrument Inverse Solver Testbed(MIIST) uses the Atmospheric Radiative TransferSimulator (ARTS) for solving the vector radiativetransfer (RT) equation in up to three spatialdimensions within a spherical geometry• Gas absorptiono Line-by-line calculationso Fast transmittance tables• Hydrometeor scattering solverso Discrete ordinateo RT4 (Evans, 1D)o Radar Single Scattering (1D or 3D)o Monte Carlo (3D)Scattering TablesHigh-fidelity hydrometeor scatteringtables are necessary for accurateand consistent forward modeling ofmulti-frequency observations• Requires full Stokes matriceso And absorption vector• Randomly oriented particleso Discrete Dipole Approximationo Characteristic Basis Function Method(coming soon)• Horizontally-oriented plateso Invariant Imbedding T-matrix MethodCloud Resolving SimulationsCloud resolving simulations (e.g.,NU-WRF) supply output consistentwith ARTS needs• Atmospheric Informationo Temperatureo Pressure / heighto Water vapor• Hydrometeor Profileso ARTS architecture ripe for explicit binmicrophysics• Examples use Morrison 2M schemeThe Olympic Mountains Experiment (OLYMPEX)Validation for GPM of mid-latitudefrontal systems approaching nearcoastalmountains from the ocean• Large collection of ground-based andairborne sensorso Radarso Radiometerso In situ• Contemporaneous with RADEXo Two sets of radar at same frequenciesRadiometer Simulation (3 km NUWRF, 20151203, 15:00)2018.12.14 7Simulate 166 GHz polarizationdifference• Corresponds to the presence of aligned icecrystals• Look at trends for both simulations andobservations• Simulations can tolerate lower resolutiono Larger domainSimulations from Observations: OLYMPEXSimulate sensor response usinggeophysical retrievals as input• Single frequency radar retrievals• Multiple scattering enhancementapparent at W band• Spatially dependent phenomenonModeling Application: 1D Retrievals03 December 2015• DC-8 and ER-2 flightso Focus on APR-3 (DC-8)• Citationo Stacked microphysics legso Qualitative comparisonso Range of frozen habitso Presence of supercooledliquid cloudsResults• Retrievals match probeso Good qualitative match• Bands of increasedreflectivity correspond tolarge Dm and highaggregate fraction• Significant amounts ofsupercooled liquid water

Adams, Ian S.↗
DOE OSTI2024

How the Galaxy–Halo Connection Depends on Large-scale Environment

We investigate the connection between galaxies, dark matter halos, and their large-scale environments at z = 0 with Illustris TNG300 hydrodynamic simulation data. We predict stellar masses from subhalo properties to test two types of machine learning (ML) models: explainable boosting machines (EBMs) with simple galaxy environment features and E(3)-invariant graph neural networks (GNNs). The best-performing EBM models leverage spherically averaged overdensity features on 3 Mpc scales. Interpretations via SHapley Additive exPlanations also suggest that in the context of the TNG300 galaxy–halo connection, simple spherical overdensity on ∼3 Mpc scales is more important than cosmic web distance features measured using the DisPerSE algorithm. Meanwhile, a GNN with connectivity defined by a fixed linking length, L, outperforms the EBM models by a significant margin. As we increase the linking length scale, GNNs learn important environmental contributions up to the largest scales we probe (L = 10 Mpc). We conclude that 3 Mpc distance scales are most critical for describing the TNG galaxy–halo connection using the spherical overdensity parameterization, but that information on larger scales, which is not captured by simple environmental parameters or cosmic web features, can further augment these models. Our study highlights the benefits of using interpretable ML algorithms to explain models of astrophysical phenomena, and the power of using GNNs to flexibly learn complex relationships directly from data while imposing constraints from physical symmetries.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Interpretation of Urinary Excretion Data From Plutonium Wound Cases Treated With DTPA: Application of Different Models and Approaches

After a chelation treatment, assessment of intake and doses is the primary concern of an internal dosimetrist. Using the urinary excretion data from two actual wound cases encountered at Los Alamos National Laboratory (LANL), this study discusses several methods that can be used to interpret intakes from the urinary data collected after one or multiple chelation treatments. One of the methods uses only the data assumed to be unaffected by chelation (data collected beyond 100 d after the last treatment). This method, used by many facilities for official dose records, was implemented by employing maximum likelihood analysis and Bayesian analysis methods. The impacts of an improper assumption about the physicochemical behavior of a radioactive material and the importance of the use of a facility-specific biokinetic model when available have also been demonstrated. Another method analyzed both the affected and unaffected urinary data using an empirical urinary excretion model. This method, although case-specific, was useful in determining the actual intakes and the doses averted or the reduction in body burdens due to chelation treatments. Finally, this approach was important in determining the enhancement factors, the behavior of the chelate, and other observations that may be pertinent to several DTPA compartmental modeling approaches being conducted by the health physics community.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Accelerated Discovery of CH 4 Uptake Capacity Metal–Organic Frameworks Using Bayesian Optimization

Abstract High‐throughput computational studies for discovery of metal–organic frameworks (MOFs) for separations and storage applications are often limited by the costs of computing thermodynamic quantities. Recent such studies at the time of writing may use ab initio results for a narrow selection of MOFs or empirical force‐field methods for larger selections. Here, a proof‐of‐concept study is conducted using Bayesian optimization on CH 4 uptake capacity of hypothetical MOFs for an existing dataset (Wilmer et al., Nature Chem. 2012, 4 , 83). It is shown that less than 0.1% of the database needs to be screened with the Bayesian optimization approach to recover the top candidate MOFs. This opens the possibility for efficient screening of MOF databases using accurate ab initio calculations for future adsorption studies on a minimal subset of MOFs. Furthermore, Bayesian optimization and the surrogate model presented here can offer interpretable material design insights and the framework will be applicable in the context of other target properties.

Taw, Eric↗