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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Covariate Dependent Sparse Functional Data Analysis

This study proposes a method to incorporate covariate information into sparse functional data analysis. The method aims at cases where each subject has a limited number of longitudinal measurements and is associated with static covariates. This research is motivated by several use cases in practice. One representative example is void swelling, a nuclear-specific material degradation mechanism. Void swelling is affected by many covariates, including alloy composition and irradiation type. How to accurately model the complicated joint effects of such covariates on the swelling process is the key to mitigating the effect of swelling and ensuring safe operation. Unlike most of the existing methods, the proposed method can handle high-dimensional covariates with the informative covariate identification procedure and sparse and irregularly spaced measurements, that is, does not require complete or dense observations. The main innovation of the proposed method is that we model the variation coming from covariates and the variation left conditioned on covariates, such that the functional principal component analysis and Gaussian process can be conducted in a unified manner. Further, we also propose a systematic approach to identify important covariates in the hypothesis testing context. The methodology is demonstrated on applications in nuclear engineering and healthcare and simulation studies.

42 ENGINEERING↗
DOE OSTI2024

Elucidating the complex interplay between thermodynamics, kinetics, and electrochemistry in battery electrodes through phase-field modeling

Abstract This article highlights applications of phase-field modeling to electrochemical systems, with a focus on battery electrodes. We first provide an overview on the physical processes involved in electrochemical systems and applications of the phase-field approach to understand the thermodynamic and kinetic mechanisms underlying these processes. We employ two examples to highlight how realistic thermodynamics and kinetics can naturally be incorporated into phase-field modeling of electrochemical processes. One is a composite battery cathode with an intercalation compound (Li x FePO 4 ) as the electrochemically active material, and the other is a displacement reaction compound (Li–Cu–TiS 2 ). With the input parameters mostly from atomistic calculations and experimental measurements, phase-field simulations allowed us to untangle the interactions among transport, reaction, electricity, chemistry, and thermodynamics that lead to highly complex evolution of the materials within battery electrodes. The implications of these observations for battery performance and degradation are discussed. Graphical abstract

Andrews, W. Beck (ORCID:0000000287824621)↗
DOE OSTI2023

Optimal mass variables for semivisible jets

Strongly coupled hidden sector theories predict collider production of invisible, composite dark matter candidates mixed with standard model hadrons in the form of semivisible jets. Classical mass reconstruction techniques may not be optimal for these unusual topologies, in which the missing transverse momentum comes from massive particles and has a nontrivial relationship to the visible jet momentum. We apply the artificial event variable network, a semisupervised, interpretable machine learning technique that uses an information bottleneck, to derive superior mass reconstruction functions for several cases of resonant semivisible jet production. We demonstrate that the technique can extrapolate to unknown signal model parameter values. We further demonstrate the viability of conducting an actual search for new physics using this method, by applying the learned functions to standard model background events from quantum chromodynamics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Exploring Gamma-Ray and Neutron Spectroscopy Missions to Mars Caves [Slides]

In this work, we investigate the sensitivity of active neutron detector systems in Mars caves using MCNP6 to model neutron transport in different cave geometries, compositions, and detectors to determine if active neutron measurements can be used to identify the hydration of cave walls vs floor vs ceilings, and to perform a concept study for potential future Mars cave missions in search of hydration and habitable regions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

22 Ne Phase Separation as a Solution to the Ultramassive White Dwarf Cooling Anomaly

The precise astrometric measurements of the Gaia Data Release 2 have opened the door to detailed tests of the predictions of white dwarf cooling models. Significant discrepancies between theory and observations have been identified, the most striking affecting ultramassive white dwarfs. Cheng et al. found that a small fraction of white dwarfs on the so-called Q branch must experience an extra cooling delay of ~8 Gyr not predicted by current models. 22 Ne phase separation in a crystallizing C/O white dwarf can lead to a distillation process that efficiently transports 22 Ne toward its center, thereby releasing a considerable amount of gravitational energy. Here, using state-of-the-art Monte Carlo simulations, we show that this mechanism can largely resolve the ultramassive cooling anomaly if the delayed population consists of white dwarfs with moderately above-average 22 Ne abundances. We also argue that 22 Ne phase separation can account for the smaller cooling delay currently missing for models of white dwarfs with more standard compositions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Impact of Molten Gallium on the Microstructure and Corrosion Behavior of Aluminum and Uranium-Aluminum Alloys for Used Nuclear Fuel Reprocessing

Test reactors around the world utilize highly enriched uranium fuel to achieve high neutron fluxes for materials testing. Once spent, the remaining uranium is a valuable resource for subsequent fuel fabrication. However, some of these test reactor cores consist of curved plate-type fuel elements, fabricated using aluminum alloy 6061 (AA6061) cladding to encapsulate a uranium-aluminum alloy (UAlx) fuel matrix. These assemblies require non-standard reprocessing approaches for uranium recovery, as aluminum dissolves readily in acidic solutions, generating large volumes of waste and complicating downstream chemical separations. In this work, we investigate a novel chemical decladding strategy based on the interaction between AA6061/UAlx and molten gallium (Ga). Ga is known to induce severe degradation of aluminum metal through liquid metal embrittlement (LME), even at relatively low Ga concentrations. By penetrating the aluminum crystal lattice, Ga disrupts grain cohesion and facilitates fracture or dissolution of the aluminum matrix. Thermodynamic analysis of the Al–Ga binary phase diagram suggests that Ga may offer a viable pathway to selectively weaken or dissolve the AA6061 cladding, and potentially the aluminum component of the UAlx fuel matrix within. To this end, parametric experiments were performed at 50 °C and 100 °C across a range of Al–Ga atomic fractions. At lower Al fractions, the AA6061 was completely molten after 2 hours of exposure to the Ga metal. In contrast, samples with higher Al fractions (0.9 Al, 0.1 Ga) contained residual solids after 2 hours, which were characterized by microstructural examination using electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM). These Al-Ga compositions were also evaluated using FactSage thermodynamic modeling to further elucidate the relationship between phase diagram behavior and LME.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Reliability Analysis of Power Grids Considering Component Failures of Variable Energy Resources

This paper proposes an improved model for the reliability assessment of power systems considering component failures of variable energy resources (VER). The inherent intermittency of VER such as solar photovoltaic (PV) and wind farms, along with their susceptibility to component failures, present significant challenges to reliable system operation. These issues, combined with power grid operation and network constraints, complicate the reliable operation of VER-integrated power systems. Here, to address these concerns, this paper introduces a reliability assessment framework that considers VER input variability, its impact on component availability, and their resulting impact on overall system reliability. Stochastic models based on discrete Markov processes are developed to incorporate variable irradiance, wind speeds, and their effects on PV and wind component failure rates. A next-event and state transition-based approach is then developed to integrate the stochastic models into a mixed-timing sequential Monte Carlo simulation framework for composite reliability assessment. Case studies on the RTS-GMLC system demonstrate the effectiveness of the proposed model in evaluating the reliability of VER-integrated systems.

Pandit, Dilip [Sandia National Laboratories (SNL-N↗
DOE OSTI2025

In-Silico Design of Next Generation Cellulose-Derived Packaging Materials (CRADA Final Report)

Developing sustainable solutions for single-use packaging is an important objective to combat the environmental crisis of plastics pollution. Most embodiments of cellulose-based packaging materials, including CellophaneTM, are completely biodegradable in both terrestrial and marine environments. However, petroleum-derived alternatives offer some performance advantages for metrics such as moisture barriers and mechanical properties. This project leverages molecular dynamics simulation to investigate how molecular modifications to cellulose-based polymer assemblies impact their material properties. An important performance criterion for the modified materials was to retain biodegradability; thus, modifications by naturally occurring, biodegradable additives were the focus of this study. Specifically, we developed models with xylan and lignin of varying monomeric compositions into the cellulose matrix. The mechanical properties were investigated by performing stress-strain simulations, and the water barrier and hydrophobicity were investigated by simulating the water contact angle. Our findings indicate that the incorporation of xylan into the cellulose matrix tends to increase the mechanical properties with an optimal loading of ~27 wt%. We also predict that orienting the nanoscale directionality of the xylan chains such that they are perpendicular to the cellulose fibrils will dramatically increase mechanical strength. In contrast, the incorporation of lignin tends to weaken the composite at all loadings investigated. Simulations of water contact angle predicted that coating polymers on the surface of the cellulose assembly creates a more hydrophobic surface than incorporating them throughout the matrix. Of the coatings investigated, lignin resulted in the most hydrophobic surface, followed by pectin and keratin, which both imparted modest increases in hydrophobicity. Future experimental work done by Futamura will focus on designing material prototypes to capitalize on the predictions of performance enhancement obtained from molecular modeling. While substantial progress was made by the simulations performed in this project, there still exists a vast parameter space that we were unable to investigate, including branching, functional group decoration, and degree of polymerization of polymer additives. However, the methods developed in this initial investigation will facilitate more rapid evaluation of the impact of molecular characteristics on the performance of biopolymer composite materials and thereby accelerate future materials discovery efforts in this area.

36 MATERIALS SCIENCE↗
DOE OSTI2026

The impact of aerosol mixing state on immersion freezing: insights from classical nucleation theory and particle-resolved simulations

Immersion freezing, initiated by ice-nucleating particles (INPs) in supercooled aqueous droplets, plays an important role in the formation of ice crystals within clouds. The efficiency of immersion freezing depends strongly on INP composition and, crucially, on the mixing state – how chemical species are distributed across the particle population. Here, we quantify the impact of aerosol mixing state on immersion freezing using a combined theoretical and particle-resolved modeling approach. We derive analytical expressions for the frozen fraction of internally and externally mixed INP populations based on classical nucleation theory, showing that the frozen fraction is sensitive to whether ice-active species are present in all particles or only in a subset of the population. We introduce a multi-species immersion freezing scheme into the particle-resolved model PartMC, using the water activity-based immersion freezing model (ABIFM) to compute freezing probabilities for mixed-composition particles. To improve computational efficiency, we implement a Binned Tau-Leaping algorithm and demonstrate an order-of-magnitude speedup with minimal accuracy loss. Simulations reproduce the analytical trends in limiting cases and extend the analysis to more general aerosol populations, where mixing state continues to exert a substantial control on frozen fraction. Sensitivity analyses across particle size, species type, and cooling condition reveal that the mixing state effect is most pronounced when small amounts of highly efficient INPs are mixed with less efficient materials. These findings underscore the need to represent aerosol mixing state explicitly in models of heterogeneous ice nucleation to reduce uncertainty in cloud-phase partitioning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Exactly Solvable Model for Strongly Interacting Electrons in a Magnetic Field

States of strongly interacting particles are of fundamental interest in physics and can produce exotic emergent phenomena and topological structures. We consider here two-dimensional electrons in a magnetic field, and, departing from the standard practice of restricting to the lowest LL, introduce a model short-range interaction that is infinitely strong compared to the cyclotron energy. Here, we demonstrate that this model lends itself to an exact solution for the ground as well as excited states at arbitrary filling factors v<1/2p and produces a fractional quantum Hall effect at fractions of the form v=n/(2pn+1), where n and p are integers. The fractional quantum Hall states of our model share many topological properties with the corresponding Coulomb ground states in the lowest Landau level, such as the edge physics and the fractional charge of the excitations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Derivation of the Structural Integrity of Residual (SIR) glass model for the enhancement of waste loading

A new model based on glass structure to allow for enhanced waste loading in nuclear waste glass while maintaining chemical durability is proposed. The model is derived by splitting the molar concentrations of the targeted starting glass composition into theoretical crystalline phases anticipated to be observed during devitrification and a residual glass. An empirically derived relationship based on maintaining the residual glass structure, determined from a calculated non-bridging oxygen content, was demonstrated to successfully screen glasses for acceptable durability. The proposed model can successfully identify durable glass compositions containing 20–35 wt% Al 2 O 3 , a concentration that would significantly increase the projected waste loading in glasses processed at the Hanford Tank Waste Treatment and Immobilization Plant.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Development of Efficient Process for Manufacturing of Thermoplastic Composites with Tailored Properties (CRADA 511)

This is a collaborative effort between Battelle Memorial Institute as manager and operator of Pacific Northwest National Laboratory (PNNL) and ESI North America Inc. (“ESI” or “Participant”) to apply computation and data analytics to the challenge of light weighting with a focus on the battery enclosures of electric vehicles (EVs). EVs use heavy batteries to increase range and power. A complex-shaped battery enclosure is required to meet a host of challenging performance requirements. The ability to virtually develop composite parts such as battery enclosure with tailored properties to meet required performance will be highly valuable to the automotive industry. However, efficient simulation of composite-manufacturing processes remains a challenging issue since simulation involves multiscale models in space and time, highly non-linear and anisotropic behavior, strongly coupled multi-physics, and complex geometries. This work will advance the state of the art by reducing the computational burden of composite optimization by using simulation data from a limited number of configurations off-line and then developing a reduced order model (ROM) using data analytics and machine learning (ML). Develop a data driven approach to link features of the material and manufacturing processes to the mechanical properties of thermoplastic composite parts.

42 ENGINEERING↗
DOE OSTI2022

Development of a Thermodynamic Database for Corrosion in Chloride MSRs

Control of the salt redox condition is the salient method for corrosion inhibition in molten salts. For chloride salt-fueled molten salt reactors of primary concern is the U(IV)/U(III) ratio, influenced directly by the Cl2 activity in the salt. Although thermodynamic assessments of MSR-relevant salts abound, none have included consideration of UCl4 which is essential to correctly describe the salt redox condition. This work reports the assessment of the NaCl-KCl-MgCl2 base salt with UCl3-UCl4 and corrosion chlorides involving Cr, Fe, and Ni as minor components and the resultant thermodynamic models. The effort utilized available thermodynamic measurements and phase equilibria complemented by differential scanning calorimetry determinations of phase equilibria. Example calculations explore the temperature and composition space to understand its influence on corrosion. The foregoing models are incorporated into an update of the Molten Salt Thermal Properties Database – Thermochemical which is publicly available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Active Learning for Rapid Targeted Synthesis of Compositionally Complex Alloys

The next generation of advanced materials is tending toward increasingly complex compositions. Synthesizing precise composition is time-consuming and becomes exponentially demanding with increasing compositional complexity. An experienced human operator does significantly better than a novice but still struggles to consistently achieve precision when synthesis parameters are coupled. The time to optimize synthesis becomes a barrier to exploring scientifically and technologically exciting compositionally complex materials. This investigation demonstrates an active learning (AL) approach for optimizing physical vapor deposition synthesis of thin-film alloys with up to five principal elements. We compared AL-based on Gaussian process (GP) and random forest (RF) models. The best performing models were able to discover synthesis parameters for a target quinary alloy in 14 iterations. We also demonstrate the capability of these models to be used in transfer learning tasks. RF and GP models trained on lower dimensional systems (i.e., ternary, quarternary) show an immediate improvement in prediction accuracy compared to models trained only on quinary samples. Furthermore, samples that only share a few elements in common with the target composition can be used for model pre-training. We believe that such AL approaches can be widely adapted to significantly accelerate the exploration of compositionally complex materials.

Chemistry↗
DOE OSTI2025

National Energy Water Treatment & Speciation (NEWTS): A Water & Critical Mineral Database and Dashboard

The scarcity of water resources, the need for beneficial water reuse, and the challenges of wastewater treatment are becoming increasingly pressing in economic, social, and environmental domains. Addressing these concerns requires effective treatment strategies to manage wastewater streams and tackle environmental and economic issues. Furthermore, the recovery of critical minerals from the waste streams associated with energy production holds the promise of offsetting treatment costs and securing local sources of valuable minerals. However, relevant data on these waste streams are dispersed and challenging to locate. The process of ingesting such data into modeling software often involves multiple steps, requiring data restructuring to meet software-input requirements. The non-standardized reporting of water data makes data aggregation and reformatting a time-consuming process. Additionally, essential attributes necessary for modeling water treatment and mineral scale formation are frequently missing. Moreover, data gaps vary depending on the region of interest. Consequently, there is a pressing need for high-quality energy-water composition data that can be easily imported into water chemistry modeling software. To address this need, the National Energy Technology Laboratory has created the National Energy Water Treatment and Speciation (NEWTS) Database and Dashboard—a free online tool catering to community leaders and water researchers. NEWTS facilitates a comprehensive understanding of the composition of energy-related wastewater streams in the United States. The datasets provide detailed concentrations and speciation of major and minor aqueous compounds in energy-related wastewater streams, including power plant leachate, acid mine drainage, brackish water, and oil and gas produced water across the United States. Many of the aqueous species are critical minerals (Li, REEs) in high demand to modernize the world’s energy infrastructure. Many of the datasets also contain volumetric flow-rates needed to model the treatment and reuse scenarios in advanced aqueous chemistry software programs. The NEWTS Database and Dashboard offer public access to hitherto challenging-to-access datasets, presented in a standardized format that is tailored for easy input into aqueous chemistry modeling software. By performing the work needed to transform dispersed, disparate data sources into unified, model-ready datasets, NEWTS serves as an essential resource in advancing water treatment research and sustainable water resource management.

produced water management↗
DOE OSTI2025

Continuum shock mixture models for Ni+Al multilayers: Individual layers and bulk equations of state

Continuum shock mixture models are reviewed and applied to determine the equations of state for five different compositions of Ni x Al y ⁠, as well as bulk Ni+Al reactive multilayers, by combining the fundamental property data for elemental nickel and aluminum. From the literature, we down-select and evaluate two analytical models for the mixture Hugoniot, i.e., the well-known method of kinetic energy averaging (KEA) and a recent model proposed by Jordan and Baer [J. Appl. Phys. 111, 083516 (2012)]. Fundamentally, the former method assumes pressure equilibrium, whereas the latter assumes a common particle velocity and mixture sound speed from compressible two-phase cavitating flows. Additionally, we construct thermodynamically complete equations of state by fitting Einstein oscillator series models for the specific heat at constant volume. Finally, the solid solution approximation is invoked for intermetallic compositions, which are not strictly physical mixtures. Overall, the KEA model provides a better fit to the available Ni x Al y and Ni+Al multilayer shock compression data; however, there are combinations of material properties where the performance of these two models is thought to be reversed. Moreover, the results of this work include the first analytical solution of Jordan–Baer that does not require numerical root finding, as well as proposed modifications to the Einstein oscillator series to incorporate some effects of local pressure–temperature equilibrium and reaction–diffusion. Future work is planned that will use these equations of state in mesoscale simulations to study shock-induced reaction in Ni+Al multilayers, and the intended application is illustrated with a brief 2D hydrocode example.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A two-equation soot-in-flamelet modeling approach applied under Spray A conditions

Soot production (including formation and oxidation) is studied in the transient, high-pressure and turbulent n-dodecane Spray A flames from the Engine Combustion Network (ECN) using computational fluid dynamics (CFD) simulations. A two-equation soot-in-flamelet modeling approach is applied within the framework of the Unsteady Flamelet Progress Variable (UFPV) model and results are validated against experimental data. Equations for soot mass fraction and soot number density derived in the mixture fraction space are solved in the context of detailed flamelet calculations. Source terms for the different steps in the soot chemistry are tabulated and incorporated in the flamelet manifold. For the reference condition, the modeling approach based on the tabulated flamelet manifold reduces the computational cost of a CFD calculation by approximately 40 times compared to a non-tabulated well-mixed (WM) modeling approach. The soot-in-flamelet approach is then extended to study the effect of ambient oxygen concentration, ambient mixture composition and ambient temperature on soot production. Results show that the modeling approach is able to capture the experimental trends for the soot volume fraction (SVF) with good quantitative agreement, especially in the soot ramp-up region.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗