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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine Learning based Correlation of the Mechanical Properties of Sub-sized and Standard-sized Specimens

Mechanical testing with sub-sized specimens is essential in the nuclear industry, offering the ability to conduct tests in confined spaces with lower irradiation and expediting material qualification. However, smaller specimens exhibit different material behavior across scales, a phenomenon known as the "specimen size effect". In this study, we compiled over 1,000 tensile testing records, covering 54 parameters such as material type, composition, manufacturing details, irradiation conditions, specimen dimensions, and tensile properties through a comprehensive literature review. We focus on correlating sub-sized and standard specimens’ tensile mechanical properties on SS316 alloy, which has the most extensive dataset available. We explore ML-based models and uncertainty quantification for tensile properties, analyze key factors influencing these properties, and compare the effectiveness of ML models with existing analytical methods in addressing the specimen size effect.

tensile properties↗
DOE OSTI2021

Assessing the Relative Climate Impact of Carbon Utilization for Concrete, Chemical, and Mineral Production

Estimates show that 6.2 gigatons of carbon dioxide (CO2) can be captured and utilized across three pathways, concrete, chemical, and minerals, by 2050. However, it is difficult to compare the climate benefit across these three carbon capture and utilization (CCU) pathways to determine the most effective use of captured CO2. The life cycle assessment methods to evaluate the climate benefit of CCU chemicals should additionally account for the change in material properties of concrete due to CO2 utilization. Furthermore, with most CO2 utilization technologies being in the early stages of research and development, the uncertainty and variability in process and inventory data present a significant challenge in evaluating the climate benefit. We present a stochastically determined climate return on investment (ROI) metric to rank and prioritize CO2 utilization across 20 concrete, chemical and mineral pathways based on the realized climate benefit. We show that two concrete pathways, which use CO2 during concrete mixing, and two chemical pathways, which produce formic acid through hydrogenation of CO2 and carbon monoxide through dry reforming of methane, generate the greatest climate ROI and are the only CCU pathways with a higher likelihood of generating a climate benefit than a climate burden.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Kilonovae Across the Nuclear Physics Landscape: The Impact of Nuclear Physics Uncertainties on r-process-powered Emission

Merging neutron stars produce “kilonovae”—electromagnetic transients powered by the decay of unstable nuclei synthesized via rapid neutron capture (the r-process) in material that is gravitationally unbound during inspiral and coalescence. Kilonova emission, if accurately interpreted, can be used to characterize the masses and compositions of merger-driven outflows, helping to resolve a long-standing debate about the origins of r-process material in the Universe. We explore how the uncertain properties of nuclei involved in the r-process complicate the inference of outflow properties from kilonova observations. Using r-process simulations, we show how nuclear physics uncertainties impact predictions of radioactive heating and element synthesis. For a set of models that span a large range in both predicted heating and final abundances, we carry out detailed numerical calculations of decay product thermalization and radiation transport in a kilonova ejecta with a fixed mass and density profile. The light curves associated with our models exhibit great diversity in their luminosities, with peak brightness varying by more than an order of magnitude. We also find variability in the shape of the kilonova light curves and their color, which in some cases runs counter to the expectation that increasing levels of lanthanide and/or actinide enrichment will be correlated with longer, dimmer, redder emission.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Unveiling Feedstock Variability: Insights into Corn Stover Conversion - Part I: Physicochemical Properties and Self-Degradation

Transforming agricultural waste into biofuels and bioproducts is crucial to advancing a low-carbon bioeconomy. However, the inherent variability in the composition and quality introduces uncertainties in the conversion efficiency and poses challenges in process development. Through integrating a high-throughput conversion system, material characterization techniques, and advanced data analysis tools, this study investigates the variability of corn stover and its subsequent impacts on carbohydrate conversion. The findings reveal that indoor storage substantially reduces the moisture and ash content and soil contamination, while other properties remain largely unchanged. Self-degradation due to microbial activity during storage decreases the carbohydrate content of corn stover but enhances glucose and xylose yields. A negative correlation is observed between sugar yields and lignin content across samples with varying ash and moisture content. The inhibitory effect of lignin diminishes in self-degraded samples likely due to the disrupted cell wall structure. Although self-degradation slightly increases cellulose crystallinity, no strong correlation was observed between the crystallinity and sugar yield. Hot water pretreatment under mild conditions effectively mitigates inherent variability, consistently improving the sugar yield from corn stover by up to 50%. By elucidating the feedstock variability and its impact on convertibility, these findings offer valuable insights into appropriate feedstock handling and management, highlighting potential strategies to address variability challenges.

09 BIOMASS FUELS↗
DOE OSTI2021

Optimal design of acoustic metamaterial cloaks under uncertainty

In this work, we consider the problem of optimal design of an acoustic cloak under uncertainty and develop scalable approximation and optimization methods to solve this problem. The design variable is taken as an infinite-dimensional spatially-varying field that represents the material property, while an additive infinite-dimensional random field represents the variability of the material property or the manufacturing error. Discretization of this optimal design problem results in high-dimensional design variables and uncertain parameters. To solve this problem, we develop a computational approach based on a Taylor approximation and an approximate Newton method for optimization, which is based on a Hessian derived at the mean of the random field. We show our approach is scalable with respect to the dimension of both the design variables and uncertain parameters, in the sense that the necessary number of acoustic wave propagations is essentially independent of these dimensions, for numerical experiments with up to one million design variables and half a million uncertain parameters. Additionally, we demonstrate that, using our computational approach, an optimal design of the acoustic cloak that is robust to material uncertainty is achieved in a tractable manner. The optimal design under uncertainty problem is posed and solved for the classical circular obstacle surrounded by a ring-shaped cloaking region, subjected to both a single-direction single-frequency incident wave and multiple-direction multiple-frequency incident waves. Finally, we apply the method to a deterministic large-scale optimal cloaking problem with complex geometry, to demonstrate that the approximate Newton method’s Hessian computation is viable for large, complex problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Exploiting Machine Learning in Multiscale Modelling of Materials

Recent developments in efficient machine learning algorithms have spurred significant interest in the materials community. The inherently complex and multiscale problems in Materials Science and Engineering pose a formidable challenge. The present scenario of machine learning research in Materials Science has a clear lacunae, where efficient algorithms are being developed as a separate endeavour, while such methods are being applied as ‘black-box’ models by others. The present article aims to discuss pertinent issues related to the development and application of machine learning algorithms for various aspects of multiscale materials modelling. The authors present an overview of machine learning of equivariant properties, machine learning-aided statistical mechanics, the incorporation of ab initio approaches in multiscale models of materials processing and application of machine learning in uncertainty quantification. In addition to the above, the applicability of Bayesian approach for multiscale modelling will be discussed. Critical issues related to the multiscale materials modelling are also discussed.

42 ENGINEERING↗
DOE OSTI2024

Machine Learning Design of Perovskite Catalytic Properties

Abstract Discovering new materials that efficiently catalyze the oxygen reduction and evolution reactions is critical for facilitating the widespread adoption of solid oxide fuel cell and electrolyzer (SOFC/SOEC) technologies. Here, machine learning (ML) models are developed to predict perovskite catalytic properties critical for SOFC/SOEC applications, including oxygen surface exchange, oxygen diffusivity, and area specific resistance (ASR). The models are based on trivial‐to‐calculate elemental features and are more accurate and dramatically faster than the best models based on ab initio‐derived features, potentially eliminating the need for ab initio calculations in descriptor‐based screening. The model of ASR enables temperature‐dependent predictions, has well calibrated uncertainty estimates and online accessibility. Use of temporal cross‐validation reveals the model to be effective at discovering new promising materials prior to their initial discovery, demonstrating the model can make meaningful predictions. Using the SHapley Additive ExPlanations (SHAP) approach, detailed discussion of different approaches of model featurization is provided for ML property prediction. Finally, the model is used to screen more than 19 million perovskites to develop a list of promising cheap, earth‐abundant, stable, and high performing materials, and find some top materials contain mixtures of less‐explored elements (e.g., K, Bi, Y, Ni, Cu) worth exploring in more detail.

25 ENERGY STORAGE↗
DOE OSTI2023

Ultrafast radiographic imaging and tracking: An overview of instruments, methods, data, and applications

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond transients or dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes are fundamental to modern technologies and applications, such as nuclear fusion energy, advanced manufacturing, communication, and green transportation, which often involve one mole or more atoms and elementary particles, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: (a.) Detectors such as high-speed complementary metal-oxide semiconductor (CMOS) cameras, hybrid pixelated array detectors integrated with Timepix4 and other application-specific integrated circuits (ASICs), and digital photon detectors; (b.) U-RadIT modalities such as dynamic phase contrast imaging, dynamic diffractive imaging, and four-dimensional (4D) particle tracking; (c.) U-RadIT data and algorithms such as neural networks and machine learning, and (d.) Applications in ultrafast dynamic material science using XFELs, synchrotrons and laser-driven sources. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Uncertainty Analyses of Molten Salt Property Measurements

Thermophysical and thermochemical property values of molten salts and associated uncertainties are used by reactor developers to evaluate reactor performance and model accident scenarios. These activities support fuel qualification objectives by demonstrating that fundamental safety functions (e.g., limit the release of radioactive materials, remove heat from the reactor, and control reactivity) can be performed effectively in a variety of conditions over the operating life of the reactor. Fuel qualification activities are still in the development phase due to a lack of reliable property data for salt mixtures being considered for use in molten salt reactors (MSR).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Titanium alloy response sensitivity to variations in spectral reconstructions of National Ignition Facility xenon line-emission x-ray sources

Thermomechanical shock experiments on the National Ignition Facility (NIF) aim to study high strain rate dynamic material response. In such experiments, the NIF laser is used to generate high fluence x-ray emission sources, which irradiate material samples of interest. Under sufficiently high x-ray energy deposition, thermomechanical impulses are generated in the materials. While it is known that the characteristics of x-ray generated impulses vary as a function of incident x-ray spectra, it remains unclear how spectral assumptions and uncertainties in NIF spectral reconstructions affect our interpretation of impulsive loading. Here, in this paper, we simulate the response of a standard titanium alloy baseline sample to synthetic analytically derived and measured NIF xenon line-emission x-ray sources with a radiation hydrodynamics code. We vary the source spectral characteristics based on different source reconstruction techniques to understand the resulting variation in baseline sample response and compare the simulated response with experimental results. We find that the response is highly sensitive to assumptions made about the spectral contents and that knowledge of spectral uncertainties bounds our understanding of the resulting material response. The results of this effort help to extend our ability to use baseline material samples to extract quantitative properties from x-ray experiments on the NIF.

Alloys↗
DOE OSTI2023

Ab initio study of tungsten-based alloys under fusion power-plant conditions

Tungsten (W) is considered a leading candidate for structural and functional materials in future fusion energy devices. The most attractive properties of tungsten for magnetic and inertial fusion energy reactors are its high melting point, high thermal conductivity, low sputtering yield, and low long-term disposal radioactive footprint. However, tungsten also presents a very low fracture toughness, primarily associated with intergranular failure and bulk plasticity, limiting its applications. In recent years, several families of tungsten-based alloys have been explored to overcome the aforementioned limitations of pure tungsten. These include tungsten-based high-entropy alloys (W-HEAs) and tungsten-based Self-passivating Metal Alloys with Reduced Thermo-oxidation or “SMART alloys” (W-SAs). Given their proximity to the plasma, it is crucial to understand how the exposure of these candidate plasma-facing materials (PFMs) to the neutron fluxes expected in fusion reactors impacts their material behavior over time. In this work, we present a computational approach that combines inventory codes and first-principles DFT electronic structure calculations to understand the behavior of transmuting tungsten-based PFMs. In particular, we calculate the changes in the chemical composition, production uncertainties, the elastic and ductility properties, and the density of states for five tungsten-based PFMs when exposed to EU-DEMO fusion first wall conditions for ten years.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Reference Materials for Laser and Xenon Flash Thermal Diffusivity Systems

This report presents the status of current standard reference materials for thermal diffusivity testing. In most thermal properties testing systems, it is common to use standard reference materials to show the reproducibility of the tests and certify the instrument is producing results within expected uncertainty range. The definition of reference materials or standards vary depending on the quality control protocols of the project. For thermal diffusivity, although there were standards available by the National Bureau of Standards (NBS) 40 years ago, there has been no recent NIST standard reference material (SRM). There are only a few sources that produce and provide traceable reference standards for thermal diffusivity. The certificate typically expires in 5 years and only applies to the specific lot of materials. Other reference materials are provided by the testing systems manufacturers. They are mostly well-known reference materials or materials used by the equipment manufacturers with historic data. However, most these reference materials no longer carry valid certificates. Various reference materials will be discussed along with the industry practice when only secondary standards are available.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Review of thermophysical property methods applied to fueled and un-fueled molten salts

Recent investigation into designs of molten salt reactors (MSR) requires accurate data of candidate salts. Review of the literature shows lacking thermophysical (TP) property data for coolants and fuel-bearing salts of Advanced High Temperature Reactors (AHTR). Measurement uncertainties of current data are even more deficient, and this affects ongoing research where MSR modeling and testing are being performed. Melting temperature, viscosity, density, specific heat capacity, and thermal and electrical conductivity measurements are often not arranged together or discussed in great detail, especially for fuel-bearing salts. Raw data from key findings spanning over 70 years for LiF, LiF-BeF 2 , LiF-NaF-KF, KCl-MgCl 2 , KF-ZrF 4 , and NaF-KF-MgF 2 have been organized and presented here. Brief description of methods used to obtain these results, in addition to the TP data of some of these salts as fuel carriers, has also been discussed. Thermal conductivity data are particularly weak due to older, high-error measurement techniques. More recent measurement systems to evaluate salt thermal conductivity show promise for obtaining consistent absolute values. This work concludes that measurement systems such as transient hot-wire method, forced Rayleigh scattering, and laser flash analysis provide the most promise to fill these data gaps and reduce current uncertainty in thermal conductivity measurements of both fueled and un-fueled molten salts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Digestion processes and elemental analysis of oxide and sulfide solid electrolytes

Detailed elemental analysis is essential for a successful development and optimization of material systems and synthesis methods. This is especially relevant for Li- and Na-containing compounds, found in state-of-the-art and next-generation battery systems. Their materials’ properties and thus the final device performance strongly depend on the crystal structure, the stoichiometry, and defect chemistry, e.g., influencing charge carrier concentration and activation energies for vacancy transport. However, a detailed quantitative analysis of light elements in a heavy matrix, featuring a broad range of solubilities and vapor pressures, is often difficult and associated with large uncertainties and thus neglected in favor of just reporting the stoichiometry as “weighed in.” Here, in this work, we report several approaches to digest and dissolve various oxide and sulfide-based materials, used in next-generation Li batteries, for elemental analysis via optical emission spectroscopy. These include the most common solid electrolytes Li-La-Ti–O, a perovskite material (LLTO), and Li-La-Zr-O which has garnet structure (LLZO). Additionally, a facile thermal digestion process is reported for a surrogate sulfide solid electrolyte (Na 2 S). The digestion procedures reported here are suitable for almost any laboratory environment and, when applied, will improve understanding of the synthesis-structure–property correlations needed to advanced batteries with all solid-state configurations.

Malkowski, Thomas F.↗
DOE OSTI2026

Uncertainty-Driven Rapid Thermodynamic Assessment of Nb-Ta-Zr System and Effects of C impurities (L25GF9298S): Annual Progress Report

An integrated computational materials engineering (ICME) method is in development for rapid thermodynamic experimental investigation and high-fidelity computational modeling of refractory multi-principal element alloys (RMPEAs). These ultra-high-temperature (UHT) alloys are of interest for structural applications in extreme environments, but deficiency of reliable data, especially melting temperatures, impedes the prediction of alloys with favorable properties. The method leverages UHT capabilities and computational expertise of LLNL’s Materials Science Division and the McCormack Lab’s UHT conical nozzle levitation (CNL) system to iteratively map the Nb-Ta-Zr phase space, with focus on the liquidus surface, through targeted experiments selected by quantifying uncertainty in the thermodynamic model fitting parameters. This method will reduce the time to map uncharted RMPEA phase space and thereby accelerate discovery and development of advanced materials for applications in extreme environments.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Efficacy of the Method of Four Coefficients to Determine Charge-Carrier Scattering

The investigation of the electronic properties of semiconductors from transport measurements (i.e., resistivity, Hall, and Seebeck coefficient measurements) is challenging due to the averaging of charge-carrier dynamics inherent in such measurements. Here, we investigate the incorporation of a fourth measurement of electronic transport, the Nernst coefficient, into the analysis, termed the method of four coefficients. This approach yields the Fermi level, effective mass, scattering exponent, and relaxation time prefactor. We begin with a review of the underlying mathematics and investigate the mapping between the four-dimensional material property and transport coefficient spaces. We then investigate how the traditional single-parabolic band method yields a single, potentially incorrect point on the solution subspace. This uncertainty can be resolved through Nernst coefficient measurements and we map the span of the ensuing subspace. We conclude with an investigation of how sensitive the analysis of transport coefficients is to experimental error for different sample types.

charge carriers↗
DOE OSTI2026

Deep Gaussian process-based cost-aware batch Bayesian optimization for complex materials design campaigns

The accelerating pace and expanding scope of materials discovery demand optimization frameworks that efficiently navigate vast design spaces with complex response surfaces while judiciously allocating limited evaluation resources. We present a cost-aware, batch Bayesian optimization scheme powered by deep Gaussian process (DGP) surrogates and a heterotopic querying strategy. Our DGP surrogate, formed by stacking GP layers, models complex hierarchical relationships among high-dimensional compositional features and captures correlations across multiple target properties, propagating uncertainty through successive layers. We integrate evaluation cost into an upper-confidence-bound acquisition extension, which, together with heterotopic querying, proposes small batches of candidates in parallel, balancing exploration of under-characterized regions with exploitation of high-mean, low-variance predictions across correlated properties. Applied to refractory high-entropy alloys for high-temperature applications, our framework converges to optimal formulations in fewer iterations with cost-aware queries than conventional GP-based BO, highlighting the value of deep, uncertainty-aware, cost-sensitive strategies in materials campaigns.

36 MATERIALS SCIENCE↗