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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A meshing framework for digital twins for extrusion based additive manufacturing

Additive manufacturing (AM) allows for manufacturing of complex three-dimensional geometries not typically realizable with standard manufacturing practices. The internal microstructure of AM components has a significant impact on mechanical, vibrational, and shock properties and permits richer design space when this is controllable. Due to complex interactions of internal geometry of an extrusion-based AM component, it is common practice to assume homogeneous behavior or to perform characterization testing on specific toolpath configurations. To avoid testing or material waste, it is necessary to develop a consistently accurate numerical simulation framework with relevant boundary value problems that can handle the complicated geometry of internal material microstructure present in AM components. Herein, a framework is proposed to directly create computational meshes suitable for finite element analysis (FEA) of the fine-scale features generated from extrusion-based AM tool paths to maintain a strong process–structure–property-performance linkage. This mesh can be manually or automatically analyzed using standard FEA simulations such as quasi-static preloading or modal analysis. The framework allows an in-silico assessment of a target AM geometry where fine-scale features greatly impact quantities of design interest such as in soft elastomeric lattices where toolpath infill can greatly influence the self-contact of a structure in compression, which we use as a motivating exemplar. This approach greatly reduces both time and resource waste present in traditional build and test design cycles for non-intuitive design spaces, and acts as a tool for use in the production of a key component of a digital twin, a mesh suitable for finite element analysis. In conclusion, it also further allows for the exploration of toolpath infill to optimize component properties beyond simple linear properties such as density and stiffness.

Additive manufacturing↗
DOE OSTI2026

Strain age cracking in a simulated heat affected zone of Inconel 740H laser-powder bed fusion components

Inconel® 740H components produced via laser-powder-bed fusion (L-PBF) additive manufacturing are ideal for supercritical CO2 primary heat exchangers. Arc welding is often needed, and subsequent post-weld heat treatment aging at 790–840 °C is required to improve strength via gamma prime (γ’) precipitation; however, strain age cracking (SAC) can occur in the heat affected zone (HAZ) during this process. This study uses stress relaxation testing at 800 °C on simulated HAZ specimens from vertically and horizontally built L-PBF IN740H to assess SAC susceptibility across heating rates of 40–3480 °C/h and weld-induced strains of 3–10 %. Vertical builds require greater mechanical energy input and exhibit longer times to fracture than horizontal builds, and time to fracture occurs sooner at slower heating rates. Creep voids were observed in γ’-denuded regions along grain boundaries, and cracking propagated along migrated grain boundaries and at interfaces between elongated secondary phases (γ’ or carbides) and the γ-matrix.

14 SOLAR ENERGY↗
DOE OSTI2026

AGR-5/6/7 Ceramography Report

The Advanced Gas Reactor (AGR) Fuel Development and Qualification Program was established in 2002 to conduct research and development on tri-structural isotropic (TRISO)-coated particle fuel for High Temperature Gas-cooled Reactors (HTGRs). All AGR irradiations have been completed: AGR-1 (Collin 2015), AGR-2 (Collin 2014), AGR-3/4 (Collin 2016) and AGR-5/6/7 (Pham 2021). The objectives of each program are presented in Figure 1.1. The purpose of the AGR-5/6/7 program was to provide a baseline fuel qualification data set to support licensing, deployment, and operation of HTGRs in the United States. To achieve these goals, the program includes fuel fabrication, irradiations of TRISO fuels and high-temperature materials, safety testing and post-irradiation examination (PIE), fuel performance modeling, and fission product (FP) transport (Sharp 2020). The AGR-5/6/7 work continues as a final irradiation program of the Advanced Reactor Technologies (ART) program, that began in February 2018 and ended in July 2020. The AGR-5/6/7 fuel compacts were irradiated for a total of approximately 360.9 effective full power days (EFPDs) (Stempien 2023), resulting in final burn-up values, on a per-compact basis, ranging from 5.66% to 15.26% fissions per initial heavy metal atom (FIMA), and fast fluence values ranging from 1.62×1025 n/m2 to 5.55×1025 n/m2 (E >0.18 MeV) (Stempien 2022)

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

A machine-learning-aided data recovery approach for predicting multi-material thermal behaviors in advanced test reactor capsules

Instrumented experiments conducted at test reactors are essential to the deployment of new advanced reactor systems. Designing new experiments and generating data on specific reactor conditions require significant investments in terms of both time and cost. Finite element analysis software can be used to create high-fidelity models of experiment environments in order to support the actual experiments, but computation time remains a concern in terms of applying outcomes to real-time usage of data (e.g., a digital twin [DT]). Here, the present research proposes a machine-learning (ML) aided approach to making temperature and displacement predictions based on the thickness of the outer gas gap on the experimental capsule used for in-pile demonstration of a novel new thermal conductivity probe in the Advanced Test Reactor (ATR). This capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. Gas gaps existed between the fuel and the rodlet, and between the inner and the outer capsule. The learning data pertained to an experimental capsule's radial distributions of temperature and displacement, as obtained based on Abaqus and the physical features. For the first step of ML sequence, the temperature was predicted using three positional parameters. Next, the displacement was predicted using seven additional parameters. Each physical feature was normalized in order to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement with the simulation results in all cases involving interpolation and extrapolation. Furthermore, data similarity enhancement increased the similarity between the training and the target data, thereby increasing the predictive accuracy of the ML models. In certain extrapolation cases involving limited original ML model accuracy, data similarity enhancement and data recovery was able to somewhat improve this accuracy.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Nm-scale electrical resistance imaging on CdTe by scanning spreading resistance microscopy

Local resistance imaging can provide information on nm-scale carrier distribution in semiconductor devices. Scanning spreading resistance microscopy (SSRM), an atomic force microscopy-based nm-scale resistance mapping technique, has been developed for carrier delineation in Si microdevices. We report on the development and validation of SSRM on CdTe materials, by testing on molecular beam epitaxy (MBE) grown CdTe films. The probe/CdTe contact resistance was suppressed sufficiently below sample's spreading resistance by pressing the probe into the sample with ∼mN contact force and applying a large sample/probe forward bias voltage (V s ), which was understood by analyzing current-voltage (I-V) involving a serially connected insulating top layer with underlying spreading resistance. The carrier concentration as deduced from the resistance measurement, using a single mobility value, is consistent with Hall measurement with a standard deviation of 14% based on a set of MBE films with carrier concentrations in the range of 10 15 –10 16 /cm 3 . The doping polarity was readily identified by flipping V s polarity, where the resistance with reverse V s is orders of magnitude larger than forward V s . While focusing on the SSRM technique validation, we also show an example on an As-doped Cd(Se,Te) polycrystalline thin film of a high-performance CdTe solar cell, which illustrates the local resistance nonuniformity with up to two orders of magnitude differences, indicating if local mobility is roughly constant, local carrier concentration can have significant nonuniformity.

14 SOLAR ENERGY↗
DOE OSTI2026

Imaging of a van der Waals spin-orbit torque system using spin ensembles in hBN

Recently, optically active spin defects embedded in two-dimensional (2D) van der Waals (vdW) crystals have emerged as a transformative quantum sensing platform to explore cutting-edge materials science. Taking advantage of excellent solid-state integrability, this new class of spin defects can be readily arranged in nanoscale proximity to target materials, showing great promise for realizing in-situ quantum sensing of microscopic spin and charge behaviors in vdW heterostructures. Here we report hexagonal boron nitride-based quantum imaging of field-free deterministic magnetic switching and electric current distributions in an all-vdW spin-orbit torque (SOT) system. By visualizing variations of nanoscale magnetic stray field profile of room-temperature 2D magnet Fe 3 GaTe 2 under different SOT conditions, we show how the magnetic switching evolves from deterministic to stochastic behavior due to the interplay between spin orientations, anisotropy and Joule heating. Micromagnetic simulations rationalize our results well, revealing the role of field-like SOT in inhibiting thermal fluctuation driven stochastic switching and chaotic multi-domain competition. This understanding, which is otherwise difficult to access by conventional transport measurements, offers valuable insights into material design, testing, and performance evaluation of next-generation vdW spintronic devices.

Imaging techniques↗
DOE OSTI2026

Evaluation of methods and improvement of predictions for specification properties of petroleum-based and alternative aviation fuels

To support our research and process modeling for liquid fuels, including blends, from petroleum and synthetic sources such as from biomass intermediates, we evaluated composition-based prediction methods and improved predictions for five key specification properties of petroleum-based and alternative aviation fuels, namely distillation temperatures (10 % distilled, t 10 , and final boiling point, t FBP ), density, flash point, net heat of combustion, and freezing point. The types of fuels included were petroleum-based jet fuels, jet-fuel surrogate mixtures, synthetic blending components obtained from different sources, and blends of Jet A with many synthetic blending components. Expanded datasets to update associated parameters allowed significant improvements for one of the prediction methods used in earlier work, namely the Modified Weighted Average method published initially by Shi et al. By considering the importance of lighter compounds for flash points and heavier compounds for freezing points, the revised Modified Weighted Average method was further improved. For liquid density, the revised Modified Weighted Average method gave the best overall results. The revised Modified Weighted Average method, the American Society for Testing and Materials D7215 method, and the D7215 method modified by another group gave comparable results for flash point, while the revised Modified Weighted Average and D3338 methods gave the best results for net heat of combustion. Freezing point was well predicted using the revised Modified Weighted Average method and showed the most significant improvements over current predictions. Distillation temperature t 10 was not well predicted, while t FBP was predicted with a mean absolute error comparable to experimental reproducibility.

09 BIOMASS FUELS↗
DOE OSTI2026

Perovskite design principles for efficient microwave dry reforming with noble metal free catalysts

Microwave absorbing catalysts have the potential to electrify high-temperature thermal reactions such as the dry reforming of methane process (DRM: CO 2 + CH 4 → 2CO + 2 H 2 ). However, microwave catalysts present unique challenges due to their dual requirements of maintaining microwave absorption in both oxidative and reductive environments and stability across a range of temperatures in inherently non-isothermal reactors. Here, catalyst candidates from the La 0.8 Sr 0.2 CoO 3 -La 0.8 Sr 0.2 NiO 3 -La 0.8 Sr 0.2 MnO 3 perovskite systems were screened (28 total) to identify promising microwave catalysts free of noble metals for dry reforming methane. The best performing candidates met two main criteria. First, they occurred at crystal phase boundaries, giving rise to a pseudocubic perovskite structure. The combined use of Goldschmidt tolerance factor and octahedral tolerance factors appeared to be suitable for predicting pseudocubic perovskites. Second, they provided a balance of reducible metal sites with an irreducible metal oxide support. The best performing catalyst was found to exsolve Ni-Co alloy particles as active sites for the DRM reaction which offered superior resistance to coking for excellent reforming efficiency and stability.

42 ENGINEERING↗
DOE OSTI2025

Multi-frequency progressive refinement for learned inverse scattering

Interpreting scattered acoustic and electromagnetic wave patterns is a computational task that enables remote imaging in a number of important applications, including medical imaging, geophysical exploration, sonar and radar detection, and nondestructive testing of materials. However, accurately and stably recovering an inhomogeneous medium from far-field scattered wave measurements is a computationally difficult problem, due to the nonlinear and non-local nature of the forward scattering process. We design a neural network, called Multi-Frequency Inverse Scattering Network (MFISNet), and a training method to approximate the inverse map from far-field scattered wave measurements at multiple frequencies. We consider three variants of MFISNet, with the strongest performing variant inspired by the recursive linearization method — a commonly used technique for stably inverting scattered wavefield data — that progressively refines the estimate with higher frequency content. MFISNet outperforms past methods in regimes with high-contrast, heterogeneous large objects, and inhomogeneous unknown backgrounds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Power performance and loads characterization of laboratory-scale cross-flow rotors fabricated using additive manufacturing

Tidal energy conversion is a relatively new application for additive manufacturing (AM), where the focus has been on fabricating axial-flow turbine blades. AM techniques add material precisely where it is needed, creating more complex shapes with less waste. Cross-flow rotor geometry presents an opportunity for AM to improve rotor performance by fabricating features that cannot be created economically via conventional manufacturing. The challenges associated with using AM in cross-flow design include water resistance and degradation over time while retaining a level of quality equivalent to conventionally machined parts. In this work, AM materials were tested by environmentally conditioning samples in a seawater tank for 5 months, followed by performance and phase-resolved load testing of laboratory-scale rotors in a hydraulic flume. We found that while metals like titanium and Inconel have excellent performance in marine environments, achieving the desired geometry and performance is difficult. Thermoplastics degraded in seawater but were easier to form into desired geometries and could exceed the performance of an aluminum control rotor. Warping and surface finish were significant detractors from AM rotor performance. These results suggest that the primary benefits of using AM for cross-flow rotors is to quickly fabricate and test unconventional rotor geometries.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Rapid Adaptation of Chemical Named Entity Recognition Using Few-Shot Learning and LLM Distillation

Named entity recognition (NER) has been widely used in chemical text mining for the automatic identification and extraction of chemical entities. However, existing chemical NER systems primarily focus on scenarios with abundant training data, requiring significant human effort on annotations. This poses challenges for applications in the chemical field, such as catalysis, where many advancements have traditionally relied on trial-and-error investigations and incremental adjustment of variables. This hinders catalysis science and technology progress in addressing emerging energy and environmental crises. In this work, we propose a few-shot NER model that can quickly adapt to extract new types of chemical entities by using only a limited number of annotated examples. Our model employs a metric-learning approach to transfer entity similarity knowledge from high-resource chemical domains (with abundant annotations) to enable effective entity recognition in low-resource specialized domains (limited annotation). We validate the effectiveness of our model on a few-shot chemical NER benchmark built based on six existing chemical NER data sets. Experiments show that the proposed few-shot NER model can achieve reasonable performance with only 5 examples per entity type and shows consistent improvement as the number of examples increases. Furthermore, we demonstrate how the proposed model can be trained with large language model (LLM) annotated data, opening a new pathway for rapid adaptation of NER systems. Furthermore, our approach leverages the knowledge broadness of large language models for chemistry while distilling this knowledge into a lightweight model suitable for efficient and in-house use.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Strip-based Scintillation Detector for Dual-readout High-granularity Calorimetry

New calorimeter technology is being developed for future collider experiments. We are developing a calorimeter that integrates two technologies, a high-granularity calorimeter and a dual-readout calorimeter, and has high time resolution at a picosecond level. The proposed calorimeter is a sampling calorimeter based on scintillation and Cherenkov detectors with high-granularity readout. The Cherenkov detector has a picosecond level time resolution. For the high-granularity scintillation detector, scintillator strips of 300mm × 30mm ×3mm-thick are aligned horizontally and vertically to realize an effective 30mm-square-cell segmentation. Prototype strips with different designs and scintillator materials were tested to compare the performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Spatial Mapping of Chiral-Induced Spin Selectivity in Chiral Perovskite via Spin-Schottky Junction

Chiral halide perovskite (c-HP) semiconductors exhibit on average a large chiral-induced spin selectivity (CISS) effect. Nevertheless, the microscopic details of CISS and its integration in opto-spintronic constructs remain nascent. Reliable reporting of CISS performance characteristics represents a significant challenge in providing the necessary design rules. We show a Kelvin probe force microscopy (KPFM) method that can quantitatively evaluate and spatially map the chirality-dependent surface contact potential difference resulting from the formation of a spin-Schottky junction. We revealed inhomogeneity in the CISS response, where low-CISS regions in the c-HP films reduce the overall macroscopic average, likely serving as a key factor in optimizing macroscopic performance. We also observed that although c-HP films made from higher precursor concentrations lead to thicker films and higher carrier concentrations with subsequent larger barrier heights in the Schottky junction, stronger spin relaxation due to non-ideal film quality reduces spin polarization.

14 SOLAR ENERGY↗
DOE OSTI2026

Nanoscale Observation and Control of Quasiparticle Induced Magnetic Noise in a Superconducting Resonator

Superconducting circuits are arguably taking a leading role in driving the ongoing quantum technological revolution. A detailed knowledge of the microscopic fluctuating electromagnetic properties plays an important role in advancing the circuitry design, testing, and material integration of cutting-edge superconducting quantum electronics. Here, in this work, we report scanning nitrogen-vacancy (NV) quantum sensing of local magnetic noise environment of an on-chip superconducting resonator. We find that quasiparticle-induced fluctuating magnetic fields can drive NV spin relaxation, which shows a peak value around the superconducting transition point of niobium at the thermal equilibrium state. External microwave driving at the resonator mode frequency significantly increases the quasiparticle density, leading to enhancement of magnetic noise. We further perform optically detected magnetic resonance measurements to demonstrate quasiparticle magnetic noise mediated off-resonant dipole coupling between the NV center and niobium resonator. Our Letter reports experimental observation of the Hebel-Slichter peak signature by an external sensor outside of a superconductor. The presented study also highlights the advantages of quantum sensors in investigating miniaturized superconducting devices, providing insights into their future performance improvements.

Li, Senlei [Georgia Institute of Technology]↗
DOE OSTI2025

ARC-DPA 1-MeV Neutron Fluence Equivalence Model for GaAs

Here, the legacy data used in the production of the American Society for Testing and Materials (ASTM) E722 standard for gallium arsenide (GaAs) were fit with the athermal recombination corrected-displacements per atom (ARC-DPA) model for the development of a novel 1-MeV neutron equivalent fluence metric. Improving on previous work, the coefficients for the ARC-DPA model were optimized using a conjugate gradient method, and the uncertainties in the observed damage in different benchmark neutron fields were sampled to quantify their contribution to the damage metric. The optimized parameters of −0.474 ± 0.03 for b arcdpa and 0.016 ± 0.002 for c arcdpa provided good agreement and indicated that the solution is not sensitive to the corresponding fluence uncertainties. Compared to the current 1-MeV neutron fluence equivalence standard for displacement damage in GaAs, our results indicate that for a thermal reactor neutron environment, displacement damage has been underestimated by around 25% and that the saturation value for the damage efficiency had been higher than previously modeled.

1-MeV equivalent fluence↗
DOE OSTI2025

Review of High‐Speed Digital Image Correlation: Advancements and Good Practices

This paper reviews the current state of the art in high‐speed (HS) and ultrahigh‐speed (UHS) digital image correlation (DIC) techniques, emphasizing their critical role in experimental research across various scientific domains. HS and UHS DIC have evolved significantly, driven by advancements in camera systems, image processing algorithms and experimental methodologies. These developments have opened new avenues for capturing and analysing dynamic events with unprecedented temporal and spatial detail, but not without introducing challenges such as optical distortions, motion blur and lighting issues that can affect measurement quality. This review advocates for standardized reporting practices in HS/UHS DIC methodologies to improve reproducibility and reliability across studies, drawing on guidelines from the International Digital Image Correlation Society (iDICs). Through a comprehensive analysis of over 150 articles, this review identifies key advancements in imaging technology and their application in six research domains: material characterization, test development, fracture mechanics, model validation, ballistic and explosive phenomena assessment and measurement uncertainties. Distinctions between two‐dimensional (2D) and stereo‐DIC applications are explored, offering insights into their practical implementation and trade‐offs. Good practices for HS/UHS DIC applications are proposed along with suggestions for future directions for this evolving field, highlighting the indispensable role of technological innovation in expanding the capabilities of optical metrology.

experimental mechanics↗
DOE OSTI2025

State of the Art in Thermal Catalytic Upgrading of Biomass and Biomass-Derived Intermediates

Biomass-derived energy sources represent a promising domestic route for fuel and chemical production, taking advantage of largely underutilized biological and waste resources. Heterogeneous catalysis plays a key role in these biomass conversion processes, as reflected by all American Society for Testing and Materials–approved pathways for producing sustainable aviation fuel proceeding through a catalytic step. This concise review seeks to establish the state of the art in thermal catalytic process development for various biomass-derived feedstocks and the current enabling capabilities that aid this development. Research needs are identified and described throughout the article, as further advancements in heterogeneous catalysis are required to improve the affordability and realize the full potential of biomass-derived products.

09 BIOMASS FUELS↗
DOE OSTI2024

Hydrogen transport in yttrium hydride under asymmetric heat

Metal hydrides are a promising moderator material for high temperature fission reactors. Yttrium hydride can be loaded to a high hydrogen density with relatively high hydrogen stability at temperatures up to 800°C. This makes yttrium hydride a potential moderator material for microreactors as foreseen in the fission surface power program. However, during the operation of such advanced reactors temperature gradients are expected which can change the local hydrogen density in the moderator. Hydrogen diffusion in metals is driven by a concentration gradient (Fick’s law) and thermal diffusion (Soret diffusion). Thermal diffusion is the transport of hydrogen, or other species, due to a temperature gradient. For example, hydrogen might migrate from the hot side of a sample to the cold side of a sample. Measuring Fickian diffusion is achieved through various permeation or absorption experiments, however measuring thermal diffusion is challenging and has rarely been performed. The Hydrogen Experimental Apparatus for Thermal Diffusion (HEATD) experiment is designed to induce thermal diffusion in samples and quench those samples so that the hydrogen distribution can be analyzed using hot vacuum extraction (HVE). One side of the sample was heated to a high temperature e.g., 800°C, while the other side of the sample is at a lower temperature. The sample was held under the applied temperature gradient for a given time until the anticipated hydrogen diffusion has occurred. The actual time depends depend on the sample composition and hydrogen concentration. The results from HVE showed that thermal diffusion took place in the specimen and the Soret coefficient was calculated.

36 MATERIALS SCIENCE↗