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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 289 records · Page 16

Toward first principles-based simulations of dense hydrogen

Accurate knowledge of the properties of hydrogen at high compression is crucial for astrophysics (e.g., planetary and stellar interiors, brown dwarfs, atmosphere of compact stars) and laboratory experiments, including inertial confinement fusion. There exists experimental data for the equation of state, conductivity, and Thomson scattering spectra. However, the analysis of the measurements at extreme pressures and temperatures typically involves additional model assumptions, which makes it difficult to assess the accuracy of the experimental data rigorously. On the other hand, theory and modeling have produced extensive collections of data. They originate from a very large variety of models and simulations including path integral Monte Carlo (PIMC) simulations, density functional theory (DFT), chemical models, machine-learned models, and combinations thereof. At the same time, each of these methods has fundamental limitations (fermion sign problem in PIMC, approximate exchange–correlation functionals of DFT, inconsistent interaction energy contributions in chemical models, etc.), so for some parameter ranges accurate predictions are difficult. Recently, a number of breakthroughs in first principles PIMC as well as in DFT simulations were achieved which are discussed in this review. Here we use these results to benchmark different simulation methods. We present an update of the hydrogen phase diagram at high pressures, the expected phase transitions, and thermodynamic properties including the equation of state and momentum distribution. Furthermore, we discuss available dynamic results for warm dense hydrogen, including the conductivity, dynamic structure factor, plasmon dispersion, imaginary-time structure, and density response functions. We conclude by outlining strategies to combine different simulations to achieve accurate theoretical predictions that are based on first principles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

H2@Scale - Validating an Electrolysis System with High Output Pressure: Cooperative Research and Development Final Report, CRADA Number CRD-18-00741

Electrolysis has been a commercially available product for a while and electrolyzers have been a proven capability to provide additional benefits (e.g. controllable load for grid services) in addition to production of hydrogen. The hydrogen output is typically compressed for storage and dispensing. Compression adds cost and decreases system reliability. Honda’s electrolyzer systems have been developed to include electrochemical compression to leverage the production system itself for at least partial compression. In this project, the team will evaluate Honda’s PEM based electrochemical compression system. The system is capable of compressing hydrogen up to 70 MPa electrochemically. Validation testing is the next step to accelerate this technology into the marketplace, as the validation will provide needed data under a variety of operation conditions and controls. These operating conditions and controls are based on over a decade of NLR research and development with low-temperature electrolysis. The validation testing will include preparing NLR’s site for third party evaluation, benchmark testing of Honda’s stack and system, and simulating operation connected to renewables or in a grid service profile. NLR’s Energy System Integration Lab will be the location for the electrolyzer validation research and integrated into the Hydrogen Infrastructure Test & Research Facility (HITRF). This will build into the existing retail style hydrogen fueling station for a fully integrated experimental setup.

08 HYDROGEN↗

Observation of a mixed close-packed structure in superionic water

The study of superionic (SI) water has been a highly active research area since its theoretical prediction. Despite significant experimental and computational efforts, its melting curve and the stability of different oxygen lattices remain debated, impacting our understanding of SI ice’s peculiar transport properties. Experimental results at lower pressures show disagreement, whereas data at higher pressures are scarce due to the extreme challenges of such experiments. In this work, we present ultrafast X-ray diffraction results of water compressed by multiple shocks to pressures up to ~ 180 GPa. At pressures exceeding 150 GPa and temperatures around 2500 K, our diffraction patterns challenge the pure FCC-SI phase model, providing experimental evidence of the mixed close-packed superionic phase predicted by advanced ab initio calculations. At lower pressures, we observe simultaneous signatures of BCC and FCC structures within a pressure-temperature range consistent with some static-compression experiments, helping to resolve contradictory results in literature. These insights offer new constraints on the stability domains of SI phases and reveal detailed structural features, such as stacking faults. Our results advance the structural understanding of high-pressure SI ice to a level approaching that of ice I polymorphs, with potential implications for water-rich interiors of giant planets.

Andriambariarijaona, Leon [Centre National de la R↗

Microstructural Coarsening Kinetics and Mechanical Property Changes in Long-Term Aged Sn–Pb–Sb Solder Joints

Tin-lead-antimony (50Sn–47Pb–3Sb wt.%) soldered assemblies were mechanically tested approximately 30 years after initial production and found to have solder joints of reduced strength. The microstructure of this solder alloy exhibits a ternary eutectic structure with Sn-rich, Pb-rich, and SnSb phases. Accelerated aging was performed to evaluate solder microstructural coarsening and associated strength of laboratory solder joints to correlate these properties to the “naturally aged” solder joints. Isothermal aging was conducted at room temperature, 55, 70, 100, and 135 °C and aging times that ranged from 0.1 to 365 days. The coarsening kinetics of the Pb-rich phase were determined through optical microscopy and image analysis methods established in previous studies on binary Sn–Pb solder. A kinetic equation was developed with time exponent n of 0.43 and activation energy of 24000 J/mol, suggesting grain boundary diffusion or other fast diffusion pathways controlling the microstructural evolution. Compression testing and Vickers microhardness showed significant strength loss within the first 20–30 days after soldering; then, the microstructure and mechanical properties changed more slowly over long periods of time. Further, by combining accelerated aging data and the microstructure-based kinetics, strength predictions were made that match well with the properties of the actual soldered assemblies naturally aged for 30 years. However, aging at the highest temperature of 135 °C produced anomalous behavior suggesting that extraneous aging mechanisms are active. Therefore, data obtained at this temperature or higher should not be used. Overall, the combined microstructural and mechanical property methods used in this study confirmed that the observed reduction in strength of ~ 30-year-old solder joints can be accounted for by the microstructural coarsening that takes place during long-term solid-state aging.

36 MATERIALS SCIENCE↗

High strain-rate strength response of single crystal tantalum through in-situ hole closure imaging experiments

The properties of crystalline materials often depend on directionality and operating conditions. Specifically, the strength of materials can depend anisotropically on crystal direction and the loading condition. To probe these effects, a preliminary series of high strain-rate (> 105/s) strength plate-impact hole closure experiments were performed on high purity single crystal Tantalum cubes. The orientation of the single crystals with respect to impact/loading were varied to provide data to inform crystal plasticity modeling efforts. The experiments consist of in-situ high-resolution X-ray radiographic imaging of the hole collapse under dynamic compression conditions to infer the material strength via its resistance to closure at increasing levels of plastic strain. The experiments are compared against hydrocode simulation predictions. Here, a comparison with simple elastic perfectly plastic strength model predictions is presented to elucidate the response of the different crystal orientations at high strain-rate and large plastic strains.

36 MATERIALS SCIENCE↗

Inverse design of cellular structures with the targeted nonlinear mechanical response

Advanced additive manufacturing capabilities have enabled a transformational ability to create sophisticated cellular structures using diverse materials. By altering the topology of the unit cell, the mechanical behavior, such as the stress-strain response during compression, can be modulated. Nevertheless, identifying a printable topology within an enormous design space that would precisely deliver the targeted nonlinear material response is challenging. We propose a data-driven generative framework based on a conditional variational autoencoder (cVAE) architecture that can inverse design the cellular structure based on the intended nonlinear stress-strain response. Trained on a dataset of structure-property pairs, the cVAE learns a compact and expressive latent space that enables efficient mapping from targets to feasible geometries. Two inference modes are explored: (1) decoder-only generation, which enables the exploration of diverse designs conditioned solely on the desired mechanical response, and (2) encoder-decoder generation, which further allows for the incorporation of desired topologies, ensuring the generated structure conforms to both mechanical properties and to desired-topology constraints. The results demonstrate that the model can generate structurally plausible and mechanically accurate designs, with the predicted stress-strain curves closely matching the targets. Even under joint conditioning, the model effectively balances geometric fidelity and functional performance.

36 MATERIALS SCIENCE↗

Efficiently predicting pressure-composition-temperature diagrams to discover low-stability metal hydrides

Quantitatively accurate computational predictions of metal hydride thermodynamics are challenging but critical for alloy performance optimization across a multitude of technological domains, including hydrogen storage, compression, purification, and getters. Recent machine learning approaches have demonstrated great success in this area, but can potentially suffer from several shortcomings since they rely on imbalanced experimental training data and can have poor out-of-distribution (ood) test performance. Here, in this study, we circumvent such pitfalls by developing a computationally efficient, first principles-based workflow for direct prediction of metal hydride phase equilibrium, i.e., the pressure-composition-temperature (PCT) diagram. We then demonstrate its utility on predicting low stability hydrides derived from compositionally complex C14 Laves phase AB2 alloys. Specifically, we computationally predict and then experimentally validate an AB 2 alloy series (z < 0.6 for Ti 2−z Zr z CrMnFeNi) with ideal hydriding thermodynamics for a two-stage metal hydride-based compressor for pressurizing boil off from liquefied hydrogen. Importantly, this study lays the groundwork for accurate and efficient discovery/optimization of ood, low-stability hydrides for which purely data-driven approaches lack sufficient accuracy.

08 HYDROGEN↗

Accuracy of Kohn–Sham density functional theory for warm- and hot-dense matter equation of state

We study the accuracy of Kohn–Sham density functional theory (DFT) for warm- and hot-dense matter (WDM and HDM). Specifically, considering a wide range of systems, we perform accurate ab initio molecular dynamics simulations with temperature-independent local/semilocal density functionals to determine the equations of state at compression ratios of 3x–7x and temperatures near 1 MK. We find very good agreement with path integral Monte Carlo benchmarks, while having significantly smaller error bars and smoother data, demonstrating the accuracy of DFT for the study of WDM and HDM at such conditions. In addition, using a Δ-machine learned force field scheme, we confirm that the DFT results are insensitive to the choice of exchange-correlation functional, whether local, semilocal, or nonlocal.

Suryanarayana, Phanish (ORCID:0000000151720049)↗

Pervasive symmetry-lowering nanoscale structural fluctuations in the cuprate La 2−𝑥 ⁢Sr 𝑥 ⁢Cu⁢O 4

The cuprate superconductors are among the most widely studied quantum materials, yet there remain fundamental open questions regarding their electronic properties and the role of structural degrees of freedom. Recent neutron and x-ray scattering measurements uncovered exponential scaling with temperature of the strength of orthorhombic fluctuations in the tetragonal phase of La 2−𝑥⁢ Sr 𝑥 ⁢Cu⁢O 4 and Tl 2 ⁢Ba 2 ⁢Cu⁢O 6+𝛿 , unusual behavior that closely resembles prior results for the emergence of superconducting fluctuations, and that points to a common origin rooted in inherent correlated structural inhomogeneity. Here, in this study, we use neutron and x-ray diffuse scattering to provide further insight into structural fluctuations in La 2−𝑥 ⁢Sr 𝑥 ⁢Cu⁢O 4 . We perform measurements up to temperatures that approach the melting point of the undoped parent compound, and we investigate the effects of in situ in-plane uniaxial elastic stress and plastic deformation. Our neutron scattering results for the parent compound La 2 ⁢Cu⁢O 4 reveal that short-range orthorhombic fluctuations persist to the maximum experimental temperature of nearly 1000 K, i.e., to a significant fraction of the crystallization temperature. At this temperature, the spatial characteristic length extracted from the momentum-space data is still about three lattice constants. The neutron scattering experiment enables quasistatic discrimination and reveals that the response is increasingly dynamic at higher temperatures. We also find that neither compressive elastic stress along the tetragonal [110] direction nor irreversible plastic deformation activating the ⟨100⟩⁢[001] slip system significantly alter the robust orthorhombic fluctuations. Overall, these results support the notion that these fluctuations couple to subtle, underlying inhomogeneity that underpins the cuprate phase diagram. Finally, in La 1.8⁢ Sr 0.2⁢ Cu⁢O 4 , we uncover low-energy structural fluctuations at a nominally forbidden reflection that are distinct from the orthorhombic distortions. While the origin of these fluctuations is not clear, they might be related to the presence of extended defects such as dislocations or stacking faults.

Spieker, R. J. [Univ. of Minnesota, Minneapolis, M↗

A Variational Autoencoder Model Toward Molecular Structure Representation Learning of Fuels

Here, in this work, a Variational Autoencoder (VAE)-based data-driven modeling framework is developed with the overarching goal of enabling fuel design. The VAE model is trained on a large dataset with several chemical species to learn a compressed latent space molecular representation. Chemical structure in the form of Simplified Molecular Input Line Entry System (SMILES) string is fed as input, encoded into the VAE latent space, and decoded back to the SMILES string using Long Short-Term Memory (LSTM) networks. Complexities of the VAE training loss function are thoroughly examined by varying the weightage (beta (𝜷) parameter) of the latent space regularization term, thereby assessing the balance between reconstruction accuracy and validity, and focusing on both accurate molecular structure reconstruction and latent space consistency. Two different strategies for 𝜷 variation are evaluated: linear annealing and cyclic annealing. In addition, the impact of total correlation adjustment and hierarchical priors is also studied with regard to the balance between reconstruction fidelity and latent space regularization, and potential issues such as posterior collapse, over-regularization, and poor disentanglement of latent variables. Overall, the best performance of the model is achieved with hierarchical priors and incrementally increasing 𝜷 from 0 to a threshold value of 0.25 over 75 epochs. The generative VAE model can be readily coupled with Quantitative Structure–Property Relationship (QSPR) analysis to develop an integrated end-to-end framework for fuel-property prediction and molecular design of novel promising fuels.

fuel design↗

Compressor is A Sensor (Final Report)

The performance of the heat pump system varies greatly depending on the refrigerant charge amount. Improving the refrigerant charge fault detection and diagnostics (FDD) method of vapor compression systems have the potential for increasing energy efficiency and reducing service cost. Previous studies to predict refrigerant charge amount are mostly empirical methods which require significant amount of experimental data for high accuracy. The primary goal of this research is to develop a charge fault detection method which requires only a few experimental data with high prediction accuracy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

AGC-4 Specimen Post-Irradiation Examination Data Interim Report

This interim report documents preliminary results of the post-irradiation examination material property testing from the fourth advanced graphite creep (AGC), AGC 4, capsule specimens. This is the fourth of a series of six irradiation test trains planned as part of the AGC experiment to fully characterize the neutron irradiation effects and radiation creep behavior of current nuclear graphite grades to moderate dose levels (=7 dpa). The AGC 4 capsule was irradiated in the Idaho National Laboratory Advanced Test Reactor at a nominal temperature of 800°C and to a peak dose of 8 dpa. Half of the AGC-4 specimens were subjected to compressive stresses to induce irradiation creep. Post-irradiation testing and measurement results are reported with the exception of thermal testing, which is still in progress, and irradiation mechanical strength testing. Additionally, some specimens initially deemed too hot to be examined in the ART Graphite laboratory may still be measured. The data reported includes specimen dimensions for both stressed and unstressed specimens to establish the irradiation creep rates, mass and dimensional data necessary to derive density, elastic constants (Young?s modulus, shear modulus, and Poisson?s ratio) from ultrasonic time of flight velocity measurements, Young?s modulus from the fundamental frequency of vibration, and electrical resistivity. A more complete evaluation of trends in the material property changes, as well as irradiation-induced creep due to the irradiation environment and applied load on the specimens, will be discussed later in AGC 4 post-irradiation examination analysis reports.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

AGC-4 Specimen Post-Irradiation Examination Data Interim Report

This interim report documents preliminary results of the post-irradiation examination material property testing from the fourth advanced graphite creep (AGC), AGC 4, capsule specimens. This is the fourth of a series of six irradiation test trains planned as part of the AGC experiment to fully characterize the neutron irradiation effects and radiation creep behavior of current nuclear graphite grades to moderate dose levels (=7 dpa). The AGC 4 capsule was irradiated in the Idaho National Laboratory Advanced Test Reactor at a nominal temperature of 800°C and to a peak dose of 8 dpa. Half of the AGC-4 specimens were subjected to compressive stresses to induce irradiation creep. Post-irradiation testing and measurement results are reported with the exception of thermal testing, which is still in progress, and irradiation mechanical strength testing. Additionally, some specimens initially deemed too hot to be examined in the ART Graphite laboratory may still be measured. The data reported includes specimen dimensions for both stressed and unstressed specimens to establish the irradiation creep rates, mass and dimensional data necessary to derive density, elastic constants (Young?s modulus, shear modulus, and Poisson?s ratio) from ultrasonic time of flight velocity measurements, Young?s modulus from the fundamental frequency of vibration, and electrical resistivity. A more complete evaluation of trends in the material property changes, as well as irradiation-induced creep due to the irradiation environment and applied load on the specimens, will be discussed later in AGC 4 post-irradiation examination analysis reports.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Cosmology with second- and third-order shear statistics for the Dark Energy Survey: Methods and simulated analysis

We present a new pipeline designed for the robust inference of cosmological parameters using both second- and third-order shear statistics. We build a theoretical model for rapid evaluation of three-point correlations using our fastnc code and integrate it into the cosmosis framework. We measure the two-point functions 𝜉 ± and the full configuration-dependent three-point shear correlation functions across all auto- and cross-redshift bins. We compress the three-point functions into the mass aperture statistic ⟨ℳ$^{3}_{ap}$⟩ for a set of 796 simulated shear maps designed to model the Dark Energy Survey Year 3 data. We estimate from it the full covariance matrix and model the effects of intrinsic alignments, shear calibration biases and photometric redshift uncertainties. We apply scale cuts to minimize the contamination from the baryonic signal as modeled through hydrodynamical simulations. We find a significant improvement of 83% on the figure of merit in the Ω m − 𝑆 8 plane when we add the ⟨ℳ$^{3}_{ap}$⟩ data to 𝜉 ± . Here, we present our findings for all relevant cosmological and systematic uncertainty parameters and discuss the complementarity of third-order and second-order statistics.

79 ASTRONOMY AND ASTROPHYSICS↗

Chemical decomposition in shock-compressed 1,1-diamino-2,2-dinitroethene (FOX-7) single crystals: Time-resolved Raman spectroscopy

Understanding the evolution of chemical decomposition—at the molecular level—in shock-compressed insensitive high explosive (IHE) single crystals is an important need. Toward this goal, time-resolved Raman spectra were measured in 1,1-diamino-2,2-dinitroethene (FOX-7) single crystals shock compressed to stresses above 20 GPa. At 22 GPa and higher stresses, the Raman peak intensities were significantly reduced, showing the onset and extent of chemical changes. At 33 GPa, no Raman peaks were observed, suggesting complete decomposition of FOX-7. The 22 GPa onset from Raman data is in marked contrast to the much higher (32 GPa) onset determined previously from wave profile measurements—showing the significantly greater sensitivity of Raman measurements for gaining insight into shock-induced molecular-level changes in IHE single crystals. The close match between the complete loss of Raman peaks at 33 GPa and the 32 GPa reaction threshold determined from continuum data makes a good case that the two results are related and they reflect significant energy release due to decomposition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

On the accuracy of compressibility transformations

This study highlights the importance of satisfying the eddy viscosity equivalence below the logarithmic layer, to deriving accurate compressibility transformations. First, we analyze the ability of known transformations to satisfy the eddy viscosity equivalence and show that the accuracy of these transformations is strongly dependent on this ability. Second, in a step-by-step manner, we devise new transformations that satisfy this hypothesis. An approach based on curve fitting of the incompressible Direct Numerical Simulation data for eddy viscosity profiles below the logarithmic layer provides an extremely accurate transformation, which motivates self-contained methods, making use of mixing length formulas in the inner region. It is shown that the accuracy of existing transformations can be significantly improved by applying these ideas, below the logarithmic layer. Motivated by the effectiveness of the formulations derived from eddy viscosity equivalence, we introduce a new integral transformation based on Reynolds number equivalence between compressible and incompressible flows. This approach is based on defining a new compressible velocity scale, which affects the accuracy of transformations. Several choices for the velocity scale are tested, and in each attempt, it is shown that the eddy viscosity equivalence plays a very important role for the accuracy of compressibility transformations.

42 ENGINEERING↗

Scaling kinetic Monte-Carlo simulations of grain growth with combined convolutional and graph neural networks

Graph neural networks (GNN) have emerged as a promising machine learning method for microstructure simulations such as grain growth. However, accurate modeling of realistic grain boundary networks requires large simulation cells, which GNN has difficulty scaling up to. To alleviate the computational costs and memory footprint of GNN, we suggest a hybrid architecture combining a convolutional neural network (CNN) based bijective autoencoder to compress the spatial dimensions, and a GNN that evolves the microstructure in the latent space of reduced spatial sizes. Our results demonstrate that the new design significantly reduces computational costs with using fewer message passing layer (from 12 down to 3) compared with GNN alone. The reduction in computational cost becomes more pronounced as the spatial size increases, indicating strong computational scalability. For the largest mesh evaluated (160 3 ), our method reduces memory usage and runtime in inference by 117× and 115×, respectively, compared with GNN-only baseline. More importantly, it shows higher accuracy and stronger spatiotemporal capability than the GNN-only baseline, especially in long-term testing. Such combination of scalability and accuracy is essential for simulating realistic material microstructures over extended time scales. The improvements can be attributed to the bijective autoencoder’s ability to compress information losslessly from spatial domain into a high dimensional feature space, thereby producing more expressive latent features for the GNN to learn from, while also contributing its own spatiotemporal modeling capability. Training data are generated from stochastic grain growth simulations, providing realistic variability for learning robust microstructure evolution. Comprehensive system validation confirms that the model is accurate, robust, and scalable.

36 MATERIALS SCIENCE↗

Nuclear quantum effects in molecular liquids across chemical space

Abstract Nuclear quantum effects (NQEs) influence many physical and chemical phenomena, particularly those involving light atoms or occurring at low temperatures. However, their impact has been carefully quantified in few systems-like water-and is rarely considered more broadly. Here we use path-integral molecular dynamics to systematically investigate NQEs on thermophysical properties of 92 organic liquids at ambient conditions. Depending on chemical constitution, we find substantial impact across thermal expansivity, compressibility, dielectric constant, enthalpy of vaporization, and notably molar volume, which shows consistent, positive quantum-classical differences up to 5%; similar, less pronounced trends manifest as isotope effects from deuteration. Using data-driven analysis, we identify three features-molar mass, classical hydrogen density, and classical thermal expansivity-that accurately predict NQEs and facilitate understanding of how characteristics like branching and heteroatom content influence behavior. This work highlights the broad relevance of NQEs in molecular liquids, while also providing a conceptual and practical framework to anticipate their impact.

Science & Technology - Other Topics↗