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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 OSTI2021

CrI 3 revisited with a many-body ab initio theoretical approach

CrI 3 has recently been shown to exhibit low-dimensional, long-range magnetic ordering from few layers to single layers of CrI 3 . The properties of CrI 3 bulk and few layered systems are uniquely defined by a combination of short-range intralayer and long-range interlayer interactions; including strong correlations, exchange and spin orbit coupling. Unfortunately, both the long-range van der Waals interactions, which are driven by dynamic, many-body electronic correlations and the competing strong intralayer correlations, present a formidable challenging for the local or semi local mean-field approximations employed in workhorse electronic structure approaches like density functional theory. In this work, we employ a sophisticated many-body approach that can simultaneously describe long- and short-range correlations. Herein, we establish that the fixed-node diffusion Monte Carlo (FNDMC) method reproduces the experimental interlayer separation distance of bulk CrI 3 for the high-temperature, monoclinic phase, with a reliable prediction of the interlayer binding energy. We subsequently employed, the FNDMC results to benchmark the accuracy of several density functional theory exchange-correlation approximations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Electronic effects in radiation-induced collision cascades in nickel

The accurate treatment of electronic effects in multi-million-atom simulations of radiation-induced collision cascades is crucial for reliable predictions of primary radiation damage. In this work, we explore the fidelity of a recently developed two-temperature molecular dynamics model implementing an electron density-dependent coupling of electronic and atomic subsystems for cascade simulations in nickel. We show that the parameter-free model realistically captures the instantaneous energy losses during all stages of the highly nonequilibrium cascade process. Our simulations predict two distinct coupling regimes, corresponding to the rapid energy losses through electronic stopping in the early stages of the cascade and to the slow equilibration through the electron-phonon coupling mechanism in the later stages, without the use of separate models or coupling terms. The intermediate stage of the cascade dynamics displays a complex energy transfer between the subsystems, which cannot be interpreted by comparison to either electronic stopping or electron-phonon coupling theories. We therefore compare the predicted atomic mixing, which is sensitive to the energy losses during the intermediate cascade stage, with experimental ion beam mixing measurements. We find good agreement with the experiments, validating the coupling model for the intermediate stage of the cascade. Predictions of final defect numbers and cluster sizes are found in line with the results from conventional electronic stopping-based methods, while significantly reducing the theoretical uncertainty in the outcomes of conventional models stemming from arbitrary choices of thresholds for different coupling terms. Our results represent a notable improvement in cascade damage predictions in nickel, providing validation of the electron density-dependent coupling model for radiation damage simulations in general. The results lead us to propose an interpretation of the electronic energy losses in the intermediate regime of velocities, where we find an effectively nonlinear dissipation.

Crystal defects↗
DOE OSTI2025

Bayesian model mixing with multireference energy density functional

Reliably predicting nuclear properties across the entire chart of isotopes is important for applications ranging from nuclear astrophysics to superheavy science to nuclear technology. To this day, however, all the theoretical models that can scale at the level of the chart of isotopes remain semiphenomenological. Because they are fitted locally, their predictive power can vary significantly; different versions of the same theory provide different predictions. Bayesian model mixing takes advantage of such imperfect models to build a local mixture of a set of models to make improved predictions. Earlier attempts to use Bayesian model mixing for mass table calculations relied on models treated at single-reference energy density functional level, which fail to capture some of the correlations caused by configuration mixing or the restoration of broken symmetries. In this study we have applied Bayesian model mixing techniques within a multireference energy density functional (MR-EDF) framework. We considered predictions of two-particle separation energies from particle number projection or angular momentum projection with four different energy density functionals—a total of eight different MR-EDF models. We used a hierarchical Bayesian stacking framework with a Dirichlet prior distribution over weights together with an inverse log-ratio transform to enable positive correlations between different models. We found that Bayesian model mixing provides significantly improved predictions compared to the participating models. Published by the American Physical Society 2025

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Microscopic optical potentials from a Green's function approach

Optical potentials are a standard tool in the study of nuclear reactions, as they describe the interaction between a target nucleus and a projectile. The use of phenomenological optical potentials built using experimental data on stable isotopes is widespread. Although successful in their dedicated domain, it is unclear whether these phenomenological potentials can provide reliable predictions for unstable isotopes. To address this problem, optical potentials based on microscopic nuclear structure input calculations prove to be crucial and are an important current line of research. In this work we present an explicit implementation of the Feshbach formalism for the systematic derivation of optical potentials using input from nuclear structure models. Numerical tools for the derivation of Green's functions associated with nonlocal potentials are presented. In conclusion, the new optical potential, based on the valence shell model, is applied to the calculations of 𝑛 + 24 Mg elastic scattering and yields a close agreement with the experimental data.

Direct reactions↗
DOE OSTI2026

Pixel-Resolved Long-Context Learning for Turbulence at Exascale: Resolving Small-scale Eddies Toward the Viscous Limit

Turbulence plays a crucial role in multiphysics applications, including aerodynamics, fusion, and combustion. Accurately capturing turbulence's multiscale characteristics is essential for reliable predictions of multiphysics interactions, but remains a grand challenge even for exascale supercomputers and advanced deep learning models. The extreme-resolution data required to represent turbulence, ranging from billions to trillions of grid points, pose prohibitive computational costs for models based on architectures like vision transformers. To address this challenge, we introduce a multiscale hierarchical Turbulence Transformer that reduces sequence length from billions to a few millions and a novel RingX sequence parallelism approach that enables scalable long-context learning. We perform scaling and science runs on the Frontier supercomputer. Our approach demonstrates excellent performance up to 1.1 EFLOPS on 32,768 AMD GPUs, with a scaling efficiency of 94\%. To our knowledge, this is the first AI model for turbulence that can capture small-scale eddies down to the dissipative range in three-dimensional turbulence at high Reynolds numbers.

Yin, Junqi [ORNL] (ORCID:0000000338435520)↗
DOE OSTI2022

Explainable Bayesian Neural Network for Probabilistic Transient Stability Analysis Considering Wind Energy

While several data-driven models have been developed for transient stability assessment, how to consider the uncertainties from load and renewable generations and provide interpretation of data-driven assessment results are still open. This paper proposes an explainable Bayesian Neural Network (BNN) for probabilistic transient stability assessment (TSA). By extracting the uncertainties from loads and wind farms, the BNN model can make a reliable prediction and quantify the prediction uncertainties. We also develop the Gradient Shap algorithm to make the global and local explanations for the probabilistic TSA model, a significant advantage over existing black-box data-driven methods. Numerical results on the modified IEEE 39-bus system show that the proposed method outperforms the existing methods in terms of prediction accuracy and uncertainty quantification capabilities. The explainability of the proposed method allows system operators to design preventive controls for enhancing system stability.

Bayesian Neural Network↗
DOE OSTI2024

HAPPA: A Modular Platform for HPC Application Resilience Analysis with LLMs Embedded

High-performance computing (HPC) systems are increasingly vulnerable to soft errors, which pose significant challenges in maintaining computational accuracy and reliability. Predicting the resilience of HPC applications to these errors is crucial for robust code protection and detailed resilience analysis. In this study, we present HAppA, a modular platform designed for HPC Application Resilience Analysis. Embedding Large Language Models (LLMs), HAppA addresses understanding the context information of long code sequences typical in HPC applications. HAppA implements a novel code representation module that chunks the code into fixed-size segments and aggregates the embeddings of these segments. Three aggregation methods have been explored: MeanPooling, MaxPooling, and LSTM-based techniques. We built a DAtaset for REsilience analysis using Fault Injection (FI), named DARE. Using our DARE dataset, HAppA is trained for regression prediction tasks. Our evaluation results demonstrate the predictive accuracy of HAppA compared to other models, particularly noting that the LSTM-based aggregation method -- HAppA-LSTM -- achieves a mean squared error (MSE) of 0.078 for SDC prediction, surpassing the existing state-of-the-art PARIS model, which recorded an MSE of 0.1172. Additionally, HAppA with the KeyBERT model extracts a list of keywords representing the source code. A comprehensive importance analysis of these keywords further elucidates the code patterns contributing to the error rate. These findings highlight the effectiveness of HAppA in analyzing the resilience of HPC applications and establish a new benchmark for predictive accuracy in resilience.

Jiang, Hailong [Kent State University]↗
DOE OSTI2022

Module-Level Thermal Interface Material Degradation in HALT

Abstract In this study, thermal interface material (TIM) degradation is driven through highly accelerated life test (HALT) using temperature cycling with a prescribed vibrational acceleration for two commercially available materials having thermal conductivities of 6.0 and 8.5 W/m K. HALT specimens were prepared by applying TIM through a 4-mil stencil over AlSiC baseplates in the shape of those used in Wolfspeed CAS325M12HM2 power electronics modules. Baseplates were mounted onto aluminum carrier blocks with embedded thermocouples to characterize the thermal resistance across the baseplate and TIM layer. Thermal dissipation into the top of the baseplates was provided by a custom heating block, which mimics the size and placement of the die junctions in CAS325 modules, applying power loads of 200, 300, and 400 W. After initial characterization, samples were transferred to the HALT chamber with one set of samples exposed to temperature cycling only (TCO) and the other temperature cycling and vibration (TCV). Both sample sets were cycled between temperature extremes of −40 °C and 180 °C with vibrations applied at a peak acceleration of 3.21 Grms. After hundreds of cycles, samples were reevaluated to assess changes in thermal resistance to provide an accelerated measure of TIM degradation. This allows for reliability prediction of useful lifetime (illustrated in a solar inverter case study herein), as well as to provide a basis for developing an accelerated testing method to related temperature cycling to faster methods of degradation. Such techniques provide a means to develop maintenance schedules for power modules for ensuring sufficient thermal performance over the operating lifetime.

Engineering↗
DOE OSTI2023

Burst Pressure Solutions of Thin and Thick-Walled Cylindrical Vessels

Pressure vessels (PVs) are widely used in the energy industry. Accurate burst pressure is critical to structural design and safe operation for both thin and thick-walled PVs. The traditional strength theories utilized a single-parameter material property, such as the yield stress or the ultimate tensile stress (UTS) to develop failure models for determining the yield or ultimate pressure carrying capacity in the PV design. The UTS-based Barlow formula is a typical burst pressure model developed from the Tresca strength theory that provides the basis for developing regulation rules and failure models for different industry design codes, such as ASME BPVC, ASME B31.3, and ASME B31G, among others. In order to reduce the conservatism of the Tresca strength model, ASME BPVC recently adapted failure models developed from the von Mises strength theory for the PV design and analysis. It has been commonly accepted that the burst pressure of pipelines depends on the UTS and strain hardening exponent, n, of the pipeline steel. An average shear stress yield theory was thus developed, and the Zhu-Leis solution of burst pressure was obtained as a function of UTS and n for thin-walled line pipes. Experiments showed that the Zhu-Leis solution provides an accurate, reliable prediction of burst pressure for defect-free thin-walled pipes. In order to extend the Zhu–Leis solution to thick-walled cylindrical PVs, this paper defined three new flow stresses, modified the traditional strength theories, and obtained three new burst pressure solutions that are valid for both thin and thick-walled cylindrical vessels. The proposed flow stresses are able to describe the tensile strength and the plastic flow response of PVs for a strain hardening steel. The associated strength theories were then developed in terms of the Tresca, von Mises, and Zhu-Leis yield criteria. From these new strength theories, three burst pressure solutions were obtained for thick-walled cylinders, where the von Mises solution is an upper bound prediction, the Tresca solution is a lower bound prediction, and the Zhu-Leis solution is an intermediate prediction of burst pressure for thick-walled cylinders. Lastly, the proposed burst pressure solutions were evaluated and validated by two large datasets of full-scale burst tests for thick-walled tubes and for thin-walled pipes.

42 ENGINEERING↗
DOE OSTI2025

Experimental Validation of a High-Temperature Test Facility for Future Additive Manufactured Supercritical Carbon Dioxide Turbine Testing

For next-generation power plants to achieve high cycle efficiencies consistent with the Department of Energy's 65% efficiency target, turbomachinery capable of operating within high-temperature power cycles must be demonstrated. Pairing additively manufactured superalloy turbines with the supercritical carbon dioxide (sCO 2 ) power cycle could enable turbine inlet temperatures approaching 1300 °C while providing flexibility in turbine cooling strategies. Development of test facilities to characterize and validate such systems is crucial. In this study, a turbine test facility capable of achieving inlet conditions of 800 °C, 11 MPa, and 0.43 kg/s while accommodating complex auxiliary cooling flow requirements has been designed and constructed to support future testing of a Haynes 282 additively manufactured 30 kW turbine-generator system with advanced cooling channels. This facility enables characterization of aerodynamic performance, leakage, and windage losses. Details of the facility's construction and operation are presented, along with experimental validation tests using an orifice as an expansion device in place of the turbine. These tests confirm that the facility can reach the required conditions, distinguish regions of achievable steady and pseudo-steady conditions, and identify the heater power required for each point in the upcoming turbine campaign. The campaign confirmed that ISO 5167-2 can reliably predict orifice mass flow rates in extreme supercritical carbon dioxide conditions with deviations of 0.5–7.5%.

42 ENGINEERING↗
DOE OSTI2021

A Theoretical Model for Computing Freezing Point Depression of Lithium-Ion Battery Electrolytes

Reliable prediction of freezing point depression in liquid electrolytes will accelerate the development of improved Li-ion batteries which can operate in low temperature environments. In this work we establish a computational methodology to calculate activity coefficients and liquidus lines for battery-relevant liquid electrolytes. Electronic structure methods are used in conjuction with classical molecular dynamics simulations and theoretical expressions for Born solvation energy, ion-atmosphere effects from Debye-Hückel theory and solvent entropic effects. The framework uses no a priori knowledge beyond neat solvent properties and the concentration of salt. LiPF 6 in propylene carbonate (PC), LiPF 6 in dimethyl carbonate (DMC) and LiClO 4 in DMC are investigated up to 1 molal with accuracy better than 3 °C when compared to experimental freezing point measurements. Finally, we find that the difference in freezing point depression between the propylene carbonate-based electrolyte and the dimethyl carbonate electrolytes originates from the difference in the solvent dielectric constant.

25 ENERGY STORAGE↗
DOE OSTI2020

Neurotransmitter networks in mouse prefrontal cortex are reconfigured by isoflurane anesthesia

This study quantified eight, small molecule neurotransmitters collected simultaneously from prefrontal cortex of C57BL/6J mouse (n=23) during wakefulness and during isoflurane anesthesia (1.3%). Using isoflurane anesthesia as an independent variable enabled evaluation of the hypothesis that isoflurane anesthesia differentially alters concentrations of multiple neurotransmitters and their interactions. Machine learning was applied to reveal higher order interactions among neurotransmitters. Using a between-subjects design, microdialysis was performed during wakefulness and during anesthesia. Concentrations (nM) of acetylcholine, adenosine, dopamine, GABA, glutamate, histamine, norepinephrine, and serotonin in the dialysis samples are reported (mean ± SD). Relative to wakefulness, acetylcholine concentration was lower during isoflurane anesthesia (1.254 ± 1.118 versus 0.401 ± 0.134, P=0.009), and concentrations of adenosine (29.456 ± 29.756 versus 101.321 ± 38.603, P<0.001), dopamine (0.0578 ± 0.0384 versus 0.113 ± 0.084, P=0.036), and norepinephrine (0.126 ± 0.080 versus 0.219 ± 0.066, P=0.010) were higher during anesthesia. Isoflurane reconfigured neurotransmitter interactions in prefrontal cortex, and the state of isoflurane anesthesia was reliably predicted by prefrontal cortex concentrations of adenosine, norepinephrine, and acetylcholine. A novel finding to emerge from machine learning analyses is that neurotransmitter concentration profiles in mouse prefrontal cortex undergo functional reconfiguration during isoflurane anesthesia. Adenosine, norepinephrine, and acetylcholine showed high feature importance, supporting the interpretation that interactions among these three transmitters may play a key role in modulating levels of cortical and behavioral arousal.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Exploring 2D X-ray diffraction phase fraction analysis with convolutional neural networks: Insights from kinematic-diffraction simulations

Abstract Deep-learning models are effective for analyzing the complex information in 2D X-ray diffraction (XRD) patterns. Accurately collecting parameters of the material sample is crucial during model training, significantly impacting model performance. In this study, we employ a kinematic-diffraction simulator to generate simulated 2D XRD patterns for Ti–6Al–4V alloy, allowing precise control of sample parameters. These simulated patterns are used to train convolutional neural networks, predicting $$\upbeta$$ β -phase volume fractions. The training data set consists exclusively of 2D XRD patterns with pure $$\upalpha$$ α - or pure $$\upbeta$$ β -phase, while the testing set incorporates patterns with intermediate phase volume fraction. In particular, we investigate how the architectures of the model influence prediction reliability and computational performance. Experimental results reveal that, with appropriate training, the convolutional neural network accurately detects intermediate phase volume fractions even trained with only pure-phase patterns, achieving a mean square error accuracy of $$9.4 \times 10^{-4}$$ 9.4 × 10 - 4 . Graphical abstract

Yue, Weiqi↗
DOE OSTI2025

Distribution of nickel(II) ions adsorbed at the muscovite mica (001)-water interface determined by in-situ resonant anomalous X-ray reflectivity

Mineral-water interfaces mediate adsorption, ion exchange, and secondary mineral formation that control element mobility in natural and engineered systems. Reliable prediction and control of these processes require a fundamental understanding of the interfacial structure that links adsorbed ion speciation to macroscopic sorption capacity and strength. Here, we determine atomic-scale changes in hydration and distribution of Ni(II) at the muscovite mica (001)-water interface using in situ high-resolution X-ray reflectivity (XR) and resonant anomalous X-ray reflectivity (RAXR) at 1 mM NiCl2 and pH 5.7. XR reveals reorganization of the primary hydration structure relative to that in deionized water: the water layer adsorbed in the cavity sites at a height of ~1.3 Å disappears, while distinct solution layers emerge at ~2.3, ~4.1, and ~5.6 Å above the basal oxygen plane. RAXR resolves three interfacial Ni(II) species: a dominant outer-sphere complex at 3.65 Å (~80% of the total coverage), a minor inner-sphere complex at 0.75 Å, and a low-coverage, more distant outer-sphere species at 5.63 Å. These three adsorbed Ni(II) species account for a total Ni(II) coverage of 0.54 ± 0.02 ion per unit cell area that compensates for the surface charge. These results highlight the role of interfacial hydration in controlling the speciation and stability of adsorbate cations on the negatively charged mica surface, providing quantitative insight into predicting the geochemical behavior of divalent metal cations in the aqueous environments.

Lee, Sang Soo↗
DOE OSTI2023

Modeling-Based Assessment of Deep Seismic Potential Induced by Geologic Carbon Storage

Induced seismicity is an inherent risk associated with geologic carbon storage (GCS) in deep rock formations that could contain undetected faults prone to failure. Modeling-based risk assessment has been implemented to quantify the potential of injection-induced seismicity, but typically simplified multiscale geologic features or neglected multiphysics coupled mechanisms because of the uncertainty in field data and computational cost of field-scale simulations, which may limit the reliable prediction of seismic hazard caused by industrial-scale CO 2 storage. The degree of lateral continuity of the stratigraphic interbedding below the reservoir and depth-dependent fault permeability can enhance or inhibit pore-pressure diffusion and corresponding poroelastic stressing along a basement fault. In conclusion, this study presents a rigorous modeling scheme with optimal geological and operational parameters needed to be considered in seismic monitoring and mitigation strategies for safe GCS.

58 GEOSCIENCES↗
DOE OSTI2020

BioGeoChemistry of Actinides (LLNL SFA OBER - SBR FY20 Program Management and Performance Report)

The focus of the BioGeoChemistry of Actinides SFA is to identify and quantify the biogeochemical processes and the underlying mechanisms that control actinide mobility in an effort to reliably predict and control the cycling and migration of actinides in the environment. The research approach includes: (1) Field Studies (Research Thrust 1) that capture actinide behavior on the timescale of decades and (2) Fundamental Laboratory Studies (Research Thrust 2) that isolate specific biogeochemical processes observed in the field. These research thrusts are underpinned by the unique capabilities and staff expertise at Lawrence Livermore National Laboratory (LLNL), allowing the BioGeoChemistry of Actinides SFA to advance our understanding of actinide migration behavior in the environment, and serve as an international resource for environmental radiochemistry research (Figure 1). Research Thrusts 1 and 2 are guided by broad central hypotheses: Thrust 1 Hypothesis: Biogeochemical processes occurring on the timescale of years to decades lead to greater actinide recalcitrance in sediments and limit their migration in surface and groundwater. Thrust 2 Hypothesis: Long-term biogeochemical processes include mineral and surface alteration, which leads to stabilization of actinide surface associations or incorporation into mineral precipitates. Our strategic goal is to use the knowledge gained from our Science Plan to advance our understanding of the behavior of actinides, providing DOE with the scientific basis for remediation and long-term stewardship of DOE’s legacy sites and, more broadly, increasing our understanding of transport phenomena in environmental systems sciences with a particular emphasis on environmentally relevant (long-term) timescales. Although our focus is on actinide biogeochemistry, the increased focus on biogeochemistry at unique Test Bed locations associated with this SFA is providing fundamental information on redox processes and associated microbiological processes that control the cycling of redox sensitive metals under dynamic and transient biogeochemical conditions.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

BioGeoChemistry at Interfaces (LLNL SFA OBER FY21 Program Management and Performance Report)

The focus of the BioGeoChemistry at Interfaces (formerly BioGeoChemistry of Actinides) SFA is to identify and quantify the biogeochemical processes and the underlying mechanisms that control actinide mobility in an effort to reliably predict and control the cycling and migration of actinides in the environment. The research approach includes: (1) Field Studies (Research Thrust 1) that capture actinide behavior on the timescale of decades and (2) Fundamental Laboratory Studies (Research Thrust 2) that isolate specific biogeochemical processes observed in the field. These Research Thrusts are underpinned by the unique capabilities and staff expertise at Lawrence Livermore National Laboratory, allowing the BioGeoChemistry at Interfaces SFA to advance our understanding of actinide migration behavior in the environment, and serve as an international resource for environmental radiochemistry research (Figure 1).

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Theoretical tools for neutrino scattering: interplay between lattice QCD, EFTs, nuclear physics, phenomenology, and neutrino event generators

Maximizing the discovery potential of increasingly precise neutrino experiments will require an improved theoretical understanding of neutrino-nucleus cross sections over a wide range of energies. Low-energy interactions are needed to reconstruct the energies of astrophysical neutrinos from supernovae bursts and search for new physics using increasingly precise measurement of coherent elastic neutrino scattering. Higher-energy interactions involve a variety of reaction mechanisms including quasi-elastic scattering, resonance production, and deep inelastic scattering that must all be included to reliably predict cross sections for energies relevant to DUNE and other accelerator neutrino experiments. This white paper discusses the theoretical status, challenges, required resources, and path forward for achieving precise predictions of neutrino-nucleus scattering and emphasizes the need for a coordinated theoretical effort involved lattice QCD, nuclear effective theories, phenomenological models of the transition region, and event generators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗