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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Low-energy electronic interactions in ferrimagnetic Sr 2 CrReO 6 thin films

We reveal in this study the fundamental low-energy landscape in the ferrimagnetic Sr 2 CrReO 6 double perovskite and describe the underlying mechanisms responsible for the three low-energy excitations below 1.4 eV. Here, based on resonant inelastic x-ray scattering and magnetic dynamics calculations, and experiments collected from both Sr 2 CrReO 6 powders and epitaxially strained thin films, we reveal a strong competition between spin-orbit coupling, Hund’s coupling, and the strain-induced tetragonal crystal field. Here, we also demonstrate that a spin-flip process is at the origin of the lowest excitation at 200 meV, and we bring insights into the predicted presence of orbital ordering in this material. We study the nature of the magnons through a combination of ab initio and spin-wave theory calculations, and show that two nondegenerate magnon bands exist and are dominated either by rhenium or chromium spins. The rhenium band is found to be flat at about 200 meV (±25 meV) through X-L-W-U high-symmetry points and is dispersive toward Γ.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Vertical temperature boundary of the pseudogap under the superconducting dome in the phase diagram of Bi 2 Sr 2 CaCu 2 O 8 + δ

We combine electronic Raman scattering experiments with cellular dynamical mean field theory and present evidence of the pseudogap in the superconducting state of various hole-doped cuprates. In Bi 2 Sr 2 CaCu 2 O 8 + δ we also track the superconducting pseudogap hallmark, a peak-dip feature, as a function of temperature T and doping p , well beyond the optimal one. We show that, at all temperatures under the superconducting dome, the pseudogap disappears at the doping p c , between 0.222 and 0.226, where also the normal-state pseudogap collapses at a Lifshitz transition. This demonstrates that the superconducting pseudogap boundary forms a vertical line in the T - p phase diagram.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Crystal plasticity finite element simulation of lattice rotation and x-ray diffraction during laser shock compression of tantalum

Here we present a crystal plasticity model tailored for high-pressure, high-strain-rate conditions that uses a multiscale treatment of dislocation-based slip kinetics. We use this model to analyze the pronounced plasticity-induced lattice rotations observed in shock-compressed polycrystalline tantalum via in situ x-ray diffraction. By making direct comparisons between experimentally measured and simulated texture evolution, we can explain how the details of the underlying slip kinetics control the degree of lattice rotation that ensues. Specifically, we show that only the highly nonlinear kinetics caused by dislocation nucleation can explain the magnitude of the rotation observed under shock compression. We demonstrate a good fit between our crystal plasticity model and x-ray diffraction data and exploit the data to quantify the dislocation nucleation rates that are otherwise poorly constrained by experiment in the dynamic compression regime.

36 MATERIALS SCIENCE↗
DOE OSTI2019

DC Impedance Model of MMC Considering Capacitor Voltage and Circulating Current Dynamics

The modular multilevel converter (MMC) is regarded as a competitive choice for future dc grids at high and medium voltage levels. To study the dc grid stability, a few MMC dc impedance models have been proposed, but their accuracy is limited by the assumption of ideal submodule voltage or neglecting the circulating current control. In this paper, a more accurate MMC dc impedance model is developed considering both the submodule capacitor voltage and circulating current dynamics. Simulation and experiment results indicate that the developed model matches with the actual MMC better than state-of-art models and can predict the dc system stability correctly.

Kong, Le↗
DOE OSTI2016

Evaluation of Performance and Opportunities for Improvements in Automotive Power Electronics Systems: Preprint

Thermal management strategies for automotive power electronic systems have evolved over time to reduce system cost and to improve reliability and thermal performance. In this study, we characterized the power electronic thermal management systems of two electric-drive vehicles--the 2012 Nissan LEAF and 2014 Honda Accord Hybrid. Tests were conducted to measure the insulated-gate bipolar transistor-to-coolant thermal resistances for both steady-state and transient conditions at various coolant flow rates. Water-ethylene glycol at a temperature of 65 degrees C was used as the coolant for these experiments. Computational fluid dynamics and finite element analysis models of the vehicle's power electronics thermal management system were then created and validated using experimentally obtained results. Results indicate that the Accord module provides lower steady-state thermal resistance as compared with the LEAF module. However, the LEAF design may provide improved performance in transient conditions and may have cost benefits.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Metabolic multistability and hysteresis in a model aerobe-anaerobe microbiome community

Major changes in the microbiome are associated with health and disease. Some microbiome states persist despite seemingly unfavorable conditions, such as the proliferation of aerobe-anaerobe communities in oxygen-exposed environments in wound infections or small intestinal bacterial overgrowth. Mechanisms underlying transitions into and persistence of these states remain unclear. Using two microbial taxa relevant to the human microbiome, we combine genome-scale mathematical modeling, bioreactor experiments, transcriptomics, and dynamical systems theory to show that multistability and hysteresis (MSH) is a mechanism describing the shift from an aerobe-dominated state to a resilient, paradoxically persistent aerobe-anaerobe state. We examine the impact of changing oxygen and nutrient regimes and identify changes in metabolism and gene expression that lead to MSH and associated multi-stable states. In such systems, conceptual causation-correlation connections break and MSH must be used for analysis. Using MSH to analyze microbiome dynamics will improve our conceptual understanding of stability of microbiome states and transitions between states.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Dealing with Measurement Uncertainties as Nuisance Parameters in Bayesian Model Calibration

In the presence of model discrepancy, the calibration of physics-based models for physical parameter inference is a challenging problem. Lack of identifiability between calibration parameters and model discrepancy requires additional identifiability constraints to be placed on the model discrepancy to obtain unique physical parameter estimates. If these assumptions are violated, the inference for the calibration parameters can be systematically biased. In many applications, such as in dynamic material property experiments, many of the calibration inputs refer to measurement uncertainties. Here, we develop a metric for identifying overfitting of these measurement uncertainties, propose a prior capable of reducing this overfitting, and show how this leads to a diagnostic tool for validation of physical parameter inference. The approach is demonstrated for a benchmark example and applied for a material property application to perform inference on the equation of state parameters of tantalum.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Learning-based framework for sensor fault-tolerant building HVAC control with model-assisted learning

As people spend up to 87% of their time indoors, intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings are essential for maintaining occupant comfort and reducing energy consumption. These HVAC systems in smart buildings rely 'on real-time sensor readings, which in practice often suffer from various faults and could also be vulnerable to malicious attacks. Such faulty sensor inputs may lead to the violation of indoor environment requirements (e.g., temperature, humidity, etc.) and the increase of energy consumption. While many model-based approaches have been proposed in the literature for building HVAC control, it is costly to develop accurate physical models for ensuring their performance and even more challenging to address the impact of sensor faults. In this work, we present a novel learning-based framework for sensor fault-tolerant HVAC control, which includes three deep learning based components for 1) generating temperature proposals with the consideration of possible sensor faults, 2) selecting one of the proposals based on the assessment of their accuracy, and 3) applying reinforcement learning with the selected temperature proposal. Moreover, to address the challenge of training data insufficiency in building-related tasks, we propose a model-assisted learning method leveraging an abstract model of building physical dynamics. Through extensive experiments, we demonstrate that the proposed fault-tolerant HVAC control framework can significantly reduce building temperature violations under a variety of sensor fault patterns while maintaining energy efficiency.

Xu, Shichao↗
DOE OSTI2017

Phase Transitions and Scaling in Systems Far from Equilibrium

Scaling ideas and renormalization group approaches proved crucial for a deep understanding and classification of critical phenomena in thermal equilibrium. Over the past decades, these powerful conceptual and mathematical tools were extended to continuous phase transitions separating distinct nonequilibrium stationary states in driven classical and quantum systems. In concordance with detailed numerical simulations and laboratory experiments, several prominent dynamical universality classes have emerged that govern large-scale, long-time scaling properties both near and far from thermal equilibrium. These pertain to genuine specific critical points as well as entire parameter space regions for steady states that display generic scale invariance. The exploration of nonstationary relaxation properties and associated physical aging scaling constitutes a complementary potent means to characterize cooperative dynamics in complex out-of-equilibrium systems. Here, this review describes dynamic scaling features through paradigmatic examples that include near-equilibrium critical dynamics, driven lattice gases and growing interfaces, correlation-dominated reaction-diffusion systems, and basic epidemic models.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

MSASGCN : Multi-Head Self-Attention Spatiotemporal Graph Convolutional Network for Traffic Flow Forecasting

Traffic flow forecasting is an essential task of an intelligent transportation system (ITS), closely related to intelligent transportation management and resource scheduling. Dynamic spatial-temporal dependencies in traffic data make traffic flow forecasting to be a challenging task. Most existing research cannot model dynamic spatial and temporal correlations to achieve well-forecasting performance. The multi-head self-attention mechanism is a valuable method to capture dynamic spatial-temporal correlations, and combining it with graph convolutional networks is a promising solution. Therefore, we propose a multi-head self-attention spatiotemporal graph convolutional network (MSASGCN) model. It can effectively capture local correlations and potential global correlations of spatial structures, can handle dynamic evolution of the road network, and, in the time dimension, can effectively capture dynamic temporal correlations. Experiments on two real datasets verify the stability of our proposed model, obtaining a better prediction performance than the baseline algorithms. The correlation metrics get significantly reduced compared with traditional time series prediction methods and deep learning methods without using graph neural networks, according to MAE and RMSE results. Compared with advanced traffic flow forecasting methods, our model also has a performance improvement and a more stable prediction performance. We also discuss some problems and challenges in traffic forecasting.

Cao, Yang↗
DOE OSTI2025

SIDDA: SInkhorn Dynamic Domain Adaptation for Image Classification

SInkhorn Dynamic Domain Adaptation (SIDDA) supplements the experiments presented in 2501.14048, SIDDA: SInkhorn Dynamic Domain Adaptation for Image Classification with Equivariant Neural Networks. SIDDA introduces a semi-supervised, automatic domain adaptation method that leverages Sinkhorn divergences to dynamically adjust the regularization in the optimal transport plan and the weighting between classification and domain adaptation loss terms during training.

Pandya, Sneh [Fermi National Accelerator Laborator↗
DOE OSTI2025

Spectral broadening and pulse shaping in the deep ultraviolet

Here, we report the generation and shaping of ∼20 femtosecond pulses in the deep ultraviolet (DUV). Our approach combines spectral broadening in a stretched hollow-core fiber with compression and shaping via an acousto-optic modulator-based pulse shaper. The pulse shaper allows for automatic compression, useful for in-air delivery of pulses to two-dimensional (2D) spectroscopy and other time-resolved experiments. Unlike previous DUV pulse generation schemes that lack phase stability, our method enables programmable generation of phase-locked pulse-pairs with attosecond-level stability. We demonstrate pulse characterization via self-diffraction frequency-resolved optical gating and dispersion scan methods in the DUV and validate performance through a pump-probe experiment resolving electronic dynamics on attosecond timescales.

Codere, Julia [Stony Brook Univ., NY (United State↗
DOE OSTI2022

Object-Based Evaluation of Tropical Precipitation Systems in DYAMOND Simulations over the Maritime Continent

The present study uses an object-based evaluation metric to examine the precipitation bias over the Maritime Continent in the global cloud-resolving models. We specifically focus on the difference between the models that directly resolve convection and those using convection parameterization. The 40-day hindcast experiments of the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domain (DYAMOND) intercomparison project are evaluated against the high-resolution satellite rainfall products. The hindcast of the Central Weather Bureau Global Forecast System (CWBGFS) under the DYAMOND protocol is also included. The results indicate that most models simulate insufficient numbers of large precipitation system [object-based precipitation system (OPS), > 370 km in scale], indicating weaker convection organization. The observation indicates that the maximum precipitation within the OPS intensifies with increasing object size. All of the models capture this positive relationship, but most of them overestimate the sensitivity. Most of the models overestimate both the frequency and intensity of small OPS (< 160 km), except for the models with convection parameterization [i.e., CWBGFS, European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS)-9 km]. Although most of the models can reproduce the observed peak time of diurnal precipitation over the land area in the Maritime Continent, the simulated fractional contribution of different sizes of OPS to the total precipitation varies from model to model, and their peak times do not follow the observed ones with delayed peak times as the size of OPS increases from small, mid-size, to large categories. Most of the models reasonably capture the mean diurnal cycle peak time, but only the models with convection parameterization and Model for Prediction Across Scales (MPAS) can represent the diurnal evolution of fractional contribution from different OPSs. The implications of the current results to the upscale processes of the tropical convection systems in the global models are also discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Coupled Experiment and Theory to Explore the Limits of Material Strength at High Strain Rates

Critical components such as detonators in Sandia's stockpile are heterogeneous materials that rely on accurate constitutive models for each material used in the component. However, experiments to study materials at high pressure are challenging and time consumptive, therefore we turn to modeling tools to refine and predict outcomes beforehand. Efficient models balance absolute physical accuracy against approximate but computationally lightweight constitutive inputs. By using a relatively small number of high fidelity simulations and experiments we have be able to broaden the predictive power of the shock response in metals and polymers. We have tailored the analysis of these simulations to determine a size dependent material strength, which can be used as constitutive model inputs for continuum hydrodynamics codes. For the shocked Cu, MD simulations show a yield strength from RMI jet growth of approximately 450MPa that depends on the details of the free surface geometry. This value is close to the yield strength of 500MPa parameterized for an elastic-perfectly-plastic strength model from experiments at the Dynamic Compression Sector at Argonne National Lab. The same analysis applied to MD simulations of PMMA jetting resulted in no clear determination of yield strength, implying a more complex RMI process in polymeric materials. Atomistic simulations of both materials are shown to be valuable training metrics and show the need for explicit strain rate dependent strength for future improvements in strength models used in continuum codes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The Role of Grain Boundary Structure and Chemistry in Materials Failure

The objective of this project was to reveal how we can design materials with a tunable mechanical behavior through interface engineering. To do so we focused on understanding slip transmission across internal surfaces and how it relates to the strength and chemistry of the internal surface. In doing so we followed an interdisciplinary approach that employed theory, experiments and molecular dynamic (MD) simulations. Our guideline was that interfaces are characterized by their own mechanically induced interface parameter. New dislocation-grain boundary interaction mechanisms were revealed and the strengthening effects of non-traditional interfaces. We began studying simple grain boundaries in Fe systems, and then moved on to newly explored systems such as graphene-metal and nanolayer composites.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Integrated Research Infrastructure Architecture Blueprint Activity (Final Report 2023)

The complexity of scientific pursuits is increasing rapidly with aspects that require dynamic integration of experiment, observation, theory, modeling, simulation, visualization, machine learning (ML), artificial intelligence (AI), and analysis. Research projects across the Department of Energy (DOE) are increasingly data and compute intensive. Innovative research teams are accelerating the pace of discovery by using high-performance computational and data tools in their research workflows and leveraging multiple research infrastructures. Additionally, several recent high-level U.S. government reports underscore the necessity of a new advanced computing ecosystem for international competitiveness and national security. International competitors are moving forward with major research infrastructure integration efforts that seek to capture a competitive advantage in the global innovation race. Owing to its unparalleled constellation of world-class experimental and observational facilities and high-performance and extreme-scale computational, data, and networking infrastructure, DOE is positioned to be a global leader in this new era of integrated science. However, this new integration paradigm will demand continuing evolution to ensure the U.S. remains a global leader in research and innovation. The DOE Office of Science (SC) has seized on the strategic importance of integration and has adopted a vision for Integrated Research Infrastructure (IRI): To empower researchers to meld DOE’s world-class research tools, infrastructure, and user facilities seamlessly and securely in novel ways to radically accelerate discovery and innovation. To respond to the evolving computational requirements of research and the competitive international innovation landscape, experimental facilities could be connected with high performance computing resources for near real-time analysis, and resources should be provided for merging enormous and diverse data for AI/ML techniques and analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

HYBRID Modeling of DETAIL Experimental Facility

Idaho National Laboratory continues to be at the forefront of advanced reactor systems and integrated energy systems research. Through the Thermal Energy Distribution System (TEDS) and Microreactor Agile Non-nuclear Test Bed (MAGNET) experiments in the Dynamic Energy Transport and Innovation Laboratory (DETAIL) within the Engineering Systems Laboratory, a mix of digital and physical testing systems can explore the operation of thermally and electrically integrated systems. Modeling of these systems has been done using the HYBRID modeling repository of the Integrated Energy Systems program. DETAIL also contains high temperature steam electrolysis (HTSE) units to produce hydrogen and generate the load imposed on MAGNET and TEDS. The dynamic modeling capabilities within the IES program describe the DETAIL components as-built and can be used to evaluate controls, process flows, and overall system conditions.

08 HYDROGEN↗
DOE OSTI2024

Coupled Aero-Hydro-Mechanical Hybrid Simulation Testing of Offshore Wind Turbines Subjected to Operational and Extreme Loading Conditions

Understanding the response of the Offshore Wind Turbine (OWT) subjected to realistic applied loads requires modeling the whole structure including its soil-foundation system. This requires unique and innovative testing facilities. OWT systems experience cyclic and dynamic loading due to wind, wave, current, rotor vibrations (i.e., 1P load) and vibrations caused by the blade shadowing effects (2P/3P loads). These loads are complicated in nature and have varying amplitudes, frequencies, and directions. Investigating the response of the entire OWT system including the soil-foundation system under these complex loading conditions, requires: (1) full understanding of the loading characteristics including: the power take-off mechanical load (1P and 3P), and areo- and hydrodynamic loads that the OWT system is subjected to; (2) testing facility with unique multidirectional loading capabilities that allows for simultaneous application of realistic wind, wave and machine loads, axial gravity loads, and induced overturning moments; and (3) unique and cost-effective testing techniques that allow for accurate analysis of the overall response of the OWT system under realistic conditions such as: Real-Time Hybrid Simulation (RTHS).

17 WIND ENERGY↗