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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Physics informed machine learning for chemistry tabulation

Modeling of turbulent combustion system requires modeling the underlying chemistry and the turbulent transport. Solving both systems simultaneously is computationally prohibitive. Instead, given the difference in scales at which the two sub-systems evolve, the two sub-systems are typically (re)solved separately. Popular approaches such as the Flamelet Generated Manifolds (FGM) use a two-step strategy where the governing reaction kinetics are pre-computed and mapped to a low-dimensional manifold, characterized by a few reaction progress variables (model reduction) and the manifold is then “looked-up” during the run-time to estimate the high-dimensional system state by the turbulent transport system. While existing works have focused on these two steps independently, in this work we show that joint learning of the progress variables and the look-up model, can yield more accurate results. Here, we build on the base formulation and implementation to include the dynamically generated Thermochemical State Variables (Lower Dimensional Dynamic Source Terms). We discuss the challenges in the implementation of this deep neural network architecture and experimentally demonstrate its superior performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Uncertain quantum computing futures and potential energy and physical resource impacts at scale

Considerable attention has recently focused on the vast energy and water demands of supercomputing, namely large-scale data centers that underpin artificial intelligence (AI), one of the great disruptors of contemporary society. Looking ahead some years from now, quantum computing is poised to disrupt established computing paradigms once again. Scientists and engineers are now working intensely to bring this century-old dream of physicists to fruition. Yet, as quantum computers begin to be integrated with classical supercomputing architectures, the implications for energy and physical resource use also need to be understood, especially how they compare to today’s AI data centers. These impacts have not yet been quantified by the research community – a notable gap in the literature, even if commercial-scale deployment of Quantum-Accelerated Computing Infrastructure (QuACI) is not expected for a few more years. This study is the first to conduct such an assessment. Using publicly available information from academic sources and private industry, we characterize multiple configurations of superconducting qubit-based, fault-tolerant quantum computers (FTQC) that could plausibly be deployed at scale in the 2030s and into the 2040s. By parameterizing these FTQC systems at a process level, we conduct a prospective scenario analysis to quantify their energy and physical resource needs. While these estimates are uncertain, given the current state of quantum technologies and their unknown future trajectories, important insights can already be drawn. One key finding is that while the electricity needs for a fleet of FTQCs are within the bounds of previous modeling studies that have explored high electricity demand futures, the needs for certain physical resources, namely water and helium-3, could pose bottlenecks to QuACI scale-up.

Computing↗
DOE OSTI2021

Superconductivity in Y 4 RuGe 8 with a Vacancy-Ordered CeNiSi 2 -Type Superstructure

In this work, we report a new compound, Y 4 RuGe 8 , with a transition metal vacancy-ordered CeNiSi 2 -type superstructure, which has a superconducting transition at 1.3 K. Y 4 RuGe 8 crystals were grown by indium flux at relatively low temperatures (below 1273 K), which makes it possible to stabilize such a vacancy-ordered phase. The crystal structure of Y 4 RuGe 8 was solved by single-crystal X-ray diffraction and confirmed by transmission electron microscopy. The as-grown Y 4 RuGe 8 crystals are always twinned, crystallizing in the space group $P\bar{1}$(no. 2) with the lattice parameters a = 5.7680(1) Å, b = 8.2042(2) Å, c = 11.5093(3) Å, α = 79.696(1)degrees, β = 88.491(1)degrees, and γ = 79.637(2)degrees; this structure is a superstructure deriving from the higher symmetry CeNiSi 2 -type structure (Cmcm, no. 63) due to the ordering of Ru vacancies. The ordering of Ru sites breaks slightly distorted Ge planes in the CeNiSi 2 prototype into infinite cis-trans Ge chains in Y 4 RuGe 8 . The presence of bulk superconductivity in Y 4 RuGe 8 is well supported by zero resistance and a jump in specific heat at the critical transition temperature. The Sommerfeld coefficient (19 mJ K -2 mol -1 ) of the specific heat is greater than that (11 mJ K -2 mol -1 ) estimated using the bare density of states (4.7 states/eV/f.u.) from first-principles calculations. The ab initio calculations indicate that 4d electrons of both Y and Ru and 4p electrons of Ge are the main contributors to the total density of states at the Fermi level in Y 4 RuGe 8 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A Multireference Quantum Krylov Algorithm for Strongly Correlated Electrons

We introduce a multireference selected quantum Krylov (MRSQK) algorithm suitable for quantum simulation of many-body problems. MRSQK is a low-cost alternative to the quantum phase estimation algorithm that generates a target state as a linear combination of non-orthogonal Krylov basis states. This basis is constructed from a set of reference states via real-time evolution; thus, avoiding the numerical optimization of parameters. An efficient algorithm for the evaluation of the off-diagonal matrix elements of the overlap and Hamiltonian matrices is discussed and a selection procedure is introduced to identify a basis of orthogonal references that ameliorates the linear dependency problem. Preliminary benchmarks on linear H 6 , H 8 , and BeH 2 indicate that MRSQK can predict the energy of these systems accurately using very compact Krylov bases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A Model for Radiolysis in a Flowing-Water Target during High-Intensity Proton Irradiation

At the Facility for Rare Isotope Beams (FRIB), interactions between heavy-ion beams and beam-dump water will create a wide variety of radionuclides which can be accessed by a technique known as “isotope harvesting”. However, irradiation of water is always accompanied by the creation of numerous radical, ionic, and molecular radiolysis products. Some of the radiolysis products have sufficiently long lifetimes to accumulate in the irradiated water and affect the harvesting chemistry. Here we investigate the formation of hydrogen peroxide, molecular hydrogen, and molecular oxygen during a high-intensity proton irradiation of a flowing-water isotope-harvesting target and compare the experimental results to simulations. The simulations kinetically model the chemical reactions occurring in the homogeneous phase of radiolysis in flowing water and establish an “effective yield”. In both the experiment and simulations, the bulk quantities of H 2 , H 2 O 2 , and O 2 are considerably lower than predicted by primary radiolysis yields (escape yields), meaning that in the high beam intensity regime the homogeneous phase reactions have a considerable impact on the overall chemical composition of the water. Further, it could be shown that for radiation which is characterized by a limited linear energy transfer, such as the here applied protons, the bulk outcome of the microscopic kinetic modeling could be estimated by a simplified steady-state model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

An Overview of Atmospheric Features Over the Western North Atlantic Ocean and North American East Coast—Part 2: Circulation, Boundary Layer, and Clouds

The Western North Atlantic Ocean (WNAO) is a complex land-ocean-atmosphere system that experiences a wide range of atmospheric phenomena, which in turn drive unique aerosol transport pathways, cloud morphologies, and boundary layer variability. This work, Part 2 of a 2-part paper series, provides an overview of the atmospheric circulation, boundary layer variability, three-dimensional cloud structure, and precipitation over the WNAO; the companion paper (Part 1) focused on chemical characterization of aerosol, gas, and wet deposition. Seasonal changes in the circulation and sea surface temperature explain a clear transition in cloud morphologies from small shallow cumulus clouds, convective clouds, and tropical storms in summer, to stratus/stratocumulus and multi-layer cloud systems associated with winter storms. Synoptic variability in cloud fields is estimated using satellite-based weather states, and the role of postfrontal conditions (cold-air outbreaks) in the development of stratiform clouds is further analyzed. Precipitation is persistent over the ocean, with a regional peak over the Gulf Stream path, where offshore sea surface temperature gradients are large and surface fluxes reach a regional peak. Satellite data show a clear annual cycle in cloud droplet number concentration along the coast in winter, and minima in summer, suggesting a marked annual cycle in aerosol-cloud interactions. The performance of four climate models are assessed against satellite cloud retrievals. The models qualitatively reproduce the annual cycle in cloud cover and liquid water path, but with large discrepancies across models, especially in the extra-tropics. The paper concludes with a summary of outstanding issues and recommendations for future work.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Neutral gas pressure dependence of ion–ion mutual neutralization rate constants using Landau–Zener theory coupled with trajectory simulations

In this computational study, we describe a self-consistent trajectory simulation approach to capture the effect of neutral gas pressure on ion–ion mutual neutralization (MN) reactions. The electron transfer probability estimated using Landau–Zener (LZ) transition state theory is incorporated into classical trajectory simulations to elicit predictions of MN cross sections in vacuum and rate constants at finite neutral gas pressures. Electronic structure calculations with multireference configuration interaction and large correlation consistent basis sets are used to derive inputs to the LZ theory. The key advance of our trajectory simulation approach is the inclusion of the effect of ion-neutral interactions on MN using a Langevin representation of the effect of background gas on ion transport. For H + – H – and Li + – H(D) – , our approach quantitatively agrees with measured speed-dependent cross sections for up to ~10 5 m/s. For the ion pair Ne + – Cl – , our predictions of the MN rate constant at ~1 Torr are a factor of ~2 to 3 higher than the experimentally measured value. Similarly, for Xe + – F – in the pressure range of ~20 000–80 000 Pa, our predictions of the MN rate constant are ~20% lower but are in excellent qualitative agreement with experimental data. Here, the paradigm of using trajectory simulations to self-consistently capture the effect of gas pressure on MN reactions advanced here provides avenues for the inclusion of additional nonclassical effects in future work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Risk-Informed Safety Analysis for Accident Tolerant Fuels

Accident Tolerant Fuels (ATF) are being tested by different nuclear vendors and research organizations and their introduction in the US light water reactors fleet is planned for the second half of the 2020`s. In the framework of the US-DOE Light-Water Reactor Sustainability program, Risk-Informed Systems Analysis pathway, researches are being conducted at the Idaho National Laboratory for developing tools and methods that can help the industry quantifying the benefits from the adoption of ATF technology. In this paper we describe the developed risk-informed methodology including the safety analysis codes improvements, and we present some results for selected accident scenarios. The developed methodology combines INL state-of-the-art deterministic Best Estimate code RELAP5-3D, and Probabilistic Risk Analysis tools RAVEN and SAPHIRE. The analyses are performed on a three-loops pressurized water reactor, simulating Station Blackout and LB-LOCA accidents and considering near-term ATFs, or FeCrAl and Chromium-coated clads. Finally, we show how, applying our methodology, the new Core Damage Frequency can be assessed. The results indicated that the main benefits in introducing near-term ATFs concern a significant reduction in the hydrogen production during accident conditions. No significant Core Damage Frequency reduction was found.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Quantum Sensing of Displacements with Stabilized Gottesman-Kitaev-Preskill States

We demonstrate how recent protocols developed for the stabilization of Gottesman-Kitaev-Preskill states can be used for the estimation of two-quadrature displacement sensing, with sensitivities approaching the multivariate quantum Cramer-Rao bound. Thanks to the stabilization, this sensor is backaction evading and can function continuously without reset, making it well suited for the detection of itinerant signals. Additionally, we provide numerical simulations showing that the protocol can unconditionally surpass the Gaussian limit of displacement sensing with prior information, even in the presence of realistic noise. Our work shows how reservoir engineering in bosonic systems can be leveraged for quantum metrology, with potential applications in force sensing, waveform estimation, and quantum channel learning.

Labarca, Lautaro [Univ. of Sherbrooke, QC (Canada)↗
DOE OSTI2022

Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks

Exascale computing will bring with it significant I/O limitations. One foreseeable consequence of such restrictions is that the user can save only a small fraction of complex simulation data to disk for subsequent analysis. An alternative is to fit statistical models to data in situ, that is, inside the simulation as it runs. This option requires extremely fast statistical estimation to avoid slowing down the simulation. Gaussian processes (GPs) have state-of-the-art predictive performance for modeling spatial data. However, standard estimation methods for GPs scale quite poorly to large data sets as parameter estimation requires inverting a covariance matrix to the size of the data set. In the presented work, we use a convolutional neural network (CNN) to predict the GP parameters for a spatial data set, from a simulation or otherwise, rather than optimize the parameters directly. Here, our presented case study models spatial data from E3SM, the Department of Energy’s Exascale climate model. The CNN is trained on synthetic data simulated from GP models with known parameters and then applied to data from the climate simulation. In the presented examples, the neural network scheme produces parameter estimates that compare well with standard methods such as maximum likelihood estimation in predictive performance but is obtained four orders of magnitude faster.

big data↗
DOE OSTI2021

Estimating Subhourly Inverter Clipping Loss From Satellite-Derived Irradiance Data

Photovoltaic system production simulations are conventionally run using hourly weather datasets. Hourly simulations are sufficiently accurate to predict the majority of long-term system behavior but cannot resolve high-frequency effects like inverter clipping caused by short-duration irradiance variability. Direct modeling of this subhourly clipping error is only possible for the few locations with high-resolution irradiance datasets. This paper describes a method of predicting the magnitude of this error using a machine learning regressor ensemble model, comprised of a random forest and an XGBoost model, and 30-minute satellite irradiance data. The method predicts a correction for each 30-minute interval with the potential to roll up into 60-minute corrections to match an hourly energy model. The model is trained and validated at locations where the error can be directly simulated from 1-minute ground data. The validation shows low bias at most ground station locations. The model is also applied to gridded satellite irradiance to produce a heatmap of the estimated clipping error across the United States. Finally, the relative importance of each predictor satellite variable is retrieved from the model and discussed.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2025

Active Learning Surrogates for Integrating Electron Microscopy and Computational Insights from Simulations in Autonomous Experiments

Artificial Intelligence (AI) combined with simulations and experiments has great potential to accelerate scientific discovery across technology and pharmaceuticals. However, the gap between simulations and experiments is challenging due to disparities in time and scale, making it difficult to estimate properties like energy and electronic states from experiments, and to provide feedback based on theoretical insights.Our research addresses the challenge by developing unique deep kernel based surrogate models that learns from microscopic images, mapping structural features to energy differences from defect formation. We start with full-training using simulated images to determine optimal settings, establishing a baseline for active learning. Using these settings from the baseline, active learning is trained, and predicts structures along simulation trajectories based on uncertainty and energetic stability, thus reducing data requirements, simulation time and computational costs. The results demonstrate that the model achieves a low average error margin of approximately 0.03 meV, indicating good performance. To enhance feature extraction and reconstruction capabilities, we developed an autoencoder-decoder as additional surrogate to create latent space to capture essential features, enabling precise comparisons between simulations and experiments. The results from this model achieved a reconstruction loss of around 0.2 and accurately reconstructed molecular structures.Overall, this work advances the steering of experiments through computational simulations by employing a surrogate models that actively predicts the trajectories of structural evolution, achieving time-to-solution comparable to experimental measurements.

Saranathan, Gayathri [Hewlett-Packard]↗
DOE OSTI2022

A Statistical Interpolation Code for Ocean Analysis and Forecasting

Abstract We present a data assimilation package for use with ocean circulation models in analysis, forecasting, and system evaluation applications. The basic functionality of the package is centered on a multivariate linear statistical estimation for a given predicted/background ocean state, observations, and error statistics. Novel features of the package include support for multiple covariance models, and the solution of the least squares normal equations either using the covariance matrix or its inverse—the information matrix. The main focus of this paper, however, is on the solution of the analysis equations using the information matrix, which offers several advantages for solving large problems efficiently. Details of the parameterization of the inverse covariance using Markov random fields are provided and its relationship to finite-difference discretizations of diffusion equations are pointed out. The package can assimilate a variety of observation types from both remote sensing and in situ platforms. The performance of the data assimilation methodology implemented in the package is demonstrated with a yearlong global ocean hindcast with a 1/4° ocean model. The code is implemented in modern Fortran, supports distributed memory, shared memory, multicore architectures, and uses climate and forecasts compliant Network Common Data Form for input/output. The package is freely available with an open source license from www.tendral.com/tsis/ .

Srinivasan, Ashwanth↗
DOE OSTI2020

Risk-Informed Safety Analysis for Accident Tolerant Fuels

Accident Tolerant Fuels (ATF) are being tested by different nuclear vendors and research organization and their introduction in light water reactors fleet is planned for the second half of the 2020`s. In the framework of the US-DOE Light-Water Reactor Sustainability program, Risk-Informed Safety Analysis pathway (LWRS-RISA), research activities are being conducted at the Idaho National Laboratory (INL) for developing tools and methods that can help the industry in quantifying the ATF introduction benefits. In this paper we describe the developed risk-informed methodology, the codes improvements and we present some results for selected accidental conditions. The developed methodology combines INL state-of-the-art deterministic Best Estimate tools like RELAP5-3D code, and Probabilistic Risk Analysis tools like RAVEN and SAPHIRE codes. The analyses are performed on a three-loops pressurized water reactor (PWR) simulating accidental conditions like Station Blackout and LB-LOCA and considering near-term ATF (FeCrAl and Chromium-coated clads). Finally, we show, through our methodologies, how the delta-Core Damage Frequency can be assessed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Up-Tower Investigation of Main Bearing Cage Slip and Loads

This paper described the operational conditions of an SRB in the main bearing position of a three-point mount wind turbine drivetrain. A commercial wind turbine was instrumented to measure load characteristics on the outer ring of the bearing, which were used to examine the roller load characteristics qualitatively and the cage slip quantitatively in a range of operational states. Further, a simple analytic model estimated the bearing loads and was used as input conditions to a three-dimensional finite element model. The finite element roller load results were compared to the measurements and contact stresses were also described.

17 WIND ENERGY↗
DOE OSTI2022

Optimal, Reliable Building-Integrated Energy Storage (Cooperative Research and Development Final Report)

The team will advance the commercial readiness of behind-the-meter (BTM) energy storage (ES) systems by employing health-conscious controls that guarantee lifetime and optimize the ES system's value stream when integrated with onsite renewable energy generation. Specifically, the team will develop ES controls that increase the net present value (NPV) of photovoltaics (PV) by 50% in markets where net-metering policies are being replaced by variable electricity pricing structures. The team will also reduce the risk of achieving a 10-year ES warranty lifetime by at least one order of magnitude. The successful two-year project will develop the controls and system enabling Eaton to commercialize the technology by 2021. The developments achieved through this project may enable wide scale adoption of stationary energy storage benefitting the public.

25 ENERGY STORAGE↗
DOE OSTI2022

Valuation of Hydrogen Technology on the Electric Grid Using Production Cost Modeling: Cooperative Research and Development Final Report, CRADA Number CRD-18-00736

This research project will estimate the value to the United States electric grid of deploying hydrogen technology (such as electrolyzers and hydrogen-fueled generation) under projected conditions of high renewable penetration. The analysis will advance the state of the art in systems-level cost-benefit analysis of hydrogen technology for the electric grid by incorporating production cost modeling results in the analysis. Large-scale grid simulation tools will be used to evaluate total system production cost and grid operation when hydrogen technology is deployed for applications such as energy storage and demand response. Scenarios will include one or more future grid mixes in the Western Interconnect (WI) with a high proportion of intermittent renewables. Electric Power Research Institute (EPRI) will work with four utility companies to refine scenarios. Results of the analysis will include comparing the net cost of hydrogen to other technologies for long duration storage, and of power-to-gas (P2G) scenarios including merchant hydrogen sale and hydrogen-fueled generation.

08 HYDROGEN↗