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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
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Accuracy of volumetric flow rate inflow/outflow measurement by integrating PIV velocity fields

In this paper, the usefulness of planar Particle Image Velocimetry (PIV) measurements to determine the volumetric flow rate from a rectangular or round jet exit is assessed. Both two-component PIV (2C–PIV) and through-plane three-component (3C) stereo PIV (SPIV) data sets were acquired at the exit of a turbulent, rectangular nozzle and a round nozzle with Reynolds numbers between 10 000 and 100 000. The PIV data sets were processed using a variety of algorithms. The time-averaged results were then spatially integrated across the jet exits and compared to a calibrated flow meter. Recommendations for each method are developed and discussed with potential drawbacks. The accuracy of the measurement was found to be a weak function of the Reynolds number of the flow. Furthermore, two-component PIV was found to underestimate the volumetric flow rate by 1%–4% depending on the integration scheme and SPIV underestimated volumetric flow rate by 2%.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Forecasting Solar-Thermal Systems Performance under Transient Operation Using a Data-Driven Machine Learning Approach Based on the Deep Operator Network Architecture

Modeling and prediction of the dynamic behavior of thermal systems operating under intermittent energy input and variable load requirements represent one of the greatest challenges in the development of efficient and reliable renewable-based power generation technologies. In this work, a data-driven machine learning modeling framework was developed based on a modified version of the Deep Operator Network architecture where the time coordinate in the trunk net is replaced with historical data of the predicting quantity. The modeling framework can be used to accurately predict the performance of renewable-based energy conversion technologies including wind- and solar-based power plants. This novel framework was applied on a solar-thermal system that consists of a solar collection loop using a flat plate collector, a power generation loop comprising an Organic Rankine Cycle, and a thermal energy storage tank connecting both loops. Variable solar irradiance, air temperature, and power load profiles were used by the Deep Operator Network to predict the State-of-Charge and the efficiency of the thermal system for several days. The results were compared with the State-of-Charge and efficiency functions calculated using a physics-based model. For a simple operation scenario, characterized by a clear sky solar irradiance profile and constant load, the standard deviation in the State-of-Charge prediction by Deep Operator Network is below 0.9% during a seven-day prediction time horizon. For the most realistic operation scenario that considers real solar irradiance and a rough load profile, the maximum standard deviation in the predictions for the State-of-Charge and efficiency are below 6.8% and 2.5%, respectively. A comparison between Deep Operator Network and Long Short Term Memory network was also performed. In general, both networks predict very well the State-of-Charge for different data density conditions; however, a higher accuracy, with a standard deviation below 2.0%, is obtained by the Deep Operator Network during three and half days using sparser training data of 20-minute points. The same accuracy for the State-of-Charge prediction with the Long Short Term Memory network is achieved only for 14 h. Average standard deviations for the State-of-Charge prediction of 1.1% with the Deep Operator Network and 1.5% with the Long Short Term Memory network are obtained for a four-day prediction time using a denser training data of 5-minute points.

DeepONet↗
DOE OSTI2025

Resolving Mesoscale Convective Systems: Grid Spacing Sensitivity in the Tropics and Midlatitudes

Abstract Mesoscale convective systems (MCSs) are a critical global water cycle component and drive extreme precipitation events in tropical and midlatitude regions. However, simulating deep convection remains challenging for modern numerical weather and climate models due to the complex interactions of processes from microscales to synoptic scales. Recent models with kilometer‐scale horizontal grid spacings offer notable improvements in simulating deep convection compared to coarser‐resolution models. Still, deficiencies in representing key physical processes, such as entrainment, lead to systematic biases. Additionally, evaluating model outputs using process‐oriented observational data remain difficult. This study presents an ensemble of MCS simulations with spanning the deep convective gray zone ( from 12 km to 125 m) in the Southern Great Plains of the U.S. and the Amazon Basin. Comparing these simulations with Atmospheric Radiation Measurement (ARM) wind profiler observations, we find greater sensitivity in the Amazon Basin compared to the Great Plains. Convective drafts converge structurally at sub‐kilometer scales, but some deficiencies remain. In both regions, simulated up and downdrafts are too deep and extreme downdrafts are not strong enough. Furthermore, Amazonian updrafts are too strong. Overall, we observe higher sensitivity in the tropics, including an artificial buildup in vertical kinetic energy at scales of , suggesting a need for 250 m in this region. Nevertheless, bulk convergence—agreement of storm‐average statistics—is achievable with kilometer‐scale simulations within a 10% error margin with 1 km providing a good balance between accuracy and computational cost.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an end-to-end machine learning model that automatically generates descriptors that capture a complex representation of a material’s structure and chemistry. This approach builds on computational topology techniques (namely, persistent homology) and word embeddings from natural language processing. It automatically encapsulates geometric and chemical information directly from the material system. We demonstrate our approach on multiple nanoporous metal–organic framework datasets by predicting methane and carbon dioxide adsorption across different conditions. Our results show considerable improvement in both accuracy and transferability across targets compared to models constructed from the commonly-used, manually-curated features, consistently achieving an average 25–30% decrease in root-mean-squared-deviation and an average increase of 40–50% in R 2 scores. A key advantage of our approach is interpretability: Our model identifies the pores that correlate best to adsorption at different pressures, which contributes to understanding atomic-level structure–property relationships for materials design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

X-ray multi-probe data acquisition: A novel technique for laser pump x-ray transient absorption spectroscopy

We report the development and implementation of a novel data acquisition (DAQ) technique for synchrotron-based laser pump X-ray Transient Absorption (XTA) spectroscopy, called X-ray Multi-Probe DAQ (XMP DAQ). This technique utilizes high performance analog to digital converters and home-built software to efficiently measure and process the XTA signal from all x-ray pulses between laser excitations. XMP DAQ generates a set of time resolved x-ray absorption spectra at thousands of different pump–probe time delays simultaneously. Two distinct XMP DAQ schemes are deployed to accommodate different synchrotron storage ring filling patterns. Current Integration (CI) DAQ is a quasi-analog technique that implements a fitting procedure to extract the time resolved absorption intensity from the averaged fluorescence detector response. The fitting procedure eliminates issues associated with small drifts in the voltage baseline and greatly enhances the accuracy of the technique. Photon Counting (PC) DAQ is a binary technique that uses a time resolved histogram to calculate the XTA spectrum. While PC DAQ is suited to measure XTA data with closely spaced x-ray pulses (~10 ns) and a low count rate (<1 detected photon/pulse), CI DAQ works best for widely spaced pulses (tens of ns or greater) with a high count rate (>1 detected photon/pulse). XMP DAQ produces a two-dimensional XTA dataset, enabling efficient quantitative analysis of photophysical and photochemical processes from the sub-nanosecond timescale to 100 μs and longer.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Slater transition methods for core-level electron binding energies

Methods for computing core-level ionization energies using self-consistent field (SCF) calculations are evaluated and benchmarked. These include a “full core hole” (or “ΔSCF”) approach that fully accounts for orbital relaxation upon ionization, but also methods based on Slater’s transition concept in which the binding energy is estimated from an orbital energy level that is obtained from a fractional-occupancy SCF calculation. A generalization that uses two different fractional-occupancy SCF calculations is also considered. The best of the Slater-type methods afford mean errors of 0.3–0.4 eV with respect to experiment for a dataset of K-shell ionization energies, a level of accuracy that is competitive with more expensive many-body techniques. An empirical shifting procedure with one adjustable parameter reduces the average error below 0.2 eV. Here, this shifted Slater transition method is a simple and practical way to compute core-level binding energies using only initial-state Kohn–Sham eigenvalues. It requires no more computational effort than ΔSCF and may be especially useful for simulating transient x-ray experiments where core-level spectroscopy is used to probe an excited electronic state, for which the ΔSCF approach requires a tedious state-by-state calculation of the spectrum. As an example, we use Slater-type methods to model x-ray emission spectroscopy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Deep Learning Approach for High-accuracy Electron Counting of Monolithic Active Pixel Sensor-type Direct Electron Detectors at Increased Electron Dose

Abstract Electron counting can be performed algorithmically for monolithic active pixel sensor direct electron detectors to eliminate readout noise and Landau noise arising from the variability in the amount of deposited energy for each electron. Errors in existing counting algorithms include mistakenly counting a multielectron strike as a single electron event, and inaccurately locating the incident position of the electron due to lateral spread of deposited energy and dark noise. Here, we report a supervised deep learning (DL) approach based on Faster region-based convolutional neural network (R-CNN) to recognize single electron events at varying electron doses and voltages. The DL approach shows high accuracy according to the near-ideal modulation transfer function (MTF) and detector quantum efficiency for sparse images. It predicts, on average, 0.47 pixel deviation from the incident positions for 200 kV electrons versus 0.59 pixel using the conventional counting method. The DL approach also shows better robustness against coincidence loss as the electron dose increases, maintaining the MTF at half Nyquist frequency above 0.83 as the electron density increases to 0.06 e−/pixel. Thus, the DL model extends the advantages of counting analysis to higher dose rates than conventional methods.

Materials Science↗
DOE OSTI2021

Bridging the gap of storage ring light sources and linac-driven free-electron lasers

High-gain free-electron lasers (FELs) are driven by short, high-charge density electron beams as only produced at dedicated single pass or recirculating linear accelerators. We describe new conceptual, technical, and modeling solutions to produce subpicosecond, up to ~100 μJ-energy extreme ultra-violet and soft x-ray FEL pulses at high- and tunable repetition rates, from diffraction-limited storage ring light source. In contrast to previously proposed schemes, we show that lasing can be simultaneous to the standard multibunch radiation emission from short insertion devices, and that it can be obtained with limited impact on the storage ring infrastructure. By virtue of the high-average power but moderate pulse energy, the storage ring-driven high-gain FEL would open the door to unprecedented accuracy in time-resolved spectroscopic analysis of matter in the linear response regime, in addition to inelastic scattering experiments.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Bhatnagar-Gross-Krook subgrid model for neutrino quantum kinetics

Here, we present a new subgrid model for neutrino quantum kinetics, which is primarily designed to incorporate effects of collective neutrino oscillations into neutrino-radiation-hydrodynamic simulations for core-collapse supernovae and mergers of compact objects. We approximate the neutrino oscillation term in a quantum kinetic equation using the Bhatnagar-Gross-Krook (BGK) relaxation-time prescription, and the transport equation is directly applicable for classical neutrino transport schemes. The BGK model is motivated by recent theoretical indications that nonlinear phases of collective neutrino oscillations settle into quasisteady structures. We explicitly provide basic equations of the BGK subgrid model for both multiangle and moment-based neutrino transport to facilitate the implementation of the subgrid model in the existing neutrino transport schemes. We also show the capability of our BGK subgrid model by comparing it to fully quantum kinetic simulations for fast neutrino-flavor conversion. We find that the overall properties can be well reproduced in the subgrid model; the error of angular-averaged survival probability of neutrinos is within ~20%. By identifying the source of error, we also discuss perspectives to improve the accuracy of the subgrid model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Multi-objective optimization of peel and shear strengths in ultrasonic metal welding using machine learning-based response surface methodology

Ultrasonic metal welding (UMW) is a solid-state joining technique with varied industrial applications. Despite of its numerous advantages, UMW has a relative narrow operating window and is sensitive to variations in process conditions. As such, it is imperative to quantitatively characterize the influence of welding parameters on the resulting joint quality. The quantification model can be subsequently used to optimize the parameters. Conventional response surface methodology (RSM) usually employs linear or polynomial models, which may not be able to capture the intricate, nonlinear input-output relationships in UMW. Furthermore, some UMW applications call for simultaneous optimization of multiple quality indices such as peel strength, shear strength, electrical conductivity, and thermal conductivity. To address these challenges, this paper develops a machine learning (ML)-based RSM to model the input-output relationships in UMW and jointly optimize two quality indices, namely, peel and shear strengths. The performance of various ML methods including spline regression, Gaussian process regression (GPR), support vector regression (SVR), and conventional polynomial regression models with different orders is compared. A case study using experimental data shows that GPR with radial basis function (RBF) kernel and SVR with RBF kernel achieve the best prediction accuracy. The obtained response surface models are then used to optimize a compound joint strength indicator that is defined as the average of normalized shear and peel strengths. In addition, the case study reveals different patterns in the response surfaces of shear and peel strengths, which has not been systematically studied in the literature. While developed for the UMW application, the method can be extended to other manufacturing processes.

42 ENGINEERING↗
DOE OSTI2025

High-fidelity forced convection simulations of the University of Wisconsin–Madison air-cooled reactor cavity cooling system

Among the various types of advanced nuclear technologies that exist, high-temperature gas-cooled reactors (HTGRs) stand out for their co-generation capabilities and exceptional passive safety systems. HTGRs rely on a reactor cavity cooling system (RCCS) to remove decay heat from the reactor pressure vessel (RPV) during the course of long-term transients. Several studies have focused on experimental and numerical modeling of the RCCS; however, the literature reveals a notable gap when it comes to high-fidelity simulations such as direct numerical simulations (DNS) or large eddy simulation (LES) models of the RCCS. An LES model was developed for forced convection inside the RCCS so as to obtain a high-fidelity representation of the RCCS at a scale never before attempted. Furthermore, the LES model was compared against three different Reynolds-averaged Navier–Stokes (RANS) models (Realizable k − ϵ, k − ω SST, and Reynolds stress model [RSM]). This comparison verified the robustness and accuracy of the turbulence modeling approach of the RANS models for the RCCS. Finally, based on the comparison between the LES and the RANS models, the k − ω SST RANS model of the experimental facility was developed and compared against the available experimental data.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

A versatile machine learning workflow for high-throughput analysis of supported metal catalyst particles

Accurate and efficient characterization of nanoparticles (NPs), particularly regarding particle size distribution, is essential for advancing our understanding of their structure-property relationship and facilitating their design for various applications. In this study, we introduce a novel two-stage artificial intelligence (AI)-driven workflow for NP analysis that leverages prompt engineering techniques from state-of-the-art single-stage object detection and large-scale vision transformer (ViT) architectures. This methodology is applied to transmission electron microscopy (TEM) and scanning TEM (STEM) images of heterogeneous catalysts, enabling high-resolution, high-throughput analysis of particle size distributions for supported metal catalyst NPs. The model's performance in detecting and segmenting NPs is validated across diverse heterogeneous catalyst systems, including various metals (Ru, Cu, PtCo, and Pt), supports (silica (SiO 2 ), γ-alumina (γ-Al 2 O 3 ), and carbon black), and particle diameter size distributions with mean and standard deviations ranging from 1.6 ± 0.2 nm to 9.7 ± 4.6 nm. The proposed machine learning (ML) methodology achieved an average F1 overlap score of 0.91 ± 0.01 and demonstrated the ability to disentangle overlapping NPs anchored on catalytic support materials. The segmentation accuracy is further validated using the Hausdorff distance and robust Hausdorff distance metrics, with the 90th percent of the robust Hausdorff distance showing errors within 0.4 ± 0.1 nm to 1.4 ± 0.6 nm. In conclusion, our AI-assisted NP analysis workflow demonstrates robust generalization across diverse datasets and can be readily applied to similar NP segmentation tasks without requiring costly model retraining.

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

Spin Splitting Energy of Transition Metals: A New, More Affordable Wave Function Benchmark Method and Its Use to Test Density Functional Theory

Accurately predicting the spin splitting energy of chemical species is important for understanding their reactivity and magnetic properties, but it is very challenging, especially for molecules containing transition metals. One impediment to progress is the scarcity of accurate benchmark data. Here we report a set of calculations designed to yield reliable benchmarks for simple transition-metal complexes that can be used to test density functional methods that are affordable for large systems of more practical interest. Various wave function methods are tested against experiment for Fe 2+ , Fe 3+ , and Co 3+ , including CASSCF, CASPT2, CASPT3, MRCISD, MRCISD+Q, ACPF, AQCC, CCSD(T), and CASPT2/CCSD(T) and also a new method called CASPT2.5, which is performed by taking the average of the CASPT2 and CASPT3 energies. Furthermore, we find that MRCISD+Q, ACPF, and AQCC require smaller active spaces for good accuracy than are required by CASPT2 and CASPT3, and this aspect may be important for calculations on larger molecules; here we find that CASPT2.5 extrapolated to a complete basis set is the most suitable method—in terms of computational cost and in terms of accuracy on monatomic systems—and therefore we chose this method for molecular benchmarks. Then Kohn–Sham density functional calculations with 60 exchange-correlation functionals are tested for FeF 2 , FeCl 2 , and CoF 2 . We find that MN15-L, M06-SX, and revM06 have very good agreement with CASPT2.5 benchmarks in terms of both the spin splitting energy and the optimized geometry for each spin state. In addition, we recommend def2-TZVP as the most suitable basis set to perform density functional calculations for molecular spin splitting energies; extra polarization functions in the basis set do not help to increase the accuracy of the spin splitting energy in KS calculations.

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

A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts

Abstract For high-throughput screening of materials for heterogeneous catalysis, scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species. However, conditioning on a single descriptor ignores the model uncertainty and leads to suboptimal prediction of the chemisorption energy. In this article, we extend the single descriptor linear scaling relation to a multi-descriptor linear regression models to leverage the correlation between adsorption energy of any two pair of adsorbates. With a large dataset, we use Bayesian Information Criteria (BIC) as the model evidence to select the best linear regression model. Furthermore, Gaussian Process Regression (GPR) based on the meaningful convolution of physical properties of the metal-adsorbate complex can be used to predict the baseline residual of the selected model. This integrated Bayesian model selection and Gaussian process regression, dubbed as residual learning, can achieve performance comparable to standard DFT error (0.1 eV) for most adsorbate system. For sparse and small datasets, we propose an ad hoc Bayesian Model Averaging (BMA) approach to make a robust prediction. With this Bayesian framework, we significantly reduce the model uncertainty and improve the prediction accuracy. The possibilities of the framework for high-throughput catalytic materials exploration in a realistic setting is illustrated using large and small sets of both dense and sparse simulated dataset generated from a public database of bimetallic alloys available in Catalysis-Hub.org.

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

Ancilla-entangling Floquet kicks for accelerating quantum algorithms

Quantum simulation with adiabatic annealing can provide insight into difficult problems that are impossible to study with classical computers. However, it deteriorates when the systems scale up due to the shrinkage of the excitation gap and thus places an annealing rate bottleneck for high success probability. Here, in this study, we accelerate quantum simulation using digital multiqubit gates that entangle primary system qubits with ancillary qubits. The practical benefits originate from tuning the ancillary gauge degrees of freedom to enhance the quantum algorithm's original functionality in the system registry. For simple but nontrivial short-ranged, infinite long-ranged transverse-field Ising models, and the hydrogen molecule model after qubit encoding, we show improvement in the time to solution by one hundred percent but with higher accuracy through exact state-vector numerical simulation in a digital-analog setting. The findings are further supported by time-averaged Hamiltonian theory.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Hvac: Removing I/O Bottleneck for Large-Scale Deep Learning Applications

Scientific communities are increasingly adopting deep learning (DL) models in their applications to accelerate scientific discovery processes. However, with rapid growth in the computing capabilities of HPC supercomputers, large-scale DL applications have to spend a significant portion of training time performing I/O to a parallel storage system. Previous research works have investigated optimization techniques such as prefetching and caching. Unfortunately, there exist non-trivial challenges to adopting the existing solutions on HPC supercomputers for large-scale DL training applications, which include non-performance and/or failures at extreme scale, lack of portability and generality in design, complex deployment methodology, and being limited to a specific application or dataset. To address these challenges, we propose High-Velocity AI Cache (HVAC), a distributed read-cache layer that targets and fully exploits the node-local storage or near node-local storage technology. HVAC seamlessly accelerates read I/O by aggregating node-local or near node-local storage, avoiding metadata lookups and file locking while preserving portability in the application code. We deploy and evaluate HVAC on 1,024 nodes (with over 6000 NVIDIA V100 GPUS) of the Summit supercomputer. In particular, we evaluate the scalability, efficiency, accuracy, and load distribution of HVAC compared to GPFS and XFS-on-NVMe. With four different DL applications, we observe an average 25 % performance improvement atop GPFS and 9% drop against XFS-on-NVMe, which scale linearly and are considered the performance upper bound. We envision HVAC as an important caching library for upcoming HPC supercomputers such as Frontier.

Khan, Awais↗
DOE OSTI2022

Multi-Fidelity Stochastic Economic Dispatch for Operating Low Carbon Power Grids

Grid operators can address the inherently stochastic nature of renewables by solving a two-stage stochastic programming model that minimizes the cost of dispatch decisions while accounting for the complex grid dynamics. It is common to use a sample average approximation to estimate the expectation of the second stage costs in this model. However, the large sample count needed for numerical accuracy makes effective modeling large-scale electric grids computationally intractable. We introduce a control variate multi-fidelity estimator for the second-stage recourse that enables high quality dispatch decisions in real-time with a reduced computational burden. We obtain a hierarchy of model fidelities by linearizing the AC power flow system representation to DC power flow, and by relaxing transmission and voltage network constraints. We evaluate the performance of our proposed method on a synthetic grid with 73 buses against a deterministic baseline with persistence forecast and a high-fidelity reference. Our analysis shows a computational speed-up of 7.62x with a minimal loss in accuracy. The multi-fidelity method is well suited to fidelity combinations that use a simplified network topology in their lower fidelity model and is an attractive option for applications where accurate grid modeling needed on a limited computational budget.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Development of a Vibration Technique Based on Geometric Optimization for Fatigue Life Evaluation of Sandwich Composite Structures

A major obstacle to obtaining cost-effective experimental data on the fatigue life of sandwich panels is the prohibitive amount of time and cost required to carry out millions of cycles. On the other hand, vibration techniques applied to sandwich geometries fail to match the stress patterns that are obtained from standard flexural fatigue tests. To overcome such limitations, a vibration-based fatigue technique is proposed, which entails the use of sandwich specimens whose geometries are optimized to reproduce the stress distribution observed during three point bend loading while vibrating at the first resonant frequency. The proposed vibration technique was experimentally validated. The results, compared with the average number of cycles to failure at different stress ratios obtained via the Three-Point Bending test, showed high levels of accuracy. The proposed method is robust and time effective and indicates the possibility of attaining fatigue lifetime prediction of a wide class of composite elements, such as sandwich panels.

36 MATERIALS SCIENCE↗