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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Cholesky Decomposition-Based Implementation of Relativistic Two-Component Coupled-Cluster Methods for Medium-Sized Molecules

A Cholesky decomposition (CD)-based implementation of relativistic two-component coupled-cluster (CC) and equation-of-motion CC (EOM-CC) methods using an exact two-component Hamiltonian augmented with atomic-mean-field integrals (the X2CAMF scheme) is reported. Furthermore, the present CD-based implementation of X2CAMF-CC and EOM-CC methods employs atomic-orbital-based algorithms to avoid the construction of two-electron integrals and intermediates involving three and four virtual indices. The CD-based implementation extends the applicability of X2CAMF-CC and EOMCC methods to medium-sized molecules with the correlation of around 1000 spinors. Benchmark calculations for uranium-containing small molecules have been performed to assess the dependence of CC results with respect to the Cholesky threshold. A Cholesky threshold of 10 –4 is shown to maintain chemical accuracy. Example calculations to illustrate the capability of the CD-based relativistic CC methods are reported for the bond dissociation energy of the uranium hexafluoride molecule, UF 6 , with up to quadruple-zeta basis sets and the lowest excitation energy in solvated uranyl ion [UO 2 2+ (H 2 O) 12 ].

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

Quantum adiabatic machine learning by zooming into a region of the energy surface

Recent work has shown that quantum annealing for machine learning, referred to as QAML, can perform comparably to state-of-the-art machine learning methods with a specific application to Higgs boson classification. Here, we propose QAML-Z, an algorithm that iteratively zooms in on a region of the energy surface by mapping the problem to a continuous space and sequentially applying quantum annealing to an augmented set of weak classifiers. Results on a programmable quantum annealer show that QAML-Z matches classical deep neural network performance at small training set sizes and reduces the performance margin between QAML and classical deep neural networks by almost 50% at large training set sizes, as measured by area under the receiver operating characteristic curve. The significant improvement of quantum annealing algorithms for machine learning and the use of a discrete quantum algorithm on a continuous optimization problem both opens a class of problems that can be solved by quantum annealers and suggests the approach in performance of near-term quantum machine learning towards classical benchmarks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Conceptual design of a high reactive-power ferroelectric fast reactive tuner

We present a novel design of a ferroelectric fast reactive tuner (FE-FRT) capable of modulating mega-VAR reactive power on a submicrosecond timescale. The high reactive power capability of our design extends the range of applications of reactive tuners to numerous applications. We present a detailed analytical model of the performance of a megawatt-class reactive power device and benchmark it against finite-element method eigenmode and frequency domain electromagnetic simulations. We introduce new features, including an annulus design for the ferroelectric capacitors and capacitive window coupling to the cavity. We consider thermal design issues and nonlinear effects in the ferroelectric. The model covers several configurations, allowing control of the frequency of superconducting and normal-conducting cavities in a variety of applications and frequencies. We calculate that the FE-FRT designed should be capable of handling around 0.45 MVAR of reactive power with around 3 kW of resistive losses, providing a frequency tuning range of 8 kHz in an example of 400 MHz cavity geometry. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Link Scheduling in Satellite Networks via Machine Learning Over Riemannian Manifolds

Low Earth Orbit (LEO) satellites play a crucial role in enhancing global connectivity, serving a complementary solution to existing terrestrial systems. In wireless networks, scheduling is a vital process that allocates time-frequency resources to users for interference management. However, LEO satellite networks face significant challenges in scheduling their links towards ground users due to the satellites’ mobility and overlapping coverage. This paper addresses the dynamic link scheduling problem in LEO satellite networks by considering spatio-temporal correlations introduced by the satellites’ movements. The first step in the proposed solution involves modeling the network over Riemannian manifolds, thanks to their representation as symmetric positive definite matrices. We introduce two machine learning (ML)-based link scheduling techniques that model the dynamic evolution of satellite positions and link conditions over time and space. To accurately predict satellite link states, we present a recurrent neural network (RNN) over Riemannian manifolds, which captures spatio-temporal characteristics over time. Furthermore, we introduce a separate model, the convolutional neural network (CNN) over Riemannian manifolds, which captures geometric relationships between satellites and users by extracting spatial features from the network topology across all links. Simulation results demonstrate that both RNN and CNN over Riemannian manifolds deliver comparable performance to the fractional programming-based link scheduling (FPLinQ) benchmark. Remarkably, unlike other ML-based models that require extensive training data, both models only need 30 training samples to achieve over 99% of the sum rate while maintaining similar computational complexity relative to the benchmark.

42 ENGINEERING↗
DOE OSTI2023

RX-ADS: Interpretable Anomaly Detection Using Adversarial ML for Electric Vehicle CAN Data

Recent year has brought considerable advancements in Electric Vehicles (EVs) and associated infrastructures/ communications. Intrusion Detection Systems (IDS) are widely deployed for anomaly detection in such critical infrastructures. This paper presents an Interpretable Anomaly Detection System (RX-ADS) for intrusion detection in CAN protocol communication in EVs. Contributions include: 1) window based feature extraction method; 2) deep Autoencoder based anomaly detection method; and 3) adversarial machine learning based explanation generation methodology. The presented approach was tested on two benchmark CAN datasets: OTIDS and Car Hacking. The anomaly detection performance of RX-ADS was compared against the state-of-the-art approaches on these datasets: HIDS and GIDS. The RX-ADS approach presented performance comparable to the HIDS approach (OTIDS dataset) and has outperformed HIDS and GIDS approaches (Car Hacking dataset). Further, the proposed approach was able to generate explanations for detected abnormal behaviors arising from various intrusions. Furthermore, these explanations were later validated by information used by domain experts to detect anomalies. Other advantages of RX-ADS include: 1) the method can be trained on unlabeled data; 2) explanations help experts in understanding anomalies and root course analysis, and also help with AI model debugging and diagnostics, ultimately improving user trust in AI systems.

42 ENGINEERING↗
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 OSTI2020

Concurrent Relaxation through Accelerated Deep Learning

CRADL captures performance metrics of machine learning algorithms operating on mesh data from multiphysics codes This proxy application is a tool to explore scalability of inference on HPC platforms, and also gather performance metrics for inference on new machine learning specific hardware. CRADL is designed to give users as fine a control as possible over an inference simulation. Users may select the number of cycles, amount of data, and batch size to pass to the accelerator of choice. Additionally the user may select a number of performance optimization libraries and flags. CRADL comes packaged with a repository of anonymized multi-physics simulation data, as well as a pretrained model for inference. The code allows a user to load their own pre-trained model and data if they wish. The code can operate in multiple parallelization schemes, with performance enhancing options such as half-precision libraries, PyTorch benchmarking, and pinned memory with non-blocking data transfers.

Zieb, KristoferJ.↗
DOE OSTI2021

Attend and Decode: 4D fMRI Task State Decoding Using Attention Models

Source code for Brain Attend and Decode paper. Functional magnetic resonance imaging (fMRI) is a neuroimaging modality that captures the blood oxygen level in a subject's brain while the subject either rests or performs a variety of functional tasks under different conditions. Given fMRI data, the problem of inferring the task, known as task state decoding, is challenging due to the high dimensionality (hundreds of million sampling points per datum) and complex spatio-temporal blood flow patterns inherent in the data. In this work, we propose to tackle the fMRI task state decoding problem by casting it as a 4D spatiotemporal classification problem. We present a novel architecture called Brain Attend and Decode (BAnD), that uses residual convolutional neural networks for spatial feature extraction and self-attention mechanisms for temporal modeling. We achieve significant performance gain compared to previous works on a 7-task benchmark from the large-scale Human Connectome Project-Young Adult (HCP-YA) dataset. We also investigate the transferability of BAnD's extracted features on unseen HCP tasks, either by freezing the spatial feature extraction layers and retraining the temporal model, or finetuning the entire model. The pre-trained features from BAnD are useful on similar tasks while finetuning them yields competitive results on unseen tasks/conditions.

Ng, BrendaM.↗
DOE OSTI2021

NMSBA: Continuous Application Benchmarking & Analysis – CABA

The CABA project is a test of a new tool, Survey,developed by Trenza to be used not only for benchmarking or profiling programs but also to allow incorporation of the information provided by Survey to be utilized in a CI, continuous integration,tool such as GitLab CI.Survey is foremost a means of assessing code performance in terms of time and operations which for computer programmers is known as benchmarking.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Flattop Benchmark CED-2 (IER-423)

The final design for the Flattop HEU Core Benchmark is presented in this report. Physical measurements will be performed on Flattop components using modern instruments and techniques to define uncertainties necessary to meet contemporary ICSBEP standards. A preliminary re-evaluation of ICSBEP HMF-0028 (Flattop HEU Core) led to the development of IER-423 CED-1, wherein total relative uncertainties in $k_{eff}$ were provided based on a new detailed model. The estimated uncertainty on the total uncertainty is +100%/-0%, given a lack of documented uncertainties on mass, dimensions, and densities of predominantly the natural uranium (NU) and highly enriched uranium (HEU) components.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

CFD Simulations to Support Pronghorn Modeling of a Molten Salt Fast Reactor

To assist with the deployment of next generation molten salt reactors (MSRs), the DOE NEAMS program is developing advanced simulation capabilities. As part of this work, the high-fidelity CFD tool, Nek5000 was used to inform model development for the engineering scale tool, Pronghorn. The particular case chosen was the EVOL molten salt fast reactor concept. This concept uses an open core design, which is well known to be sensitive to the particular geometry and can have complex flow behavior that varies across a range of conditions. This makes it an idea candidate for demonstration of the Hi2Lo concept of using higher fidelity models to benchmark lower fidelity models. Simulations of the EVOL design were performed using both LES (high-fidelity) and RANS (moderate-fidelity) in Nek5000. The LES results indicated discrepancies in 2-D axisymmetric RANS, indicating that the core is better modeled in RANS with a 3-D wedge representation of a portion of the core, which can accurately account for the effect of the inlet channels. Finally, results from the 3-D RANS were used to modify an existing turbulence model in Pronghorn. Improvement in the calibrated Pronghorn model was demonstrated. Future work is suggested to focus on expanding the LES calculation to provide a more direct point of comparison for both the RANS and Pronghorn models as well as incorporating more of the relevant MSR physics (such as delayed neutron precursor tracking) into the model.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Zero and Finite Temperature Quantum Simulations Powered by Quantum Magic

We introduce a quantum information theory-inspired method to improve the characterization of many-body Hamiltonians on near-term quantum devices. We design a new class of similarity transformations that, when applied as a preprocessing step, can substantially simplify a Hamiltonian for subsequent analysis on quantum hardware. By design, these transformations can be identified and applied efficiently using purely classical resources. In practice, these transformations allow us to shorten requisite physical circuit-depths, overcoming constraints imposed by imperfect near-term hardware. Importantly, the quality of our transformations is t u n a b l e : we define a 'ladder' of transformations that yields increasingly simple Hamiltonians at the cost of more classical computation. Using quantum chemistry as a benchmark application, we demonstrate that our protocol leads to significant performance improvements for zero and finite temperature free energy calculations on both digital and analog quantum hardware. Specifically, our energy estimates not only outperform traditional Hartree-Fock solutions, but this performance gap also consistently widens as we tune up the quality of our transformations. In short, our quantum information-based approach opens promising new pathways to realizing useful and feasible quantum chemistry algorithms on near-term hardware.

Physics↗
DOE OSTI2026

Techno-Economic Evaluation of Electrified Vehicle Options in Drayage Fleets

The electrification of drayage fleets offers potential economic and operational benefits, but the financial viability of electrified vehicles remains sensitive to battery cost, energy price, and fleet usage patterns. While total cost of ownership (TCO) is a useful benchmark, fleet operators and investors are equally concerned with investment performance metrics such as payback period (PB) and Internal Rate of Return (IRR), which better reflect financial risks and investment return timelines. This study develops a unified techno-economic framework that jointly evaluates TCO, PB, and IRR to determine when electrified trucks become cost-effective alternatives to diesel trucks. Building on a previously developed cost modeling tool and using real-world telematics data from a Class 8 drayage fleet at the Port of Savannah, the analysis incorporates projected battery cost trajectories, electricity and diesel price trends, vehicle efficiency improvements, and multiple battery capacities. Parameter ranges reflect widely cited projections and observed drayage-duty-cycle variability. A surrogate-modeling method approximates economic performance across thousands of battery cost–electricity price combinations, enabling high-resolution identification of conditions that achieve TCO parity, acceptable PB thresholds, and target IRR levels. Additionally, the study estimates the evolving share of the fleet that can feasibly electrify over time under multiple economic metrics. This integrated framework offers a novel, data-driven approach to inform risk-aware decision-making for fleet electrification and supports investment planning under evolving cost and operational conditions.

Sun, Ruixiao [ORNL] (ORCID:0000000341768676)↗
DOE OSTI2022

Novel Real- Time EMT- TS Modeling Architecture for Feeder Blackstart Simulations

This paper presents the development and benchmarking of a novel real-time electromagnetic-transient and transient-stability (EMT-TS) modeling architecture for distribution feeder restoration studies. The work presents for the first time in the literature a real-time EMT-TS testbed in which the grid-forming unit is simulated in EMT domain, operating as the slack bus of the phasor domain while including unbalanced voltage conditions. To evaluate the performance and limitations of the proposed model, an equivalent EMT testbed is developed to serve as a benchmark. First, the co-simulation framework and the domain coupling methodology are introduced. Next, the steady state operation of the EMT-TS and EMT models are matched. Then, tests are conducted to analyze the transient performance of the proposed EMT-TS model when compared to its EMT benchmark. Results reveal that when utilizing a battery energy storage system (BESS) as the grid-forming unit, the EMT-TS testbed can maintain high accuracy for typical load steps.

Paduani, Victor↗
DOE OSTI2023

Calibration strategy of the PROSPECT-II detector with external and intrinsic sources

This paper presents an energy calibration scheme for an upgraded reactor antineutrino detector for the Precision Reactor Oscillation and Spectrum Experiment (PROSPECT). The PROSPECT collaboration is preparing an upgraded detector, PROSPECT-II (P-II), to advance capabilities for the investigation of fundamental neutrino physics, fission processes and associated reactor neutrino flux, and nuclear security applications. P-II will expand the statistical power of the original PROSPECT (P-I) dataset by at least an order of magnitude. The new design builds upon previous P-I design and focuses on improving the detector robustness and long-term stability to enable multi-year operation at one or more sites. The new design optimizes the fiducial volume by elimination of dead space previously occupied by internal calibration channels, which in turn necessitates the external deployment. In this paper, we describe a calibration strategy for P-II. Here, the expected performance of externally deployed calibration sources is evaluated using P-I data and a well-benchmarked simulation package by varying detector segmentation configurations in the analysis. The proposed external calibration scheme delivers a compatible energy scale model and achieves comparable performance with the inclusion of an additional AmBe neutron source, in comparison to the previous internal arrangement. Most importantly, the estimated uncertainty contribution from the external energy scale calibration model meets the precision requirements of the P-II experiment.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

CI/CD Efforts for Validation, Verification and Benchmarking OpenMP Implementations

Software developers must adapt to keep up with the changing capabilities of platforms so that they can utilize the power of High-Performance Computers (HPC), including exascale systems. OpenMP, a directive-based parallel programming model, allows developers to include directives to existing C, C++, or Fortran code to allow node level parallelism without compromising performance. This paper describes our CI/CD efforts to provide easy evaluation of the support of OpenMP across different compilers using existing testsuites and benchmark suites on HPC platforms. Our main contributions include (1) the set of a Continuous Integration (CI) and Continuous Development (CD) workflow that captures bugs and provides faster feedback to compiler developers, (2) an evaluation of OpenMP (offloading) implementations supported by AMD, HPE, GNU, LLVM, and Intel, and (3) evaluation of the quality of compilers across different heterogeneous HPC platforms. With the comprehensive testing through the CI/CD workflow, we aim to provide a comprehensive understanding of the current state of OpenMP (offloading) support in different compilers and heterogeneous platforms consisting of CPUs and GPUs from NVIDIA, AMD, and Intel.

Jarmusch, Aaron↗
DOE OSTI2023

Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets

Abstract Recently, deep convolutional neural networks (CNNs) have revolutionized image “super resolution” (SR), dramatically outperforming past methods for enhancing image resolution. They could be a boon for the many scientific fields that involve imaging or any regularly gridded datasets: satellite remote sensing, radar meteorology, medical imaging, numerical modeling, and so on. Unfortunately, while SR-CNNs produce visually compelling results, they do not necessarily conserve physical quantities between their low-resolution inputs and high-resolution outputs when applied to scientific datasets. Here, a method for “downsampling enforcement” in SR-CNNs is proposed. A differentiable operator is derived that, when applied as the final transfer function of a CNN, ensures the high-resolution outputs exactly reproduce the low-resolution inputs under 2D-average downsampling while improving performance of the SR schemes. The method is demonstrated across seven modern CNN-based SR schemes on several benchmark image datasets, and applications to weather radar, satellite imager, and climate model data are shown. The approach improves training time and performance while ensuring physical consistency between the super-resolved and low-resolution data. Significance Statement Recent advancements in using deep learning to increase the resolution of images have substantial potential across the many scientific fields that use images and image-like data. Most image super-resolution research has focused on the visual quality of outputs, however, and is not necessarily well suited for use with scientific data where known physics constraints may need to be enforced. Here, we introduce a method to modify existing deep neural network architectures so that they strictly conserve physical quantities in the input field when “super resolving” scientific data and find that the method can improve performance across a wide range of datasets and neural networks. Integration of known physics and adherence to established physical constraints into deep neural networks will be a critical step before their potential can be fully realized in the physical sciences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Uncertainty Quantification Framework for Predicting Material Response with Large Number of Parameters: Application to Creep Prediction in Ferritic-Martensitic Steels Using Combined Crystal Plasticity and Grain Boundary Models

This paper presents an uncertainty quantification (UQ) framework for the physics-based model prediction of material response with a large number of parameters. The application problem presented in this work is that of predicting creep in Grade 91 steel at 600°C. The material response is defined with a physically based microstructural model with constitutive equations emulating several observed phenomena in Grade 91 and embodied into an explicit geometry mesoscale finite element model for prior austenite grains and grain boundaries. Creep within the grains and in grain boundaries are represented by crystal plasticity for dislocation motion and a physics-based model for cavity growth and nucleation, respectively. The creep behavior of this material is influenced by several parameters, some of which have a wide range of variation based on experimental data. UQ combined with microstructural modeling can discover the core microstructural causes of experimental variability, leading to improved materials with lower variability in critical long-term material properties. In this study, we investigate the model's uncertainty to identify material properties that may be modified during production to increase creep life and analyze different components of the crystal plasticity model for improvements. For this purpose, a quantity of interest is defined as time to minimum creep rate, which correlates well to the creep failure of the material. A deep neural network model was trained and validated to be used as a surrogate for the finite element model. Then, a variance-based sensitivity analysis is performed on the surrogate model to find the Sobol indices of the input parameters in respect to the output quantity of interest. The Sobol indices are used to reduce the dimensionality of the model. Generalized polynomial chaos expansion is used on the reduced basis models to propagate the uncertainty from the input parameters to the quantity of interest using the deep neural network surrogate model. These results are benchmarked against uncertainty propagation using Monte Carlo simulations. In conclusion, the UQ performed through the reduced basis model captures almost all the uncertainty in the model with significantly fewer simulations, making it possible to perform the UQ directly via simulations with the finite element model rather than surrogate machine-learned models.

36 MATERIALS SCIENCE↗