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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

LANL-SNL Collaboration on NCS Validation

During 2016, nuclear criticality safety (NCS) practitioners from SNL and code developers from LANL collaborated in several areas of interest to the DOE/NNSA Nuclear Criticality Safety Program (NCSP). This collaboration involved: - Testing of the preliminary release of the MCNP6- Whisper methodology, with feedback to the developers, - Sharing of the benchmark catalogs (the collection of MCNP input files and benchmark results), with 1101 cases from LANL and 866 cases from SNL, - Comparison and analysis of 357 benchmarks common to both catalogs, - Investigation of the impact of the different benchmark catalogs on sensitivity-uncertainty based NCS validation results from MCNP6-Whisper, - Investigation of the impact of randomized selections from the benchmark catalog on sensitivity-uncertainty based validation results from MCNP6-Whisper. - Investigation of the use of MCNP6-Whisper in selecting benchmarks for use in NCS validation for unique, nonstandard, legacy fuel applications. This paper summarizes the collaboration work and initial results. It must be noted that the results described herein are preliminary and need further research and detailed analysis. However, the initial results are very interesting, and it is important to share them with the NCSP community.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Uncertainty Quantification Study of CTF for the OECD/NEA LWR Uncertainty Analysis in Modeling Benchmark

This work describes the results of a quantitative uncertainty analysis of the thermal-hydraulic subchannel code for nuclear engineering applications, Coolant Boiling in Rod Arrays-Three Field (COBRA-TF). CTF is used, which is a version of COBRA-TF developed in cooperation between the Consortium for Advanced Simulation of Light Water Reactors and North Carolina State University. Four steady-state cases from Phase II Exercise 3 of the Organisation for Economic Co-operation and Development/Nuclear Energy Agency Light Water Reactor Uncertainty Analysis in Modeling (UAM) Benchmark are analyzed using the statistical analysis tool, Design Analysis Kit for Optimization and Terascale Applications (Dakota). The input parameters include boundary condition, geometry, and modeling uncertainties, which are selected using a sensitivity study and then defined based on expert judgment. Here, a forward uncertainty quantification method with Latin hypercube sampling (LHS) is used, where the sample size is based on available computational resources.

42 ENGINEERING↗
DOE OSTI2018

Modular system for probabilistic fracture mechanics analysis of embrittled reactor pressure vessels in the Grizzly code

In light water reactor (LWR) nuclear power plants, the reactor pressure vessel (RPV) plays an essential safety role, and its integrity must be ensured during a variety of transient loading conditions. These can include off-normal conditions such as a pressurized thermal shock (PTS), as well as transients encountered during normal startup, shutdown, and testing of the reactor. Exposure to irradiation and elevated temperatures embrittles the RPV’s steel over time, making it increasingly susceptible to failure due to propagation of fractures that could initiate at the locations of flaws introduced during the manufacturing process. As long-term operation scenarios are being considered for LWRs in the United States, it is important to have a flexible simulation tool that can be used to perform probabilistic evaluations of RPV integrity under a wide variety of conditions and incorporate improved predictive models of RPV steel embrittlement. The Grizzly code is being developed to meet these needs. This paper describes Grizzly’s modular architecture, provides results of benchmarking studies of various components of Grizzly, and demonstrates the application of Grizzly on a model that includes plume effects that are difficult to represent in other codes being used in current practice.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Case study on the utility of hepatic global gene expression profiling in the risk assessment of the carcinogen furan

Furan is a chemical hepatocarcinogen in mice and rats. Its previously postulated cancer mode of action (MOA) is chronic cytotoxicity followed by sustained regenerative proliferation; however, its molecular basis is unknown. To this end, we conducted toxicogenomic analysis of B3C6F1 mouse livers following three week exposures to non-carcinogenic (0, 1, 2 mg/kg bw) or carcinogenic (4 and 8 mg/kg bw) doses of furan. We saw enrichment for pathways responsible for cytotoxicity: stress-activated protein kinase (SAPK) and death receptor (DR5 and TNF-alpha) signaling, and proliferation: extracellular signal-regulated kinases (ERKs) and TNF-alpha. We also noted the involvement of NF-kappaB and c-Jun in response to furan, which are genes that are known to be required for liver regeneration. Furan metabolism by CYP2E1 produces cis-2-butene-1,4-dial (BDA), which is required for ensuing cytotoxicity and oxidative stress. NRF2 is a master regulator of gene expression during oxidative stress and we suggest that chronic NFR2 activity and chronic inflammation may represent critical transition events between the adaptive (regeneration) and adverse (cancer) outcomes. Another objective of this study was to demonstrate the applicability of toxicogenomics data in quantitative risk assessment. We modeled benchmark doses for our transcriptional data and previously published cancer data, and observed consistency between the two. Margin of exposure values for both transcriptional and cancer endpoints were also similar. In conclusion, using furan as a case study we have demonstrated the value of toxicogenomics data in elucidating dose-dependent MOA transitions and in quantitative risk assessment. - Highlights: • Global gene expression changes in furan-exposed mouse livers were analyzed. • A molecular mode of action for furan-induced hepatocarcinogenesis is proposed. • Key pathways include NRF2, SAPK, ERK and death receptor signaling. • Important roles for TNF-alpha, c-Jun, and NF-κB in tumorigenesis are proposed. • BMD and MoE values from transcriptional and apical data are compared.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Resolution of Selectivity Steps of CO Reduction Reaction on Copper by Quantum Monte Carlo

Electrochemical reduction of carbon monoxide to valuable fuels and chemicals on copper surfaces remains a challenging area in catalysis due to a limited understanding of adsorption mechanisms and reaction pathways. Although density functional theory (DFT)-based studies have investigated these processes, their accuracy varies across different functionals. Here, in this study, we present the application of fixed-node diffusion Monte Carlo (FNDMC) to benchmark the adsorption energies of CO*, H*, and key CO reduction reaction (CORR) intermediates, COH* and CHO* on the Cu(111) surface. Our results for CO* and H* adsorption energies closely align with experimentally measured chemisorption reactions, highlighting the limitations of DFT and providing site-specific energy comparisons that are often not available experimentally. Additionally, we explore the effect of explicit solvation, demonstrating how water stabilizes the COH* over CHO*, thus suggesting a critical role of COH* in CORR. Finally, we release our high-accuracy FNDMC benchmarks for testing and developing new DFT functionals for electrocatalysis. Overall, this study underscores the potential of FNDMC for detailed surface chemistry studies and offers new insights into catalytic processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

The Harvard organic photovoltaic dataset

Presented in this work is the Harvard Organic Photovoltaic Dataset (HOPV15), a collation of experimental photovoltaic data from the literature, and corresponding quantum-chemical calculations performed over a range of conformers, each with quantum chemical results using a variety of density functionals and basis sets. It is anticipated that this dataset will be of use in both relating electronic structure calculations to experimental observations through the generation of calibration schemes, as well as for the creation of new semi-empirical methods and the benchmarking of current and future model chemistries for organic electronic applications.

14 SOLAR ENERGY↗
DOE OSTI2014

Electronic and optical properties of pure and modified diamondoids studied by many-body perturbation theory and time-dependent density functional theory

Diamondoids are small diamond nanoparticles (NPs) that are built up from diamond cages. Unlike usual semiconductor NPs, their atomic structure is exactly known, thus they are ideal test-beds for benchmarking quantum chemical calculations. Their usage in spintronics and bioimaging applications requires a detailed knowledge of their electronic structure and optical properties. In this paper, we apply density functional theory (DFT) based methods to understand the electronic and optical properties of a few selected pure and modified diamondoids for which accurate experimental data exist. In particular, we use many-body perturbation theory methods, in the G{sub 0}W{sub 0} and G{sub 0}W{sub 0}+BSE approximations, and time-dependent DFT in the adiabatic local density approximation. We find large quasiparticle gap corrections that can exceed thrice the DFT gap. The electron-hole binding energy can be as large as 4 eV but it is considerably smaller than the GW corrections and thus G{sub 0}W{sub 0}+BSE optical gaps are about 50% larger than the Kohn-Sham (KS) DFT gaps. We find significant differences between KS time-dependent DFT and GW+BSE optical spectra on the selected diamondoids. The calculated G{sub 0}W{sub 0} quasiparticle levels agree well with the corresponding experimental vertical ionization energies. We show that nuclei dynamics in the ionization process can be significant and its contribution may reach about 0.5 eV in the adiabatic ionization energies.

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

A perspective on the pathway to a scalable quantum internet using rare-earth ions

The ultimate realization of a global quantum internet will require advances in scalable technologies capable of generating, storing, and manipulating quantum information. The essential devices that will perform these tasks in a quantum network are quantum repeaters, which will enable the long-range distribution of entanglement between distant network nodes. Here, in this review, we provide an overview of the primary functions of a quantum repeater and discuss progress that has been made toward the development of repeaters with rare-earth ion doped materials while noting challenges that are being faced as the technologies mature. We give particular attention to erbium, which is well suited for networking applications. Finally, we provide a discussion of near-term benchmarks that can further guide rare-earth ion platforms for impact in near-term quantum networks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Development of a predictive capability of short-pulse laser-driven broadband x-ray radiography

High intensity, short-pulse laser interaction with a solid metal target produces broadband hard x-rays potentially for various applications of x-ray radiography. In this work, experimental benchmarking of numerical modelling for short-pulse laser-driven broadband x-ray radiography is presented. Angular dependent x-ray spectra are first calculated with a hybrid particle-in-cell code, Large Scale Plasma (LSP), using fast electron parameters inferred from an analysis of measured bremsstrahlung signals. Subsequently, a calculated x-ray spectrum in the direction of radiography is used in photon transport calculations using a Monte Carlo code, Particle and Heavy Ion Transport code System (PHITS), to simulate a radiographic image including a modelled 3D test object, an x-ray attenuation filter and an image plate detector. Simulated radiographic images are compared with measurements obtained in an experiment using a 50-TW Leopard short-pulse laser at the University of Nevada Reno. Results show that simulations reproduce the experimental images well for three different attenuation filters (plastic, aluminium, and brass), while 1D transmission profiles for the plastic and aluminium filters are quantitatively in good agreement. The modelling approach established in this work could be used as a predictive tool to simulate radiographic images of complex 3D solid objects at any arbitrary angular position or to optimize experimental components such as the source spectrum, x-ray attenuation filters and a detector type depending on a radiographic object without carrying out radiographic experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Assessing the quality of large-area monolayer graphene grown on liquid copper for size-selective ionic/molecular membrane separations

Monolayer graphene growth on liquid copper (Cu) has attracted attention due to advantages of a flat/smooth catalytic growth surface, high synthesis temperature (>1080 °C) as well as the possibility of forming graphene domains that are mobile on the liquid Cu with potential to minimize grain boundary defects and self-assemble into a continuous monolayer film. However, the quality of monolayer graphene grown on liquid copper and its suitability for size-selective ionic/molecular membrane separations has not been evaluated/studied. Here, we probe the quality of monolayer graphene grown on liquid Cu (via a metallurgical process, HSMG ® ) using Scanning Electron Microscope (SEM), High-resolution transmission electron microscope (HR-TEM), Raman spectroscopy and report on a facile approach to assess intrinsic sub-nanometer to nanometer-scale defects over centimeter-scale areas. We demonstrate high transfer yields of monolayer graphene (>93% coverage) from the growth substrate to polyimide track etched membrane (PITEM, pore diameter ~200 nm) supports to form centimeter-scale atomically thin membranes. Next, we use pressure-driven transport of ethanol to probe defects > 60 nm and diffusion-driven transport of analytes (KCl ~0.66 nm, L-Tryptophan ~0.7–0.9 nm, Vitamin B12 ~1–1.5 nm and Lysozyme ~3.8–4 nm) to probe nanoscale and sub-nanometer scale defects. Diffusive transport confirms the presence of intrinsic sub-nanometer to nanometer scale defects in monolayer graphene grown on liquid Cu are no less than that in high-quality graphene synthesized via chemical vapor deposition (CVD) on solid Cu. Our work not only benchmarks quality of graphene grown on liquid copper for membrane applications but also provides fundamental insights into the origin of intrinsic defects in large-area graphene synthesized via bottom-up processes for membrane applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Fast convolutional neural networks on FPGAs with hls4ml

We introduce an automated tool for deploying ultra low-latency, low-power deep neural networks with convolutional layers on field-programmable gate arrays (FPGAs). By extending the hls4ml library, we demonstrate an inference latency of 5 µs using convolutional architectures, targeting microsecond latency applications like those at the CERN Large Hadron Collider. Considering benchmark models trained on the Street View House Numbers Dataset, we demonstrate various methods for model compression in order to fit the computational constraints of a typical FPGA device used in trigger and data acquisition systems of particle detectors. In particular, we discuss pruning and quantization-aware training, and demonstrate how resource utilization can be significantly reduced with little to no loss in model accuracy. We show that the FPGA critical resource consumption can be reduced by 97% with zero loss in model accuracy, and by 99% when tolerating a 6% accuracy degradation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Pile-up mitigation using attention

Abstract Particle production from secondary proton-proton collisions, commonly referred to as pile-up, impair the sensitivity of both new physics searches and precision measurements at large hadron collider (LHC) experiments. We propose a novel algorithm, Puma , for modeling pile-up with the help of deep neural networks based on sparse transformers. These attention mechanisms were developed for natural language processing but have become popular in other applications. In a realistic detector simulation, our method outperforms classical benchmark algorithms for pile-up mitigation in key observables. It provides a perspective for mitigating the effects of pile-up in the high luminosity era of the LHC, where up to 200 proton-proton collisions are expected to occur simultaneously.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

SPIKANs: separable physics-informed Kolmogorov–Arnold networks

Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving partial differential equations (PDEs) in scientific computing. While PINNs typically use multilayer perceptrons (MLPs) as their underlying architecture, recent advancements have explored alternative neural network structures. One such innovation is the Kolmogorov–Arnold Network (KAN), which has demonstrated benefits over traditional MLPs, including faster neural scaling and better interpretability. The application of KANs to physics-informed learning has led to the development of Physics-Informed KANs (PIKANs), enabling the use of KANs to solve PDEs. However, despite their advantages, KANs often suffer from slower training speeds, particularly in higher-dimensional problems where the number of collocation points grows exponentially with the dimensionality of the system. To address this challenge, we introduce Separable Physics-Informed Kolmogorov–Arnold Networks (SPIKANs). This novel architecture applies the principle of separation of variables to PIKANs, decomposing the problem such that each dimension is handled by an individual KAN. This approach drastically reduces the computational complexity of training without sacrificing accuracy, facilitating their application to higher-dimensional PDEs. Through a series of benchmark problems, we demonstrate the effectiveness of SPIKANs, showcasing their superior scalability and performance compared to PIKANs and highlighting their potential for solving complex, high-dimensional PDEs in scientific computing.

Kolmogorov-Arnold networks↗
DOE OSTI2024

CHARM-SYCL & IRIS: A Tool Chain for Performance Portability on Extremely Heterogeneous Systems

Performance portability is becoming crucial as high-performance computing systems become increasingly heterogeneous. We have many options for CPUs and accelerators (e.g., GPUs) but also for non-Von Neumann architectures such as field-programmable gate arrays. This paper presents the CHARM-SYCL unified programming environment for multiple accelerator types as a performance-portable programming environment. It uses the IRIS library developed at Oak Ridge National Laboratory as the back end accelerator runtime. IRIS has a high-performance scheduler to distribute tasks across accelerators. This design allows us to run an application from the same source on multiple systems with multiple configurations. We provide three types of portability with CHARM-SYCL: Portable Workflow, Compiler and Runtime Portability, and Application and Performance Portability. We implement a Monte Carlo simulation benchmark code on the CHARM-SYCL execution environment and demonstrate that our programming environment can accommodate extremely heterogeneous systems.

Fujita, Norihisa↗
DOE OSTI2015

Maximum Likelihood Source Localization Using Elpasolite as a Dual Gamma Neutron Directional Detector

The problem of accurately detecting extremely low levels of nuclear radiation is rapidly increasing in importance in nuclear counter-proliferation, verification, and environmental and waste management. Because the 239Pu gamma signature may be weak, for instance, even when compared to the natural terrestrial background, coincidence counting with the 239Pu neutron signature may improve overall 239Pu detection sensitivity. However, systems with sufficient multiple-particle detectors require demonstration that the increased sensitivity be sufficiently high to overcome added cost and weight. We report the results of measurements and calculations to determine sensitivity that can be gained in detecting low levels of nuclear radiation from use of a relatively new detector technology based on elpasolite crystals. We have performed investigations exploring cerium (Ce3+)-doped elpasolites Cs2LiYCl6:Ce3+0.5% (CLYC) and Cs2LiLa(Br6)90%(Cl6)10%:Ce3+0.5% (CLLBC:Ce). These materials can provide energy resolution (r(E) = 2.35σ(E)/E) as good as 2.9% at 662 keV (FWHM). The crystals show an excellent neutron and gamma radiation response. The goals of the investigation were to set up the neutron/gamma pulse shape discrimination electronics for elpasolite detectors; perform limited static source benchmarking, testing, and evaluation to validate system performance; and explore application of a maximum likelihood algorithm for source location. Data were measured and processed through a maximum likelihood estimation algorithm, providing a direction to the radioactive source for each individual position. The estimated directions were good representations for the actual directions to the radioactive source. This paper summarizes the maximum likelihood results for our elpasolite system.

maximum likelihood, radiation detectors, neutron d↗
DOE OSTI2011

A Survey of Recent Advances in Transmission Network Switching

Optimal Transmission Switching for electric power networks is a research topic of great interest for both researchers and electric power engineers. Transmission switching is commonly considered a corrective mechanism tool for overloaded networks; however in recent years it has been sought as a means to reduce power loss in the network. Ultimately the objective of this analysis is to provide smart tools for power and economic dispatch planning, allowing utilities and engineers to utilize existing infrastructure as a means of automatic control. The optimization problem is a complex multi-objective search including criteria such as system power loss, equality/non-equality constraints, islanding management, and radial topology configuration in the case of distribution networks. Many optimization methods have been adopted for the transmission switching problem in past years using various heuristic search algorithms. The scope of methodology extends from tried and true methods such as simulated annealing to more modern methods like neural network optimization. Solution benchmarks under consideration commonly include solution state accuracy, computational burden and solution time. The objective of this presentation is to provide a comprehensive account of development in transmission switching methodologies. Benchmarking and testing strategies will be discussed as well as application of the Transmission Expansion Planning Tool in finding transmission switching solutions. Limited Discrepancy Search (LOS) and Probe Discrepancy Search (PDS) Algorithms are compared with more classic optimization strategies, as seen in defining literature and publications in this research field.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Evaluating Trade-offs in Potential Exascale Interconnect Topologies

This study looks at the high-level trade-offs for interconnect architectures that are likely to be available in HPC machines in the 2021-2022 timeframe. The study uses multiple simulation models to look at the effects of varying machine size, injection bandwidth and topology for a selected set of benchmark communication patterns. The studies show the sensitivity of various applications to these network parameters. This study can help inform decisions on node size versus number of nodes, which topology is most efficient for our applications, how much injection bandwidth to provision, etc. These simulations only look at interconnects with maximally configured global bandwidth, and studies of networks configured with less will be the subject of future milestones.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Physically Motivated Deep Learning to Superresolve and Cross Calibrate Solar Magnetograms

Abstract Superresolution (SR) aims to increase the resolution of images by recovering detail. Compared to standard interpolation, deep learning-based approaches learn features and their relationships to leverage prior knowledge of what low-resolution patterns look like in higher resolution. Deep neural networks can also perform image cross-calibration by learning the systematic properties of the target images. While SR for natural images aims to create perceptually convincing results, SR of scientific data requires careful quantitative evaluation. In this work, we demonstrate that deep learning can increase the resolution and calibrate solar imagers belonging to different instrumental generations. We convert solar magnetic field images taken by the Michelson Doppler Imager (resolution ∼2″ pixel −1 ; space based) and the Global Oscillation Network Group (resolution ∼2.″5 pixel −1 ; ground based) to the characteristics of the Helioseismic and Magnetic Imager (resolution ∼0.″5 pixel −1 ; space based). We also establish a set of performance measurements to benchmark deep-learning-based SR and calibration for scientific applications.

Muñoz-Jaramillo, Andrés (ORCID:0000000247160840)↗