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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Development of fast-running eighth core nodal solution

A fast-running, eighth-core solution technique has been developed for the quick core calculations in ANC, the Westinghouse core analysis code based on the nodal expansion method, and the BEACON{sup TM} Core Monitoring System (BEACON). This technique, referred to as 'Fast Eighth' (Fast8), enables users to obtain satisfactory macroscopic core calculation results such as reactivity, axial offset and assembly power much more quickly than calculations performed in the standard full-core or quarter-core geometry. Fast8 uses three techniques: folding the radial geometry into eighth-core geometry, reducing the axial geometry into a coarse axial mesh geometry, and smearing the spacer grids into nodes on-the-fly after the first calculation in the standard geometry. These geometry adjustments in Fast8 contribute to the reduction of the number of neutronic and material nodes while maintaining the reaction rates in each node. The axial mesh condition dependency in Fast8 is investigated in a xenon transient calculation in single assembly geometry, and the Fast8 maximum axial mesh size is determined based on these results. Fast8 is then applied to the load swing calculation, and the results are compared with the reference, full-core geometry, solution. The critical boron concentration and axial offset of Fast8 show good agreement with the reference solutions, and that Fast8 is a good technique for obtaining macroscopic core calculation results in significantly shorter time. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Fiats: Functional inference and training for surrogates

Fiats provides a platform for research on the training and deployment of neural-network surrogate models for computational science. Fiats also supports exploring, advancing, and combining functional, object-oriented, and parallel programming patterns in Fortran 2023. As such, the Fiats name has dual expansions: “Functional Inference And Training for Surrogates” or “Fortran Inference And Training for Science.” Fiats inference and training procedures are pure and therefore satisfy a language constraint imposed on procedure invocations inside Fortran’s parallel loop construct: do concurrent. Furthermore, the Fiats training procedures are built around a do concurrent parallel reduction. Several compilers can automatically parallelize do concurrent on Central Processing Units (CPUs) or Graphics Processing Units (GPUs). Fiats thus aims to achieve performance portability through standard language mechanisms.

Rouson, Damian [Lawrence Berkeley National Laborat↗
DOE OSTI2024

A portable parton-level event generator for the high-luminosity LHC

The rapid deployment of computing hardware different from the traditional CPU+RAM model in data centers around the world mandates a change in the design of event generators for the Large Hadron Collider, in order to provide economically and ecologically sustainable simulations for the high-luminosity era of the LHC. Parton-level event generation is one of the most computationally demanding parts of the simulation and is therefore a prime target for improvements. We present a production-ready leading-order parton-level event generation framework capable of utilizing most modern hardware and discuss its performance in the standard candle processes of vector boson and top-quark pair production with up to five additional jets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

ChIMES: A Machine-Learned Interatomic Model Targeting Improved Description of Condensed Phase Chemistry in Energetic Materials

In this report we detail completion of a Physics and Engineering Model Level Two Milestone targeting improved reactive interatomic potentials (IAPs) for energetic materials (EM) through machine learning. The specific goals of this milestone were to develop, validate, and document a new reactive molecular dynamics method for EM, based on machine learning by (1) generating databases of first-principles-derived forces, stresses, and energies for HN3 and 3,4-bis(3-nitrofurazan- 4-yl)furoxan (DNTF) (2) generate atomistic force fields from these databases via ML, and (3) benchmark model performance against first principles calculations. These goals were achieved by (1) further developing a machine learned reactive IAP and generation approach (i.e. the Chebyshev Interaction Model for Efficient Simulation or “ChIMES”), for which resulting IAPs can approach the predictive power of quantum-mechanical approaches at a fraction of the computational expense, and (2) applying the ChIMES framework to develop models for HN3 and DNTF. We find that for simple energetic materials like HN3, high accuracy ChIMES models can be obtained through application of a fitting approach that does not use active machine learning. We demonstrate the suitability of ChIMES models for simulations involving EM by using the HN3 model in multiscale shock technique simulations to predict the HN3 Chapman-Jouguet detonation state and investigate chemical evolution out to 1 ns following shock compression. This model is then used in larger direct shock (DS) simulations for a preliminary investigation of how bubbles (i.e. voids) influence material response under shock compression. We find that more complex EM (i.e. DNTF) necessitate a more sophisticated fitting approach, and develop a new active learning method and python tool to meet this challenge. We demonstrate that this fitting approach yields ChIMES models that out-perform commonly used standard reactive IAPs as well as semi-empirical quantum methods, and discuss the systematic improvability of these actively learned ChIMES models. We also describe challenges related to model development for EM such as DNTF, for which few experimental or previous simulation data are available (e.g. which could otherwise inform generation of training data). To overcome this issue, we establish a semi-empirical quantum ChIMES capability which can be used to efficiently map out relevant thermodynamic and configurational space, and generate ChIMES-IAP training data in a multiscale manner. We also show that these semi-empirical quantum ChIMES models can be used to generate predictions for the shock Hugoniot (the Hugoniot is the locus of thermodynamic states found in a shocked material) equation of state, investigate related thermochemistry, and explore carbon condensation following shock compression. This work represents a substantial advance in our atomistic modeling capability for EM that will provide much needed information on the chemistry of detonation for continued development of continuum models based on the Cheetah thermochemical code.

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

Small Punch Testing of Molybdenum-99 Targets

Northstar Medical Radioisotopes is developing an accelerator-based method to produce 99 Mo, which is a parent isotope of the commonly used 99m Tc medical isotope. The Mo targets being designed for the accelerator will be produced from enriched 100 Mo, also known as aMo . Pressed and sintered powder feedstock is used to fabricate aMo targets, producing 29 mm disk-shaped targets. The targets are subjected to 1–6 days in line of an electron beam with subsequent dissolution of the disk to retain the 99 Mo, which decays to 99m Tc at radio-pharmacies. The press and sinter method is advantageous because the inherent porosity produced by this method enables increased surface area and therefore increased flow of dissolution media, decreasing the dissolution time and reducing the need for a highly acidic media. Although porosity aids in dissolution, it reduces the mechanical strength and ductility. Targets require good mechanical integrity when subjected to the conditions in the accelerator. Therefore, Northstar is seeking methods to rapidly test disk samples after fabrication to assure mechanical performance metrics are achieved. This report details the design and testing of a small punch test (SPT) that accommodates the 29 mm disk. Initial data were used to relate the SPT data to tensile properties, such as the yield strength (YS), ultimate tensile strength (UTS), and total elongation to failure. Although the SPT has been established as a somewhat reliable method for testing metallic materials, few studies have applied the SPT to refractory materials such as Mo. Based on tensile testing performed at Oak Ridge National Laboratory on different Mo samples, correlation between the Mo tensile and SPT properties could be performed, establishing a standard calibration that could be applied to other Mo samples. To test the efficacy of the SPT with Mo, multiple different disk batches were fabricated under different conditions (e.g., pressure, lubricant) with commercially available pure Mo powder. Generally, only a UTS could be well defined because the press and sinter disks failed under brittle fracture, making it difficult to determine the YS and elongation. Compared with disks fabricated with aMo powder, the aMo samples underperformed their pure nat Mo counterparts. This report summarizes the current status of the SPT, but further evaluation is needed before it can be applied as a reliable quality assurance tool.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

MicroED for rapid and comprehensive characterization of the unknown small molecule composition of crude plant and soil extracts

This project implemented at PNNL a method introduced recently for de novo structure elucidation of small organic molecules: Micro-Electron Diffraction (henceforth microED). Micro-ED is an alternative to X-ray diffraction (XRD) or Nuclear Magnetic Resonance (NMR) for atomic-resolution structure elucidation of small organic or biological molecules, and it can succeed in solving structures when these well-established methods fail. In particular, microED offers the possibility of determining structures from far smaller quantities of material than would be required for NMR or even XRD, or from substances that fail to form crystals suitable (i.e., large enough) for X-ray crystallography, because microED requires only microcrystalline samples: in principle, a single crystal with ca. 100 nm dimensions would be suitable. MicroED is performed typically with standard electron microscopy (EM) instrumentation. The substantial potential of microED has drawn significant attention and examples of its use to solve difficult structure elucidation problems where X-ray or NMR methods failed has been described recently.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Autonomous Fueling System for Heavy-Duty Fuel Cell Electric Trucks

The motivation for this project stemmed from the challenges associated with rapidly refueling heavy-duty hydrogen fuel cell electric trucks (FCETs). Current manual refueling processes for fast refueling involve large, heavy equipment (e.g., hoses three times heavier than standard) and pose ergonomic risks and potential for equipment damage. The goal was to develop and test an autonomous fueling system to improve ergonomics, enhance safety, increase equipment durability through design improvements, and potentially speed up the fueling process. This project aimed to add to the understanding of autonomous systems in the context of heavy-duty hydrogen refueling, evaluating the technical effectiveness of potential concepts. A successfully developed system would benefit the public by facilitating the adoption of zero-emission heavy-duty transport, reducing reliance on manual labor for a physically demanding task, and potentially improving the safety and efficiency of hydrogen refueling infrastructure. The major accomplishment during the project's active period was the completion of the system-level architecture task. This involved establishing a detailed list of system requirements covering interfaces, environmental conditions, regulatory compliance, industry standards, safety, security, performance capabilities, and optional features. Five key use cases for the autonomous system were also identified. However, due to internal restructuring at Nikola, the necessary resources could not be allocated to continue the project. Consequently, Nikola opted to discontinue the project. The award was mutually terminated by Nikola and the DOE.

08 HYDROGEN↗
DOE OSTI2025

Evaluating Tools and Technologies for Monitoring Baleen Whales During Offshore Wind Foundation Installation

Impacts to baleen whales are of particular concern in the context of offshore wind development because of their conservation status, the overlap of their habitat with current and planned offshore wind energy facilities, and their expected sensitivity to low-frequency sounds, such as those produced during pile driving. Therefore, this report focuses on monitoring technologies capable of detecting this species group. The scope of this report is to assess the current state of technologies and methods for monitoring baleen whales in low- and no-light conditions in conjunction with offshore wind foundation installation. This work considers the performance of various sensing technologies that can be used to observe the presence of baleen whales near offshore construction activities. The scope of work includes: 1. Technology Performance Metrics – Identify metrics that can be used to assess technology performance. 2. Technology Evaluation – Synthesize the past performance of technologies with respect to the performance metrics based on available literature. 3. Technology Characterization Framework – Describe a potential framework to characterize technology performance with a standardized approach. 4. Technology Research and Development Needs – Identify future needs and opportunities for research, development, and deployment to improve technologies.

17 WIND ENERGY↗
DOE OSTI2025

Machine Learning Analysis of Temperature-Strain Relationships for Structural Health Monitoring of Pipes: Self-powered wireless sensor system for health monitoring of liquid-sodium cooled fast reactors

This report presents machine learning (ML) analysis of temperature-strain relationships for structural health monitoring of nuclear reactor stainless steel (SS) pipes with the strain gauge sensor directly printed on the pipe with a 3D conformal aerosol jet printer. We investigate correlations for two sensor pairs installed on the same SS304 pipe: commercial K-type thermocouple with a printed gold strain gauge (TC3-SG3), and commercial K-type thermocouple with commercial Kyowa strain gauge (TC0-SG0). The temperature ranges for the sensor pairs TC0-SG0 and TC3-SG3 are 20.00°C to 266.37°C and 39.95°C to 219.28°C respectively. ML algorithms in this study include Linear Regression (baseline method), Ridge Regression, Lasso Regression, and Gradient Boosting. Performance evaluation metrics include Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), R 2 Score, and Explained Variance. Using advanced feature engineering techniques, we extracted 27 temperature-based features and 30 strategic inclusion features. The best performance was obtained with the Gradient Boosting method, which achieves prediction accuracy of R 2 = 0.9999 and RMSE = 7.69 μStrain for TC0-SG0, and R 2 = 0.9998 and RMSE = 18.03 μStrain for TC3-SG3. While the temperature-strain correlations are weaker for the gauge directly printed on the pipe than for the commercial strain gauge, deployment-ready performance exceeding industry standards is achieved for both sensor pairs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Solution-Processed Oxide Thin Films for Perovskite Photovoltaics (Final Report)

The overarching goal of the voucher project is to evaluate the suitability of NexTC materials as functional layers in perovskite photovoltaic technology. There are several different perovskite device configurations that are being pursued for commercialization. Most are superstrate configuration (light enters the device through the glass substrate) and can be subdivided into two categories: PIN and NIP. At minimum, PIN devices have a transparent conductor (typically Indium Tin Dioxide [ITO] or Fluorine-Doped Tin Oxide [FTO]) and hole transport layer (HTL) deposited on glass before the perovskite is deposited. Alternatively, NIP devices have a transparent conductor and electron transport layer (ETL) deposited before the perovskite is deposited. NexTC produces several inks that can be deposited to replace or modify the transparent conductor and/or the ETL or HTL, depending on the device. NLR will produce PIN and NIP perovskite devices on substrates that have been coated by NexTC and compare to NLR-standard devices that perform among the best for research labs across the world. The work will ultimately expose NexTC products to the up-and-coming perovskite PV industry.

14 SOLAR ENERGY↗
DOE OSTI2026

Improving unfolding and systematic uncertainty estimation using generative diffusion networks (Final Technical Report)

This final technical report summarizes the key accomplishments on the unfolding using diffusion model project, a DOE award received by PI Pierre-Hugues Beauchemin at Tufts University. This project main goal was to investigate the potential of diffusion models for unfolding experimental High Energy Physics data from detector effects while controlling systematics uncertainties. The project accomplished its goals by completing the following objectives: 1) Performing an object-by-object, event-by-event unfolding of various kinematic distributions reconstructed from detector data in HEP in a way that keeps correlations between unfolded observables while demonstrating competitive performance compared to standard algorithms used in the field; 2) Address the generalization problem by developing an unfolding algorithm capable to correctly infer the underlying distributions of observables and processes never seen before, while controlling the dominant theoretical uncertainties affecting the process, therefore increasing the effectiveness, the precision, and the applicability of the developed algorithm; 3) Understand the theoretical foundations between the developed algorithm so to extend it to applications beyond experimental HEP, for broader benefits to the society. This report provides an overview of the accomplishments related to each of these key objectives.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Cellulose molecular weight of corn stover with cotreatment

Here we present a dataset of cellulose molecular weights from corn stover after a cotreatment process. The corn stover sample was subjected to a combination of mechanical disruption (ball milling) and consolidated bioprocessing (CBP) process using the bacterium C. thermocellum at 60 grams/L solids loadings. Cellulose was isolated from control, no cotreatment control, and cotreatment corn stover. Cellulose was then derivatized using anhydrous pyridine and phenyl isocyanate over 48 h at 70 ºC. The reaction was quenched by anhydrous methanol. Methanol and water mixture (7:3, v/v) was added dropwise to promote precipitation of the cellulose derivative. The solids were collected by filtration and thoroughly washed with the methanol and water mixture followed by water. The cellulose derivative was dried overnight under vacuum at 40°C. The obtained cellulose tricarbanilates was then dissolved in tetrahydrofuran (THF) at 1.0 mg/mL and the solution was filtered through a 0.45 µm PTFE filter and placed in an auto-sampler vial. The molecular weight of cellulose was measured by Agilent GPC SECurity 1200 system equipped with four Waters Styragel columns (i.e., HR0.5, HR2, HR4, and HR6) and a UV detector (270 nm). THF was used as the mobile phase. Data collection and processing was performed by Polymer Standards Service WinGPC Unity software (Build 6807). The molecular weight was calculated relative to the polystyrene calibration curve. The data on molecular weights provided information about the effects of cotreatment on corn stover cellulose molecular changes.

Cellulose, molecular weights, corn stover, cotreat↗
DOE OSTI2024

Globally optimal interferometry with lossy twin Fock probes

Parity or quadratic spin (e.g., J z 2 ) readouts of a Mach–Zehnder (MZ) interferometer probed with a twin Fock (TF) input state allow saturating the optimal sensitivity attainable among all mode-separable states with a fixed total number of particles but only when the interferometer phase θ is near zero. When more general Dicke state probes are used, the parity readout saturates the quantum Fisher information (QFI) at θ = 0, whereas better-than-standard quantum limit performance of the J z 2 readout is restricted to an o ( N ) occupation imbalance. We show that a method of moments readout of two quadratic spin observables J z 2 and J + 2 + J − 2 is globally optimal for Dicke state probes; i.e., the error saturates the QFI for all θ . In the lossy setting, we derive the time-inhomogeneous Markov process describing the effect of particle loss on TF states, showing that the method of moments readout of four at-most-quadratic spin observables is sufficient for globally optimal estimation of θ when two or more particles are lost. The analysis culminates in a numerical calculation of the QFI matrix for distributed MZ interferometry on the four-mode state | N 4 , N 4 , N 4 , N 4 〉 and its lossy counterparts, showing that an advantage for the estimation of any linear function of the local MZ phases θ 1 and θ 2 (compared to independent probing of the MZ phases by two copies of | N 4 , N 4 〉 ) appears when more than one particle is lost.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Inpainting Galactic Foreground Intensity and Polarization Maps Using Convolutional Neural Networks

The Deep Convolutional Neural Networks (DCNNs) have been a popular tool for image generation and restoration. In this work, we applied DCNNs to the problem of inpainting non-Gaussian astrophysical signal, in the context of Galactic diffuse emissions at the millimetric and submillimetric regimes, specifically Synchrotron and Thermal Dust emissions. Both signals are affected by contamination at small angular scales due to extragalactic radio sources (the former) and dusty star-forming galaxies (the latter). Furthermore, we compare the performance of the standard diffusive inpainting with that of two novel methodologies relying on DCNNs, namely Generative Adversarial Networks and Deep-Prior. We show that the methods based on the DCNNs are able to reproduce the statistical properties of the ground-truth signal more consistently with a higher confidence level.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Spurious Correlations between Galaxies and Multiepoch Image Stacks in the DESI Legacy Surveys

A nonnegligible source of systematic bias in cosmological analyses of galaxy surveys is the on-sky modulation that is caused by foregrounds and variable image characteristics, such as observing conditions. Standard mitigation techniques perform a regression between the observed galaxy density field and sky maps of the potential contaminants. Such maps are ad hoc lossy summaries of the heterogeneous sets of coadded exposures that contribute to the survey. We present a methodology for addressing this limitation, and we extract spurious correlations between the observed distributions of galaxies and arbitrary stacks of single-epoch exposures. We study four types of galaxies (luminous red galaxies, emission-line galaxies, quasars, and Lyman-break galaxies) in the three regions of the DESI Legacy Surveys (North, South, and Dark Energy Survey), resulting in 12 samples with varying levels and types of contamination. We find that the new technique outperforms the traditional ones in all cases, and is able to remove higher levels of contamination. This paves the way for new methods that extract more information from multiepoch galaxy survey data and mitigate large-scale biases more effectively.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A Dark Siren Measurement of the Hubble Constant with the LIGO/Virgo Gravitational Wave Event GW190412 and DESI Galaxies

Abstract We present a measurement of the Hubble Constant H 0 using the gravitational wave event GW190412, an asymmetric binary black hole merger detected by LIGO/Virgo, as a dark standard siren. This event does not have an electromagnetic counterpart, so we use the statistical standard siren method and marginalize over potential host galaxies from the Dark Energy Spectroscopic Instrument (DESI) survey. GW190412 is well-localized to 12 deg 2 (90% credible interval), so it is promising for a dark siren analysis. The dark siren value for H 0 = 85.4 − 33.9 + 29.1 km s −1 Mpc −1 , with a posterior shape that is consistent with redshift overdensities. When combined with the bright standard siren measurement from GW170817 we recover H 0 = 77.96 − 5.03 + 23.0 km s −1 Mpc −1 , consistent with both early and late-time Universe measurements of H 0 . This work represents the first standard siren analysis performed with DESI data, and includes the most complete spectroscopic sample used in a dark siren analysis to date.

Ballard, W.↗
DOE OSTI2020

Competitiveness Improvement Project Informational Workshop

The National Renewable Energy Laboratory (NREL) is hosted an in-person workshop and webinar for the distributed wind Competitiveness Improvement Project (CIP) on Tuesday, December 17, 2019, at NREL's Flatirons Campus. The CIP is a periodic solicitation issued by NREL on behalf of the U.S. Department of Energy's Wind Energy Technologies Office. Through a competitive process, component suppliers and manufacturers of small- to medium-sized wind turbine technology are awarded cost-shared subcontracts to optimize their designs, develop advanced manufacturing processes, and perform turbine testing. The CIP aims to make wind energy cost competitive with other distributed generation technologies and increase the number of wind turbine designs certified to national performance and safety standards.

17 WIND ENERGY↗
DOE OSTI2021

Convolutional Neural Networks for the CHIPS Neutrino Detector R&D Project.

The CHerenkov detectors In mine PitS (Chips) neutrino detector R&D project aims to develop novel strategies and technologies for very large yet ‘cheap as chips’ water Cherenkov neutrino detectors. Via deployment in a body of water, use of commercially available components, and instrumentation coverage optimisation for the study of exclusively accelerator beam neutrinos, Chips will enable megaton scale detectors to become a reality at the cost of $200k-$300k per kt of sensitive mass. During the summer of 2019 a prototype Chips detector, Chips-5, was deployed into the Wentworth 2W disused mine pit in northern Minnesota, 7 mrad off the NuMI beam axis. A novel data acquisition system was introduced using cheap single-board computers and open-source software. This work presents a novel approach to water Cherenkov neutrino detector event reconstruction and classification. Three forms of a Convolutional Neural Network, a type of deep learning algorithm, have been trained to reject cosmic muon events, classify beam events, and estimate neutrino energies, all using only the raw detector event as input. When evaluated on the expected distribution of Chips-5 events, this new approach is shown to be robust and explainable as well as providing a significant performance increase over the standard likelihood-based reconstruction and simple neural network classification. Promisingly, the performance presented here is comparable to the more complex (and expensive) neutrino oscillation experiments within the field.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗