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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Airborne hyperspectral imaging of cover crops through radiative transfer process-guided machine learning

Cover cropping between cash crop growing seasons is a multifunctional conservation practice. Timely and accurate monitoring of cover crop traits, notably aboveground biomass and nutrient content, is beneficial to agricultural stakeholders to improve management and understand outcomes. Currently, there is a scarcity of spatially and temporally resolved information for assessing cover crop growth. Remote sensing has a high potential to fill this need, but conventional empirical regression operated with coarse-resolution multispectral data has large uncertainties. Therefore, this study utilized airborne hyperspectral imaging techniques and developed new process-guided machine learning approaches (PGML) for cover crop monitoring. Specifically, we deployed an airborne hyperspectral system covering visible to shortwave-infrared wavelengths (400–2400 nm) to acquire high spatial (0.5 m) and spectral (3–5 nm) resolution reflectance over 23 cover crop fields across Central Illinois in March and April of 2021. Airborne hyperspectral surface reflectance with high spectral and spatial resolution can be well matched with field data to quantify cover crop traits. Furthermore, the PGML models were pre-trained by synthetic data from soil-vegetation radiative transfer modeling (one million records), and then fine-tuned with field data of cover crop biomass and nutrient content. Results show that airborne hyperspectral data with PGML can achieve high accuracy to predict cover crop aboveground biomass (R 2 = 0.72, relative RMSE = 15.16%) and nitrogen content (R 2 = 0.69, relative RMSE = 16.59%) through leave-one-field-out cross-validation. Unlike the pure data-driven approach (e.g., partial least-squares regression), PGML incorporated radiative transfer knowledge and obtained higher predictive performance with fewer field data. Meanwhile, with field data for model fine-tuning, PGML predicted biomass more accurately than the inversion of radiative transfer models. Here we also found that the red edge has a high contribution in quantifying aboveground biomass and nitrogen content, followed by green and shortwave spectra. This study demonstrated the first attempt of utilizing hyperspectral remote sensing to accurately quantify cover crop traits. We highlight the strength of PGML in exploiting sensing data to quantify ecosystem variables to advance agroecosystem monitoring for sustainable agricultural management.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Physics-guided machine learning for improved accuracy of the National Solar Radiation Database

The National Solar Radiation Database (NSRDB) provides high-resolution spatiotemporal solar irradiance data for the entire globe. The NSRDB uses a two-step Physical Solar Model (PSM) to compute the effects of clouds and other atmospheric variables on the solar radiation reaching the surface of the Earth. Physical and optical cloud properties are fundamental inputs to the PSM and are derived from the National Oceanic and Atmospheric Administration's Geostationary Operational Environmental Satellites. This paper describes recent improvements to the NSRDB driven by physics-guided machine learning methods for cloud property retrieval. The impacts of these new methods on the NSRDB irradiance data are validated using an extensive set of ground measurement sites, showing significant improvement for all sites. We report on average, the mean absolute percentage error for global horizontal irradiance and direct normal irradiance show reductions of 2.16 and 3.95 percentage points respectively for all daylight conditions, 5.92 and 17.39 percentage points respectively for cloudy conditions, and 9.00 and 22.59 percentage points respectively for gap-filled cloudy conditions. These new methods will help improve the quality and accuracy of the irradiance and cloud data in the NSRDB.

14 SOLAR ENERGY↗
DOE OSTI2023

Train, Inform, Borrow, or Combine? Approaches to Process–Guided Deep Learning for Groundwater–Influenced Stream Temperature Prediction

Although groundwater discharge is a critical stream temperature control process, it is not explicitly represented in many stream temperature models, an omission that may reduce predictive accuracy, hinder management of aquatic habitat, and decrease user confidence. We assessed the performance of a previously-described process-guided deep learning model of stream temperature in the Delaware River Basin (USA). We found lower accuracy (root mean square error [RMSE] of 1.71 versus 1.35°C) and stronger seasonal bias (absolute mean monthly bias of 1.06 vs. 0.68°C) for reaches primarily influenced by deep groundwater as compared to atmospheric conditions. We then tested four approaches for improving groundwater process representation: (a) a custom loss function leveraging the unique patterns of air and water temperature coupling characteristic of different temperature drivers, (b) inclusion of additional groundwater-relevant catchment attributes, (c) incorporation of additional process model outputs, and (d) a composite model. The custom loss function and the additional attributes significantly improved the predictive accuracy in groundwater-dominated reaches (RMSE of 1.37 and 1.26°C) and reduced the seasonal bias (absolute mean monthly bias of 0.44 and 0.48°C), but neither approach could identify holdout groundwater reaches. Variable importance analysis indicates the custom loss function nudges the model to use the existing inputs more efficiently, whereas with the added features the model relies on a broader suite of inputs. This analysis is a substantial step toward more accurately representing groundwater discharge processes in stream temperature models and will improve predictive accuracy and inform habitat management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A guided ion beam investigation of UO 2 + thermodynamics and f orbital participation: Reactions of U + + CO 2 , UO + + O 2 , and UO + + CO

A guided ion beam tandem mass spectrometer was employed to study the reactions of U + + CO 2 , UO + + O 2 , and the reverse of the former, UO + + CO. Reaction cross sections as a function of kinetic energy over about a three order of magnitude range were studied for all systems. The reaction of U + + CO 2 proceeds to form UO + + CO with an efficiency of 118% ± 24% as well as generating UO 2 + + C and UCO + + O. The reaction of UO + + O 2 forms UO 2 + in an exothermic, barrierless process and also results in the collision-induced dissociation of UO + to yield U + . In the UO + + CO reaction, the formation of UO 2 + in an endothermic process is the dominant reaction, but minor products of UCO + + O and U + + (O + CO) are also observed. Analysis of the kinetic energy dependences observed provides the bond energies, D 0 (U + –O) = 7.98 ± 0.22 and 8.05 ± 0.14 eV, D 0 (U + –CO) = 0.73 ± 0.13 eV, and D 0 (OU + –O) = 7.56 ± 0.12 eV. The values obtained for D 0 (U + –O) and D 0 (OU + –O) agree well with the previously reported literature values. To our knowledge, this is the first experimental measurement of D 0 (U + –CO). Furthermore, an analysis of the oxide bond energies shows that participation of 5f orbitals leads to a substantial increase in the thermodynamic stability of UO 2 + relative to ThO 2 + and especially transition metal dioxide cations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

The bond energy of UN + : Guided ion beam studies of the reactions of U + with N 2 and NO

In this work, a guided ion beam tandem mass spectrometer was used to study the reactions of U + with N 2 and NO. Reaction cross sections were measured over a wide range of energy for both systems. In each reaction, UN + is formed by an endothermic process, thereby enabling the direct measurement of the threshold energy and determination of the UN + bond dissociation energy. For the reaction of U + + N 2 , a threshold energy (E 0 ) of 4.02 ± 0.11 eV was measured, leading to D 0 (UN + ) = 5.73 ± 0.11 eV. The reaction of U + + NO yields UO + through an exothermic, barrierless process that proceeds with 94 ± 23% efficiency at the lowest energy. Analysis of the endothermic UN + cross section in this reaction provides E 0 = 0.72 ± 0.11 eV and, therefore, D 0 (UN + ) = 5.78 ± 0.11 eV. Averaging the values obtained from both reactions, we report D 0 (UN + ) = 5.76 ± 0.13 eV as our best value (uncertainty of two standard deviations). Combined with precise literature values for the ionization energies of U and UN, we also derive D 0 (UN) = 5.86 ± 0.13 eV. Both bond dissociation energies agree well with high-level theoretical treatments in the literature. The formation of UN + in reaction of U + with NO also exhibits a considerable increase in reaction probability above ~3 eV. Theory suggests that this may be consistent with the formation of UN + in excited quintet spin states, which we hypothesize are dynamically favored because the number of 5f electrons in reactants and products is conserved.

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

Numerical assessment of the impact of the guiding-centre approximation on fast ion simulations in NSTX

Guiding-centre (GC) and full-orbit (FO) simulations of the beam-injected fast ion distribution and the corresponding neutron emissivity have been carried out for magnetohydrodynamics-quiescent National Spherical Torus eXperiment (NSTX) plasmas, using a combination of ASCOT5 and DRESS, to assess the suitability of the GC approximation for fast ions in NSTX. It was found that GC and FO simulations predicted substantially different steady-state distributions in both position and velocity space and different neutron emissivity profiles, leading to a 15% reduction in the predicted global neutron rate for FO relative to GC. These changes accompany a higher magnetic moment in FO, and correspond to a change in particle orbits from co-passing to trapped and stagnation orbits. ASCOT5 was also benchmarked against TRANSP/NUBEAM with input loaded entirely from TRANSP/NUBEAM output files, with agreement found between the GC simulations when finite Larmor radius (FLR) corrections were omitted. ASCOT5 FO and TRANSP/NUBEAM with FLR produced fast ion distributions which differed in localised regions, but predicted global neutron rates which agree within 3%.

ASCOT↗
DOE OSTI2024

Heritable, multinucleotide deletions in plants using viral delivery of a repair exonuclease and guide RNAs

Abstract CRISPR/Cas9-mediated mutagenesis typically results in short insertion/deletion mutations, which are often too small to disrupt the function of cis-acting regulatory elements. Here, we describe a highly efficient in planta gene editing approach called VirTREX2-HLDel that achieves heritable multinucleotide deletions in both protein-coding genes and noncoding DNA regulatory elements. VirTREX2-HLDel uses RNA viruses to deliver both the 3 prime repair exonuclease 2 (TREX2) and single-guide RNAs. Our method enables recovery of multiplexed heritable deletions and increases the heritable gene editing frequency at poorly edited sites. We identified functional conservation and divergence of MICRORNA164 (miR164) in Nicotiana benthamiana and tomato (Solanum lycopersicum) using VirTREX2-HLDel and observed previously uncharacterized phenotypes in plants with large deletions at this locus. Our viral delivery method reduces the need for tissue culture and will accelerate the understanding of protein-coding and regulatory regions in plants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Physics-Guided Machine Learning for Prediction of Cloud Properties in Satellite-Derived Solar Data

With over 20 years of high-resolution surface irradiance data covering most of the western hemisphere, the National Solar Radiation Database (NSRDB) is a vital public data asset. The NSRDB uses a two-step Physical Solar Model (PSM) that explicitly considers the effects of clouds and other atmospheric variables on radiative transfer. High-quality physical and optical cloud properties derived from satellite imagery are perhaps the most important data inputs to the PSM, representing the greatest source of radiation attenuation and scattering. However, traditional methods for cloud property retrieval have their own limitations and are unable to accurately predict cloud properties outside of nominal conditions. We introduce a physics-guided neural network that can accurately predict cloud properties when traditional methods fail or are inaccurate. Using this framework, we show reductions in relative Root Mean Square Error (RMSE) for Global Horizontal Irradiance (GHI) up to 13 percentage points for timesteps that previously had missing or low-quality cloud property data. We expect that this methodology will be effective in improving the quality of cloud property and solar irradiance data in the NSRDB.

cloud properties↗
DOE OSTI2026

Multi‐Decadal Dynamics of Wetland Methane Emissions Revealed by Knowledge‐Guided Machine Learning

Measurement of methane fluxes (FCH 4 ) from natural systems, such as wetlands, has lagged far behind carbon dioxide fluxes. Short and fragmented wetland FCH 4 data limit our ability to assess its long-term dynamics and potential climate feedbacks. Extrapolating short-term FCH 4 records to recent decades remains challenging for both process-based models and data-driven machine learning (ML) approaches. Here, we develop a knowledge-guided ML framework that integrates eddy covariance (EC) FCH 4 observations, field warming experiments, and biogeochemical knowledge to reconstruct the long-term FCH 4 budgets and trends. Focusing on the 11 longest EC monitoring sites in the AmeriFlux network, we found considerable variability in multi-decadal trends of wetland FCH 4 , with increases up to 14% per decade from 2000 to 2024. We also found that the strength of these increasing trends declines from high to low latitudes, highlighting the vulnerability of northern wetlands. This work presents novel and robust reconstructions of long-term wetland FCH 4 , offering critical benchmark datasets for bottom-up ecosystem models and advancing fundamental understanding of wetland biogeochemistry.

AmeriFlux site↗
DOE OSTI2022

Nondestructive Evaluation of Stress Corrosion Cracking in a Welded Steel Plate Using Guided Ultrasonic Waves

Stress corrosion cracking (SCC) had occurred in early-generation high-level nuclear waste tanks constructed by welding carbon steel. This paper describes an ultrasonic inspection system and its fundamental ability to detect and quantify the length of SCC on thick welded steel plates. The finite element method (FEM) was applied to simulate the welding process to estimate the welding residual stress field. Growth of stress corrosion cracks is driven by crack stress intensities exceeding the subcritical cracking threshold intensity. The subject plate was experimentally inspected with ultrasonic nondestructive evaluation (NDE) techniques to characterize the extent of SCC. The NDE system uses a piezoelectric transducer to generate guided waves in the thick steel plate, and a scanning laser Doppler vibrometer (SLDV) to measure multidimensional time–space wavefield data over a user-defined scanning area in the plate surface. The measured wavefield data can show wave interactions in a localized area in the plate due to the presence of the discontinuities of the SCC. To generate an inspection image that can precisely show the crack’s location and/or the dimension, the wavefield data are further processed to generate inspection image that maps the entire sample plate so the crack can be clearly identified in the plate while its length can be readily estimated. In conclusion, the ultrasonic test results for crack length agree well with the visually estimated length and are close to that predicted by the FEM for cracks in the weld residual stress field.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Advanced Energy Design Guide for K-12 School Buildings (AEDG) Training: Cooperative Research and Development Final Report, CRADA Number CRD-18-00761

U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (BTO), Commercial Buildings Integration (CBI) Program supports the adoption of zero energy ready buildings design practices through targeted Science, Technology, Engineering, and Mathematics (STEM) workforce development. The National Renewable Energy Laboratory (NREL) identified the opportunity to provided targeted training to those professionals that have the greatest potential to impact adoption and successful design outcomes of zero energy ready buildings projects. In January 2018, ASHRAE released Achieving Zero Energy: Advanced Energy Design Guide for K-12 School Buildings (AEDG). The AEDG was developed in collaboration with NREL, American Institute of Architects (AIA), American Society of Heating, Refrigeration and Air-conditioning Engineers (ASHRAE), U.S. Green Building Council (USGBC), and Illuminating Engineers Society (IES). The purpose of this CRADA was to create a partnership between NREL, AIA, ASHRAE, USGBC, and IES to create and host on-demand, web-based training based on the AEDG.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Cyber100 Compass User Guide

This document provides an overview of the Cyber100 Compass tool and serves as a guide to users interested in understanding the cybersecurity risks facing their clean energy transition. The National Renewable Energy Laboratory (NREL) developed the Cyber100 Compass tool for cyber risk assessment at the system-of-systems level for system planners trying to reach high levels of renewables. Two offices of the U.S. Department of Energy (DOE) - the Office of Electricity and the Office of Cybersecurity, Energy Security, and Emergency Response-funded NREL to develop this framework that will enable grid system planners to understand and mitigate cybersecurity risk for grids transitioning to high levels of renewable generation, including 100%. Cyber100 Compass can help systems planners apply the principle of security-by-design at scale. Investing in an upfront understanding of system-of-systems (where the constituent systems are operating entities of the different renewable resources) risks from high-renewable grids will result in a more resilient grid at a lower long-term cost.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Safely Exploring Solar: A Guide for Austin Energy Customers

It's easy to get overwhelmed as you start to explore your solar options. There is a wealth of information available out there, particularly online, and it can be hard to know who to trust. This informational guide has been adapted from Austin Energy's Solar Education Course and other national best practice resources, as of May 1, 2023. It will help you understand some of the key concepts and resources available to help you on your solar journey if you have a home in Austin Energy's service territory and are considering installing solar. This document includes: (1) Austin Energy Programs and Services Related to Solar; (2) Tips for Spotting a Solar Scam; (3) Things to Know When Designing Your System; (4) Key Considerations When Shopping for a Solar Contractor; and (5) Final Takeaways.

14 SOLAR ENERGY↗
DOE OSTI2024

Application Guide for the Cyber-Resilient Design Framework for Hybrid Systems

As the energy landscape evolves, hybrid power plants—integrating multiple renewable energy sources (e.g., solar, wind, and battery storage) with the bulk power electric system—play a crucial role in meeting growing energy demands. However, with a hybrid power plant’s increased number of components, complex network connectivity and digitization of controls, these systems face growing cybersecurity risks. To help mitigate these risks, Idaho National Laboratory (INL) presents this application guide specifically designed for operators and systems engineers to evaluate the cybersecurity resilience of their hybrid power plant’s design.

14 SOLAR ENERGY↗
DOE OSTI2024

Physics-Guided Machine Learning (PGML) for Improved Aerostructure Manufacturing

The Physics-Guided Machine Learning (PGML) for Improved Aerostructure Manufacturing project objective is to automatically tune machining parameter predictions from physics-based models using process data and Bayesian machine learning. The intent is to enable a step change in aerospace manufacturing by combining machine learning, physics-based process models, and sensors/data in a comprehensive digital environment that simultaneously considers the computer numerically controlled (CNC) machining center capabilities, the workpiece material and geometry, and the workpiece support (fixturing). The project hypothesis is that this combination will enable improved performance in machining operations.

42 ENGINEERING↗
DOE OSTI2026

Municipal Fleet Action Plan Guide

This action plan template and guide have been designed to support light-duty or medium-duty municipal fleet managers as they plan to adopt electric vehicles. The resource is an adaptation of content created by the National Laboratory of the Rockies (NLR), with support from World Resources Institute (WRI), for participants in the U.S. Department of Energy's Energy to Communities peer-learning cohort, Charting a Path to Municipal Fleet Electrification. Fifteen participating municipalities joined monthly cohort workshops from July through December 2024 to learn and plan for their own fleet electrification. Clean Cities and Communities coalitions supported participating municipalities throughout the cohort series by conducting fleet analysis and planning activities on their behalf. This template is an adapted version of cohort activities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Image-Guided Intraoperative Assessment of Surgical Margins in Oral Cavity Squamous Cell Cancer: A Diagnostic Test Accuracy Review

The assessment of resection margins during surgery of oral cavity squamous cell cancer (OCSCC) dramatically impacts the prognosis of the patient as well as the need for adjuvant treatment in the future. Currently there is an unmet need to improve OCSCC surgical margins which appear to be involved in around 45% cases. Intraoperative imaging techniques, magnetic resonance imaging (MRI) and intraoral ultrasound (ioUS), have emerged as promising tools in guiding surgical resection, although the number of studies available on this subject is still low. The aim of this diagnostic test accuracy (DTA) review is to investigate the accuracy of intraoperative imaging in the assessment of OCSCC margins. By using the Cochrane-supported platform Review Manager version 5.4, a systematic search was performed on the online databases MEDLINE-EMBASE-CENTRAL using the keywords “oral cavity cancer, squamous cell carcinoma, tongue cancer, surgical margins, magnetic resonance imaging, intraoperative, intra-oral ultrasound”. Ten papers were identified for full-text analysis. The negative predictive value (cutoff < 5 mm) for ioUS ranged from 0.55 to 0.91, that of MRI ranged from 0.5 to 0.91; accuracy analysis performed on four selected studies showed a sensitivity ranging from 0.07 to 0.75 and specificity ranging from 0.81 to 1. Image guidance allowed for a mean improvement in free margin resection of 35%. IoUS shows comparable accuracy to that of ex vivo MRI for the assessment of close and involved surgical margins, and should be preferred as the more affordable and reproducible technique. Both techniques showed higher diagnostic yield if applied to early OCSCC (T1–T2 stages), and when histology is favorable.

60 APPLIED LIFE SCIENCES↗