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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Recovery Act - Measurement of Parity Violation in Deep Inelastic Scattering and Studies of the Nucleon Spin Structure at JLab 6 and 11 GeV

The program proposed contains two ingredients which aim to address aspects of two of the three research frontiers of nuclear science as identified in the 2007 NSAC Long Range Plan. The first topic, a test of the current Standard Model, is an ongoing project focusing on measurements of the parity-violating asymmetry in ~e-2H deep inelastic scattering (PVDIS). The PVDIS measurement is complementary to other completed or ongoing low- to medium-energy tests of the Standard Model. As the first, exploratory, step, an experiment using a 6 GeV electron beam will be carried out from October to December 2009 at the Thomas Jefferson National Accelerator Facility (JLab). Meanwhile, a program using the upgraded JLab 11 GeV beam is being planned. The PVDIS program as a whole will provide the first precision data on the axial quark neutral-weak coupling constants. This will either put the current Standard Model to a test that has never been done before, or reveal information on where to look for New Physics beyond the current Standard Model. The PVDIS program will also provide results on hadronic physics effects such as charge symmetry violation. The second part of the proposed program uses spin observables to address the research frontier concerning QCD and structure of the nucleon. An experiment using the JLab 6 GeV beam in 2001 showed that, contrary to predictions from perturbative quantum chromodynamics (pQCD), while the valence up quark’s spin is parallel to the nucleon’s spin, the valence down quark’s spin is not. In order to test the limit of QCD in describing the nucleon spin structure to a region beyond the 6 GeV kinematics, this measurement will be extended to a more energetic, “deeper” valence quark region using the upgraded JLab 11 GeV beam with a polarized 3 He target. Although the two topics of the proposed program appear to focus on different physics, for the upgraded JLab 11 GeV beam, both will utilize a new, yet-to-be-built large acceptance spectrometer, called “SoLID”. Once built, SoLID can be used for other topics such as using PVDIS on a proton target to measure the valence quark distributions, using PVDIS on a polarized target to measure new electro-weak interference structure functions of the nucleon, and semi-inclusive deep inelastic scattering experiments. SoLID will greatly enhance the exploration potential of JLab. The proposed program will first focus on completion of the 6 GeV PVDIS experiment, its data analysis and publishing physics results. This 6 GeV experiment will explore whether precision PVDIS measurements are feasible, will set limits on the hadronic physics effects, and will improve our knowledge on the quark neutral weak couplings, optimally by a factor of six. Starting early 2010, efforts will be spent on preparation for the 11 GeV program, focusing on simulations of SoLID, optimization and construction of the SoLID detector package, and studies of the polarized 3 He target and its improvements. Funding support for the whole program is requested here.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Transforming Public Housing with Deep Energy Retrofits

Open Market ESCO’s (OME) Transforming Public Housing through Deep Energy Retrofits project demonstrated new design and financing solutions for implementing deep energy retrofits (DERs) in occupied low-income multifamily housing. The Project performed an integrated project delivery process, including designing low-carbon retrofit solution packages, construction pricing, and financing. The Project developed a constructible and financeable DER scope for a 102-unit extremely low-income multifamily property in Boston. This Project demonstrated a replicable approach for streamlining and implementing DER projects in occupied housing, including real solutions for overcoming design complexities and cost barriers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Spatio-temporally shaped deep UV laser source for ultrabright photocathodes using novel upconversion techniques (Final Report)

The goal of this project was to take a VUV laser system designed by Kapteyn-Murnane Labs (KMLabs)—the Hyperion VUV, and increase its functionality, efficiency, and applicability by further probing the fundamental physics taking place in the system and optimizing the system performance. More specifically, DOE accelerator applications—particularly for XFELs that require low-emittance electron beams, require precisely controlled ultrashort pulses in the deep-UV at 250-260 nm to drive the photocathode electron source. This is conventionally done using an ultrafast infrared laser, with subsequent stages of nonlinear upconversion in solid-state nonlinear-optical crystal media (principally BBO, which is commonly used for UV upconversion). However, maintaining high spatial mode beam quality, as well as precisely controlled temporal profile, has proven a persistent challenge. As a possible alternative to this approach, KMLabs has demonstrated a system that generates vacuum ultraviolet light (VUV) by upconverting pulses from an infrared ultrafast laser in a gas-filled hollow waveguide, driving a process of highly cascaded harmonic generation (HCHG). Because HCHG upconversion is done in a gas, rather than a solid, at high intensity in a guided-wave geometry, it retains an excellent beam spatial profile as well as a large spectral bandwidth that allows for control over the temperal structure of the pulse. A challenge, however, is in energy scaling of this HCHG technique to the ~1-20 μJ pulses required for photocathode applications. To-date, the pulse energies used in published HCHG work are sub-10-nJ. This project proposed to explore ways to generate higher-energy pulses using HCHG, as well as to investigate spectral and temporal shaping of the deep UV pulses by pulse shaping the infrared laser used to seed the process. Although we showed that our approach works for generating light at 259 nm, the total pulse energy achieved seems limited to approximately the same value as past work. However, the techniques we investigated may well have benefits for extending this source to much shorter wavelength output.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Polymers in Deep Eutectic Solvents

The project investigated the behavior of polymers in ionic liquids such as deep eutectic solvents using an array of techniques. These included the development of atomistic force fields, coarse graining these force fields to the united atom level, large scale molecular dynamics (MD) simulations using new thermostat algorithms, and machine learning (ML) methods for the phase behavior. The project demonstrated the feasibility and accuracy of first principles force fields for ionic liquids, deep-eutectic solvents, and urea-water mixtures. A novel hierarchical coarse graining method was then used to develop accurate and efficient united-atom models, using which microsecond simulations were performed for polymers in ionic liquids. These simulations were in quantitative agreement with experiment, thus resolving previous controversies. Methods were also developed to obtain the potential of mean force between complex ions in solution. Finally, supervised ML methods were developed for the phase behavior of polymers in ionic liquids.

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

Griffin Capability Improvements in Support of Ex-core Deep-Penetration Problems

Advanced reactor designs, especially portable reactors that are designed to be located closer to humans and operate autonomously, require the ability to accurately compute the ex-core neutron and gamma flux solutions in terms of shielding design optimization to reduce dose rates at the vessel boundary and detector signal prediction to drive the reactor control system. The Nuclear Energy Advanced Modeling and Simulation program has prioritized improvements to the Griffin discrete ordinates (SN) solver for deep-penetration problems in fiscal year 2025. Significant advancements have been made to the Griffin methodologies for solving ex-core deep-penetration problems for steady-state, fixed-source and transient calculations. This work presents the methodology improvements as well as a comprehensive demonstration with a Transient Test Reactor model and measurements.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

A Public Data Set of Auto-Generated Geotagged PV Site Equipment, Generated via Deep Learning

In this research, we present a data set over 100 photovoltaic (PV) sites in TX, which have been automatically geotagged via a fully autonomous deep learning (DL) pipeline. Specifically, locations of inverters, tracker/fixed tilt rows, batteries, and substations are labeled algorithmically. To ensure high data quality, all systems have been reviewed manually and any deep learning errors have been corrected. This public data set, as well as the open-sourced pipeline used to generate it, is valuable for site planning, modelling, and insurance purposes. Given time and resources, we hope to extend the data set to additional states/regions in the US.

14 SOLAR ENERGY↗
DOE OSTI2016

Evaluating an Exterior Insulation and Finish System for Deep Energy Retrofits

This report provides a case study of a deep energy retrofit using a site-applied exterior insulation and finish system (EIFS). Prefabricated EIFS panels are also discussed as a potential wall insulation retrofit solution. EIFSs are common in new and retrofit commercial construction. Such products typically consist of five layers installed over the top of a water resistive barrier as follows: (1) Adhesive (2) Foam insulation (3) Base coat (4) Reinforcing mesh embedded in the base coat (5) Top or finish coat. They can be applied to new or existing buildings that employ wood or masonry construction, and can utilize an insulation thickness of up to 16 in. However, such systems are rarely used in lowrise residential retrofits. Reasons for this might include a lack of demand for or knowledge of the systems in residential retrofit industry, high cost, and/or lack of suitable distribution channels serving the residential market. While the final appearance of an EIFS installation resembles stucco, it is a distinctly different system. Stucco is a generic cementitious-based material, whereas EIFSs are proprietary synthetic formulations distributed by manufacturers to a network of authorized applicators. An EIFS is composed of polymeric (organic) bonded aggregate and cement reinforced with a glass mesh. Stucco is made of inorganic cement, sand, and lime. In this project, a home was retrofitted using a site-applied 4-in.-thick EIFS. Site-specific details were developed as required for the residential retrofit application. Site work and the costs of the EIFS were documented. The case study home was modeled using version 2 of the National Renewable Energy Laboratory-developed Building Energy Optimization (BEopt) energy and cost analysis software. While a package utilizing the 4-in. EIFS is the optimal retrofit of the options analyzed in terms of annualized energy-related costs, it is higher cost than replacing the siding with midrange vinyl alone. However, compared to high-end vinyl, the EIFS package has a lower annualized energy cost. This held true for both the Islip, New York (climate zone 4) and Boston (climate zone 5) case studies. The report discusses the risks, selection criteria, interactions with other building systems, cost, performance, and other aspects of using an EIFS in a deep energy retrofit. EIFSs do not require special site safety precautions beyond general construction site precautions. Moisture design is important. Trapping moisture between two vapor/air barriers is a risk of all wall systems, including EIFS cladding applications if an interior vapor barrier exists and if there is not adequate ventilation through the drainage plane behind the insulation layer. Face-sealed approaches that rely on exposed sealants do not provide acceptable rain control or durability and are very risky. Generally, fire codes permit EIFSs for most building types and conditions.

airtightness↗
DOE OSTI2022

Optimal vocabulary selection approaches for privacy-preserving deep NLP model training for information extraction and cancer epidemiology

With the use of artificial intelligence and machine learning techniques for biomedical informatics, security and privacy concerns over the data and subject identities have also become an important issue and essential research topic. Without intentional safeguards, machine learning models may find patterns and features to improve task performance that are associated with private personal information. The privacy vulnerability of deep learning models for information extraction from medical textural contents needs to be quantified since the models are exposed to private health information and personally identifiable information. The objective of the study is to quantify the privacy vulnerability of the deep learning models for natural language processing and explore a proper way of securing patients’ information to mitigate confidentiality breaches. The target model is the multitask convolutional neural network for information extraction from cancer pathology reports, where the data for training the model are from multiple state population-based cancer registries. This study proposes the following schemes to collect vocabularies from the cancer pathology reports; (a) words appearing in multiple registries, and (b) words that have higher mutual information. We performed membership inference attacks on the models in high-performance computing environments. The comparison outcomes suggest that the proposed vocabulary selection methods resulted in lower privacy vulnerability while maintaining the same level of clinical task performance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Crystal Structure Prediction of Binary Alloys via Deep Potential

Predicting crystal structure has been a challenging problem in physics and materials science for a long time. A reliable energy calculation engine combined with an efficient global search algorithm, such as particle swarm optimization algorithm or genetic algorithm, is needed to conduct crystal structure prediction. In recent years, machine learning-based interatomic potential energy surface models have been proposed, potentially allowing us to perform crystal structure prediction for systems with the accuracy of density functional theory (DFT) and the speed of empirical force fields. In this paper, we employ a previously developed Deep Potential model to predict the intermetallic compound of the aluminum–magnesium system, and find six meta-stable phases with negative or nearly zero formation energy. In particular, Mg 12 Al 8 shows excellent ductility and Mg 5 Al 27 has a high Young's modulus. Based on our benchmark results, we propose a relatively robust structure screening criterion that selects potentially stable structures from the Deep Potential-based convex hull and performs DFT refinement. By using this criterion, the computational cost needed to construct the convex hull with ab initio accuracy can be dramatically reduced.

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

Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation

Machine learning and artificial intelligence (AI/ML) methods are beginning to have significant impact in chemistry and condensed matter physics. For example, deep learning methods have demonstrated new capabilities for high-throughput virtual screening, and global optimization approaches for inverse design of materials. Recently, a relatively new branch of AI/ML, deep generative models (GMs), provide additional promise as they encode material structure and/or properties into a latent space, and through exploration and manipulation of the latent space can generate new materials. These approaches learn representations of a material structure and its corresponding chemistry or physics to accelerate materials discovery, which differs from traditional AI/ML methods that use statistical and combinatorial screening of existing materials via distinct structure-property relationships. However, application of GMs to inorganic materials has been notably harder than organic molecules because inorganic structure is often more complex to encode. In this work we review recent innovations that have enabled GMs to accelerate inorganic materials discovery. We focus on different representations of material structure, their impact on inverse design strategies using variational autoencoders or generative adversarial networks, and highlight the potential of these approaches for discovering materials with targeted properties needed for technological innovation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

DeepComplex: A Web Server of Predicting Protein Complex Structures by Deep Learning Inter-chain Contact Prediction and Distance-Based Modelling

Proteins interact to form complexes. Predicting the quaternary structure of protein complexes is useful for protein function analysis, protein engineering, and drug design. However, few user-friendly tools leveraging the latest deep learning technology for inter-chain contact prediction and the distance-based modelling to predict protein quaternary structures are available. To address this gap, we develop DeepComplex, a web server for predicting structures of dimeric protein complexes. It uses deep learning to predict inter-chain contacts in a homodimer or heterodimer. The predicted contacts are then used to construct a quaternary structure of the dimer by the distance-based modelling, which can be interactively viewed and analysed. The web server is freely accessible and requires no registration. It can be easily used by providing a job name and an email address along with the tertiary structure for one chain of a homodimer or two chains of a heterodimer. The output webpage provides the multiple sequence alignment, predicted inter-chain residue-residue contact map, and predicted quaternary structure of the dimer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google’s Colaboratory

X-ray micro-computed tomography (X-ray μCT) has enabled the characterization of the properties and processes that take place in plants and soils at the micron scale. Despite the widespread use of this advanced technique, major limitations in both hardware and software limit the speed and accuracy of image processing and data analysis. Recent advances in machine learning, specifically the application of convolutional neural networks to image analysis, have enabled rapid and accurate segmentation of image data. Yet, challenges remain in applying convolutional neural networks to the analysis of environmentally and agriculturally relevant images. Specifically, there is a disconnect between the computer scientists and engineers, who build these AI/ML tools, and the potential end users in agricultural research, who may be unsure of how to apply these tools in their work. Additionally, the computing resources required for training and applying deep learning models are unique, more common to computer gaming systems or graphics design work, than to traditional computational systems. To navigate these challenges, we developed a modular workflow for applying convolutional neural networks to X-ray μCT images, using low-cost resources in Google’s Colaboratory web application. Here we present the results of the workflow, illustrating how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate. We expect that this framework will accelerate the adoption and use of emerging deep learning techniques within the plant and soil sciences.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Deep learning models for interpretation of point of care ultrasound in military working dogs

Introduction: Military working dogs (MWDs) are essential for military operations in a wide range of missions. With this pivotal role, MWDs can become casualties requiring specialized veterinary care that may not always be available far forward on the battlefield. Some injuries such as pneumothorax, hemothorax, or abdominal hemorrhage can be diagnosed using point of care ultrasound (POCUS) such as the Global FAST® exam. This presents a unique opportunity for artificial intelligence (AI) to aid in the interpretation of ultrasound images. In this article, deep learning classification neural networks were developed for POCUS assessment in MWDs. Methods: Images were collected in five MWDs under general anesthesia or deep sedation for all scan points in the Global FAST® exam. For representative injuries, a cadaver model was used from which positive and negative injury images were captured. A total of 327 ultrasound clips were captured and split across scan points for training three different AI network architectures: MobileNetV2, DarkNet-19, and ShrapML. Gradient class activation mapping (GradCAM) overlays were generated for representative images to better explain AI predictions. Results: Performance of AI models reached over 82% accuracy for all scan points. The model with the highest performance was trained with the MobileNetV2 network for the cystocolic scan point achieving 99.8% accuracy. Across all trained networks the diaphragmatic hepatorenal scan point had the best overall performance. However, GradCAM overlays showed that the models with highest accuracy, like MobileNetV2, were not always identifying relevant features. Conversely, the GradCAM heatmaps for ShrapML show general agreement with regions most indicative of fluid accumulation. Discussion: Overall, the AI models developed can automate POCUS predictions in MWDs. Preliminarily, ShrapML had the strongest performance and prediction rate paired with accurately tracking fluid accumulation sites, making it the most suitable option for eventual real-time deployment with ultrasound systems. Further integration of this technology with imaging technologies will expand use of POCUS-based triage of MWDs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

An observational study of entrainment rate in deep convection

This study estimates entrainment rate and investigates its relationships with cloud properties in 156 deep convective clouds based on in-situ aircraft observations during the TOGA-COARE (Tropical Ocean Global Atmosphere Coupled Ocean Atmosphere Response Experiment) field campaign over the western Pacific. To the authors’ knowledge, this is the first study on the probability density function of entrainment rate, the relationships between entrainment rate and cloud microphysics, and the effects of dry air sources on the calculated entrainment rate in deep convection from an observational perspective. Results show that the probability density function of entrainment rate can be well fitted by lognormal, gamma or Weibull distribution, with coefficients of determination being 0.82, 0.85 and 0.80, respectively. Entrainment tends to reduce temperature, water vapor content and moist static energy in cloud due to evaporative cooling and dilution. Inspection of the relationships between entrainment rate and microphysical properties reveals a negative correlation between volume-mean radius and entrainment rate, suggesting the potential dominance of homogeneous mechanism in the clouds examined. The entrainment rate and environmental water vapor content show similar tendencies of variation with the distance of the assumed environmental air to the cloud edges. Their variation tendencies are non-monotonic due to the relatively short distance between adjacent clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Deep Learning-Based Adaptive Remedial Action Scheme with Security Margin for Renewable-Dominated Power Grids

The Remedial Action Scheme (RAS) is designed to take corrective actions after detecting predetermined conditions to maintain system transient stability in large interconnected power grids. However, since RAS is usually designed based on a few selected typical operating conditions, it is not optimal in operating conditions that are not considered in the offline design, especially under frequently and dramatically varying operating conditions due to the increasing integration of intermittent renewables. The deep learning-based RAS is proposed to enhance the adaptivity of RAS to varying operating conditions. During the training, a customized loss function is developed to penalize the negative loss and suggest corrective actions with a security margin to avoid triggering under-frequency and over-frequency relays. Simulation results of the reduced United States Western Interconnection system model demonstrate that the proposed deep learning–based RAS can provide optimal corrective actions for unseen operating conditions while maintaining a sufficient security margin.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting

Advancing the capabilities of earthquake nowcasting, the real-time forecasting of seismic activities, remains crucial for reducing casualties. This multifaceted challenge has recently gained attention within the deep learning domain, facilitated by the availability of extensive earthquake datasets. Despite significant advancements, the existing literature on earthquake nowcasting lacks comprehensive evaluations of pre-trained foundation models and modern deep learning architectures; each focuses on a different aspect of data, such as spatial relationships, temporal patterns, and multi-scale dependencies. This paper addresses the mentioned gap by analyzing different architectures and introducing two innovative approaches called Multi Foundation Quake and GNNCoder. We formulate earthquake nowcasting as a time series forecasting problem for the next 14 days within 0.1-degree spatial bins in Southern California. Earthquake time series are generated using the logarithm energy released by quakes, spanning 1986 to 2024. Our comprehensive evaluations demonstrate that our introduced models outperform other custom architectures by effectively capturing temporal-spatial relationships inherent in seismic data. The performance of existing foundation models varies significantly based on the pre-training datasets, emphasizing the need for careful dataset selection. However, we introduce a novel method, Multi Foundation Quake, that achieves the best overall performance by combining a bespoke pattern with Foundation model results handled as auxiliary streams.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Deep-Learning-Based Segmentation of Keyhole in In-Situ X-ray Imaging of Laser Powder Bed Fusion

In laser powder bed fusion processes, keyholes are the gaseous cavities formed where laser interacts with metal, and their morphologies play an important role in defect formation and the final product quality. The in-situ X-ray imaging technique can monitor the keyhole dynamics from the side and capture keyhole shapes in the X-ray image stream. Keyhole shapes in X-ray images are then often labeled by humans for analysis, which increasingly involves attempting to correlate keyhole shapes with defects using machine learning. However, such labeling is tedious, time-consuming, error-prone, and cannot be scaled to large data sets. To use keyhole shapes more readily as the input to machine learning methods, an automatic tool to identify keyhole regions is desirable. In this paper, a deep-learning-based computer vision tool that can automatically segment keyhole shapes out of X-ray images is presented. The pipeline contains a filtering method and an implementation of the BASNet deep learning model to semantically segment the keyhole morphologies out of X-ray images. The presented tool shows promising average accuracy of 91.24% for keyhole area, and 92.81% for boundary shape, for a range of test dataset conditions in Al6061 (and one AliSi10Mg) alloys, with 300 training images/labels and 100 testing images for each trial. Prospective users may apply the presently trained tool or a retrained version following the approach used here to automatically label keyhole shapes in large image sets.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Dataset, Code, and Models for Training Deep Learning Potentials for Low Temperature Plasma-Surface Interactions

This repository contains datasets, training scripts, and finished models, and test simulations used in the development of DeepREBO— a machine-learned interatomic potential trained to emulate the REBO2 empirical potential. The data was generated to study deep potential development for simulations of plasma-surface interactions. It uses an active learning framework, starting from a minimal dataset and iteratively expanding it. Included are those generated datasets, the trained models, and simulations used to evaluate the performance of the training process. This resource supports reproducibility and provides a reference framework for training deep potentials in plasma-surface interaction studies.

active learning↗