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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Machine Learning-driven Molecular Design for Therapeutic Discovery

The ongoing novel coronavirus pandemic (COVID-19) has highlighted the need for new therapeutics to counter the threat of emerging viral pathogens. The main proteases are a promising target for developing antiviral inhibitors. In this work, we utilized a novel combination of artificial intelligence-driven iterative design of covalent inhibitor candidates, physics-based computational modeling of protein-inhibitor interactions, and “All in One” Native MS biophysical assay screening and characterization of therapeutic candidates. With our existing expertise in hit generation using a particular scaffold as a starting point, we first generated tens of thousands of compounds that preserve the key scaffold. In order to optimize the candidates, we calculated about 136 descriptors consisting of 2D and 3D features for molecules targeting the SARS-CoV-2 Main protease (Mpro). These compounds were initially filtered according to properties and further sorted by predicted binding affinity using our automated docking modeling and machine learning methods. We tested a handful of candidates and identified two as inhibitors of Mpro with micromolar affinities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Interfacing Atmospheric Flow Tube to Ion Mobility Spectrometry: FY 2021 Final Report

An atmospheric flow tube (AFT) provides extended ion-molecule reaction times of 2-3 seconds compared to milliseconds in typical mass spectrometers. This results in several orders of magnitude enhanced sensitivity allowing for vapor detection at the parts-per-quadrillion levels when an AFT is coupled to a mass spectrometer (MS). The goal of this project was to demonstrate the feasibility of integrating an AFT ionization source to a more portable ion mobility spectrometer (IMS). This project was successful in integrating AFT to IMS, as shown in Figure ES.1. The PNNL team demonstrated measurable ion signal from an AFT-IMS with a Faraday plate detector. Reasonable ion signals were detectable at low flow rates approaching 1 m/s. This is close to the flow rates used with the AFT-MS, which has demonstrated parts-per-quadrillion vapor detection. Vapor detection enables non-contact detection of a variety of illicit substances for various applications including cargo screening, mail sorting facilities, and checkpoints. With further improvements to enhance detection, AFT-IMS has the potential to allow ultra-trace vapor detection in a more portable device.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Remapping of Data Between One-Dimensional Meshes

In this report we present two approaches to data remapping between one-dimensional meshes implemented with the c++ programming language. Our goal was to test the performance of two search algorithms, linear and binary, and verify the accuracy of our implementations of the two methods. We first introduce the concept of data remap and meshing components, as well as their various uses. We then delve into the differences between point-wise and conservative remap, the algorithms used in the implementations, and lastly confirm the implementations work as intended when given various inputs. We expect that, after profiling, the binary search algorithm will be more efficient than the linear algorithm for sorted sets of data, the point-wise remap implementation to accurately approximate the data transfer between two meshes, and the conservative remap implementation to conserve the area underneath the curve of two distinct meshes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

BETO Visit: Investigating Large-Scale Cultivation Processes [Slides]

Questions: How important is density management in a scaled system?; What processes are currently proposed/used, and how are they deployed?; What sort of information (tools/experimentation) would be helpful?; What can be learned that can inform better strain, cultivation systems, operations and tools development for increased productivities?; Beginning with the end in mind?

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

EOSPAC User's Manual: Version 6.5 Second Edition (Rev. 3)

The EOSPAC utility package is a collection of interface routines, which can be used to access the SESAME data library and perform various data adjustments and interpolations on the SESAME data. The SESAME data library contains both thermodynamic (e.g., equation of state) and transport coefficients (e.g., opacity and conductivity). Note, for simplicity, the term EOS (equation of state) used herein includes both thermodynamic variables and transport coefficients. The EOSPAC utility package is designed to be used by physics codes (henceforth ”host codes”) written in multiple languages and on multiple platforms. The remainder of this manual is organized into several sections. Chapter 2 discusses conventions such as data organization and routine names. Chapter 3 provides a general overview of basic theory and models implemented within EOSPAC. Chapter 4 provides a general overview of how to use the EOSPAC interface library. Chapters 5 to 7 describe the public interfaces of EOSPAC in detail. Chapter 8 provides a brief introduction to some related tools, which may be of use to the user. Chapter 9 provides details related to some selected numerical features of EOSPAC. Chapter 10 gives examples for using the interface routines described in chapters 5 to 7. Chapter 11 provides technical support contact information. Chapter 12 contains a brief set of acknowledgments. Chapter 13 contains a list of referenced documents. Finally, chapter 14 lists the “table types: mnemonic conventions”, “table types: grouped by category, sorted by name”, “table types: eospac version 5 cross reference”, “options: setup phase”, “data information parameters”, “meta-data information parameters”, “options: interpolation phase”, and the “error codes”.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Pressurized-Water Reactor Core Design Demonstration with Genetic Algorithm Based Multi-Objective Plant Fuel Reload Optimization Platform

LWRS M3 milestone report due September 15, 2023. This report summarizes development and demonstration activities of PRLO optimization platform built in Risk Analysis and Virtual ENviroment (RAVEN), specifically: (1) Improvement of multi-objective non-dominated sorting genetic algorithm II (NSGA-II) to handle large size of objectives and constraints, (2) Demonstration of pressurized water reactor core design with NSGA-II multi-objective optimization platform, and (3) Single-objective optimized core design including system analysis and fuel performance feedback.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

An Integrated Approach to Predicting Ash Deposition and Heat Transfer in Coal-Fired Boilers

The overall goal of this project is to develop via measurements and simulations an advanced online technology to predict, monitor and manage fireside ash deposition in a coal-fired boiler allowing for more efficient operations under a range of load conditions and fuel property variability. With this in place fuel sorting and blending can be done upstream and operations can be optimized to compensate for load and fuel properties. In support of this objective, three experimental campaigns were undertaken during the course of the project to measure ash deposition rates within the boiler at different fuel flow rates and its ash composition variability. Simulations of the experimental conditions representing actual geometry, operational scenarios in terms of air flow rates, coal flow rates as well as coal compositions, heating values, and particle size distributions were also carried out. Deposition rates were predicted using a unique particle kinetic energy and viscosity based ash deposition methodology whose validity was ascertained by comparing against deposition rate measurements for widely varying operating conditions and ash compositions in a lab-scale furnace. With a unique end-to-end combustion modeling methodology established and different simulation scenarios carried out, the results from our computational fluid dynamic (CFD) simulations in conjunction with the plant data summarized in this report were used to refine Microbeam Technology Incorporated’s MTI CSPI-CT Tool to predict and monitor fire-side ash deposition under a range of load conditions and fuel property variability in real time.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Advanced Algal Biofoundries for the Production of Polyurethane Precursors

The primary goal of the BEEPs project was to develop a process that could accelerate the development of algae as bioproduction platforms, from initial chemical product concept to an economically viable market supply. Under this program we elected to develop strains of algae that could generate polyurethane precursors, while simultaneously developing basic genetic tools to enable improved algal production systems. This program was specifically designed to incorporate National Laboratories as a means to utilize the expertise and facilities for new bio-production platforms. To that end, we designed a program to collaborate with the Agile BioFoundry at Lawrence Berkeley National Laboratory (LBNL) and computational platforms at Pacific Northwest National Laboratory (PNNL). In addition to these National Laboratory partners, we also had academic partners from UC Davis and Georgia Tech, as well as commercial partners Algenesis Materials and BASF. To achieve these goals, we initially focused on developing the genetic tools and high throughput screening technologies necessary to generate and assess production of polymer precursors (succinic acid) in algae and cyanobacteria, including advanced promoters and biosensors. In parallel, we computationally identified potential production bottlenecks and then used the developed genetic tools to increase production rates and yields. Constant feedback of data was used in conjunction with machine learning, high-throughput cell sorting, and synthetic biology, for additional targeted metabolic engineering. Multiple rounds of tool design, building, testing, and learning were supplied to partners at PNNL and LBNL to develop new models and tools that could expedite bioproduction platform development and increase yield performance. We targeted, and achieved, the FOA requirement yield metric of 20 g/L, as a milestone and deliverable from at least one of our engineered strains for the production of succinic acid.

09 BIOMASS FUELS↗
DOE OSTI2024

Crush Gun Impact of Lightly Confined Explosive with Rounded Projectiles at Nominal Velocity 35 m/s

Blunt, crushing impact of high explosives can cause internal shear heating sufficient to ignite a deflagration. Low-velocity impact of this sort is plausible in handling accident scenarios, e.g. if a charge is dropped or an object is dropped onto a charge. This mechanism can also be activated during sub-shock fragment impact. Efforts to model this viscous shear heating mechanism are currently being developed and refined. The Crush Gun apparatus was created to provide a controlled low-velocity impact stimuli to explosive samples. Infrared thermography captures temporal- and spatially-resolved heating that occurs during the impact event.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Preliminary results addressing material qualification using combined ion irradiation and modeling data

Additively-manufactured (AM) materials have attracted increasing attention in recent years as a new method to make novel and customized components. While AM and conventionally produced materials are compositionally similar, they do possess different microstructures, necessitating assessment of materials produced via AM for their behavior in reactor environments. Some microstructures unique to AM materials, such as compositional micro-inhomogeneity and dislocation cell structures, are of particular importance since they may lead to different radiation performance. The performance of AM materials for advanced nuclear reactor applications is of interest to the Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy. The AMMT program aims to demonstrate its new accelerated development and qualification methods via laser powder bed fusion (LPBF) 316 stainless steel (SS). Focusing on material bearing both 316L and 316H specifications, we integrate ion irradiation and modeling. This year, we focus on answering foundational questions related to process variability, alloy chemistry variation, and microchemical segregation. Experimental results provide information and motivate questions to the modeling effort, which aims to develop the ability to model radiation-driven microstructural evolution in additively-manufactured 316 stainless steel under a variety of advanced reactor conditions, including different temperatures, neutron spectra, and fluxes, in a sort of "virtual experiment". We perform in-situ and ex-situ ion irradiations and microstructural characterizations to support the development of AM materials for reactor applications, develop a phase field model of radiation-induced segregation in additively manufactured material with high angle grain boundaries and dislocation cells, and investigate the effect of carbon and chromium content on point defect behavior.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Plant Disease Detection Technology Assessment

Visual inspections by US Customs and Border Protection agriculture specialists have identified approximately 20,000 regulated, quarantined pests each year in agricultural products entering the United States. Most of these pests identified in the Agriculture Quarantine and Inspection (AQI) program are insects. Many pathogens are difficult to detect in agricultural products, particularly in early stages of infection. New technologies can help to detect plant pathogens and the diseases that they cause. This technology assessment was performed for the US Department of Homeland Security Science and Technology Directorate (DHS S&T) through the Food, Agriculture, and Veterinary Defense (FAV-D) program to identify emerging technologies that could address this hard problem. These emerging technologies differ in their diagnostic sensitivities and specificities, as well as in their measurement time and training requirements. New instruments that detect volatile organic compounds characteristic of plant disease or pathogens could provide a less invasive inspection method. Dogs, which can successfully detect many concealed agricultural products, have also been trained to detect some plant pests and pathogens. Simple immunological tests offer sensitive and specific detection of many pathogens at the point of use. Advanced imaging methods that use AI to sort fruits and vegetables and recognize anomalies at high speeds could be used in cooperation with exporters to improve food quality and reduce pests. Advances in nucleic acid–based detection methods that have become gold standards for confirmatory diagnostics are now making those methods available for faster, point-of-use detection. These new methods should be developed in the context of AQI operational requirements, which apply risk-based sampling protocols to protect agriculture and facilitate commerce and passenger transit.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Calibrating LArPix for TinyTPC

LArTPCs provide sensitivity to GeV signals, such as accelerator neutrinos and part of the supernova neutrino spectrum. TinyTPC is a LArTPC test stand for R&D of LAr doping to expand the reach of LArTPCs down to the 1-10 MeV range, which would substantially enhance the flagship analyses of experiments like DUNE, while enabling low energy analyses. We aim to dope LAr with Xe and pho- tosensitive dopants to expand the LArTPC range by converting hard-to-detect scintillation light to efficiently detected ionization charge. A critical element of the data analysis in TinyTPC is calibrating the readout. This poster will cover the calibration of TinyTPC, a pixelated liquid argon detector, where we find the distance a muon travels through each pixel. We then calculate the energy loss of muons traveling through the detector from cosmic data. We can reconstruct the path of the particles through the TPC using a density-based clustering algorithm designed to sort straight cosmic muon tracks from low energy radioactive decay curled paths and electronic noise

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Precision Plant Biomass Characterization in Agriculture: Harnessing Machine Learning and Hyperspectral Imaging [Slides]

Efficient Biomass Separation Object detection of anatomical parts (Cob, Stalk, Husk) in IR images enables precise separation, improving preprocessing (e.g., drying, grinding) for biofuel production. Detailed Biomass Characterization with Hyperspectral Data Hyperspectral imaging captures spectral signatures of biomass, allowing for the identification of specific traits like moisture content, lignin levels, and nutrient composition, leading to optimized treatments for each biomass part. Enhanced Feedstock Quality By leveraging hyperspectral data, feedstock can be processed based on its chemical composition, improving conversion efficiency and biofuel yield. Automation for Large-Scale Operations Automated object detection and hyperspectral data analysis reduce manual labor, ensuring accurate sorting and faster processing, making large-scale biofuel production more efficient. Maximized Biomass Utilization Accurate identification of biomass properties minimizes waste and ensures that each part is processed according to its highest biofuel potential.

09 BIOMASS FUELS↗
DOE OSTI2024

Justice Underpinning Science and Technology Research (JUST-R) Offline Tool: A Tool for Guiding Researchers in Addressing Energy Justice Considerations in Early-Stage Research

The Justice Underpinning Science and Technology Research (JUST-R) Metrics Framework is a set of metrics for assessing the energy justice implications of technologies that are currently under research and development (R&D). The JUST-R Metrics Framework guides researchers through an analysis of the many facets of their research processes, from material inputs to knowledge sources, that may contribute to energy injustice both during the research period and when the technology is scaled. This JUST-R Offline Tool consists of an Excel file and PDF guide to aid researchers, engineers, and project managers in their application of the JUST-R Metrics Framework. This tool enables researchers to 1) evaluate the baseline energy justice implications of their research; 2) develop justice-oriented changes to the research process; and 3) track the implementation of proposed changes. The metrics included in the framework are sorted into five aspects of research: Team Dynamics, Sources & Inputs, Processes & Protocols, Waste & Hazards, and Results & Dissemination. The JUST-R Tool can be found here: https://www.nrel.gov/analysis/just-r.html.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Assessing the Current State of U.S. Energy Equity Regulation and Legislation (2024 Update) [Slides]

Lawrence Berkeley National Laboratory (LBNL) partnered with E9 Insight (E9) to update the 2022 database of executive, legislative, and regulatory actions focusing on energy equity and directed at electricity and natural gas utilities. The resulting database contains 307 energy equity actions, which consist of documents (e.g., bills, dockets, and executive orders) identified through keyword searches associated with energy equity. Based on our review, almost half of states (32 + DC) were taking some sort of action on energy equity (i.e., executive order, PUC activity, agency plan, or executive bill). In the report, we explored temporal trends to track state actions over time across regions, outcomes, equity tenets, and objectives. We also explored what drivers led to what outcomes. Drivers were organized into legislative, regulatory, executive, and stakeholder-driven.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Circularity Futures Workshop Series: Summary Report

The aim of this report is to synthesize key feedback received from the three-part Circularity Futures workshop series held in Spring 2024. The workshop series was conducted by the National Renewable Energy Laboratory (NREL) on behalf of U.S. Department of Energy, Office Energy Efficiency and Renewable Energy (EERE), and was broken into three workshops: Workshop 1 - Circularity Analysis Needs and Priorities; Workshop 2 - Circularity Metrics and Indicators; and Workshop 3 - Circularity Data. Together, the workshops focused on identifying the existing priorities and gaps in the circularity modeling space, understanding different stakeholders' use and interpretation of circularity metrics and indicators, identifying common data gaps and data quality challenges, and assessing the robustness of available solutions. The workshop series brought a diverse group of stakeholders - including representatives from U.S. government offices, national labs, nonprofit organizations, industry, and academia - to collect first-hand feedback on needs, priorities, challenges and opportunities in the circularity modeling and analysis space. The workshop discussions highlighted numerous common needs, priorities and challenges among the interviewed groups. Several topics were frequently discussed, including: 1) Circularity as a pathway for sustainable economic growth: While circularity is generally defined in terms of resource conservation and reducing wasteful disposal of materials, participants agreed that circular strategies should serve broader economic, environmental, and social goals. It is therefore crucial for circularity analysis to look beyond waste reduction and instead evaluate a variety of impact metrics such as cost savings, job creation, air quality, and pollutant emissions. Mutli-criteria decision-making frameworks may be useful for making sense of disparate metrics and evaluating tradeoffs between impact categories.; 2) Economic and social factors are not well understood: Underdevelopment of existing end-of-life (EOL) management infrastructure, inconsistent standardization codes and policy space in reusing recycled content, and suboptimal collection and sorting strategies collectively contribute to uncertainty about the economic potential of circular pathways. The latter observation is consistent among all technologies but more emphasized for renewable energy systems. Social impacts of circularity practices are less understood and less researched than other sustainability aspects.; 3) Inconsistent methods for assessing emerging technologies: LCA and TEA results vary widely depending on the assumptions made with regards to market adoption of new technologies. Emerging technologies suffer limited availability of data needed to conduct a robust circularity analysis. Yet, understanding projected impacts of proposed nascent technology is a key need for different stakeholder groups.; and 4) Lack of temporally and geospatially explicit data: There is a need for open data that represents variations in circularity technologies over time and location. The lack thereof leads to aggregated and potentially misrepresented results in circularity analysis. Sensitivity analyses should be included to verify whether options perceived as more sustainable align with real-world practices.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

A Data-Agnostic, Continuous Machine Learning Framework for Application in High Energy Physics and Beyond: Phase 1 Final Scientific/Technical Report

This Phase 1 effort has focused on the development of continual learning frameworks for use in machine learning, specifically in the applied context of High Energy Physics (HEP). Machine learning (ML) is a transformative technology by which computers, typically through the use of neural networks, are able to perform tasks with proficiency that rivals or surpasses that of human users. Model Degradation & Catastrophic Forgetting are two undesired phenomena which can occur in ML where the performance of a model degrades when either deployed on novel data streams, or trained on novel data which are sufficiently different than the data the models were initially trained on. A natural example where these sorts of effects can be observed is in the performance of detectors in harsh environments, where the detector signature may change over the lifetime of the detector as it ages and deteriorates — precisely what occurs in the experiments conducted in HEP. Real world HEP data is therefore an excellent test-ground and use-case for Continual Learning paradigms, which are techniques used in ML to counteract these problems. Ensemble learning is one such technique, where multiple smaller models are trained on subsets of the overall data and are ensembled together during inference. The intuition behind this technique is that, although there are shifts in the distributions which govern the incoming data streams, these shifts are not expected to be homogeneous or global. If a sufficient diversity in solutions within the various sub-models has been achieved, then at least one sub-model is expected to retain its performance within the overall ensemble. One further strength of this approach is that the architectures of the various models do not need to be identical, and in fact even different modalities of data can naturally be combined in this way. This work focused on applying ensemble learning techniques to derive results using two main datasets, anomaly detection in HEP data & time-series forecasting in semiconductor manufacturing data. Semiconductor manufacturing involves data with surprising similarity to that of HEP (e.g. wafer maps look very similar to digi-occupancy maps) and Cerium Lab’s prominence within the semiconductor industry makes semiconductor manufacturing a natural opportunity for commercialization of this work. Our efforts have led to two strong results. The first is that we evaluated the proposed ensembling techniques using previously proposed machine learning architectures for use in anomaly detection, namely AutoEncoder based models and their derivatives. We also developed new architectures which have not been evaluated in this context before. In fact, this work marks the first use of Vision Transformers for anomaly detection in HEP. Second, we demonstrated that ensemble learning significantly improves model performance in scenarios prone to degradation, validating its effectiveness across both HEP and semiconductor datasets. These results further support ensemble learning as a powerful strategy for mitigating catastrophic forgetting and maintaining robust performance in evolving data environments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Macromolecules & Manufacturing Science

Outline • SRNL Overview • Mission overview • Polymers enabling the mission • R&D Highlights • Polymers in radiation environments • Tooling in shielded cells • Packaging for nuclear material shipments • Polymers supporting tank waste remediation • Ref electrode • Epoxy and polymer grout • Polymers for fusion energy • Deuterium labelling • Polymers for additive manufacturing • Coalescence and blends: experimental and predictive • Process modelling and sorting through big data (peregrine and latticeJ)

Chatham, Camden [Savannah River National Laborator↗