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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Final Report (October 2024): University of Tennessee, Knoxville (UTK) contribution to: FusMatML: Machine Learning Atomistic Modeling for Fusion Materials Collaborative Project led by Dr. Aidan Thompson, Sandia National Laboratory

The rapid growth of the field of Machine Learning Inter-Atomic Potentials (MLIAP) has lead to a profusion of methods, all of which have some similarity to each other, but each also restricted to particular design choices, often arrived at in a rather ad hoc fashion. Beyond anecdotal evidence, and some benchmarking studies on specific problems, little progress has been made in developing design principles for MLIAPs. The goal of this project is to use machine learning, data science, and uncertainty quantification methods to optimize the design choices for MLIAP.

Density functional theory, Helium and Hydrogen↗
DOE OSTI2017

Building the Sun4Cast System: Improvements in Solar Power Forecasting

The Sun4Cast System results from a research-to-operations project built on a value chain approach, and benefiting electric utilities’ customers, society, and the environment by improving state-of-the-science solar power forecasting capabilities. As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers. The project followed a value chain approach to determine key research and technology needs to reach desired results. Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, the basis of the system beyond about 6 h. For short-range (0-6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short to mid-term irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach, and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed. As a result, this paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

In-situ investigation of strengthening and strain hardening mechanisms of Cu-added medium-Mn steels by synchrotron-based high-energy X-ray diffraction

A novel Cu-added medium-Mn steel with a chemical composition of Fe–0.27C–9.1Mn–1.86Al–3.3Cu (wt.%) was designed and subjected to intercritical annealing (IA) temperature range from 620 °C to 680 °C for 1 h. The ultimate tensile strength (UTS) increases and the yielding strength (YS) decreases with the IA temperature increasing. The YS of 824 MPa, UTS of 1222 MPa, total elongation (TE) of 55%, and product of strength and elongation (PSE) of 67.2 GPa·% are achieved after IA at 660 °C. Transmission electron microscopy confirmed that Cu-rich nanoparticles precipitate in the ferrite. The in-situ high-energy X-ray diffraction (HE-XRD) experiments show that at the beginning of plastic deformation, both austenite and ferrite bear the applied load. The load is mainly undertaken by martensite with effective transformation-induced plasticity (TRIP) effect triggered. The YS of ferrite is significantly higher than that of austenite. The individual contribution of solid solution strengthening, grain refinement strengthening, dislocation strengthening, and precipitation strengthening in ferrite and austenite is analyzed. The discrepancy between the YS of ferrite and austenite is mainly attributed to the precipitation strengthening due to the Cu-rich nanoparticles precipitation. The moderate mechanical stability and the collaboration of TRIP and twinning-induced plasticity (TWIP) effects of austenite contributed to the enhanced strain hardening capability and resulted in large ductility.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Planck 2015 results: I. Overview of products and scientific results

The European Space Agency’s Planck satellite, which is dedicated to studying the early Universe and its subsequent evolution, was launched on 14 May 2009. It scanned the microwave and submillimetre sky continuously between 12 August 2009 and 23 October 2013. In February 2015, ESA and the Planck Collaboration released the second set of cosmology products based ondata from the entire Planck mission, including both temperature and polarization, along with a set of scientific and technical papers and a web-based explanatory supplement. This study gives an overview of the main characteristics of the data and the data products in the release, as well as the associated cosmological and astrophysical science results and papers. The data products include maps of the cosmic microwave background (CMB), the thermal Sunyaev-Zeldovich effect, diffuse foregrounds in temperature and polarization, catalogues of compact Galactic and extragalactic sources (including separate catalogues of Sunyaev-Zeldovich clusters and Galactic cold clumps), and extensive simulations of signals and noise used in assessing uncertainties and the performance of the analysis methods. The likelihood code used to assess cosmological models against the Planck data is described, along with a CMB lensing likelihood. Finally, scientific results include cosmological parameters derived from CMB power spectra, gravitational lensing, and cluster counts, as well as constraints on inflation, non-Gaussianity, primordial magnetic fields, dark energy, and modified gravity, and new results on low-frequency Galactic foregrounds.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Operation of the Australian Store.Synchrotron for macromolecular crystallography

The Store.Synchrotron service, a fully functional, cloud computing-based solution to raw X-ray data archiving and dissemination at the Australian Synchrotron, is described. The Store.Synchrotron service, a fully functional, cloud computing-based solution to raw X-ray data archiving and dissemination at the Australian Synchrotron, is described. The service automatically receives and archives raw diffraction data, related metadata and preliminary results of automated data-processing workflows. Data are able to be shared with collaborators and opened to the public. In the nine months since its deployment in August 2013, the service has handled over 22.4 TB of raw data (∼1.7 million diffraction images). Several real examples from the Australian crystallographic community are described that illustrate the advantages of the approach, which include real-time online data access and fully redundant, secure storage. Discoveries in biological sciences increasingly require multidisciplinary approaches. With this in mind, Store.Synchrotron has been developed as a component within a greater service that can combine data from other instruments at the Australian Synchrotron, as well as instruments at the Australian neutron source ANSTO. It is therefore envisaged that this will serve as a model implementation of raw data archiving and dissemination within the structural biology research community.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Neural Networks for Modeling and Control of Particle Accelerators

Particle accelerators are host to myriad nonlinear and complex physical phenomena. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems, as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. The purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

SU-F-T-163: Improve Proton Therapy Efficiency: Report of a Workshop

Purpose: The technology of proton therapy, especially the pencil beam scanning technique, is evolving very quickly. However, the efficiency of proton therapy seems to lag behind conventional photon therapy. The purpose of the abstract is to report on the findings of a workshop on improvement of QA, planning and treatment efficiency in proton therapy. Methods: A panel of physicists, clinicians, and vendor representatives from over 18 institutions in the United States and internationally were convened in Knoxville, Tennessee in November, 2015. The panel discussed several topics on how to improve proton therapy efficiency, including 1) lean principle and failure mode and effects analysis, 2) commissioning and machine QA, 3) treatment planning, optimization and evaluation, 4) patient positioning and IGRT, 5) vendor liaison and machine availability, and 6) staffing, education and training. Results: The relative time needed for machine QA, treatment planning & check in proton therapy was found to range from 1 to 2.5 times of that in photon therapy. Current status in proton QA, planning and treatment was assessed. Key areas for efficiency improvement, such as elimination of unnecessary QA items or steps and development of efficient software or hardware tools, were identified. A white paper to summarize our findings is being written. Conclusion: It is critical to improve efficiency by developing reliable proton beam lines, efficient software tools on treatment planning, optimization and evaluation, and dedicated proton QA device. Conscious efforts and collaborations from both industry leaders and proton therapy centers are needed to achieve this goal and further advance the technology of proton therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Groundwater table elevation and temperature from 2015 to 2024 across Meander C at the Lower Montane site in the East River Watershed, Colorado.

This groundwater level elevation and temperature data package is aimed at improving the predictive understanding of hydro-biogeochemical processes at the lower montane site in the East River Watershed, Colorado. The dataset is obtained using pressure transducers placed in shallow wells in the floodplain. This dataset contains data from wells ER-CPA1 to ER-CPA6. Another dataset contains the data from wells at nearby Locations. Each file contains the water level elevation and the water temperature. Water level elevation has been obtained using the barometric pressure from the pressure transducer (Hobos sensor) in the well, barometric pressure from a sensor in air located at the same site (lower montane), depth from top-of-casing (TOC) to sensor measurement point, and TOC elevation. Data have been checked with a few measurements of water table depths. A real-time kinematic (RTK) global positioning system (GPS) has been used to survey the TOC (data in file Well_Location.csv). The water level elevation is given in UTM13N Geoid2012AB. While depth to water level is not present in the data files, it can be easily calculated with the TOC and distance to ground provided in the GPS coordinate file. The dataset quality is discussed in Collection/Analysis section of the methods. Time-series of measurements were initially added to the archive for the period 2015 to 2019, and later updated with time-series until 2024 (end of data collection). The dataset contains 7 *.csv data files, and 3 *.csv metadata files. Feel free to contact the author with any questions or collaboration interests. The publication year was updated from "2020" to "2025" to reflect the revised version of this dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Board 160: Empowerment in STEM Day: Introducing High School Girls to Careers at National Laboratories (Work in Progress)

In the US, women are still vastly underrepresented in STEM (science, technology, engineering, and mathematics) careers, and various studies have shown that girls' interest in STEM careers wane as high school progresses. With this challenge in mind, Empowerment in STEM Day was organized by Lawrence Berkeley National Laboratory (LBNL), hosting 47 high school students from 6 public high schools in the area. This one-day event was designed and executed through a collaboration between the Women's Support and Empowerment Council (WSEC) and the K-12 STEM Education and Outreach Program at LBNL. The main goal of this program was to provide high school girls, who have little access to STEM career role-models in their immediate surroundings with insights into how a career in STEM looks like. Invitations to participate in the program were sent out to six local high schools in the Bay area asking educators to identify female students that were interested in STEM. Each high school participant was provided with an opportunity to experience a national laboratory environment, learn more about summer workshops and paid summer research internship opportunities at LBNL for high school students, and engage directly with LBNL's employees through job shadow, career mapping and speed networking sessions. In this paper, we will present an overview of the event organization, challenges faced during planning and execution of the event, discuss the lessons learned from the first Empowerment in STEM Day and suggest strategies for incorporating such events at other national laboratories and academic institutions as part of a vital effort into recruiting and retaining more high school girls in STEM-based careers. Additionally, since this was the first in-person event hosted by LBNL's K-12 Program after the pandemic, we will also share the strategies implemented at the event so as to engage both remote and on-site employees as volunteers.

Bose, Baishakhi↗
DOE OSTI2017

Next Generation Workload Management and Analysis System for Big Data

We report on the activities and accomplishments of a four-year project (a three-year grant followed by a one-year no cost extension) to develop a next generation workload management system for Big Data. The new system is based on the highly successful PanDA software developed for High Energy Physics (HEP) in 2005. PanDA is used by the ATLAS experiment at the Large Hadron Collider (LHC), and the AMS experiment at the space station. The program of work described here was carried out by two teams of developers working collaboratively at Brookhaven National Laboratory (BNL) and the University of Texas at Arlington (UTA). These teams worked closely with the original PanDA team – for the sake of clarity the work of the next generation team will be referred to as the BigPanDA project. Their work has led to the adoption of BigPanDA by the COMPASS experiment at CERN, and many other experiments and science projects worldwide.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Utilization of SRNL-developed radiation-resistant polymer in high radiation environments

The radiation-resistant polymer developed by the Savannah River National Laboratory is adaptable for multiple applications to enhance polymer endurance and effectiveness in radiation environments. SRNL offers to collaborate with TEPCO in evaluation, testing, and utilization of SRNL’s radiation-resistant polymer in the D&D of the Fukushima Daiichi NPS. Refinement of the scope and associated costs will be conducted in consultation with TECPO.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A Spatiotemporal Sequence Forecasting Platform to Advance the Prediction of Changing Spatiotemporal Patterns of CO 2 Concentration by Incorporating Human Activity and Hydrological Extremes

Focal Areas (2): Predictive modeling using AI techniques and AI-derived model components. It describes the use of AI and other tools to design a prediction system comprising a hierarchy of models. Specifically, this white paper focuses on the development of a collaborative deep learning platform for evaluating and predicting spatiotemporal relationships between the hydrological and carbon cycles and includes capabilities for considering biogenic and anthropogenic inputs to these systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

NNSA/CEA Workflow Workshop Report 2021

Researchers from three NNSA labs plus CEA recently met for a half-day workshop on scientific and engineering workflows in March of 2021. Tools, projects and use cases from each institution were described. A wide range of unique capabilities and requirements were represented. The workshop highlights the fact that the CEA/NNSA workflows space mirrors the broader open science workflows community, with multiple technologies under development in a number of science domains and under a number of funding streams, with differing capabilities and focuses. Despite the number of tools, presentations and discussions have shown that these tools cover specific mission spaces at the four labs, each with distinctive capabilities that do not completely overlap with each other. We believe there is a strong interest in the short term for sharing lessons learned and collaborating on benchmarks and site evaluation of projects. This report, prepared by the NNSA/CEA Workflows Working Group, briefly summarizes the presentations in the areas of domain specific workflows, end user environments, data management, job and resource management, and infrastructure, and then identifies six broad areas for potential collaboration. A key finding is that users could benefit from greater interoperability, compatibility, and composability of the workflow technologies under development and that point-to-point collaboration opportunities should be identified to explore these aspects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Modeling of Reactor Design and Optimization for Scale-Up of the Catalyxx Process for Ethanol Conversion to Higher Alcohol Biofuels

This report summarizes the results of a collaborative efforts between Oak Ridge National Laboratory (ORNL) and Catalyxx Inc. to investigate scale-up of Catalyxx’s Ethanol upgrading to higher alcohols process. The study was funded by the U.S. Department of Energy (DOE) Bioenergy Technologies Office (BETO) under CRADA (Cooperative Research and Development Agreement) No: NFE-20-08396. The project is part of the Direct Funding Opportunity (DFO) for Computational Science to Enable Bioenergy program which utilized computational toolsets developed by the Consortium for Computational Physics and Chemistry, a multi-laboratory consortium in BETO. The report here summarizes a packed-bed reactor modeling effort spanning the range from lab to industrial scale (from 4 gram to 5-ton catalyst beds), and examining reactor design, process optimization strategies, and suggested design and operating conditions for Catalyxx’s ethanol upgrading plants. The results in this report have been shared in monthly steering meetings and presentations are available in the shared data house owned by Catalyxx. The modeling effort helped to define optimum operation conditions for maximum alcohol selectivity and yield: i.e., temperature control scenarios ranging from adiabatic to isothermal, feed rate, pressure, and inlet H 2 /Ethanol ratio. The modeling results were verified at lab-(4 gram) and pre-pilot (4 kg) scales and has been used to evaluate a 5-ton packed-bed reactor and identify operating conditions to maximize the butanol yield. Special focus was given to understanding mass-transfer effects in the pre-pilot and pilot-scale reactors, over the domain of flow rate, pressure, feed composition, pellet size, shape, porosity, bed voidage, and reactor dimensions (i.e., length/diameter). Modeling was also used to evaluate innovative reactor design concepts such as water removal to improve alcohol selectivity and yield, and a reactor with an additional side inlet to facilitate quenching. These concepts were thoroughly explored, and potential benefits were disclosed. The results in this report are summarized and described qualitatively to protect the IP rights of Catalyxx. The details have been shared with the Catalyxx team in the regular steering meetings. At the end of the project, Catalyxx Inc. announced a successful demonstration of pilot scale operation in Seville, Spain.

02 PETROLEUM↗
DOE OSTI2024

Low RF Loss DC Conductive Ceramic for High Power Input Coupler Windows for SRF Cavities

Euclid Techlabs, LLC is developing a low RF loss/DC conductive MgTi ceramics for high power input coupler windows for SRF cavities, under DOE SBIR phase II grant DE-SC0017150. JLab was subcontracted to: fabricate two 500 kW capable pre-stressed coaxial windows; provide an RF testing fixture, and test the window with low power and high power RF. This collaboration helped to develop a better material for high power RF windows, with low RF loss and less prone to arcing, with the potential to improve the state of the art high power RF window performance.

36 MATERIALS SCIENCE↗