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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Human Factors Design for Particle Accelerator Control Room Interfaces

Fermilab, the birthplace of many scientific discoveries in physics and particle accelerator sciences, is in the midst of a widescale modernization effort. The Accelerator Control Operations Research Network (ACORN project’s goal is to modernize the accelerator control system by replacing end-of-life power supplies and enhance future operations of the Fermilab accelerator complex with megawatt particle beams. Within ACORN, opportunities for process improvement concerning software development, human-system interface design, and task performance are also being considered. Human factors researchers from Idaho National Laboratory in collaboration with usability experts from Fermilab, are currently investigating human-centered design improvements for the accelerator control system. For example, substantial tribal knowledge and memory recall are required to effectively operate the accelerator system. This contributes to high cognitive workload and potential burnout of accelerator operators. Developing guidance for consistent visual and functional design enables a more intuitive interaction and relieves operators of cognitive burden. Additionally, developing more intuitive and integrated interfaces can also lead to improved accelerator efficacy by empowering operators with greater understanding and control of the systems. The challenge in developing such interfaces is in designing for a wide variety of user goals, system specifications, and level of experience in users. The challenges need to be met while e also considering the maintainability of the control system. The purpose of this paper is to detail the human factors process and design within the ACORN project, describe results gathered thus far, and discuss the larger implications for this work.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

FY19 Status Report on the Computing Systems for the Yucca Mountain Project TSPA-LA Models and Preliminary Testing of a Selected Process Model

Sandia National Laboratories continued evaluation of total system performance assessment (TSPA) computing systems for the previously considered Yucca Mountain Project. This was done to maintain the operational readiness of the computing infrastructure (computer hardware and software) and knowledge capability for total system performance assessment) type analysis, as directed by the National Nuclear Security Administration (NNSA), DOE 2010. The FY19 task included continued operation of the cluster; maintenance of the TSPA-LA models (with Gold Sim 9.60.300); preliminary assessment of the status of the Infiltration Model (a process model that feeds the TSPA-LA). In addition, precautionary actions were needed to extend the life of the cluster hardware. To do that, three new nodes were added to the cluster. In the event any of the original nodes fail they will be replaced with the new nodes, thereby maintaining the core capability. The 2014 cluster and supporting software systems are currently fully operational to support TSPA-LA type analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Mechanisms of Methane Hydrate Formation in Geological Systems

Natural gas hydrate is ice-like mixture of gas (mostly methane) and water that is widely found in sediments along the world's continental margins and within and beneath permafrost and glaciers in a near-surface depth interval where the pressure is sufficiently high and temperature sufficiently low for gas hydrate to be stable. We categorize the myriad of geological gas hydrate deposits into five characteristic types. We then review the multiple quantitative models that have proposed to describe the genesis of these deposits and describe how each may have formed. We emphasize the importance of coupling multiphase flow (free gas and liquid water) and multicomponent reactive transport with geological history to describe the dynamical processes of gas hydrate formation and evolution in geological systems. A better insight into the kinetics of methane formation from microbial biogenesis and the processes of multiphase flow at the pore scale will advance our knowledge of how these systems form. By understanding the generation and evolution of gas hydrate through time, we will better decipher the role of gas hydrate in the carbon cycle, its potential to contribute to climate change and geohazards, and how to design optimal strategies for gas production from hydrate reservoirs.

58 GEOSCIENCES↗
DOE OSTI2021

FY2021 Status Report on the Computing Systems for the Yucca Mountain Project TSPA-LA Models and Testing of Selected Process Models

Sandia National Laboratories continued evaluation of the total system performance assessment (TSPA) for License Application (LA) computing systems for the previously considered Yucca Mountain Project (YMP). This was done to maintain the operational readiness of the computing infrastructure (computer hardware and software) and knowledge capability for total system performance assessment) type analysis, as directed by the National Nuclear Security Administration (NNSA), DOE 2010. The FY21 task included continued operation of the cluster; maintenance of the TSPA-LA models (with GoldSim 9.60.300); continued assessment of the status of the Infiltration Model; (a process model that feeds the TSP -LA) and preliminary assessments of the Unsaturated Zone Flow Model and the Saturated Zone Flow and Transport Model Abstraction (process models that feed the TSPA-LA). The 2014 cluster and supporting software systems are currently fully operational to support TSPA-LA type analyses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution

Recent works exploring deep learning application to dynamical systems modeling have demonstrated that embedding physical priors into neural networks can yield more effective, physically-realistic, and data-efficient models. However, in the absence of complete prior knowledge of a dynamical system's physical characteristics, determining the optimal structure and optimization strategy for these models can be difficult. In this work, we explore methods for discovering neural state space dynamics models for system identification. Starting with a design space of block-oriented state space models and structured linear maps with strong physical priors, we encode these components into a model genome alongside network structure, penalty constraints, and optimization hyperparameters. Demonstrating the overall utility of the design space, we employ an asynchronous genetic search algorithm that alternates between model selection and optimization and obtains accurate physically consistent models of three physical systems: an aerodynamics body, a continuous stirred tank reactor, and a two tank interacting system.

genetic algorithms, neural architecture search, ne↗
DOE OSTI2026

Knowledge Graph for End-to-End Traceability of an Integrated Human-Earth System Model

Integrated human-Earth system models inform energy-water-land system dynamics and policies, yet their results are difficult to trace through input-data, model structure, scenario configurations, and solved outputs. Because this information is siloed across disconnected artifacts, process-based IAMs have historically lacked a unified, queryable representation. Such lack of traceability prevents researchers from systematically isolating the multi-sector drivers of complex outcomes (such as tracing water-scarcity results back to distant energy-system dynamics) or conducting holistic uncertainty attribution across hundreds of interacting parameters. To address this concern, our work documents the software engineering process of a knowledge graph that unifies these four layers for the Global Change Analysis Model (GCAM-USA_Reference scenario, GCAM v9.1). The graph was built as a relational property graph in DuckDB from the run’s own artifacts: the input-preparation dependency map (gcamdata chunk map), the model’s XML input files, the run configuration, and the results database (BaseX), successfully mapping the model’s declared structure. The resulting graph comprises 204,321 nodes and 1,687,814 edges across 16 node types and 15 edge types, with approximately 16.3 million time-series values stored separately to maintain structural efficiency. To ensure representation fidelity, every edge carries an epistemic-status annotation recording the warrant for the relationship (structural, provenance, dependency, or model-derived), and a machine-readable provenance ledger classifying the origin of every schema element. Evaluation against a fixed five-benchmark suite with locked baselines reports zero structural orphans, zero dangling edge endpoints, and 100% of output-producing technologies traceable to raw input files. Two interactive interfaces present the graph, including a serverless browser application built on DuckDB-Wasm. By establishing the first end-to-end provenance framework for an IAM, this work enables researchers and scientists to systematically audit complex policy scenarios, debug model structures, and trace policy-relevant outputs to their data origins in real time.

Artifical Intelligence↗
DOE OSTI2025

RAG for FLAG: AI Assistance for a Physics Code

Artificial intelligence (AI) has quickly become an important tool in scientific research, where significant efforts are underway to develop tools that will expedite the research process. One area of particular impact is scientific software, which can be particularly complex, and therefore time consuming to learn and use effectively. AI assistants are increasingly helping to streamline the process by performing tasks such as interactively answering user questions or suggesting solutions. Los Alamos National Laboratory (LANL) develops several advanced scientific codes, such as FLAG, which can be used to run multiphysics simulations. With this study, our goal was to develop an AI assistant for FLAG that could help make the process of understanding the software and running physics simulations more efficient. To develop an AI assistant for FLAG, we used a method called retrieval-augmented generation (RAG), which is a technique that uses information from relevant data sources to enhance the accuracy of large language models (LLMs). We used the FLAG user manual and other FLAG documentation as the knowledge base for the RAG system. When a user provides a query, RAG retrieves relevant sections from the knowledge base in response, then uses those excerpts to generate grounded and contextually rich answers. We found that our AI assistant was able to provide context aware answers and source references to user queries. To evaluate performance, we developed a set of 40 benchmark questions and compared the accuracy of the responses to those of two standard LLMs without retrieval. Our AI assistant significantly outperformed the standard LLMs at answering FLAG-related questions, with an 82.5% accuracy rate, compared to 47.5% for both of the standard LLMs. This has the potential to make the process of learning and using FLAG much easier, especially for new users. Ultimately, it supports LANL’s broader mission by empowering scientists and engineers to focus more on discovery and analysis rather than on navigating complex software systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Abstract Machine Models and Proxy Architectures for Exascale Computing

To achieve exascale computing, fundamental hardware architectures must change. The most significant consequence of this assertion is the impact on the scientific and engineering applications that run on current high performance computing (HPC) systems, many of which codify years of scientific domain knowledge and refinements for contemporary computer systems. In order to adapt to exascale architectures, developers must be able to reason about new hardware and determine what programming models and algorithms will provide the best blend of performance and energy efficiency into the future. While many details of the exascale architectures are undefined, an abstract machine model is designed to allow application developers to focus on the aspects of the machine that are important or relevant to performance and code structure. These models are intended as communication aids between application developers and hardware architects during the co-design process. We use the term proxy architecture to describe a parameterized version of an abstract machine model, with the parameters added to elucidate potential speeds and capacities of key hardware components. These more detailed architectural models are formulated to enable discussion between the developers of analytic models and simulators and computer hardware architects. They allow for application performance analysis and hardware optimization opportunities. In this report our goal is to provide the application development community with a set of models that can help software developers prepare for exascale. In addition, through the use of proxy architectures, we can enable a more concrete exploration of how well new and evolving application codes map onto future architectures. This second version of the document addresses system scale considerations and provides a system-level abstract machine model with proxy architecture information.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Advanced Algal Biofoundries for the Production of Polyurethane Precursors [Abstract]

The collaboration between PNNL, LBNL, and UCSD will develop novel algae bioproduction platforms for the generation of polymer precursors. To achieve this, we will design and construct genetic tools for enhanced production of target chemicals. Biosensors will be developed to detect target chemical production. We will expand our knowledge of metabolic and regulatory systems that control or inhibit biosynthesis of the target molecules, and use this knowledge base to optimize production titers. Finally, we will integrate the knowledge gained from each activity to produce a set of the most promising production strains used to demonstrate the economic viability of producing polymer precursors in algal bioproduction platforms at scale under either heterotrophic or photosynthetic growth.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Industrial Energy Management During a Pandemic: Lessons Learned from DOE Better Plants Program Partners

The COVID-19 pandemic is causing many challenges for manufacturers, especially those that depend on workers whose jobs cannot be performed remotely. This has detrimentally impacted the energy management workforce, energy support system performance, as well as operations and supply chains.This article discusses four primary challenges for industrial energy management caused by the pandemic, strained budget for energy projects, increased energy intensity caused by low production rates and other safety practices, limited access to energy systems, and the lost knowledge and experience of senior staff. This article also proposes some potential solutions for these challenges that include implementing no- and low-cost energy conservation measures, adopting alternative financing options, improving shutdown procedures, reducing baseloads, creating an effective online energy-monitoring system, performing virtual energy assessments, building a robust EnMS, and attending online and in-person training events.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Round Robin Measurements of Molten Salt Properties for LiF-NaF-KF (FLiNaK) and NaCl-KCl Mixtures

The development, operation, and regulation of nuclear reactors that utilize molten salts as fuel or as heat transfer media require knowledge of the thermal properties of the salt systems and quantification of the corresponding uncertainties. Knowledge of molten salt properties is also necessary for applications in material synthesis, processing, separations, solar thermal power generation, and energy storage. A round robin was conducted with national laboratory and university participants from twenty-one laboratories in five countries to compare property measurements, to better understand uncertainties, and to identify possible best practices. Two salt mixtures, each from a common batch, were distributed to participants for evaluation: equimolar NaCl-KCl and 45.0LiF-13.7NaF-41.3KF mol % (FLiNaK). Measurements were performed to determine the major constituent composition, oxygen content, density, thermal expansivity, melting point, and thermal conductivity. Error analysis was performed on each measurement for uncertainty quantification for each type of property that was explored. Finally, the resulting discussion of the methodologies used in this work is meant to lay the groundwork for the development of standard methods and reference materials for future high-temperature property measurements on halide melts.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Electrochemical Reduction of Flue Gas Carbon Dioxide to Commercially Viable C2-C4 Products (Final Report)

This is the final scientific/technical report for a DOE project focused on the electrochemical conversion of CO 2 in non-aqueous solvents to novel products. Electrochemical reduction of CO 2 provides an attractive route to produce valuable fuels and chemicals that can simultaneously lower greenhouse gas emissions when powered by renewable electricity. While recent technological advances have shown the feasibility of industrial CO 2 electroreduction, many challenges remain to improve this technology and expand the list of economically viable products. The vast majority of electrochemical CO 2 reduction research has been conducted in aqueous media under neutral to alkaline conditions, leading to commonly reported products including carbon monoxide, formic acid, methane, methanol, ethylene, acetic acid, and ethanol. In comparison, non-aqueous media for CO 2 reduction has been underexplored but represents a possible avenue to yield new products and improved operating conditions. The aim of the project was to convert waste CO 2 in the form of flue gas to a multicarbon C2 - C4 chemical product in a reactor designed to achieve economically competitive values of current density and selectivity. The project strived to advance the technology readiness of an electrochemical CO 2 reduction process in alcohol solvents from the proof-of-concept stage to a device capable of meeting performance metrics for commercial viability. In the initial plan, the University of Louisville researchers were to focus on investigating the electrochemical process and improving the faradaic efficiency for novel C2 – C4 species, while also working on a parallel effort to build a practical electrolysis reactor to markedly increase the CO 2 reduction current density. The reactor development effort also aimed to engineer a dual-electrolyte feed strategy with non-aqueous catholyte and aqueous anolyte to promote water oxidation as the coupling anodic half-reaction to enable a sustainable and economical overall process. At the outset, the University of North Dakota was to investigate the feasibility of operating directly from coal-derived flue gas without separate capture and purification. The research team sought to determine impurity effects and test mitigation strategies, as well as engineer the gaseous feed system for high reactor tolerance to lower CO 2 concentration. In the last half year of the project, the focus was planned to shift to integrating the advances in the catalysis, electrochemical conditions, reactor design, and flue gas compatibility into a fully functional device and improve it for maximum current density and faradaic efficiency for C2 – C4 species. Knowledge of the full system components, constraints, and maximum performance was then to be used as the basis for a thorough technoeconomic analysis (TEA) and life cycle analysis (LCA) at the end of the project.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Biodiversity and disease risk in an algal biofuel system: An experimental test in outdoor ponds using a before-after-control-impact (BACI) design

For outdoor cultivation of algal feedstocks to become a commercially viable and sustainable option for biofuel production, algal cultivation must maintain high yields and temporal stability in environmentally variable outdoor ponds. One of the main challenges is mitigating disease outbreaks that leads to culture crashes. Drawing on predictions from the ‘dilution effect’ hypothesis, in which increased biodiversity is thought to reduce disease risk in a community, a teste of whether algal polycultures would reduce disease risk and improve feedstock production efficiencies compared to monocultures was performed. While the positive benefits of biodiversity on disease risk have been demonstrated in various systems, to the best of our knowledge this is the first test in an algal biofuel system. Here, the results a before-after-control-impact (BACI) experimental design to compare mean monoculture (control) and polyculture (impact) yield, stability, and productivity before and after fungal infection when grown in 400-L outdoor raceway ponds are presented. It has been found that polycultures did not experience a reduction in disease risk compared to monocultures or differ in production efficiencies throughout the course of the 43-day experiment. These results show that polyculture feedstocks can maintain similar levels of productivity, stability, and disease resistance to that of a monoculture. Determining whether these results are generalizable or represent one case study requires additional outdoor experiments using a larger variety of host and pathogen species.

09 BIOMASS FUELS↗
DOE OSTI2024

L4-3 Developing DOE M441.1-1 Compliant Container Components: Filter Cup Experimental Fixture Test Data Verification

This document serves to provide technical documentation and justification for using the Filter Cup Experimental Fixture (FCEP) for filter efficiency and pressure drop testing in the Enhanced Filter Test System (EFTS). This document assumes that the reader has prior knowledge related to both of these systems, as well as the SAVY-4000® nuclear material container series filter technology.

42 ENGINEERING↗
DOE OSTI2023

Understanding and Predictability of Integrated Mountain Hydroclimate (Workshop Report)

Mountainous systems cover approximately 23% of Earth’s land and are distributed across all continents. They can capture and store atmospheric moisture that is then cycled through the terrestrial surface and subsurface system, released to downstream communities, and cycled back to the atmosphere. Mountain hydroclimate—characterized by steep gradients, geological, ecological, and biogeochemical diversity—is influenced by topographic forcing and elevated warming and susceptible to large subseasonal to multidecadal variability and rapid changes. Terrestrial hydrological and biogeochemical cycles also experience cascading effects from global warming impacts, such as multidecadal declines in mountain snowpack, longer growing seasons, and increased frequency and severity of extreme events like droughts and wildfires. However, little is known about the effects of these impacts and their feedbacks on climate systems and surface-subsurface compartments. Also unknown are the full implications of changing hydroclimate and extreme events on hydro biogeochemical cycles across atmosphere, terrestrial, and human systems in mountain regions and beyond. This knowledge gap is critical, given human reliance on mountain systems for stable water supply and quality. Mountain systems’ increasing vulnerability to climate change and human perturbations motivates the need to improve understanding of integrated mountain hydroclimate (IMHC) systems and their feedbacks and impacts on humans across scales. However, due to large heterogeneity and strong gradients, coupled natural-human processes in mountain regions present significant challenges for observations, modeling, predictions, and projections. Motivated by gaps in mountain hydroclimate understanding, observations, and modeling and the need for credible projections of future changes, the U.S. Department of Energy’s (DOE) Biological and Environmental Research (BER) program organized a virtual workshop on “Understanding and Predictability of Integrated Mountain Hydroclimate.” Sponsored by BER’s Earth and Environmental Systems Sciences Division (EESSD), the workshop aimed to inform and catalyze EESSD’s growing interests in enhancing predictive understanding of IMHC. Organizers structured the workshop to identify (1) knowledge gaps, (2) observational and modeling challenges, (3) short-term (1 to 3 years) and long-term (10 years and beyond) research opportunities, and (4) strategies for fostering collaboration and coordination. To address the outstanding challenges of IMHC, the workshop included two sessions organized by disciplinary, cross-disciplinary, and crosscutting science topics. The disciplinary and cross-disciplinary topics focused on essential IMHC elements: atmosphere, terrestrial, and human systems and their interactions. Breakout sessions on disciplinary and cross-disciplinary topics facilitated identification of crosscutting topics and central emerging themes. Session 1 focused on connecting existing DOE investments to accelerate progress related to scientific challenges in understanding mountain hydroclimate. In Session 2, participants further explored key Session 1 takeaways through the lens of multiagency collaborations and coordination.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Robotics on KM-IT platform - 19107

The Knowledge Management Information Tool (KM-IT) is a web-based system developed to maintain and preserve the Department of Energy's (DoE's) knowledge base. The system was developed by Florida International University's Applied Research Center (FIU-ARC) with the support of the D and D community including the DOE Office of Environmental Management, and with the collaboration and support of the DoE's Energy Facility Contractors Group (EFCOG) and the former ALARA Centers at Hanford and Savannah. The KM-IT system is a community driven system tailored to serve the technical issues faced by the workforce across the DOE Complex. This KM-IT web-based interactive system is operational and available for use at www.dndkm.org and is currently comprised of the following modules: Web Crawler, Hotline, Technology, Hanford's ALARA Center Reports, Specialist Directory, Lessons Learned, Best Practices, Video/Picture Library, Vendors and Industry News. The Web Crawler dynamically searches through the KM-IT repository as well as the web and displays search results based on the search criteria. The Hotline allows registered users to post questions/problems related to D and D and to receive solutions from a subject-matter specialist. The Technology module serves as repository of D and D technologies and contains technology/ demonstration fact sheets and vendor information. D and D community members can add Lessons Learned, Best Practices, and Pictures/Videos to the D and D repository, after a formal approval process. The Vendor module provides a directory of commercial vendors who provide D and D related technologies, supplies, and services. KM-IT makes an excellent use of the knowledge that exists within the D and D community by allowing D and D project managers around the DOE complex to collaborate by sharing innovative ideas, past experiences, and practices and by maintaining a directory of subject matter specialists. FIU is working on developing a new knowledge base using the existing KM-IT framework to provide focused information on robotics. FIU will develop the robotics technology and vendor modules to provide information related to robotics technologies and its application to many different areas including D and D, remote systems and waste processing. This robotics knowledge base will provide the ability to search through the information repository as well as display customized summary information including robotics technology descriptions, benefits, limitations, videos and pictures. The vendor module will provide information related to industry vendors for these technologies and include updated vendor contact information. The vendor module will also provide a search capability based on keywords as well as categories. The robotics knowledge base will publish commercially available technologies as well as the technologies that are still in the research and development phase with national laboratories and academic universities. The robotics KM-IT will be developed in phases to support all of the features of the KM-IT platform and will be published on www.rkmit.org. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗