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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

SU-F-BRD-10: Improving Plan Delivery Efficiency of Intensity Modulated Proton Plans with Prioritized Optimization

Purpose: To integrate treatment delivery time into plan optimization in spot scanning intensity modulated proton therapy. Utilizing a dedicated research treatment planning system we present an optimization approach to explore the trade-off between the correlated parameters treatment time and plan quality on an astrocytoma patient case. Methods: The planning system is based on prioritized optimization, a stepwise approach of implementing clinical goals. After each optimization step, dosimetric achievements are turned into hard constraints to maintain the achieved plan quality. Prior achievements can be violated by a so-called slip-factor which allows to study possible trade-offs of conflicting goals. Plan quality is obtained in the first two steps, while the third step optimizes delivery efficiency by working on the spot weight distribution via four alternative Methods: elimination of low weighted spots (1), elimination of spots hardly contributing to PTV dose, followed by reoptimization of the resulting smaller optimization problem (2), reduction of spot weights variance within each energy layer (3), and reduction of the overall spot weight sum (4). Treatment times were calculated assuming either constant or variable beam current depending on the lowest spot weight. Results: Delivery efficiency can be improved remarkably without influencing the plan quality. Absolute time savings depend on the utilized method and facility properties. By varying slip-factor and spot reduction limits, a border of worsening quality is detectable for all methods.Deleting low weighted spots by 10% results in a noticeable decrease in minimum target dose. Further reduction results in more heterogeneous dose distributions and insufficient coverage. Option 2 showed constant plan quality for spot reductions of more than 10%. Conclusion: Including treatment time optimization as a final step into prioritized optimization allows for more efficient treatment plans by redistributing the spot patterns without compromising plan quality, which has to remain the main intent of radiotherapy treatment. The authors report grants from German Research Foundation (DFG) during the conduct of the study.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Quantitative Universal Option Prioritizer (QUOP) v1.0.0

Quantitative Universal Option Prioritizer (QUOP) is a three-layer analytical hierarchy process (AHP) based multi-criteria decision-making option evaluation and prioritization tool. Decision-making often entails evaluation of options. Such competing options can either be mutual substitutes or cumulative means to achieve an objective. The options themselves can be methods, tools, technologies, processes, etc. Stakeholders involved in achieving the objective may view each option characteristic differently, depending on their needs and preferences. The QUOP tool enables a streamlined quantitative collaborative process of weighing the opinion of each stakeholder in evaluating the options. The evaluation may also be performed under different scenarios, for instance, various option potential estimates, prices, or end-user demand projections. The QUOP tool can find application in both public and private sectors, as well as on an individual level. The major advantage of using the QUOP decision-making tool is in documenting the quantified stakeholder opinion, therefore the background to each decision made, while simultaneously maintaining high transparency and stakeholder involvement. The tool is particularly suitable for decisions involving a high number of options and option characteristics, that is, aspects of each option that matter to the stakeholders

Grahovac, Milica↗
DOE OSTI2024

Flux REaction TArget Prioritization (Flux RETAP) v1

Metabolic engineering is evolving rapidly as a result of new advances in synthetic biology and automation, as well as the irruption of machine learning (ML). ML has been shown to provide the predictive power synthetic biology lacked and needed, and to be able to effectively guide the metabolic engineering process. However, current technical limitations prevent the independent application of ML approaches to metabolic engineering without the use of previous biological knowledge in the form of a prioritized list of desirable engineering targets. Here, we present FluxRETAP, a simple and computationally inexpensive method that leverages the prior mechanistic knowledge embedded in genome-scale metabolic models (GSMs) for suggesting targets for genetic overexpression, downregulation or deletion, with the final goal of increasing metabolite production. FluxRETAP captured 100% of reaction targets experimentally verified to improve Escherichia coli isoprenol production in the literature accessible to us, 50% of targets that experimentally improved taxadiene production in E. coli and ~60% of genetic targets from a verified minimal constrained cut-set in Pseudomonas putida while providing additional high priority targets that could be tested. Overall, FluxRETAP is an efficient algorithm for identifying a prioritized list of testable genetic and reaction targets which can also be utilized in ML pipelines.

Czajka, Jeffrey [Battelle Memorial Institute, Paci↗
DOE OSTI2023

Use-Inspired, Process-Oriented GCM Selection: Prioritizing Models for Regional Dynamical Downscaling

Dynamical downscaling is a crucial process for providing regional climate information for broad uses, using coarser-resolution global models to drive higher-resolution regional climate simulations. The pool of global climate models (GCMs) providing the fields needed for dynamical downscaling has increased from the previous generations of the Coupled Model Intercomparison Project (CMIP). However, with limited computational resources, the need for prioritizing the GCMs for subsequent downscaling studies remains. GCM selection for dynamical downscaling should focus on evaluating processes relevant for providing boundary conditions to the regional models and be inspired by regional uses such as the response of extremes to changes in the boundary conditions. This leads to the need for metrics representing processes of relevance to diverse stakeholders and subregions of a domain. Procedures to account for metric redundancy and the statistical distinguishability of GCM rankings are required. Further, procedures for selecting realizations from ensembles of top-performing GCM simulations can be used to span the range of climate change signals in multiple ways. As a result, distinct weighting of metrics and prioritization of particular realizations may depend on user needs. We provide high-level guidelines for such region-specific evaluations and address how CMIP7 might enable dynamical downscaling of a representative sample of high-quality models across representative shared socioeconomic pathways (SSPs).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

QTG-Finder2: A Generalized Machine-Learning Algorithm for Prioritizing QTL Causal Genes in Plants

Linkage mapping has been widely used to identify quantitative trait loci (QTL) in many plants and usually requires a time-consuming and labor-intensive fine mapping process to find the causal gene underlying the QTL. Previously, we described QTG-Finder, a machine-learning algorithm to rationally prioritize candidate causal genes in QTLs. Although it showed good performance, QTG-Finder could only be used in Arabidopsis and rice because of the limited number of known causal genes in other species. Here we tested the feasibility of enabling QTG-Finder to work on species that have few or no known causal genes by using orthologs of known causal genes as training set. The model trained with orthologs could recall about 64% of Arabidopsis and 83% of rice causal genes when the top 20% ranked genes were considered, which is similar to the performance of models trained with known causal genes. The average precision was 0.027 for Arabidopsis and 0.029 for rice. We further extended the algorithm to include polymorphisms in conserved non-coding sequences and gene presence/absence variation as additional features. Using this algorithm, QTG-Finder2, we trained and cross-validated Sorghum bicolor and Setaria viridis models. The S. bicolor model was validated by causal genes curated from the literature and could recall 70% of causal genes when the top 20% ranked genes were considered. Furthermore, we applied the S. viridis model and public transcriptome data to prioritize a plant height QTL and identified 13 candidate genes. QTL-Finder2 can accelerate the discovery of causal genes in any plant species and facilitate agricultural trait improvement.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Conservation Habitat Assessment and Mitigation Prioritization (CHAMP) for the Hanford Site: Identifying Priority Conservation Areas

This report presents the initial phase of the Conservation Habitat Assessment and Mitigation Prioritization (CHAMP) for the Hanford Site. This phase of the habitat assessment and prioritization identifies priority conservation areas based on current health, size, and status of native habitats and species and initiates the identification of priority mitigation areas. The products from this analysis form the foundation for continued assessments. The impetus for the CHAMP is to take a landscape approach to evaluating habitat quality on the U.S. Department of Energy, Richland Operations Office (DOE-RL)-managed portion of the Hanford Site (study area) and use the results to determine areas for conserving, restoring, mitigating, and connecting habitats.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

US National Institutes of Health Prioritization of SARS-CoV-2 Variants

Since late 2020, SARS-CoV-2 variants have regularly emerged with competitive and phenotypic differences from previously circulating strains, sometimes with the potential to escape from immunity produced by prior exposure and infection. The Early Detection group is one of the constituent groups of the US National Institutes of Health National Institute of Allergy and Infectious Diseases SARS-CoV-2 Assessment of Viral Evolution program. Here, the group uses bioinformatic methods to monitor the emergence, spread, and potential phenotypic properties of emerging and circulating strains to identify the most relevant variants for experimental groups within the program to phenotypically characterize. Since April 2021, the group has prioritized variants monthly. Prioritization successes include rapidly identifying most major variants of SARS-CoV-2 and providing experimental groups within the National Institutes of Health program easy access to regularly updated information on the recent evolution and epidemiology of SARS-CoV-2 that can be used to guide phenotypic investigations.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1978

A prioritized set of physiological measurements for future spaceflight experiments

A set of desired experimental measurements to be obtained in future spaceflights in four areas of physiological investigation are identified. The basis for identifying the measurements was the physiological systems analysis performed on Skylab data and related ground-based studies. An approach for prioritizing the measurement list is identified and discussed with the use of examples. A prioritized measurement list is presented for each of the following areas; cardiopulmonary, fluid-renal and electrolyte, hematology and immunology, and musculoskeletal. Also included is a list of interacting stresses and other factors present in spaceflight experiments whose effects may need to be quantified.

Source record↗
NASA NTRS1991

A proven knowledge-based approach to prioritizing process information

Many space-related processes are highly complex systems subject to sudden, major transients. In any complex process control system, a critical aspect is rapid analysis of the changing process information. During a disturbance, this task can overwhelm humans as well as computers. Humans deal with this by applying heuristics in determining significant information. A simple, knowledge-based approach to prioritizing information is described. The approach models those heuristics that humans would use in similar circumstances. The approach described has received two patents and was implemented in the Alarm Filtering System (AFS) at the Idaho National Engineering Laboratory (INEL). AFS was first developed for application in a nuclear reactor control room. It has since been used in chemical processing applications, where it has had a significant impact on control room environments. The approach uses knowledge-based heuristics to analyze data from process instrumentation and respond to that data according to knowledge encapsulated in objects and rules. While AFS cannot perform the complete diagnosis and control task, it has proven to be extremely effective at filtering and prioritizing information. AFS was used for over two years as a first level of analysis for human diagnosticians. Given the approach's proven track record in a wide variety of practical applications, it should be useful in both ground- and space-based systems.

Corsberg, Daniel R.↗
NASA NTRS1992

Exploration technology prioritization

A series of outlines and graphs describing NASA's Space Exploration Initiative (SEI) technology prioritization are presented. Prioritization criteria and preliminary critical technology priorities for a first lunar outpost and a Mars and permanently-manned lunar mission are addressed.

Dula, Alex↗
NASA NTRS2021

Report on Workshop on Artificial Intelligence in Strategic Planning and Science Prioritization

This report details the observations from a two-day virtual workshop, held May 12-13, 2020, focused on whether, and how, artificial intelligence (AI) could assist humans in strategic planning, specifically in science and technology prioritization. The participants identified several “key challenges” that AI might tackle in this area. To further understand the value of these key challenges the workshop then developed related test cases that would demonstrate specifically how AI/machine learning (ML) could provide assistance to humans. Approximately 40 subject matter experts (SMEs), with backgrounds in AI, strategic planning for science, and scientific data, were gathered for the conference. This report collates the details of the output of the workshop. The “best” test cases include (in no particular order):Use of AI to assist in selecting Decadal Survey priorities. * Use of AI to identify new, or previously unidentified, science topics for prioritization. * Using AI to better label and increase discoverability of scientific literature and proposals. * Use of AI to enhance current observation capabilities for scientific missions. * Using AI to mitigate biases in selection of proposal reviewers and membership of advisory committees. Examination of these test cases indicates that Natural Language Processing (NLP) is a common capability found in most of the ”best” (top-rated) test cases and is a valuable, multi-purpose tool which enables ML in this area.

strategic planning↗
NASA NTRSDate not supplied

Transforming Science Prioritization Processes Using Artificial Intelligence

Artificial Intelligence (AI) and Machine Learning (ML) have potential to augment significantly the current labor-intensive processes of science prioritization, specifically by the National Academies’ Decadal Survey on behalf of NASA and NSF. Here we summarize what we believe to be the first exploratory demonstration-of-concept results from an application of AI/ML to Survey science prioritization. Specifically, we applied Latent Dirichlet Allocation (LDA) and Natural Language Processing (NLP) to reveal trends in published astrophysics research that may indicate science priorities and which could be applied to strategic planning. For the purpose of the work that we summarize here, AI/ML is able to analyze – that is, to “understand,” in a manner of speaking – a vast amount of text to reveal complex relationships among research topics, including the growth or decline of science community activities in those topics over time. We trained ourselves and AI/ML algorithms by using ~400,000 abstracts in the period 1998 to 2010 to “forecast” the Academies’ Astro2010 recommendations and compare with the solicited white papers. Comparing our results with actual Astro2010 recommendations allowed us to identify candidate metrics that better predicted the actual results of the Survey. We found, for example, that Compound Annual Growth Rate (CAGR) of papers published in a topic area is a good proxy measure for importance of this topic area of research. With this training complete, we identified candidate astrophysics astrophysics science priorities for the 2021+ period using the research during 2007 - 2019 . We conclude that appropriate application of AI can potentially significantly reduce the current workload of the Decadal Survey processes and reveal otherwise unrecognized characteristics in the body of astronomical research. We emphasize throughout the exploratory nature of our work, encouraging colleagues to pursue promising results further. Our most critical governing assumption was that increased (or decreased) research activity can be used to identify scientific or technology topic areas worthy of increased (or decreased) future emphasis. We discuss advantages, limitations, and recognize the “black box” nature of our technique. We note ethics issues associated, for example, with using AI/ML to reveal “hidden” meanings and biases in published work. Furthermore, inevitable improvements in AI may soon enable widespread and welcome identification of and advocacy for science and technology priorities by disparate and diverse groups and organizations. Consequently, we continue to urge a near-term, in-depth evaluation of appropriate applications of AI, including implications and consequences, as well as support for multiple follow-on assessments, of which ours is only a beginning.

Artificial Intelligence↗
DOE OSTI2012

Prioritizing Acquisition Pathways in the State Level Concept

The International Atomic Energy Agency's (IAEA) Department of Safeguards has launched a project to further develop the State-level concept for the planning, implementation, and evaluation of safeguards activities. In order to further evolve the safeguards system an emphasis is placed on integrating inspection-related activities and the State evaluation process to draw safeguards conclusions in the most efficient way. The credible implementation of acquisition pathway analysis is central to the success of the IAEA's State-level concept. NNSA's Office of Nuclear Safeguards and Security (NA-241) The Next Generation Safeguards Initiative (NGSI) is sponsoring Los Alamos National Laboratory (LANL) to produce a study that will examine the use of acquisition pathway analysis in: (1) Developing a State-specific, State-level approach (SLA) and Annual Implementation Plan (AIP); (2) Maximizing the utility of the physical model; and (3) Supporting resource allocation decisions through a pathway prioritization. To deal with the challenge of developing an effective and efficient SLA, this study looks at: (1) Prioritizing proliferation pathways based on an assessment of a State's capabilities and assumed proliferation strategies; and (2) Relevant State behavior (e.g., transparency, cooperation, etc.) while avoiding subjective judgments about States themselves. The study makes use of case studies and concrete examples in order to illustrate how new concepts and approaches will be implemented, and how they may differ from more traditional safeguards approaches.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2012

Prioritization Risk Integration Simulation Model (PRISM) For Environmental Remediation and Waste Management - 12097

The PRISM (Prioritization Risk Integration Simulation Model), a computer model was developed to support the Department of Energy's Office of Environmental Management (DOE-EM) in its mission to clean up the environmental legacy from the Nation's nuclear weapons materials production complex. PRISM provides a comprehensive, fully integrated planning tool that can tie together DOE-EM's projects. It is designed to help DOE managers develop sound, risk-informed business practices and defend program decisions. It provides a better ability to understand and manage programmatic risks. The underlying concept for PRISM is that DOE-EM 'owns' a portfolio of environmental legacy obligations (ELOs), and that its mission is to transform the ELOs from their current conditions to acceptable conditions, in the most effective way possible. There are many types of ELOs - - contaminated soils and groundwater plumes, disused facilities awaiting D and D, and various types of wastes waiting for processing or disposal. For a given suite of planned activities, PRISM simulates the outcomes as they play out over time, allowing for all key identified uncertainties and risk factors. Each contaminated building, land area and waste stream is tracked from cradle to grave, and all of the linkages affecting different waste streams are captured. The progression of the activities is fully dynamic, reflecting DOE-EM's prioritization approaches, precedence requirements, available funding, and the consequences of risks and uncertainties. The top level of PRISM is the end-user interface that allows rapid evaluation of alternative scenarios and viewing the results in a variety of useful ways. PRISM is a fully probabilistic model, allowing the user to specify uncertainties in input data (such as the magnitude of an existing groundwater plume, or the total cost to complete a planned activity) as well as specific risk events that might occur. PRISM is based on the GoldSim software that is widely used for risk and performance assessment calculations. PRISM can be run in a deterministic mode, which quickly provides an estimate of the most likely results of a given plan. Alternatively, the model can be run probabilistically in a Monte Carlo mode, exploring the risks and uncertainties in the system and producing probability distributions for the different performance measures. The PRISM model demonstrates how EM can evaluate a portfolio of ELOs, and transform the ELOs from their current conditions to acceptable conditions, utilizing different strategic approaches. There are many types of ELOs - contaminated soils and groundwater plumes, disused facilities awaiting D and D, and various types of wastes waiting for processing or disposal. This scope of work for the PRISM process and the development of a dynamic simulation model are a logical extension of the GoldSim simulation software used by the OCRWM to assess the long-term performance for the Yucca Mountain Project and by NNSA to assess project risk at its sites. Systems integration modeling will promote better understanding of all project risks, technical and nontechnical, and more defensible decision-making for complex projects with significant uncertainties. It can provide effective visual communication and rapid adaptation during interactions with stakeholders (Administration, Congress, State, Local, and NGO). It will also allow rapid assessment of alternative management approaches. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Prioritization of Model Support Activities for Performance and Safety Assessments - 17111

and tank farm closure consider the performance of a wide variety of wastes, facilities and natural systems over very long time frames. Within the United States Department of Energy - Office of Environmental Management (DOE-EM), several PAs are currently being developed and similar applications are underway at numerous locations around the world. The Savannah River National Laboratory (SRNL) is providing technical support for PAs at several DOE-EM sites and developed recommendations for approaches to prioritize model support activities to help focus efforts on activities that would be expected to significantly influence decision-making. A general approach has been developed to identify data needs for the modeling, foster integration and communication between the PA team and the groups providing model support, and document the basis for prioritization of specific model support efforts. The approach addresses development of the rationale for efforts to address key needs and also describe the rationale for not pursuing efforts that are not expected to influence the conclusions of the PA. The approach will be of use for groups conducting PAs for waste disposal and can also be applied for other modeling efforts for site restoration activities (e.g., decommissioning, environmental remediation, etc.). (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Prioritizing Nuclear Materials for SAM-3 Neutron Irradiation Campaign: Structural and Cladding Materials Candidates

This report outlines a framework for selecting structural and cladding materials for the Nuclear Science User Facilities (NSUF) SAM-3 neutron irradiation campaign to support the advancement of nuclear energy technologies. The document begins with an introduction that provides background context, highlights the motivations for launching a new irradiation campaign, and defines the overall objectives. The core of the report describes the design considerations for the irradiation campaign, including capsule configurations, irradiation temperature ranges, and target dose levels (defined by displacements per atom, or dpa). The material recommendation was guided by the Specimen Identification and Prioritization (SIP) Working Group, a multidisciplinary team of experts representing national laboratories, academia, industry, federal government and agency. This group played a central role in identifying candidate materials, evaluating technical justifications, and ensuring alignment with boarder programmatic goals. A detailed set of criteria for material prioritization is then presented, taking into account reactor relevance, performance gaps, advanced manufacturing methods, and emerging material classes. Based on the input of SIP working group, specific materials were selected and justified for inclusion in the irradiation campaign by the NSUF leadership and its U.S. Department of Energy (DOE)-Office of Nuclear Energy (NE) management. The final section provides recommended capsule designs, summarizing critical parameters such as material type, fabrication method, sample geometry, irradiation conditions, and specimen quantities. This report serves as a foundation for executing a focused and high-impact neutron irradiation campaign aimed at addressing key materials challenges for both existing and advanced nuclear reactors.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

Alternative Analysis and Prioritization of Department of Energy “TBD” Materials with No Identified Disposition Pathway

The Department of Energy (DOE) complex manages a significant inventory of excess nuclear materials for which disposition pathways have not been identified, commonly referred to as "To Be Determined" (TBD) items. The Disposition Pathways Program, initiated in FY2018, provides a standardized framework for identifying viable disposition pathways for these materials. In 2023, the program undertook a comprehensive review and systematic analysis of the remaining TBD material groups, updating the assessment from the 2020 TBD Study using the 2022 fiscal year-end Nuclear Material Inventory Assessment (NMIA) as the primary data source. This effort aimed to utilize quantitative analysis to down-select from a wide range of potential disposition options and prioritize the remaining pathways to facilitate informed, risk-based decision-making for future programmatic funding and execution. This paper details the alternative analysis methodology used for screening and prioritization, highlighting the key criteria, ranking process, and resultant recommendations. The study successfully narrowed fifty-two potential disposition options down to nineteen, providing a focused path forward for addressing a longstanding challenge within the DOE complex.

Ramsey, Catherine [Savannah River National Laborat↗
DOE OSTI2023

Chemical Asset Prioritizer (CAP) v.1.0

SAND2024-08627O The Chemical Asset Prioritizer is a web application designed for facility management on how to safely store chemicals. App users fill out a survey about the facility and the chemicals on the site. The Chemical Asset Prioritizer then determines which chemicals are at the highest priority and provide recommendations on how to secure them. The web application will likely be licensed to a company that will host it for use by numerous federal entities, universities, and other government agencies. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Johnson II, Alfred↗