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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 OSTI2019

Benchmark Datasets Task 2 Report: Use Case Development and Prioritization Results June 2019

The Benchmark Datasets project is a four-year effort to collect and curate building operational and environmental data, with the goal of addressing data gaps that have prevented stakeholders from achieving greater levels of energy efficiency and load flexibility. The effort is led by the U.S. Department of Energy (DOE) Building Technologies Office (BTO) with performers from National Renewable Energy Laboratory (NREL), Lawrence Berkeley National Laboratory (LBNL), Oak Ridge National Laboratory (ORNL), and Pacific Northwest National Laboratory (PNNL). Task 2 of the FY19 project involved identifying and prioritizing use cases that would benefit from targeted data collection and curation. NREL led this task with support from LBNL, ORNL, and PNNL. This report, which was submitted as an internal document to DOE in June 2019, summarizes the method used by the Task 2 team to select high-priority use cases and data needs that formed the focus of the remainder of the project. The high-priority use cases continue to inform subsequent efforts to down-select candidate data sources and engage potential partners.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2006

START Analysis for ESAS Capability Needs Prioritization

START is a tool to optimize research and development primarily for NASA missions. It was developed within the Strategic Systems Technology Program Office, a division of the Office of the Chief Technologist at NASA's Jet Propulsion Laboratory. START is capable of quantifying and comparing the risks, costs, and potential returns of technologies that are candidates for funding. START can be enormously helpful both in selecting technologies for development -- within the constraints of budget, schedule, and other resources -- and in monitoring their progress. START's methods are applicable to everything from individual tasks to multiple projects comprising entire programs of investigation. They can address virtually any technology assessment and capability prioritization issue. In this report, START is used to analyze the capability needs using data from NASA's Exploration Systems Architecture Study (ESAS).

optimization↗
DOE OSTI2013

Technology Prioritization: Transforming the U.S. Building Stock to Embrace Energy Efficiency

The U.S. Buildings sector is responsible for about 40% of the national energy expenditures. This is due in part to wasteful use of resources and limited considerations made for energy efficiency during the design and retrofit phases. Recent studies have indicated the potential for up to 30-50% energy savings in the U.S. buildings sector using currently available technologies. This paper discusses efforts to accelerate the transformation in the U.S. building energy efficiency sector using a new technology prioritization framework. The underlying analysis examines building energy use micro segments using the Energy Information Administration Annual Energy Outlook and other publically available information. The tool includes a stock-and-flow model to track stock vintage and efficiency levels with time. The tool can be used to investigate energy efficiency measures under a variety of scenarios and has a built-in energy accounting framework to prevent double counting of energy savings within any given portfolio. This tool is developed to inform decision making and estimate long term potential energy savings for different market adoption scenarios.

energy efficiency↗
DOE OSTI2025

Redefining Design for Remanufacturing: A Practical Methodology for Prioritizing Remanufacturing Design Rules

Products are often discarded when they fail or no longer meet user needs. These outcomes are frequently shaped by early design decisions. While remanufacturing offers a sustainable alternative by restoring products to like‐new condition, its potential is often limited by designs that do not consider remanufacturing from the outset. This research addresses that challenge by introducing a structured Design for Remanufacturing (DfRem) methodology and a CAD‐integrated tool to support real‐time design decisions. The DfRem framework introduces a new primary design function focused on preserving product functionality across its life cycle. It is supported by a fault tree that identifies failure modes that limit remanufacturing potential and a hierarchy of design principles including Prevent, Minimize, Relocate, Restore, and others. Each principle is linked to actionable design rules that help engineers reduce the need for remanufacturing or improve its efficiency when necessary. To operationalize this framework, we developed CAD plugins for Autodesk Inventor and PTC Creo. These tools use a state machine model to present prioritized design rules based on selected failure modes and user input. By embedding DfRem logic directly into widely used CAD environments, the tool enables engineers to make sustainability‐informed decisions without disrupting existing workflows. Furthermore, this approach highlights the critical role of design in enabling circular and resource‐efficient product development, making remanufacturing a more practical and accessible strategy during the early stages of product design.

CAD↗
DOE OSTI2023

A multiscale landscape approach for prioritizing river and stream protection and restoration actions

River and stream conservation programs have historically focused on a single spatial scale, for example, a watershed or stream site. Recently, the use of landscape information (e.g., land use and land cover) at multiple spatial scales and over large spatial extents has highlighted the importance of incorporating a landscape perspective into stream protection and restoration activities. Previously, we developed a novel framework that links information about watershed-, catchment-, and reach-scale integrity with stream biological condition using scatterplots and a landscape integrity map. Here we examined an application of this approach for streams in urban and other settings in King County, Washington State, United States, where we related stream macroinvertebrate condition to two indices of landscape integrity, the US Environmental Protection Agency's (USEPA) nationally available Index of Watershed Integrity (IWI) and Index of Catchment Integrity (ICI). We generated a scatterplot of IWI versus ICI for sample sites, where points represented site macroinvertebrate condition from poor to good. The same data were also visualized as a landscape integrity map that displayed catchments of King County according to the level of watershed and catchment integrity (high or low IWI/ICI). Almost three-quarters of poor-condition sites were associated with high-integrity watersheds and catchments (i.e., underperforming sites), which suggested that either one or both national indicators were insufficient for this area, and that sites underperformed because of local-scale factors. In response, we used a catchment-scale indicator related to forest condition (PctForestCat) after examining several GIS-based dispersal indicators from the National Hydrography Dataset and other candidates from the USEPA's StreamCat dataset. We then compared the results of the scatterplots and maps based on the current and original analyses and found that many of the sites previously classified as underperforming now performed as expected, that is, they were poor-condition sites in poor-condition catchments. This analysis demonstrates how results based on a national dataset can be improved by developing an alternative that represents regionally important stressors. The methods used to develop an effective landscape indicator based on StreamCat datasets, and the utility of the multiscale approach, could provide important tools for prioritizing, optimizing, and communicating stream conservation actions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Sustainability as the key to prioritize investments in public infrastructures

Infrastructure construction, one of the biggest driving forces of the economy nowadays, requires a huge analysis and clear transparency to decide what projects have to be executed with the few resources available. With the aim to provide the public administrations a tool with which they can make their decisions easier, the Sustainability Index of Infrastructure Projects (SIIP) has been defined, with a multi-criteria decision system called MIVES, in order to classify non-uniform investments. This index evaluates, in two inseparable stages, the contribution to the sustainable development of each infrastructure project, analyzing its social, environmental and economic impact. The result of the SIIP allows to decide the order with which projects will be prioritized. The case of study developed proves the adaptability and utility of this tool for the ordinary budget management.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

An application of the Analytic Hierarchy Process for prioritizing user preferences in the design of a Home Energy Management System

Most demand response programs focus primarily on commercial and industrial loads. The sheer number of decision-influencing variables combined with a lack of a comprehensive understanding of human behavior make it difficult to develop response programs that encourage greater residential consumer participation. This paper presents a multi-criteria decision making approach using actual survey data for identifying user preferences. The novelty of the work is in considering functionality, cost, and carbon emissions for a given set of home appliances toward energy management. The paper explains the design methodologies used to prioritize the preferences from 1023 survey participants. Furthermore, the results are expected to inform the design of Home Energy Management Systems (HEMS) for participating in demand response programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Improving the performance of first- and last-mile mobility services through transit coordination, real-time demand prediction, advanced reservations, and trip prioritization

Socio-demographic trends and recent economic development patterns have resulted in travel behavior changes that call for more flexible and accessible public transit options. Because flexible transit services vary in scope, size, and service type, new data-informed methods are useful to optimize services based on the specific needs of local communities and riders. In this study, real-world demand and vehicle trajectory data were used to evaluate and optimize system performance for an existing first-mile–last-mile (FMLM) service in Robinson Township, PA. A general FMLM model for arbitrary demand and service supply was then developed to quantify system performance—both travel time costs and day-to-day reliability—for various operational polices considering spatio-temporal demand variation and transportation network dynamics. Heuristics were used for optimal real-time vehicle routing in sizable real-world networks accommodating various service types and scopes. In this case study, total user costs were reduced by 18.6% when rides were coordinated with mainline fixed-route transit. Predictive routing strategies were shown to marginally improve system performance under sparse and variable spatio-temporal demand. The case study also highlights potentially large travel time and user reliability improvements—reductions of 51% and 53.8%, respectively—when trip requests were made in advance of their desired pickup time. Finally, we show that travel time reliability can be improved for time-inflexible trips with trip prioritization without increasing total user costs. These results were stable to changes in demand density.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2012

Prioritizing material recovery for end-of-life printed circuit boards

Highlights: Black-Right-Pointing-Pointer Material recovery driven by composition, choice of ranking, and weighting. Black-Right-Pointing-Pointer Economic potential for new recycling technologies quantified for several metrics. Black-Right-Pointing-Pointer Indicators developed for materials incurring high eco-toxicity costs. Black-Right-Pointing-Pointer Methodology useful for a variety of stakeholders, particularly policy-makers. - Abstract: The increasing growth in generation of electronic waste (e-waste) motivates a variety of waste reduction research. Printed circuit boards (PCBs) are an important sub-set of the overall e-waste stream due to the high value of the materials contained within them and potential toxicity. This work explores several environmental and economic metrics for prioritizing the recovery of materials from end-of-life PCBs. A weighted sum model is used to investigate the trade-offs among economic value, energy saving potentials, and eco-toxicity. Results show that given equal weights for these three sustainability criteria gold has the highest recovery priority, followed by copper, palladium, aluminum, tin, lead, platinum, nickel, zinc, and silver. However, recovery priority will change significantly due to variation in the composition of PCBs, choice of ranking metrics, and weighting factors when scoring multiple metrics. These results can be used by waste management decision-makers to quantify the value and environmental savings potential for recycling technology development and infrastructure. They can also be extended by policy-makers to inform possible penalties for land-filling PCBs or exporting to the informal recycling sector. The importance of weighting factors when examining recovery trade-offs, particularly for policies regarding PCB collection and recycling are explored further.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Prioritization of Early-Stage Research and Development of a Hydrogel-Encapsulated Anaerobic Technology for Distributed Treatment of High Strength Organic Wastewater

This study aims to support the prioritization of research and development (R&D) pathways of an anaerobic technology leveraging hydrogel-encapsulated biomass to treat high-strength organic industrial wastewaters, enabling decentralized energy recovery and treatment to reduce organic loading on centralized treatment facilities. To characterize the sustainability implications of early-stage design decisions and to delineate R&D targets, an encapsulated anaerobic process model was developed and coupled with design algorithms for integrated process simulation, techno-economic analysis, and life cycle assessment under uncertainty. Across the design space, a single-stage configuration with passive biogas collection was found to have the greatest potential for financial viability and the lowest life cycle carbon emission. Through robust uncertainty and sensitivity analyses, we found technology performance was driven by a handful of design and technological factors despite uncertainty surrounding many others. Hydraulic retention time and encapsulant volume were identified as the most impactful design decisions for the levelized cost and carbon intensity of chemical oxygen demand (COD) removal. Encapsulant longevity, a technological parameter, was the dominant driver of system sustainability and thus a clear R&D priority. Ultimately, we found encapsulated anaerobic systems with optimized fluidized bed design have significant potential to provide affordable, carbon-negative, and distributed COD removal from high strength organic wastewaters if encapsulant longevity can be maintained at 5 years or above.

Anaerobic Treatment↗
DOE OSTI2024

Prioritizing Mentorship as Scientific Leaders

Scientific careers are rarely straight paths. This article emphasizes the crucial role of mentorship in navigating scientific careers and sustaining innovation in STEMM fields. Effective mentorship can have a positive impact on graduate students' research productivity, research self-efficacy, degree completion, and program satisfaction. Despite its importance, mentorship is often an overlooked and underappreciated component of scientific training. As members of the 2022 CAS Future Leaders class, representing ten countries and various chemistry subdisciplines, we share our mentorship experiences to suggest actions to promote healthy and inclusive mentor-mentee relationships in chemistry. The article explores the importance of mentorship, outlines impactful strategies, and offers insights into how to create a scientific community that values and prioritizes effective mentorship.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Epidermal growth factor receptor cascade prioritizes the maximization of signal transduction

Many studies have been performed to quantify cell signaling. Cell signaling molecules are phosphorylated in response to extracellular stimuli, with the phosphorylation sequence forming a signal cascade. The information gain during a signal event is given by the logarithm of the phosphorylation molecule ratio. The average information gain can be regarded as the signal transduction quantity (ST), which is identical to the Kullback–Leibler divergence (KLD), a relative entropy. We previously reported that if the total ST value in a given signal cascade is maximized, the ST rate (STR) of each signaling molecule per signal duration (min) approaches a constant value. To experimentally verify this theoretical conclusion, we measured the STR of the epidermal growth factor (EGF)-related cascade in A431 skin cancer cells following stimulation with EGF using antibody microarrays against phosphorylated signal molecules. The results were consistent with those from the theoretical analysis. Thus, signaling transduction systems may adopt a strategy that prioritizes the maximization of ST. Furthermore, signal molecules with similar STRs may form a signal cascade. In conclusion, ST and STR are promising properties for quantitative analysis of signal transduction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

FluxRETAP: a REaction TArget Prioritization genome-scale modeling technique for selecting genetic targets

MOTIVATION: Metabolic engineering is rapidly evolving as a result of new advances in synthetic biology tools and automation platforms that enable high throughput strain construction, as well as the development of machine learning tools (ML) for biology. However, selecting genetic engineering targets that effectively guide the metabolic engineering process is still challenging. ML can provide predictive power for synthetic biology, but current technical limitations prevent the independent use of ML approaches without previous biological knowledge. RESULTS: Here, we present FluxRETAP, a simple and computationally inexpensive method that leverages the prior mechanistic knowledge embedded in genome-scale models for suggesting targets for genetic overexpression, downregulation or deletion, with the final goal of increasing the production of a desired metabolite. This method can provide a list of desirable engineering targets that can be combined with current ML pipelines. FluxRETAP captured 100% of reaction targets experimentally verified to improve Escherichia coli isoprenol production, 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. AVAILABILITY AND IMPLEMENTATION: FluxRETAP is implemented in python and released under the creative commons license. The implementation and code are freely available at: https://github.com/JBEI/FluxRETAP.

Czajka, Jeffrey J↗
DOE OSTI2019

Cybersecurity Vulnerability Mitigation Framework through Empirical Paradigm (CyFEr): Prioritized Gap Analysis

Cybersecurity vulnerability assessment tools, frameworks, methodologies, and processes are commonly used to understand the cybersecurity maturity and posture of a system or a facility. Although, those tools are strictly developed based on standards defined by organizations such as the National Institute of Standards and Technology (NIST) and the U.S. Department of Energy, the majority of these tools and frameworks do not provide a platform to prioritize the requirements to reach a desired cybersecurity maturity. To address that challenge, we have been developing a framework and a software application called the cybersecurity vulnerability mitigation framework through empirical paradigm (CyFEr). The efficacy of CyFEr was evaluated by implementing it on the NIST Cybersecurity Framework (CSF). This paper provides a detailed architecture of CyFEr and demonstrates its application to CSF by testing against a real-world cyber-attack that targeted industrial control systems in a critical infrastructure facility.

Gourisetti, Sri Nikhil G.↗
DOE OSTI2020

A genoscape–network model for conservation prioritization in a migratory bird

Migratory animals are declining worldwide and coordinated conservation efforts are needed to reverse current trends. We devised a novel genoscape-network model that combines genetic analyses with species distribution modeling and demographic data to overcome challenges with conceptualizing alternative risk factors in migratory species across their full annual cycle. We applied our method to the long distance, Neotropical migratory bird, Wilson's Warbler (Cardellina pusilla). Despite a lack of data from some wintering locations, we demonstrated how the results can be used to help prioritize conservation of breeding and wintering areas. For example, we showed that when genetic, demographic, and network modeling results were considered together it became clear that conservation recommendations will differ depending on whether the goal is to preserve unique genetic lineages or the largest number of birds per unit area. More specifically, if preservation of genetic lineages is the goal, then limited resources should be focused on preserving habitat in the California Sierra, Basin Rockies, or Coastal California, where the 3 most vulnerable genetic lineages breed, or in western Mexico, where 2 of the 3 most vulnerable lineages overwinter. Alternatively, if preservation of the largest number of individuals per unit area is the goal, then limited conservation dollars should be placed in the Pacific Northwest or Central America, where densities are estimated to be the highest. Overall, our results demonstrated the utility of adopting a genetically based network model for integrating multiple types of data across vast geographic scales and better inform conservation decision-making for migratory animals.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-452: Identifying Inefficiencies in Radiation Oncology Workflow and Prioritizing Solutions for Process Improvement and Patient Safety

Purpose: Successful radiation therapy requires multi-step processes susceptible to unnecessary delays that can negatively impact clinic workflow, patient satisfaction, and safety. This project applied process improvement tools to assess workflow bottlenecks and identify solutions to barriers for effective implementation. Methods: We utilized the DMAIC (define, measure, analyze, improve, control) methodology, limiting our scope to the treatment planning process. From May through December of 2014, times and dates of each step from simulation to treatment were recorded for 507 cases. A value-stream map created from this dataset directed our selection of outcome measures (Y metrics). Critical goals (X metrics) that would accomplish the Y metrics were identified. Barriers to actions were binned into control-impact matrices, in order to stratify them into four groups: in/out of control and high/low impact. Solutions to each barrier were then categorized into benefit-effort matries to identify those of high benefit and low effort. Results: For 507 cases, the mean time from simulation to treatment was 235 total hours. The mean process and wait time were 60 and 132 hours, respectively. The Y metric was to increase the ratio of all non-emergent plans completed the business day prior to treatment from 47% to 75%. Project X metrics included increasing the number of IMRT QAs completed at least 24 hours prior to treatment from 19% to 80% and the number of non-IMRT plans approved at least 24 hours prior to treatment from 33% to 80%. Intervals from simulation to target contour and from initial plan completion to plan approval were identified as periods that could benefit from intervention. Barriers to actions were binned into control-impact matrices and solutions by benefit-effort matrices. Conclusion: The DMAIC method can be successfully applied in radiation therapy clinics to identify inefficiencies and prioritize solutions for the highest impact.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-CD-209-06: LET-Based Adjustment of IMPT Plans Using Prioritized Optimization

Purpose: In-vitro experiments suggest an increase in proton relative biological effectiveness (RBE) towards the end of range. However, proton treatment planning and dose reporting for clinical outcome assessment has been based on physical dose and constant RBE. Therefore, treatment planning for intensity-modulated proton therapy (IMPT) is unlikely to transition radically to pure RBE-based planning. We suggest a hybrid approach where treatment plans are initially created based on physical dose constraints and prescriptions, and are subsequently altered to avoid high linear energy transfer (LET) in critical structures while limiting the degradation of the physical dose distribution. Methods: To allow fast optimization based on dose and LET we extended a GPU-based Monte-Carlo code towards providing dose-averaged LET in addition to dose for all pencil beams. After optimizing an initial IMPT plan based on physical dose, a prioritized optimization scheme is used to modify the LET distribution while constraining the physical dose objectives to values close to the initial plan. The LET optimization step is performed based on objective functions evaluated for the product of physical dose and LET (LETxD). To first approximation, LETxD represents a measure of the additional biological dose that is caused by high LET. Regarding optimization techniques, LETxD has the advantage of being a linear function of the pencil beam intensities. Results: The method is applicable to treatments where serial critical structures with maximum dose constraint are located in or near the target. We studied intra-cranial tumors (high-grade meningiomas, base-of-skull chordomas) where the target (CTV) overlaps with the brainstem and optic structures. Often, high LETxD in critical structures can be avoided while minimally compromising physical dose planning objectives. Conclusion: LET-based re-optimization of IMPT plans represents a pragmatic approach to bridge the gap between purely physical dose-based and RBE-based planning. It can be realized with fast GPU-based Monte-Carlo dose calculation. The project was in part supported by NIH grants U19 CA 021239-36 and C06 CA 059267.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

VULCON: A System for Vulnerability Prioritization, Mitigation, and Management

Vulnerability remediation is a critical task in operational software and network security management. In this paper, an effective vulnerability management strategy, called VULCON (VULnerability CONtrol), is developed and evaluated. The strategy is based on two fundamental performance metrics: i). Time-to-Vulnerability Remediation (TVR) and; ii). Total Vulnerability Exposure (TVE). VULCON takes as input real vulnerability scan reports, metadata about the discovered vulnerabilities, asset criticality, and personnel resources. VULCON uses a mixed integer multi-objective optimization algorithm to prioritize vulnerabilities for patching, such that the above performance metrics are optimized subject to the given resource constraints. VULCON has been tested on multiple months of real scan data from a Cyber-Security Operations Center (CSOC). Results indicate an overall Total Vulnerability Exposure reduction of 8.97\% when VULCON optimizes a realistic security analyst workforce's effort. Additionally, it is demonstrated that VULCON can determine monthly resources required to maintain a target TVE score. As such, VULCON provides valuable operational guidance for improving vulnerability response processes in CSOCs.

Farris, Katheryn A.↗