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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Agent-Based, Bottom-Up Medium- and Heavy-duty Electric Vehicle Economics, Operation, Charging and Adoption (Research Performance Final Report)

This is the research performance final report for the project entitled: Agent-Based, Bottom-Up Medium- and Heavy-duty Electric Vehicle Economics, Operation, Charging and Adoption This project was able to achieve the DOE’s goals of developing new modeling tools to understand MDHD vehicle operation and adoption. The first modeling tool is a fleet-level techno-economic analysis model capable of estimating energy use and associated environmental and cost impacts for electrified and conventional vehicles of any MDHD vocation, using real-world cost and operations data, including approaches to optimizing schedules for charging and/or vehicle dispatch. The second modeling tool is a system-level, bottom-up, agent-based adoption model capable of generating geographically-resolved estimates of market projections for MDHD vehicles and charging infrastructure. These tools will be developed and published to serve dual purposes as analysis tools for researchers, and decision-support tools for decision makers within the MDHD system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

A framework and tool for designing cost-effective, resilient, and circular net-zero supply chains under uncertainty with an application to multilayer plastic films

While 55% of Fortune 500 companies have committed to achieving net-zero emissions and/or zero-waste operations by 2035, only 2% are currently on track, revealing a critical gap between ambition and action. Designing supply chains that reduce both emissions and waste is a complex non-intuitive, multi-objective challenge, compounded by the high costs of new technologies and the need for resilient, profitable solutions. This paper aims to address this challenge by presenting a generic framework and multi-objective optimization formulation for designing cost-effective, circular, and resilient supply chains under uncertainty, implemented through a user-friendly decision-support tool with intuitive data visualization capabilities, enabling communication of results to both technical and non-technical stakeholders. We demonstrate the application of this framework in the context of multilayer plastic films (barrier films), which are widely used in food packaging and composite materials. The model quantifies trade-offs across three objectives: minimizing global warming potential, maximizing circularity, and minimizing cost. A key contribution of this work is the explicit modeling of technological resilience, the ability of supply chains to maintain function under disruption. In the cost-minimization case, the resilience constraint makes the design approximately three times more expensive in the short-term metric, but shifts the system from relying on a single recovery pathway to a portfolio of four recovery pathways, improving the robustness of the optimization solution under uncertainty. Lastly, we introduce TranZero, a decision-support tool that integrates material flow analysis, hotspot identification, and optimization-based scenario planning to support net-zero and circularity decisions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

FieLDS

Field Landscape Decision Support (FieLDS) is a decision support tool that assists agricultural producers and farm managers in selecting parts of the field that can be converted to bioenergy plants, like perennial grasses, from the conventional use, like corn or soybean growth. That conversion of parts of the field is done based on the specific landscape properties and the agricultural producer’s priorities. The decision support tool generates a map of the field layout and spatially marks where to place each plant based on the spatial input from the user. Also, the user selects which priorities (sustainability indicators) are most relevant to them in their decision-making. By allowing the user to set the weights among the priorities, the tool empowers the user to generate field designs specific to their needs instead of imposing the developer’s set of priorities, and increases the transparency of the decision process.

Vazhnik, Veronika↗
DOE OSTI2022

Pit Production Strategic Analysis Tool

The LANL Pit Production Strategic Analysis tool is an executive-level decision support tool for planning related to the demand and production of pits. The spreadsheet-based tool includes decision factors for stockpile program demand combined with production and infrastructure planning at PF-4, Savanna River, and future production options. The tool graphically compares supply and demand under different scenarios. The tool is based on NNSA planning documents and process analyses.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Radiation Case Strategic Analysis Tool [Fact Sheet]

The LANL Radiation Case Production Planning Tool is an executive-level decision support tool for planning related to the demand and production of radiation cases. The spreadsheet-based tool includes decision factors for stockpile program demand combined with production and infrastructure planning at Y-12. The tool displays graphical comparisons of supply and demand under different scenarios. The tool is based on NNSA planning documents and process analyses from Y12.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2021

High Value Opportunities to Advance Automation in Electric Grid Control Rooms

Grid operators and electric utilities are increasingly driven to develop automated processes and introduce decision support tools to reduce cognitive demand on System Operations staff and aid in the operational decision-making process. However, System Operation staff are traditionally hesitant to implement and accept new solutions, automation processes, and tools within the control room. This paper explores high-value opportunities to advance automation in electric grid control rooms for the purpose of improving grid safety, reliability, and resiliency during normal and emergency operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Wind Systems Integration Workshop

The U.S. Department of Energy’s Wind Energy Technologies Office (WETO) Wind Systems Integration Workshop was held to facilitate an exchange of information and to solicit feedback to inform WETO’s near- to mid-term research priorities and to accelerate near-term, rapid deployment and integration of wind technologies at both the transmission and distribution levels. Workshop participants identified key research challenges and opportunities for grid services, power electronics, modeling and decision-support tools, transmission and distribution system coordination, and applying energy equity principles to wind grid integration research.

17 WIND ENERGY↗
DOE OSTI2025

A Typology of Decision-Making Tasks for Visualization

Despite decision-making being a vital goal of data visualization, little work has been done to differentiate decision-making tasks within the field. While visualization task taxonomies and typologies exist, they often focus on more granular analytical tasks that are too low-level to describe large complex decisions, which can make it difficult to reason about and design decision-support tools. In this paper, we contribute a typology of decision-making tasks that were iteratively refined from a list of design goals distilled from a literature review. Our typology is concise and consists of only three tasks: CHOOSE, ACTIVATE, and CREATE. Although decision types originating in other disciplines exist, we provide definitions for these tasks that are suitable for the visualization community. Our proposed typology offers two benefits. First, the ability to compose and hierarchically organize the tasks enables flexible and clear descriptions of decisions with varying levels of complexities. Second, the typology encourages productive discourse between visualization designers and domain experts by abstracting the intricacies of data, thereby promoting clarity and rigorous analysis of decision-making processes. We demonstrate the benefits of our typology through four case studies, and present an evaluation of the typology from semi-structured interviews with experienced members of the visualization community who have contributed to developing or publishing decision support systems for domain experts. Our interviewees used our typology to delineate the decision-making processes supported by their systems, demonstrating its descriptive capacity and effectiveness. Finally, we present preliminary findings on the usefulness of our typology for visualization design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Self Configuring Digital Twin for Optimizing E-Waste Recycling

Electronic waste recycling industry needs a decision support tool for optimizing their processes to become cost competitive. We developed a software called CMAT, Comprehensive Manufacturing Assessment Tool. The aim of CMAT is to provide the e-waste recycling companies with a fully customizable decision support framework that analyzes the optimal supply chain configurations. The software optimizes the logistics operations, helps to identify the best recycling process configuration, and generates valuable insights regarding the economic performance of different categories of e-waste. The ultimate purpose of the tool is to assist the users developing a digital twin of their processes and to provide insights on questions pertinent to the e-waste recycling industry including how to increase efficiency and reduce costs, energy consumption, and greenhouse gas emissions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Progress in the Verification and Validation Efforts for START: A Spent Fuel Routing Tool

The U.S. Department of Energy’s (DOE) Office of Nuclear Energy (DOE-NE) is planning for an integrated waste management approach to transport, store, and dispose of spent nuclear fuel (SNF) and other high-level radioactive waste (HLW) as part of the Integrated Waste Management (IWM) program [1]. In support of this effort, the Stakeholder Tool for Assessing Radioactive Transportation (START) is being developed within the IWM program [2, 3]. This is a web-based decision support tool that can be used to analyze geospatial data related to the transportation of SNF and HLW. START is designed as a web-based application using an ArcGIS server through which the user can select the origin and destination of the route [4]. This is followed by selection of the mode of transportation of choice based on user preference. Some of the modes available to the user include rail, heavy haul truck, and barge. The option of utilizing more than one mode of transportation (intermodal transportation) is also implemented in START. A few examples of intermodal transportation include barge to rail, and heavy haul truck to rail. It must be noted that some routes might not have access to all modes of transportation, depending on the infrastructure availability at the origin and destination sites. Users can also select any stops or barriers they would like to introduce in the routes. This is followed by selection of the routing criteria of interest. Three primary routing options available to the user include minimum population, minimum distance, and minimum time. Apart from that, a few other options include accounting for a weighted average of the three aforementioned routing options. The next step involves the selection of the buffer distance of interest which includes the two choices available of 800 m and 2500 m, respectively. Finally, an option to select any prohibited rail carriers that the user does not wish to use is available. After making these selections, a route is ready to be created.

Gadey, Harish Reddy↗
DOE OSTI2023

Agent-based simulation and child protection systems: Rationale, implementation, and verification

Simulation models are an important tool used in health care and other disciplines to support operational research and decision-making. In the child protection literature, simulation models are an under-utilized source of research evidence. Here, in this paper, we describe the rationale for and the development of an agent-based simulation of a child protection system in the US. Using the investigation, prevention service, and placement histories of 600,000 children served in an urban child welfare system, we walk the reader through the development of a prototype known as OSPEDALE. The governing equations built into OSPEDALE probabilistically simulate the onset of investigations. Then, drawing from empirical survival distributions, the governing equations trace the probability of subsequent interactions with the system (recurrence of maltreatment, service referrals, and placement) conditional on the characteristics of children, their assessed risk level, and prior child protection system involvement. As an initial test of OSPEDALE's utility, we compare empirical admission counts with counts generated from OSPEDALE. Though the verification step is admittedly simple, the comparison shows that OSPEDALE replicates the empirical count of new admissions closely enough to justify further investment in OSPEDALE. Management of public child protection systems is increasingly research evidence-dependent. The emphasis on research evidence as a decision-support tool has elevated evidence acquired through randomized clinical trials. Though important, the evidence from clinical trials represents only one type of research evidence. Properly specified, simulation models are another source of evidence with real-world relevance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Verification and Validation of the Stakeholder Tool for Assessing Radioactive Transportation (START) – 22323

The U.S. Department of Energy (DOE) Office of Integrated Waste Management is planning for the eventual transportation, storage, and disposal of spent nuclear fuel (SNF) and high-level radioactive waste (HLW) from nuclear power plant and DOE sites. The Stakeholder Tool for Assessing Radioactive Transportation (START) is a web-based, geospatial decision-support tool developed for evaluating routing options and other aspects of transporting SNF and HLW, covering rail, truck, barge, and intermodal infrastructure and operations in the continental United States. The verification and validation (V&V) process is intended to independently assess START to provide confidence in the ability of START to accurately provide intended results. The V&V process checks the START tool using a variety of methods, ranging from independent hand calculations to comparison of START performance and results to those of other codes. The V&V activity was conducted independently from the START development team with opportunities to provide feedback and collaborate throughout the process. The V&V analyzed attributes of transportation routes produced by START, including route distance and both population and population density captured within buffer zones around routes. Population in the buffer zone, population density in the buffer zone, and route distance were all identified as crucial outputs of the START code and were subject to V&V tasks. Some of the improvements identified through the V&V process were standardizing the underlying population data in START, changing the projection of the population raster data, and changes to the methodology used for population density to improve its applicability for expected users. This collaboration also led to suggested improvements to some of the underlying shape file segments within START. A formal series of test routes went through the V&V process from START version 3.2.1 following the implementation of the recommended improvements to START. The V&V of the buffer zone population and distance reported in START were achieved by utilizing a custom workflow developed in QGIS. This platform was chosen because it is fully independent of START. The buffer zone population and route distances in START showed excellent agreement to independent V&V test results. Over 200 route test cases were run; in all cases, the percent difference in the population within the buffer zone was less than +/- 5%, and the majority of cases were below +/- 1%. These cases were also duplicated in ArcMap, which is Environmental Systems Research Institute’s (ESRI) GIS desktop application. The ArcMap results also show good agreement; however, they cannot be considered independent of START because START also uses ESRI software. The V&V tasks are currently addressing population density and estimated radiation dose in START as well as implementing further automation of the V&V process to allow larger suites of test routes to be run more easily and quickly by less experienced GIS users. These improvements to the V&V process will make it a valuable tool for the development team and the stakeholders so that each new iteration of START can easily be checked for quality and consistency. This paper and presentation will describe the V&V process, findings and implications, and ongoing V&V activities.

START, DOE, Transporation, dose, GIS↗
DOE OSTI2021

Analytical Framework for Homeowner-Focused Computational Cost-Benefit Mitigation Decision Making

As a result of increasing windstorm losses in the United States over the past 50 years, a variety of residential wind hazard mitigation strategies have been recommended. Wind hazard mitigation undoubtedly reduces windstorm losses; however, little customized information is available to support the economic decision-making process for individual stakeholders, especially at the consumer level. Therefore, there is a need for decision support tools to calculate, evaluate, and present the economic benefits of mitigation over variable decision-making time horizons for unique building characteristics in specific geographical locations. This paper presents a cost-benefit computational decision-making recommendation framework that evaluates and presents the cost effectiveness of mitigation, customized based on location, years of interest, and building characteristics. The decision-making framework consists of two cores. The user-independent core and user-dependent core which consists of eight steps, divided into four subprocesses: (1) data acquisition, (2) data evaluation, (3) cost-benefit calculation, and (4) results comparison and decision making. To demonstrate the framework, a wood-framed single-family home in light suburban terrain in Golden Meadow, Louisiana, is used as a case study, considering retrofit mitigation with a $15,000 budget and a decision-making time horizon of 30 years. The case study provides six mitigation options, with cost ranging between approximately $2,000 and $15,000 and net benefit/cost ratios ranging from 2.4 to 13. Once fully implemented in a decision making information tool, the developed mitigation recommendation framework will provide valuable information that can be used by home designers, builders, owners, and other stakeholders to better understand the economic facets of wind hazard mitigation, providing critical information as part of the overall decision-making process.

Orooji, Fatemeh↗
DOE OSTI2022

Development and Validation of a Non-Invasive, Chairside Oral Cavity Cancer Risk Assessment Prototype Using Machine Learning Approach

Oral cavity cancer (OCC) is associated with high morbidity and mortality rates when diagnosed at late stages. Early detection of increased risk provides an opportunity for implementing prevention strategies surrounding modifiable risk factors and screening to promote early detection and intervention. Historical evidence identified a gap in the training of primary care providers (PCPs) surrounding the examination of the oral cavity. The absence of clinically applicable analytical tools to identify patients with high-risk OCC phenotypes at point-of-care (POC) causes missed opportunities for implementing patient-specific interventional strategies. This study developed an OCC risk assessment tool prototype by applying machine learning (ML) approaches to a rich retrospectively collected data set abstracted from a clinical enterprise data warehouse. We compared the performance of six ML classifiers by applying the 10-fold cross-validation approach. Accuracy, recall, precision, specificity, area under the receiver operating characteristic curve, and recall–precision curves for the derived voting algorithm were: 78%, 64%, 88%, 92%, 0.83, and 0.81, respectively. The performance of two classifiers, multilayer perceptron and AdaBoost, closely mirrored the voting algorithm. Integration of the OCC risk assessment tool developed by clinical informatics application into an electronic health record as a clinical decision support tool can assist PCPs in targeting at-risk patients for personalized interventional care.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Web-Based Tools for Data-Informed Remedy Optimization: Software Theory and User Guide

This report documents the development and application of two web-based decision-support tools for pump-and-treat (P&T) groundwater remediation systems: PTOLEMY (Pump-and-Treat Optimized Location Evaluation to Maximize Yields) and OPTIMA (Optimization for Pump-and-Treat Implementation, Management, & Assessment). These tools enhance remedy design and management by leveraging advanced computational methods – specifically deep learning and multi-objective optimization – within a user-friendly platform. By integrating data-driven models with established hydrogeological knowledge, PTOLEMY and OPTIMA enable more efficient evaluation of well placement and operational strategies, helping site managers balance multiple remediation objectives under complex conditions. Both tools are implemented as modules within the SOCRATES (Suite Of Comprehensive Rapid Analysis Tools for Environmental Sites) web platform, which provides data access, visualization, and analytics to support remedy optimization across sites in the U.S. Department of Energy Office of Environmental Management complex. PTOLEMY is a rapid screening module designed to identify promising locations for new extraction wells. It employs a multi-channel three-dimensional convolutional neural network (MC3D-CNN) trained on high-fidelity simulation data to predict the relative performance (in terms of contaminant mass recovery) of potential well sites. Through an interactive web interface, PTOLEMY visualizes the probability of high performance across a site, highlighting areas where an extraction well is likely to yield above-threshold contaminant removal over a multi-year period. PTOLEMY’s map-based displays and exportable results support transparent communication of screening analyses. By focusing attention on the most favorable candidate locations, the tool augments traditional engineering judgment and physics-based modeling, providing a data informed basis for subsequent detailed evaluations. OPTIMA is a multi objective optimization module designed to find wellfield layouts and operating schedules that meet various cleanup goals. It quickly evaluates thousands of candidate setups – combinations of well locations, timing, and rates – and returns a small set of best trade-off options for comparison. At its core, OPTIMA uses a U-Net-based surrogate model – a deep-learning emulator of a groundwater flow and transport simulator – to dramatically accelerate scenario evaluations. Coupling this fast surrogate with the NSGA-II (Non-dominated Sorting Genetic Algorithm II) evolutionary algorithm, OPTIMA explores a wide decision space of well locations and schedules to identify Pareto-optimal solutions that trade off key objectives (e.g., minimizing cleanup time, maximizing contaminant mass removal, and minimizing plume extent). The tool outputs a family of optimal configurations and visualizes their trade-offs (Pareto frontiers of cleanup metrics and maps of optimized well placements). Site managers can use these results to understand the range of viable strategies and to select candidate designs for more detailed verification. OPTIMA is currently under active development and not yet fully released; this guide provides early documentation to support planning and gather user feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

PRIMO - The P&A Project Optimizer

In support of the Methane Emissions Reduction Program (MERP) and under the National Methane Emissions Reduction Initiative (NEMRI), the National Energy Technology Laboratory (NETL) and NETL site support contractors are developing and releasing an open-source decision-support tool (“PRIMO”) to help organizations determine which marginal conventional wells (MCWs) or other low-producing wells make the best candidates for plugging utilizing MERP funds, while also optimizing subsequent plugging and abandonment (P&A) campaigns for both program impact and efficiency. The framework—which is fully customizable—provides three main capabilities: 1. Ranking candidate wells (based on user preferences) 2. Identifying high-impact, high-efficiency P&A project candidates (with transparently computed scores for relevant metrics) 3. Comparing competing P&A projects quantitatively (through transparently computed project impact and efficiency scores) As such, PRIMO is a versatile, fully customizable tool that is meant to support organizations in making data-based, transparent, and defensible well selection and P&A project design decisions. For questions, comments or feedback, please contact primo@netl.doe.gov.

MERP↗
DOE OSTI2021

Development of a Regional Roadmap for Source Network and Storage Deployment for CarbonSAFE Illinois - Macon County (Task 10)

Research documented in this report includes (1) resource estimates for the Lower Mt. Simon Sandstone in the Illinois Basin, including storage and cost estimates, and (2) regional roadmaps for carbon capture and storage (CCS) deployment using five capture facilities throughout the Illinois Basin: Archer Daniels Midland Decatur (ADM Decatur), Abbott Power Plant, City Water, Light, and Power (CWLP), One Earth Energy, and Prairie State Energy Campus (PSEC). Resource estimates were developed using the Sequestration of CO 2 Tool (SCO2T) and data provided by the Illinois State Geological Survey (ISGS), and regional roadmaps for CCS deployment were developed in SimCCS Gateway, a decision-support tool for designing CCS infrastructure.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Policy-Driven Sustainable Saline Drainage Disposal and Forage Production in the Western San Joaquin Valley of California

Environmental policies to address water quality impairments in the San Joaquin River of California have focused on the reduction of salinity and selenium-contaminated subsurface agricultural drainage loads from westside sources. On 31 December 2019, all of the agricultural drainage from a 44,000 ha subarea on the western side of the San Joaquin River basin was curtailed. This policy requires the on-site disposal of all of the agricultural drainage water in perpetuity, except during flooding events, when emergency drainage to the River is sanctioned. The reuse of this saline agricultural drainage water to irrigate forage crops, such as ‘Jose’ tall wheatgrass and alfalfa, in a 2428 ha reuse facility provides an economic return on this pollutant disposal option. Irrigation with brackish water requires careful management to prevent salt accumulation in the crop root zone, which can impact forage yields. The objective of this study was to optimize the sustainability of this reuse facility by maximizing the evaporation potential while achieving cost recovery. This was achieved by assessing the spatial and temporal distribution of the root zone salinity in selected fields of ‘Jose’ tall wheatgrass and alfalfa in the drainage reuse facility, some of which have been irrigated with brackish subsurface drainage water for over fifteen years. Electromagnetic soil surveys using an EM-38 instrument were used to measure the spatial variability of the salinity in the soil profile. The tall wheatgrass fields were irrigated with higher salinity water (1.2–9.3 dS m -1 ) compared to the fields of alfalfa (0.5–6.5 dS m -1 ). Correspondingly, the soil salinity in the tall wheatgrass fields was higher (12.5 dS m -1 –19.3 dS m -1 ) compared to the alfalfa fields (8.97 dS mm -1 –14.4 dS mm -1 ) for the years 2016 and 2017. Better leaching of salts was observed in the fields with a subsurface drainage system installed (13–1 and 13–2). The depth-averaged root zone salinity data sets are being used for the calibration of the transient hydro-salinity computer model CSUID-ID (a one-dimensional version of the Colorado State University Irrigation Drainage Model). This user-friendly decision support tool currently provides a useful framework for the data collection needed to make credible, field-scale salinity budgets. In time, it will provide guidance for appropriate leaching requirements and potential blending decisions for sustainable forage production. This paper shows the tie between environmental drainage policy and the role of local governance in the development of sustainable irrigation practices, and how well-directed collaborative field research can guide future resource management.

54 ENVIRONMENTAL SCIENCES↗