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At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

DOE OSTI2026

RxnRover/amlro

AMLRO (Active Machine Learning Reaction Optimizer) is an open-source framework designed to accelerate chemical reaction optimization using active learning with classical machine learning regression models. AMLRO integrates space-filling sampling strategies (e.g., Sobol and Latin Hypercube sampling) with iterative model training, prediction, and experiment selection to efficiently navigate complex reaction spaces. The platform supports multiple regression models, flexible multi-objective definitions, and user-defined parameter bounds, enabling data-efficient optimization from small initial datasets. AMLRO is designed for ease of use by experimentalists and can operate as a standalone decision-support tool or be integrated into closed-loop automated experimentation workflows.

Kulathunga, Dulitha Prasanna [Iowa State Universit↗

DOE OSTI2026

BLEECAM™ (Benchmarking Life Cycle Environmental, Economic, and Social Metrics for Critical and Advanced Minerals and Materials) [SWR-25-125]

The National Laboratory of the Rockies' (NLR) Benchmarking Life Cycle Environmental, Economic, and Social Metrics for Critical and Advanced Minerals and Materials (BLEECAM™) is an open-source, integrated decision-support tool for evaluating the impacts, risks, and trade-offs across U.S. and global materials supply chains. Funded by the U.S. Department of Energy, BLEECAM supports supply chain and market analysis. The tool integrates multi-objective supply chain optimization, system dynamics, network design, lifecycle assessment, techno-economic modeling, and social impact assessment methods to evaluate how supply chains evolve over time, geography, and deployment scenarios. BLEECAM also supports analysis related to energy infrastructure, data centers and digital infrastructure, advanced manufacturing, and other sectors that depend on critical materials.

Khalifa, SherifA. [National Laboratory of the Rock↗

DOE OSTI2020

Comprehensive Approach to Measure the Mobility Energy Productivity of Freight Transport

Freight travel accounts for a major share of the energy consumed in the transportation sector in any country, and the United States is no exception. Understanding and modeling freight movement are critical, particularly in the context of capturing the impact of emerging technologies on freight travel and its externalities. The domain of freight modeling and forecasting has been gaining pace in recent years, but advancement in comprehensive freight performance metrics is still lagging. Conventional freight performance metrics such as truck-miles, ton-miles, or value-miles are unidimensional and aggregate in nature, making them unsuitable to accurately capture the impact of emerging transportation trends on the performance or productivity of freight systems. Addressing the research need, this paper presents the “Freight Mobility Energy Productivity” metric to quantify freight productivity of current as well as future freight systems, accounting for various costs associated with freight transport. The proposed metric was implemented using data from the Freight Analysis Framework along with other published sources, and shows intuitive results in quantifying freight productivity. Further, a scenario analysis exercise was conducted to test the capability of the metric in tracking improvements in system-level freight productivity as a result of vehicle electrification. The relative differences in Freight Mobility Energy Productivity scores help identify which zones benefit from the vehicle powertrain technology improvement. The results of the scenario analysis reinforce confidence that the proposed metric can be used as a decision support tool in assessing the productivity of existing as well as future freight trends and technologies.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Incorporating adaptation and resilience into an integrated watershed and coral reef management plan

Changing environmental conditions are forcing natural resource managers and communities to adapt their strategies to account for global shifts in precipitation, temperature, sea level and more, all of which are occurring in addition to local human impacts. Adapting to threats from climate change requires a fundamental shift in the practice of natural resource management through the development of forward-looking “climate-smart” goals and strategies. Here we present a proof-of-concept application of a decision-support tool to help design climate-smart management actions for the watershed and coral reef management plan for Guánica Bay watershed in southwest Puerto Rico. We also explore the connection between adaptation planning and coral reef resilience, using a recently developed Puerto Rico-wide reef resilience assessment. In the first phase of the study, we used the publicly available Adaptation Design Tool to draft initial climate-smart versions of twelve proposed management actions. In the second phase, two actions (dirt road management on steep slopes, and coral reef restoration) were further refined through consultations with local experts to make more detailed design adjustments; this included the option to use information from the coral reef resilience assessment to inform design improvements. The first phase resulted in moderately detailed assessments that broadly accounted for anticipated direct and indirect effects of climate change on the planned management actions. The second phase resulted in more site-specific technical assessments and additional important design details. The expert panel charged with discussing climate-smart reef restoration around Guánica used the reef resilience assessment to guide discussion of reef restoration, highlighting the importance of having such information available for adaptation planning. This study demonstrates how climate change impacts can be effectively incorporated into a management plan at the most granular level of planning and how a structured, formalized process can be as valuable as the resulting adaptation information.

Gibbs, David A. (ORCID:0000000173898569)↗

DOE OSTI2020

Designing a dynamic vulnerability index to COVID-19 for New Mexico, USA

Motivation: Non-congregational shelters are a place where COVID-19 patients can quarantine away from their household until recovered. NM wants to establish non-congregational shelters for COVID-19 patients and first responders; Where? How many rooms? Create a decision support tool to assess which counties have the most vulnerable population to COVID-19; Socioeconomic status, community practices.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

NREL CITIES Support (CRADA CRD-14-00558 Final Report)

The Technical University of Denmark has received a Grant from the Danish Council for Strategic Research for the project "CITIES - Centre for IT-Intelligent Energy Systems in Cities", a strategic research centre, which involves industrial partners and universities. The Alliance for Sustainable Energy, LLC will participate with DTU as an industrial partner in "CITIES" under this Cooperative Research and Development Agreement (CRADA). The Centre for IT based Intelligent Energy Systems in Cities (CITIES) Project is a Danish national research center focused on work in building short-term operational integrated energy system models that feed longer term plam1ing models, considering the spatio-temporal variations, interactions, dynamics and stochastics in the energy system, from production to consumption. CITIES will harness the extensive expertise and experience of its industrial and academic partners to conduct meaningful research with tangible outputs, including a roadmap to a fossil free future. Decision support tools will be developed to inform stakeholders of the impact of investment, control and policy measures and to identify further opportunities for energy system efficiency. Dissemination and transferring knowledge is a key value to this CRADA, and the use of results by Danish policymakers, industry and society is vital. All participants in the Centre for IT based Intelligent Energy Systems in Cities (CITIES) Project are therefore encouraged to use all means to communicate their findings to society. Wherever possible, and compatible with applicable intellectual property regulations, publications should be produced and disseminated under Open Science, Data, Source and Access principles.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Renewable Thermal Energy Systems: Characterization of the Most Important Thermal Energy Applications in Buildings and Industry (Report 1)

This report is the first in a three-report series that evaluates the provision of renewable heat for industry and buildings via current and prospective renewable thermal energy system (RTES) technologies. The RTES project has undertaken initial research focused on technologies that could be suited for industrial process heat applications at different temperature levels, and, where possible, gathered performance and cost data for these technologies. This project does not directly evaluate RTES for distributed residential or commercial applications, nor does it yet include documented cases or modeling of RTES using geothermal, biomass, waste heat, renewable fuels like renewable natural gas, or hydrogen production. The three technical reports are summarized as follows: Renewable Thermal Energy Systems: Characterization of the Most Important Thermal Energy Applications in Buildings and Industry (Report 1), this report: summary of thermal demands of U.S. industry and buildings, and relevant hybrid RTES configurations; Renewable Thermal Energy Systems: Systemic Challenges and Transformational Policies (Report 2): discussion of socio-technical characteristics of RTES, innovation challenges, and supporting policies. Available at: https://www.nrel.gov/docs/fy23osti/83020.pdf; Renewable Thermal Energy Systems: Modeling Developments and Future Directions (Report 3): Energy yield and performance modeling of RTES, techno-economic analysis via case studies, and proposed development of a user decision support tool. Available at: https://www.nrel.gov/docs/fy23osti/83021.pdf.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Surveillance Workload Divinator (SWORD)

The LANL Surveillance Workload Divinator (SWORD) is a high-level decision support tool that reproduces and predicts the annual values for stockpile and production disassembly and inspections (D&I) and rebuilds. SWORD, a spreadsheet-based tool, can be used to assess the surveillance workload under various planning scenarios. Results are shown graphically and updated dynamically for each weapon system or for the entire stockpile. Surveillance workload is calculated from stockpile surveillance guidance1 and from Production and Planning Directive (P&PD) data.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2025

Projected Operational Energy Life Cycle Data Development: 2025 Update

This report documents the data sources and methods used to develop the energy life cycle data for use by the National Institute of Standards and Technology (NIST) Engineering Laboratory (EL) to be used for the development of measurement science and incorporation into decision-support tools for evaluating building and facility capital investments. The data are posted separately under DOI 10.18141/2575194.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

National Hydropower Fish Passage Database User's Guide and Methodology

Fish passage facilities are used to mitigate the impacts of hydropower dams on migratory fish in rivers, but information on the location, types, and characteristics of this infrastructure is incomplete at a national scale. Researchers at Oak Ridge National Laboratory partnered with federal agency and industry stakeholders to create the first national-scale database of fish passage infrastructure at U.S. hydropower developments. This database contains information of great value to a broad range of stakeholders; it includes fish passage facility engineering characteristics, targeted fish species, operational schedules, and costs. This data resource addresses a large gap in knowledge of the deployment of fish passage technology and is freely available to members of the hydropower community, including federal and state regulators and resource agencies, non-governmental organizations, industry, and other user groups to support project planning and regulatory (re)licensing activities. This database supports the U.S. Department of Energy Water Power Technologies Office objective to develop decision support tools and data resources that improve environmental performance and ensure hydropower’s long-term value to the American public.

13 HYDRO ENERGY↗

DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on Banshee Distribution Network

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

ORNL National Hydropower Fish Passage Database

Fish passage facilities are used to mitigate impacts of hydropower dams to migratory fish in rivers, but information on the location, types, and characteristics of this infrastructure is incomplete at a national scale. Researchers at Oak Ridge National Laboratory (ORNL) partnered with fish passage engineers and hydropower experts from the US Fish and Wildlife Service (USFWS), the National Oceanic Atmospheric Administration’s National Marine Fisheries Service (NOAA NMFS), and the Low Impact Hydropower Institute (LIHI) to create the first national scale database of fish passage infrastructure at US hydropower developments. This database consists of the ORNL_Fish_Passage_Dataset.zip file with 13 individual .csv files that contain information on fish passage facility engineering characteristics, targeted fish species, operational schedule, and costs. which is of great value to a diverse range of stakeholders. This information was collected between December 2023 and July 2025 from project partners, other hydropower stakeholders, online datasets available for download, and a stakeholder questionnaire Information. This data resource addresses a large gap in knowledge of the deployment of fish passage technology and is freely available to members of the hydropower community, including federal and state regulators and resource agencies, non-governmental organizations (NGOs), industry, and other user groups to support project planning and regulatory (re)licensing activities. This database supports the US Department of Energy Water Power Technologies Office objective to develop decision support tools and data resources that improve environmental performance and ensure hydropower’s long-term value to the American public.

Matson, Paul [Oak Ridge National Laboratory (ORNL)↗

DOE OSTI2024

Emerging Trends and Technologies Used for the Identification, Detection, and Characterisation of Plant-Parasitic Nematode Infestation in Crops

Accurate identification and estimation of the population densities of microscopic, soil-dwelling plant-parasitic nematodes (PPNs) are essential, as PPNs cause significant economic losses in agricultural production systems worldwide. This study presents a comprehensive review of emerging techniques used for the identification of PPNs, including morphological identification, molecular diagnostics such as polymerase chain reaction (PCR), high-throughput sequencing, meta barcoding, remote sensing, hyperspectral analysis, and image processing. Classical morphological methods require a microscope and nematode taxonomist to identify species, which is laborious and time-consuming. Alternatively, quantitative polymerase chain reaction (qPCR) has emerged as a reliable and efficient approach for PPN identification and quantification; however, the cost associated with the reagents, instrumentation, and careful optimisation of reaction conditions can be prohibitive. High-throughput sequencing and meta-barcoding are used to study the biodiversity of all tropical groups of nematodes, not just PPNs, and are useful for describing changes in soil ecology. Convolutional neural network (CNN) methods are necessary to automate the detection and counting of PPNs from microscopic images, including complex cases like tangled nematodes. Remote sensing and hyperspectral methods offer non-invasive approaches to estimate nematode infestations and facilitate early diagnosis of plant stress caused by nematodes and rapid management of PPNs. This review provides a valuable resource for researchers, practitioners, and policymakers involved in nematology and plant protection. It highlights the importance of fast, efficient, and robust identification protocols and decision-support tools in mitigating the impact of PPNs on global agriculture and food security.

Plant Sciences↗

DOE OSTI2020

A Comprehensive Approach to Measure the Mobility Energy Productivity of Freight Transport: Preprint

Freight travel accounts for a major share of the energy consumed in the transportation sector in any country, and the United States is no exception. Understanding and modeling freight movement are critical, particularly in the context of capturing the impact of emerging technologies on freight travel and its externalities. The domain of freight modeling and forecasting is gaining pace in the recent years, but advancement in comprehensive freight performance metrics is still lagging. Conventional freight performance metrics such as truck-miles, ton-miles, or value-miles are unidimensional and aggregate in nature, making them unsuitable to accurately capture the impact of emerging transportation trends on the performance or productivity of freight systems. Addressing the research need, this paper presents the “Freight Mobility Energy Productivity” metric to quantify freight productivity of current as well as future freight systems, accounting for various costs associated with freight transport. The proposed metric was implemented using data from the Freight Analysis Framework along with other published sources, which shows intuitive results in quantifying freight productivity. Further, a scenario analysis exercise was conducted to test the capability of the metric in tracking improvements in system-level freight productivity as a result of vehicle electrification. The relative differences in Freight Mobility Energy Productivity scores help identify which zones benefit from the vehicle powertrain technology improvement. The results of the scenario analysis reinforce confidence that the proposed metric can be used as a decision support tool in assessing the productivity of existing as well as future freight trends and technologies.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Times (and the Grid) are A-Changin'

<span style="background-color: rgb(255, 255, 255); font-family: "Neue Plak", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", Helvetica, Tahoma, Arial, sans-serif; font-size: 16px; color: rgb(111, 114, 135);">This webinar highlighted two datasets that provide forward-looking projections of electric grid GHG emissions for the United States. These data sets were created to help present-day analyses and decision-support tools incorporate what we know about the ongoing evolution of the U.S. electric grid. <span style="background-color: rgb(255, 255, 255); font-family: "Neue Plak", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", Helvetica, Tahoma, Arial, sans-serif; font-size: 16px; color: rgb(111, 114, 135);">Additionally, the webinar compared these two databases to other data sources to summarize differences (i.e., attributional versus consequential emissions rates, age and source of underlying data, regionalization approach, and boundary conditions) and highlighted the value and need for expert guidance when an analyst or developer is choosing how to represent electric-sector GHG emissions. <span style="background-color: rgb(255, 255, 255); font-family: "Neue Plak", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", Helvetica, Tahoma, Arial, sans-serif; font-size: 16px; color: rgb(111, 114, 135);">This webinar was intended to set the stage for ACLCA roundtable events to further discuss these issues.

Jamieson, Matthew↗

DOE OSTI2024

OpenStudio Development, Maintenance, and Support

BEM is a key decision support tool for energy efficiency, demand flexibility, and electrification at the building and building stock levels. The OpenStudio SDK maximizes the value of BEM by enhancing the productivity, consistency, and transparency with which it is applied. OpenStudio enables BEM experts to encapsulate domain and process knowledge in such a way that makes it usable by a range of stakeholders.

BEM↗

DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on the Banshee Distribution Network: Preprint

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Computational Tools and Workflows for Quantitative Risk Assessment and Decision Support for Geologic Carbon Storage Sites: Progress and Insights from the U.S. DOE’s National Risk Assessment Partnership

The 2005 Intergovernmental Panel on Climate Change (IPCC) Special Report on CCS raised the profile of CO2 capture and storage (CCS) as an important technology for reducing greenhouse gas (GHG) emissions. CCS is now recognized as a key component of most climate change mitigation scenarios. Since publication of that report the international research, development, and deployment (RD&D) community has advanced key technical aspects, clarified regulatory requirements, explored value chain and infrastructure solutions, and developed incentive paradigms to enable and promote large-scale deployment of CCS. These efforts have included research to better characterize geologic storage resources, to improve injection performance and storage efficiency, to assess and manage subsurface environmental risks, and to advance monitoring technologies to assure system conformance. These efforts have helped to build confidence in the viability of geologic carbon storage (GCS), but stakeholder concerns about long-term risks and liability associated with GCS remain a hurdle to broad acceptance and large-scale deployment of CCS. Since 2010, the U.S. DOE’s National Risk Assessment Partnership (NRAP) – a research collaboration between five contributing national laboratories – has worked to establish and demonstrate methods and tools to quantify and manage the subsurface environmental risks associated with GCS, amidst uncertainty. This work supports the Office of Fossil Energy and Carbon Management Carbon Transport and Storage Program’s goal of advancing safe and secure commercial-scale GCS deployment. To address the technical challenge of simulating the physical response of the GCS site to large-scale CO2 injection, NRAP has adopted an approach that relies on coupling computationally efficient reduced-order and/or data-driven proxy models of important system components (i.e., storage reservoir, sealing caprock, leakage pathways, intermediate formations, overlying groundwater aquifers, and the atmosphere) in integrated assessment framework. That integrated model of the physical system is complemented with fit-for purpose functionality to support site characterization and risk-related decisions. The recently released NRAP Phase II toolset includes the Open-Source Integrated Assessment Model (NRAP-Open-IAM) for evaluation of trends in leakage risk and potential impact, tools to support monitoring design optimization (Designs for Risk Evaluation and Management – DREAM v3.0 and Passive Seismic Monitoring Tool - PSMT), and tools for state of stress evaluation (State-of-Stress Analysis Tool - SOSAT) and forecasting induced seismicity risk. The NRAP team has also released a pair of reports describing conceptual workflows to incorporate physics-based, quantitative risk assessment into many of the design, planning, operation, and closure decisions for GCS projects. An online catalogue highlights published studies where these tools and methods are demonstrated. In this presentation, the utility of these products to assess risks and address key stakeholder questions will be highlighted through examples, and related insights about the safety and security of geologic carbon storage in qualified storage sites will be discussed. The prospect of rapid, large-scale deployment of GCS technology to aggressively reduce anthropogenic CO2 emissions requires careful consideration of interference between multiple commercial-scale storage projects within a basin. Going forward, NRAP is expanding and adapting site-scale risk quantification tools and methods to enable assessment of risks and inform management decisions for basin-scale deployment. Increasingly, this work will leverage next-generation approaches for surrogate modelling, fast prediction, and advanced visualization enabled by machine learning and artificial intelligence to promote virtual learning, scenario evaluation, and augment risk-based decision making.

quantitative risk assessment, geologic carbon stor↗