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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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DOE OSTI2023

Deep Deception: Exemplars of Adversarial Machine Learning and Countermeasures Applicable to International Safeguards

As a follow-up to our more comprehensive report on Adversarial Machine Learning (AML), here we provide demonstrations of AML attacks against the Limbo image database of UF6 cylinders in a variety of orientations and amongst a variety of distractor images. We demonstrate the Carlini & Wagner AML attack against a subset of Limbo images, with 100% attack success rate; meaning all attacked images were misclassified by a highly accurate trained model, yet the image changes were imperceptible to the human eye. We also demonstrate successful attacks against segmented images (images with more than one targeted object). Finally, we demonstrated the Fast Fourier Transform countermeasure that can be used to detect AML attacks on images. The intent of this and our previous report is to inform the IAEA and stakeholders of both the promise of machine learning, which could greatly improve the efficiency of surveillance monitoring, but also of the real threat of AML and potential defenses.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Summary of Massachusetts Clean Energy Center's Workforce Needs Assessment for the City of Lawrence

In July 2023, the Massachusetts Clean Energy Center (MassCEC) published an employment report titled “Powering the Future: A Massachusetts Clean Energy Workforce Needs Assessment.” This report was intended to support the State in meeting its 2030 decarbonization goals by identifying both existing workforce and training resources as well as gaps and barriers that need to be addressed in order to scale the State’s workforce to meet future demand. The following is a summary of the content that is relevant to Lawrence, MA. This was completed as part of the US Department of Energy’s Communities LEAP (Local Energy Action Program) Pilot supporting the Lawrence Stakeholders Coalition (LSC) led by Browning the Green Space in partnership with Groundwork Lawrence, All In Energy, and the City of Lawrence.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Floating Offshore Wind US Manufacturing and Commercialization: Cooperative Research and Development (Final Report)

NREL assessed the supply chain and workforce considerations for the OCG-Wind floater technology, a floating semi-submersible offshore wind substructure, as well sharing vessel needs to inform their installation strategy. This technical assistance was in support of the FLoating Offshore Wind ReadINess (FLOWIN) Prize Phase 2 submission. NREL provided an assessment of domestic supplier capabilities for the main components of their floating offshore wind platform design and analyzed US regional and national supply chain constraints and gaps. Thirteen interviews with companies including steel distributors, forges, foundries, ports, large component fabricators, subcomponent fabricators, and secondary suppliers provided key insights such as 1) assembly ports are the key infrastructure barrier standing in the way of unlocking the domestic assembly and component fabrication for steel-based FOW platforms, 2) domestic steel producers can supply the types and quantities of steel necessary for FOW platforms, and 3) coordination between stakeholders will be a vital part of successfully developing the supply chain and infrastructure needed to domestically produce FOW platforms. In the workforce assessment, NREL documented a step-by-step approach to conduct a place-based assessment of the foundational workforce consideration for recruiting, upskilling, and retaining a workforce, such as supportive local and state policy, nearby education and training programs, and existing relevant industry. This approach was applied to Tacoma, Washington. Tacoma was indicated to have the potential be a successful location for fabrication and assembly of floating offshore wind energy in terms of workforce development. To share data on vessel requirements to install the OCG-Wind floater, NREL compiled resources that help answer the questions related to anchor handling tug vessels, shared a database of cable laying vessels, and answered questions on complying with the Jones Act.

17 WIND ENERGY↗
DOE OSTI2022

First Plant Cell Atlas symposium report

The Plant Cell Atlas (PCA) community hosted a virtual symposium on December 9 and 10, 2021 on single cell and spatial omics technologies. The conference gathered almost 500 academic, industry, and government leaders to identify the needs and directions of the PCA community and to explore how establishing a data synthesis center would address these needs and accelerate progress. This report details the presentations and discussions focused on the possibility of a data synthesis center for a PCA and the expected impacts of such a center on advancing science and technology globally. Community discussions focused on topics such as data analysis tools and annotation standards; computational expertise and cyber-infrastructure; modes of community organization and engagement; methods for ensuring a broad reach in the PCA community; recruitment, training, and nurturing of new talent; and the overall impact of the PCA initiative. These targeted discussions facilitated dialogue among the participants to gauge whether PCA might be a vehicle for formulating a data synthesis center. The conversations also explored how online tools can be leveraged to help broaden the reach of the PCA (i.e., online contests, virtual networking, and social media stakeholder engagement) and decrease costs of conducting research (e.g., virtual REU opportunities). Major recommendations for the future of the PCA included establishing standards, creating dashboards for easy and intuitive access to data, and engaging with a broad community of stakeholders. The discussions also identified the following as being essential to the PCA's success: identifying homologous cell-type markers and their biocuration, publishing datasets and computational pipelines, utilizing online tools for communication (such as Slack), and user-friendly data visualization and data sharing. In conclusion, the development of a data synthesis center will help the PCA community achieve these goals by providing a centralized repository for existing and new data, a platform for sharing tools, and new analytical approaches through collaborative, multidisciplinary efforts. A data synthesis center will help the PCA reach milestones, such as community-supported data evaluation metrics, accelerating plant research necessary for human and environmental health.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

DOE Microreactor Program: Summary of Experimental Capabilities Development and Activities

This document provides a summary of the experimental capabilities and activities developed and currently performed under the U.S. Department of Energy’s (DOE’s) Microreactor Program. These capabilities, along with other laboratory capabilities, are available to support developer and other stakeholder needs. Specific experimental capabilities described in this document include the single primary heat extraction and removal emulator (SPHERE), the microreactor agile non-nuclear experimental test bed (MAGNET), microreactor applications research validation and evaluation (MARVEL), yttrium hydride fabrication, the Hypatia critical experiment series, and a brief overview of the National Criticality Experiments Research Center (NCERC), which provides the platform to conduct experiments and training with critical assemblies and fissionable material.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Towards Trust-Augmented Visual Analytics for Data-Driven Energy Modeling

The promise of data-driven predictive modeling is being increasingly realized in various science and engineering disciplines, where experts are used to the more conventional, simulation-driven modeling practices. However, trust remains a bottleneck for greater adoption of machine learning-based models for domain experts, who might not be necessarily trained in data science. In this paper, we focus on the building energy domain, where physics-based simulations are being complemented or replaced by machine learning-based methods for forecasting energy supply and demand at various spatio-temporal scales. We study the trust problem in close collaboration with energy scientists and engineers and describe how visual analytics can be leveraged for alleviating this trust bottleneck for stakeholders with varying degrees of expertise and analytics goals in this domain.

Kandakatla, Akshith R.↗
DOE OSTI2025

Investigation of the Performance and Explainability Tradeoffs for Machine-Learning Models for Predictive Maintenance of Circulating Water Systems in Nuclear Power Plants

Predictive maintenance (PdM) has shown great potential for achieving substantial cost savings and enhancing the economic competitiveness of nuclear power plants (NPPs) in today's energy market. Among the different modeling approaches that exist, machine learning (ML) tools in particular have a demonstrated ability to handle high dimensional and multivariate data and to extract hidden relationships within data in industrial environments. While ML methods show great potential, their lack of explainability---especially for black-box models---is a major hurdle to their adoption. Moreover, considering the supposed trade-off between explainability and performance challenges, careful consideration must be made as to which of these quality aspects takes precedence in light of multiple modeling options, resource availability, and domain characteristics. The present work evaluates the performance of six ML models, each with a different degree of explainability, in classifying the conditions of circulating water pumps (CWPs) by utilizing sensor data from nuclear power plants. To determine the drivers behind the trade-offs presented by this array of models, this work also tests different combinations of CWP units as the training and testing data, degrees of data imbalance, and objective functions for hyperparameter tuning. It was found that black-box models tend to afford superior performance in cases where there are far more instances of one type of labeled data than of any other type. It is recommended that a guided procedure be followed for designing and delivering an ML system that is sufficiently explainable to all involved stakeholders.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Developing Smart Building Technology Modules to Enhance Workforce Preparedness: A Case for AI-Driven Academic and Professional Education

Smart building technologies are resources that improve building energy efficiency and resilience, reduce carbon emissions, and provide load flexibility to the grid. However, in both academic curricula and building professionals’ continuing education, there is a lack of systematic instruction on methods to integrate multiple energy systems including distributed energy resources (DER), smart building technologies, AI (Artificial Intelligence) tools and key concepts, components, and controls, including “Internet of Things” (IoT) devices. In today’s dynamic workforce, this major gap in smart building technology education prevents stakeholders from being able to attract talent with an understanding and preparation to adopt smart building technologies in building design and operations. A federally funded project included a partnership between Slipstream and Texas A&M University (TAMU) to develop a semester-long smart building curriculum for engineering college students with the ability to adapt the contents for workforce development of professionals in building services. The final product consists of 16 training videos adapted for building professionals and the public. The educational content and training materials cover the benefits of building energy systems, the latest sensor technologies and IoT devices, all with a focus on smart building technologies. The key drivers are on topics related to smart building controls (i.e., energy management information systems), smart building control platforms, cybersecurity, grid-interactive-efficient buildings (GEBs), smart building control methods, and occupant-centric control. Although not explicitly included the technologies nod to the need for AI driven technologies to prepare engineers and industry professionals to be future ready. This paper describes the project approach, provides outlines of the training materials, and identifies lessons learned in creating the content for this course. The authors suggest ways to scale the instruction of smart building concepts to empower the workforce to accelerate the adoption of smart building technologies and AI-based teaching and learning in higher education and building sector.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

INDUSTRY ENGAGEMENT TO ESTABLISH A ROBUST SECURITY APPROACH TO MOBILE RADIOACTIVE SOURCES

The mobile radiological sources used in the well-logging and radiography industries are of sufficient curie quantities to be categorized as desirable material for malicious actors. Beyond the security risk posed by these sources, there is also an understanding of the potential damage, both reputational and monetary, that a lost source would have on the licensee and the industry overall. Identifying and communicating the risk these sources pose with impacted stakeholders is a critical first step in developing a security approach. Common day-to-day operations within both industries drive the unique security challenge of mobile sources. From storage facilities, transportation vehicles, temporary storage locations, and use in the field, each phase creates challenges regarding source control and accountability. All aspects of the operational use of these sources needs to be fully understood in order to address security equipment enhancements, policies, procedures, and training. This paper will leverage more than ten years of experience that ORS has gained working closely with industry partners and mobile radiological source users across the well-logging and radiography industries. It will identify the risk posed by mobile radiological sources, clearly define the operational phases of each industry, identify security best practices of mobile sources, and discuss what long-term, sustainable security looks like within these industries. In addition, it will explore areas of a robust security approach that are not commonly given priority in these industries, such as alarm adjudication and response.

Office of Radiological Security↗
DOE OSTI2024

Scenario storyline discovery for complex multi-actor human-natural systems

This poster was presented at the AGU Fall Meeting 2024. Abstract:Scenario analysis is a useful tool for assessing the impacts of future conditions or alternative strategies. However, the common practice of focusing on a small number of predetermined scenarios can limit our understanding of key uncertainties, and fail to represent diverse stakeholder impacts. Exploratory modeling approaches have been developed to address these issues by simulating a wide range of possible futures and system perspectives. A challenge with these approaches is that they often involve large ensemble experiments which limit interpretability and usability. We recently introduced the FRamework for Narrative Storylines and Impact Classification (FRNSIC; pronounced ``forensic''), a scenario discovery framework that helps users identify scenario storylines that capture key system dynamics and as well as important outcomes. In this poster presentation, we present training materials to support the generalizable application of the framework to other multi-actor systems with complex dynamics. Specifically, we will present a step-by-step methodological typology of tools and methods that can be used to generate and classify plausible states of the world on key metrics and consequential dynamics. The typology will also discuss potential implications of these choices and their applicability to different systems.

Colorado River↗
DOE OSTI2025

Retrospective on decadal progress of the NOAA/NPS ocean noise reference station network

The National Oceanic and Atmospheric Administration (NOAA), in partnership with the U.S. National Park Service (NPS), established the Ocean Noise Reference Station Network (NRS) in 2014 as a foundational component of NOAA’s Ocean Noise Strategy. This long-term effort aims to characterize baseline ocean ambient sound conditions across diverse marine environments and to inform management of noise impacts on protected species and habitats within U.S. waters. The NRS is now composed of 13 autonomous passive acoustic monitoring stations strategically positioned across the U.S. Exclusive Economic Zone (EEZ), extending from Arctic regions to tropical waters in depths ranging from 33 to 4,790 m. These locations include several National Marine Sanctuaries and National Parks, such as the recently designated Chumash Heritage National Marine Sanctuary off the coast of California. Each station is equipped to continuously sample low-frequency underwater sound at five kHz, enabling the detection of anthropogenic, geophysical, and biological acoustic signals. To date the network has sampled over 72 years of calibrated acoustic data. The spatial breadth and consistent methodology of the NRS allow for comparative acoustic assessments across diverse marine ecosystems. In addition to applied research functions, the NRS has served as a platform for education and training, offering opportunities for students to develop skills for marine science and data analysis. Looking forward, the NRS project team is focused on network expansion, improved data delivery, and broader integration with collaborative scientific initiatives. NRS recordings are being archived in partnership with NOAA’s National Centers for Environmental Information to enhance accessibility and long-term utility. Efforts are underway to develop standardized metadata and summary products to accompany raw audio files, making the data more usable for a wide range of stakeholders in the ocean science community. The NRS is evolving into a fully integrated national framework for ocean sound monitoring that supports scientific inquiry, management decision-making, national security interests, and public engagement with ocean acoustic environments.

Long-term monitoring↗
DOE OSTI2021

High Energy Physics Network Requirements Review (Final Report, July-October 2020)

Throughout 2020, ESnet and the Office of High Energy Physics (HEP) of the DOE SC organized an ESnet requirements review of HEP-supported activities. Preparation for this event included identification of key stakeholders: program and facility management, research groups, technology providers, and a number of external observers. These individuals were asked to prepare formal case study documents about their relationship to the HEP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward. A series of pre-planning meetings better prepared case study authors for this task, along with guidance on how the review would proceed in a virtual fashion.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Coupled social and infrastructure approaches for enhancing solar energy adoption. Final Report

The goal of this project was to work with rural electric cooperatives to facilitate the diffusion of solar energy adoption in households located in the rural and semi-urban areas of Virginia by identifying social and behavioral factors that might be unique to rural regions; and develop a model to calculate the solar adoption propensity score for household based on their demographics, social and behavioral characteristics which would provide an objective metric to cooperatives that can be further used to do targeted marketing of rooftop solar panels. This was achieved through the following tasks: (1) Conducted a survey of the members of Virginia electric cooperatives to identify demographic, social, financial and behavioral attributes of individuals who are likely to adopt rooftop solar panels. (2) Developed a highly detailed, data-driven, agent-based model of the population of Virginia, focusing on the rural regions. (3) Developed diffusion models that use social, behavioral, and demographic factors, and peer effects to study their impact on solar adoption in rural areas. (4) Built a prototype tool based on the diffusion model to help study market segmentation in rural areas and made it available to National Rural Electric Cooperative Association (NRECA). (5) Results and recommendations derived from the model were provided to NRECA to be shared with participating cooperatives. (6) Results were published in peer reviewed journals, conference proceedings and book chapters, and ideas disseminated through presentations and newsletters. There were several important methodological contributions made under this project which are detailed in the published papers, including: (1) Built a decision-adjusted model for predicting adoptors with imbalanced training data; (2) designed seeding strategies to maximize adoption given a fixed budget; (3) built a methodology to compare different agent based models; (4) created models to identify important factors that influence decision to adopt solar panels; and (5) built a methodology for building household profiles of solar generation to study the duck curve phenomenon. The team included members from the University of Virginia (lead), National Rural Electric Cooperative Association (NRECA), Arizona State University, Virginia Tech and Sandia National Laboratory. Note that no individual entity or stakeholder has incentive to promote solar in rural regions. Most of the research and work focuses around urban regions where the potential for growth in solar adoption is higher due to higher population density. This puts rural areas at a disadvantage. By improving the diffusion of solar adoption in rural parts of the country, we can not only provide clean energy to rural areas but also promote job growth and improves energy independence.

14 SOLAR ENERGY↗
DOE OSTI2025

Microreactor Optimization Using Simulation And Economics (mouse)

Microreactor Optimization Using Simulation and Economics (MOUSE) is a tool that integrates both nuclear microreactor design and reactor economics to provide comprehensive evaluations and optimizations. This tool enables stakeholders to explore the interplay between technical and economic variables, guiding them towards effective and competitive microreactor solutions. For the reactor core simulations, MOUSE leverages the OpenMC Monte Carlo Particle Transport Code to perform detailed core simulations for various microreactor designs. The included OpenMC models are 2D core designs of a Liquid Metal Thermal Microreactor (LMTR), a Gas-Cooled TRISO-Fueled Microreactor (GCMR), and a Heat Pipe Microreactor. Beyond core design, MOUSE includes simplified calculations for: - Calculating the masses of heat exchangers within the system. - Mechanical power of pumps. - Estimating the area occupied by various buildings within the nuclear plant. For the economic analysis, MOUSE provides detailed bottom-up cost estimates, encompassing a wide range of costs including preconstruction costs, direct costs, indirect costs, training costs, financial costs, operation & maintenance (O&M) costs, and fuel costs. These cost estimations are developed using data from the MARVEL project and additional literature sources, enabling the calculation of total capital costs and levelized cost of energy for both first-of-a-kind and nth-of-a-kind microreactors. MOUSE also enables analysis of the cost drivers and competitiveness in the electricity market. MOUSE allows users to modify a wide array of technical and economic parameters to evaluate different scenarios and their impacts. Examples of these parameters include: Fuels, coolants, or reflector materials Enrichment levels Control drum materials and geometry Fuel pin geometry and materials Moderator pin geometry and materials Reactor core and reflector dimensions Packing factor for the TRISO particles Nuclear reactor power and reactor burnup Number of sensors Shielding thickness Reactor vessel and guard vessel dimensions Operational staff requirements Number of emergency shutdowns Levelization period Interest rate Construction duration Since MOUSE is powered by the WATTS toolkit, it supports optimization studies, parametric analyses, and uncertainty calculations/propagation. The optimization techniques enable users to identify optimal design and economic configurations. The parametric analysis tools allow users to explore the sensitivity of various parameters, while uncertainty propagation helps quantify the impact of uncertainties on overall performance and cost. User Interface and Workflow: Currently, MOUSE is a command-line-based tool. Users can input various reactor design or economic parameters, modify the designs, run simulations, and visualize results through comprehensive data visualization and reporting capabilities. The typical workflow involves setting up the reactor model, defining economic parameters, running simulations, and analyzing the results to make informed decisions. By combining advanced design calculations with detailed economic modeling, MOUSE provides a robust framework for optimizing nuclear microreactor technologies, enhancing their competitiveness, and guiding stakeholders towards innovative and cost-effective solutions.

Hanna, Botros [Idaho National Laboratory (INL), Id↗
DOE OSTI2022

Calista Energy Management Assistance Initiative

The Calista Energy Management Assistance Initiative (CEMAI) provided technical assistance and capacity building for 56 Tribal communities in the Calista Region of Alaska as an effort to reduce costs, improve operational efficiency, enhance human capacity, and job opportunities. CEMAI catalyzed and guided numerous efforts into a consolidated and effective initiative that brought rural energy best practices, economies of scale, operational efficiencies, human capacity, and economic development to the forefront. Calista Corporation (Calista) is one of thirteen Alaska Native Regional Corporations created under the Alaska Native Claims Settlement Act of 1971 (ANCSA) in the settlement of aboriginal land claims. Calista was incorporated in Alaska on June 12, 1972. The Calista Region covers Alaska’s Bethel and Kusilvak (formerly Wade Hampton) Census areas and includes 56 communities. Calista partnered with Nuvista Light and Electric Cooperative (Nuvista) on the Department of Energy, Office of Indian Energy (DOE-OIE), CEMAI project. Nuvista is a non-profit that seeks to reduce energy costs and provide renewable sources of energy to the people of western Alaska. It is founded and led by a non-profit, Tribe, Native Corporations (including Calista), energy organizations, and Alaska Native stakeholders in the Yukon-Kuskokwim Delta (YK-D) Region; covering the same service boundaries and communities served by Calista. This partnership allowed Calista to direct the project work with local energy experts to respond to community needs. In this arrangement, Calista added credibility and regional accountability while Nuvista added energy-specific expertise and skill set in project management. The delivery of the CEMAI Workplan included short-term or on-demand responses to specific technical assistance requests from communities in addition to longer-term strategically directed activities such as workshops, coordinated training, and capacity development efforts across the region. CEMAI’s Workplan aimed to enhance communities’ readiness for establishing renewable energy projects and implementing energy efficiency initiatives. The CEMAI objectives included: (1) Identifying common operational needs and improvement opportunities for entities with an energy interest or responsibility. (2) Providing access to multi-level expertise to address existing challenges. (3) Developing specialized training programs to build local capacity and community readiness. (4) Nurturing the creation of regional support networks. (5) Increasing access to regional, state, and federal energy initiatives, funding, and expertise. (6) Improving technical skills, provide livable wages, and job opportunities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Evaluation of an Accident Tolerant Fuel Leak in the Advanced Test Reactor

Accident Tolerant Fuels (ATF), which are nuclear fuel sources designed to withstand operational irregularities and incidents, have been a topic of interest in the nuclear industry for several decades. Interest in ATF technology surged following the 2011 accident at Fukushima Daiichi in Japan. At the Advanced Test Reactor (ATR), one of Idaho National Laboratory’s (INL) four operating nuclear reactors, the ATF program is a collaborative effort between the national laboratory and various stakeholders within the nuclear industry. This program focuses on the research and development of novel fuel compositions, cladding, and component materials with enhanced accident-resistant properties. During one of ATR’s 60-day operating cycles in 2024, the reactor experienced five unplanned shutdowns. Following the fifth shutdown, radiation monitors detected an increase in radiation levels coming from the loop piping. Subsequent water samples confirmed the cause was a leak of fission products from the ATF experiment, designated as ATF-2C. The source of the leak was identified as the instrumented section of the test train. The primary discussions in this paper are 1) the design of the ATF test train, 2) the operating parameters leading up to and following the detection of the leak, and 3) the quantification and characterization of the released fission products.

Accident Tolerant Fuels↗
DOE OSTI2025

Evaluation of an Accident Tolerant Fuel Leak in the Advanced Test Reactor

Accident Tolerant Fuels (ATF), which are nuclear fuel sources designed to withstand operational irregularities and incidents, have been a topic of interest in the nuclear industry for several decades. Interest in ATF technology surged following the 2011 accident at Fukushima Daiichi in Japan. At the Advanced Test Reactor (ATR), one of Idaho National Laboratory’s (INL) four operating nuclear reactors, the ATF program is a collaborative effort between the national laboratory and various stakeholders within the nuclear industry. This program focuses on the research and development of novel fuel compositions, cladding, and component materials with enhanced accident-resistant properties. During one of ATR’s 60-day operating cycles in 2024, the reactor experienced five unplanned shutdowns. Following the fifth shutdown, radiation monitors detected an increase in radiation levels coming from the loop piping. Subsequent water samples confirmed the cause was a leak of fission products from the ATF experiment, designated as ATF-2C. The source of the leak was identified as the instrumented section of the test train. The primary discussions in this presentation are 1) the design of the ATF test train, 2) the operating parameters leading up to and following the detection of the leak, and 3) the quantification and characterization of the released fission products.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Realizing the Residential Electrification Opportunity: One Heat Pump at a Time

Space and water heating in residential buildings is a major contributor of greenhouse gas (GHG) emissions in the United States. Advanced electric heat pumps (HP) and heat pump water heaters (HPWH) are poised to provide a low carbon alternative to traditional fossil-based space and water heating. Widespread deployment of heat pumps could help address the significant portion of building emissions and primary energy used in American households, however much work needs to be done to close the knowledge gap between like-for-like fossil equipment replacement and switching to HPs for the average contractor. The Pacific Northwest National Laboratory (PNNL) has been working on closing this knowledge gap through the development of decision tools and resources targeted towards contractors and installers. Developed in coordination with stakeholders and experienced contractors from a variety of geographic regions, these tools help streamline the process of sizing and selecting residential HPs and HPWHs for key use cases. Along with the complementary Retrofit Decision Tool developed by PNNL, the decision tools will help contractors understand the importance of whole building considerations when choosing HPs including envelope upgrades, duct assessments, and electrical assessments to ensure optimal selection and performance of the HP or HPWH. They also provide direct links to resources and best practices developed by PNNL as well as external entities to further help contractor education and training. This paper describes the development of these tools, and their role in moving the existing space and water heating market towards HPs to help realize the country’s decarbonization goals.

heat pump, heat pump water heater, Residential bui↗