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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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RMD @ NSLS-II helps pave the path to a DMMSC: Developing expertise in techniques and experimental methods for DMMSC science; Improving capabilities for models to become microstructurally aware; Workforce training and development; Help refine DMMSC scientific requirements; Complementary to DMMSC XFEL science (EDXD/EXAFS)
Quantum Information Science (QIS) is expected to profoundly change the practice of science and engineering in the coming decades. QIS technology exploits quantum phenomena for performing tasks that are impossible to do today and is a rapidly progressing field, fueled by large investments from the private sector and governments. Its importance to the U.S. economy and national security is underscored by the National Quantum Initiative Act (NQIA) passed in December 2018, which creates a coordinated multiagency program to support research and training in QIS. After the NIQA signed into law, NETL has launched an initiative to apply QIS to problems encountered in energy technology development. In Jan. 2020, an Quantum for Energy Systems & Technologies (QUEST) working group was formed to establish a workforce capable of developing, reviewing, managing, and advising on QIS-related technologies for NETL and FECM. Since then, the QUEST team have been working on developing quantum sensing technology and performing quantum computing to solve energy-related problems. This poster summarized the QUEST team’s activities & accomplishments on QIS targeting energy-related applications.
As the U.S. offshore wind market prepares for rapid growth, our understanding of specific workforce requirements and pathways to meet those requirements is not keeping pace. Without this deeper understanding, there is a risk that the workforce will not develop as efficiently or robustly as is needed to fully realize the benefits of offshore wind. The "U.S. Offshore Wind Workforce Assessment" provides a more detailed assessment of the workforce demand, supply, and pathways to support key stakeholders including industry, state and local governments, educational organizations, and unions in their efforts to attract, educate, train, and retain a domestic workforce to support this burgeoning industry.
Workforce planning deals with determining the number of employees and associated skills necessary to meet the future operational needs of an organization. A workforce system consists of six elements: recruitment, attrition, promotion, training, retention, and scheduling. Historically, several workforce modeling and analysis methodologies have been developed to capture these elements. This paper reviews the results of workforce and manpower models published within peer-reviewed literature between 1959 and 2021 to provide an in-depth analysis of current models. The focus of this review is on analytical, simulation, and empirical models found in literature that were collected based on a citation requirement and keyword search criteria. Results demonstrate the trends in workforce modeling research and discuss the common uses of each model type and the advantages/disadvantages related to each model. Based on the common attributes of workforce systems, the discussion focuses on the most frequently used model type for each element and the best use for each model. Lastly, recommendations are made for the development of workforce models that allow the most comprehensive view of the workforce systems of the future.
Fusion energy, if successfully commercialized and globally adopted, could substantially increase U.S. energy dominance and provide essentially unlimited baseload power for the national grid. It also stands to provide the U.S. with a strong source of economic growth through the 2030s and 2040s, giving the U.S. a new and highly internationally competitive industry for export. Fusion can also provide the increased electrical power generation capacity required to power current and future artificial intelligence (AI) activities. In October 2024, the CleanTech Alliance held a fusion energy commercialization regional hub workshop alongside its annual Seattle Fusion Week conference. The event brought together fusion companies, researchers, economic development experts, federal and state government representatives, and power utilities from the western states of Washington, California, Colorado and Nevada. Attendees discussed the potential of regionally focused commercialization of fusion energy in three pillars: technology, workforce, and economic development. Mel Clark, President & CEO, CleanTech Alliance, and Dr. Javier Garay, Associate Dean for Research and Professor of Mechanical and Aerospace Engineering, at the University of California San Diego (UCSD) and Founding Director of the UCSD Fusion Engineering institute, are co-PIs for this project. The project is sponsored by the U.S. Department of Energy’s Office of Fusion Energy Sciences (FES) through a Field Work Proposal to Pacific Northwest National Laboratory (PNNL) with Karl Mueller as point-of-contact. Chris Ajemian (Principal, Ajemian Consulting, LLC) and Dr. Christopher Keane (Professor of Physics and former Vice-President for Research, Washington State University) co-organized the workshop and were the editors for this report. This report details the current state of the work of the four western states to develop industry-led collaboration with national laboratories, universities, and government at all levels to hasten the commercialization of fusion energy. It provides a summary of fusion energy R&D needs, presents the ideas the attendees at the workshop identified for deepening regional collaboration, and makes findings and recommendations for next steps.
There are more than 100,000 jobs in the U.S. wind industry today, and the second-fastest growing job in the United States in 2017 was wind technician. A vibrant wind industry needs workers, and students who graduate from wind energy education and training programs need jobs. The goal of this research is to better understand the needs of wind-related businesses, education and training requirements, and the make-up of current and future domestic workforces. Educators are developing and training future workers. Educational institutions need to know which courses to provide to connect students with potential employers and to justify their wind energy programs by being able to place graduates into well-paying jobs. In interviews with 250 wind energy firms and 50 educational institutions, many respondents reported difficulty hiring qualified candidates, while many educational institutions reported graduates not finding jobs in the wind industry. We refer to this mismatch as the 'workforce gap.' This conference poster explores this gap.
This WTEC panel report assesses the international research and development activities in the field of Simulation- Based Engineering and Science (SBE&S). SBE&S involves the use of computer modeling and simulation to solve mathematical formulations of physical models of engineered and natural systems. SBE&S today has reached a level of predictive capability that it now firmly complements the traditional pillars of theory and experimentation/observation. As a result, computer simulation is more pervasive today – and having more impact – than at any other time in human history. Many critical technologies, including those to develop new energy sources and to shift the cost-benefit factors in healthcare, are on the horizon that cannot be understood, developed, or utilized without simulation. A panel of experts reviewed and assessed the state of the art in SBE&S as well as levels of activity overseas in the broad thematic areas of life sciences and medicine, materials, and energy and sustainability; and in the crosscutting issues of next generation hardware and algorithms; software development; engineering simulations; validation, verification, and uncertainty quantification; multiscale modeling and simulation; and SBE&S education. The panel hosted a U.S. baseline workshop, conducted a bibliometric analysis, consulted numerous experts and reports, and visited 59 institutions and companies throughout East Asia and Western Europe to explore the active research projects in those institutions, the computational infrastructure used for the projects, the funding schemes that enable the research, the collaborative interactions among universities, national laboratories, and corporate research centers, and workforce needs and development for SBE&S.
Artificial intelligence (AI) and machine learning (ML) methods have had significant impacts in science and technology in recent years. These methods for generating models from datasets or logic-based algorithms that emulate aspects of human performance can similarly accelerate the fields of nuclear applications, science, and technology toward the IAEA goals of contributing to peace, health, and prosperity. In order to accomplish advances with AI in general and ML in particular across these fields, IAEA can play a significant role by establishing, hosting and curating centralised resources, including databases, adhering to FAIR (findable, accessible, interoperable and reusable) principles and Open Science best practices, providing stewardship of data sharing, supporting training efforts and development of relevant workforces, as well as enabling connections among the scientific, technology, mathematics, AI and ethics communities. Many areas can benefit from the use of AI in the realm of nuclear applications. In human health, these areas include clinical research, epidemiology, nutrition, medical imaging, radiotherapy and education of health professionals. AI-based tools are also being used to facilitate different clinical tasks in imaging, computer-assisted diagnosis in mammography and lung cancer screening programmes, and dose prediction in nuclear medicine procedures. ML methods in particular may also increase the efficiency and accuracy of the analysis of computerised tomography and dual-energy absorptiometry scans for body composition and bone analysis. The application of AI methods to nuclear and related technologies in food and agriculture can lead to significant advances and improved efficiency in the optimisation of agricultural production, food product development, management of supply chains, food safety and food authenticity control. In the water and environmental sector, AI can help inform policies to mitigate the world’s water problems. The application of AI techniques to hydrology and environmental sciences is expected to improve patterns identification and enable model predictions under a changing climate.
Motivating K-12 students to pursue careers in science, technology, engineering, and mathematics (STEM) is an effort that requires consistent engagement. Throughout the K-12 student timeline, STEM educators need to continuously pivot their teaching and update their educational materials to motivate the next generation of students. Once students begin their undergraduate education, university professors need to encourage them to consider pursuing graduate studies to ensure that a qualified future workforce can be developed for research and teaching. In this paper, we present our work on developing STEM materials for K-12 student engagement. Our K-12 materials target the grid integration of hydrogen assets that are suitable to engage students in the classroom. Our undergraduate materials target hands-on projects and collaboration with industry to connect classroom learning with real-world applications and needs in renewable energy. Our work leverages available open-source models and tools to create projects for undergraduate students and motivate their interest in pursuing research topics in graduate-level education.
Part of creating a successful solar industry is developing a capable workforce. It has been repeatedly shown that organizations and companies are more productive when they have diverse and inclusive cultures. The solar industry in the U.S. performs well in some aspects (non-white workforce, positive work environment) but drastically under-performs in many others. We discuss these metrics and how this impacts the renewable energy industry and the solar research community. We highlight some initial actions which are necessary to begin to remediate these deficiencies, such as demographic tracking, recruiting strategies, and mentoring. We also present the historical and ongoing efforts to improve the diversity and inclusion of the Photovoltaic Specialists Conference, particularly through the work of this year’s Diversity and Inclusion (D&I) oversight committee. This year’s D&I committee is implementing diversity tracking, a women’s lunch, a diversity lunch, childcare resources, pronouns on badges, gender-neutral restrooms, addressing accessibility issues, and publishing a diversity statement and code of conduct to the PVSC website.
This report evaluates the future costs, benefits, and other impacts of renewable energy used to meet current state renewable portfolio standards (RPSs). It also examines a future scenario where RPSs are expanded. The analysis examines changes in electric system costs and retail electricity prices, which include all fixed and operating costs, including capital costs for all renewable, non-renewable, and supporting (e.g., transmission and storage) electric sector infrastructure; fossil fuel, uranium, and biomass fuel costs; and plant operations and maintenance expenditures. The analysis evaluates three specific benefits: air pollution, greenhouse gas emissions, and water use. It also analyzes two other impacts, renewable energy workforce and economic development, and natural gas price suppression. This analysis finds that the benefits or renewable energy used to meet RPS polices exceed the costs, even when considering the highest cost and lowest benefit outcomes.
The U.S. Department of Energy (DOE) Office of Science (SC) Offices of High Energy Physics (HEP) and Advanced Scientific Computing Research (ASCR) convened a programmatic Exascale Requirements Review on June 10–12, 2015, in Bethesda, Maryland. This report summarizes the findings, results, and recommendations derived from that meeting. The high-level findings and observations are as follows. Larger, more capable computing and data facilities are needed to support HEP science goals in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of the demand at the 2025 timescale is at least two orders of magnitude — and in some cases greater — than that available currently. The growth rate of data produced by simulations is overwhelming the current ability of both facilities and researchers to store and analyze it. Additional resources and new techniques for data analysis are urgently needed. Data rates and volumes from experimental facilities are also straining the current HEP infrastructure in its ability to store and analyze large and complex data volumes. Appropriately configured leadership-class facilities can play a transformational role in enabling scientific discovery from these datasets. A close integration of high-performance computing (HPC) simulation and data analysis will greatly aid in interpreting the results of HEP experiments. Such an integration will minimize data movement and facilitate interdependent workflows. Long-range planning between HEP and ASCR will be required to meet HEP’s research needs. To best use ASCR HPC resources, the experimental HEP program needs (1) an established, long-term plan for access to ASCR computational and data resources, (2) the ability to map workflows to HPC resources, (3) the ability for ASCR facilities to accommodate workflows run by collaborations potentially comprising thousands of individual members, (4) to transition codes to the next-generation HPC platforms that will be available at ASCR facilities, (5) to build up and train a workforce capable of developing and using simulations and analysis to support HEP scientific research on next-generation systems.
These slides were presented at a webinar on January 9, 2017. The slides overview a report that evaluates the future costs, benefits, and other impacts of renewable energy used to meet current state renewable portfolio standards (RPSs). It also examines a future scenario where RPSs are expanded. The analysis examines changes in electric system costs and retail electricity prices, which include all fixed and operating costs, including capital costs for all renewable, non-renewable, and supporting (e.g., transmission and storage) electric sector infrastructure; fossil fuel, uranium, and biomass fuel costs; and plant operations and maintenance expenditures. The analysis evaluates three specific benefits: air pollution, greenhouse gas emissions, and water use. It also analyzes two other impacts, renewable energy workforce and economic development, and natural gas price suppression. The analysis finds that the benefits or renewable energy used to meet RPS polices exceed the costs, even when considering the highest cost and lowest benefit outcomes.
Atmospheric convection spans a wide range of spatial and temporal scales and involves complex interactions with the surrounding dynamic and thermodynamic environment, particularly over tropical continental regions. These processes remain a major source of uncertainty in weather and climate models, including persistent biases in the diurnal cycle of convective precipitation that directly affect estimates of climate sensitivity. Addressing these challenges requires the combined use of high-resolution observations and cloud-resolving modeling frameworks. In this context, the DOE Atmospheric Radiation Measurement (ARM) program’s Large-Eddy Simulation ARM Symbiotic Simulation and Observation (LASSO) activity provides a powerful platform that pairs comprehensive observations with numerical simulations to enable process-level understanding of atmospheric convection. Within this context, this Research and Development Partnership Pilot (RDPP) project was designed to initiate and expand DOE ARM/ASR research capacity at minority-serving institutions, while advancing scientific understanding of convective processes over the Amazon rainforest. Consistent with the RDPP mission, the project emphasized partnership development, training, and workforce capacity building alongside exploratory research activities. On the scientific side, the project produced two peer-reviewed journal articles, and one manuscript currently under review (see list in section 3.1). Together, these studies combine long-term ARM observations and cloud-resolving and convection-permitting modeling to investigate the environmental controls on the shallow-to-deep convective transition during the Amazon wet season. The results demonstrate the central role of early-day moisture preconditioning and large-scale dynamical forcing in regulating isolated deep convection, provide mechanistic insight into convective evolution, and establish physically informed modeling frameworks for future sensitivity experiments. These scientific outcomes are described in sections 2.1 to 2.3 and were disseminated in 8 conference presentations (see section 3.2) and 5 invited talks (see section 3.3), reflecting broad engagement with our community. Equally important, the project achieved its RDPP capacity-building objectives (see section 2.4). A sustained research partnership was established among the University of Maryland, Baltimore County (UMBC), Morgan State University (MSU), and Howard University (HU), and extended to include collaboration with Pacific Northwest National Laboratory (PNNL). The project organized multiple multi-day training events focused on ARM data, LASSO simulations, and quantitative analysis methods, directly engaging students, postdoctoral researchers, and faculty across institutions. These activities broadened participation in ASR research and led to independent adoption of LASSO workflows by students beyond the immediate project team. Finally, the project successfully positioned the participating institutions to pursue future DOE research. Preliminary scientific results, coupled with strengthened partnerships and technical capacity, enabled the submission of follow-on proposals to DOE ASR funding opportunities. In this way, the project fulfilled the RDPP goal of seeding durable research capacity and laying the foundation for larger-scale, sustained engagement with DOE ARM and ASR programs.
This final technical report summarizes the activities and accomplishments in the period from February 2023 thru January 2026. The objective of the proposed research is to investigate the physical mechanisms and processes underlying the formation of structures and patterns in systems with plasma-surface interactions. In the past decades, there have been extensive studies on the interaction of glow discharges, dielectric barrier discharges, and arc discharges with confining or intervening surfaces. The advancement of the understanding of these phenomena is not only of fundamental scientific interest and relevance to the knowledge of the plasma state, but also with profound implications in various technological applications. The research will integrate theoretical, computational, and experimental work within an innovative framework of data assimilation, i.e., optimally combining model predictions with measurements. The scientific merit of this research has three aspects. Firstly, it extends the studies of plasma-surface interactions to systems with insulator surfaces and multi-layer systems, while existing studies are predominantly on electrode surfaces. Secondly, it expects to develop a novel data-driven modeling approach based on data assimilation to enhance the predictive and control capabilities, which could make transformative contributions to basic plasma research. Thirdly, it will shed new light on outstanding problems related to formation of patterns interfacing plasmas. This project also aims to launch an education and outreach initiative at Texas A&M University-Kingsville, a non-R1, minority-serving institution in South Texas. The initiative is structured as a four-tier pyramid. Tier one will be a webinar series for culture and capacity building to inform broader audience in the region about the research fields of plasma science and engineering. Tier two will be the creation and offering of an upper-level undergraduate course on introductory plasma physics, which will help with the recruitment for the upper tiers. On tier three, we will engage and mentor senior design students to conduct work toward the research goal of this project. There will also be a certificate program on general plasma science for undergrad and graduate students, part of which will be lab training at Princeton University. Tier four will be the supervision and mentoring of Ph.D. students. Therefore, this project will systematically expand the talent pipeline, broaden participation from communities historically and geographically underrepresented in DOE SC research portfolio, significantly improve the research and education capacity at the PI’s institution, and contribute to developing a diverse workforce in plasma science and engineering.
Harnessing the sheer discovery potential of GW Astronomy will require bold, deliberate,and sustained efforts to train and develop the requisite workforce. The next decaderequires a strategic plan to build - from the ground up - a robust, open, andwell-connected GW Astronomy community with deep participation from traditionalastronomers, physicists, data scientists, and instrumentalists. This basic infrastructure issorely needed as an enabling foundation for research. We outline a set ofrecommendations for funding agencies, universities, and professional societies to helpbuild a thriving, diverse, and inclusive new field.
Part of creating a successful solar industry is developing a capable workforce. It has been repeatedly shown that organizations and companies are more productive when they have diverse and inclusive cultures. The solar industry in the U.S. performs well in some aspects (non-white workforce, positive work environment) but drastically under-performs in many others. We discuss these metrics and how this impacts the renewable energy industry and the solar research community. We highlight some initial actions which are necessary to begin to remediate these deficiencies, such as demographic tracking, recruiting strategies, and mentoring. We also present the historical and ongoing efforts to improve the diversity and inclusion of the Photovoltaic Specialists Conference, particularly through the work of this year’s Diversity and Inclusion (D&I) oversight committee. This year’s D&I committee is implementing diversity tracking, a women’s lunch, a diversity lunch, childcare resources, pronouns on badges, gender-neutral restrooms, addressing accessibility issues, and publishing a diversity statement and code of conduct to the PVSC website.
Part of creating a successful solar industry is developing a capable workforce. It has been repeatedly shown that organizations and companies are more productive when they have diverse and inclusive cultures. The solar industry in the U.S. performs well in some aspects (non-white workforce, positive work environment) but drastically under-performs in many others. We discuss these metrics and how this impacts the renewable energy industry and the solar research community. We highlight some initial actions which are necessary to begin to remediate these deficiencies, such as demographic tracking, recruiting strategies, and mentoring. We also present the historical and ongoing efforts to improve the diversity and inclusion of the Photovoltaic Specialists Conference, particularly through the work of this year’s Diversity and Inclusion (D&I) oversight committee. This year’s D&I committee is implementing diversity tracking, a women’s lunch, a diversity lunch, childcare resources, pronouns on badges, gender-neutral restrooms, addressing accessibility issues, and publishing a diversity statement and code of conduct to the PVSC website.