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DOE OSTI2022

The Science of Scientific Software Development and Use

Increasingly powerful and affordable computing has revolutionized scientific and scholarly discovery across a broad range of fields. Computing relies on software, which has been rapidly growing in scope, diversity, and complexity. At the same time, the methods, processes, and tools used to produce and utilize this essential software are often ad hoc, and the study and improvement of them is often done without the benefit of direct funding or prioritization. Consequently, concerns are growing about the productivity of the developers and users of scientific software, its sustainability, and the trustworthiness of the results that it produces. The US Department of Energy Office of Science (DOE-SC) is at the forefront of modern software-enabled scientific discovery across numerous areas of computational, experimental, and observational science, including major investments in national user facilities that support these activities. In December 2021, the DOE-SC Office of Advanced Scientific Computing Research (ASCR) convened a workshop on basic research needs for the Science of Scientific Software Development and Use (SSSDU). Through keynote presentations, lightning talks, and breakout groups, participants discussed the current practice of software development, maintenance, evolution, and use, and considered how the scientific method could be used to examine these practices and develop more evidence-based approaches to enhance the impact of software and computing on all areas of science. Workshop participants identified three priority research directions (PRDs) and three important crosscutting themes that center on the following overarching insight: software has become an essential part of modern science that impacts new discovery, policy, and technological development. To have full confidence in science delivered via software, we must improve the processes and tools that help us create and use it, and this enhancement requires a deep understanding of the diverse array of teams and individuals doing the work. The full workshop report will be available at https://doi.org/10.2172/1846009.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

A Multiscale Investigation of the Mechanisms Controlling Materials Degradation in the Fusion Energy Environment (DE-SC0006661: Final Report)

Realizing the promise of fusion as a commercially attractive energy source requires the development of advanced structural materials capable of sustained operation in an incredibly hostile environment. The fusion environment presents many challenges associated with high cyclic thermal-mechanical loadings, welding and joining disparate materials, and achieving chemical compatibility with coolants and tritium breeders. Yet, the overarching concern is the degradation of physical and mechanical properties, resulting from a neutron energy spectrum peaked at 14 MeV. The high-energy fusion neutron irradiation produces both displacement damage and high levels of hydrogen and helium through transmutation reactions. Advanced materials development for use in such a hostile environment is predicated on understanding the underlying mechanisms responsible for physical and material property degradation. This project has closely combined computational, theoretical and experimental techniques within a multiscale materials science paradigm to determine the mechanisms controlling material degradation in the fusion environment.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Optimization of key energy and performance metrics for drug product manufacturing

During the development of pharmaceutical manufacturing processes, detailed systems-based analysis and optimization are required to control and regulate critical quality attributes within specific ranges, to maintain product performance. As discussions on carbon footprint, sustainability, and energy efficiency are gaining prominence, the development and utilization of these concepts in pharmaceutical manufacturing are seldom reported, which limits the potential of pharmaceutical industry in maximizing key energy and performance metrics. Based on an integrated modeling and techno-economic analysis framework previously developed by the authors, this study presents the development of a combined sensitivity analysis and optimization approach to minimize energy consumption while maintaining product quality and meeting operational constraints in a pharmaceutical process. The optimal input process conditions identified were validated against experiments and good agreement resulted between simulated and experimental data. Here, the results also allowed for a comparison of the capital and operational costs for batch and continuous manufacturing schemes under nominal and optimized conditions. Using the nominal batch operations as a basis, the optimized batch operation results in a 71.7% reduction of energy consumption, whereas the optimized continuous case results in an energy saving of 83.3%.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Pathways to a Sustainable Aviation Ecosystem: Life Cycle Analysis with Blockchain Carbon Accounting

When linked with the National Renewable Energy Laboratory's (NREL's) rigorous life cycle assessment methodologies, blockchain technology can enable aviation stakeholders to quantify and transact on the impacts and benefits of sustainable aviation fuels and technologies. NREL can develop high-fidelity carbon modeling tools that enable apples-to-apples comparisons across technologies, fuels, companies, and jurisdictions.

aviation↗

DOE OSTI2021

Development and Application of a Data-Driven Methodology for Validation of Risk-Informed Safety Margin Characterization Models

The document is the Final Technical Progress Report for the Nuclear Energy University Program’s Integrated Research Project (IRP) on “Development and Application of a Data-Driven Methodology for Validation of Risk-Informed Safety Margin Characterization Models” that supports the LWR Sustainability Program’s RISMC R&D Pathway. The project goal is to develop and demonstrate a data-driven methodology for validation of advanced computer models used in nuclear power plant safety analysis. Specifically, the advanced computer models are those in the toolkit developed to support risk-informed safety margin characterization (RISMC), an integrated deterministic/probabilistic safety analysis methodology developed in the Department of Energy’s Light Water Reactor Sustainability (LWR-S) program. The report reflects the progress made towards the project’s stated goal by contributions by researchers and graduate students from universities, with support from researchers from national laboratories and industry companies. The project organization, effort coordination and technical implementation are summarized, followed by discussion of main findings, issues, and path forward. Selected chapters provide a more detailed description of tasks, approaches and respective findings and recommendations. Noteworthy are contributions that serve as guidelines for methodology development. It is also noted that this report is complemented by other milestone reports (as stand-alone deliverables) that provide detailed discussion of the technical developments. The project results have been documented in a number (12) dissertations and these, 50+ peer-reviewed publications in technical journals and conference proceedings.

42 ENGINEERING↗

DOE OSTI2024

A review on machine learning-guided design of energy materials

Abstract The development and design of energy materials are essential for improving the efficiency, sustainability, and durability of energy systems to address climate change issues. However, optimizing and developing energy materials can be challenging due to large and complex search spaces. With the advancements in computational power and algorithms over the past decade, machine learning (ML) techniques are being widely applied in various industrial and research areas for different purposes. The energy material community has increasingly leveraged ML to accelerate property predictions and design processes. This article aims to provide a comprehensive review of research in different energy material fields that employ ML techniques. It begins with foundational concepts and a broad overview of ML applications in energy material research, followed by examples of successful ML applications in energy material design. We also discuss the current challenges of ML in energy material design and our perspectives. Our viewpoint is that ML will be an integral component of energy materials research, but data scarcity, lack of tailored ML algorithms, and challenges in experimentally realizing ML-predicted candidates are major barriers that still need to be overcome.

36 MATERIALS SCIENCE↗

DOE OSTI2020

CO 2 –Based Dual–Tone Resists for Electron Beam Lithography

The increasing global environmental and energy crisis has urgently motivated the advancement of sustainable materials. Significant effort has been focused on developing new materials to replace the fossil-based resists in the semiconductor industry based on greener sources such as ice, dry ice, small organic molecules, and proteins. Such resist materials, however, have yet to meet the stringent requirements of high sensitivity, high resolution, reliable repeatability, and good compatibility with the current protocols. To this end, CO 2 -based polycarbonates (CO 2 -PCs) obtained from the copolymerization of CO 2 and epoxides are demonstrated as sustainable dual-tone (positive & negative tone) resists for electron beam lithography. By adjusting the chemical structure, developing agent, and molecular weight, the CO 2 -PCs present high sensitivities to electron beam (1.3/120 μC cm -2 ), narrow critical dimensions (29/58 nm), and moderate line edge roughness (4.6/26.7 nm) for negative and positive resists, respectively. Here, a deep understanding of the exposure mechanism of CO 2 -PC resists is provided on the basis of the Fourier transform infrared, Raman, and electron ionization mass spectroscopy. 2D photonic crystal devices are fabricated using the negative and positive CO 2 -PC resists, respectively, and both devices show distinct colors derived from their well-defined nanostructures, indicating the great practical potential of CO 2 -derived electron beam resists.

nanofabrication↗

DOE OSTI2022

Enabling Regulatory and Business Models for Broad Microgrid Deployment (White Paper)

This white paper is one of seven being prepared for the Department of Energy (DOE) Microgrid Research & Development (R&D) program as part of a strategy development effort for the next 10 years. The seven white papers focus on the following areas: 1. Program vision, objectives, and R&D targets in 5 and 10 years, 2. T&D co-simulation of microgrid impacts and benefits, 3. Building blocks for microgrids, 4. Microgrids as a building block for the future grid, 5. Advanced microgrid control and protection, 6. Integrated models and tools for microgrid planning, designs, and operations, 7. Enabling regulatory and business models for broad microgrid deployment. This white paper is focused on Topic 7, as a sustainable regulatory and business environment for microgrid development is a foundational element for securing DOE's vision for the future role of microgrids in the U.S. electric sector. The objective of this white paper is to systematically characterize regulatory issues involved in microgrid deployment and microgrid business models, and from this evidence identify a robust and well-justified set of research recommendations for the Department of Energy Office of Electricity, informing programmatic vision, objectives and activities for the DOE Microgrid R&D Program.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Contextualizing Wind Turbine Blade Waste: Comparison to Other Global Waste Streams

Worldwide wind energy generation capacity has grown rapidly over the past several decades, and wind turbines installed at the beginning of this wave of growth are approaching the end of their design lifetimes. As an increasing number of wind power plants reach their end of life, both decommissioning and repowering (i.e., dismantling or refurbishing existing turbines and commissioning new ones) will produce waste material from the retired wind turbines, foundations, and balance of plant. However, the amount and type of waste, particularly for wind blades, is often mischaracterized. Although wind turbine components are largely recyclable, the blades are typically made of fiberglass composites, which can present challenges for material recovery and reuse. Within the USA, the accumulation of wind turbine blades in landfills has raised questions about whether the continued expansion of wind energy is sustainable if it results in substantial future waste. This study compares the mass and volume of potential global wind blade waste to other waste streams. It also discusses the materials used to manufacture wind turbine blades and summarizes current options for material redesign, recycling (recovery and reuse), repurposing, and disposal of used blades. The analysis indicates that, although wind turbine blades could represent 14% of the composite market by 2027, the potential future mass and volume of wind turbine blade waste is relatively small compared to other industries. These findings suggest that although the development of scalable, economically viable, and environmentally sustainable methods for wind turbine manufacturing, repurposing, and recycling is important, it may make sense to take advantage of synergies among multiple industries in recycling composite waste, rather than focusing solely on wind turbine blades. From a global perspective, larger sustainability, recycling, and waste stream reduction impacts can be made in other industries, such as transportation and construction.

17 WIND ENERGY↗

DOE OSTI2024

Depolymerizable and recyclable luminescent polymers with high light-emitting efficiencies

Luminescent polymers are of great interest in a number of photonic technologies, including electroluminescence, bioimaging, medical diagnosis, bio-stimulation and security signage. Incorporating depolymerizability and recyclability into luminescent polymers is pivotal for promoting their sustainability and minimizing their environmental impacts at the end of the product lifecycle, but existing strategies often compromise the light-emitting efficiencies. Here, in this study, we develop a strategy that utilizes cleavable moiety to create depolymerizable and recyclable thermally activated delayed fluorescence (TADF) polymers without compromising their high light-emitting efficiencies. The electroluminescent devices based on the TADF polymers achieved a high external quantum efficiency of up to 15.1 %. The TADF polymers can be depolymerized under either mild acidic or heating conditions, with precise control of the kinetics, and the obtained pure monomers can potentially be isolated and repolymerized for subsequent life applications. This work promotes the end-of-life environmental friendliness and circularity of luminescent materials, paving the way to a sustainable photonic industry. Developing depolymerizable and recyclable polymers with high light-emitting efficiencies is of vital importance for sustainable photonic technologies, but remains challenging. Here the authors design a strategy to develop such polymers based on the use of controllable cleavable moiety.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Recent Developments in Sensor Technologies for Enabling the Hydrogen Economy

Efforts to create a sustainable hydrogen economy are gaining momentum as governments all over the world are investing in hydrogen production, storage, distribution, and delivery technologies to develop a hydrogen infrastructure. This involves transporting hydrogen in gaseous or liquid form or using carrier gases such as methane, ammonia, or mixtures of methane and hydrogen. Hydrogen is a colorless, odorless gas and can easily leak into the atmosphere leading to economic loss and safety concerns. Therefore, deployment of robust low-cost sensors for various scenarios involving hydrogen is of paramount importance. Here, we review some recent developments in hydrogen sensors for applications such as leak detection, safety, process monitoring in production, transport and use scenarios. The status of methane and ammonia sensors is covered due to their important role in hydrogen production and transportation using existing natural gas and ammonia infrastructure. This review further provides an overview of existing commercial hydrogen sensors and also addresses the potential for hydrogen as an interferent gas for currently used sensors. This review can help developers and users make informed decisions about how to drive hydrogen sensor technology forward and to incorporate hydrogen sensors into the various hydrogen deployment projects in the coming decade.

08 HYDROGEN↗

DOE OSTI2023

Recent Developments in Sensor Technologies for Enabling the Hydrogen Economy

08 HYDROGEN↗

DOE OSTI2025

Nanoindentation’s imprint on an advanced society: Toward application conditions at the extremes

Abstract As society strives for a more sustainable future, key technologies depend on advanced materials development. Nanoindentation enables highly localized nanoscale measurement of mechanical properties, crucial for optimizing the performance of functional materials. Coupled with chemical charging, variable temperatures, or high deformation speeds, it offers insights into pressing materials science challenges necessary for sustainable energy transitions. This article explores the role of nanoindentation in advancing vital next-generation technologies. Graphical abstract

36 MATERIALS SCIENCE↗

DOE OSTI2026

Detecting and Characterizing Fracture Zones Using a Convolutional Neural Network

This project directly supports the Geothermal Technologies Office (GTO) objectives outlined in the Multi-Year Program Plan (MYPP) by advancing two key research areas: “Exploration and Characterization” and “Data, Modeling, and Analysis.” This project has successfully demonstrated a pre-drilling ability to image and characterize the distribution and connectivity of subsurface faults and fractures, key parameters for identifying permeable pathways that enable geothermal fluids to circulate and produce energy. Specifically, we developed and implemented innovative machine learning methodologies to enhance geothermal exploration. Large-scale faults were detected using a Convolutional Neural Network (CNN), while small-scale fractures were characterized using a novel Double-Beam Neural Network (DBNN). These tools have proven both technically effective and cost-efficient by reducing reliance on expensive exploratory drilling. Through collaboration with our geothermal industry partner, this research has significantly advanced techniques for identifying hidden geothermal systems and extending the productive lifespan of existing geothermal fields. We applied our methods to two geothermal fields—Soda Lake (Nevada) and Lightning Dock (New Mexico)—to identify shallow steam-charged fracture zones and characterize deep faults at depths of 1.5-2 km. The steam zone identified at the Soda Lake geothermal field showed excellent agreement with prior drilling data, validating the effectiveness of our approaches. In addition, the analysis revealed three new prospective drilling targets for further development and verification. The outcomes of this project improve our scientific understanding of geothermal reservoir behavior, enhance exploration efficiency, extend the economic life of existing geothermal plants. Ultimately, these advancements contribute to GTO’s goal of achieving more sustainable, affordable, and data-driven geothermal energy development across the United States.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Transformative Pathways for U.S. Industry: Unlocking American Innovation

The United States (U.S.) is undergoing an energy transformation that will depend on continued U.S. innovation. Although U.S. industry has been foundational to the nation’s economic growth and prosperity, it has also given rise to decades’ worth of industrial pollutants in our air and water, which acutely impact the most vulnerable communities, as well as greenhouse gas (GHG) emissions contributing to climate risk. At the same time, U.S. industry is facing growing competitive pressures. Global investors and financial regulations are increasingly focusing on emissions footprints, governments are developing emissions-based trade adjustments and procurement specifications, and downstream demand for low-carbon products is emerging. Developing cost-competitive solutions to meet these needs provides an opportunity to fundamentally transform U.S. industry and sharpen its competitive edge, while reducing the GHG emissions and adverse environmental and health impacts (see Figure ES-1). Innovation is central to this transformation. Pathways to Commercial Liftoff: Industrial Decarbonization, which provides a descriptive fact base on what is needed to reach commercial scale in the marketplace, estimates that over 60% of emissions reduction for the industrial sector will need to come from technologies that are still nascent today. This report, Transformative Pathways for U.S. Industry,3 focuses on the pathways that rely on the nascent and innovative technologies that were too early for consideration in the Pathways to Commercial Liftoff report. Targeted and sustained public and private investment in research, development, demonstration, and deployment is required to catalyze innovation and meet this moment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Segmenting the target audience for transportation demand management programs: An investigation between mode shift and individual characteristics

Understanding the characteristics of travelers and situations that are more likely to switch from single occupancy vehicles (SOV) to more sustainable mobility options can help transportation agencies develop better behavior change strategies. Existing mode shift programs, however, rarely identify their target audience, which could make the programs less effective. Here, to investigate the demographics and trip characteristics of those who are more likely to shift from SOV to carpooling, public transit, and micromobility (e.g. walking, biking), a matrix decomposition audience segmentation method is proposed and applied on travel survey data. The results of this research show that for short-distance leisure or social trips, young and middle-income people are more likely to shift to these mobility options from SOV. This study provides a comprehensive and profound understanding of the characteristics of the target audience that can inform policies to create more targeted behavior change strategies to reach their sustainability goals.

42 ENGINEERING↗

DOE OSTI2021

Development of Digital Twin Predictive Model for PWR Components: Updates on Multi Times Series Temperature Prediction Using Recurrent Neural Network, DMW Fatigue Tests, System Level Thermal-Mechanical-Stress Analysis

The long-term operation (LTO) of nuclear power plant (NPP) beyond their original design life of 40 years, can lead to more material damage associated with cyclic fatigue under thermal-mechanical loading cycles and associated long-term exposure of reactor material to the deleterious reactor-coolant environments. However, under this LTO condition the reactor components can still safely operate but may require more frequent Nondestructive Evaluation (NDE) of reactor components. Frequent NDE requirement may lead to frequent shutdown of the NPP. This in turn can lead to power outage and additional NDE-inspection-cost related economic loss. The economic loss can be minimized by reducing uncertainty in life estimation of safety-critical pressure boundary components and by implementing more digital approach such as by using upcoming digital-twin (DT) technology for predicting the structural states (e.g., time and location dependent inside/outside thickness temperature, stress, strain, plastic deformation, etc.) and associated fatigue life of a component in real time. Towards this goal Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program is working on the development of a DT framework that can be used for real time environmental fatigue prediction of reactor components. The DT framework is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics such as finite element (FE) based modeling tools. Towards this overall goal, following are some of the major contributions made during the FY21: 1) Multiple 82/182 dissimilar metal weld (DMW) specimens (both solid-weld and joint-weld representing the actual reactor multi-metal nozzles) were fatigue tested. The resulting fatigue lives were compared to the NUREG-6909 based best-fit and design fatigue curves. Additionally, the results of 52/152 DMW fatigue specimens (which were recently tested at Republic of Korea under the sponsorship of International Nuclear Energy Research Initiative - INERI program) were compared to the NUREG-6909 based best-fit and design fatigue curves. From the comparison of 82/182 and 52/152 DMW test data with NUREG-6909 best-fit curve, most of the reported test data fall way away from the NUREG-6909 suggested best-fit or mean curve. The NUREG-6909 suggested best-fit curve is the best-fit curve of austenitic stainless steel and due to lack of enough data on Nickel-based welds, this is currently being used for predicting the life of Nickel-alloy-based welded components. However, the above observation may require higher scaling factor (e.g., ASME suggested factor of 20 on cycles rather than the current NUREG-6909 suggested factor of 12 on cycles) for scaling the austenitic-stainless-steel best-fit-curve for estimating the design or safe-life of a welded component. Accordingly, for example, if a DMW component experience a strain amplitude of 0.6% the PWR-water life of the component would be 52 cycles instead of 85 cycles. However, more DMW tests are required to further ascertain the above-mentioned observations. 2) A system level CAD and finite element model were developed which consists of reactor pressure vessel (RPV), part of steam generator (SG), part of pressurizer (PRZ), hot leg (HL), and surge line (SL). This is with detailed nozzle geometry and thermal-mechanical material properties of different metals to simulate realistic thermal-mechanical stress under connected system global thermal-mechanical boundary conditions. 3) Different system level heat transfer analyses were performed with estimation of relevant heat transfer coefficients. The resulting data were used in subsequent system level thermal-mechanical stress analysis and for generating spatial-temporal training and validation data for a system level digital-twin based temperature predictor. Transient heat transfer analyses were performed considering thermal boundary condition under design-basis (DB) loading and EDF (Électricité de France) data-based grid-load-following (EDF-GLF) loading cycles. 4) System level thermal-mechanical stress analysis was performed for identifying damage-prone hotspots and for future extension of the model for cyclic state prediction. From the system-level model simulation under DB loading cycle it is found that HL and the SL nozzle that connect to the HL can experience significant stress and strain and could be one of the weakest links in the overall reactor coolant system (RCS). 5) An AI/ML based DT model was developed for multi-time-series temperature prediction at any inside/outside thickness locations of PWR pressure boundary components. This is by using Recurrent-neural-network (RNN) and keras machine learning libraries. The RNN model was validated against two laboratory test-based data sets with one obtained through ANL’s in-air fatigue test system and other through PWR-water test loop. The experimentally validated DT model further validated against FE model results to predict thermal scarification related spatialtemporal temperatures at random locations of a component. The well validated DT model was then used for demonstrating spatial-temporal temperature prediction under 100+ years of reactor operation subjected to combined DB, EDF-GLF and randomized grid-load-following (RANDOMGLF) loading Cycles. The expert-elicitation DT model framework was developed assuming field/input/process measurements can be available from a few existing plant sensors and can readily be used by the NPP operators. The above temperature prediction model will feed to the next-step stress analysis model based on which the life of a component can be predicted in realtime, which is one of our future works.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

From Neglect to Progress: Assessing Social Sustainability and Decent Work in the Tourism Sector

Measuring social sustainability performance involves assessing firms’ implementation of social goals, including working conditions, health and safety, employee relationships, diversity, human rights, community engagement, and philanthropy. The concept of social sustainability is closely linked to the notion of decent work, which emphasizes productive work opportunities with fair income, secure workplaces, personal development prospects, freedom of expression and association, and equal treatment for both genders. However, the tourism sector, known for its significant share of informal labor-intensive work, faces challenges that hinder the achievement of decent work, such as extended working hours, low wages, limited social protection, and gender discrimination. This study assesses the social sustainability of the Portuguese tourism industry. The study collected data from the “Quadros do Pessoal” statistical tables for the years 2010 to 2020 to analyze the performance of Portuguese firms in the tourism sector and compare them with one another and with the overall national performance. The study focused on indicators such as employment, wages, and work accidents. The findings reveal fluctuations in employment and remuneration within the tourism sector and high growth rates in the tourism sector compared to the national average. A persistent gender pay gap is identified, which emphasizes the need to address this issue within the tourism industry. Despite some limitations, such as the lack of comparable data on work quality globally, incomplete coverage of sustainability issues, and challenges in defining and measuring social sustainability indicators, the findings have implications for policy interventions to enhance social sustainability in the tourism industry. By prioritizing decent work, safe working conditions, and equitable pay practices, stakeholders can promote social sustainability, stakeholder relationships, and sustainable competitive advantage. Policymakers are urged to support these principles to ensure the long-term sustainability of the tourism industry and foster a more inclusive and equitable society. This study provides insights for Tourism Management, sustainable Human Resource Management, Development Studies, and organizational research, guiding industry stakeholders in promoting corporate social sustainability, firm survival, and economic growth.

Santos, Eleonora (ORCID:0000000346930804)↗