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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 OSTI2025

OLCF’s Advanced Computing Ecosystem (ACE): FY25 Update for Ongoing Efforts

The advent of widespread use of artificial intelligence (AI) and machine learning (ML) models in science, coupled with fast data production rates of scientific instruments strain the traditional batch-oriented high-performance computing (HPC) environment. As scientific exploration continues to require more data and faster processing and analysis, new emerging technologies and capabilities to enable cross-facility and time-sensitive workflows are required for seamless integration of HPC and experimental facilities. The Advanced Computing Ecosystem (ACE) is a strategic initiative within the Oak Ridge Leadership Computing Facility (OLCF) established in 2024 to support the development of cutting-edge technologies to advance computational research and infrastructure at OLCF and across the Department of Energy (DOE). Several DOE initiatives are spearheading the evolution of the scientific landscape by blurring facility boundaries and connecting the user facilities to advance scientific capabilities and ensure energy dominance. The DOE Integrated Research Infrastructure (IRI) program is one example that is laying a foundation to support complex cross-facility workflows. The IRI program aims to integrate diverse computational resources, data infrastructures, and scientific instruments to facilitate collaboration and accelerate scientific discovery. The Interconnected Science Ecosystem (INTERSECT) initiative at Oak Ridge National Laboratory (ORNL) is another example that aims to revolutionize scientific research through AI-driven, interconnected autonomous laboratories and research facilities. Finally, the American Science Cloud (AmSC), recently announced in the “One Big Beautiful Bill”, aims to leverage prior infrastructure efforts of the IRI and automation and AI efforts of INTERSECT (and others) to build a federated, AI-augmented AmSC platform to unify the DOE’s computing, experimental, and data resources to catalyze scientific innovation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Load-Shifting Strategies for Cost-Effective Emission Reductions at Wastewater Facilities

Significant hourly variation in the carbon intensity of electricity supplied to wastewater facilities introduces an opportunity to lower emissions by shifting the timing of their energy demand. This shift could be accomplished by storing wastewater, biogas from sludge digestion, or electricity from on-site biogas generation. However, the life cycle emissions and cost implications of these options are not clear. Here, we present a multiobjective optimization framework for comparing cost- and emission-minimizing load-shifting strategies at a California case study facility with a relatively low carbon intensity grid and high spread in peak and off-peak electricity prices. We evaluate cost and emission trade-offs from the optimal flexible operation of both existing infrastructure and optimally sized energy flexibility upgrades. We estimate energy-related emission reductions of up to 9.0% with flexible operation of existing infrastructure and up to 16.8% with optimally sized storage upgrades. Only a fraction of these potential savings are realized under actual industrial energy tariffs and the EPA’s recommended social cost of carbon. Energy flexibility may hold promise as a short-term emission-saving solution for the wastewater sector, but the extent of savings is heavily dependent on the cost of carbon, electricity tariffs, and emission intensity of the regional electricity grid.

climate↗
DOE OSTI2025

Advancing Grid Resilience through Smart Charge Management: Findings from Maryland’s Pilot

This report presents research findings from a four-year Smart Charge Management (SCM) pilot program conducted by Maryland’s largest electric utilities—Baltimore Gas and Electric (BGE), Potomac Electric Power Company (Pepco), and Delmarva Power & Light (DPL)—to evaluate strategies for optimizing electric vehicle (EV) charging loads and enhancing grid stability. Supported by the U.S. Department of Energy (DOE), Argonne National Laboratory collaborated with all project partners and examined the effectiveness of Time-of-Use (TOU) and Load Balancing (LB) strategies in managing peak demand, deferring costly infrastructure upgrades, and reducing grid constraints at the feeder level. Using charging data from over 4,600 EV drivers, the study analyzed SCM’s impact on the distribution systems of BGE and Pepco, which consists of over 2000 feeders. Unlike prior research that focused on system-wide trends or synthetic feeders, this analysis offers granular, feeder-level insights based on real-world operational data. It highlights how transformer density, load profiles, and infrastructure constraints influence smart charging performance. Results show feeder-level conditions play a crucial role in SCM effectiveness, with most feeders benefiting more from LB, while TOU-based SCM may be sufficient for others. By 2035, LB reduced peak charging loads by 27% on average, compared to 23% under TOU-based SCM, though some feeders saw reductions exceeding 35%, while others experienced minimal impact. Feeders with higher transformer utilization and limited capacity benefited more from LB, which more effectively distributed charging demand during off-peak hours. Beyond reducing grid constraints, SCM offers long-term operational and financial benefits. By shifting EV charging demand strategically, utilities can optimize asset utilization, delay infrastructure investments, and enhance grid performance. In terms of infrastructure upgrade deferrals, at the feeder level, LB consistently reduced peak charging loads and resulting infrastructure upgrade costs, particularly in high EV enrollment areas, decreasing the number of overloaded transformers by up to 35%, while TOU-based SCM achieved 20-30% reductions depending on feeder characteristics. At the system level, LB has the potential to defer total upgrade costs by $\$$186 million for BGE, compared to $\$$159 million under TOU-based SCM. For Pepco, TOU-based SCM performed slightly better, deferring upgrade costs by $\$$30 million, compared to $\$$29 million under LB. Section 4.5 reviews some of the system differences between BGE and Pepco. However, as EV adoption scales, TOU-based SCM will introduce secondary peak charging loads, reinforcing the need for more advanced, adaptive SCM approaches to prevent new grid challenges. As EV adoption continues to grow, feeder-level managed charging strategies will be essential for mitigating grid stress, improving infrastructure efficiency, and maintaining energy affordability for consumers. This report provides critical insights for utilities, Public Utility Commissions (PUCs), and state agencies on the role of feeder-specific smart charging in infrastructure planning, policy development, and grid modernization. The findings underscore the importance of tailored, data-driven SCM solutions that align with local grid conditions, ensuring a resilient, cost-effective transition to increasing EV adoption while safeguarding distribution system performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

An Evaluation Framework for State Energy Offices' Energy Efficiency and Clean Energy Workforce Programs

Historic levels of funding for energy efficiency and clean energy projects from the Bipartisan Infrastructure Law and Inflation Reduction Act will be flowing through State Energy Offices (SEOs) in the coming years, including funds specifically for supporting workforce development efforts. This report offers a workforce evaluation framework that they can use to track and measure the progress, performance, and effectiveness of energy efficiency and clean energy workforce programs that they fund and/or implement. This evaluation step is essential to ensuring that SEOs' workforce efforts remain relevant to the evolving needs of the energy efficiency and clean energy sector and advance a workforce that is inclusive and diverse thus maximizing the success of their programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Leveraging AI and Spatial Data to Unlock Pipeline Integrity Insights: NETL’s Advanced Infrastructure Integrity Model (AIIM)

Maintaining the integrity of natural gas infrastructure plays a critical role in ensuring energy security. Robust, data-driven foundational AI models for pipeline integrity can help address risk management and mitigation issues. Trusted foundational models can help with industry adoption and accelerate innovation by enhancing integrity predictions, reduce costs, and informing infrastructure build-out. The AIIM dashboard was released in 2022 and utilizes multi-ML models for ensemble-type insights. It was expanded to include analytics on reported incidents. It was developed as an ESRI Dashboard to support data visualization & interrogation and contains pipeline data and model results.

Advanced Infrastructure Integrity Model (AIIM)↗
DOE OSTI2024

County Electric Vehicle Home Charging Access Shares from the 2030 National Charging Network Study

This file contains modeled county-level home electric vehicle (EV) charging access shares from the study, "The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure" by Wood et al. (2023). These are based on modeling in "There's No Place Like Home: Residential Parking, Electrical Access, and Implications for the Future of Electric Vehicle Charging Infrastructure" by Ge et al. (2021). Wood, Eric, et al. 2023. The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure. Golden, CO: National Renewable Energy Laboratory. NREP/TP-5400-85654. https://www.nlr.gov/docs/fy23osti/85654.pdf . Ge, Yanbo, et al. 2021. There's No Place Like Home: Residential Parking, Electrical Access, and Implications for the Future of Electric Vehicle Charging Infrastructure. Golden, CO: National Renewable Energy Laboratory. NREL/TP-5400-81065. https://www.nlr.gov/docs/fy22osti/81065.pdf .

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

NETL LCA Update US DOE Special Session, ACLCA 2024

This work was presented at the American Center for Life Cycle Assessment 2024 Conference in Snowbird, UT on September 24, 2024. The evaluation of the environmental advantages and disadvantages of energy technology and policy options requires the comparison of those options on a common basis, and as such, it needs to include not only obvious impacts like converting fuel to useful energy via combustion, but of infrastructure construction, extraction, processing and transportation of fuels, and transport of the final energy product to an end user. Further, it requires comparison across multiple categories of impacts, as single metric perspectives can miss important cross-media tradeoffs. At the Department of Energy’s National Energy Technology Laboratory, life cycle analysis is used as a tool and framework for undertaking a broad range of these types of evaluations. This overview will describe the NETL LCA work over the past year.

Jamieson, Matthew↗
DOE OSTI2025

From Quantum to Classical: Sensing Materials, Methods, and Platforms for Energy

This presentation covers several ongoing sensors projects at NETL, including the use of quantum sensors for energy sector applications (e.g. infrastructure monitoring, critical metal detection, grid modernization) as well as NETL-developed materials and platforms for detecting economically critical metals including rare earth elements, cobalt, and aluminum. Taken together, these projects demonstrate NETL's leadership in unleashing American energy through the development of high performance sensors for reliable energy production and consumption.

critical minerals↗
DOE OSTI2026

Technical Assistance for Guam Power Authority

Through the Energy Technology Innovation Partnership Project (ETIPP), Guam Power Authority learned approaches to evaluate existing infrastructure to maximize distributed local energy generation without damaging existing system components.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Mobility Energy Productivity (MEP) Metric: Partnerships, Applications, and Key Enhancements

The Mobility Energy Productivity (MEP) metric is a holistic measure of transportation systems performance that quantifies the ability of individuals to reach destinations in a cost-efficient and energy-efficient manner. This presentation highlights recent partnerships that have advanced the adoption of MEP as a decision-support tool by various agencies, stakeholders, and researchers. Applications include evaluating multimodal accessibility, comparing system-level energy impacts, and informing infrastructure investment strategies. Key enhancements to the metric - such as expanded regional applications and incorporation of emerging technologies - are also discussed. Together, these efforts highlight the potential of MEP to inform data-driven decisions that shape future transportation systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Sparsely Dispersed CeO x ‑Stabilized Pt Nanoparticles Overcome Pt Loading–Durability Trade-Off for Highly Durable Heavy-Duty Fuel Cells

Proton-exchange-membrane fuel cells (PEMFCs) are clean and sustainable mobile power sources for transportation. Recently, their deployment in heavy-duty vehicles (HDVs) has attracted growing interest owing to their high energy scalability and lower infrastructure requirements. However, to meet the stringent requirements for efficiency and long-term durability for HDV applications, PEMFCs typically employ a relatively high platinum group metal (PGM) loading (>0.2 mg PGM /cm 2 ). This elevated PGM loading significantly increases the stack and system costs, surpassing the U.S. Department of Energy (DOE) target of $\$ 60$/kW for commercial viability. Reducing PGM loading while maintaining performance and durability remains a central challenge for HDV fuel cells. Here we exploit metal oxide–Pt interactions and utilize the strong CeO x –Pt interaction to design a CeO x @Pt catalyst structure with exceptional durability. At a low total PGM loading (0.1 mg PGM /cm 2 ), the CeO x @Pt/C catalyst demonstrates high fuel cell performance (8.8 kW/g PGM ) and stability (power retention >90%) after the challenging HDV durability testing (90,000 accelerated-stress-test cycles). With the CeO x @Pt/C catalyst, we showcase over 70% reduction in Pt cost from the M2FCT target (to $\$ 9$/kW), highlighting its promising potential for enabling stable and cost-effective fuel cell systems for heavy-duty applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Ensuring electromagnetic compatibility in grid-connected power converters: Challenges, standards, and compliance strategies

In today's rapidly advancing world, electronic devices and systems are fundamental to a wide range of industries, including renewable energy and global telecommunications infrastructure. However, as these devices become more complex and widespread, the risk of electromagnetic interference (EMI) also increases, underscoring the importance of stringent Electromagnetic Compatibility (EMC) requirements for maintaining system integrity. This paper addresses the specific challenges associated with grid-connected power converters (GCPCs), which are critical in integrating renewable energy into existing power grids. It explores the complexities of EMI in the context of GCPCs, particularly given the recent emergence of tailored EMC standards for these systems. The paper also highlights the shortcomings of applying generic or unrelated standards to GCPCs, often leading to inadequate compliance and testing protocols. Through a detailed analysis of existing standards and recent advancements in product-specific EMC requirements, this paper provides a comprehensive overview of the current landscape, offering guidance to stakeholders on navigating the intricate EMC compliance landscape, with a focus on methodologies, testing procedures, and the evolving regulatory environment for GCPCs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Center for Wind Energy

The purpose was to establish the physical infrastructure and additional measurement equipment for a center focused on wind energy research, education, technology development and community outreach. The Center is located on the Richardson campus of UTD. The Center for Wind Energy will bring together university, civic and industry partners to create and test new technologies to realize the potential of wind to support the energy needs of the nation.

17 WIND ENERGY↗
DOE OSTI2026

Scenario-based analysis of electric vehicle adoption in the United States: Technology, infrastructure, and electricity pricing

This work investigates the impact of battery technology advancement, charging infrastructure development, and time-of-use (TOU) electricity pricing on vehicle adoption by 6 powertrain types in the United States through 2050. Using the Market Acceptance of Advanced Automotive Technologies (MA3T) model, we simulate 15 scenarios, examining individual cost factors and their combinations. We assess outcomes through market share, consumer surplus, and energy consumption. Results show that battery cost reductions are the strongest driver of EV adoption, increasing 2050 battery electric vehicle (BEV) share by 27 percentage points over baseline, raising annual consumer surplus by $511 per household, and reducing cumulative energy consumption by 16,610 trillion Btu. These gains are two to five times larger than those from other individual factors. Reducing home charging installation costs produces moderate impact, while TOU pricing alone yields only small gains, raising 2050 BEV market share by 1–2 percentage points. However, when cost factor improvements are combined, their effects are amplified beyond simple additivity. Pairing modest battery cost reductions with charging installation cost reductions and TOU pricing results in the largest 2050 BEV sales combined impact. The analysis demonstrates that moderate progress targeting multiple cost barriers may be more impactful than focusing on any single barrier.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Modeling Distributed Computing Infrastructures for HEP Applications

Predicting the performance of various infrastructure design options in complex federated infrastructures with computing sites distributed over a wide area network that support a plethora of users and workflows, such as the Worldwide LHC Computing Grid (WLCG), is not trivial. Due to the complexity and size of these infrastructures, it is not feasible to deploy experimental test-beds at large scales merely for the purpose of comparing and evaluating alternate designs. An alternative is to study the behaviours of these systems using simulation. This approach has been used successfully in the past to identify efficient and practical infrastructure designs for High Energy Physics (HEP). A prominent example is the Monarc simulation framework, which was used to study the initial structure of the WLCG. New simulation capabilities are needed to simulate large-scale heterogeneous computing systems with complex networks, data access and caching patterns. A modern tool to simulate HEP workloads that execute on distributed computing infrastructures based on the SimGrid and WRENCH simulation frameworks is outlined. Studies of its accuracy and scalability are presented using HEP as a case-study. Hypothetical adjustments to prevailing computing architectures in HEP are studied providing insights into the dynamics of a part of the WLCG and candidates for improvements.

Horzela, Maximilian↗
DOE OSTI2026

Countrywide LED Lighting Retrofit for Safety, Visibility, and Energy Efficiency (Final Technical Report)

The project supported national clean energy goals by reducing electricity demand and improving energy efficiency in public infrastructure. Tasks defined in the Statement of Project Objectives included site assessments, retrofit planning, lighting design optimization, installation, monitoring, and reporting. All milestones and go/no-go decision points were successfully met.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗