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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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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Low-energy H- beam injectors for particle accelerators: upgrade requirements, challenges and future plans [Slides]

High brightness, negative hydrogen ion sources are used extensively in large, accelerator-based, user facilities operating worldwide. Negative hydrogen beams have become the preferred means of filling circular accelerators and storage rings as well as enabling efficient extraction from cyclotrons. Much larger beams of H- ions are also utilized for neutral beams injection into fusion machines and will be discussed in a separate companion presentation. Some of the accelerator facilities include the US Spallation Neutron Source (SNS), Japan Proton Accelerator Research Complex (J-PARC), Rutherford Appleton Laboratory (RAL-ISIS), Los Alamos Neutron Science Center (LANSCE), Fermi National Accelerator Laboratory (FNAL), Brookhaven National Laboratory (BNL), the CERN LHC injector, the Chinese Spallation Neutron Source (CSNS) as well as numerous installations of D-Pace (licenced by TRIUMF) ion sources used mainly with cyclotrons. Many facilities are currently working on various improvement / upgrade projects, both in the near and long term, which are driving further development of their ion source and LEBT (Low Energy Beam Transport) and, in some cases, their overall front end injector system. This report will first provide a simple description of each facilities existing ion source and LEBT and summarize the operational parameters which are currently being routinely injected into their accelerators. Next, the parametric goals of each of the facilities upgrade projects are specified as well as development efforts currently underway to meet these requirement and improvement goals. It is hoped that that this work will not only capture the current state-of-the-art of worldwide H- beam injectors and clarify the research goals and efforts of the community in general but will also encourage further inter laboratory collaborations.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Artificial Intelligence for Data Center Operations (AIOps): Cooperative Research and Development (Final Report)

High performance computing data centers will increasingly need to rely on automation to keep pace with exascale growth in compute capability and to manage and optimize the data center environment and facility resources. Artificial intelligence and machine learning approaches provide the means to improve HPC data center operational efficiency, by learning historical trends and training models to operate on real-time data collected from both IT and facilities sources. NREL has developed methods of real-time collection, aggregation and streaming of these data in the ESIF HPC Data Center and has collected a significant dataset of relevant metrics across computer systems, racks, environmental, building and utility sources for research into various predictive analytics problems. HPE's Advanced Technology Group (ATG) is doing comprehensive research into exascale monitoring and management for High Performance Computing (HPC) systems (hereinafter HPE's Data Monitoring/ Management Technology). NREL and HPE will collaborate to add Artificial Intelligence (AI) to NREL's real-time data collection/ aggregation/ streaming system and HPE's Data Monitoring/ Management System, with the goal of improving the operational efficiency of NREL's Energy Systems Integration Facility (ESIF) HPC Data Center through data analytics on both historical and real-time data from IT systems and facilities operations. This collaboration will consist of efforts in Data Management, Data Analytics, and AI/ML Optimization for both manual and autonomous intervention in data center operations. This will be a multi-year, multi-staged effort with a goal towards building capabilities for an Advanced Smart Facility, and demonstration of these techniques in the NREL ESIF HPC Data Center.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Report of the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science

This report summarizes insights from the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science, which convened more than 40 experts from national laboratories, academia, industry, and community organizations to chart a path toward more powerful, sustainable, and collaborative scientific software ecosystems. To address urgent challenges at the intersection of high-performance computing (HPC), AI, and scientific software, participants envisioned agile, robust ecosystems built through socio-technical co-design—the intentional integration of social and technical components as interdependent parts of a unified strategy. This approach combines advances in AI, HPC, and software with new models for cross-disciplinary collaboration, training, and workforce development. Key recommendations include building modular, trustworthy AI-enabled scientific software systems; enabling scientific teams to integrate AI systems into their workflows while preserving human creativity, trust, and scientific rigor; and creating innovative training pipelines that keep pace with rapid technological change. Pilot projects were identified as near-term catalysts, with initial priorities focused on hybrid AI/HPC infrastructure, cross-disciplinary collaboration and pedagogy, responsible AI guidelines, and prototyping of public-private partnerships. This report presents a vision of next-generation ecosystems for scientific computing where AI, software, hardware, and human expertise are interwoven to drive discovery, expand access, strengthen the workforce, and accelerate scientific progress.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications

The Modular Autonomous Research System (MARS) was created to address a key challenge in scientific discovery: experiments are often slow, require significant manual labor, and generate data that is not easily integrated across different tools. This limits how quickly scientists can explore new materials, processes, and chemical reactions. Our motivation was to design a system that makes research faster, more reliable, and adaptable by combining automation with artificial intelligence. By doing so, we aimed to reduce human error, accelerate discovery, and allow researchers to quickly test many possibilities that would otherwise take months or years. Our approach was to build a flexible platform that connects laboratory robots, measurement instruments, and a central data system, all guided by artificial intelligence. MARS integrates liquid handling robots, robotic arms, and plate readers with an intelligent decision-making system that chooses the most informative experiments to run next. This creates a closed loop where experiments are performed automatically, the data is analyzed in real time, and new conditions are immediately tested. Through this work, we demonstrated that MARS can carry out multiple experiments with little or no human intervention, adapt to different scientific problems, and handle uncertain or noisy measurements in a robust way. The results show that modular and intelligent automation can significantly accelerate the pace of discovery, providing a model for future self-driving laboratories. This approach addresses the growing scientific need for adaptable, data-driven research platforms that can keep up with the complexity and scale of modern science.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Electric Vehicle Charging for Residential and Commercial Energy Codes: Technical Brief

Numerous studies show that sales of electric vehicles (EVs) have grown consistently over recent years in the U.S. The U.S. Energy Information Administration (EIA) estimated 3 million EVs were on the road in 2022, and the Edison Electric Institute (EEI) forecasts a total of 26.4 million EVs on the road by 2030. Based on this forecast, EEI projects the need for an additional 12.9 million EV charge ports by 2030. If EV charging infrastructure fails to keep pace with sales of EVs it could result in consumers stranded without options to power their vehicles. EVs are capable of providing substantial benefits to the consumers. EVs are less expensive to operate than conventional internal combustion engine vehicles, have lower maintenance costs, and have the convenience of fueling (charging) at home or work. Studies conducted in California show that costs associated with installing EV charging infrastructure can be substantially more expensive for retrofit scenarios compared to new construction, making inclusion of EV infrastructure in new construction codes a cost-effective policy option to increase infrastructure to meet growing demands. PNNL tracks adoption of mandatory EV provisions across the U.S. As of December 20, 2024, 12 states (California, Oregon, Washington, Colorado, New Mexico, Illinois, Maryland, Delaware, New Jersey, Rhode Island, Massachusetts and Vermont) and 53 local governments have added EV provisions to their building codes, local ordinances and zoning requirements. Originally published in 2022, this tech brief has been revised to align with recent model energy code committee discussions and published EV infrastructure code language. This technical brief summarizes market trends, costs and benefits, and provides sample code language for EV charging infrastructure for consideration to be included in model codes, such as the International Energy Conservation Code (IECC) and ANSI/ASHRAE/IES Standard 90.1, as well as directly by states and local governments in their building codes. The technical brief summarizes related efforts undertaken by states and local governments, and builds upon language considered during the 2021 and 2024 IECC development cycles.

2021 IECC↗
DOE OSTI2026

National Laboratory of the Rockies (NLR) 2025 U.S. Geothermal Market Report

The 2025 U.S. Geothermal Market Report updates and expands on the 2021 U.S. Geothermal Power Production and District Heating Market Report with the inclusion of geothermal heat pumps (GHPs) for both distributed and centralized heating and cooling applications. The report updates technology and cost trends in the geothermal power generation industry as well as an uptick in market activities, especially those of next-generation geothermal power technologies, including enhanced geothermal systems (EGS) and closed-loop geothermal (CLG). This report also tracks policy and market drivers that have influenced the direction and growth pace of the U.S. geothermal industry over the years, and especially since 2020.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Irrigation Infrastructure and Modernization – Setting a Baseline: Estimates of Irrigation Water Conveyance Infrastructure Extents and Composition and the Potential Water, Energy, and Economic Benefits of Modernization in the Western U.S.

In the Western United States (U.S.), water delivery for irrigation is still largely managed using century-old equipment and designs. Modernization of this vital water conveyance infrastructure, such as piping of earthen canals, is known to improve water availability and water quality for farmers, while saving energy and enabling new hydropower. However, there is sparse information about the extent of irrigation water delivery infrastructure, which makes it challenging to estimate the cost of upgrades at scale and the potential benefits of accelerating modernization work. This report estimates a variety of previously unquantified data points related to irrigation water delivery infrastructure in the Western U.S. to support stakeholders interested in nationwide modernization planning. The findings should be considered approximations, useful for understanding the scale, range, or variability of these indicators. Taken together, the findings of this report illustrate some of the challenges and opportunities involved in modernizing the agricultural water delivery infrastructure in the Western U.S. Accelerating the pace of modernization could strengthen the long-term resilience of U.S. food systems while providing significant economic, energy, water, and environmental benefits.

13 HYDRO ENERGY↗
DOE OSTI2025

MLCommons Science Benchmarks

Benchmarks are a cornerstone of modern machine learning practice, providing standardized eval- uations that enable reproducibility, comparison, and scientific progress. Yet, as AI systems particularly deep learning models become increasingly dynamic, traditional static benchmarking approaches are losing their relevance. Models rapidly evolve in architecture, scale, and capability; datasets shift; and deployment contexts continuously change, creating a moving target for evaluation. Without adaptive benchmarking frame- works, both scientific assessment and real-world de- ployment risk becoming misaligned with actual system behavior. Drawing on our experience from MLCommons, educa- tional initiatives, and government programs such as the DOE s Million Parameter Consortium, we identify key barriers that hinder the broader adoption and utility of benchmarking in AI. These include substantial resource demands, limited access to specialized hardware, lack of expertise in benchmark design, and uncertainty among practitioners about how to relate benchmark results to their own application domains. Moreover, current benchmarks often emphasize peak performance on leadership-class hardware, offering limited guidance for more diverse, real-world deployment scenarios. We argue that benchmarking itself must become dy- namic in order to incorporate evolving models, updated data, and heterogeneous computational platforms while maintaining transparency, reproducibility, and inter- pretability. Democratizing this process requires not only technical innovation, but also systematic educational efforts spanning undergraduate to professional levels to develop sustained expertise in benchmark design and use. Finally, benchmarks should be framed and com- municated to support application-relevant comparisons, enabling both developers and users to make informed, context-sensitive decisions. Advancing dynamic and inclusive benchmarking practices will be essential to ensure that evaluation keeps pace with the evolving AI landscape and supports responsible, reproducible, and accessible AI deployment.

Hawks, Benjamin G. [Fermilab]↗
DOE OSTI2024

Shunt-Connected FACTS and Synchronous Condensers

In recent years, the electric-transmission system has undergone a significant transformation marked by a greater integration of renewable-energy sources like wind and solar, the phasing out of thermal generation plants, and a concerted effort towards electrifying energy consumption. To keep pace with the integration of renewable-energy sources and the escalating demands of industries and households, it is imperative to expand and modernize the existing power infrastructure. These upgrades are essential to maintain grid stability, enhance power delivery, and boost overall efficiency of the system. However, challenges have emerged with the growing complexity of power grids, particularly in the realms of voltage control, transient stability, and power-quality management.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Navigating Integration: Key Challenges for Data Centers, Nuclear Stakeholders, and Utility Operators

The rapid expansion of data centers, driven by the exponential growth in data-processing and storage needs, presents significant challenges and opportunities for various stakeholders, including data center developers, nuclear energy providers, and utility companies. Data centers are projected to consume 6.7–12% of United States (U.S.) electricity by 2028, driven by artificial intelligence (AI) and cloud-computing demands. Nuclear energy offers reliability and dispatchable baseload power, but data centers need power now while nuclear still needs time to address siting, fast power ramping, and regulatory hurdles. Utilities must keep pace with the unprecedented acceleration of large load interconnection requests and urgently adapt to high-density loads while maintaining grid stability, reliability, and accelerating interconnection timelines. This report dives into these challenges and proposes key collaboration strategies to streamline data center integration that aligns with recent federal initiatives like America’s AI Action Plan and related executive orders that emphasize the importance of data center growth, nuclear energy expansion, and maintaining a competitive edge in the global AI race.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2026

Oakland University Cybersecurity Center (Final Scientific/Technical Report)

This report summarizes the outcomes of Award DE-CR0000023, “Oakland University Cybersecurity Center,” a 31-month project funded by the U.S. Department of Energy Office of Cybersecurity, Energy Security, and Emergency Response (CESER). The project addressed cybersecurity risks facing small and medium-sized manufacturers (SMMs) transitioning to Industry 4.0. The project integrated customer discovery, applied research, and cybersecurity training development. A total of 51 cybersecurity assessments identified significant gaps in baseline practices, incident response, and workforce capability. Research efforts produced a scalable mitigation framework tailored to SMM environments, and workforce analysis identified persistent talent gaps. Eight cybersecurity training modules were developed and deployed via Oakland University’s Professional and Continuing Education (PACE) platform. All objectives were completed, with 98.93% federal budget utilization and cost share exceeding requirements. The project establishes a scalable model for strengthening cybersecurity resilience and workforce capacity across U.S. manufacturing supply chains.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Aggregation and Grid Security Workshop Report

The Aggregation and Grid Security Workshop - held on June 17-18, 2025, National Laboratory of the Rockies (NLR) in Golden, Colorado - brought together approximately 40 external stakeholders from the energy sector, including VPP owner/operators, aggregators, OEMs, utilities, testing & certification labs, trade associations, and cybersecurity vendors. Led by key facilitators, the workshop focused on addressing cybersecurity challenges and enhancing grid resilience for aggregated Distributed Energy Resources (DERs) and Virtual Power Plants (VPPs). The workshop was catalyzed by recognition that traditional, rearward-looking regulatory frameworks are insufficient to keep pace with technological change. There is a "missing understanding" of risk, an "absent security basis" for managing it, and an "untenable responsibility" due to unclear ownership and requirements. The workshop aimed to shift the mindset from reacting to past crises to proactively preparing for emerging threats, fostering forward resilience through risk simulation and collaborative action. This report summarizes the outcomes of the workshop, marking it a significant step toward a secure, reliable and affordable energy future.

14 SOLAR ENERGY↗
DOE OSTI2026

Event-Driven 3D Sensing Architecture

Some events happen too quickly to capture twice. Whether tracking satellites, monitoring aircraft, observing autonomous systems, or recording high speed industrial processes, imaging systems often have only one opportunity to collect the right information. If a camera is focused on the wrong distance, uses the wrong exposure, or cannot keep pace with rapid motion, important details may be lost forever. Los Alamos National Laboratory has developed an event-driven imaging architecture that preserves more information during data collection than a conventional camera. Instead of committing to a single image, the system captures data that can later be used to digitally adjust focus and exposure while reconstructing the scene in three dimensions. The result is a more flexible approach for imaging dynamic environments where conventional cameras can struggle.

42 ENGINEERING↗
DOE OSTI2026

Development and implementation of high-throughput proteomic and metabolomics assays by using advanced chromatographic and mass spectrometric systems (CRADA Final Report)

The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOFMS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Overall, the project advanced rapid metabolomics by combining liquid chromatography, ion mobility spectrometry, and data-independent mass spectrometry with machine learning. This multidimensional approach uses retention time, collision cross-section, precursor mass, and fragment-ion information to distinguish chemically similar metabolites that can be difficult to resolve using conventional liquid- or gas-chromatography methods. The resulting workflow also provided automated metabolite-identification error estimates, addressing a recognized need for statistical confidence measures in metabolomics.

Petzold, Christopher [Lawrence Berkeley National L↗
DOE OSTI2024

CRISPR-Cas9/Cas12a systems for efficient genome editing and large genomic fragment deletions in Aspergillus niger

CRISPR technology has revolutionized fungal genetic engineering by accelerating the pace and expanding the feasible scope of experiments in this field. Among various CRISPR-Cas systems, Cas9 and Cas12a are widely used in genetic and metabolic engineering. In filamentous fungi, both Cas9 and Cas12a have been utilized as CRISPR nucleases. In this work we first compared efficacies and types of genetic edits for CRISPR-Cas9 and -Cas12a systems at the polyketide synthase (albA) gene locus in Aspergillus niger. By employing a tRNA-based gRNA polycistronic cassette, both Cas9 and Cas12a have demonstrated equally remarkable editing efficacy. Cas12a showed potential superiority over Cas9 protein when one gRNA was used for targeting, achieving an editing efficiency of 86.5% compared to 31.7% for Cas9. Moreover, when employing two gRNAs for targeting, both systems achieved up to 100% editing efficiency for single gene editing. In addition, the CRISPR-Cas9 system has been reported to induce large genomic deletions in various species. However, its use for engineering large chromosomal segments deletions in filamentous fungi still requires optimization. Here, we engineered Cas9 and -Cas12a-induced large genomic fragment deletions by targeting various genomic regions of A. niger ranging from 3.5 kb to 40 kb. Our findings demonstrate that targeted engineering of large chromosomal segments can be achieved, with deletions of up to 69.1% efficiency. Furthermore, by targeting a secondary metabolite gene cluster, we show that fragments over 100 kb can be efficiently and specifically deleted using the CRISPR-Cas9 or -Cas12a system. Overall, in this paper, we present an efficient multi-gRNA genome editing system utilizing Cas9 or Cas12a that enables highly efficient targeted editing of genes and large chromosomal regions in A. niger.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Developing a media formulation to sustain ex vivo chloroplast function

Chloroplasts are critical organelles in plants and algae responsible for accumulating biomass through photosynthetic carbon fixation and cellular maintenance through metabolism in the cell. Chloroplasts are increasingly appreciated for their role in biomanufacturing, as they can produce many useful molecules, and a deeper understanding of chloroplast regulation and function would provide more insight for the biotechnological applications of these organelles. However, traditional genetic approaches to manipulate chloroplasts are slow, and generation of transgenic organisms to study their function can take weeks to months, significantly delaying the pace of research. To develop chloroplasts themselves as a quicker and more defined platform, we isolated chloroplasts from the green algae, Chlamydomonas reinhardtii, and examined their photosynthetic function after extraction. Combined with a metabolic modeling approach using flux-balance analysis, we identified key metabolic reactions essential to chloroplast function and leveraged this information into reagents that can be used in a “chloroplast media” capable of maintaining chloroplast photosynthetic function over time ex vivo compared to buffer alone. We envision this could serve as a model platform to enable more rapid design-build-test-learn cycles to study and improve chloroplast function in combination with genetic modifications and potentially as a starting point for the bottom-up design of a synthetic organelle-containing cell.

Chlamydomonas reinhardtii↗
DOE OSTI2024

Challenges of open data in aquatic sciences: issues faced by data users and data providers

Free use and redistribution of data (i.e., Open Data) increases the reproducibility, transparency, and pace of aquatic sciences research. However, barriers to both data users and data providers may limit the adoption of Open Data practices. Here, we describe common Open Data challenges faced by data users and data providers within the aquatic sciences community (i.e., oceanography, limnology, hydrology, and others). These challenges were synthesized from literature, authors’ experiences, and a broad survey of 174 data users and data providers across academia, government agencies, industry, and other sectors. Through this work, we identified seven main challenges: 1) metadata shortcomings, 2) variable data quality and reusability, 3) open data inaccessibility, 4) lack of standardization, 5) authorship and acknowledgement issues 6) lack of funding, and 7) unequal barriers around the globe. Our key recommendation is to improve resources to advance Open Data practices. This includes dedicated funds for capacity building, hiring and maintaining of skilled personnel, and robust digital infrastructures for preparation, storage, and long-term maintenance of Open Data. Further, to incentivize data sharing we reinforce the need for standardized best practices to handle data acknowledgement and citations for both data users and data providers. We also highlight and discuss regional disparities in resources and research practices within a global perspective.

54 ENVIRONMENTAL SCIENCES↗