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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 253 records · Page 14

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DOE OSTI2017

Development of Manufacturing Technology to Accelerate Cost Reduction of Low Concentration and

The purpose of this project was to accelerate deployment of cost-effective US-based manufacturing of Solaria’s unique c-Si module technology. This effort successfully resulted in the development of US-based manufacturing technology to support two highly-differentiated, market leading product platforms. The project was initially predicated on developing Solaria’s low-concentration PV (LCPV) module technology which at the time of the award was uniquely positioned to exceed the SunShot price goal of $0.50/Wp for standard c-Si modules. The Solaria LCPV module is a 2.5x concentrator that leverages proven, high-reliability PV module materials and low silicon cell usage into a technology package that already had the lowest direct material cost and leading Levelized Cost of Electricity (LCOE). With over 25 MW commercially deployed globally, the Solaria module was well positioned to continue to lead in PV module cost reduction. Throughout the term of the contract, market conditions changed dramatically and so to did Solaria’s product offerings to support this. However, the manufacturing technology developed for the LCPV module was successfully leveraged and optimized to support two new and different product platforms. BIPV “PowerVision” and High-efficiency “PowerXT” modules. The primary barrier to enabling high-volume PV module manufacturing in the US is the high manual labor component in certain unique aspects of our manufacturing process. The funding was used to develop unique manufacturing automation which makes the manual labor components of these key processes more efficient and increase throughput. At the core of Solaria’s product offerings are its unique and proprietary techniques for dicing and re-arranging solar cells into modules with highly-differentiated characteristics that address key gaps in the c-Si market. It is these techniques that were successfully evolved and deployed into US-based manufacturing site with SunShot funding. Today, Solaria is currently positioned to become the market leader with these two technologies over the coming 24 months largely due to the successful innovations of the underlying manufacturing technology. This success will leverage US-based manufacturing technology and the associated US-jobs to support. Solaria views the project as highly successful and a great example of SunShot funding enabling the creating of US jobs and the deployment of ubiquitous solar energy products.

14 SOLAR ENERGY↗

DOE OSTI2026

ARM FY2026 Radar Plan

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility maintains a suite of advanced atmospheric radar systems that serve as critical tools in ARM’s mission to provide continuous, high-quality observations for advancing the understanding and modeling of atmospheric processes. These radar systems enable detailed characterization of clouds, precipitation, and dynamic structures in the atmosphere, supporting a broad range of scientific applications. The number of deployed systems exceeds what current staffing levels can fully support for continuous 24/7/365 operation. As such, it is essential to have a clearly defined and community-informed plan that prioritizes radar operations and communicates ARM’s strategy for sustaining and evolving these observational assets. This FY2026 Radar Plan outlines ARM’s approach to managing its radar portfolio—balancing scientific impact, operational feasibility, and long-term sustainability. It reflects ARM’s continued commitment to delivering calibrated, well-documented radar data products that enable process-level studies and support the development and evaluation of weather and climate models. Through this plan, ARM aims to ensure transparency in decision-making, alignment with user needs, and support for innovative science across the facility’s fixed and mobile observatories. Given uncertainties around the Fiscal Year (FY) 2026 budget, this plan was developed to assume business as usual and will be updated as budgets and plans may change. It should be noted that, given the limited timeframe involved, this plan will be more succinct than previous plans.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Two-mode squeezing over deployed fiber coexisting with conventional communications

Explore the source record for details and available documents.

Chapman, Joseph↗

DOE OSTI2022

Seasonal Fluctuations in Iron Cycling in Thawing Permafrost Peatlands

In permafrost peatlands, up to 20% of total organic carbon (OC) is bound to reactive iron (Fe) minerals in the active layer overlying intact permafrost, potentially protecting OC from microbial degradation and transformation into greenhouse gases (GHG) such as CO 2 and CH 4 . During the summer, shifts in runoff and soil moisture influence redox conditions and therefore the balance of Fe oxidation and reduction. Whether reactive iron minerals could act as a stable sink for carbon or whether they are continuously dissolved and reprecipitated during redox shifts remains unknown. We deployed bags of synthetic ferrihydrite (FH)-coated sand in the active layer along a permafrost thaw gradient in Stordalen mire (Abisko, Sweden) over the summer (June to September) to capture changes in redox conditions and quantify the formation and dissolution of reactive Fe(III) (oxyhydr)oxides. We found that the bags accumulated Fe(III) under constant oxic conditions in areas overlying intact permafrost over the full summer season. In contrast, in fully thawed areas, conditions were continuously anoxic, and by late summer, 50.4 ± 12.8% of the original Fe(III) (oxyhydr)oxides were lost via dissolution. Periodic redox shifts (from 0 to +300 mV) were observed over the summer season in the partially thawed areas. This resulted in the dissolution and loss of 47.2 ± 20.3% of initial Fe(III) (oxyhydr)oxides when conditions are wetter and more reduced, and new formation of Fe(III) minerals (33.7 ± 8.6% gain in comparison to initial Fe) in the late summer under more dry and oxic conditions, which also led to the sequestration of Fe-bound organic carbon. Our data suggest that there is seasonal turnover of iron minerals in partially thawed permafrost peatlands, but that a fraction of the Fe pool remains stable even under continuously anoxic conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Diurnal Trends and Meteorological Factors Influencing the Variability of Fluorescent Bioaerosol in Mt. Crested Butte, Colorado During SAIL

Abstract Despite the crucial role of bioaerosol particles (BAP) in our climate system, local ecosystems, and human health, our grasp on their atmospheric interactions is hampered by a lack of high‐resolution and long‐term data, which is essential for understanding their abundance and variability in response to meteorological conditions. To discern these relationships in a high‐altitude mountainous terrain is even less well‐studied. Therefore, we deployed a Wideband Integrated Bioaerosol Sensor (WIBS‐Neo) for three months during the first biologically active season of the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign in summer 2022 on Mt. Crested Butte in Colorado (elevation 3,140 m ASL). Here, we report real‐time fluorescent BAP (FBAP) data collected from 15 June to 13 September 2022, sampled from within the ARM Aerosol Observing System (AOS). To our knowledge, these are the first high altitude (>3,000 m ASL) continuous measurements of FBAP made in North America. During this deployment, we observed on average 21% and as many as 48% (hourly maximum) of particles within the detection size range ( to ) of the WIBS as FBAP. Our analysis presents the diurnal cycles for seven distinct types of FBAP, showing unique patterns and, in some cases, correlations with temperature and solar radiation cycles. Abundance and composition of FBAP varied with relative humidity and precipitation. Precipitation events appeared to both cause emission and removal of FBAP, whereas dust and smoke events had no significant effect, highlighting the critical role of meteorology on FBAP at high altitudes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Artificial Intelligence and Digital Engineering as Enablers for System Engineering in the Energy Sector

Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world’s energy demands and build energy security.

42 - ENGINEERING↗

DOE OSTI2025

Artificial Intelligence and Digital Engineering as Enablers for Systems Engineering in the Energy Sector

42 - ENGINEERING↗

DOE OSTI2023

Renewable Thermal Energy Systems: Systemic Challenges and Transformational Policies (Report 2)

Two important challenges for RTES are the need for significant further innovation and deployment, particularly in the United States. In the face of continued success of other renewable energy technologies, such as solar photovoltaics (PV) and land-based wind energy for electricity, it is worthwhile to evaluate the current state of select RTES technologies in the context of technology innovation systems and energy transitions. This report marks a departure from the existing body of research we are aware of and have contributed to relating to RTES applications for industrial process heat in the United States. Our objectives for this report are fourfold: 1) Begin considering the challenges of RTES deployment in the United States through application of socio-technical and transitions frameworks; 2) Begin applying the theory of RTES as configurational technology innovation systems in the United States and for industrial process heat specifically; 3) Review RTES innovation, policies, and market formation activities in the contexts of Objectives 1 and 2; 4) Identify future paths for RTES research. Ultimately, the challenges associated with scaling the deployment of RTES in the United States are related to the need to develop a well-functioning innovation system that can overcome resistance from a highly stable socio-technical regime within a shrinking window of time to address the climate crisis, U.S. industrial competitiveness, and volatile energy commodity price shocks. Current efforts toward scaling for RTES applications in industry and buildings emphasize the need for further cost reductions, but not for interventions that support actors and their interactions and that increase the legitimacy of the technologies. As a result, there are significant opportunities to analyze the social contexts of RTES that can then be used to inform the design of effective, transformational policy portfolios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

A Novel Segmentation Algorithm for the ARM User Facility All-Sky Imagers Using Machine Learning Applications

Cloud cover plays a pivotal role in modulating the Earth's energy budget through the reflection of incoming solar radiation and the trapping of outgoing longwave radiation. Ground-based all-sky imagers offer an objective assessment of cloud cover that can be used to estimate solar irradiance, classify cloud types, track cloud movement, and serve as a benchmark 10 for the evaluation of satellite and reanalysis data products. The Atmospheric Radiation Measurement (ARM) user facility has utilized all-sky imagers for more than 25 years to monitor cloud cover and augment its comprehensive suite of atmospheric measurements. Following the retirement of its Total Sky Imager (TSI), ARM recently deployed the TSI’s successor, the All Sky Imager (ASI-16 camera systems). To provide a smooth transition and continuity to the vast amount of knowledge gathered by the TSI over the years, while addressing typical deployment issues, we developed a novel pixel segmentation algorithm, 15 the ASI Sky Cover (ASISKYCOVER). ASISKYCOVER builds on the different strengths and properties of the TSI processing algorithm while integrating machine learning techniques, ensuring data validity and accuracy across diverse atmospheric conditions. It enhances cloud cover characterization with new features such as artifact detection and uncertainty quantification. ASISKYCOVER also includes cloud cover estimates for near-zenith (narrow field-of-view) and reduces susceptibility to false detections. This study introduces ASISKYCOVER, details its algorithm framework, and demonstrates its capabilities using a 20 year-long dataset from the ARM Southern Great Plains site. Comparisons with co-located TSI data and other ARM measurements, such as zenith-pointing radars and lidars, are presented, underscoring the ASISKYCOVER’s potential to improve cloud cover analyses and data evaluation efforts, as well as to be integrated into higher-level data products that synergize instrument suites to generate new and insightful information

Silber, Israel↗

DOE OSTI2024

Physics-informed heterogeneous graph neural networks for DC blocker placement

The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts. One such approach involves placing transformer neutral blocking devices, which interrupt the path of geomagnetically induced currents (GICs) to limit their impact. The high cost of these devices and the sparsity of transformers that experience high GICs during GMD events, however, calls for a sparse placement strategy that involves high computational cost. To address this challenge, we developed a physics-informed heterogeneous graph neural network (PIHGNN) for solving the graph-based dc-blocker placement problem. Our approach combines a heterogeneous graph neural network (HGNN) with a physics-informed neural network (PINN) to capture the diverse types of nodes and edges in ac/dc networks and incorporates the physical laws of the power grid. We train the PIHGNN model using a surrogate power flow model and validate it using case studies. Results demonstrate that PIHGNN can effectively and efficiently support the deployment of GIC dc-current blockers, ensuring the continued supply of electricity to meet societal demands. Furthermore, our approach has the potential to contribute to the development of more reliable and resilient power grids capable of withstanding the growing threat that GMDs pose.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

The electric vehicles-solar photovoltaics Nexus: Driving cross-sectoral adoption of sustainable technologies

Residential and transportation energy consumption account for more than one-half of the overall energy consumption in the United States. Adoption of electric vehicles (EVs) can play a key role in decarbonizing the transportation sector, while the adoption of renewable energy sources (e.g., solar photovoltaics [PVs]) could bring similar benefits to the residential energy sector and in turn support transport electrification. Although the market shares for both EVs and PVs continue to grow, both of these emerging technologies are deployed rather disjointly, without considering the existence of potential similarities among users who own (or aspire to own) these technologies. This might be due to lack of understanding of the behavioral interdependence in consumer preferences toward these technologies. To fill this gap in knowledge, this study utilizes data from the 2018 WholeTraveler Transportation Behavior Study to develop an integrated model system that explores interactive EV and PV adoption behaviors. A structural equation model is employed that incorporates direct effects as well as error correlations among the adoption behaviors for EVs and PVs. Model results indicate that the adoption behavior for both these technologies is indeed interconnected and significantly influenced by attitudes, values, and personality traits. Findings from this research suggest that incentives (e.g., subsidies) that drive bundled adoption of EV-PV systems could accelerate the adoption of both of these sustainable technologies. In conclusion, this study highlights the need to consider transport and building energy-efficient technology adoption behavior in a single integrated structure.

14 SOLAR ENERGY↗

DOE OSTI2025

WLCG transition from X.509 to Tokens: Progress and Outlook

Since 2017, the Worldwide LHC Computing Grid (WLCG) has been working towards enabling token-based authentication and authorization throughout its entire middleware stack.Taking guidance from the WLCG Token Transition Timeline, published in 2022, substantial progress has been achieved not only in making middleware compatible with the use of tokens, but also in understanding the limitations of the WLCG Common JWT Profiles, first published in 2019. Significant scalability experience has been gained from Data Challenge 2024, during which millions of files were transferred with tokens used as credentials - a significant percentage of the total transfers completed.Besides describing the state of affairs in the transition to tokens, revisions to the WLCG token profile, and the evolving road maps, this contribution also covers the corresponding transition from VOMS-Admin to INDIGO-IAM services, with continuing improvements in terms of functionality as well as deployment.

Dack, Thomas [Rutherford Appleton Laboratory]↗

DOE OSTI2020

Global Experiences with HPC Operational Data Measurement, Collection and Analysis

As we move into the exascale era, supercomputers grow larger, denser, more heterogeneous, and ever more complex. Operating such machines reliably and efficiently requires deep insight into the operational parameters of the machine itself as well as its supporting infrastructure. To fulfill this need, early adopter sites have started the development and deployment of Operational Data Analytics (ODA) frameworks allowing the continuous monitoring, archiving, and analysis of near realtime performance data from the machine and infrastructure levels, providing immediately actionable information for multiple operational uses. To understand their ODA goals, requirements, and use cases, we have conducted a survey among eight early adopter sites from the US, Europe, and Japan that operate top 50 high-performance computing systems. We have assessed the technologies leveraged to build their ODA frameworks, identified use cases and other push and pull factors that drive the sites' ODA activities, and report on their operational lessons.

Ott, Michael↗

DOE OSTI2024

Unveiling Temporal Performance Deviation: Leveraging Clustering in Microservices Performance Analysis

As the market for cloud computing continues to grow, an increasing number of users are deploying applications as microservices. The shift introduces unique challenges in identifying and addressing performance issues, particularly within large and complex infrastructures. To address this challenge, we propose a methodology that unveils temporal performance deviations in microservices by clustering containers based on their performance characteristics at different time intervals. Showcasing our methodology on the Alibaba dataset, we found both stable and dynamic performance patterns, providing a valuable tool for enhancing overall performance and reliability in modern application landscapes.

Clustering↗

DOE OSTI2025

The Rise of US Photovoltaics: An Exploration of Land Use Density Trends in Large‐Scale Solar Facility Attributes, 2012–2021

An energy transition is underway in the United States; renewable energy generation is now on par with coal and nuclear generation. The number of large-scale solar photovoltaic facilities increased approximately tenfold between 2012 and 2021, with an associated 25-fold increase in cumulative installed capacity. With ambitious decarbonization and renewable energy deployment goals at both the federal and state levels, deployments of large-scale solar photovoltaic facilities will continue apace. This growth is likely to be complex with ripples of impacts felt throughout different aspects of society, and thus accurate solar land use metrics allowing more accurate predictions are of value to policymakers, planners, and other stakeholders in the future photovoltaic build-out. In this paper, we leverage data from the newly released US Large-Scale Solar Photovoltaic Database to examine recent trends in large-scale solar photovoltaic land use. We analyze the relationships between solar array capacity density (W/acre) and a range of facility attributes to better understand the future land requirements of solar capacity expansion over the coming years. Installed capacity was the single strongest determinant of solar array area. However, we found substantial variation in capacity density across facility attributes, including mount type, latitude, urbanicity, time, and prior land use.

14 SOLAR ENERGY↗

DOE OSTI2016

Thorium Fuel Cycle Option Screening in the United States

As part of a nuclear fuel cycle Evaluation and Screening (E&S) study, a wide-range of thorium fuel cycle options were evaluated and their performance characteristics and challenges to implementation were compared to those of other nuclear fuel cycle options based on criteria specified by the Nuclear Energy Office of the U.S. Department of Energy (DOE). The evaluated nuclear fuel cycles included the once-through, limited, and continuous recycle options using critical or externally-driven nuclear energy systems. The E&S study found that the continuous recycle of 233U/Th in fuel cycles using either thermal or fast reactors is an attractive promising fuel cycle option with high effective fuel resource utilization and low waste generation, but did not perform quite as well as the continuous recycle of Pu/U using a fast critical system, which was identified as one of the most promising fuel cycle options in the E&S study. This is because compared to their uranium counterparts the thorium-based systems tended to have higher radioactivity in the short term (about 100 years post irradiation) because of differences in the fission product yield curves, and in the long term (100,000 years post irradiation) because of the decay of 233U and daughters, and because of higher mass flow rates due to lower discharge burnups. Some of the thorium-based systems also require enriched uranium support, which tends to be detrimental to resource utilization and waste generation metrics. Finally, similar to the need for developing recycle fuel fabrication, fuels separations and fast reactors for the most promising options using Pu/U recycle, the future thorium-based fuel cycle options with continuous recycle would also require such capabilities, although their deployment challenges are expected to be higher since such facilities have not been developed in the past to a comparable level of maturity for Th-based systems.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2014

Subtask 2.17 - CO 2 Storage Efficiency in Deep Saline Formations

As the field of carbon capture and storage (CCS) continues to advance, and large-scale implementation of geologic carbon dioxide (CO 2 ) storage progresses, it will be important to understand the potential of geologic formations to store meaningful amounts of CO 2 . Geologic CO 2 storage in deep saline formations (DSFs) has been suggested as one of the best potential methods for reducing anthropogenic CO 2 emission to the atmosphere, and as such, updated storage resource estimation methods will continue to be an important component for the widespread deployment of CCS around the world. While there have been several methodologies suggested in the literature, most of these methods are based on a volumetric calculation of the pore volume of the DSF multiplied by a storage efficiency term and do not consider the effect of site-specific dynamic factors such as injection rate, injection pattern, timing of injection, pressure interference between injection locations, and overall formation pressure buildup. These volumetric methods may be excellent for comparing the potential between particular formations or basins, but they have not been validated through real-world experience or full-formation injection simulations. Several studies have also suggested that the dynamic components of geologic storage may play the most important role in storing CO 2 in DSFs but until now have not directly compared CO 2 storage resource estimates made with volumetric methodologies to estimates made using dynamic CO 2 storage methodologies. In this study, two DSFs, in geographically separate areas with geologically diverse properties, were evaluated with both volumetric and dynamic CO 2 storage resource estimation methodologies to compare the results and determine the applicability of both approaches. In the end, it was determined that the dynamic CO 2 storage resource potential is timedependent and it asymptotically approaches the volumetric CO 2 storage resource potential over very long periods of time in the two systems that were evaluated. These results indicate that the volumetric assessments can be used as long as the appropriate storage efficiency terms are used and it is understood that it will take many wells over very long periods of time to fully realize the storage potential of a target formation. This subtask was funded through the Energy & Environmental Research Center (EERC)– U.S. Department of Energy (DOE) Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FC26-08NT43291. Nonfederal funding was provided by the IEA Greenhouse Gas R&D Programme.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Moving Toward an Optimal and Automated Geospatial Network for CCUS Infrastructure

Modifications in the global climate are being driven by the anthropogenic release of greenhouse gases (GHG) including carbon dioxide (CO 2 ) (Middleton et al. 2014). CO 2 emissions have, for example, been directly linked to an increase in total global temperature (Seneviratne et al. 2016). Strategies that limit CO 2 emissions—like CO 2 capture, utilization, and storage (CCUS) technology—can greatly reduce emissions by capturing CO 2 before it is released to the atmosphere. However, to date CCUS technology has not been developed at a large commercial scale despite several promising high profile demonstration projects (Middleton et al. 2015). Current CCUS research has often focused on capturing CO 2 emissions from coal-fired power plants, but recent research at Los Alamos National Laboratory (LANL) suggests focusing CCUS CO 2 capture research upon industrial sources might better encourage CCUS deployment. To further promote industrial CCUS deployment, this project builds off current LANL research by continuing the development of a software tool called SimCCS, which estimates a regional system of transport to inject CO 2 into sedimentary basins. The goal of SimCCS, which was first developed by Middleton and Bielicki (2009), is to output an automated and optimal geospatial industrial CCUS pipeline that accounts for industrial source and sink locations by estimating a Delaunay triangle network which also minimizes topographic and social costs (Middleton and Bielicki 2009). Current development of SimCCS is focused on creating a new version that accounts for spatial arrangements that were not available in the previous version. This project specifically addresses the issue of non-unique Delaunay triangles by adding additional triangles to the network, which can affect how the CCUS network is calculated.

54 ENVIRONMENTAL SCIENCES↗