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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Innovative Strategies for Long-Term Monitoring of Complex Groundwater Plumes at DOE’s Legacy Sites (Workshop Report)

Most remaining Department of Energy (DOE) sites will require extended periods of institutional control, especially at complex groundwater sites where attenuation-based strategies have been implemented to facilitate closure. The current practice of monitoring—obtaining and analyzing contaminant concentration in groundwater samples at numerous wells—will account for a large portion of the projected life-cycle at these DOE sites unless a new approach is adopted. State-of-the-art technologies are being developed, including in situ sensors, geophysics, radiation mapping, numerical modeling and AI/ML. These technologies can optimize monitoring strategies in space and time, provide spatially extensive information at vulnerable regions and/or provide more continuous monitoring at lower cost. As part of DOE’s Office of Environmental Management (DOE-EM’s) efforts to advance long-term monitoring systems, an in-person/virtual hybrid workshop was hosted by Savannah River National Laboratory (SRNL) on January 24 and 25, 2023, in Augusta, Georgia. Because DOE-EM’s complex sites will eventually be transferred to DOE’s Office of Legacy Management (DOE-LM), representatives of DOE-LM were important participants in the workshop. The purpose of the workshop was to identify challenges and opportunities for deploying advanced technologies for long-term monitoring at DOE sites. The key questions during the workshop were: 1) the regulatory acceptance of replacing a process that traditionally has used laboratory sampling and analysis of groundwater samples, and 2) the application of this strategy to the southwestern arid sites that include many of the remaining DOE-EM and DOE-LM complex groundwater plumes. Characteristics common to most arid sites present both limitations and opportunities for advanced technologies. DOE-EM has funded a National Laboratory team from SRNL, Lawrence Berkeley National Laboratory (LBNL), and Pacific Northwest National Laboratory (PNNL) to establish the overarching framework of long-term monitoring by systematically combining advanced hardware and software technologies. This project is titled “Advanced Long-Term Environmental Monitoring Systems (ALTEMIS)” and is sponsored by the DOE-EM Technology Development Program. The multi-laboratory team is currently developing and testing innovative monitoring strategies, including the use of in situ groundwater sensors, geophysics, drone/satellite-based remote sensing, reactive transport modeling, and artificial intelligence/machine learning (AI/ML). The project’s demonstration testbed is at the Savannah River Site (SRS) F-Area Seepage Basins, where a well-characterized complex groundwater plume composed of uranium and other radionuclides is in the latter stages of remediation. The workshop included more than 70 participants, presentations, a field visit to F-Area, breakout working groups, and large group discussion. Participants developed recommendations on five topics: in situ sensors, spatially integrative tools, challenges to regulatory acceptance, AI/ML strategies, and transitioning sites to DOE-LM.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Mitigating Data Center Impact on Grid Stability: A Coordinated Control Strategy Using Verrus StabiliGrid Architecture

Large data centers, which now represent a significant and growing share of the total U.S. grid load, can inadvertently destabilize the electrical grid when they disconnect simultaneously during brief voltage disturbances. The July 10, 2024, Eastern Interconnection incident, in which a sub-100-millisecond transmission fault triggered the cascading loss of approximately 1,500 MW of data center load, illustrates this vulnerability. While commercial battery energy storage systems (BESS) deployed in data centers provide device-level fault ride-through per IEEE 1547, they lack coordination with facility protection logic and uninterruptible power supplies (UPS), limiting their effectiveness as grid-stabilizing assets. This report presents the Verrus StabiliGrid architecture, a coordinated control framework that integrates BESS, UPS, and point-of-interconnection (POI) protection settings to enable data centers to ride through both undervoltage and overvoltage grid contingencies without disconnecting. The four-step strategy encompasses: (1) high-resolution power quality monitoring to detect the grid state during events such as undervoltage, overvoltage, underfrequency, and overfrequency; (2) POI protection settings that allow for extended ride-through and grid-connected operation during grid contingencies; (3) grid state-driven autonomous dispatch of assets to improve grid resilience by reducing power draw during undervoltage or absorbing more power during overvoltage events; and (4) coordinated post-recovery dispatch of data center assets to restore firm load to pre-contingency levels. Validated through controller-hardware-in-the-loop (C-HIL) simulations at the National Laboratory of the Rockies, results show grid import restoration to pre-fault levels within 100 milliseconds of voltage recovery. This work advances the ability of data centers to transition from passive, disturbance-sensitive loads to active participants in grid stability, a capability increasingly required by emerging NERC and ERCOT regulatory frameworks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A Materials Perspective on Direct Recycling of Lithium-Ion Batteries: Principles, Challenges and Opportunities

As the dominant means of energy storage technology today, the widespread deployment of lithium-ion batteries (LIBs) would inevitably generate countless spent batteries at their end of life. From the perspectives of environmental protection and resource sustainability, recycling is a necessary strategy to manage end-of-life LIBs. Compared with traditional hydrometallurgical and pyrometallurgical recycling methods, the emerging direct recycling technology, rejuvenating spent electrode materials via a non-destructive way, has attracted rising attention due to its energy efficient processes along with increased economic return and reduced CO 2 footprint. This review investigates the state-of-the-art direct recycling technologies based on effective relithiation through solid-state, aqueous, eutectic solution and ionic liquid mediums and thoroughly discusses the underlying regeneration mechanism of each method regarding different battery chemistries. It is concluded that direct regeneration can be a more energy-efficient, cost-effective, and sustainable way to recycle spent LIBs compared with traditional approaches. Additionally, it is also identified that the direct recycling technology is still in its infancy with several fundamental and technological hurdles such as efficient separation, binder removal and electrolyte recovery. Finally, in addressing these remaining challenges, this review proposes an outlook on potential technical avenues to accelerate the development of direct recycling toward industrial applications.

25 ENERGY STORAGE↗
DOE OSTI2024

Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications

Run to run variability in parallel programs caused by floating-point non-associativity has been known to significantly affect reproducibility in iterative algorithms, due to accumulating errors. Non-reproducibility can critically affect the efficiency and effectiveness of correctness testing for stochastic programs. Recently, the sensitivity of deep learning training and inference pipelines to floating-point non-associativity has been found to sometimes be extreme. It can prevent certification for commercial applications, accurate assessment of robustness and sensitivity, and bug detection. New approaches in scientific computing applications have coupled deep learning models with high-performance computing, leading to an aggravation of debugging and testing challenges. Here we perform an investigation of the statistical properties of floating-point non-associativity within modern parallel programming models, and analyze performance and productivity impacts of replacing atomic operations with deterministic alternatives on GPUs. We examine the recently-added deterministic options in PyTorch within the context of GPU deployment for deep learning, uncovering and quantifying the impacts of input parameters triggering run to run variability and reporting on the reliability and completeness of the documentation. Finally, we evaluate the strategy of exploiting automatic determinism that could be provided by deterministic hardware, using the Groq LPUTM accelerator for inference portions of the deep learning pipeline. We demonstrate the benefits that a hardware-based strategy can provide within reproducibility and correctness efforts.

Shanmugavelu, Sanjif↗
DOE OSTI2022

2020 Hydrogen and Fuel Cell Technologies Market Report

This report features summaries of relevant hydrogen and fuel cell activity in 2020, consistent with prior annual reports across applications and sectors. However, 2020 was no ordinary year. Despite pandemic hardships, the hydrogen and fuel cell industry not only stepped up to assist with humanitarian and healthcare efforts but saw continued momentum in several growing market sectors with numerous partnerships, initiatives, and investments. Using hydrogen and fuel cell technologies to provide an environmental and economic pathway to clean, reliable power was a key feature of world economic recovery and energy infrastructure plans. Today, hydrogen energy and fuel cell systems are being developed and deployed in a range of stationary power, portable power, and transportation applications around the world. Throughout 2020, there were significant achievements in each sector, building on years of research and development (R&D), field testing, large-scale demonstration projects, and real-world customer experience. The heavy-duty truck market in particular continued to make substantial gains, with new players entering the space and a coalition of companies pledging to produce and deploy more than 100,000 fuel cell trucks in the next two decades. To provide hydrogen for trucks and other end users, larger and larger electrolyzer projects were announced throughout the year. Collectively, there were more than 430 megawatts (MW) of electrolyzer systems either deployed, ordered, or proposed around the world. This 2020 report also provides special “Spotlights” on the collective industry response to the COVID-19 pandemic as well as international commitments and hydrogen strategies, providing overviews of significant activity during and after the initial onset of COVID-19 around the world.

08 HYDROGEN↗
NASA NTRS2003

Stray-Light Correction of the Marine Optical Buoy

In ocean-color remote sensing, approximately 90% of the flux at the sensor originates from atmospheric scattering, with the water-leaving radiance contributing the remaining 10% of the total flux. Consequently, errors in the measured top-of-the atmosphere radiance are magnified a factor of 10 in the determination of water-leaving radiance. Proper characterization of the atmosphere is thus a critical part of the analysis of ocean-color remote sensing data. It has always been necessary to calibrate the ocean-color satellite sensor vicariously, using in situ, ground-based results, independent of the status of the pre-flight radiometric calibration or the utility of on-board calibration strategies. Because the atmosphere contributes significantly to the measured flux at the instrument sensor, both the instrument and the atmospheric correction algorithm are simultaneously calibrated vicariously. The Marine Optical Buoy (MOBY), deployed in support of the Earth Observing System (EOS) since 1996, serves as the primary calibration station for a variety of ocean-color satellite instruments, including the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Japanese Ocean Color Temperature Scanner (OCTS) , and the French Polarization and Directionality of the Earth's Reflectances (POLDER). MOBY is located off the coast of Lanai, Hawaii. The site was selected to simplify the application of the atmospheric correction algorithms. Vicarious calibration using MOBY data allows for a thorough comparison and merger of ocean-color data from these multiple sensors.

Brown, Steven W.↗
NASA NTRS2017

Implicitly Coordinated Detect and Avoid Capability for Safe Autonomous Operation of Small UAS

As the airspace becomes increasingly shared by autonomous small Unmanned Aerial Systems (UAS), there would be a pressing need for coordination strategies so that aircraft can safely and independently maneuver around obstacles, geofences, and traffic aircraft. Explicitly coordinating resolution strategies for small UAS would require additional components such as a reliable vehicle-to-vehicle communication infrastructure and standardized protocols for information exchange that could significantly increase the cost of deploying small UAS in a shared airspace. This paper explores a novel approach that enables multiple aircraft to implicitly coordinate their resolution maneuvers. By requiring all aircraft to execute the proposed approach deterministically, it is possible for all of them to implicitly agree on the region of airspace each will be occupying in a given time interval. The proposed approach lends itself to the construction of a suitable feedback mechanism that enables the real-time execution of an implicitly conflict-free path in a closed-loop manner dealing with uncertainties in aircraft speed. If a network infrastructure is available, the proposed approach can also exploit the benefits of explicit information.

Balachandran, Swee↗
DOE OSTI2025

Systems Analysis of Biomass and Coal Co-firing Power Plants with Deep Carbon Capture Toward Net-zero Emissions

Achieving a net-zero emission economy in the United States requires integrating diverse low-carbon and negative-emission technologies into the existing fossil fuel-dominant power fleet. Potential technologies from the low-carbon portfolio include renewable power, fossil power with carbon capture and storage (CCS), bioenergy with CCS (BECCS), and direct air capture (DAC). Renewable power is a clean energy source but has to pair with costly battery storage to provide dispatchable electricity. Fossil power with CCS offers dispatchable electricity yet still relies on DAC to offset residual emissions, even when deploying deep CCS with more than 90% CO2 capture. Coal-biomass co-firing with CCS, a subset of BECCS, is a reliable energy production technology that can be retrofitted from existing electricity generation units (EGUs). Power plant retrofit maximizes the use of the current U.S. coal power fleet without the need for large-scale deployment of new renewable power, battery storage, or DAC. Retrofitting coal-biomass co-firing with deep CCS in EGUs is a promising option, but not a universal solution. Biomass co-firing at a power plant introduces economic challenges and indirectly poses pressure on land and water resources. Meanwhile, retrofitting deep CCS affects plant efficiency and raises electricity generation costs. Overall, the technical feasibility and economic viability of plant retrofits vary across EGUs, as they are contingent upon the regional availability of biomass, unit-specific characteristics, site-specific fuel supply costs, and adjacent CO2 storage potential. Government incentives like 45Q can improve the retrofit viability, though the impact requires further quantification. A comprehensive analysis at the unit level is essential to address the question regarding the fate of the U.S. coal-fired electricity generation fleet toward the net-zero emission goal. This study conducts a systematic techno-economic-environmental assessment of EGUs to identify the viability of biomass co-firing and deep CCS retrofits in the U.S. coal-fired power fleet. Specifically, it characterizes the techno-economic performance of deep carbon capture, estimates life cycle greenhouse gas (GHG) emissions, and conducts a fleet-level assessment on retrofit viability. The key objectives are (1) to estimate the unit-specific performance and retrofitted cost under various biomass co-firing levels and CO2 capture rates; (2) to determine the possibility of reaching net-zero emission at the fleet level; (3) to quantify the cumulative capacities that are suitable for plant retrofits under current and future biomass supply scenarios; and (4) to improve the understanding of policy impacts on such retrofits to help the power sector’s transition to a net-zero economy. Techno-economic Model of Deep Carbon Capture. This study develops the performance and economic models for Monoethanolamine-based post-combustion CO2 capture at 95–99% capture rates. The process is simulated in Aspen Plus, analyzing the performance of carbon capture technology by varying the plant sizes, solvent lean loading, CO2 concentrations, and flue gas inlet temperature. Based on the key inputs and output parameters of CO2 capture, a reduced-order performance model of deep carbon capture is formulated. In addition, an engineering-economic model integrating the performance metrics is developed to estimate the capital as well as operation and maintenance (O&M) costs. Capital cost estimations follow the framework of the Integrated Environmental Control Model (IECM) and incorporate data regressions from three technical reports by IECM, the National Energy Technology Laboratory (NETL), and the National Renewable Energy Laboratory. The O&M cost estimation utilizes the actual inventory consumption rate and labor requirements. Both performance and cost models are embedded into IECM v13.0-beta, a fossil-fuel power plant modeling tool. Life Cycle Assessment of Power Plants. This study estimates the GHG emissions of power plants through life cycle assessment (LCA). The LCA scope includes fuel supply, combustion-based power generation, and CO2 transport and storage. The fuel-based life cycle module is designed following the framework of the NETL Unit Process Library and CO2U LCA Guidance Toolkit. The module is then incorporated into IECM v13.0-beta. The process-based LCA is applied to estimate the GHG emissions of coal and biomass supply, coal- and coal-biomass co-firing power plant operation, as well as CO2 pipeline transport and geographical sequestration. An uncertainty analysis is conducted to quantify the variability and uncertainty associated with the LCA using the Latin Hypercube Sampling (LHS) method. Fleet-level Assessment. This study evaluates the technical and economic feasibility of selected coal-fired EGUs, examines the role of tax credits in retrofit viability, and assesses the competitiveness of retrofitted units against other low-carbon options. Unit screening identifies EGUs for the study, focusing on new, efficient baseload units with air pollution controls. The power plant databases are then established to organize unit-specific information on performance and operating conditions from the relevant public databases. Biomass for co-firing retrofits is selected based on home and neighboring county availability, ensuring sustained operation with at least a 5% co-firing level. The CO2 storage site is determined by state-level storage potential, with ArcGIS Pro and NETL CO2 Saline Storage Cost Model used to identify the optimal balance between the nearest transport distances and affordable storage costs. The latest IECM v13.0-beta is then employed to configure and evaluate the eligible EGUs with or without the deployment of deep CCS and biomass co-firing. A supply curve is established to illustrate the cumulative installed capacity suitable for retrofits at different cost levels. A sensitivity analysis on tax credits for carbon sequestration is performed. Finally, a unit-level cost comparison is conducted among retrofitted plants, renewable power with battery storage, and abated fossil fuels with DAC. Expected Results. This study evaluates the technical, economic, and environmental metrics of each EGU across an array of CO2 capture rates and biomass co-firing level scenarios. Unit-level comparisons will identify critical factors influencing technical performance. The supply curves with and without tax incentives will provide insights into the impact of tax credits on biomass co-firing and CCS deployment. The cost comparisons with renewables and DAC-retrofit will assess the competitiveness of the retrofitted units. Life cycle emissions from each unit will be assessed to identify the scenarios under which net-zero emissions can be achieved. These analyses are expected to determine the total coal-fired capacity suitable for serving as a low-carbon energy source with or without tax incentives. The study results are novel in identifying optimal unit-specific strategies for producing carbon-neutral power, whether through retrofitting EGUs with deep CCS, biomass co-firing, DAC, or installing renewable power with battery. The findings will provide insight into nationwide efforts to ensure reliable, affordable, and low-carbon electricity. It also will inform investment decisions and policies in the deployment of deep carbon capture and negative emission technologies for a net-zero energy future.

Biomass Co-firing↗
NASA NTRSDate not supplied

A Case Study on the Challenges and Opportunities for the Deployment of PHM Capabilities in Existing Engineering Systems

The field of Prognostics and Health Management (PHM) of engineering systems has experienced considerable growth over the last decade. From benefits associated with faster and more powerful hardware in the form of wireless sensors, edge devices, and general computing capabilities (GPU’s and cloud computing), to development of powerful algorithms for anomaly detection and remaining useful life (RUL) estimation, the number of engineering systems featuring advanced diagnostics and prognostics capabilities continues to grow at an increasingly faster pace. However, the deployment of PHM capabilities as part of the upgrade of existing engineering systems presents multiple challenges to the PHM practitioner charged with retrofitting such systems. Issues include a lack of specific instrumentation needed to capture the signals of interest; insufficient data and sampling rates required for fault detection and diagnosis, and for detection of failure/degradation indicators; and difficulties in the identification of a system’s nominal behavior as a result of age induced degradation. Today’s PHM practitioner must be able to quickly identify and assess these types of issues to effectively evaluate and select the optimal PHM strategies required to achieve the desired results. This paper presents results from the preliminary evaluation of the High-Pressure Gas Facility (HPGF) infrastructure at NASA’s Stennis Space Center in Hancock County, Mississippi. This evaluation is part of a feasibility study conducted prior to the deployment of prognostics and diagnostics capabilities in the pumps skids of the liquid nitrogen (LN2) system of the HPGF.

Condition Based Maintenance↗
DOE OSTI2025

A review on advances in oxidative coupling of methane (OCM) for industrial use and prospects of CO 2 –H 2 O splitting integration

The discovery of shale gas reserves has encouraged the development of direct methods for methane conversion into valuable chemicals, offering an alternative to indirect approaches that involve an energy-intensive and intermittent syngas production step, leading to high CO 2 emissions. Amongst the direct methods, the oxidative coupling of methane (OCM) is a potential pathway to reduce CO 2 emissions and can produce commodity chemicals such as ethylene, a chemical regarded as central to the petrochemical industry. Even though OCM has been studied for over four decades, the technology still has not found commercial application. Amongst the challenges regarding industrial deployment of OCM, the most significant one is the requirement of a high ethylene yield of 30 % which is currently reported to be around 20 %. Moreover, the highly exothermic nature of the process and controlling the carbon selectivity over oxides of carbon (COx) is the heart of the problem. Numerous researchers have presented promising results in terms of catalysts, reactor designs and feeding strategies for OCM. However, due to lack of inclusiveness in the results, none of the combination of catalysts, reactors and system optimizations has been able to bring about its industrial viability. The current paper presents an extensive review of the noteworthy attempts to achieve industrial targets for OCM. Moreover, a comprehensive criteria is presented which highlights the desired end state for the industrial deployment of OCM technology. Furthermore, the criteria is based on literature survey and a comparison with industrially deployed ethylene production plants i.e., naphtha or ethane steam cracker plants. Finally, a novel integration technology is presented which includes a combination of OCM and CO 2 -H 2 O splitting in a chemical looping reactor design to enable efficient energy utilization and minimal heat losses to the environment.

CO2 Splitting↗
DOE OSTI2025

Risk assessment of wellbore leakage during underground hydrogen storage

The expansion of renewable energy sources would require large-scale energy storage options to overcome the intermittent nature of these sources. Underground hydrogen storage (UHS) in depleted hydrocarbon reservoirs offers a scalable and practical energy storage solution. These reservoirs are chosen for their availability and large capacity, but the unique properties of hydrogen raise concerns about potential leakage pathways, particularly through wellbores. In this study, we develop and apply, for the first time, reduced-order models (ROMs) specifically designed for efficient leakage risk prediction in UHS systems operating in depleted hydrocarbon reservoirs. Using 3,000 high-fidelity simulation scenarios, we examine the influence of 11 key parameters, including reservoir and aquifer depths, wellbore permeability and porosity, initial saturations of water, oil and gas fractions (hydrogen, light, intermediate, and heavy hydrocarbons), reservoir pressure multiplier, and the aquifer-to-reservoir volume ratio, to simulate leakage behavior over a 1,000-year timescale. We train ROMs using a two-step classification-regression approach, achieving R 2 values exceeding 99 % across all targets. These ROMs effectively capture the leakage evolution and identify critical controls of leakage, guiding the design of mitigation strategies. Results indicate that gas leakage occurs in about 27 % of scenarios as early as five years post-operation, reaching volumes of up to 106 ft3. Oil leakage is less frequent (~17 %) and typically begins decades later. Our findings also show that hydrogen often migrates first, owing to its smaller molecular size and higher buoyancy, followed by heavier hydrocarbons. Over time, these heavier components contribute significantly to the total leaked volume, reinforcing the need for targeted monitoring and remediation strategies. Our analysis highlights that deeper storage reservoirs, shallower aquifers, and low-permeability wellbores significantly reduce leakage risks. In conclusion, this work offers a robust framework for risk-informed UHS deployment, supporting energy security through reliable large-scale hydrogen storage while safeguarding environmental integrity.

08 HYDROGEN↗
DOE OSTI2023

Taming the wild edge of smart grid – Lessons from transactive energy market deployments

For two decades, the idea of using market-based approaches in field deployments that coordinate the flexible operation of electricity customer assets has occupied the efforts of transactive energy practitioners. While the purported benefits of this distributed decision-making approach have been well explored, the practical aspects of implementing such a system to address real-world problems are just beginning to emerge. This report surveyed 24 field-deployed programs and interviewed experts instrumental in these deployments. The results of the survey and interviews reveal the diversity of designs and applications. They highlight the technical promise of the approaches as well as challenges with system integration, sustainable business strategy, and regulatory policy obstacles. Insights from the survey offer considerations to direct future effort and investment.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Photovoltaic inverter-based quantification of snow conditions and power loss

Snow is a significant challenge for photovoltaic (PV) systems at northern latitudes, where the pace of deployment is rapid but snow-related power losses can exceed 30% of annual production. Accurate snow-related power loss estimation methods for utility-scale sites can support snow mitigation strategies, inform resource planning and validate predictive snow-loss models. This study builds on our previous work on inverter-based detection of snow, and its implications for utility-scale power production, by validating the accuracy of our snow-loss method across different PV sites and system designs and highlighting its value in bringing greater visibility to PV plant operations in winter. Our estimation method is both novel and scalable, requiring only standard monitoring data to correlate snow-related losses with meteorological data. As demonstrated here, our validation method involved three main steps: 1) estimation of performance losses for multiple systems by comparing measured inverter data to modeled data; 2) application of a detection framework to identify which performance losses are snow-related; and 3) comparison of snow-related losses among three utility-scale sites differing in tilt angle. Results show that utility-scale systems at higher tilt angles consistently shed snow more quickly/completely than their lower-tilt counterparts. Further, monthly and seasonal snow losses are inversely and non-linearly correlated with tilt angle when normalized for cumulative snowfall. These results are consistent with the findings of previous studies and support the broad applicability of this method to fixed-tilt utility-scale PV systems around the world that routinely experience snow-related performance losses.

Cooper, Emma C. (ORCID:0000000190554098)↗
DOE OSTI2025

TrioSim: A Lightweight Simulator for Large-Scale DNN Workloads on Multi-GPU Systems

Deep Neural Networks (DNNs) have become increasingly capable of performing tasks ranging from image recognition to content generation. The training and inference of DNNs heavily rely on GPUs, as GPUs' massively parallel architecture delivers extremely high computing capability. With the growing complexity of DNNs and the size of training datasets, training DNNs with a large number of GPUs is becoming a prevalent strategy. Researchers have been exploring how to design software and hardware systems for GPU farms to achieve the best utilization, efficiency, and DNN accuracy during training or inference. However, when designing and deploying such systems, designers usually rely on testing on physical hardware platforms equipped with many GPUs, incurring high costs that are almost prohibitive for system designers to test different configurations and designs, even for highly resourceful companies. While an alternative solution is to test on GPU simulators, they are often too slow for these l

Li, Ying [William & Mary, Williamsburg, VA, USA] (↗
DOE OSTI2024

Transactive Energy Practices Survey

For nearly two decades, the idea of using market-based approaches in field deployments that coordinate the flexible operation of electricity customer assets has occupied the efforts of transactive energy practitioners. While the purported benefits of this distributed decision-making approach encourage transactive energy designs have been well explored, the practical aspects of implementing such a system to address real-world problems are just beginning to emerge. This report surveyed 24 field-deployed programs and interviewed experts instrumental in these deployments. The results of the survey and interviews reveal the diversity of designs and applications. They highlight the technical promise of the approaches as well as challenges with system integration, sustainable business strategy, and regulatory policy obstacles. Insights from the survey offer considerations to direct future effort and investment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Potential Challenges of HyBlend Storage in a Methane Reservoir Located in Southwestern United States

Hydrogen has been identified as a flexible energy carrier with zero or negative emissions across multiple energy systems. It is possible to utilize hydrogen by storing Hyblend, or hydrogen gas blended with methane, in existing natural gas infrastructure. However, the compatibility of adapting the current CH4 storage strategies to include H2 injection has not been fully demonstrated. It is essential that we understand the impact of H2 gas on the naturally occurring microbial community of subsurface storage reservoirs before deploying large-scale H2-CH4 storage. We designed a series of experiments that allowed us to identify potential geochemical and microbial challenges of HyBlend Storage in existing methane reservoirs. First, we collected and characterized field fluid samples from a methane reservoir located in southwestern United States. Next, we used these field fluid samples to complete a series of short-term reactor experiments at reservoir conditions (80 °C and ~1,000 psi) for a natural gas (100% CH4) and HyBlend(80% CH4/20% H2) storage environment to measure the transformation of gas content. We conducted both biotic and abiotic (sterilized) measurements to accurately understand and decouple abiotic and microbially driven processes. Overall, we found that our field sample was characterized by a diverse microbial community with the metabolic capacity for sulfur reduction, iron reduction, and acetogenesis. Across our reactors, there was minimal change in geochemistry.

hydrogen storage↗
DOE OSTI2025

Potential Risks Associated with Short-Term Hydrogen Storage in Methane Reservoirs

Hydrogen (H2) has been identified as a flexible energy carrier with zero emissions. It is possible to utilize H2 by storing Hyblend, or H2 blended with CH4, in existing natural gas infrastructure. However, the compatibility of adapting the current CH4 storage strategies to include H2 injection has not been fully demonstrated. It is essential that we understand the impact of H2 gas on the naturally occurring microbial community of subsurface storage reservoirs before deploying large-scale H2-CH4 storage. We designed a series of experiments that allowed us to identify potential challenges of HyBlend Storage in existing methane reservoirs (Figure 1). First, we characterized two field fluid samples from a methane reservoir located in Western United States. Next, we used these samples to complete a series of short-term reactor experiments at reservoir conditions for a natural gas (100% CH4) and HyBlend (80% CH4/20% H2) storage environment.

geomicrobiology↗
NASA NTRS1992

Status of the NASA SETI Sky Survey microwave observing project

The Sky Survey observing program is one of two complementary strategies that NASA plans to use in its microwave Search for Extraterrestrial Intelligence (SETI). The primary objective of the Sky Survey is to search the entire sky over the frequency range 1000-10,000 MHz for evidence of narrow band signals of extraterrestrial, intelligent origin. Spectrum analyzers with upwards of 10 million channels and data rates in excess of 10 gigabits per second are required to complete the survey in less than 7 years. To lay the foundation for the operational SETI Sky Survey, a prototype system has been built to test and refine real time signal detection algorithms, to test scan strategies and observatory control functions, and to test algorithms designed to reject radio frequency interference. This paper presents a high level description of the prototype hardware and reports on the preparations to deploy the system to the 34-m antenna at the research and development station of NASA's Deep Space Communication Complex, Goldstone, California.

Klein, M. J.↗