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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Hydrogen/Metal Hydride Based Heat Pump System for Large HVAC Applications Utilizing an Ionic Liquid Desiccant Subsystem [Slides]

A multi-stage electrochemical hydrogen compressor incorporates membrane-electrode-assemblies (MEAs) separated by proton exchange membranes (PEMs) in series to reach higher pressures, when a current is passed through the MEA protons and electrons are generated at the anode. The protons are electrochemically driven across the membrane to the cathode, after which they combine with the rerouted electrons to form hydrogen, which is fed to the hydrogen compressor to be oxidized at the anode of each cell to form protons and electrons. This type of compressor has no moving parts and can be applied to refrigeration systems technologies. This project investigated the viability of commercialization of heat pumps based on electrochemical compressor technology.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Improving Grid Awareness by Empowering Utilities with Machine Learning and Artificial Intelligence

Gap filling time series data typically depends on linear interpolation. More recently gap filling advancements include machine learning techniques. However, none leverage advanced learning approach that uses cohort training or a neighborhood informed approach, which is described in this report. The report also describes a physics informed approach using Reduced Order Models (ROM). There are several methods to capture the nature of the detailed system in aggregated models, however there is a trade-off for these methods developed for multiple applications. These methods have specific requirements and applications that includes consideration of dynamics or covering a larger range of operating conditions, etc. The various methods of aggregation are: 1) Thevenin equivalents for downstream networks 2) Equivalent feeder representation to capture downstream network losses accurately 3) Structured reduced order models for dynamics 4) System identification-based ROM (abstract dynamical model) Methods described in items 1 and 2 above are ideal for steady-state models and useful for this application. Of these two methods, based on the data availability, the targeted application, the reduced order model that is proposed to be developed is the equivalent feeder model representation. This includes a structure of the reduced order model whose parameters can be determined by the system load and losses with the meter measurements.

14 SOLAR ENERGY↗
DOE OSTI2024

Reduced LCoE CSP Through Utilizing Process Gas Lubricated Bearings in Oil-Free Drivetrains

The GE Research team proposed to advance the TRL level of process lubricated gas bearings from TRL3 based on the DE-EE0007109 program to TRL5 addressing a critical risk item that radial gas bearings can provide satisfactory rotordynamic performance in supporting a representative rotor for sCO2 Power Cycles. The ability to apply process lubricated gas bearings can provide cost savings with reduction of the associated lube oil systems and an efficiency benefit worth 2% efficiency for the turbomachinery configuration first presented in DE-EE005804. The process lubricated gas bearings are enablers for advanced hermetically sealed configurations that have the potential for significant advancement in sCO2 Power Cycles for CSP applications including the 2% efficiency increase referred to above in addition to OPEX savings with no need to refill the CO2 valued at 60k USD/yr and cost reductions of 50 USD/kWe all leading to a potential 0.95¢/kWh reduction positively impacting CSP goals.

14 SOLAR ENERGY↗
DOE OSTI2024

A Framework for Identifying Building Energy Models of Localized Utility Service Areas Using Smart Meter Data

Bottom-up load modeling of buildings offers a versatile approach to simulating baseline demand and scenarios of future technology evolution and adoption at the individual building level. This capability is essential to understanding how future load shapes may change with the adoption of electric equipment and vehicles, particularly as it relates to grid planning and infrastructure investments. Traditionally, grid planning techniques have used historical load data to predict future load and infrastructure needs. However, with the anticipated rise in adoption of electrification technologies such as heat pumps and electric vehicles, historical data become less reliable predictors of the future. By employing ResStock, a high-fidelity building stock modeling tool, we can fine-tune electrification scenarios and aggregate models to represent varying geographic resolutions of the grid system, while considering the underlying features of homes. This may enable a more accurate and responsive approach to anticipate and plan for the evolving landscape of energy demands. We present a new framework that leverages building stock energy modeling to identify building models that align with the load shapes and housing attributes of buildings with AMI data. This approach applies two model layers: (1) a classification step that identifies the presence of air conditioning, electric heating, and electric water heating, and (2) an optimization routine that identifies building energy models aligning with load profile data from advanced metering infrastructure meters. This report demonstrates one approach to deploying this framework, and presents results for three test cases that use both modeled and AMI data to assess performance. For a test case using AMI data in Fort Collins, Colorado, we observed a median monthly electricity load CV-RMSE of 16.6%, and a top ten daily heating and cooling median absolute percent error of 7.7% and 8.3%, respectively. For each AMI meter, we identify a set of potential energy models so that downstream use-cases can account for uncertainty driven by variability of baseline technologies and occupant behavior, which impact the response to electrification and energy efficiency scenarios. Our results indicate that ResStock has potential as a scalable solution for modeling residential energy demand at local grid resolutions. Its performance depends on location-specific factors, underlying building characteristics, and the level of aggregation, offering a path towards more precise and adaptive distribution grid planning for the evolving energy landscape.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Klickitat Valley Health Central Utility Plant Modernization

This project replaced legacy heating and cooling equipment at Klickitat Valley Health (KVH), a rural hospital in Goldendale, Washington. Work included constructing a new chiller building, installing modern hydronic piping systems, replacing the main Emergency Department air handler (Air Handlining Unit (AHU-1)), upgrading electrical systems, and converting the Emergency Department wing from electric reheat to hydronic heating and cooling. Commissioning, testing, adjusting, and balancing were completed under this grant. These upgrades improve energy efficiency, operational resilience, and system reliability while preparing the campus for future electrification and renewable energy integration. They also support growth in healthcare services for a rural population. The project directly reduces energy costs and provides a foundation for future decarbonization measures, including a FEMA-funded microgrid and a large-scale ground source heat pump (GSHP) project. These follow-on initiatives would not have been possible without the hydronic backbone built through this project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Biogas Utilization in Refuse Power Plants (BURP 2 )

The BURP 2 project investigated the technical and financial viability of co firing biogas with waste coal for power generation while using carbon capture and sequestration to achieve net negative emissions. A comparative assessment integrating geospatial mapping, technoeconomic analysis, and life cycle analysis was carried out to evaluate retrofitting an existing coal fired facility in West Virginia, versus developing a new greenfield power plant in Kentucky located near a low quality coal resource. The study found that CO 2 capture rates of 90% or higher, in combination with biogas feedstocks such as animal manure, could significantly reduce global warming potential compared to plants using neither biogas nor CO 2 capture systems. Economic feasibility depended heavily on federal tax credits and proximity to fuel sources. In both greenfield and retrofit scenarios, access to biogas played a key role. Because the retrofit site was located close to existing biogas resources, it represented a feasible option, whereas the greenfield site, being far from pipelines or biogas sources, would require prohibitively expensive biogas transport infrastructure. Overall, the research showed that repurposing waste or low-quality coal with renewable biogas and CO 2 capture systems could provide a viable approach for reducing carbon emissions in power production, if biogas resources are easily accessible and available in sufficient quantities.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

A Guide for Public Utility Commissions: Building Internal Technical Capacity and Recruiting Talent for Grid Resilience

This guide offers insight into how PUCs can strategically expand their technical workforce to meet evolving gird resilience demands. It outlines critical skill sets needed to support informed regulatory decision-making around resilience, such as modeling and weather forecasting, electric power systems analysis, and advanced data interpretation. In addition, it provides strategies for developing technical talent both internally and through additional recruitment efforts. Two appendices provide a list of grid resilience training resources and a compendium of sample job descriptions that reflect grid resilience technical expertise for PUC consideration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

A Methodology for Measuring Blade Clearance on an Operating Utility-Scale Wind Turbine

This report describes the deployment of eleven laser sensors to measure the clearance between blades and tower in a 1.5-MW wind turbine whose rotor was mounted first in upwind and then in downwind configurations. The experimental recordings are compared to the numerical predictions generated by an aeroservoelastic model of the turbine. Good agreement is found between the two datasets, although discrepancies up to 30~cm are observed. The sources of this error are discussed. This methodology is found to be a valuable resource for the validation of the numerical predictions of the flapwise deflections of wind turbine blades. The accurate prediction of these deflections is increasingly important as wind turbines grow in size and become increasingly flexible.

17 WIND ENERGY↗
DOE OSTI2025

Connected Residential Communities with Enhanced Resiliency and both Customer and Utility Attributes (Final Technical Report)

This report is a compilation of information from Quarter Progress Reports submitted to the Department of Energy’s Office of Energy Efficiency Building Technologies Office (BTO) by SunPower Corporation. The report has been uploaded to OSTI by DOE as a substitute for the required Final Technical Report which was never received from the project recipient.

14 SOLAR ENERGY↗
DOE OSTI2025

AI-powered municipal solid waste management: a comprehensive review from generation to utilization

The accumulation of municipal solid waste (MSW) continues to rise due to burgeoning population, rapid global urbanization and economic growth, intensifying ecological concerns associated with landfills and greenhouse gas (GHG) emissions. Over the past 2 decades, global waste generation has surged by 50%, with one-third remaining uncollected and about 70% sent to landfills. This review examines the critical role of integrating emerging technologies, such as advanced sensors and artificial intelligence (AI), into end-to-end MSW management to alleviate landfill burdens. The suitability of various AI tools for different stages of MSW management is assessed, alongside the deployment of advanced sensors including hyperspectral cameras, computer vision systems, and internet of things (IoT) devices for material identification. Applications of genetic algorithms and reinforcement learning for optimizing collection routes, reducing costs, and lowering emissions are highlighted. Life cycle assessment (LCA) across all stages of MSW management is also reviewed, along with future trends in leveraging generative AI, natural language processing (NLP), and agent-based AI systems to analyze waste generation patterns and public sentiment. Efficient collection and handling can be enhanced through route optimization with geographic information systems and real-time bin-level monitoring. Furthermore, sensor-embedded, real-time object detection systems paired with robotics enable material characterization and automated sorting, thereby lowering costs and diverting waste from landfills into value-added products for diverse industrial sectors including packaging, chemicals, textiles, metals and glass, transportation, and electronics industries. Without intervention, global waste is projected to reach 4.54 billion tons by 2050, contributing direct economic costs of $\$$400 billion and roughly 2.38 billion tons of CO 2 -equivalent emissions annually. This review demonstrates how AI-driven, end-to-end solutions for MSW management can mitigate economic and environmental challenges, while directly supporting the United Nations Sustainable Development (UNDP) goals related to innovation and infrastructure (SDG 9), sustainable cities (SDG 11), responsible consumption and production (SDG 12), and climate action (SDG 13).

09 BIOMASS FUELS↗
DOE OSTI2020

Update on the United States Advanced Ultra-Supercritical Program with Full Scale Component Fabrication to Develop Utility Scale Power Plants with 760°C Steam Temperature

Following the successful completion of a 15-year effort to develop and test materials that would allow coal-fired power plants to be operated at advanced ultra-supercritical (A-USC) steam conditions, a United States-based consortium is presently engaged in Phase 2 of a project which includes an advanced manufacturing effort to complete US-based supply chain development for full commercial scale (800-850 MWe) A-USC components made of nickel-based alloys, components operating at up to 760°C. The project, funded under U.S. Department of Energy Award No. DE-FE0025064, falls under the US DOE overall goal to Develop cost-effective, reliable technologies to improve the efficiency of new and existing coal-fired power plants. A-USC steam cycles have the potential to improve cycle efficiency, reduce fuel costs, and reduce greenhouse gas emissions. Current development and demonstration efforts are focused on enabling the construction of A-USC plants, operating with steam temperatures as high as 1400°F (760°C) and steam pressures up to 5000 psi (35 MPa), which can potentially increase cycle efficiencies to 47% HHV (higher heating value), or approximately 50% LHV (lower heating value), and reduce CO 2 emissions by roughly 25%, compared to today’s U.S. fleet. A-USC technology provides a lower-cost method to reduce CO 2 emissions, compared to CO 2 capture technologies, while retaining a viable coal option for owners of coal generation assets. Among the specific goals of Phase 2 of the ComTest Project are to: validate that components made from advanced nickel-based alloys can operate and perform under A-USC conditions accelerate the development of a U.S.-based supply chain for the full complement of A-USC components close the remaining gaps and reduce the risks for manufacturing components from advanced materials for commercial demonstration fabricate full-scale versions of selected key components made of nickel-based alloys validate a qualified U.S. supply chain, to provide greater cost certainty for components obtain American Society of Mechanical Engineers (ASME) code approval for new materials, components and processes Additional benefit: Manufacturing technology will be applicable to other advanced fossil energy high temperature cycles, and can support increased flexibility of existing power plants The ComTest project is managed by Energy Industries of Ohio, and technically directed by the Electric Power Research Institute, Inc., with General Electric designing the A-USC components. As stated earlier, the current phase of the effort is primarily funded by the U.S. Department of Energy, through the National Energy Technology Laboratory. This presentation outlines the motivation for the project, explains the project’s structure and schedule, and provides some of the technical details on the design of the ComTest components.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Update on the United States Advanced Ultra-Supercritical Program with Full Scale Component Fabrication to Develop Utility Scale Power Plants with 760°C Steam Temperature

Following the successful completion of a 15-year effort to develop and test materials that would allow coal-fired power plants to be operated at advanced ultra-supercritical (A-USC) steam conditions, a United States-based consortium is presently engaged in Phase 2 of a project which includes an advanced manufacturing effort to complete US-based supply chain development for full commercial scale (800-850 MWe) A-USC components made of nickel-based alloys, components operating at up to 760°C. The project, funded under U.S. Department of Energy Award No. DE-FE0025064, falls under the US DOE overall goal to Develop cost-effective, reliable technologies to improve the efficiency of new and existing coal-fired power plants. A-USC steam cycles have the potential to improve cycle efficiency, reduce fuel costs, and reduce greenhouse gas emissions. Current development and demonstration efforts are focused on enabling the construction of A-USC plants, operating with steam temperatures as high as 1400°F (760°C) and steam pressures up to 5000 psi (35 MPa), which can potentially increase cycle efficiencies to 47% HHV (higher heating value), or approximately 50% LHV (lower heating value), and reduce CO 2 emissions by roughly 25%, compared to today’s U.S. fleet. A-USC technology provides a lower-cost method to reduce CO 2 emissions, compared to CO 2 capture technologies, while retaining a viable coal option for owners of coal generation assets. Among the specific goals of Phase 2 of the ComTest Project are to: validate that components made from advanced nickel-based alloys can operate and perform under A-USC conditions accelerate the development of a U.S.-based supply chain for the full complement of A-USC components close the remaining gaps and reduce the risks for manufacturing components from advanced materials for commercial demonstration fabricate full-scale versions of selected key components made of nickel-based alloys validate a qualified U.S. supply chain, to provide greater cost certainty for components obtain American Society of Mechanical Engineers (ASME) code approval for new materials, components and processes Additional benefit: Manufacturing technology will be applicable to other advanced fossil energy high temperature cycles, and can support increased flexibility of existing power plants The ComTest project is managed by Energy Industries of Ohio, and technically directed by the Electric Power Research Institute, Inc., with General Electric designing the A-USC components. As stated earlier, the current phase of the effort is primarily funded by the U.S. Department of Energy, through the National Energy Technology Laboratory. This presentation outlines the motivation for the project, explains the project’s structure and schedule, and provides some of the technical details on the design of the ComTest components.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Autonomous Operations for Advanced Reactors Utilizing Supervisory Control

Automation is a critical tenet of reactor plant operations as reliance on nuclear energy increases. Nuclear power plants require a large workforce which does not scale with output; that is, the cost per megawatt increases as reactor output becomes smaller. The economic viability of advanced reactors, particularly small modular reactors (SMRs) and microreactors, requires a significantly reduced onsite workforce. The logical solution is establishing a systematic process of elimination of reliance on human operators, and to the extent possible, replacing these actions with automated functions. In this paper, we propose a method for such transformation to establish a robust technical basis to enable transition to autonomy. Our method is based on finite state automata (FSA)—also known as finite state machines (FSMs). Relying on this method allows us to exploit the rich set of mathematical proofs available in the field of regular languages. FSA are one of the mathematical tools to model discrete event systems (DES). These properties are applied to produce an automated startup controller for the Massachusetts Institute of Technology Research Reactor (MITR). The startup procedure is captured in terms of discrete changes from one state to another while an independent supervisory control system directs the sequence of states and alerts a human in the event of an abnormal operation. First, the design and behavior of the MITR rod control system were modeled in Simulink. Then, the startup procedure was applied to the rod control system and the DES performed a startup by procedurally withdrawing rods to the subcritical position. The simulation also stops rod motion in response to an uncontrollable event and restarts rod motion once the event has been cleared.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗