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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Modeling-Based Assessment of Deep Seismic Potential Induced by Geologic Carbon Storage

Induced seismicity is an inherent risk associated with geologic carbon storage (GCS) in deep rock formations that could contain undetected faults prone to failure. Modeling-based risk assessment has been implemented to quantify the potential of injection-induced seismicity, but typically simplified multiscale geologic features or neglected multiphysics coupled mechanisms because of the uncertainty in field data and computational cost of field-scale simulations, which may limit the reliable prediction of seismic hazard caused by industrial-scale CO 2 storage. The degree of lateral continuity of the stratigraphic interbedding below the reservoir and depth-dependent fault permeability can enhance or inhibit pore-pressure diffusion and corresponding poroelastic stressing along a basement fault. In conclusion, this study presents a rigorous modeling scheme with optimal geological and operational parameters needed to be considered in seismic monitoring and mitigation strategies for safe GCS.

58 GEOSCIENCES↗
DOE OSTI2012

Decision Paper for the Disposition of FS-65 Shipping Containers, Support Equipment, and Mixed Oxide (MOX) Material Contents (Rev. 1.1)

A description of the containers, contents, and disposition alternatives follow. Table 1 provides a rough order of magnitude cost and schedule duration estimate for the 2 disposition alternatives. Cost and schedule data are rounded to two significant figures and the durations are based on 1735 working hours per year. The sub-task durations and personnel are not totaled for the tasks because many of the sub-tasks will be conducted in parallel. Once an Alternative is selected a detailed resource loaded schedule will be developed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2012

Decision Paper for the Disposition of FS-65 Shipping Containers, Support Equipment, and Mixed Oxide (MOX) Material Contents (Rev. 1.2)

A description of the containers, contents, and disposition alternatives follow. Table 1 provides a rough order of magnitude cost and schedule duration estimate for the 2 disposition alternatives. Cost and schedule data are rounded to two significant figures and the durations are based on 1735 working hours per year. The sub-task durations and personnel are not totaled for the tasks because many of the sub-tasks will be conducted in parallel. Once an Alternative is selected a detailed resource loaded schedule will be developed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

2015 Standard Scenarios Annual Report: U.S. Electric Sector Scenario Exploration

This report is one of several products resulting from an initial effort to provide a consistent set of technology cost and performance data and to define a conceptual and consistent scenario framework that can be used in the National Renewable Energy Laboratory’s (NREL’s) future analyses. The long-term objective of this effort is to identify a range of possible futures of the U.S. electricity sector in which to consider specific energy system issues through (1) defining a set of prospective scenarios that bound ranges of key technology, market, and policy assumptions and (2) assessing these scenarios in NREL’s market models to understand the range of resulting outcomes, including energy technology deployment and production, energy prices, and carbon dioxide (CO2) emissions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

Evaluation of Early Performance Results for Massachusetts Homes in the National Grid Pilot Deep Energy Retrofit Program

This research project evaluates post-retrofit performance measurements, energy use data and construction costs for 13 projects that participated in the National Grid Deep Energy Retrofit Pilot program. The projects implemented a package of measures defined by performance targets for building enclosure components and building enclosure air tightness. Nearly all of the homes reached a post-retrofit air tightness result of 1.5 ACH 50. Homes that used the chainsaw retrofit technique along with roof insulation, and wall insulation applied to the exterior had the best air tightness results and the lowest heating and cooling source energy use. Analysis of measure costs and project objectives yielded a categorization of costs relative to energy performance objectives. On average about ½ of the energy-related measure costs correspond primarily to energy-related objectives, and 20% of energy-related measure costs relate primarily to non-energy objectives.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Evaluation of Early Performance Results for Massachusetts Homes in the National Grid Pilot Deep Energy Retrofit Program

This research project evaluates post-retrofit performance measurements, energy use data and construction costs for 13 projects that participated in the National Grid Deep Energy Retrofit Pilot program. The projects implemented a package of measures defined by performance targets for building enclosure components and building enclosure air tightness. Nearly all of the homes reached a post-retrofit air tightness result of 1.5 ACH 50. Homes that used the chainsaw retrofit technique along with roof insulation, and wall insulation applied to the exterior had the best air tightness results and the lowest heating and cooling source energy use. Analysis of measure costs and project objectives yielded a categorization of costs relative to energy performance objectives. On average about 1/2 of the energy-related measure costs correspond primarily to energy-related objectives, and 20% of energy-related measure costs relate primarily to non-energy objectives.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Low Cost Manufacturing of Advanced Silicon-Based Anode Materials

Under the financial support of award DE-EE0007312, Group14 Technologies (G14) has progressed from the lab (~g) to lab-pilot (~kg) manufacturing of a novel silicon-carbon (Si-C) composite that is highly promising as an anode material for next-generation lithium-ion batteries. G14’s Si-C is a drop in for graphite in lithium ion battery anodes, providing for increased energy density while lowering the cost on a capacity basis. At the foundation of the Si-C particle is an amorphous carbon scaffold, with its porosity fine-tuned for promoting intra-particle growth of the desired type and size (amorphous, nano-sized) of high capacity silicon, while also proving for internal volume to ameliorate silicon’s expansion upon lithiation. Both the carbon and silicon within the Si-C composite are produced from readily available, low-cost precursors. At lab scale, we demonstrated the ability to produce Si-C capable of achieving capacity of 1000 mAh/g with minimum 500 cycle stability (up to 1000 cycles with pre-lithiation) in full cell, coin cell lithium ion batteries. Based on such lab experience, we down-selected process and equipment suitable for producing Si-C at lab-pilot scale. The chosen equipment was purchased, installed and commissioned at Group14’s pilot manufacturing facility in Woodinville, WA. In turn, the ability to produce kg quantities facilitated the shipment of Si-C to various third parties. The promising feedback from such third parties included achievement of high first cycle efficiency (up to 92%) and 800 cycle stability in full cell, pouch cell lithium ion batteries. Beyond this highly promising electrochemical performance, it is important to note that G14’s Si-C has also been developed for low cost. Based on estimated production costs, electrochemical performance data, and modeling employing the BatPaC model, we project <$125/kWh for lithium ion battery packs containing G14’s Si-C.

25 ENERGY STORAGE↗
DOE OSTI2019

Accelerated Fuel Qualification (AFQ) Workshop I Summary Report

The first AFQ workshop was held on Friday, May 31, 2019, in Washington, DC, with over 30 expert participants from industry (General Atomics, Framatome, Lightbridge, TerraPower, Westinghouse), national labs (ANL, INL, LANL, ORNL), DOE-NE, NRC and academia (University of Florida and University of Tennessee). The overall goal of the workshop was to discuss how modeling and experiments can be simultaneously exploited to markedly reduce the years of data (and associated costs) that are currently required for deployment of new nuclear fuels utilizing the new methodology of Accelerated Fuel Qualification (AFQ). There were nine presentations given during the workshop, which included an overview presentation, five case studies, summary of relevant national lab capabilities and on-going DOE programs, as well as perspective from the NRC.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Fuel Economy and Cost Estimates for Medium and Heavy Duty Trucks

The U.S. Department of Energy’s (DOE’s) Vehicle Technologies Office (VTO) and Hydrogen and Fuel Cell Technologies Office (HFTO) aim to develop sustainable, affordable and efficient technologies for transportation of goods and people. These offices seek to advance DOE investments in advanced transportation component technologies and powertrains that translate to the estimated potential vehicle-level fuel savings contained herein. As part of quantifying these benefits, we have been asked to take a three-decades-long view of the fuel-saving vehicle technologies that will likely be implemented in medium- and heavy-duty trucks. In this work, we focus on technologies funded by VTO and HFTO that we expect to see during this time frame. Simulation was carried out for more than 20 types of trucks, ranging from Class 2 (the smallest medium-duty trucks, which span from Class 2b–6) to Class 8 (the largest heavy-duty trucks, which span Classes 7 and 8). The output of this study provides the fuel consumption and estimated purchase prices for trucks that employ advanced technologies, where both types of output offer a vehicle-level perspective and estimated projections about the future of advanced-technology medium- and heavy-duty trucks, as well as cost and performance data pairs that can inform other advanced transportation studies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

SECARB-USA: Preliminary Regional Assessment of Potential Storage Complexes, Including Capacities and Costs, Based on Application of NRAP and SCO2T

This subtask focused on producing estimates of CO 2 injection rates, storage capacity, and sequestrations costs across the entire Southeast Regional CO 2 Utilization and Storage Acceleration Partnership (SECARB-USA) region. These estimates will then be used in subsequent tasks that rely on understanding CO 2 storage and sequestration cost estimates. The data are generated and reported using 10x10 km (100 km 2 ) grid cells across based on the NATCARB dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Supply Chain Energy and Greenhouse Gas Analysis Using the Materials Flows through Industry (MFI) Tool: Examination of Decarbonization Technology Scenarios for the U.S. Glass Manufacturing Sector

Glass manufacturing is a major part of the U.S. economy, and glass products in multiple sectors - flat glass, container glass, and fiberglass - are a part of everyday life. Despite glass manufacturing's long history, there are still many opportunities for glass production to improve in terms of both in overall energy consumption and greenhouse gas (GHG) emission reduction. Scenarios for the flat, container, and fiberglass sectors show the effect that each improvement has on primary energy demand and GHG emissions, starting from an industry baseline that reflects current manufacturing practices. Using the National Renewable Energy Laboratory's Material Flows through Industry tool, multiple scenarios are examined for each glass sector by applying a combination of energy reduction, increased electrification, hydrogen cofiring, a more renewable electric grid, and increased use of cullet. Additional decarbonization options are possible for glass but are left for later analysis due to cost, feasibility, or data issues. Primary energy demand can be reduced by 75%-83% compared to baseline inputs, depending on the sector, and GHG emissions are reduced by 82%-86%. Applying the maximum level of improvements listed here has the potential to save over 300 million GJ and 25 million metric tons of GHGs by 2050.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Hawaii Solar Desalination Project

This project built the world’s largest Forward Osmosis (FO) solar thermal desalination plant at the NELHA Hawaii Ocean Science and Technology Park for a high visibility “on sun” demonstration of seawater desalination for agricultural applications. The project included re-commissioning an existing 2MW micro-dish solar thermal array and coupling it to a next-generation 500m3/day FO system. The purpose of the project was to validate FO at commercial scale; to verify that the water produced is safe for potential downstream agricultural users; to gather data on capital cost, energy consumption and operating expense for the technology; and to demonstrate that FO can be successfully coupled to low-grade thermal resources. The plant implemented advancements in FO component technology, including hot nano-filtration, polymeric heat exchangers, draw solutions, and improved FO membranes, and operated from June 2022 to September 2023 - producing 30gpm of permeate water. Thermal energy was below 30kWh/m 3 during operation, establishing a new benchmark in thermal desalination technology, with recovery rates at times exceeding 65%, well above what commercial RO systems can achieve. Combining the plant capital cost (CAPEX) and the operating expenses (OPEX) yields a Levelized Cost of Water (LCOW) that is competitive with existing carbon-intensive technologies such as RO. The ability of the plant to operate from a renewable resource paves the way toward a low-carbon desalination future using low-grade thermal resources.

14 SOLAR ENERGY↗
DOE OSTI2024

Continuous integration data-driven platform of industrial-scale subsurface storage for real-time analytics

This project helped address the growing need for efficient and scalable models to support geological carbon and energy storage, which are crucial for achieving net-zero emissions. Traditionally accurate high-fidelity numerical models have been used to simulate relevant storage processes under a handful of processes, however such models are computationally demanding, making uncertainty quantification impractical. Consequently, we first developed a machine learning framework, based on Graph Neural Operators (GNOs), to improving the accuracy of model predictions for a fixed computational budget. We then developed an Ensemble of Improved Neural Operators (ENO), which uses bagging and Monte Carlo dropout techniques, to further improve prediction accuracy. Lastly, we developed the way to explain progressive transfer learning methods to reduce the amount of training data and computational cost of training (i.e., reduce trainable parameters) when using our models for multiple storage sites. Our numerical investigation, which used real-world case studies, demonstrated that our framework can significantly improve the safety and efficiency of geological storage operations, with potential applications in other domains such as geothermal reservoirs and climate modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Retro-Commissioning Sensor Suitcase for Energy Efficiency (CRADA Final Report)

The Sensor Suitcase project aimed to enable cost-effective, data-driven retro-commissioning (RCx) in small commercial buildings through a self-installable toolkit consisting of portable sensors and diagnostic software. Results showed that the Sensor Suitcase could identify common operational faults leading to 5–15% energy savings opportunities, with automated reports requiring minimal user interpretation. The project adhered closely to its Statement of Work (SOW) but evolved the reporting interface and sensor logging duration based on field feedback. The development and validation of diagnostics are documented in PNNL technical reports and internal analysis summaries.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Agent-Based, Bottom-Up Medium- and Heavy-duty Electric Vehicle Economics, Operation, Charging and Adoption (Research Performance Final Report)

This is the research performance final report for the project entitled: Agent-Based, Bottom-Up Medium- and Heavy-duty Electric Vehicle Economics, Operation, Charging and Adoption This project was able to achieve the DOE’s goals of developing new modeling tools to understand MDHD vehicle operation and adoption. The first modeling tool is a fleet-level techno-economic analysis model capable of estimating energy use and associated environmental and cost impacts for electrified and conventional vehicles of any MDHD vocation, using real-world cost and operations data, including approaches to optimizing schedules for charging and/or vehicle dispatch. The second modeling tool is a system-level, bottom-up, agent-based adoption model capable of generating geographically-resolved estimates of market projections for MDHD vehicles and charging infrastructure. These tools will be developed and published to serve dual purposes as analysis tools for researchers, and decision-support tools for decision makers within the MDHD system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Energy and Comfort Impacts of High Performance Facades in Office Buildings

Building facades have a major effect on energy use, occupant comfort, and well-being. Yet adoption of high-performance facades is often slowed by limited data and unclear cost benefits. To address this gap, Oak Ridge National Laboratory, in collaboration with the Facade Tectonics Institute, conducted whole-building energy simulations to evaluate fenestration technologies for small and medium office buildings across three weather locations: hot (Tampa, Florida, weather zone 2A), mixed (New York, New York, weather zone 4A), and cold (Rochester, Minnesota, weather zone 6A). The simulations included parametric variations in window-to-wall ratio (30%–70%), U-values (0.1–1 Btu/h∙ft 2 ∙°F), solar heat gain coefficients (0.2–0.8), and solar control strategies and devices (e.g., interior shades and switchable glazing). Performance metrics included annual cooling, heating, and total heating, ventilation and air-conditioning (HVAC) energy use intensity, as well as nonenergy factors such as useful daylight illuminance, glare frequency, and thermal comfort during typical office hours (8 a.m.–6 p.m.). Results indicate that cooling energy consumption is most sensitive to solar heat gain coefficient (SHGC) in hot weather, whereas heating energy consumption is strongly influenced by U-value in cold weather. Total HVAC reflects these trade-offs, showing up to 60% difference for a window in total HVAC energy use intensity between different combinations of U-value and SHGC. Daylight dimming generally reduces cooling loads but can increase heating demands in colder locations. Interior solar shades improve useful daylight and reduce discomfort glare, whereas switchable glazing delivers the largest cooling reductions in hot weather but may increase heating loads in winter by limiting passive solar gains. Thermal comfort improves with lower window-to-wall ratios and lower SHGC in hot locations and with lower U-values in cold locations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Modeling and simulation to investigate the electrification potential of medium- and heavy-duty vehicle fleets

This project involves developing and integrating new modeling tools to simulate the dynamics of electric medium- and heavy-duty fleet vehicle adoption. A technical and economic modeling tool, combining a data-driven hardware cost model with a cost-optimal charging strategy microsimulation, enables tailored analysis of the costs and benefits of electrifying individual fleets. Next, a novel text synthesis process, applied to a curated corpus of literature, quantifies trade-offs between technical, economic, and other factors in the fleet vehicle procurement decision. The outcomes of these tasks combine with knowledge from recent literature on fleet decision processes to specify the vehicle procurement model used by fleets in an agent-based model of the medium- and heavy-duty electric vehicle market. This model embodies an especially disaggregated approach to adoption modeling, internalizing factors and dynamics that conventional adoption models externalize. In particular, explicitly modeling the formation and diffusion of opinions among agents enables experiments that conventional models cannot support. Demonstrations show, for example, that increasing the extent of interactions between populations with different proclivities to electric vehicles has an asymmetrical outcome. High-proclivity electric vehicle adoption is generally unaffected as interactions increase, but low-proclivity adoption is accelerated. By representing individual fleets' requirements and costs at a high level of detail, incorporating an adoption decision model informed by a wide body of empirical research, and broadening the array of variables and dynamics available for experimentation, this integrated model offers a new way to understand the urgent challenge of eliminating emissions from the most emissions-intensive transportation sectors.

Trinko, David A.↗