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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Micromobility Integrated Transit and Infrastructure for Efficiency (MITIE)

Nearly omnipresent in many cities of all sizes across the United States, micromobility vehicles-e-scooters, manual bicycles, e-bicycles, and larger seated electric scooters-are notably missing from SMART Mobility research. This project aims to expand the spectrum of modes currently being researched within SMART Mobility by exploring micromobility as an important tool toward meeting energy-efficient mobility goals. It expands on findings from SMART Mobility 1.0 that revealed preferences to reduce transportation-related expenses through use of a network of mobility-as-a-service (MaaS) and other shared mobility options, and builds on findings from a 2019 Vehicle Technology Analysis Program (VTAP) funded micromobility project conducted by our team. We will explore multiple facets of micromobility, including behavior and decision-making, the integration of micromobility within transportation infrastructure, energy estimates, and operations. Guiding research questions include: (1) What are the potential energy savings from low, medium, and high market penetration of micromobility (in passenger, multimodal, and freight domains)? (2) Which scenarios for micromobility use and related enablement of increased public transit use should be modeled/considered in the SMART 2.0 Workflow? (3) To what degree can micromobility supplement/complement transit system operations? (4) What are people's preferences towards micromobility? How do preferences vary across various sociodemographic segments? How can this knowledge inform operations? (5) What are optimal strategies to attain high user adoption and shift users toward more energy-efficient mode choices in terms of micromobility operation? How do these strategies affect energy savings, person-miles traveled, lifecycle energy use, and adoption rates? These questions will be addressed through applied research in five project emphasis areas: (1) Energy estimates of micromobility for Workflow scenarios: Expand and refine previous micromobility work to augment the Workflow approaches to modeling urban travel. (2) Multimodal connection with transit: Utilizing Mobility-Energy Productivity (MEP) tools to evaluate multimodal travel patterns enabled by micromobility, including assessing how to reduce barriers of inequity of access to mobility options and destinations. (3) Mode choice, induced demand, and infrastructure: Understanding the mode shift induced through micromobility to inform energy impact analysis. (4) Energy optimization of micromobility operations: Identification of micromobility operations parameters and development of operations scenarios to better understand present-day micromobility operations for integration into the Workflow, in partnership with BEAM and POLARIS modeling teams. (5) Micro-freight: Characterize the current state of micro-freight activities, including energy effects and geospatial analyses, to inform Workflow.

ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗
DOE OSTI2023

Micromobility Integrated Transit and Infrastructure for Efficiency (MITIE)

Nearly omnipresent in many cities of all sizes across the United States, micromobility vehicles-e-scooters, manual bicycles, e-bicycles, and larger seated electric scooters-are notably missing from SMART Mobility research. This project aims to expand the spectrum of modes currently being researched within SMART Mobility by exploring micromobility as an important tool toward meeting energy-efficient mobility goals. It expands on findings from SMART Mobility 1.0 that revealed preferences to reduce transportation-related expenses through use of a network of mobility-as-a-service (MaaS) and other shared mobility options, and builds on findings from a 2019 Vehicle Technology Analysis Program (VTAP) funded micromobility project conducted by our team. We will explore multiple facets of micromobility, including behavior and decision-making, the integration of micromobility within transportation infrastructure, energy estimates, and operations. Guiding research questions include: 1) what are the potential energy savings from low, medium, and high market penetration of micromobility (in passenger, multimodal, and freight domains)? 2) which scenarios for micromobility use and related enablement of increased public transit use should be modeled/considered in the SMART 2.0 Workflow? 3) to what degree can micromobility supplement/complement transit system operations? 4) what are people's preferences towards micromobility? How do preferences vary across various sociodemographic segments? How can this knowledge inform operations? 5) what are optimal strategies to attain high user adoption and shift users toward more energy-efficient mode choices in terms of micromobility operation? How do these strategies affect energy savings, person-miles traveled, lifecycle energy use, and adoption rates? These questions will be addressed through applied research in five project emphasis areas: 1) energy estimates of micromobility for Workflow scenarios: Expand and refine previous micromobility work to augment the Workflow approaches to modeling urban travel; 2) multimodal connection with transit: Utilizing Mobility-Energy Productivity (MEP) tools to evaluate multimodal travel patterns enabled by micromobility, including assessing how to reduce barriers of inequity of access to mobility options and destinations; 3) mode choice, induced demand, and infrastructure: Understanding the mode shift induced through micromobility to inform energy impact analysis; 4) energy optimization of micromobility operations: Identification of micromobility operations parameters and development of operations scenarios to better understand present-day micromobility operations for integration into the Workflow, in partnership with BEAM and POLARIS modeling teams; 5) micro-freight: Characterize the current state of micro-freight activities, including energy effects and geospatial analyses, to inform Workflow.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Integrated Systems-to-Atoms (S2A) Framework for Designing Resilient and Efficient Hydrogen Infrastructure Solutions

The success of a future clean hydrogen infrastructure will depend on technology performance, operating conditions, and system configuration, which will be integrated to meet specific end-use requirements. We introduce a Systems-to-Atoms framework to cosimulate material, device, and system design for clean hydrogen storage and transport. Here we present a demonstration scenario in which hydrogen is distributed using a liquid organic hydrogen carrier to 700 bar refueling stations. Cross-scale analysis on hydrogen release at the refueling station reveals that while a high reaction pressure (up to 100 bar) may not enhance the catalyst performance, it can lead to system-level cost savings (up to 5% of total refueling station cost) due to savings in downstream gas compression. Further, a typically low-performing dehydrogenation catalyst material (e.g., copper) may be preferred over a high-performing catalyst (e.g., palladium) at different operating conditions when considering overall system affordability and supply chain resilience.

08 HYDROGEN↗
DOE OSTI2020

2020 Transportation Research Board Workshop: Infrastructure Spatial Sensing at Intersections: The Potential to Enhance Safety and Enable Automation

The sensing technology that is enabling vehicle automation also can revolutionize traffic intersection safety, control, and efficiency. Infrastructure spatial sensing technology refers to the deployment of sensors that can detect and track all objects in the field of view - for example, mounting lidar sensors, radar, and video imaging at an intersection and fusing the data to produce a three-dimensional dynamic operational awareness. Combining such sensing capability with fail-safe vehicle communications can facilitate applications such as eco-approach and departure, optimizing efficient vehicle-signal coordination, and reducing traffic accidents through increasing safety. Furthermore, evidence is mounting suggesting that infrastructure-based sensing is needed in order to safely deploy automated public mobility alongside the normal vehicle and pedestrian traffic in our cities. Six speakers were invited to present their work from various aspects (safety, efficiency, and automation) addressing infrastructure-based sensing at the workshop on Infrastructure Spatial Sensing hosted in Washington DC as part of the Transportation Research Board Annual Meeting in January 12, 2019. A summary of the presentation are included herein.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

The water use of data center workloads: A review and assessment of key determinants

The global importance of data center water use is increasing with the rapid growth of digitalization and artificial intelligence. This study analyzes the factors influencing workload-level water use, measured in liters consumed per workload, to guide water-saving strategies in data centers. Our findings reveal workload-level water use variations exceeding 10,000-fold, driven by over 1000-fold differences in water consumption per kilowatt hour of server electricity consumed and approximately 10-fold differences in server workload efficiency. Key determinants are ranked as server efficiency, electrical grid water consumption factors, server utilization, cooling system type, infrastructure efficiency, climate zone, inactive server percentage, and server refresh cycle. Notably, there is no single recipe for minimizing water use; instead, optimal outcomes depend on tailored combinations of these factors. This analysis addresses critical knowledge gaps by identifying the determinants of data center water use and exploring their achievable minima under diverse site-specific constraints.

Data centers↗
NASA NTRS2007

Fabrication Infrastructure to Enable Efficient Exploration and Utilization of Space

Unlike past one-at-a-time mission approaches, system-of-systems infrastructures will be needed to enable ambitious scenarios for sustainable future space exploration and utilization. Fabrication infrastructure will be needed to support habitat structure development, tools and mechanical part fabrication, as well as repair and replacement of ground support and space mission hardware such as life support items, vehicle components and crew systems. The fabrication infrastructure will need the In Situ Fabrication and Repair (ISFR) element, which is working in conjunction with the In Situ Resources Utilization (ISRU) element, to live off the land. The ISFR Element supports the entire life cycle of Exploration by: reducing downtime due to failed components; decreasing risk to crew by recovering quickly from degraded operation of equipment; improving system functionality with advanced geometry capabilities; and enhancing mission safety by reducing assembly part counts of original designs where possible. This paper addresses the fabrication infrastructures that support efficient, affordable, reliable infrastructures for both space exploration systems and logistics; these infrastructures allow sustained, affordable and highly effective operations on the Moon, Mars and beyond.

Howell, Joe T.↗
NASA NTRS2008

On-Site Fabrication Infrastructure to Enable Efficient Exploration and Utilization of Space

Unlike past one-at-a-time mission approaches, system-of-systems infrastructures will be needed to enable ambitious scenarios for sustainable future space exploration and utilization. So what do we do when we get to the moon for sustainable exploration. On-site fabrication infrastructure will be needed to support habitat structure development, tools and mechanical part fabrication, as well as repair and replacement of ground support and space mission hardware such as life support items, vehicle components and crew systems. The on-site fabrication infrastructure will need the In Situ Fabrication and Repair (ISFR) element, which is working in conjunction with the In Situ Resources Utilization (ISRU) element, to live off the land. The ISFR element has worked closely with the ISRU element in the past year to assess the ability of using lunar regolith as a viable feedstock for fabrication material. Preliminary work has shown promise and the ISFR Element will continue to concentrate on this activity. Fabrication capabilities have been furthered with the process certification effort that, when completed, will allow for space-qualified hardware to be manufactured. Materials being investigated include titanium and aluminum alloys as well as lunar regolith simulants with binders. This paper addresses the latest advancements made in the fabrication of infrastructures that support efficient, affordable, reliable infrastructures for both space exploration systems and logistics; infrastructures that allow sustained, affordable and highly effective operations on the Moon and beyond.

Howell, Joe T.↗
DOE OSTI2025

Advancing Grid Resilience through Smart Charge Management: Findings from Maryland’s Pilot

This report presents research findings from a four-year Smart Charge Management (SCM) pilot program conducted by Maryland’s largest electric utilities—Baltimore Gas and Electric (BGE), Potomac Electric Power Company (Pepco), and Delmarva Power & Light (DPL)—to evaluate strategies for optimizing electric vehicle (EV) charging loads and enhancing grid stability. Supported by the U.S. Department of Energy (DOE), Argonne National Laboratory collaborated with all project partners and examined the effectiveness of Time-of-Use (TOU) and Load Balancing (LB) strategies in managing peak demand, deferring costly infrastructure upgrades, and reducing grid constraints at the feeder level. Using charging data from over 4,600 EV drivers, the study analyzed SCM’s impact on the distribution systems of BGE and Pepco, which consists of over 2000 feeders. Unlike prior research that focused on system-wide trends or synthetic feeders, this analysis offers granular, feeder-level insights based on real-world operational data. It highlights how transformer density, load profiles, and infrastructure constraints influence smart charging performance. Results show feeder-level conditions play a crucial role in SCM effectiveness, with most feeders benefiting more from LB, while TOU-based SCM may be sufficient for others. By 2035, LB reduced peak charging loads by 27% on average, compared to 23% under TOU-based SCM, though some feeders saw reductions exceeding 35%, while others experienced minimal impact. Feeders with higher transformer utilization and limited capacity benefited more from LB, which more effectively distributed charging demand during off-peak hours. Beyond reducing grid constraints, SCM offers long-term operational and financial benefits. By shifting EV charging demand strategically, utilities can optimize asset utilization, delay infrastructure investments, and enhance grid performance. In terms of infrastructure upgrade deferrals, at the feeder level, LB consistently reduced peak charging loads and resulting infrastructure upgrade costs, particularly in high EV enrollment areas, decreasing the number of overloaded transformers by up to 35%, while TOU-based SCM achieved 20-30% reductions depending on feeder characteristics. At the system level, LB has the potential to defer total upgrade costs by $\$$186 million for BGE, compared to $\$$159 million under TOU-based SCM. For Pepco, TOU-based SCM performed slightly better, deferring upgrade costs by $\$$30 million, compared to $\$$29 million under LB. Section 4.5 reviews some of the system differences between BGE and Pepco. However, as EV adoption scales, TOU-based SCM will introduce secondary peak charging loads, reinforcing the need for more advanced, adaptive SCM approaches to prevent new grid challenges. As EV adoption continues to grow, feeder-level managed charging strategies will be essential for mitigating grid stress, improving infrastructure efficiency, and maintaining energy affordability for consumers. This report provides critical insights for utilities, Public Utility Commissions (PUCs), and state agencies on the role of feeder-specific smart charging in infrastructure planning, policy development, and grid modernization. The findings underscore the importance of tailored, data-driven SCM solutions that align with local grid conditions, ensuring a resilient, cost-effective transition to increasing EV adoption while safeguarding distribution system performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Formal methods approach to the charging facility location problem for battery electric vehicles

Battery electric vehicles (BEVs) are becoming more prevalent as improvements in battery technology and energy management continue to be made. As the number of electric vehicles grows, the demand for fast-charging stations is expected to increase dramatically. Thus, building new charging station infrastructure efficiently will be key to reducing upfront costs while meeting consumer demands. In this work, we propose a method for choosing a set of charging station locations that are optimized based on a set of given common vehicle demand points. As part of this solution, we also offer a novel abstraction of the road network on which energy-efficient paths that account for charge-time delays may be found. The current algorithm chooses the optimal charging locations for a single agent which has a route objective specified using temporal logic. To demonstrate the proposed method, the running example shows how a charging station could be chosen for an electric delivery vehicle. Simulations were run on sample road networks with a given set of demand points to service and potential charging station locations to compare. The method is shown to successfully rank potential charging stations in terms of their expected average charging time cost.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Energy-Efficient and Resilient Infrastructure: Simulation, Validation, and Installation

Advanced, high-performance computing at the National Renewable Energy Laboratory (NREL) has enabled access to vast data resources with cutting-edge software techniques to understand, design, plan for, and maintain energy-efficient and resilient infrastructure. We have focused on cities and airports, but the technology we have developed will easily translate to seaports, inland ports, military installations, or other complex and large-scale energy-intensive systems. We can digitally simulate and explore current and future scenarios to make datadriven decisions for optimizing advanced energy systems, transportation and building operations, infrastructure planning and expansion, and battery storage to guide short- and long-term investments, electrification strategies, and integration of new technologies.

Athena↗
DOE OSTI2026

Advanced Data Center Energy Opportunities: Cloud and Infrastructure CoP - Data Center Energy and Efficiency with AI Adoption

The NLR portion of the "Cloud & Infrastructure CoP - Data Center Energy and Efficiency with AI Adoption" web meeting will cover data center locations, energy use and load growth, best practices, performance metrics, transition to direct liquid cooled data center equipment, and NLR's approach to optimizing data center.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Low-carbon fuels for spark-ignited engines: A comparative study of compressed natural gas and liquefied petroleum gas on a CFR engine with exhaust gas recirculation

Decades of work on low-carbon fuels have established their potential for substantial emissions reductions; however, their adoption is still limited by infrastructure concerns and engine efficiency deficits. As infrastructures have begun to evolve, research on strategies that maximize engine efficiency through interactions with fuel properties must also now take center place. This paper compares the performance, emissions, and combustion characteristics of two forefront low-carbon fuels: compressed natural gas (CNG) and liquefied petroleum gas (LPG) in a cooperative fuel research (CFR) engine over a range of compression ratios and engine loads. The effects of exhaust gas recirculation (EGR), end-gas auto-ignition, and a novel combustion control tool, the combustion intensity metric (CIM), were also evaluated at different stoichiometric engine operating conditions. In comparison to LPG, CNG operation demonstrated an extended knock-free regime, allowing engine operation at higher engine loads and compression ratios, but LPG operation exhibited enhanced combustion characteristics with higher peak pressures and faster apparent heat release rates (AHRR). LPG operation achieved higher brake thermal efficiencies and lower equivalent CO 2 emissions compared to CNG operation at the tested engine loads and compression ratios. LPG demonstrated significantly higher EGR tolerance limits compared to CNG, with a maximum of 28% EGR rate, compared to 23% for CNG. This improved EGR dilution tolerance was responsible for a 90% reduction in NOx emissions for LPG compared to a maximum of 70% with CNG. EGR dilution also exhibited more effective knock mitigation potential with LPG, suppressing knock intensity values by up to 98% and transitioning the engine operation towards normal combustion from heavy knocking conditions. As a result, the CIM was found to decrease burn durations and improve the quality of combustion by controlling the desired fraction of end-gas auto-ignition.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRSDate not supplied

Enabling Reliable, Fault-Tolerant Autonomous Lunar Habitats with High-Performance Spaceflight Computing

The lunar surface presents unfavorable constraints and harsh living conditions. To address these challenges, autonomous habitats will require complex integrated systems that combine advanced software, high-performance hardware, and cutting-edge sensors to ensure sustainability, safety, and operational efficiency. Consequently, maintaining a sustainable presence on the Moon requires reliable infrastructure and efficient development, precise monitoring, and utilization of resources within a lunar installation. These elements are essential not only to ensure that lunar settlement can be long-term, self-sustaining, and resource-efficient, but also to serve as a foundation for future missions and eventual human habitation on Mars. Humans are not native to the Moon; therefore, our survival and ability to thrive will depend on autonomous systems that can foster safety and resilience through high-availability architectures, graceful degradation, and highly fault-tolerant spaceflight hardware capable of continuing operation during failures. This requires advanced human-rated distributed systems architectures with specialized electronics, scalable capabilities, and an integrated design approach. Unlike current practices focused on short-term missions and regularly maintained components, permanent lunar compute systems must be designed for extended operations beyond mission durations. This paper explores the necessity of transitioning toward fault- tolerant, highly autonomous hardware systems designed for multi-year missions. It also identifies critical subsystems that require high levels of autonomy, supported by radiation-hardened processors and extreme thermal loads, which are essential to mitigate long-term degradation and ensure sustainable lunar habitation. Finally, the paper aligns with NASA’s identified Civil Space Shortfalls, particularly in high-performance onboard computing, advanced data acquisition, extreme-environment avionics, radiation monitoring and countermeasures, and autonomous health management. It proposes NASA’s new High-Performance Spaceflight Computing (HPSC) processor as a turnkey solution, delivering 100 times the performance-per-watt of legacy rad-hard CPUs and enabling onboard AI, edge computing, and fault-tolerant features essential for sustained lunar autonomy and beyond.

Sarkis S Mikaelian↗
DOE OSTI2022

Development and Evaluation of Chip-Enabled Raised Pavement Markers for Lane Line Detection

Increased energy consumption from autonomous vehicle (AV) sensors and computational load as well as upfront costs of sensors are barriers to broad AV adoption. For high quality and reliable perception of the driving environment, incoming data from multiple sensors need to be fused together using advanced computational algorithms, which requires a high compute load. As an alternative, infrastructure-based sensors can be designed to facilitate perception and sensing by supporting vehicle-to-infrastructure (V2I) information exchange. This work presents the initial development and evaluation of a novel energy efficient infrastructure-based sensor. The sensor, a chip-enabled raised pavement marker (CERPM), is capable of wireless communications to exchange environment information with AVs. As a test case, the developed CERPM is applied in real-world driving to perform lane line and drivable region detection for an AV. It is shown that CERPMs can be utilized to effectively detect the lane line and drivable region, which can improve perception while reducing the compute load.

Sharma, Sachin↗
DOE OSTI2022

GridDS: Data Science Toolkit for Energy Grid Data

According to the U.S. Energy Information Administration (EIA), the demand for energy is expected to increase 50% by the year 20501. While energy standards, such as the Institute of Electrical and Electronics Engineers (IEEE) Standard 1547, (Basso 2015) and monitoring with wide area management systems (WAMS) (Liu 2017, Zhou 2016) have enabled large scale data collection and storage, the application of this data in mitigating costs associated with increased consumer demand is an ongoing focus for energy research. This ubiquitous data collection presents a promising opportunity for machine learning and data science to improve efficiency of distributed energy resources (DERs). The GridDS software toolkit is designed to leverage advanced metering infrastructure (AMI), outage management systems data (OMS), Supervisory control Data Acquisition (SCADA), and geographic information systems (GIS) to forecast future energy demands and detect incipient grid failures. GridDS is a python software library designed to be modular and generalizable to data recorded by DERs. In adapting to disparate datasets recorded by various WAMS, GridDS provides a range of unique functionality not presently implemented in current WAMS which have highly specific software infrastructure by design. GridDS functionality ranges from data specification and preparation, to training and validation for state of the art machine learning, to interactive data visualization. For data intake, GridDS combines: Pandera: a library for creating data specifications. TimeScaleDB: a postgresSQL database infrastructure for efficient storage of timeseries data. Dataset class: A custom dataset class / interface that ensures modularity between a range of synthetic and live recorded datasets. Is

Ladd, Alexander↗
DOE OSTI2022

SimCCS: CCS Infrastructure Decision Support [Slides]

SimCCS can help determine optimal, regional network of CO 2 sources, CO 2 sinks and CO 2 transport infrastructure that meet desired CCS goals. Deployment of large-scale CCS will require large-scale regional infrastructure. Strategic investments in developing regional CO 2 transportation infrastructure can facilitate timely CCS deployment (including at large-scale). Economies of scale can help to minimize costs. Development of effective & efficient CCS infrastructure requires taking into consideration characteristics of point sources (for capture), region (for transport) and sinks (for storage).

58 GEOSCIENCES↗
NASA NTRS2017

Evolution of A Distributed Live, Virtual, Constructive Environment for Human in the Loop Unmanned Aircraft Testing

NASA's Unmanned Aircraft Systems Integration in the National Airspace System Project is conducting human in the loop simulations and flight testing intended to reduce barriers associated with enabling routine airspace access for unmanned aircraft. The primary focus of these tests is interaction of the unmanned aircraft pilot with the display of detect and avoid alerting and guidance information. The projects integrated test and evaluation team was charged with developing the test infrastructure. As with any development effort, compromises in the underlying system architecture and design were made to allow for the rapid prototyping and open-ended nature of the research. In order to accommodate these design choices, a distributed test environment was developed incorporating Live, Virtual, Constructive, (LVC) concepts. The LVC components form the core infrastructure support simulation of UAS operations by integrating live and virtual aircraft in a realistic air traffic environment. This LVC infrastructure enables efficient testing by leveraging the use of existing assets distributed across multiple NASA Centers. Using standard LVC concepts enable future integration with existing simulation infrastructure.

UAS in the NA↗