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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

An HPC-Based Hydrothermal Finite Element Simulator for Modeling Underground Response to Community-Scale Geothermal Energy Production

Geothermal heat, as renewable energy, shows great advantage with respect to its environmental impact due to its significantly lower CO2 emissions than conventional fossil fuel. Open and closed-loop geothermal heat pumps, which utilize shallow geothermal systems, are an efficient technology for cooling and heating buildings, especially in urban areas. Integrated use of geothermal energy technologies for district heating, cooling, and thermal energy storage can be applied to optimize the subsurface for communities to provide them with multiple sustainable energy and community resilience benefits. The utilization of the subsurface resources may lead to a variation in the underground environment, which might further impact local environmental conditions. However, very few simulators can handle such a highly complex set of coupled computations on a regional or city scale. We have developed high-performance computing (HPC) based hydrothermal finite element (FE) simulator that can simulate the subsurface and its hydrothermal conditions at a scale of tens of km. The HPC simulator enables us to investigate the subsurface thermal and hydrologic response to the built underground environment (such as basements and subways) at the community scale. In this study, a coupled hydrothermal simulator is developed based on the open-source finite element library deal.II. The HPC simulator was validated by comparing the results of a benchmark case study against COMSOL Multiphysics, in which Aquifer Thermal Energy Storage (ATES) is modeled and a process of heat injection into ATES is simulated. The use of an energy pile system at the Treasure Island redevelopment site (San Francisco, CA, USA) was selected as a case study to demonstrate the HPC capability of the developed simulator. The simulator is capable of modeling multiple city-scale geothermal scenarios in a reasonable amount of time.

Sun, Xiang↗
DOE OSTI2026

Heterogeneous Multi-Domain Dataset Synthesis to Facilitate Privacy and Risk Assessments in Smart City IoT

The emergence of the Smart Cities paradigm and the rapid expansion and integration of Internet of Things (IoT) technologies within this context have created unprecedented opportunities for high-resolution behavioral analytics, urban optimization, and context-aware services. However, this same proliferation intensifies privacy risks, particularly those arising from cross-modal data linkage across heterogeneous sensing platforms. To address these challenges, this paper introduces a comprehensive, statistically grounded framework for generating synthetic, multimodal IoT datasets tailored to Smart City research. The framework produces behaviorally plausible synthetic data suitable for preliminary privacy risk assessment and as a benchmark for future re-identification studies, as well as for evaluating algorithms in mobility modeling, urban informatics, and privacy-enhancing technologies. As part of our approach, we formalize probabilistic methods for synthesizing three heterogeneous and operationally relevant data streams—cellular mobility traces, payment terminal transaction logs, and Smart Retail nutrition records—capturing the behaviors of a large number of synthetically generated urban residents over a 12-week period. The framework integrates spatially explicit merchant selection using K-Dimensional (KD)-tree nearest-neighbor algorithms, temporally correlated anchor-based mobility simulation reflective of daily urban rhythms, and dietary-constraint filtering to preserve ecological validity in consumption patterns. In total, the system generates approximately 116 million mobility pings, 5.4 million transactions, and 1.9 million itemized purchases, yielding a reproducible benchmark for evaluating multimodal analytics, privacy-preserving computation, and secure IoT data-sharing protocols. To show the validity of this dataset, the underlying distributions of these residents were successfully validated against reported distributions in published research. We present preliminary uniqueness and cross-modal linkage indicators; comprehensive re-identification benchmarking against specific attack algorithms is planned as future work. This framework can be easily adapted to various scenarios of interest in Smart Cities and other IoT applications. By aligning methodological rigor with the operational needs of Smart City ecosystems, this work fills critical gaps in synthetic data generation for privacy-sensitive domains, including intelligent transportation systems, urban health informatics, and next-generation digital commerce infrastructures.

IoT↗
DOE OSTI2022

tell: a Python package to model future total electricity loads in the United States

The purpose of the Total ELectricity Load (tell) model is to generate 21st century profiles of hourly electricity load (demand) across the Conterminous United States (CONUS). tell loads reflect the impact of climate and socioeconomic change at a spatial and temporal resolution adequate for input to an electricity grid operations model. tell uses machine learning to develop profiles that are driven by projections of climate/meteorology and population. tell also harmonizes its results with United States (U.S.) state-level, annual projections from a national- to global-scale energy-economy model. This model accounts for a wide range of other factors affecting electricity demand, including technology change in the building sector, energy prices, and demand elasticities, which stems from model coupling with the U.S. version of the Global Change Analysis Model (GCAM-USA). tell was developed as part of the Integrated Multisector Multiscale Modeling (IM3) project. IM3 explores the vulnerability and resilience of interacting energy, water, land, and urban systems in response to compound stressors, such as climate trends, extreme events, population, urbanization, energy system transitions, and technology change

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management

The urban drainage system constantly facing flooding issues in coastal and urban areas. Robust and accurate urban flood management, particularly considering fast-moving compound floods, is crucial to minimize the impact of flood disasters in coastal cities. Till now, Ho Chi Minh City (HCMC) lacks an effective means of urban flood management because of flood risk communication among residents. Existing flood risk communication tools rely on post-disaster flood model outcomes and data. Therefore, this research proposes a real-time Early Urban Flooding Warning System (EUFWS) integrated with a user-friendly web and app interface. The backbone of this system consists of flood models developed using machine learning (ML) algorithms, combined with big data and Web-GIS visualization, with ML serving as the core for constructing the EUFWS. EUFWS offer several key advantages: they are available at all times, accessible from anywhere, and provide a real-time, multi-user working platform. Additionally, the system is flexible, allowing for the easy addition of components and services and scalable, adjusting to workload demands. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. Research results indicate that EUFWS supported decision-makers to be effectively risk informed and make intelligent decisions during urban flood emergencies. Finally, this underscores the significant potential of integrating ML and information technology to enhance the management of smart urban drainage systems in flood-prone cities worldwide.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A simulation-based assessment of technologies to reduce heat emissions from buildings

Heat emissions from buildings are part of anthropogenic heat leading to urban overheating. This paper aims to assess how technologies (i.e., energy conservation measures - ECMs), used to decrease energy use, may also reduce heat emissions from buildings. This study employs the physics-based engine EnergyPlus to simulate the main components of heat emissions from buildings to ambient air: envelope, zone, and systems. Hourly simulations are run for IECC single- and multi-family reference models with three representative climates: Miami, Baltimore, and Chicago. The results show that the performance of ECMs varies among weather, seasons, and residential typologies. Particularly, some ECMs (i.e., cool coatings, heat pumps, additional insulation, energy-awareness occupants) show a strong decrease in heat emissions, yet they are not always correlated with proportional decreases in energy use. When all ECMs are combined, the reductions are larger on heat emissions (89%) than on site energy (65%) from the base cases. During summer in Miami, the combination of ECMs shows a decrease in heat emissions from the building surface component of 80% during daytime, 92% for the HVAC component and a counterbalanced increase in the zone component of 88%, bringing to a daily decrease in total heat emissions. The main contributions of this study are quantifying how typical ECMs influence residential building heat emissions using EnergyPlus simulations and informing urban planners and stakeholders on prioritizing measures for mitigating urban overheating problems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Truck platooning in the U.S. national road network: A system-level modeling approach

Truck platooning enables a group of trucks to move close together, which helps reduce truck fuel use and increase effective road capacity. In this paper, a system-level equilibrium model is developed to characterize spontaneous truck platooning with coexistence of non-platooning vehicles in a network, by explicitly accounting for the interlocking relationship among platoon formation time, truck fuel saving, and increase in effective road capacity. To equilibrate the relationships, an algorithm is proposed which involves a diagonalization approach and a bush based algorithm to solve decomposed subproblems. The condition of proportionality is imposed to obtain unique traffic flows for each class of vehicles on road links. In addition, a spatially constrained multivariate clustering technique is employed to construct origin/destination zones that are smaller than the coarse Freight Analysis Framework (FAF) zones, while maintaining reasonable computational burden for network traffic assignment. Model implementation in the U.S. shows that platooning could lead to 7.9% fuel saving among platoonable trucks in 2025 and a comparable increase in effective capacity of platoonable road links, which would account for 60% of rural interstate roads. The fuel saving and road capacity improvement translate into an annual cost reduction of $\$$868 million for the U.S. intercity trucking sector and reduced road infrastructure investment needs worth $\$$4.8 billion. Extensive sensitivity analysis further reveals that fuel saving of platoonable trucks increases with platoon size but decreases with inter-truck distance in a platoon. Fuel saving potential suggests that priority should be given to rural rather than urban roads in deploying platooning technologies. As we expected, greater market penetration of platooning technologies means higher fuel saving and greater increase in effective road capacity.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

NREL Transforms Energy for Innovative Smart and Connected Communities

Smart and connected communities use technology to better manage their urban energy systems and improve the quality and performance of government services by leveraging big data for data-driven decisions. NREL helps these communities reach their clean energy goals through cutting-edge expertise in planning, data, analytical tools, and technical support.

partnering with cities↗
DOE OSTI2020

Hydrogen Refueling Reference Station Lot Size Analysis for Urban Sites

Hydrogen Fueling Infrastructure Research and Station Technology (H2FIRST) is a project initiated by the DOE in 2015 and executed by Sandia National Laboratories and the National Renewable Energy Laboratory to address R&D barriers to the deployment of hydrogen fueling infrastructure. One key barrier to the deployment of fueling stations is the land area they require (i.e. "footprint"). Space is particularly a constraint in dense urban areas where hydrogen demand is high but space for fueling stations is limited. This work presents current fire code requirements that inform station footprint, then identifies and quantifies opportunities to reduce footprint without altering the safety profile of fueling stations. Opportunities analyzed include potential new methods of hydrogen delivery, as well as alternative placements of station technologies (i.e. rooftop/underground fuel storage). As interest in heavy-duty fueling stations and other markets for hydrogen grows, this study can inform techniques to reduce the footprint of heavy-duty stations as well. This work characterizes generic designs for stations with a capacity of 600 kg/day hydrogen dispensed and 4 dispenser hoses. Three base case designs (delivered gas, delivered liquid, and on-site electrolysis production) have been modified in 5 different ways to study the impacts of recently released fire code changes, colocation with gasoline refueling, alternate delivery assumptions, underground storage of hydrogen, and rooftop storage of hydrogen, resulting in a total of 32 different station designs. The footprints of the base case stations range from 13,000 to 21,000 ft 2 . A significant focus of this study is the NFPA 2 requirements, especially the prescribed setback distances for bulk gaseous or liquid hydrogen storage. While the prescribed distances are large in some cases, these setback distances are found to have a nuanced impact on station lot size; considerations of the delivery truck path, traffic flow, parking, and convenience store location are also important. Station designs that utilize underground and rooftop storage can reduce footprint but may not be practical or economical. For example, burying hydrogen storage tanks underground can reduce footprint, but the cost savings they enable depend on the cost of burial and the cost land. Siting and economic analysis of station lot sizes illustrate the benefit of smaller station footprints in the flexibility and cost savings they can provide. This study can be used as a reference that provides examples of the key design differences that fueling stations can incorporate, the approximate sizes of generic station lots, and considerations that might be unique to particular designs.

08 HYDROGEN↗
DOE OSTI2022

Automated Mobility Platforms: A Framework for Versatile, Energy-Efficient Urban Transportation for the 21st Century: Preprint

This paper presents automated mobility platforms (AMPs) as a framework to deliver high-quality urban mobility. AMPs leverage advances in sensing technology developed from automated roadway vehicles to provide opportunities for efficient movement of people and goods in dense urban environments, campuses, and large facilities like airports. In this paper, we layout the concept of AMPs and how they can serve the mobility needs of the population by providing service at the intersection of micromobility (services such as electric scooters or bikes), personal rapid transit, and moving walkways. Several key attributes and justification for the AMPs concept are described, including improvements to accessibility (inherent in design) covering both socioeconomic and disability equity, safety, energy efficiency, cost-effectiveness, urban land and space management, and scalability. We summarize the results from an ongoing national laboratory-sponsored, faculty-supported, student-led investigation into AMPs that includes stakeholder engagement and technical feasibility assessment.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

A Convergence of Public-Private Benefits in Denver: Surveys and Analyses to Inform Urban Mobility-, Energy-, Infrastructure- and Behavior-Related Innovation: Preprint

Cities, public transit agencies, and new private ride hailing services seek to understand emerging traveler dynamics, the shifting demographics of urban travelers,and new energy-efficient mobility opportunities. This includes exploring how new infrastructure investments, public and private mobility services, and smart-phone mobility apps are reshaping behaviors, demands (e.g. mobility-on-demand services), travel experiences and energy-efficient urban travel preferences. Currently, cities and metropolitan regions are providing and experimenting with many new mobility options, technologies, and personalized information services at the intersection of urban mobility, energy, and infrastructure systems (e.g., new commuter rail). To date, technology alone has not been able to crack the nut of 'creating faster trip times, less congestion, safer streets, and cleaner air for its citizens through fewer cars on the road'. This paper focuses on this gap by offering new concepts and potential for integrated approaches. Accommodating more vehicles miles traveled in cities, without increases in person miles traveled (PMT), could be costly, generating: 1) tremendous demands for new infrastructure, land, road space, materials, and energy; 2) higher traffic fatality risks; and 3) worsening air quality. Therefore, this study focuses on reducing single occupancy vehicle use by enhancing integrated mobility, helping transit and ridehailing increase occupancy in ways that also reduce energy use, and improve quality of life for urban travelers and communities. This study focuses on a survey of urban travelers in Denver, as a representative case study for metropolitan regions experiencing rapid growth, ageing populations, increased urban sprawl, traffic-related delays, and inefficient energy use per PMT.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Planning decentralized urban renewable energy systems using algal cultivation for closed-loop and resilient communities

To tackle climate challenges, communities need to harvest renewable energy and resources on site locally to close the loops for enhancing the resilience of communities facing unpredictable and uncertain future changes. A decentralization planning of urban renewable energy systems is proposed by treating urban waste streams and producing biomass through applying algal biotechnology. When applying algal technology as a renewable and decentralized energy source in urban systems, the overall performance can vary by levels of urban nutrients, solar and CO 2 resources, and the transportation cost when considering its application to different urban densities, urban form, and the spatial scale of urban settings. This research explores three potential impacts on the algal system’s energy performance: (1) urban density, (2) urban form in different contexts, and (3) spatial scale. The research examines the impacts by testing urban settings given in actual contexts in Atlanta, Georgia, USA. Four neighborhoods representing the high-density urban, mid-density urban, mixed suburban, and typical suburban areas are investigated. The density-scale–performance relationships are explored through testing different urban forms of neighborhoods in both hypothetical and actual neighborhood settings. A GIS-based model is developed to estimate the overall energy performance of the decentralized renewable energy system in urban environments. Results show that the energy performance is positive mainly for high-density urban neighborhoods with small-to-medium scales, up to 0.36 MJ per ton of municipal solid wastes for actual settings and 0.37 MJ for hypothetical cases. Neighborhoods with higher density have higher energy performance while up scaling has negative effects on the energy performance with a low degree of significance. Optimal scales are found as a 1-km radius in real test beds and 1.3 km in hypothetical settings, in which the results show trade-offs between scaling effects in the system efficiency gain and the transportation cost increase.

Quan, Steven Jige↗
DOE OSTI2021

Large-Scale Classification of Urban Structural Units From Remote Sensing Imagery

Remote sensing in combination with deep learning has become instrumental for efficiently and accurately classifying land-use and land-cover across large geographic areas. These technologies have also been successful in characterizing urban environments in terms of their structural units, structure types, or morphological regions. In these approaches, an urban area is partitioned into regions that exhibit homogeneous physical characteristics. However, existing approaches are typically limited to a single city, use inconsistent typologies, and lack scalability and generalization capacity. In this article, we propose an urban structural units categorization scheme and demonstrate its utility by applying it to 13 cities. Inspired by the lack of scalability and generalization capacity in urban structural units mapping, we extend the reach of deep learning and conduct a set of classification experiments in all 13 cities. These experiments offer insights into the strengths and limitations of deep neural networks for classifying urban structural units over diverse geographic regions and on heterogeneous collections of satellite imagery. The efficacy of the proposed deep learning approach is compared to a baseline method of multiscale image features and support vector machines. Our validation on five cities shows that better performance is achieved with deep neural networks. Additionally, we evaluate the impact of input size, model depth, and spatial pyramid pooling to assess the generalization capacity of deep neural networks.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Solar HBCU Innovation in Microgrids & Resiliency with Solar + Storage

The Breaking Barriers project was designed to improve electricity resilience at four Historically Black Colleges and Universities (HBCUs) in West Atlanta, as well as within the surrounding energy-burdened community. To do so, the project undertook work to design and construct innovative urban energy resiliency hubs integrating microgrid technology, solar generation, and energy storage in these West Atlanta colleges and communities. The hubs will help Atlanta HBCUs and the energy-burdened broader community be more resilient, in addition to informing new course curricula at Atlanta University Center campuses and inspiring similar efforts at other HBCUs and beyond.

14 SOLAR ENERGY↗
DOE OSTI2022

Hands-On Computer Science: The Array of Things Experimental Urban Instrument

Chicago's Array of Things (AoT) project is aptly described as a technology experiment or a "smart city" prototype. The concept of such an extensible "instrument" arose within a larger translational research vision applying computer science and engineering research for the multidimensional benefit of people and communities in cities. The AoT project hypothesized that wireless intelligent sensor networks could enable both quantitative social science and urban monitoring while also stimulating youth interest in science and technology. Successful deployment of such sensor networks could provide open data from urban measurements not only in support of diverse research questions-in environmental dynamics, urban architecture, engineering, and social sciences-but also informing community groups and city planners. Further, the AoT project and its successor SAGE project are a computer science and engineering experiment, but its success is inextricably tied to community engagement and experiential education. Simply put, community acceptance is a prerequisite to installing and testing the instrument.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Leveraging SETS resilience capabilities for safe-to-fail infrastructure under climate change

As the rehabilitation of infrastructure is outpaced by changes in the profile, frequency, and intensity of extreme weather events, infrastructure’s service disruptions and failures become increasingly likely. Safe-to-fail approaches for infrastructure planning and design improve the capacity of cities to adapt for uncertain climate futures by identifying social, ecological, and technological systems (SETS) capabilities to prepare for potential failure scenarios. In this work, we argue for transforming infrastructure planning and design to effectively utilize safe-to-fail approaches by navigating the opportunities and trade-offs of SETS resilience capabilities. From a technological vantage point, traditional infrastructure planning approaches account for social and ecological domains as external design conditions rather than embedded system characteristics. Safe-to-fail approaches directly challenge the isolation of the technological domain by necessitating a recognition that SETS domains are interconnected and interdependent in infrastructure systems, as such risks and system capabilities for resilience must be managed cohesively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Atmospheric emission of nanoplastics from sewer pipe repairs

Nanoplastic particles are inadequately characterized environmental pollutants with significant adverse effects on aquatic and atmospheric systems, causing drastic impacts on human health through inhalation, ingestion, and skin penetration. At present, it is explicitly assumed that environmental nanoplastics (EnvNP) are weathering fragments of microplastic or larger plastic debris discharged into terrestrial and aquatic environments, while atmospheric occurrence of EnvNP is solely attributed to aerosolization by wind and other mechanical forces. However, sources and emissions of unintended EnvNP are poorly understood and are therefore largely unaccounted for in various risk assessments. In this work, we show that large quantities of EnvNP may be directly emitted into the atmosphere as steam laden waste components discharged from a commonly used technology to repair sewer pipes in urban areas. Comprehensive chemical analysis of the discharged waste condensate reveals abundant presence of insoluble colloids, which after drying form solid organic particles with composition and viscosity consistent with EnvNP. We suggest that airborne emissions of EnvNP from these globally used sewer repair practices may be prevalent in highly populated urban areas. Airborne emissions of these EnvNP particles may have important implications on air quality and toxicological levels in large urban areas that need to be mitigated.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

SEIN: Breaking Barriers Resilient Energy System Analysis [Slides]

The Solar Energy Innovation Network (SEIN) is a collaborative research program that supports multi-stakeholder teams in researching and sharing solutions to real-world challenges associated with solar energy adoption. The Breaking Barriers project was selected to participate in the Solar Energy Innovation Network, Round 2, and was led by Groundswell, a D.C.-based clean energy project developer. The Breaking Barriers team included Partnership for Southern Equity, Atlanta University Center campus facility managers and professors, the City of Atlanta's Neighborhood Planning Unit T, and Georgia Power Company. The project aimed to design and construct innovative urban energy resiliency hubs integrating microgrid technology, solar generation, and energy storage in Atlanta colleges and communities. The hubs will help these historically Black colleges and universities (HBCUs) and the energy-burdened broader community in West Atlanta be more resilient, in addition to informing new course curricula at Atlanta University Center campuses. With many possible options for the system's battery size, the Breaking Barriers team needed insight into the relationships between BESS size, economic performance, and resilience at Spelman College's Manley Center. These insights are crucial for entering procurement negotiations with project developers, establishing resilience capabilities that the HBCU campuses can plan around, and guiding the team's fundraising targets. This analysis includes estimates of PV and battery performance, costs, savings, and resilience for multiple battery sizes. In order to provide power during a grid outage, the resilient energy system also needs to be connected to the Manley Center in a safe and island-able manner (electrically isolated from the grid). Analysis of potential electrical configurations and estimated setup costs is key to successfully entering a required interconnection agreement with Georgia Power, as well as informing requests for engineering firms to construct the system. This analysis includes conceptual options and rough order of magnitude cost estimates for electrically interconnecting the resilient energy system to the Manley Center and the grid. The Breaking Barriers team used this analysis to select preferred system characteristics and design for the resilient energy system.

14 SOLAR ENERGY↗