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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Citizen science coupled with machine learning to quantify green-blue infrastructure cooling potential in Maricopa County, Arizona

Here, this study investigates the spatiotemporal cooling performance of green and blue infrastructure (GBI) in the Dobson Ranch urban neighborhood in Phoenix, Arizona. We leveraged citizen science near-surface (2 m) air temperature (Tair) measurements to train a highly accurate Tair predicting LightGBM machine learning model (R 2 : 0.986, MAE: 0.251 °C, RMSE: 0.585 °C). On June 16, 2024, the park area exhibited approximately 1 °C cooling effect (relative to the neighborhood mean) during both day and night. In contrast, the nearby artificial lake exhibited a stronger cooling effect of 2.4 °C during the day but a slight warming of 0.3 °C at night. At 00:00, locations 50 m downwind of the park were 0.3 °C warmer than the park, while locations 50 m upwind were 0.8 °C warmer. At 11:00, we observed that the downwind area is 0.8 °C cooler and the upwind area is 0.6 °C warmer—at the same 50 m distances relative to the park. We also observed 1 °C cooler and warmer effects respectively at the same 50 m downwind and upwind locations at 19:00 on June 17, 2024. Our data-driven analysis highlights potential limitations of car-traverse measurements, showing that failure to account for temporal variations during the traverse can lead to overestimation of Tair at night and underestimation during the day. Our analysis also showed only a weak correlation (coefficient: 0.48) between Landsat-derived land surface temperature (LST) and model predicted Tair at the time of the local Landsat overpass (∼11.00). This highlights the potential error of relying solely on LST for human thermal exposure analysis—particularly within the heterogenous built-environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

District-Scale Analysis of Electricity Load and Strategies to Improve Energy Reliability Using Prototype District Models

Projected increases in electricity demand in the U.S. highlight the urgent need for effective load management to ensure grid reliability. As the building sector accounts for approximately 75% of electricity usage, enhancing energy efficiency and flexibility in this sector is crucial. Adopting district-level approaches offers significant advantages over traditional individual building analyses by enabling shared infrastructure and economies of scale. To navigate the data and computational challenges associated with modeling energy at the district level, prototype district models have been proposed as holistic, system-level solutions that capture complex interactions within typical configurations. This study presents these models as a reference tool for analyzing district-scale energy systems across various climate zones in the U.S. Developed with input from stakeholders, these models integrate varied building characteristics, inter-building connections, and energy system interactions. A case study utilizing the Urban Edge prototype district model, implemented on the URBANopt™ platform, evaluates multiple demand scenarios and the impact of distributed energy resources such as fuel-fired backup generators, photovoltaic systems, and batteries. Findings suggest that while new electric systems can significantly reduce annual energy use, they may also elevate peak electricity loads, with a notable 43% increase in heating-dominant climate zone 5B. The optimal backup power solutions vary based on location, influenced by factors such as utility rates and incentives. For example, PV and batteries perform well in high-cost regions like New York City, while diesel backup generators are more suitable for backup needs in climate zone 3A, such as Atlanta. Thus, this research highlights the importance of prototype district models for future district-scale energy planning.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Calibration and field deployment of low-cost sensor network to monitor underground pipeline leakage

Recent technological advances in methane detection have improved leak detection and repair. However, current methods to reliably measure methane concentrations rely on expensive instruments or demand significant labor input. There is interest in using affordable methane sensors that are responsive to ppmv level changes in methane concentrations in both urban and rural environments for monitoring underground natural gas pipeline leaks. This is especially relevant for situations where potentially significant leaks cannot be repaired immediately or smaller leaks that require long-term monitoring and further evaluation. In this work, a low-cost sensor unit, equipped with a metal oxide sensor, was designed, built, and calibrated over a wide range of methane concentrations and environmental conditions in preparation for field application. A network of these sensors was then installed at the test site and used to measure methane concentrations at ground level above known sub-surface natural gas emissions which emulated underground gas pipeline leaks. This low-cost sensor network measured over 4 days total for the two different known leakage rates. Results demonstrate that the sensors can continuously measure relative methane variability for extended periods but require calibration for a wide range of temperature and humidity conditions to properly determine absolute gas (i.e., methane) concentrations. Furthermore, when a regression analysis was conducted to evaluate the effects of meteorological parameters on methane concentration, air temperature and wind speed have strong impacts on the concentration. Overall, the network approach allows improved identification of leak location and monitoring of underground natural gas leaks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure

Disease surveillance systems allow public health agencies to respond to emerging diseases before they become widespread. Developing such systems requires identifying optimal ways to monitor in the context of an epidemic outbreak; this problem is known as sensor selection. Contact networks represent the dynamics of interaction in a population and are used to model how a disease spreads in a population and to explore strategies of sensor selection. We evaluated five sensor selection strategies on their ability to provide an early warning of a COVID-like outbreak in synthetic contact networks encapsulated in four network scenarios. Three of these scenarios assessed different aspects of community structure. The fourth scenario employed a contact network representing the population and interactions of 6.8 million people in New York City, constructed from an agent-based simulation using census and transportation data. This scenario exemplifies how sensor selection strategies may perform in a real-world, urban context. Our findings suggest that the choice of the optimal strategy depends heavily on the community structure of the network. Strategies that select highly connected nodes or maximize network coverage are the optimal surveillance strategy for outbreak detection in many network community structures. However, a naive implementation of these strategies may fail to provide an early warning at all—including in the New York City scenario. Moreover, these methods are impractical for real-world use as they require knowledge of the underlying contact network. Instead, a selection strategy that starts with a set of random nodes and then performs a random walk through a chain of neighbors reliably provides early warnings without requiring prior knowledge of the network. We find this method, called “random chain”, to be the most pragmatic for implementation in a real-world disease surveillance context.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Accelerating room air conditioner efficiency in India: Grid, economic, and policy implications through 2035

India is poised for a rapid surge in space cooling demand, driven by rising incomes, urbanization, and intensifying heat. Between 2025 and 2035, the country is expected to add 130–150 million new room air conditioners (ACs). If Minimum Energy Performance Standards (MEPS) continue to improve at the historical rate of 2–3 % annually, room ACs alone could contribute over 180 GW to peak electricity demand by 2035-nearly 30 % of the projected national total. This study evaluates the impact of an accelerated MEPS trajectory, proposing to raise the 1-star threshold to ISEER 5.0 by 2027, ISEER 6.3 by 2030, and ISEER 7.4 by 2033. Drawing on engineering cost analysis, stock turnover modeling, and retail pricing data, we find that this pathway could reduce peak demand by over 60 GW, save 118 TWh of electricity annually, avoid 49 MtCO₂ of electricity-related emissions per year, avert ₹7.5 trillion (∼US$85 billion) in power system investments, and yield ₹0.7–2.3 trillion (∼US$8–26 billion) in net consumer savings by 2035. Contrary to affordability concerns, empirical trends show that higher efficiency does not increase AC prices. These results highlight the value of ambitious MEPS as a cost-effective strategy for improving grid reliability, reducing emissions, and advancing consumer welfare in emerging economies.

Abhyankar, Nikit↗
DOE OSTI2022

Receiver willingness to participate in off-hour service programs

Service trips, a frequently overlooked segment of urban traffic, represent a disproportionately large share of the negative impacts associated with commercial activity. The reason is that although service trips are less frequent than freight trips, they often take longer and thus occupy a significant share of commercial parking. Demand management programs seeking to move service trips off-hours can potentially reduce congestion and emissions but they have been understudied. Here, this paper describes research conducted to investigate receivers’ willingness to participate in off-hour programs for planned service activity. The research reported in this paper analyzes results from a survey of 189 business establishments in New York City and the Capital Region in New York State using descriptive analyses of the data, and by estimating a discrete choice model to gain insight into how business characteristics influence willingness to participate in off-hour services. To assess the impacts of a hypothetical off-hour services program, the authors applied the discrete choice model, together with service trip attraction models, to quantify the hours of daytime parking that could be eliminated by the proposed program in certain ZIP Codes in New York City and the Albany area. The research conducted led to the identification of numerous policy implications that will help policymakers understand and maximize the potential benefits of implementing off-hour service programs.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Siting and sizing of public–private charging stations impacts on household and electric vehicle fleets

To facilitate the provision of electric vehicle charging stations (EVCS) in urban areas, this study investigates the benefits of co-locating fleet-owned chargers with public charging stations to enable construction incentives and cord-sharing cost savings. Shared EVCS can serve charging demand from both user types: private (household) EV owners and those managing fleet vehicles – like shared and fully automated EV (SAEV) fleets. Using POLARIS to simulate all person-travel across the 6-county Austin, Texas region, new EVCS were sited and sized with DC fast-charging (DCFC) plugs to lower operating and construction costs while providing public + private (PP) service across an 81-square-mile core geofence (where 200 SAEVs were active) over 24-hour days. When co-location is permitted, 115 DCFC cords were added to the 23 existing (publicly available) stations to enable SAEVs and household EVs (HHEVs) charging access, within the geofence. Each 250-mile-range SAEV was simulated to travel an average of 330 miles per day, serve over 92 person-trips, and recharge 2.7 times a day (for 2.4 h per session). The new DCFC plugs were primarily added to public EVCS at shopping centers and schools, and in residential settings along freeways. The average plug served 4.8 EVs per day. Most co-located PP EVCS permitted immediate (no-wait) charging, except for 2 stations along freeways that averaged 8 min of wait time to begin charging. In conclusion, the co-location strategy lowered fleet owners’ initial EVCS construction costs by 12 % (thanks to cord-sharing to avoid cord duplication), while reducing SAEV wait times to just 3.1 min (versus 10.7 min if SAEV managers had to build and operate their own EVCS).

EV charging modeling↗
DOE OSTI2023

Non-unimodal and non-concave relationships in the network Macroscopic Fundamental Diagram caused by hierarchical streets

Unimodal, concave relationships between average network productivity and accumulation or density aggregated across spatially compact regions of urban networks—so called network Macroscopic Fundamental Diagrams (MFDs)—have recently been shown to exist on homogeneous street networks. When present, MFD relationships facilitate the modeling of traffic congestion at a regional level and have led to the development of various regional traffic control strategies. However, real street networks are not homogeneous—they generally have a hierarchical structure where some streets (e.g., arterials) promote higher mobility than others (e.g., local roads). Here, this paper examines how the presence of hierarchical roadway structures may potentially cause non-unimodal patterns in a network's MFD. These are observed using three types of tools: analytical models of simple network structures, simulations of various idealized roadway networks, and empirical data. The impacts of street hierarchy depend on how vehicles use different roadway types to move within the network; i.e., their routing strategy. The findings suggest that the presence of roadway hierarchies may lead to MFDs that have non-unimodal or non-concave patterns on the free-flow branch when vehicles route themselves according to user equilibrium principles, which is closest to what would be observed in realistic situations. Such patterns are contrary to what is traditionally assumed in most MFD-based modeling frameworks. However, the unimodal and concave MFD should be expected under system optimal routing conditions that maximize network productivity for a given traffic state.

42 ENGINEERING↗
DOE OSTI2020

Reducing ridesourcing empty vehicle travel with future travel demand prediction

Ridesourcing services provide alternative mobility options in several cities. Their market share has grown exponentially due to the convenience they provide. The use of such services may be associated with car-light or car-free lifestyles. However, there are growing concerns regarding their impact on urban transportation operations performance due to empty, unproductive miles driven without a passenger (commonly referred to as deadheading). This paper is motivated by the potential to reduce deadhead mileage of ridesourcing trips by providing drivers with information on future ridesourcing trip demand. Future demand information enables the driver to wait in place for the next rider’s request without cruising around and contributing to congestion. A machine learning model is employed to predict hourly and 10-minute future interval travel demand for ridesourcing at a given location. Using future demand information, we propose algorithms to (i) assign drivers to act on received demand information by waiting in place for the next rider, and (ii) match these drivers with riders to minimize deadheading distance. Real-world data from ridesourcing providers in Austin, TX (RideAustin) and Chengdu, China (DiDi Chuxing) are leveraged. Results show that this process achieves 68%–82% and 53%–60% reduction of trip-level deadheading miles for the RideAustin and DiDi Chuxing sample operations respectively, under the assumption of unconstrained availability of short-term parking. Deadheading savings increase slightly as the maximum tolerable waiting time for the driver increases. Further, it is observed that significant deadhead savings per trip are possible, even when a small percent of the ridesourcing driver pool is provided with future ridesourcing demand information.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Joint routing of conventional and range-extended electric vehicles in a large metropolitan network

Range-extended electric vehicles combine the higher efficiency and environmental benefits of battery-powered electric motors with the longer mileage and autonomy of conventional internal combustion engines. This combination is particularly advantageous for time-constrained delivery routing in dense urban areas, where battery recharging along routes can be too time-consuming to economically justify the use of all-electric vehicles. However, switching from electric to conventional fossil fuel modes also results in higher costs and emissions and lower efficiency. This paper analyzes this heterogeneous vehicle routing problem and describes two solution methods: an exact branch-price-and-cut algorithm and an iterated tabu search metaheuristic. From a methodological perspective, we find that the exact algorithm consistently obtains tight lower bounds that also serve to certify the metaheuristic solutions as near-optimal. From a policy standpoint, we examine a large-scale real-world case study concerning parcel deliveries in the Chicago metropolitan area and quantify various operational metrics including energy costs and vehicle miles traveled. We find that by deploying roughly 20% of range -extended vehicles with a modest all-electric range of 33 miles, parcel distributors can save energy costs by up to 17% while incurring less than 0.5% increase in vehicle miles traveled. Increasing the range to 60 miles further reduces costs by only 4%, which can alternatively be achieved by decreasing the average service time by 1 minute or increasing driver working time by 1 hour. In conclusion, our study reveals several key areas of improvement on which vehicle manufacturers, distributors, and policy makers can focus their attention.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Multiscale geographically and temporally weighted regression (MGTWR): exploring the spatiotemporal heterogeneity of EV market adoption

As an innovative vehicle technology, electric vehicles are experiencing growing sales and have made significant inroads into the traditional automotive market in the United States and around the world. However, EV adoption rates vary significantly across space and over time, influenced by a complex interplay of socio-economic and infrastructural factors alongside federal and state policies. Here, this paper presents a comprehensive spatial–temporal investigation of EV market adoption within one city in the US, that of Chicago, utilizing Multiscale Geographically and Temporally Weighted Regression (MGTWR) alongside Multiscale Geographically Weighted Regression (MGWR). The aim is to unravel the spatial and temporal dynamics affecting EV adoption and to explore how the influence of various determinants of EV adoption, such as demographic factors and economic conditions, vary spatially. Moreover, by utilizing MGTWR, we provide insights into the evolution of these relationships over time, offering a predictive outlook on future EV market growth. Our findings, with an 86.6% prediction accuracy for EV market adoption, tailored policy measures to support accelerated EV adoption. Methodologically, this work advances MGWR frameworks by integrating temporal dynamics to examine nonstationary processes in spatially disaggregated contexts. These findings offer evidence–based guidance for policymakers, urban planners, and stakeholders in the automotive industry, supporting the transition toward a more sustainable and efficient transportation system.

EV Market Adoption↗
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 OSTI2023

Do parcel lockers reduce delivery times? Evidence from the field

Common-carrier parcel lockers have emerged as a secure, automated, self-service means of delivery consolidation in congested urban areas, which are believed to mitigate last-mile delivery challenges through reducing out-of-vehicle delivery times and consequently vehicle dwell times at the curb. However, little research exists to empirically demonstrate the environmental and efficiency gains from this technology. In this study, we designed a nonequivalent groups pre-test/post-test control experiment to estimate the causal effects of a parcel locker on delivery times in a residential building in downtown Seattle, WA. Here, the causal effects are measured in terms of vehicle dwell time and the time delivery couriers spend inside the building, through the difference-in-difference method and using a similar nearby residential building as a control. The results showed a statistically significant decrease in time spent inside the building, and small yet insignificant reduction in delivery vehicle dwell time at the curb. The locker was also well received by the building managers and residents.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

A consumer-centric approach to quantify efficiency of receiving goods purchased via online

Virtual participation in shopping activities has increased exponentially in the past four years compared to the last couple of decades. E-tailing or online shopping offers the convenience of goods reaching a consumer instead of a consumer traveling to a store, but it has downsides like geographical service variability and negative social externalities such as increased energy consumption and emissions. This study proposes a novel approach to quantify e-tailing efficiency from the consumers’ viewpoint. The methodology is innovative in its integration of accessibility theory with energy and cost impedance factors and consideration of delivering and picking up goods purchased via online. The methodology is implemented for the San Francisco Bay Area and is subject to scenarios that see enhancements to various facets of online shopping delivery. Results demonstrate that increasing the frequency of e-commerce deliveries helps improve e-tailing efficiency in rural locations, while improvements in energy and cost aspects of delivery modes are seen to improve e-tailing efficiencies in the central parts of the region. The approach proposed can provide valuable insights on where people have limited benefits from online shopping and how emerging delivery mechanisms can change the quality of the e-commerce experience within a city. This research offers a replicable framework for assessing e-commerce systems in diverse geographic contexts, contributing to the development of equitable and environmentally sustainable urban freight systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Local-scale panmixia in the lichenized fungus Xanthoria parietina contrasts with substantial genetic structure in its Trebouxia photobionts

Microsatellite markers can provide valuable information about gene flow and population history. We developed and tested new microsatellites for the nitrophilic lichenized fungus Xanthoria parietina and studied its genetic diversity and structure within the urban area of Munich, Bavaria. We compared its local genetic pattern with that of its photobiont partner Trebouxia decolorans, for which existing microsatellites were applied. For comparison, a reference site with clean air was included in the sampling. We found support for three genetic clusters in the fungus X. parietina, which occurred intermingled in collecting sites. There was a high degree of admixture within fungal populations and individuals, and analysis of molecular variance revealed a lack of population structure in the mycobiont. The Trebouxia photobiont, in contrast, exhibited structured populations which grouped into two to five genetic clusters, and individuals showed less admixture than in the mycobiont. This indicates that the two lichen partners differ in their ability to move around in the landscape. The microsatellite markers we report are polymorphic and are suitable for population genetic studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Modeled Emission of Hydroxyl and Ozone Reactivity from Evaporation of Fragrance Mixtures

Volatile chemical products (VCPs) account for increasing fractions of organic carbon emitted to the atmosphere, particularly in urban areas. Fragrances are potentially reactive components that are added to many VCPs. To better constrain these emissions, 11 commercially available liquid fragrance mixtures were characterized for their composition and their evaporation modeled. Here, emissions of mass, hydroxyl reactivity, and ozone reactivity were estimated by modeling under four different scenarios. Fragrance compounds were generally less than one-half the mass of fragrance mixtures, with the balance comprised of solvents and plasticizers and unresolved mass thought to be dominated by plasticizers. The results showed that terpenes and terpenoids account for nearly all of the emitted mass and reactivity while only comprising ~10% w/w on average of the liquid fragrance mixtures. Most of the reactivity is emitted within hours, with ozone reactivity evolving more rapidly than OH reactivity and comprised almost entirely of terpenes. Limonene, a common fragrance constituent, dominates the reactivity of emitted carbon. Generally, 20–40% of the potential hydroxyl reactivity contained in the fragrance mixture does not evaporate on time scales sufficient to have an impact on local or regional air quality.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Midlatitude Ozone Depletion and Air Quality Impacts from Industrial Halogen Emissions in the Great Salt Lake Basin

We report aircraft observations of extreme levels of HCl and the dihalogens Cl 2 , Br 2 , and BrCl in an industrial plume near the Great Salt Lake, Utah. Complete depletion of O 3 was observed concurrently with halogen enhancements as a direct result of photochemically produced halogen radicals. Observed fluxes for Cl 2 , HCl, and NO x agreed with facility-reported emissions inventories. Bromine emissions are not required to be reported in the inventory, but are estimated as 173 Mg year –1 Br 2 and 949 Mg year –1 BrCl, representing a major uncounted oxidant source. A zero-dimensional photochemical box model reproduced the observed O 3 depletions and demonstrated that bromine radical cycling was principally responsible for the rapid O 3 depletion. Inclusion of observed halogen emissions in both the box model and a 3D chemical model showed significant increases in oxidants and particulate matter (PM 2.5 ) in the populated regions of the Great Salt Lake Basin, where winter PM 2.5 is among the most severe air quality issues in the U.S. The model shows regional PM 2.5 increases of 10%–25% attributable to this single industrial halogen source, demonstrating the impact of underreported industrial bromine emissions on oxidation sources and air quality within a major urban area of the western U.S.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗