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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Win-win transportation strategies for India: Linking air pollution and climate mitigation

Here, this article analyzes road transport in India to explore linkages between air pollution and climate change policies in the transportation sector. Five teams modeled five policy scenarios – fuel efficiency, electrification, alternative fuels, modal shifts, and moderation in transport demand – to explore which policy brings the largest synergetic effects in reducing carbon dioxide (CO 2 ) and particulate matter (PM 2.5 ) emissions. The teams also modeled the comprehensive scenario which included policy measures from individual scenarios. The paper concludes that all of the measures provide strong co-benefits in reducing air pollutant and CO 2 emissions. The modeling results show that increased energy efficiency of passenger and freight vehicles has the largest protentional for reducing both CO 2 and PM 2.5 emissions. It is possible to reach even a larger reduction of air pollutants and CO 2 emissions by combining several policy measures in the comprehensive scenario.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Model predictive control for optimal dispatch of chillers and thermal energy storage tank in airports

Cost of energy consumption is one of the biggest operational cost for airports, and it is increasing from time to time as airports expand to support growing number of passengers. Various factors affect the energy consumption including efficiency of airport Heating Ventilation and Air conditioning (HVAC) systems, which in turn depends on the efficiency of individual subsystems. Here, in this paper, we present an optimal scheduling method for the central plant system at Dallas Fort Worth airport, involving chillers, pumps, and a thermal energy storage (TES) system. A model predictive control (MPC) problem is formulated to minimize both energy and demand charge costs while satisfying the cooling needs of the airport. The proposed Mixed-Integer Nonlinear Programming (MINLP) formulation includes performance curve based models for chillers and pumps and a simplified state of charge model for TES. The formulation also includes predictions of cooling load and chilled water return temperature. Simulation results for a month in summer show savings around 10% compared to the baseline. Initial recommendations based on insights from simulation results to the manual operation procedures resulted in significant savings. Field test results show a 7% chiller efficiency improvement.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Modeling and mitigating airborne pathogen risk factors in school buses

Computational fluid dynamics (CFD) models were developed to simulate the impact of different ventilation scenarios on airborne exposure risks in a 72-passenger school bus. Here, scenarios and factors that were investigated included a moving vs. stationary bus, impacts of a heating unit within the bus, and impacts of alternative ventilation scenarios with different combinations of openings (e.g., windows, door, emergency hatch). Results of the simulations showed that when the bus was stationary, use of the heater increased receptor concentrations unless there was another opening. When the bus was moving, simulations with at least two sets of openings separated from each other in the forward and aft directions produced a through-flow condition that reduced concentrations via dilution from outside air by a factor of ten or more. A single opening in a moving bus generally increased concentrations throughout the cabin due to increased mixing with minimal ventilation. The cumulative exposure risk (time-averaged concentrations) was found to be inversely correlated to the air exchange rate. Stationary and moving-bus scenarios that yielded above ~20 air changes per hour resulted in the lowest cumulative exposures. Recommendations from this study were implemented in new safety and operating procedures by the Albuquerque Public Schools Transportation Center.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A data-driven operational model for traffic at the Dallas Fort Worth International Airport

Airports are on the front line of significant innovations, allowing the movement of more people and goods faster, cheaper, and with greater convenience. As air travel continues to grow, airports will face challenges in responding to increasing passenger vehicle traffic, which leads to lower operational efficiency, poor air quality, and security concerns. This paper evaluates methods for traffic demand forecasting combined with traffic microsimulation, which will allow airport operations staff to accurately predict traffic and congestion. Using two years of detailed data describing individual vehicle arrivals and departures, aircraft movements, and weather at Dallas-Fort Worth (DFW) International Airport, we evaluate multiple prediction methods including the Auto Regressive Integrated Moving Average (ARIMA) family of models, traditional machine learning models, and DeepAR, a modern recurrent neural network (RNN). We find that these algorithms are able to capture the diurnal trends in the surface traffic, and all do very well when predicting the next 30 minutes of demand. Longer forecast horizons are moderately effective, demonstrating the challenge of this problem and highlighting promising techniques as well as potential areas for improvement. Traffic demand is not the only factor that contributes to terminal congestion, because temporary changes to the road network, such as a lane closure, can make benign traffic demand highly congested. Combining a demand forecast with a traffic microsimulation framework provides a complete picture of traffic and its consequences. The result is an operational intelligence platform for exploring policy changes, as well as infrastructure expansion and disruption scenarios. To demonstrate the value of this approach, we present results from a case study at DFW Airport assessing the impact of a policy change for vehicle routing in high demand scenarios. This framework can assist airports like DFW as they tackle daily operational challenges, as well as explore the integration of emerging technology and expansion of their services into long term plans.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Home charging for all: Techno-economic and life cycle assessment of multi-unit dwelling electric vehicle charging hubs

Ubiquitous electric vehicle adoption can drastically reduce greenhouse gas emissions (GHG) but will require equitable home charging infrastructure for all residences. Unlike single-family homes, Multi-Unit Dwellings (MUD) currently lack market share and access to nearby charging infrastructure partly due to expensive capital costs for reluctant residential property-owners. Further, this work evaluates the levelized cost of charging (LCOC) for Battery Electric Vehicles (BEVs) at MUD community charging hubs through a techno-economic analysis (TEA) that leverages real-world charging data and costs. Three different MUD charging types are investigated for a baseline and optimistic case: 1.9-kW Level 1 (L1), 6.6-kW Level 2 (L2), and 50-kW Direct Current Fast Charging (DCFC) stations under three different ownership models: resident, utility, and private company. Results demonstrate L1 and L2 chargers to be less expensive than the gasoline equivalent across the United States. Further, utility and private company ownership models, which avoid initial costs for residential property owners, result in a large LCOC premium for L1 and moderate LCOC premium for L2 relative to the resident ownership model. In contrast, DCFC is shown to be expensive for baseline scenarios but economical for optimistic scenarios especially under private company ownership. This work also performs a cradle to grave (C2G) life cycle assessment (LCA) of an average passenger BEV and gasoline conventional vehicle (CV) using yearly (grid mix & vehicle parameters), hourly (grid mix), and state-level (grid mix) resolution. Results show BEVs to have lower GHG emissions (-86% to-10%) than gasoline CVs in the contiguous U.S. Next, the system boundary of the TEA is extended to the total cost of ownership (TCO) of BEVs. The TCO is then coupled with the C2G GHG emissions to calculate the cost of GHG emissions reduction. Ultimately, the cost of GHG emissions reduction from MUD BEVs relative to gasoline CVs is shown to be negative for every scenario except baseline DCFC, meaning MUD BEV charging infrastructure can be a cost-effective endeavor to reduce GHG emissions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Realizing "Net-Zero-Carbon" Sustainable Aviation Fuel

The aviation industry needs renewable and fungible jet fuel - commonly referred to as sustainable aviation fuel (SAF) - to minimize emissions and increase efficiencies. SAF can displace the same, if not more, CO2 equivalent emissions (CO2eq), reduce contrail formation, and increase engine-aircraft efficiencies. Flight represents ~10% of transportation greenhouse gas emissions, with the global consumption of ~400 billion liters of fossil jet fuel in 2019 releasing over 900 million tons of CO2eq. Even with impacts from the COVID-19 global pandemic, the aviation sector is projected to grow with passenger miles expected to double by 2050, relative to 2010. As such, there is an urgent need to produce drop-in aviation fuel from renewables with a dramatically lower carbon footprint.

biojet↗
DOE OSTI2022

Electric vehicle post-crash Recovery—Stranded energy issues and mitigation strategy

In post-crash situations, passengers, bystanders, and first responders are exposed to the immediate safety risks of stranded energy in electric vehicle (EV) batteries. Stranded energy is the energy remaining inside any undamaged or damaged battery following an accident. A potentially damaged battery with an unknown state of safety might go into thermal runaway in the absence of proper monitoring, diagnostics, controls, and handling—thereby leading to potential loss of life and property damage. Therefore, it is imperative to develop standard guidelines, diagnostic methods, and tools to handle stranded energy appropriately and minimize safety risks from immediately after an EV accident to final disposal or re-entry to the road. This perspective paper identifies the challenges associated with handling stranded energy and proposes some potential solution pathways. Further, we hope to draw attention of the research and development community, original equipment manufacturers, and policymakers for motivating new insights and research needs in mitigating safety implications associated with stranded energy.

25 ENERGY STORAGE↗
DOE OSTI2024

High Elevation Radiation Array (HERA) detectors for airborne thunderstorm investigations

A high-energy atmospheric physics phenomenon, referred to as a terrestrial gamma ray flash (TGF), is associated with lightning and produces large bursts of energetic photon radiation. TGFs will be investigated using a suite of gamma-ray instruments designed and constructed to fly on ten United States Air Force (USAF) WC-130J Hurricane Hunter aircraft as part of an aircrew ionization study led by the Air Force Institute of Technology (AFIT) and the United States Air Force School of Aerospace Medicine (USAFSAM), in cooperation with the 53rd Weather Reconnaissance Squadron (WRS). Each instrument consists of one NaI and one plastic detector, a GPS timing device, and an instrument computer that performs data acquisition. High Elevation Radiation Array (HERA) detectors will be employed to maximize the chances of observing TGFs near their source and to gain a better understanding of their origin, mechanism, ubiquity, and to assess potential hazards posed to military and commercial aircrew and passengers. The HERA program, deployed on 10 separate Air Force aircraft over a multi-year campaign, will result in thousands of observational flight hours and be the largest concerted effort to date to observe TGFs in situ through aircraft observations. In this paper, we give an overview of the scientific goals of this campaign and how the HERA instruments have been designed to meet those goals. Here, we include a detailed description of the HERA instrument, along with mass model and signal processing simulations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Automating ridehailing services would reduce pooling, especially among women

Here, this study investigates how autonomous vehicles (AVs) could transform pooled (shared) ridehailing services, focusing on the impacts of fare reductions, the absence of drivers/staff, and psychological attributes such as trust in other passengers and privacy concerns. We distinguish between the automation of driving tasks and the removal of human driver/staff from the vehicle, providing novel insights into the factors influencing AV ridehailing adoption. Using a national survey with stated preference (SP) choice experiments and psychometric questions, we analyze the complex interactions of ridehailing fare, pooled ridehailing service quality, and latent attitudes on ridehailing choices. Our findings suggest that the elimination of drivers/staff from fully autonomous ridehailing could lead to a shift from pooled to solo rides, particularly among female travelers who may have greater concerns about trust and safety in unstaffed AVs. This study highlights the importance of addressing trust and comfort beyond fare discounts to ensure the inclusivity and widespread adoption of pooled AV ridehailing. These insights underscore the need for ridehailing providers and policymakers to prioritize trust-building measures, user-centered AV design that offers greater privacy, and dynamic pricing strategies, to ensure inclusive and widespread adoption of pooled AV services.

Autonomous vehicle↗
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 OSTI2021

Estimating Energy Bounds for Adoption of Shared Micromobility

Shared micromobility has garnered widespread popularity in recent years, but limited attention has been given to the energy impacts of trips replaced by micromobility. This paper investigates the energy bounds of shared micromobility adoption. Travel demand data at the national and city level were analyzed to identify trips that can be served through micromobility, and scenarios with varying levels of micromobility adoption were evaluated. Results show that peak adoption of shared micromobility can reduce energy consumption from reported passenger travel by 1% at the national level and 2.6% at the city level, with micromobility-induced transit trips identified as the largest contributor for energy reduction. Sensitivity analysis was carried out to show how the energy impacts would change with various levels of key micromobility-related parameters, and results show distance threshold having a stronger influence on the energy impacts, compared to redistribution energy intensity.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Multi-stage charging and discharging of electric vehicle fleets

Fleets of electric vehicles will likely shift electricity demand, and the effect of upstream charging emissions will come from generation sources that are dispatched in response. This study proposes a multi-stage charging and discharging problem to translate low-cost energy transactions into vehicle dispatch decisions. A day-ahead charging optimization problem minimizes electricity purchases and marginal emissions damages, with energy transactions becoming targets in an optimization-based dispatch strategy for an on-demand shared autonomous electric vehicle (SAEV) fleet. The framework was tested for Austin, Texas, using an agent-based simulator. Fleets can schedule charging to lower daily power costs (averaging 15.5% or $\$0.79$/day/SAEV) while reducing health damages from generation-related pollution (2.8% or $\$0.43$/day/SAEV). Finally, fleet managers can increase profits ($\$8$ per SAEV per day) by adopting a multi-stage charging and discharging strategy that can serve more passengers per day than price-agnostic dispatch strategies.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

MiR-21–3p triggers cardiac fibroblasts pyroptosis in diabetic cardiac fibrosis via inhibiting androgen receptor

Highlights: • HG aggravates CFs pyroptosis and fibrosis. • MiR-21–3p enhances CFs pyroptosis due to AR down regulation. • MiR-21–3p targeting AR in HG triggers CFs pyroptosis and fibrosis. MicroRNA-21 has been implicated in diabetic complication, including diabetic cardiomyopathy. However, there is limited information regarding the biological role of the miR-21 passenger strand (miR-21–3p) in diabetic cardiac fibrosis. The aim of this study was to investigate the role of miR-21–3p and its target androgen receptor in STZ-induced diabetic cardiac fibrosis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Can Cobalt Be Eliminated from Lithium-Ion Batteries?

Following the discovery of LiCoO 2 (LCO) as a cathode in the 1980s, layered oxides have enabled lithium-ion batteries (LIBs) to power portable electronic devices that sparked the digital revolution of the 21st century. Since then, LiNi x Mn y Co z O 2 (NMC) and LiNi x Co y Al z O 2 (NCA) have emerged as the leading cathodes for LIBs in electric vehicle (EV) application and have become crucial components in the fight against global warming. However, the surging demand for LIBs has led to an extremely tight supply. The EV sector has already dominated the LIB market, even though EV sales were only 2–3% of total passenger vehicle sales in 2020. EV sales are expected to grow 10-fold by the end of this decade, and close to 90% of the total LIB demand will come from the EV sector. As a result, LIB manufacturers must aggressively ramp up cell production to keep pace with the immense growth of the EV market, a quest that brings scrutiny to the cost and sustainability of current LIB manufacturing practices. Here, cathodes are a critical component that largely determines the energy density and 40–50% of the total cell cost in LIBs. Rigorous consideration of the cathode performance and material cost is crucial in sustaining EV adoption. In this Viewpoint, we discuss why using cobalt in cathodes is unsustainable in the long run and highlight the features of cobalt-free cathodes.

25 ENERGY STORAGE↗
DOE OSTI2022

Light-Duty Vehicle Transportation Policy and Implication on Greenhouse Gas Emissions

The transportation sector accounts for 16% of global greenhouse gas (GHG) emissions and is under formidable pressure to decarbonize. With a growing number of countries making commitments to achieve carbon neutrality or “net-zero” emissions within the next few decades, it is imperative for transportation researchers and policymakers to understand the viable pathways towards achieving carbon neutrality for light-duty transport. This chapter discusses the transportation policies and GHG emissions of the three largest markets in the world—the U.S., China, and the European Union. The life cycle GHG emissions of various vehicle technologies are evaluated while highlighting the regional and temporal differences. We then use market penetration and fleet models, developed specifically for each market, to comprehensively assess the light-duty transport energy demand and GHG emissions under various scenarios. The modeling results show that battery electric vehicles (BEVs) will increase in market share, but internal combustion engine vehicles (ICEV) will continue to dominate the passenger vehicle stock in the next 20 years under most scenarios. Improving ICEV efficiency can play a critical role in meeting GHG regulations in the near- and medium-term. BEVs, whose GHG emissions are highly dependent on the source of electricity generation, will play an essential role in the long-term as the electric grid becomes cleaner. In summary, transportation policies should be technology agnostic and consider emissions based on the whole life cycle. Moreover, a holistic approach to reducing transportation GHG emissions is key to achieving global environmental goals.

Ou, Shawn↗
DOE OSTI2022

Short–Term Forecasting of Wind Gusts at Airports Across CONUS Using Machine Learning

Short–term forecasting of wind gusts, particularly those of higher intensity, is of great societal importance but is challenging due to the presence of multiple gust generation mechanisms. Wind gust observations from eight high–passenger–volume airports across the continental United States (CONUS) are summarized and used to develop predictive models of wind gust occurrence and magnitude. These short–term (same hour) forecast models are built using multiple logistic and linear regression, as well as artificial neural networks (ANNs) of varying complexity. A suite of 19 upper–air predictors drawn from the ERA5 reanalysis and an autoregressive (AR) term are used. Stepwise procedures instruct predictor selection, and resampling is used to quantify model stability. All models are developed separately for the warm (April–September) and cold (October–March) seasons. Results show that ANNs of 3–5 hidden layers (HLs) generally exhibit higher hit rates than logistic regression models and also improve skill with respect to wind gust magnitudes. However, deeper networks with more HLs increase false alarm rates in occurrence models and mean absolute error in magnitude models due to model overfitting. For model skill, inclusion of the AR term is critical while the majority of the remaining skill derives from wind speeds and lapse rates. A predictive ceiling is also clearly demonstrated, particularly for the strong and damaging gust magnitudes, which appears to be partially due to ERA5 predictor characteristics and the presence of mixed wind climates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Design of diverse, functional mitochondrial targeting sequences across eukaryotic organisms using variational autoencoder

Mitochondria play a key role in energy production and metabolism, making them a promising target for metabolic engineering and disease treatment. However, despite the known influence of passenger proteins on localization efficiency, only a few protein-localization tags have been characterized for mitochondrial targeting. To address this limitation, we leverage a Variational Autoencoder to design novel mitochondrial targeting sequences. In silico analysis reveals that a high fraction of the generated peptides (90.14%) are functional and possess features important for mitochondrial targeting. We characterize artificial peptides in four eukaryotic organisms and, as a proof-of-concept, demonstrate their utility in increasing 3-hydroxypropionic acid titers through pathway compartmentalization and improving 5-aminolevulinate synthase delivery by 1.62-fold and 4.76-fold, respectively. Moreover, we employ latent space interpolation to shed light on the evolutionary origins of dual-targeting sequences. Overall, our work demonstrates the potential of generative artificial intelligence for both fundamental research and practical applications in mitochondrial biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

New roads and challenges for fuel cells in heavy-duty transportation

The recent release of hydrogen economy roadmaps for several major countries emphasizes the need for accelerated worldwide investment in research and development activities for hydrogen production, storage, infrastructure and utilization in transportation, industry and the electrical grid. Due to the high gravimetric energy density of hydrogen, the focus of technologies that utilize this fuel has recently shifted from light-duty automotive to heavy-duty vehicle applications. Decades of development of cost-effective and durable polymer electrolyte membrane fuel cells must now be leveraged to meet the increased efficiency and durability requirements of the heavy-duty vehicle market. This Review summarizes the latest market outlooks and targets for truck, bus, locomotive and marine applications. Required changes to the fuel-cell system and operating conditions for meeting Class 8 long-haul truck targets are presented. The necessary improvements in fuel-cell materials and integration are also discussed against the benchmark of current passenger fuel-cell electric vehicles.

08 HYDROGEN↗