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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Exploring impacts of electricity tariff on charging infrastructure planning: An activity-based approach

In the past decade, electric vehicles (EVs) have gained popularity for their efficiency and environmental benefits. Advances in battery technology and charging equipment have yielded long-range EVs and fast-charging. However, many major cities lack adequate charging infrastructure for daily EV use. This study addresses this gap by integrating activity-based modeling, charging behavior simulation, and charging infrastructure optimization. The research utilizes the POLARIS agent-based transportation model to accurately capture user activities, trip patterns, and traffic flows. Additionally, the study investigates the impact of fixed and spatiotemporal electricity rate distributions on optimal charging infrastructure deployment. The framework is applied to the Chicago regional area network and analyzed under various EV ownership scenarios. Further, the results reveal significant impacts of the charging pricing strategy on user decision-making and charging demand distribution. There is also a need for consistent pricing policies in charging infrastructure planning and operational phases to avoid drops in service quality.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Linking transportation agent-based model ($\mathrm{ABM}$) outputs with micro-urban social types ($\mathrm{MUSTs}$) via typology transfer for improved community relevance

The human relationship with transportation is shaped by social, economic, demographic, and urban form variables, or socio-spatial factors. The spatial dynamics of these are key to generating and interpreting outputs of transportation models that are most relevant for a community and the diverse mobility needs of its members. Here we present a typology transfer framework, grounded in socio-spatial dynamics shaping people's mobility, to take transportation-themed regional mobility model outcomes, in this case from two agent-based models (ABMs), and extrapolate them to other cities, with less time and resource intensity than new ABM development. The typology transfer process first identifies micro-urban social types (MUSTs) using socio-spatial factors, then defines city types based on spatial patterns of MUSTs to assess across which cities transfer results are likely to best hold. Lastly, a typology transfer multiplier matrix extrapolates a given variable, in our case the Mobility Energy Productivity (MEP) metric, to another city. The full process demonstration uses ABM results from Chicago (POLARIS model) and San Francisco (BEAM model), applying them to New York City. We discuss how MEP or other outputs can be appropriately estimated and used for integrated, human-centered mobility analysis. Key findings include that this MUST framework of user-defined dependent and independent variables allows tailoring ABM results and interpretations to specific community needs and data availability. Findings clarify that positive outcomes can be targeted towards user groups, based on sociospatial characteristics, using a typology approach, such as inclusive access to mobility choices, transportation affordability, and greater efficiency in resource use.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

A modeling pipeline to relate municipal wastewater surveillance and regional public health data

As COVID-19 becomes endemic, public health departments require improved passive indicators, which are independent of voluntary testing data, to estimate the prevalence of COVID-19 in local communities. Quantification of SARS-CoV-2 RNA from wastewater has the potential to be a powerful passive indicator. However, connecting measured SARS-CoV-2 RNA to community prevalence is challenging. We have developed a generalized methodology to improve the predictive power of wastewater measurements and applied it to data collected from treatment plants in the Chicago area. We built and compared a set of multi-linear regression models, which incorporate pepper mild mottle virus (PMMoV) as a population biomarker, Bovine coronavirus (BCoV) as a recovery control, and wastewater system flow rate into a corrected estimate for SARS-CoV-2 RNA concentration. For our data, models with BCoV performed better than those with PMMoV, but all model estimates of prevalence significantly improved correlation compared to doing no correction. We also investigate the utility of RNA measurements in wastewater as a leading indicator of COVID-19 trends. We do this in a rolling manner for corrected wastewater data and for other prevalence indicators, and statistically compare the temporal relationship between new increases in the wastewater data and those in other prevalence indicators. We find that wastewater trends often lead other COVID-19 indicators in predicting new surges.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Integrating Systems Thinking into Teaching Emerging Technologies.

Collaborations and partnerships across disciplines are becoming increasingly recognized as valuable endeavors toward solving emerging global challenges in our rapidly changing world. Thus, it is crucial to create STEM educational strategies that infuse diverse and interconnected perspectives among scientists, policymakers, and the general public. Systems thinking represents one such pedagogical approach, in which a holistic framework empowers both teachers and students to recognize how fundamental concepts taught in the classroom can be used as resourceful tools to better address complex, multicomponent modern challenges. For chemical educators, topics on technology (the "T" in STEM education) represent a particularly meaningful avenue to introduce and study the effectiveness of systems thinking, but there have been few examples reported to date. Here, we show that a molecular engineering course introduced at the University of Chicago 5 years ago ("Introduction to Emerging Technologies") provides a model course to this end, where explicitly designing in elements of systems thinking to the course could further strengthen students' understanding of the already well suited, holistic course topics. Integrating systems thinking would illuminate how tissue engineering, nanomedicine, batteries, quantum information, and meat alternatives are developed from multiple, interrelated fields as well as from basic science to a practical reality. For each technology, we outline the direct connections between the current course topics and future opportunities to incorporate systems thinking strategies, challenging students to identify the interdependence of components in a successful technology. Finally, to offer further insight for instructors interested in utilizing systems thinking, we broaden our discussion to reflect on the challenges of redesigning any STEM course to utilize systems thinking, and we present corresponding teaching strategies to overcome them.

Interdisciplinary/Multidisciplinary↗
DOE OSTI2020

The Evolving Distribution of Relative Humidity Conditional Upon Daily Maximum Temperature in a Warming Climate

Abstract The impacts of heat waves in a warming climate depend not only on changing temperatures but also on changing humidity. Using 35 simulations from the Community Earth System Model Large Ensemble (CESM LENS), we investigate the long‐term evolution of the joint distribution of summer relative humidity (RH) and daily maximum temperature ( Tmax ) near four U.S. cities (New York City, Chicago, Phoenix, and New Orleans) under the high‐emissions Representative Concentration Pathway (RCP) 8.5. We estimate the conditional quantiles of RH given Tmax with quantile regression models, using functions of temperature for each city in July for three time periods (1990–2005, 2026–2035, and 2071–2080). Quality‐of‐fit diagnostics indicate that these models accurately estimate conditional quantiles for each city. As expected, each quantile of Tmax increases from 1990–2005 to 2071–2080, while mean RH decreases modestly. Conditional upon a fixed quantile of Tmax , the median and high quantiles of RH decrease, while those of the Heat Index (HI) and dew point both increase. This result suggests that, despite a modest decrease in median relative humidity, heat stress measured by metrics considering both humidity and temperature in a warming climate will increase faster than that measured by temperatures alone would indicate. For a fixed Tmax , the high quantiles of RH (and thus of HI and dew point) increase from 1990–2005 to 2071–2080 in all four cities. This result suggests that the heat stress of a day at a given Tmax will increase in a warming climate due to the increase of RH.

Yuan, Jiacan↗
DOE OSTI2023

A Coupled Deep Learning Model for Estimating Surface NO 2 Levels from Remote Sensing Data: 15-Year Study Over the Contiguous United States

This study proposes a novel two-step deep learning (DL) model for estimating surface NO 2 concentrations using satellite data over the contiguous United States (CONUS) from 2005 to 2019. The first phase of the model uses partial convolutional neural network (PCNN), an advanced DL model that accurately imputes gaps between surface NO 2 stations and creates 5,478 daily-mean NO 2 grids (PCNN-NO 2 ) of the 2005-2019 period over the study area. We then feed the PCNN-NO 2 , along with other predictor variables, into a deep neural network (DNN) to estimate surface NO 2 levels, achieving exceptional performance with a correlation coefficient of 0.975 to 0.978, a mean absolute bias of 0.99 ppb to 1.38 ppb, and a root mean square error of 1.47 ppb to 1.97 ppb. Spatial cross-validation results also indicate strong spatial performance of PCNN-DNN surface NO 2 estimates. In addition to its accurate estimates, the PCNN-DNN model consistently generates estimated NO 2 grids without any missing values, improving the quality of various applications such as emission reduction strategies and public health studies. Between 2005 and 2019, the 5,478 daily estimated NO 2 grids over the CONUS reveal significant reductions in NO 2 levels in fourteen major urban environments: Washington D.C. (-43%), New York (-45%), Los Angeles (-38%), Chicago (-25%), Boston (-43%), Houston (-34%), Dallas (-40%), Philadelphia (-41%), Phoenix (-38%), Detroit (-20%), Denver (-23%), Atlanta (-0.7%), Cincinnati (-38%), and Pittsburgh (-56%). Furthermore, the study shows that the denser urban regions that in-situ stations are installed in, the higher the difference between in-situ observations and regional-mean NO 2 levels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Hyper‐Local Temperature Prediction Using Detailed Urban Climate Informatics

The accurate modeling of urban microclimate is a challenging task given the high surface heterogeneity of urban land cover and the vertical structure of street morphology. Recent years have witnessed significant efforts in numerical modeling and data collection of the urban environment. Nonetheless, it is difficult for the physical‐based models to fully utilize the high‐resolution data under the constraints of computing resources. The advancement in machine learning (ML) techniques offers the computational strength to handle the massive volume of data. In this study, we proposed a modeling framework that uses ML approach to estimate point‐scale street‐level air temperature from the urban‐resolving meso‐scale climate model and a suite of hyper‐resolution urban geospatial data sets, including three‐dimensional urban morphology, parcel‐level land use inventory, and weather observations from a sensor network. We implemented this approach in the City of Chicago as a case study to demonstrate the capability of the framework. The proposed approach vastly improves the resolution of temperature predictions in cities, which will help the city with walkability, drivability, and heat‐related behavioral studies. Moreover, we tested the model's reliability on out‐of‐sample locations to investigate the modeling uncertainties and the application potentials to the other areas. This study aims to gain insights into next‐gen urban climate modeling and guide the observation efforts in cities to build the strength for the holistic understanding of urban microclimate dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Observational Constraints on the Spatial Effect of Greenness and Canopy Cover on Urban Heat in a Major Midlatitude City

Abstract Urban heat stress is a critical issue, particularly in cities where dense infrastructure and limited green space exacerbate temperature extremes. This study investigates the impact of greenery (EVI2), canopy cover (CC), impervious cover (IC), and water bodies on heat index in Chicago using high‐resolution data from the Heat Watch campaign. We find that EVI2, CC and proximity to water body significantly reduce heat while IC increases heat, particularly in the afternoon when solar radiation is intense. Additionally, the effective radius that land cover impacts heat is smaller in the afternoon. The combined effect analysis indicates that enhancing total greenness, not just canopy cover, is the most effective strategy to reduce heat. This study underscores the importance of strategic vegetation management, highlighting the critical role of integrated approaches in reducing urban heat.

Lee, Jangho↗
DOE OSTI2023

Storylines for unprecedented heatwaves based on ensemble boosting

Recent temperature extremes have shattered previously observed records, reaching intensities that were inconceivable before the events. Could the possibility of an event with such unprecedented intensity as the 2021 Pacific Northwest heatwave have been foreseen, based on climate model information available before the event? Could the scientific community have quantified its potential intensity based on the current generation of climate models? Here, we demonstrate how an ensemble boosting approach can be used to generate physically plausible storylines of a heatwave hotter than observed in the Pacific Northwest. We also show that heatwaves of much greater intensities than ever observed are possible in other locations like the Greater Chicago and Paris regions. In order to establish confidence in storylines of ‘black swan’-type events, different lines of evidence need to be combined along with process understanding to make this information robust and actionable for stakeholders.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Citywide indoor air sampling mirrors wastewater and clinical for environmental surveillance of respiratory viruses

Wastewater surveillance of respiratory pathogens can provide timely estimates of viral activity and disease trends in a population. Indoor air surveillance could be used similarly with some advantages but remains largely unvalidated at the community -scale. Here, an indoor air surveillance program was employed as part of public health environmental surveillance in Chicago, Illinois, USA. Ten air samplers were placed in healthcare and congregate living settings across the city. Weekly air samples were evaluated for influenza A, influenza B, respiratory syncytial virus, and SARS -CoV-2 over two respiratory virus seasons (2023 -2025). Citywide, aggregated air sample positivity and viral load were closely correlated with local clinical case and wastewater surveillance data across all respiratory viruses. Virus trends in air data often preceded clinical and wastewater, although this varied across pathogens and respiratory virus seasons. Further, whole -genome sequencing of SARS -CoV-2 showed close correlation of variant proportions across all datasets. At the building -scale, air samples obtained from a single sampling device provided efficient respiratory virus surveillance, with respiratory pathogen levels mirroring citywide clinical surveillance data. These data demonstrate that air surveillance can provide respiratory virus case and variant trend data at a building or community -scale, serving as an alternative or complementary tool for public health environmental surveillance.

Wilton, Rosemarie↗
DOE OSTI2021

A synthetic building operation dataset

Abstract This paper presents a synthetic building operation dataset which includes HVAC, lighting, miscellaneous electric loads (MELs) system operating conditions, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals. The data is created with 1395 annual simulations using the U.S. DOE detailed medium-sized reference office building, and 30 years’ historical weather data in three typical climates including Miami, San Francisco, and Chicago. Three energy efficiency levels of the building and systems are considered. Assumptions regarding occupant movements, occupants’ diverse temperature preferences, lighting, and MELs are adopted to reflect realistic building operations. A semantic building metadata schema - BRICK, is used to store the building metadata. The dataset is saved in a 1.2 TB of compressed HDF5 file. This dataset can be used in various applications, including building energy and load shape benchmarking, energy model calibration, evaluation of occupant and weather variability and their influences on building performance, algorithm development and testing for thermal and energy load prediction, model predictive control, policy development for reinforcement learning based building controls.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Synthetic residential load models for smart city energy management simulations

The ability to control tens of thousands of residential electricity customers in a coordinated manner has the potential to enact system-wide electric load changes, such as reduce congestion and peak demand, among other benefits. To quantify the potential benefits of demand-side management and other power system simulation studies (e.g. home energy management, large-scale residential demand response), synthetic load datasets that accurately characterize the system load are required. This study designs a combined top-down and bottom-up approach for modelling individual residential customers and their individual electric assets, each possessing their own characteristics, using time-varying queueing models. The aggregation of all customer loads created by the queueing models represents a known city-sized load curve to be used in simulation studies. The three presented residential queueing load models use only publicly available data. An open-source Python tool to allow researchers to generate residential load data for their studies is also provided. The simulation results presented consider the ComEd region (utility company from Chicago, IL) and demonstrate the characteristics of the three proposed residential queueing load models, the impact of the choice of model parameters, and scalability performance of the Python tool.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Validation of Hygrothermal Simulations with Wall Performance Experiments in an Environmental Chamber

Oak Ridge National Laboratory is developing a web tool, built on a rule-based expert system, that aids stakeholders in designing energy-efficient moisture-durable walls. Currently, the tool’s expert system database is mostly populated with expert opinion, but work is being done to incorporate durability assessment based on stochastic hygrothermal modeling. Chamber experiments have been conducted to validate the hygrothermal models. The measured temperature and humidity have been compared with those predicted by the hygrothermal model. Experiments and comparison to one-dimensional hygrothermal modeling results were completed with two walls, a Structural Insulated Panel (SIP) based wall and a Concrete Masonry Unit (CMU) based wall. The two walls were succumbed to typical Chicago weather in ORNL’s Heat, Air and Moisture chamber. The walls were sequentially exposed to different scenarios, including diffusion, wetting, air leakage and solar radiation. For the most part, the hygrothermal simulations can be successfully used to predict the performance of these wall systems for the moisture transport phenomenon. Errors between measured and simulated values decreased as measurements got closer to the interior side of the wall. The root mean square error was larger for relative humidity (up to 17.5%-RH for CMU) than for temperature (up to 4.5°C for CMU wall). The errors were larger for the CMU wall than the wood frame wall. The phenomenon, including liquid water, caused large discrepancies between measurement and simulation results, and simulated results showed slower drying of materials than measured results. The one-dimensional nature of the simulation model made simulating air leaks difficult but not impossible.

Salonvaara, Mikael↗
DOE OSTI2023

O’Hare Airport Short-Term Ground Transportation Modal Demand Forecast Using Gaussian Processes

Here, the principal objective of this study is to analyze the spatial and temporal variation of ground transportation airport demand and provide demand forecast to inform planning capability and explore alternatives for investments to accommodate airport growth. Because of its good adaptability and strong generalization ability for dealing with high-dimensional input, small-sample, and nonlinear spatial data, Gaussian process (GP) regression is used to provide forecast estimates using data from transportation network company (TNC) trips and urban rail passengers at Chicago's O'Hare International Airport. TNC airport trips differ significantly, with three times more distance, more than twice the travel time, and half of the share requests compared with nonairport trips. This highlights the need for separate demand models. Hourly analysis of the rail service indicates that this is likely heavily used by airport workers, whereas TNC services focus on travelers because of variations in the peak demand hours. Heteroscedastic GP regression is implemented because of differences in trip variance between night and day hours. Estimates are given for weekdays and weekend trips, and the 95% confidence intervals are calculated. The introduction of flight schedule information into the models shows marginal improvements in their performance. However, fitting a GP regression becomes computationally expensive with increased sample size and the introduction of spatial components. Transportation planners and policymakers can use the results and methods implemented in this study to optimize transportation assets and provide long-range simulations of the current and future conditions in the area.

42 ENGINEERING↗
DOE OSTI2020

Observation of shock-front separation in multi-ion-species collisional plasma shocks

We observe shock-front separation and species-dependent shock widths in multi-ion-species collisional plasma shocks, which are produced by obliquely merging plasma jets of a He/Ar mixture (97% He and 3% Ar by initial number density) on the plasma liner experiment. Visible plasma emission near the He-I 587.6-nm and Ar-II 476.5–514.5-nm lines is simultaneously recorded by splitting a single visible image of the shock into two different fast-framing cameras with different narrow bandpass filters (589 ± 5 nm for observing the He-I line and 500 ± 25 nm for the Ar-II lines). For conditions in these experiments (pre-shock ion and electron densities ≈5×10 14 cm –3 , ion and electron temperatures of ≈2.2 eV, and relative plasma-merging speed of 22 km/s), the observationally inferred magnitude of He/Ar shock-front separation and the shock widths themselves are <1 cm, which correspond to ~50 post-shock thermal ion–ion mean free paths. These experimental length scales are in reasonable qualitative and quantitative agreement with results from 1D multi-fluid simulations using the CHICAGO code. However, we note there are differences between the experimentally inferred and simulation-predicted ionization states and line emission intensities, particularly in the post-shock region. Overall, the experimental and simulation results are consistent with theoretical predictions that the lighter He ions diffuse farther ahead within the overall shock front than the heavier Ar ions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Kinetic simulations of anomalous resistivity in high-temperature current carrying plasmas

Anomalous resistivity caused by lower hybrid drift instabilities (LHDIs) has been extensively studied in the literature, and has been invoked to explain the rates of magnetic diffusion and plasma profile evolution in the low-density plasma periphery of theta and screw pinch plasmas. In this article, we present a fully kinetic simulation study of LHDIs with a realistic mass ratio using the particle-in-cell code Chicago over a large range of drift speeds, 0.5 < v$^{2}_{di}$/v$^{2}_{ti}$ < 14.5, plasma β, β ≥ 2×10 –5 , and temperature ratios, 0.1 < T e /T i < 10. The resistivity quantified from the simulations is compared with the analytic estimates from the literature and a generalized resistivity expression is presented, which can be applied over the entire range of plasma environments that were simulated in this study. This expression uses the amplitude of the LHDI fluctuations resulting from instability saturation by electron resonance broadening rather than plateau formation, current relaxation, or ion trapping. The generalized resistivity expression is appropriate for full-scale fluid simulations that encounter a large range of plasma conditions, and has better agreement with the resistivity quantified from kinetic simulations than previous expressions in the literature, particularly at low plasma β. Anomalous resistivity has also been considered as an explanation of the rapid magnetic field dissipation during magnetic reconnection events at large plasma β. At a large β, the magnetic field fluctuations of LHDIs can dominate the electrostatic fluctuations and this article investigates enhanced collisionality due to the nonlinear magnetic force, δJ×δB/c.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

The Manhattan Project Nuclear Science and Technology Developments at Los Alamos: A Special Issue of Nuclear Technology

The year 2020 marked the 75th anniversary of the Trinity experiment, the world’s first nuclear explosion, on July 16, 1945, near Alamogordo, New Mexico. Trinity was a vital proof step toward the culmination of the Manhattan Project and the end of World War II. The technical accomplishments made by scientists and engineers from the United States, United Kingdom, and Canada (some originating in Germany, Hungary, Italy, France, and other countries) were recognized by many events in 2020, including a visit to New Mexico’s Los Alamos National Laboratory by U.S. Department of Energy (DOE) National Nuclear Security Administration (NNSA) dignitaries; historical documentaries. The importance of Trinity as a foundational accomplishment for the broad nuclear science and engineering community is clear; indeed, New Mexico’s chapter of the American Nuclear Society (ANS) is referred to as the Trinity Section. The events surrounding Trinity have even entered into high culture with recent performances of John Adams’s opera Doctor Atomic in San Francisco, Amsterdam, Chicago, New York, and Santa Fe.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Do automated vehicle (AV) enthusiasts value travel time differently from cautious travelers? an exploration of travelers’ attitudes towards AV

The anticipation of automated vehicles (AV) has resulted in diverse research topics, including understanding the adoption and usage of this emerging mode. Using attitudinal indicators, this paper identifies prevalent types of attitudes towards AV. It then explores the variation of those attitudes across different demographic groups and quantifies the influence of these attitudes on the preference for AV. The paper uses stated preference survey data collected among respondents of Chicago in 2019. The exploratory factor analysis of the attitudinal indicators identifies four factors, namely 'Cautious Travelers', 'Hedonists', 'Utilitarians', and 'Safety-First'. Next, the paper develops an integrated choice and latent variable model to test the influence of these four types of attitudes on the preference for AV. Finally, the results indicate that the cautious travelers associate higher Value of Travel Time (VOTT) to AV and enthusiasts associate lower VOTT to AV than a base model without such attitudes.

33 ADVANCED PROPULSION SYSTEMS↗