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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 OSTI2020

Municipal Adaptation to Changing Curbside Demands: Findings from Semi-Structured Interviews with Ten U.S. Cities

Emerging mobility services (e.g. ride-hailing, e-commerce, micro-mobility, etc.), are generating novel and rapidly growing demands to use curbside space, with potentially large impacts on mobility, energy consumption, and related outcomes. This presents both opportunities and challenges to municipal agencies responsible for managing this interface between the road network and adjacent land uses, as legacy practices require updating. In this study, we employ a semi-structured interviewing approach to establish how municipalities are adapting to these new pressures on their curbside. We interviewed senior staff responsible for curbside policy of ten large U.S. municipalities, with populations ranging from ~250,000 to ~5,000,000 (and the majority of which are the central city of their metropolitan region). We document a trend of organizational restructuring to more formally include curbside management teams, with the majority of interviewees also reporting increased staffing. Respondents reported that operational failures at their curbside (e.g. demand in excess of capacity) have impacts on safety, capacity, and emergency vehicle mobility, with impacts highly concentrated spatially and temporally (e.g. late evenings in nightlife districts). We document a diversity of data flows between ride-hailing operators (e.g. Uber, Lyft) and municipalities, with some cities reporting obtaining types of data that other cities report not receiving, despite requesting such data. Finally, respondents consistently expressed a desire for new data streams and methodologies to help manage the curbside of the future. It is hoped that establishing the state of practice in this rapidly changing context will be of use to practitioners facing similar pressures as those of our interviewees.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

A hybrid data–model approach to map soil thickness in mountain hillslopes

Abstract. Soil thickness plays a central role in the interactions between vegetation, soils, and topography, where it controls the retention and release of water, carbon, nitrogen, and metals. However, mapping soil thickness, here defined as the mobile regolith layer, at high spatial resolution remains challenging. Here, we develop a hybrid model that combines a process-based model and empirical relationships to estimate the spatial heterogeneity of soil thickness with fine spatial resolution (0.5 m). We apply this model to two aspects of hillslopes (southwest- and northeast-facing, respectively) in the East River watershed in Colorado. Two independent measurement methods – auger and cone penetrometer – are used to sample soil thickness at 78 locations to calibrate the local value of unconstrained parameters within the hybrid model. Sensitivity analysis using the hybrid model reveals that the diffusion coefficient used in hillslope diffusion modeling has the largest sensitivity among all input parameters. In addition, our results from both sampling and modeling show that, in general, the northeast-facing hillslope has a deeper soil layer than the southwest-facing hillslope. By comparing the soil thickness estimated between a machine-learning approach and this hybrid model, the hybrid model provides higher accuracy and requires less sampling data. Modeling results further reveal that the southwest-facing hillslope has a slightly faster surface soil erosion rate and soil production rate than the northeast-facing hillslope, which suggests that the relatively less dense vegetation cover and drier surface soils on the southwest-facing slopes influence soil properties. With seven parameters in total for calibration, this hybrid model can provide a realistic soil thickness map with a relatively small amount of sampling dataset comparing to machine-learning approach. Integrating process-based modeling and statistical analysis not only provides a thorough understanding of the fundamental mechanisms for soil thickness prediction but also integrates the strengths of both statistical approaches and process-based modeling approaches.

58 GEOSCIENCES↗
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 OSTI2021

Signal Origin of Electrochemical Strain Microscopy and Link to Local Chemical Distribution in Solid State Electrolytes

Electrochemical strain microscopy (ESM) is a distinguished method to characterize Li-ion mobility in energy materials with extremely high spatial resolution. The exact origin of the cantilever deflection when the technique is applied on solid state electrolytes (SSEs) is currently discussed in the literature. Under-standing local properties and influences on ion mobility in SSEs is of utmost importance to improve such materials for next generation batteries. Here, the exact signal formation process of ESM when applied on sodium super ionic conductor (NASICON)-type SSE containing Na- and Li-ions is investigated. Changes in the dielectric properties, which are linked to the local chemical composition, are found to be responsible for the observed contrast in the deflection of the cantilever instead of a physical volume change as a result of Vegard´s Law. The cantilever response is strongly reduced in areas of high sodium content which is attributed to a reduction of the tip-sample capacitance in comparison to areas with high lithium content. This is the first time a direct link between electrostatic forces in contact mode and local chemical information is demonstrated on SSEs. Furthermore, the results open up new possibilities in information gain since dielectric proper-ties are sensitive to subtle changes in local chemical composition.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Coupled cluster and dislocation dynamics modeling of microstructure evolution in irradiated materials

We develop here a coupled cluster and dislocation dynamics framework to study the microstructure evolution of irradiated materials. The framework not only accounts for the three dimensional diffusion of radiation-generated clusters, but also their interaction with dislocation networks and the resultant climb motion of discrete dislocations within finite crystals. The framework is solved with a superposition solution scheme, and is applied to investigate the evolution of the irradiation-induced dislocation loops in zirconium (Zr), considering the effects of various bias factors including the diffusion anisotropy difference (DAD) of interstitials and interstitial clusters, the dislocation bias of defects to discrete dislocation segments, and the production bias of defects from the radiation cascade. We find that the DAD is the most critical factor influencing the kinetics of the loop evolution in Zr, while the recombination/interaction of mobile defects can induce a strong spatial dependence of the loop evolution together with the DAD. Here, the method is also adopted to study the evolution of interstitial $\langle$a$\rangle$ and vacancy $\langle$c$\rangle$ dislocation loop ensembles consistent with the microstructure observed during irradiation-induced growth of Zr. Our findings not only reveal the spatial dependence of the size and ellipticity of the dislocation loops, but also suggest a limit on the anisotropy factor of interstitials to reproduce the co-growth of $\langle$a$\rangle$ and $\langle$c$\rangle$ loops in zirconium, in good agreement with experimental observations and other simulation results.

Bias factors↗
DOE OSTI2024

Connected Traffic Signal Coordination Optimization Framework through Network-Wide Adaptive Linear Quadratic Regulator–Based Control Strategy

Traffic congestion in metropolitan areas causes several significant challenges, such as longer travel times, decreased productivity, increased fuel consumption and vehicle emissions, and even severe injuries during crashes. Traffic signal control is a management approach to reduce traffic congestion and allocate the appropriate right of way for safety and mobility efficiency, both in temporal and spatial domains. Here, this study proposes a network-wide adaptive signal control coordination optimization framework based on the linear quadratic regulator algorithm. The traffic flow conditions driven by signal control inputs are formulated based on their network-wide state-space representation. After modeling traffic control regulation constraints, an adaptive linear quadratic regulator algorithm is designed to maximize the network-wide total throughput under the current conditions. Optimal signal control split time durations for multiple intersections in the network are derived by solving the algebraic Riccati equation. Furthermore, the recursive least square parameter estimation method is employed to quantify dynamic traffic condition changes. To verify the effectiveness of this proposed signal control framework, both simulation and real-world experimental tests are conducted for multiple intersections in downtown Chattanooga, Tennessee, United States. In preparation for real-world experimental tests, pipelines for real-time data processing implementation and historical traffic flow data analysis are conducted. The test results demonstrate that the proposed control framework achieves a decrease in travel time by up to 19.4%, total time spent (TTS) by up to 11.9%, and relative queue balance (RQB) by up to 15.6%. The research findings indicate that the proposed signal control framework can be generalized to handle large scale signal control optimization network-wide.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Ultrawide bandgap semiconductor heterojunction p–n diodes with distributed polarization-doped p-type AlGaN layers on bulk AlN substrates

Ultrawide bandgap heterojunction p–n diodes with polarization-induced AlGaN p-type layers are demonstrated using plasma-assisted molecular beam epitaxy on bulk AlN substrates. Current–voltage characteristics show a turn-on voltage of Vbi≈5.5 V, a minimum room temperature ideality factor of η≈1.63, and more than 12 orders of current modulation at room temperature. A stable current operation of the ultrawide bandgap semiconductor diode is measured up to a temperature of 300 °C. The one-sided n+–p heterojunction diode design enables a direct measurement of the spatial distribution of polarization-induced mobile hole density in the graded AlGaN layer from the capacitance–voltage profile. The measured average mobile hole density is p∼5.7×1017 cm−3, in close agreement with what is theoretically expected from distributed polarization doping. Light emission peaked at 260 nm (4.78 eV) observed in electroluminescence corresponds to interband radiative recombination in the n+ AlGaN layer. A much weaker deep-level emission band observed at 3.4 eV is attributed to cation-vacancy and silicon complexes in the heavily Si-doped AlGaN layer. These results demonstrate that distributed polarization doping enables ultrawide bandgap semiconductor heterojunction p–n diodes that have wide applications ranging from power electronics to deep-ultraviolet photonics. These devices can operate at high temperatures and in harsh environments.

Physics↗
DOE OSTI2021

Understanding the Drivers of Mobility during the COVID-19 Pandemic in Florida, USA Using a Machine Learning Approach

As of March 2021, the State of Florida, U.S.A. had accounted for approximately 6.67% of total COVID-19 (SARS-CoV-2 coronavirus disease) cases in the U.S. The main objective of this research is to analyze mobility patterns during a three month period in summer 2020, when COVID-19 case numbers were very high for three Florida counties, Miami-Dade, Broward, and Palm Beach counties. To investigate patterns, as well as drivers, related to changes in mobility across the tri-county region, a random forest regression model was built using sociodemographic, travel, and built environment factors, as well as COVID-19 positive case data. Mobility patterns declined in each county when new COVID-19 infections began to rise, beginning in mid-June 2020. While the mean number of bar and restaurant visits was lower overall due to closures, analysis showed that these visits remained a top factor that impacted mobility for all three counties, even with a rise in cases. Our modeling results suggest that there were mobility pattern differences between counties with respect to factors relating, for example, to race and ethnicity (different population groups factored differently in each county), as well as social distancing or travel-related factors (e.g., staying at home behaviors) over the two time periods prior to and after the spike of COVID-19 cases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

A Customizable Metric to Provide a Comprehensive Picture of the Mobility Potential of a Location

Accessibility in a geo-spatial context refers to the ease of reaching a variety of opportunities from a given location. Accessibility theories (and resulting metrics) have traditionally focused on quantifying access to specific opportunities (such as jobs), or focused on specific modes (such as car, bike, etc.). Such approaches often fall short of providing a comprehensive picture of the true accessibility potential of a location as a combination of multiple modes to multiple types of destinations. Addressing this drawback, a novel metric labeled the ‘Mobility Energy Productivity (MEP) Metric’ was developed at the National Renewable Energy Laboratory to quantify the mobility potential of a location to connect people to goods, services, and employment using a variety of modes, while accounting for time, energy, and affordability. The MEP metric has been integrated with advance travel behavior models to compute the changes in mobility potential for various future scenarios, such as introduction of automated vehicles, and/or electric vehicles—but does so at the aggregate, or average-citizen level. The MEP in its initial iteration is not customized to the particular socio-economic contingents, or even to an individual whose modal availability or pattern of trip making may substantially differ from the average. Addressing this gap, this research effort extends the MEP framework from a static state to a more tailored and dynamic state, one in which an individual, or group can customize the metric for their unique characteristics, such as modes, activity patterns, and time-of-day preferences. The extended MEP metric framework can now be integrated to assess the customized mobility energy productivity of an individual.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2025

String membrane nets from higher-form gauging: An alternate route to 𝑝-string condensation

We present a new perspective on the $p$-string condensation procedure for constructing 3+1D fracton phases by implementing this process via the gauging of higher-form symmetries. Specifically, we show that gauging a 1-form symmetry in 3+1D that is generated by Abelian anyons in isotropic stacks of 2+1D topological orders naturally results in a 3+1D $p$-string condensed phase, providing a controlled non-perturbative construction that realizes fracton orders. This approach clarifies the symmetry principles underlying $p$-string condensation and generalizes the familiar connection between anyon condensation and one-form gauging in two spatial dimensions. We demonstrate this correspondence explicitly in both field theories and lattice models: in field theory, we derive the foliated field theory description of the $\mathbb{Z}_N$ X-Cube model by gauging a higher-form symmetry in stacks of 2+1D $\mathbb{Z}_N$ gauge theories; on the lattice, we show how gauging a diagonal 1-form symmetry in isotropic stacks of $G$-graded string-net models leads to string-membrane-nets hosting restricted mobility excitations. This perspective naturally generalizes to spatial dimensions $d \geq 2$ and provides a step towards building an algebraic theory of $p$-string condensation.

Anyons↗
DOE OSTI2020

Adapting the Electron Beam from SEM as a Quantitative Heating Source for Nanoscale Thermal Metrology

The electron beam (e-beam) in the scanning electron microscopy (SEM) provides an appealing mobile heating source for thermal metrology with spatial resolution of ~1 nm, but the lack of systematic quantification of the e-beam heating power limits such application development. Here, we systemically study e-beam heating in LPCVD silicon nitride (SiN x ) thin-films with thickness ranging from 200 to 500 nm from both experiments and complementary Monte Carlo simulations using the CASINO software package. There is good agreement about the thickness-dependent e-beam energy absorption of thin-film between modeling predictions and experiments. Using the absorption results, we then demonstrate adapting the e-beam as a quantitative heating source by measuring the thickness-dependent thermal conductivity of SiN x thin-films, with the results validated to within 7% by a separate Joule heating experiment. The results described here will open a new avenue for using SEM e-beams as a mobile heating source for advanced nanoscale thermal metrology development.

42 ENGINEERING↗
DOE OSTI2022

Non-destructive depth-resolved characterization of residual strain fields in high electron mobility transistors using differential aperture x-ray microscopy

Localized residual stress and elastic strain concentrations in microelectronic devices often affect the electronic performance, resistance to thermomechanical damage, and, likely, radiation tolerance. A primary challenge for the characterization of these concentrations is that they exist over sub-μm length-scales, precluding their characterization by more traditional residual stress measurement techniques. Here, in this study, we demonstrate the use of synchrotron x-ray-based differential aperture x-ray microscopy (DAXM) as a viable, non-destructive means to characterize these stress and strain concentrations in a depth-resolved manner. DAXM is used to map two-dimensional strain fields between the source and the drain in a gallium nitride (GaN) layer within high electron mobility transistors (HEMTs) with sub-μm spatial resolution. Strain fields at various positions in both pristine and irradiated HEMT specimens are presented in addition to a preliminary stress analysis to estimate the distribution of various stress components within the GaN layer. γ-irradiation is found to significantly reduce the lattice plane spacing in the GaN along the sample normal direction, which is attributed to radiation damage in transistor components bonded to the GaN during irradiation.

42 ENGINEERING↗
DOE OSTI2023

Highly Resolved Projections of Passenger Electric Vehicle Charging Loads for the Contiguous United States: Results From and Methods Behind Bottom-Up Simulations of County-Specific Household Electric Vehicle Charging Load (Hourly 8760) Profiles Projected Through 2050 for Differentiated Household and Vehicle Types

This report documents enhancements made to the TEMPO (Transportation Energy & Mobility Pathway Options TM ) model to project spatially, demographically, and temporally resolved national-scale EV charging load profiles and describes three scenarios and corresponding datasets created for the NREL demand-side grid (dsgrid) project in support of bulk power systems modeling. In brief, TEMPO was enhanced to disaggregate national and annual energy demand projections into household and county-level projections of passenger electric vehicle (EV) hourly charging load profiles (8760 profiles), accounting for consumer, travel, and temperature variations that impact EV energy demand. In alignment with NREL's forward-looking grid modeling, three scenarios for EV adoption covering 2020-2050 were created-- Annual Energy Outlook (AEO) Reference Case, Electrification Futures Study (EFS) High Electrification, and All EV Sales by 2035 --and associated datasets have been included in the dsgrid platform for public use.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Low-temperature paddlewheel effect in glassy solid electrolytes

Glasses are promising electrolytes for use in solid-state batteries. Nevertheless, due to their amorphous structure, the mechanisms that underlie their ionic conductivity remain poorly understood. Here, ab initio molecular dynamics is used to characterize migration processes in the prototype glass, 75Li2S–25P2S5. Lithium migration occurs via a mechanism that combines concerted motion of lithium ions with large, quasi-permanent reorientations of PS4 3- anions. This latter effect, known as the ‘paddlewheel’ mechanism, is typically observed in high temperature crystalline polymorphs. In contrast to the behavior of crystalline materials, in the glass paddlewheel dynamics contribute to Lithium-ion mobility at room temperature. Paddlewheel contributions are confirmed by characterizing spatial, temporal, vibrational, and energetic correlations with Lithium motion. Furthermore, the dynamics in the glass differ from those in the stable crystalline analogue, γ-Li3PS4, where anion reorientations are negligible and ion mobility is reduced. These data imply that glasses containing complex anions, and in which covalent network formation is minimized, may exhibit paddlewheel dynamics at low temperature. Consequently, these systems may be fertile ground in the search for new solid electrolytes.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Interactive effects of salinity, redox, and colloids on greenhouse gas production and carbon mobility in coastal wetland soils

Coastal wetlands, including freshwater systems near large lakes, rapidly bury carbon, but less is known about how they transport carbon either to marine and lake environments or to the atmosphere as greenhouse gases (GHGs) such as carbon dioxide and methane. This study examines how GHG production and organic matter (OM) mobility in coastal wetland soils vary with the availability of oxygen and other terminal electron acceptors. We also evaluated how OM and redox-sensitive species varied across different size fractions: particulates (0.45–1μm), fine colloids (0.1–0.45μm), and nano particulates plus truly soluble (<0.1μm; NP+S) during 21-day aerobic and anaerobic slurry incubations. Soils were collected from the center of a freshwater coastal wetland (FW-C) in Lake Erie, the upland-wetland edge of the same wetland (FW-E), and the center of a saline coastal wetland (SW-C) in the Pacific Northwest, USA. Anaerobic methane production for FW-E soils were 47 and 27,537 times greater than FW-C and SW-C soils, respectively. High Fe 2+ and dissolved sulfate concentrations in FW-C and SW-C soils suggest that iron and/or sulfate reduction inhibited methanogenesis. Aerobic CO 2 production was highest for both freshwater soils, which had a higher proportion of OM in the NP+S fraction (64±28% and 70±10% for FW-C and FW-E, respectively) and organic C:N ratios reflective of microbial detritus (5.3±5.3 and 5.3±7.0 for FW-E and FW-C, respectively) compared to SW-C, which had a higher fraction of particulate (58±9%) and fine colloidal (19±7%) OM and organic C:N ratios reflective of vegetation detritus (11.4 ± 1.7). The variability in GHG production and shifts in OM size fractionation and composition observed across freshwater and saline soils collected within individual and across different sites reinforce the high spatial variability in the processes controlling OM stability, mobility, and bioavailability in coastal wetland soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Spatial Impacts of Electric Vehicle Charging on Power Grid Stability: A Downtown Atlanta Case Study

The rapid increase in electric vehicle (EV) charging demand poses a potential risk to power grid stability, particularly as the spatial distribution of this demand remains underexplored. Existing research often focuses on technical optimization models while overlooking the geographic and human dynamics that affect energy consumption. This study addresses this gap by incorporating mobility data to estimate both building energy use and EV charging demand while also considering geographic factors for a better understanding of grid load. Using agent-based simulations and the Open-Source Distribution System Simulator, the study evaluates the effect of various EV penetration scenarios on grid voltage and unbalance. The results show that, although voltage remains within acceptable limits at lower EV penetration rates, significant voltage drop and unbalance occur as EV penetration exceeds 40%, particularly in residential areas with high charging demand. This study offers a framework for integrating spatial analysis and mobility data in power network simulations, providing insights for future EV infrastructure planning.

Pan, Melrose [ORNL] (ORCID:000000031627448X)↗
DOE OSTI2025

BAMCensus (The Behavior and Advanced Mobility Census Dataset Aggregator) [SWR-25-120]

This software is a high-performance tool developed in Rust for downloading and processing large-scale geospatial datasets, specifically focusing on US Census data. It is designed to address scaling limitations found in existing tools, such as R's [tidycensus](https://walker-data.com/tidycensus/), by providing performant streaming dataset JOIN operations between various US Census datasets (like ACS and LEHD) and their corresponding geometries stored on the TIGER/Lines web server. The tool automates the process of joining these data sources, returning aggregated data to the user based on a specified census GEOID type. The tool automates the process of joining these data sources, returning aggregated data to the user based on a specified census GEOID type. Its primary motivation stems from the need for a high-performance solution to combine spatial datasets with graph traversals within the context of mobility analysis tooling being developed at NREL's Behavior and Advanced Mobility (BAM) group.

Fitzgerald, Robert [National Renewable Energy Labo↗