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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

SMART Mobility. Modeling Workflow Development, Implementation, and Results Capstone Report

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019, and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the SMART Mobility Modeling Workflow effort. The SMART Mobility Modeling Workflow was developed to evaluate new transportation technologies such as connectivity, automation, sharing, and electrification through multi-level systems analysis that captures the dynamic interactions between technologies. By integrating multiple models across different levels of fidelity and scale, the Workflow yields insights about the influence of new mobility and vehicle technologies at the system level. For information about the other Pillars, please refer to the relevant pillar’s Capstone Report.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Behavior, Energy, Autonomy, Mobility Modeling Framework

The Behavior, Energy, Autonomy, and Mobility (BEAM) model is an integrated, agent-based travel demand simulation framework. Individual agents express preferences through a utility-maximizing evolutionary algorithm that minimizes each individual's cost and time spent traveling via diverse modal options, including the competition for scarce supply resources such as parking spaces and charging infrastructure. BEAM simulates the essential elements that compose a dynamic transportation system. From the road network, parking and charging infrastructure, to the transit system and a synthetic population with plans and preferences, the virtual system is an amalgamation of multiple spatially resolved layers that together represent an integrated transportation system. BEAM is an extension to the MATSim (Multi-Agent Transportation Simulation) model, where agents employ reinforcement learning across successive simulated days to maximize their personal utility through plan mutation (exploration) and selecting between previously executed plans (exploitation). The BEAM model shifts some of the behavioral emphasis in MATSim from across-day planning to within-day planning, where agents dynamically respond to the state of the system during the mobility simulation. In BEAM, agents can plan across all major modes of travel including driving, walking, biking, transit, and demand-responsive ride hailing.

Bae, Sangjae↗
DOE OSTI2024

Behavior, Energy, Autonomy, Mobility Modeling Framework (BEAM) v1.0

The Behavior, Energy, Autonomy, and Mobility (BEAM) model is an integrated, agent-based travel demand simulation framework. Individual agents express preferences through a utility- maximizing evolutionary algorithm that minimizes each individual’s cost and time spent traveling via diverse modal options, including the competition for scarce supply resources such as parking spaces and charging infrastructure. BEAM simulates the essential elements that compose a dynamic transportation system. From the road network, parking and charging infrastructure, to the transit system and a synthetic population with plans and preferences, the virtual system is an amalgamation of multiple spatially resolved layers that together represent an integrated transportation system. BEAM is an extension to the MATSim (Multi-Agent Transportation Simulation) model, where agents employ reinforcement learning across successive simulated days to maximize their personal utility through plan mutation (exploration) and selecting between previously executed plans (exploitation). The BEAM model shifts some of the behavioral emphasis in MATSim from across-day planning to within- day planning, where agents dynamically respond to the state of the system during the mobility simulation. In BEAM, agents can plan across all major modes of travel including driving, walking, biking, transit, and demand-responsive ride hailing. It is designed to integrate with other open source transportation models, such as ActivitySim.

Lazarus, Jessica↗
DOE OSTI2021

Grid-Integrated Electric Mobility Model (GEM) v1.0

Transportation is the fastest-growing source of greenhouse gas (GHG) emissions and energy consumption globally. The convergence of shared mobility, vehicle automation, and electrification has the potential to drastically reduce transportation impacts, but requires careful integration with rapidly evolving electricity systems. We have developed the GEM Model (Grid-Integrated Electric Mobility) to examine these interactions with a U.S.-wide simulation framework encompassing private electric vehicles (EVs); shared automated EVs (SAEVs); charging infrastructure; controlled EV charging; and a grid economic dispatch model to simulate mobility futures exclusively using EVs. We find that an SAEV fleet 9% the size of today's active vehicles can satisfy trip demand with only 2.6 million chargers (0.2 per EV). Controlled EV charging can also reduce electricity demand variability, significantly reducing GHG emissions and decreasing solar curtailment by about one-third. While private EVs with uncontrolled charging would reduce GHG emissions by 53% compared to gasoline vehicles, SAEVs could achieve a 70% reduction.

Sheppard, Colin↗
DOE OSTI2014

Simulation and characterization of millimeter-wave InAlN/GaN high electron mobility transistors using Lombardi mobility model

A gate length of 0.2 μm InAlN/GaN high electron mobility transistor on SiC substrate is obtained with a maximum current gain cutoff frequency (f{sub T}) of 65.8 GHz and a maximum power gain cutoff frequency (f{sub max}) of 143.6 GHz. Lombardi model, which takes interface roughness scattering into consideration, has been introduced to model the transconductance (g{sub m}) degradation. The simulated g{sub m} and f{sub T} with Lombardi model are 69% and 58% lower than the ones without considering interface roughness scattering, respectively. Further analysis show experimental g{sub m}, gate capacitance (C{sub g}), and f{sub T} are consistent with results based on Lombardi model.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Development of an Advanced Simulator to Model Mobility Control and Geomechanics during CO 2 Floods

The simulator is an isothermal, three-dimensional, four-phase, compositional, equation-of–state (EOS) simulator. We have named the simulator UTDOE-CO2 capable of simulating various recovery processes (i.e., primary, secondary waterflooding, and miscible and immiscible gas flooding). We include both the Peng-Robinson EOS and the Redlich-Kwong EOS models. A Gibbs stability test is also included in the model to perform a phase identification test to consistently label each phase for subsequent property calculations such as relative permeability, viscosity, density, interfacial tension, and capillary pressure. Our time step strategy is based on an IMPEC-type method (implicit pressure and explicit concentration). The gridblock pressure is solved first using the explicit dating of saturation-dependent terms. Subsequently, the material balance equations are solved explicitly for the total concentration of each component. The physical dispersion term is also included in the governing equations. The simulator includes (1) several foam model(s) for gas mobility control, (2) compositional relative permeability models with the hysteresis option, (3) corner point grid and several efficient solvers, (4) geomechanics module to compute stress field as the result of CO 2 injection/production, (5) the format of commercial visualization software, S3graf from Science-soft Ltd., was implemented for user friendly visualization of the simulation results. All tasks are completed and the simulator was fully tested and delivered to the DOE office including a user’s guide and several input files and the executable for Windows Pcs. We have published several SPE papers, presented several posters, and one MS thesis is completed (V. Pudugramam, 2013) resulting from this DOE funded project.

02 PETROLEUM↗
DOE OSTI2014

Quantification of the solid-state charge mobility in a model radical polymer

We establish that an oft-used radical polymer, poly(2,2,6,6-tetramethylpiperidinyloxy methacrylate) (PTMA), has a solid-state hole mobility value on the order of 10 -4 cm 2 V -1 s -1 in a space charge-limited device geometry. Despite being completely amorphous and lacking any $π$-conjugation, these results demonstrate that the hole mobility of PTMA is comparable to many well-studied conjugated polymers [e.g., poly(3-hexylthiophene)]. Furthermore, we show that the space charge-limited charge carrier mobility of these macromolecules is only a weak function of temperature, in contrast to many thermally-activated models of charge transport in polymeric materials. This key result demonstrates that the charge transport in radical polymers is inherently different than that in semicrystalline, conjugated polymers. These results establish the mechanism of solid-state charge transport in radical polymers and provide macromolecular design principles for this emerging class of organic electronic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Exploring the Utility-Privacy Trade-Off: Impacts of Semantic and Visit Types Ambiguities on Human Mobility Simulation

Humans are in perpetual movement, constantly traversing buildings, cities, waters, oceans, and countries. Mobility stands out as a major driving force shaping our modern societies. Capturing and explaining human behavior in a world of eight billion distinct mobility agendas is a complex challenge. With the rise of interconnected devices and platforms, such as smartphones, wearables, and point-of-interest data, largescale behavioral data has become more accessible, enabling rich insights into mobility patterns. However, the widespread availability of such data introduces significant ethical challenges. Detailed mobility data can inadvertently reveal sensitive personal information, including individuals' locations, habits, social interactions, and even political or religious affiliations. Beyond privacy breaches, the ethical implications of uncovering and potentially manipulating underlying behavioral patterns demand attention. Striking a balance between the utility of mobility models and the protection of individual privacy is therefore paramount. This paper explores the utility-privacy trade-offs in human mobility modeling, focusing on the impacts of introducing semantic and visit type ambiguities. By systematically examining how these ambiguities affect the fidelity of simulated trajectories and privacy risks, we provide a framework for evaluating ethical and privacy-conscious modeling practices. Our findings emphasize the need for methods that safeguard privacy without undermining the usefulness of mobility models, contributing to the responsible advancement of mobility science in alignment with ethical standards and societal expectations.

Amichi, Licia [ORNL] (ORCID:0000000177631394)↗
DOE OSTI2017

Quantum Chemical Mass Spectrometry: Verification and Extension of the Mobile Proton Model for Histidine

The quantum chemical mass spectrometry for materials science (QCMS{sup 2}) method is used to verify the proposed mechanism for proton transfer – the Mobile Proton Model (MPM) – by histidine for ten XHS tripeptides, based on quantum chemical calculations at the DFT/B3LYP/6-311+G* level of theory. The fragmentations of the different intermediate structures in the MPM mechanism are studied within the QCMS{sup 2} framework, and the energetics of the proposed mechanism itself and those of the fragmentations of the intermediate structures are compared, leading to the computational confirmation of the MPM. In addition, the calculations suggest that the mechanism should be extended from considering only the formation of five-membered ring intermediates to include larger-ring intermediates. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Coordinated PEV Charging for Distribution System Management

Plug-in electric vehicles (PEVs) can help to reduce worldwide dependence on petroleum and carbon emissions. In the past a few years, PEVs have received considerable attention as an eco-friendly and cost-effective alternative to conventional gasoline vehicles. PEVs consume higher power from the grid during charging compared to conventional residential loads. Therefore, the emerging fleet of PEVs will introduce a considerable amount of additional load on power system. On the other hand, majority of PEVs are parked for more than 90% of the time, making them ideal for providing various services through Electric Vehicle Supply Equipment (EVSE), a.k.a. charging stations which are PEVs’ connection points to the power system. In this project, coordinated PEV charging are developed to provide distribution system services, considering PEV characteristics, realistic travel pattern, charging behaviors, and EVSE power rating and availability. The main efforts and contributions are summarized as follows. • Mobility models and EVSE charging capability/availability are indispensable for evaluating PEV charging coordination strategies and developing PEV charging control. Many existing studies utilize simplified mobility models assuming that the entire vehicle fleet returns home in the evening and is parked at home until the next morning. Some other PEV models better represent diversified home arrival and departure time, but cannot capture varying charging flexibility and capability at different locations, and therefore are not appropriate to study the impacts of public EVSE on PEV load and utilization. In this work, a mobility and charging flexibility model is proposed to better represent the temporal availability and varying charging capability from PEV onboard charger and EVSE. • Based on the mobility model, a generalized optimization method is proposed to evaluate different PEV charging coordination strategies. In existing literature, different algorithms and methods need to be designed to evaluate each charging strategy from both vehicle owners’ and system’s perspective. With the optimization method proposed in this work, different charging control strategies can be studied and compared by only updating objective functions. Moreover, optimization tricks are provided to convert the optimization problems to equivalent linear programming problems, which can be efficiently solved with existing solvers. • An innovative scheduling and control framework is proposed to enable smart PEV charging for grid services while meeting PEV owners’ travel needs. In the proposed framework, each set of EVSE is equipped with a controller that estimates charging power and energy flexibility based on vehicle characteristics, EVSE power rating, battery energy state, and upcoming trip information. With the simplified charging flexibility model received from each EVSE controller, the central coordinator determines the optimal power allocation for a look-ahead time window for the given grid services. The proposed charging control can help reduce the computational complexity and communication requirement compared with existing methods. It is also scalable to the expanding PEV fleet and robust to uncertainties in upcoming vehicle trips and future system condition. • The proposed charging strategy evaluation and smart charging control methods are applied to one of the prototypical feeders developed by the Pacific Northwest National Laboratory (PNNL) for case studies. Detailed trip information extracted from the National Household Travel Survey (NHTS) is used to represent travel and parking patterns. Different scenarios are simulated to evaluate the performance of the proposed methods and understand how PEV mobility and public EVSE availability affect PEV load and potential grid services.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Electrified autonomous freight benefit analysis on fleet, infrastructure and grid leveraging Grid-Electrified Mobility (GEM) model

Fast-growing freight activities over the decades have become one of the major contributors to air pollution, leading to many efforts in freight decarbonization and electrification. However, the development of freight electrification is slow due to technological uncertainty, slow charging, high capital cost, etc. This paper analyzes the potential impact and benefit of heavy-duty vehicle (HDV) electrification and automation on fleet cost, infrastructure cost, the electricity grid, and environmental outcomes. In this work, we extended the vehicle electrification benefit analysis tool: Grid-Electrified Mobility (GEM) model, which had primarily been used to study light-duty passenger vehicles (LDVs), to analyze heavy-duty vehicle electrification. The extended model is derived for freight transportation electrification, and different freight electrification and automation adoption scenarios were analyzed. We find that the increased penetration of automated electric freight fleets within other types of electrified freight fleets from 1% to 99% will result in an overall cost reduction of 18.2%, fleet size reduction of 20.4%, and lower peak load reduction of 14.3%.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

D and D decision model and mobile application for selection of fixative, strippable coating, and decontamination gel products - 15696

Deactivation and decommissioning (D and D) is the process of taking an active/excess/abandoned facility to a final disposition end state. D and D presents unique hazards that must be addressed from a safety, programmatic, environmental, and technological standpoint. This definition is especially applicable to Savannah River Site's 235-F facility. The facility was constructed in the 1950's as part of the weapons complex materials production and fabrication. The most significant quantities of residual contamination remaining in 235-F building is in the form of Plutonium-238. A Decision Model was created using MatLab to work hand-in-hand with a product list containing commercially available D and D products. This model was created to further enhance and accelerate the decontamination process. It is envisioned that D and D engineers will be able to use this Decision Model in the field via a mobile device. It allows the user to select four inputs to match their situation. The model searches the database and returns the products that fit the criteria selected by the user. This Decision Model will be made available in a web-based application and as a mobile application to ensure end users can access it when needed. The MatLab code recognizes what position in the text box is selected and then this input is converted to the corresponding string. The string is then compared with the information in the product list in the specified column in order to reduce processing time. Then the products that contain the string inputted are displayed on the table and are separated by category. The web-based version of the model will receive three specified criteria and sub-criteria from the user and update the table with products that fit each criteria. A web-based version of this Decision Model is under way and will be set up on the D and D KM-IT platform. There will be two versions of this mobile application: for devices with internet connection and for those that do not have internet connection. This will ensure end users have access to the Decision application at any time or place during the D and D process. A mobile version of the model will also be developed in the near future.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

A self-consistent first-principle based approach to model carrier mobility in organic materials

Transport through thin organic amorphous films, utilized in OLEDs and OPVs, has been a challenge to model by using ab-initio methods. Charge carrier mobility depends strongly on the disorder strength and reorganization energy, both of which are significantly affected by the details in environment of each molecule. Here we present a multi-scale approach to describe carrier mobility in which the materials morphology is generated using DEPOSIT, a Monte Carlo based atomistic simulation approach, or, alternatively by molecular dynamics calculations performed with GROMACS. From this morphology we extract the material specific hopping rates, as well as the on-site energies using a fully self-consistent embedding approach to compute the electronic structure parameters, which are then used in an analytic expression for the carrier mobility. We apply this strategy to compute the carrier mobility for a set of widely studied molecules and obtain good agreement between experiment and theory varying over several orders of magnitude in the mobility without any freely adjustable parameters. The work focuses on the quantum mechanical step of the multi-scale workflow, explains the concept along with the recently published workflow optimization, which combines density functional with semi-empirical tight binding approaches. This is followed by discussion on the analytic formula and its agreement with established percolation fits as well as kinetic Monte Carlo numerical approaches. Finally, we skatch an unified multi-disciplinary approach that integrates materials science simulation and high performance computing, developed within EU project MMM@HPC.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

A Deep Gravity model for mobility flows generation

The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. When information about mobility flows is not available for a particular region of interest, we must rely on mathematical models to generate them. In this work, we propose Deep Gravity, an effective model to generate flow probabilities that exploits many features (e.g., land use, road network, transport, food, health facilities) extracted from voluntary geographic data, and uses deep neural networks to discover non-linear relationships between those features and mobility flows. Our experiments, conducted on mobility flows in England, Italy, and New York State, show that Deep Gravity achieves a significant increase in performance, especially in densely populated regions of interest, with respect to the classic gravity model and models that do not use deep neural networks or geographic data. Deep Gravity has good generalization capability, generating realistic flows also for geographic areas for which there is no data availability for training. Finally, we show how flows generated by Deep Gravity may be explained in terms of the geographic features and highlight crucial differences among the three considered countries interpreting the model’s prediction with explainable AI techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

A charge carrier transport model for donor-acceptor blend layers

Highly efficient organic solar cells typically comprise donor-acceptor blend layers facilitating effective splitting of excitons. However, the charge carrier mobility in the blends can be substantially smaller than in neat materials, hampering the device performance. Currently, available mobility models do not describe the transport in blend layers entirely. Here, we investigate hole transport in a model blend system consisting of the small molecule donor zinc phthalocyanine (ZnPc) and the acceptor fullerene C{sub 60} in different mixing ratios. The blend layer is sandwiched between p-doped organic injection layers, which prevent minority charge carrier injection and enable exploiting diffusion currents for the characterization of exponential tail states from a thickness variation of the blend layer using numerical drift-diffusion simulations. Trap-assisted recombination must be considered to correctly model the conductivity behavior of the devices, which are influenced by local electron currents in the active layer, even though the active layer is sandwiched in between p-doped contacts. We find that the density of deep tail states is largest in the devices with 1:1 mixing ratio (E{sub t} = 0.14 eV, N{sub t} = 1.2 × 10{sup 18 }cm{sup −3}) directing towards lattice disorder as the transport limiting process. A combined field and charge carrier density dependent mobility model are developed for this blend layer.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗