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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

What Makes You Hold on to That Old Car? Joint Insights From Machine Learning and Multinomial Logit on Vehicle-Level Transaction Decisions

What makes you hold on to that old car? While the vast majority of household vehicles are still powered by conventional internal combustion engines, the progress of adopting emerging vehicle technologies will critically depend on how soon the existing vehicles are transacted out of the household fleet. Leveraging a nationally representative longitudinal data set, the Panel Study of Income Dynamics, this study examines how household decisions to dispose of or replace a given vehicle are: 1) influenced by the vehicle’s attributes, 2) mediated by households’ concurrent socio-demographic and economic attributes, and 3) triggered by key life cycle events. Coupled with a newly developed machine learning interpretation tool, TreeExplainer, we demonstrate an innovative use of machine learning models to augment traditional logit modeling to both generate behavioral insights and improve model performance. We find the two gradient-boosting-based methods, CatBoost and LightGBM, are the best performing machine learning models for this problem. The multinomial logistic model can achieve similar performance levels after its model specification is informed by TreeExplainer. Both machine learning and multinomial logit models suggest that while older vehicles are more likely to be disposed of or replaced than newer ones, such probability decreases as the vehicles serve the family longer. Pickup trucks and sport utility vehicles are less likely to be disposed of or replaced than cars, and leased vehicles are more likely to be transacted than owned vehicles. We find that married families, families with higher education levels, homeowners, and older families tend to keep their vehicles longer. Life events such as childbirth, residential relocation, and change of household composition and income are found to increase vehicle disposal and/or replacement. We provide additional insights on the timing of vehicle replacement or disposal, in particular, the presence of children and childbirth events are more strongly associated with vehicle replacement among younger parents.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Efficient ab initio calculations of electron-defect scattering and defect-limited carrier mobility

Electron-defect ($e - \mathrm{d}$) interactions govern charge carrier dynamics at low temperature, where they limit the carrier mobility and give rise to phenomena of broad relevance in condensed matter physics. Ab initio calculations of $e - \mathrm{d}$ interactions are still in their infancy, mainly because they require large supercells and computationally expensive workflows. Here we develop an efficient ab initio approach for computing elastic $e - \mathrm{d}$ interactions, their associated $e - \mathrm{d}$ relaxation times (RTs), and the low-temperature defect-limited carrier mobility. The method is applied to silicon with simple neutral defects, such as vacancies and interstitials. Contrary to conventional wisdom, the computed $e - \mathrm{d}$ RTs depend strongly on carrier energy and defect type, and the defect-limited mobility is temperature dependent. These results highlight the shortcomings of widely employed heuristic models of $e - \mathrm{d}$ interactions in materials. Finally, our method opens avenues for studying $e - \mathrm{d}$ scattering and low-temperature charge transport from first principles.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Mass Transfer Between Recirculation and Main Flow Zones: Is Physically Based Parameterization Possible?

Recirculation zones (RZs) are typical in many geophysical flows. These zones form near irregular solid boundaries and are separate from the main flow. Since mass can be trapped and later released locally from RZs, bulk transport can exhibit long tails—an anomalous behavior that is challenging to predict. The underlying RZ mass transfer and retention in this situation is poorly understood despite common parameterization by effective exchange coefficients in mobile-immobile (MIM) domain models. We viewed the mass transfer process using computationally resolved flow and transport fields inside two-dimensional rough fractures. RZs were delineated by a novel technique followed by quantification of mass transfer across the interface with the main flow zone. The results showed that the first-order mass transfer coefficient is a function of Reynolds number and velocity difference between the RZ and bulk flow. A distributed mobile-immobile model with the directly estimated parameters accurately reproduced bulk anomalous transport. As the distributed mobile-immobile model is not yet predictive, its development showed that mass transfer coefficients for flows involving RZs are physically meaningful, potentially predictable, and useful for elucidating local mass transfer processes within the general framework of mobile-immobile transport modeling.

58 GEOSCIENCES↗
DOE OSTI2023

Wetting of a Hydrophobic Surface: Far-IR Action Spectroscopy and Dynamics of Microhydrated Naphthalene

The interaction of water and polycyclic aromatic hydrocarbons is of fundamental importance in areas as diverse as materials science and atmospheric and interstellar chemistry. The interplay between hydrogen bonding and dipole–π interactions results in subtle dynamics that are challenging to describe from first principles. Here, we employ far-IR action vibrational spectroscopy with the infrared free-electron laser FELIX to investigate naphthalene with one to three water molecules. We observe diffuse bands associated with intermolecular vibrational modes that serve as direct probes of the loose binding of water to the naphthalene surface. These signatures are poorly reproduced by static DFT or Møller–Plesset computations. Instead, a rationalization is achieved through Born–Oppenheimer Molecular Dynamics simulations, revealing the active mobility of water over the surface, even at low temperatures. Therefore, our work provides direct insights into the wetting interactions associated with shallow potential energy surfaces while simultaneously demonstrating a solid experimental–computational framework for their investigation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

X-ray computed tomography-based porosity analysis: Algorithms and application for porous woody biomass

The properties of lignocellulosic biomass, such as bulk physical, thermal, and mechanical properties, as well as the mobility of enzymes or catalysts, are largely affected by porosity, pore structures, and pore size distribution. While X-ray computed tomography (CT) has been introduced to effectively produce 3D volumetric images of material microstructure, a quantitative porosity analysis from the 3D images has never been done. This work introduces a first-of-its-kind X-ray CT-based quantitative porosity analysis method and applies the developed toolkit to characterize the internal porosity distribution of loblolly pine. A sample loblolly pine chip was scanned by a nano-resolution X-ray CT system and digitally reconstructed after a sequence of image processing operations. Here, a comprehensive porosity analysis was then performed to quantify the envelope porosity, the spatial distribution of local porosity, and the directional porosity. Solutions to the various challenges in image processing, porosity calculation, and large data handling are provided.

42 ENGINEERING↗
DOE OSTI2025

FIRE: A Failure-Adaptive RL Framework for Edge Computing Migrations

In edge computing, users' service profiles are migrated between edge servers due to user mobility. Reinforcement Learning (RL) frameworks have been proposed to do so, often trained on simulated data. However, existing RL frameworks overlook occasional server failures, which although rare, impact latency-sensitive applications like AR/VR and real- time obstacle detection. These rare failures, being not adequately represented in historical training data, pose a challenge for data-driven RL algorithms. We introduce FIRE, a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment. We propose FIRE-ImRE, an importance sampling-based Q-learning algorithm, which samples rare events proportionally to their impact on the value function. FIRE considers delay, migration, failure, and backup placement costs across individual and shared service profiles. We prove FIRE-ImRE's boundedness and convergence to optimality. Next, we introduce novel deep Q-learning (FIRE-ImDQL) and actor critic (FIRE-ImACRE) versions of our algorithm to enhance scalability. Here, we extend our framework to accommodate users with varying risk tolerances of rare failure events. Through trace-driven experiments, we show that FIRE reduces edge computing costs compared to vanilla RL and the greedy baseline in the event of failures.

Edge computing↗
DOE OSTI2017

Enabling Real-Time Volume Rendering of Functional Magnetic Resonance Imaging on an iOS Device

Powerful non-invasive imaging technologies like computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI) are used daily by medical professionals to diagnose and treat patients. While 2D slice viewers have long been the standard, many tools allowing 3D representations of digital medical data are now available. The newest imaging advancement, functional MRI (fMRI) technology, has changed medical imaging from viewing static to dynamic physiology (4D) over time, particularly to study brain activity. Add this to the rapid adoption of mobile devices for everyday work and the need to visualize fMRI data on tablets or smartphones arises. However, there are few mobile tools available to visualize 3D MRI data, let alone 4D fMRI data. Building volume rendering tools on mobile devices to visualize 3D and 4D medical data is challenging given the limited computational power of the devices. This paper describes research that explored the feasibility of performing real-time 3D and 4D volume raycasting on a tablet device. The prototype application was tested on a 9.7” iPad Pro using two different fMRI datasets of brain activity. The results show that mobile raycasting is able to achieve between 20 and 40 frames per second for traditional 3D datasets, depending on the sampling interval, and up to 9 frames per second for 4D data. While the prototype application did not always achieve true real-time interaction, these results clearly demonstrated that visualizing 3D and 4D digital medical data is feasible with a properly constructed software framework.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Technical Characterization and Benefit Evaluation of 5G-Enabled Grid Data Transport and Applications

This report summarizes the Year 1 work of Pacific Northwest National Laboratory’s (PNNL’s) 5G Fabricated Resource and Asset Management Encompassment for energy infrastructure (Energy FRAME) project funded by the Department of Energy Office of Science’s Advanced Scientific Computing Research Program. 5G is a breakthrough technology that enables a fully mobile and connected society, and a 5G-enabled digital continuum will be one of the critical foundations for a clean energy economy and grid modernization. In collaboration with PNNL’s Advanced Wireless Communication team and Center for Advanced Technology Evaluation team, the project team has been evaluating the system performance of 5G testbeds in the PNNL 5G Innovation Studio, and has formulated a co-simulation test case of power system transmission, distribution, and communication (T&D&C) networks considering 5G technology and high penetration of distributed energy resources. The methodology developed in the 5G Energy FRAME project can be customized to fit different future grid scenarios to evaluate multiple (dynamic) configurations (computing, sensing, communication, environment) for different stakeholders. In summary, our main technical highlights in project Year 1 are as follows: 1) Technical characterization of 5G standalone architectures, 2) Formulation of co-simulation test case of T&D&C networks embedded with 5G, 3) Initial benefit evaluation of 5G communication platform for grid use cases, and 4) Additional extended discussions on edge computing, artificial intelligence and machine learning, and high-performance computing and cloud computing adoptions. In addition, a collection of system performance data is shared through the publicly available weblink, https://www.pnnl.gov/projects/5g-energy-frame/publications

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Improving Thermal Management Strategies for Data Centers: A Physical Testbed Incorporating Small Modular Reactor and Microreactor Technology

This study aims to accelerate the demonstration of various thermal management systems for data centers using nuclear-generated heat to enhance energy and grid reliability. Utilizing mobile containerized and stationary test beds at INL's High Performance Computing (HPC) facility, this project integrates with various nuclear-related energy systems testing facilities. Key components include immersion cooling apparatus, absorption chillers, and adjustable thermal management simulators. Tasks involve acquiring necessary hardware, sensors, and cooling apparatus, engaging with data center industry stakeholders, and providing a testing platform for algorithms, models, tools, and software. The objective is to expedite the deployment of nuclear-powered data centers, thereby improving energy reliability and affordability.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2017

Understanding of the Elemental Diffusion Behavior in Concentrated Solid Solution Alloys

As one of the core effects on the high-temperature structural stability, the so-called “sluggish diffusion effect” in high-entropy alloy (HEA) has attracted much attention. Experimental investigations on the diffusion kinetics have been carried out in a few HEA systems, such as Al-Co-Cr-Fe-Ni and Co-Cr-Fe-Mn-Ni. However, the mechanisms behind this effect remain unclear. To better understand the diffusion kinetics of the HEAs, a combined computational/experimental approach is employed in the current study. In the present work, a self-consistent atomic mobility database is developed for the face-centered cubic (fcc) phase of the Co-Cr-Fe-Mn-Ni quinary system. The simulated diffusion coefficients and concentration profiles using this database can well describe the experimental data both from this work and the literatures. The validated mobility database is then used to calculate the tracer diffusion coefficients of Ni in the subsystems of the Co-Cr-Fe-Mn-Ni system with equiatomic ratios. The comparisons of these calculated diffusion coefficients reveal that the diffusion of Ni is not inevitably more sluggish with increasing number of components in the subsystem even with homologous temperature. Taking advantage of computational thermodynamics, the diffusivities of alloying elements with composition and/or temperature are also calculated. Furthermore, these calculations provide us an overall picture of the diffusion kinetics within the Co-Cr-Fe-Mn-Ni system.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Transforming Energy Through Computational Excellence: A Virtual Test-Drive of Energy Scenarios

Researchers at the National Renewable Energy Laboratory (NREL) are conducting groundbreaking research to decarbonize transportation systems and reduce emissions. As they plan for growing populations and related demands on aging infrastructure and energy systems, local transportation departments and city planners are facing significant, complex challenges. New approaches to planning and operations, as well as capitalizing on rapidly advancing technology, can address mobility and energy-centric challenges, including cost and energy efficiency, emissions reduction, decarbonization, and congestion.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

N-Doping improves charge transport and morphology in the organic non-fullerene acceptor O-IDTBR

Molecular doping has been shown to improve the performance of various organic (opto)electronic devices. When compared to p-doped systems, research into n-doped organic small-molecules is relatively limited, primarily due to the lack of suitable dopants and the often encountered unfavourable microstructural effects. These factors have prevented the use of n-doping in a wider range of existing materials, such as non-fullerene acceptors (NFAs), that have already shown great promise for a range of (opto)electronic applications. Here, we show that several different molecular n-dopants, namely [1,2-b:2',1'-d]benzo[i][2.5]benzodiazocine potassium triflate adduct (DMBI-BDZC), tetra-n-butylammonium fluoride (TBAF) and 4-(2,3-dihydro-1,3-dimethyl-1H-benzimidazol-2-yl)-N,N-dimethylbenzenamine (N-DMBI), can be used to n-dope the molecular semiconductor O-IDTBR, a promising NFA, and increase the electron field-effect mobility to >1 cm 2 V -1 s -1 . By combining complementary experimental techniques with computer simulations of doping and charge carrier dynamics, we show that improved charge transport arises from synergistic effects of n-type doping and morphological changes. Specifically, a new, previously unreported dopant-induced packing orientation results in one of the highest electron mobility values reported to-date for an NFA molecule. Overall, this work highlights the importance of dopant–semiconductor interactions and their impact on morphology, showing that dopant-induced molecular packing motifs may be generic and a key element of the charge transport enhancement observed in doped organics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

In Silico Human Mobility Data Science: Leveraging Massive Simulated Mobility Data (Vision Paper)

Human mobility data science using trajectories or check-ins of individuals has many applications. Recently, we have seen a plethora of research efforts that tackle these applications. However, research progress in this field is limited by a lack of large and representative datasets. The largest and most commonly used dataset of individual human trajectories captures fewer than 200 individuals, while datasets of individual human check-ins capture fewer than 100 check-ins per city per day. Thus, it is not clear if findings from the human mobility data science community would generalize to large populations. Since obtaining massive, representative, and individual-level human mobility data is hard to come by due to privacy considerations, the vision of this work is to embrace the use of data generated by large-scale socially realistic microsimulations. Informed by both real data and leveraging social and behavioral theories, massive spatially explicit microsimulations may allow us to simulate entire megacities at the person level. The simulated worlds, which do not capture any identifiable personal information, allow us to perform “in silico” experiments using the simulated world as a sandbox in which we have perfect information and perfect control without jeopardizing the privacy of any actual individual. In silico experiments have become commonplace in other scientific domains such as chemistry and biology, permitting experiments that foster the understanding of concepts without any harm to individuals. This work describes challenges and opportunities for leveraging massive and realistic simulated alternate worlds for in silico human mobility data science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Preliminary Logistics Modeling of the Integrated Wash Aid Treatment Emergency Reuse System - 19495

We employed Analysis of Mobility Platform (AMP) and GoldSim software to model the logistics of deploying materials to implement the Integrated Wash Aid Treatment Emergency Reuse System (IWATERS) in a hypothetical cesium-contamination scenario covering a four-block strip of downtown Chicago, IL. In this deployment of IWATERS, buildings were washed down via fire-hosing and contaminated wash solution was collected in the city's waste/stormwater collection system. The contaminated wash water was treated to remove Cs via ad hoc sand/clay filtration beds, and the treated wash solution was recycled during continuing decontamination operations. A detailed task timeline for the IWATERS deployment and information (or assumptions) on the availability and location of required resources for operations provided input parameters, as well as a framework, to enable the AMP and GoldSim simulations. From the decision to deploy IWATERS to the end of operations, including the completion of processing all contaminated wash water, took 49 days with decontamination teams active for 35 days and treatment bed teams active for 37 days. The modeling results showed the availability of vermiculite clay (based on data supplied by its commercial vendors/distributors) as the limiting factor for deploying treatment bed teams and, consequentially, the recycling rate of wash water and the timeline for decontamination operations. These simulations represent the first attempt at understanding the material and personnel requirements for large-scale IWATERS operation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Measuring Fundamental Improvements in Sustainable Urban Mobility: The Mobility-Energy Productivity Metric

Recent technological advancements in mobility are creating many options for connecting citizens with employment, goods, and services, particularly in urban areas where modes such as bike and car shares, electric scooters, ridesourcing, and ridesharing are proliferating at a rapid pace. Analysis and tools for overall transportation planning are dominated by urban regional travel demand models whose roots in highway operations poorly reflect the system dynamics in denser areas where parking costs, convenience, and availability - not to mention sustainability concerns and quality of life - are driving people to an ever-greater spectrum of mobility services. In this paper, we present a new paradigm for evaluating mobility options within an urban area. First developed for the U.S. Department of Energy's Energy Efficient Mobility System research program, this metric is termed the mobility-energy productivity (MEP) metric. At its heart, the MEP metric measures accessibility and appropriately weights it with travel time, cost, and energy of modes that provide access to opportunities in any given location. The proposed metric is versatile in that it can be computed from readily available data sources or derived from outputs of regional travel demand models. End times associated with parking, curb access, cost, and reliability and frequency of service need to be carefully considered to obtain an appropriate and accurate perspective when computing the metric. Ultimately, the MEP metric can be used to reflect the impacts of new mobility technologies (transportation network companies, electric scooters), business models (car shares and bike shares), and land-use practices (such as transit-oriented development) on sustainable urban mobility. This paper lays out the need, requirements, and framework for this new metric, and offers it, in collaboration with the American Society for Civil Engineers (ASCE), as a foundational metric for smart city assessment.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2019

Measuring Fundamental Improvements in Sustainable Urban Mobility: The Mobility-Energy Productivity Metric: Preprint

Recent technological advancements in mobility are creating many options for connecting citizens with employment, goods, and services, particularly in urban areas where modes such as bike and car shares, electric scooters, ridesourcing, and ridesharing are proliferating at a rapid pace. Analysis and tools for overall transportation planning are dominated by urban regional travel demand models whose roots in highway operations poorly reflect the system dynamics in denser areas where parking costs, convenience, and availability - not to mention sustainability concerns and quality of life - are driving people to an ever-greater spectrum of mobility services. In this paper, we present a new paradigm for evaluating mobility options within an urban area. First developed for the U.S. Department of Energy's Energy Efficient Mobility System research program, this metric is termed the Mobility-Energy Productivity (MEP) metric. At its heart, the MEP metric measures accessibility and appropriately weights it with travel time, cost, and energy of modes that provide access to opportunities in any given location. The proposed metric is versatile in that it can be computed from readily available data sources or derived from outputs of regional travel demand models. End times associated with parking, curb access, cost, and reliability and frequency of service need to be carefully considered to obtain an appropriate and accurate perspective when computing the metric. Ultimately, the MEP metric can be used to reflect the impacts of new mobility technologies (transportation network companies, electric scooters), business models (car shares and bike shares), and land-use practices (such as transit-oriented development) on sustainable urban mobility. This paper lays out the need, requirements, and framework for this new metric, and offers it, in collaboration with the American Society for Civil Engineers (ASCE), as a foundational metric for Smart City assessment.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2019

Full in silico DFT characterization of lanthanum and yttrium based oxynitride semiconductors for solar fuels

Finding new solar-energy absorber materials is one of the most significant challenges in artificial photosynthesis. Starting from the experimentally available LaTaON 2 and LaTiO 2 N oxynitrides, we use DFT to propose new sunlight absorbing semiconductors. The synthetically unknown YTaON 2 and YTiO 2 N are semiconductors with indirect band gaps of 2.7 eV and 2.9 eV, respectively. For the first time, we compute within Boltzmann transport theory the DOS-averaged effective mass and mobility of LaTaON 2 , LaTiO 2 N, YTaON 2 and YTiO 2 N. Our first principles calculations indicate that the Y-based materials possess advantageous dielectric (ε r ≥ 47), optical (α ≥ 10 4 cm –1 near band edge), and charge transport properties (m e,h*DOS ≈ 0.2; μ e,h ≈ 10 2 cm 2 V –1 s –1 ) for the conversion of solar energy into storable fuels. Furthermore, our work leads to the complete in silico design of semiconductors for water splitting.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗