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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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Decoupling Carrier Dynamics and Energy Transport in Ultrafast Near-Field Nanoscopy
Ultrafast near-field optical nanoscopy has emerged as a powerful platform to characterize low-dimensional materials. While analytical and numerical models have been established to account for photoexcited carrier dynamics, quantitative evaluation of the associated pulsed laser heating remains elusive. Here, we decouple the photocarrier density and temperature increase in near-field nanoscopy by integrating the two-temperature model (TTM) with finite-difference time-domain (FDTD) simulations. These results reveal that the electron–phonon coupling in a silicon film after femtosecond laser excitation is most pronounced within approximately 3 ps–substantially shorter than the photocarrier decay time scale at tens of picoseconds. Moreover, the coupled TTM-FDTD method indicates that ultrafast laser heating can cause up to a 14% variation in the near-field signal at a 220 μJ/cm 2 pump pulse fluence. Our numerical results are further validated by transient experiments, highlighting the potential of this method for investigations of carrier and thermal phenomena in emerging nanomaterials and nanodevices.
Temperature dependence of energy transport in the Z 3 chiral clock model
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Energy transport between heat baths with oscillating temperatures
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Exciton-Condensate-Like Amplification of Energy Transport in Light Harvesting
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Diffuse energy transport and coda-wave interferometry for resonant transmission between reverberant structures
Approximate analytic and numerical theories are developed with which to model, and compare with laboratory measurements, the diffuse ultrasonic field transmitted from one elastic body to another through a resonant coupling. Particular focus is on the sensitivity of the transmitted field to perturbations in the natural frequency of the coupling, and on the manifestation of such perturbations as time delays in the second body. Immediate application is to measurements of slow dynamic elastic nonlinearity. It is found that the developed approximate theories do a good job of predicting the time dependence of mean square transmitted signal and signal spectra. They also predict the time delays, both their erratic character and their means. Analytic dependence of these time shifts on the fractional frequency perturbation df/f is derived.
Sedov-Taylor-like blast waves as an in-situ measure of energy transport in laboratory experiments.
Abstract not provided.
Energy transport in magnetized laser plasmas
We present the first volumetric measurements of magnetic field, density, and temperature in laser-produced plasmas and shock waves. The results were obtained in one of the only rep-rated (1 Hz) HED experiment currently operating.
Self-similar mesh convergence, asymptotic convergence, and energy transport analysis
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Multi-scale Analysis of Surface Heterogeneity Induced Convection on Isentropic Coordinates
This research uses semi-idealized simulations to investigate multi-scale processes induced by the heterogeneity of soil moisture observed during the 2016 Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) field campaign. The semi-idealized simulations have realistic land heterogeneity, but large-scale winds are removed. Analysis on isentropic coordinates enables the tracking of circulation that transports energy vertically and facilitates the identification of the primary convective processes induced by realistic land heterogeneity. The isentropes associated with upward motion are found to connect the ground characterized by high latent heat flux to cloud bases directly over the ground with high sensible heat flux, while isentropes associated with downward motion connect precipitation to the ground characterized by high sensible heat fluxes. The mixing of dry, warm parcels ascending from the ground with high sensible heat fluxes and moist parcels from high latent heat regions leads to cloud formation. This new mechanism explains how soil moisture heterogeneity provides the key ingredients such as buoyancy and moisture for shallow cloud formation. We also found that the sub-mesoscale dominates upward energy transport in the boundary layer, while mesoscale circulations contribute to vertical energy transport above the boundary layer. Our novel method better illustrates and elucidates the nature of land atmospheric interactions under irregular and realistic soil moisture patterns.
Energy 101 Transportation [Slides]
The Energy 101: Transportation presentation, developed for the Energy Technology Innovation Partnership Project (ETIPP), provides an overview of transportation. It covers fundamental concepts, technologies, considerations, case studies, and additional resources.
Advanced Computing, Data Science, and Artificial Intelligence Research Opportunities for Energy-Focused Transportation Science
The Energy Efficient Mobility Systems (EEMS) technology landscape is complex and rapidly evolving, which provides both tremendous opportunities and formidable challenges. Significant alterations to the mobility landscape are underway due to the advent of vehicle and infrastructure connectivity, autonomous driving, and rapid passenger- and freight-vehicle electrification. Advanced computing will play an increasingly important role in enabling the EEMS program to understand and identify the most important levers to improve the energy productivity of future integrated mobility systems. It is also driving new approaches to mobility and the research to unlock an affordable, efficient, safe, and accessible transportation future. Driving much of this change is the collection, analysis, and strategic use of massive amounts of diverse, complex data from infrastructure and vehicles with on-board sensors and data storage and transmission capabilities. Diverse and representative data are key to implementing approaches to maximize mobility energy productivity. While high-fidelity modeling of integrated transportation networks has strengthened our understanding of dynamic movement and behavior patterns, existing tools must be expanded beyond their current focus. This work necessitates data infrastructure investments (e.g., secure-streaming data platforms driven by ubiquitous sensors and video analytics) as well as investments in critical capabilities for large-scale automated analysis and organization using modern machine learning, statistics, and artificial intelligence. Other chief needs include agile, large-scale storage that can be quickly searched and queried for relevant data to support validation and model development, data-sharing agreements, and formatting standards for key data types. The future of public transit must be explored in greater detail, research must inform design, and opportunities must be identified for improving the mobility productivity of public transit in both urban and rural America.
Holistic Transportation and Energy Modeling
This project advances the development and application of the TEMPO transportation energy systems model to support DOE analysis of evolving mobility futures. Recent work has focused on improving model transparency, performance, and fidelity through a redesigned software architecture and updated technology adoption scenarios aligned with the latest data and assumptions. Ongoing efforts aim to expand TEMPO's ability to assess transportation affordability and consumer decision-making, including vehicle ownership dynamics and distributional impacts across households. These enhancements position TEMPO to better inform policy and R&D decisions by providing more behaviorally realistic and policy-relevant insights into transportation energy use, technology adoption, and system-level outcomes.
Launch Alaska Transportation and Energy Accelerator (LATEA)
The Launch Alaska Transportation and Energy Accelerator (LATEA), funded through the U.S. Department of Energy Office of Technology Commercialization’s Energy Program for Innovation Clusters (EPIC),advanced deployment of innovative and efficient transportation and energy technology in Alaska from October 2021 through June 2025. The project was designed to leverage Launch Alaska’s accelerator model to identify, recruit, and support transportation technology companies with novel solutions to market needs while building the stakeholder networks, demonstration opportunities, and institutional capacity necessary to accelerate commercialization in one of the most challenging operating environments in the United States.
Frontiers in Thermal Transport and Energy Conversion: Proceedings of a Workshop
Thermal transport and energy conversion has remained an active field for at least 200 years, with numerous opportunities for discoveries and new applications. Recently, experiments have advanced researchers’ understanding of basic physics, and how new discoveries might translate into applications in energy, materials, quantum technologies, and other areas. The National Academies convened a workshop on April 11, 2019 to identify and assess the frontier of current research in the field of thermal transport and energy conversion. Discussions involved topics related to thermal transport and quasi-particle hydrodynamics, thermal transport beyond the quasiparticle paradigm, the thermal hall effect from neutral spin excitations in frustrated quantum magnets, quantization of the thermal hall conductivity at small hall angles, and thermal spin transport, including spin-Seebeck and magnon drag effects. These topics were strategically selected with the goal of uncovering key challenges, opportunities, and issues in order to guide future efforts and investments to advance the field. This publication offers a condensed summary of the discussions and presentations from the workshop, which was unclassified and open to the public.
Neuromorphic heat transport effects in a molecular junction
Understanding energy transport at the nanoscale is an open and fundamental challenge in the molecular sciences with direct implications for the design of new electronics, computing devices, and materials. While nanoscale energy transport under steady-state conditions has been studied extensively, there is much less known about energy transport under time-dependent driving forces, particularly in the far-from-equilibrium regime. In this work, we use nonequilibrium molecular dynamics simulations and stochastic thermodynamics to investigate energy transport in a well-studied nanoscale system—a molecular junction—subjected to a time-periodic temperature gradient. The primary observation is that molecular junctions can exhibit heat transport hysteresis, a phenomenon in which the heat flux through a system depends not only on the instantaneous value of a time-dependent temperature bias but also on the temporal history of that bias. The presented findings illustrate that molecular junctions can exhibit the specific memory effect—heat transport hysteresis—that is essential for the design of thermal neuromorphic computers. This work elucidates a potential pathway toward the realization of such devices.
Validation of the single-event method for low-energy electron transport via stopping power calculations with $\mathrm{MCNP}$
Monte Carlo simulations of low-energy ( <50 keV) electron transport in matter are essential for a broad range of application fields. Several Monte Carlo codes have developed specialized treatments for this case, but a comprehensive validation of low-energy electron transport for general-purpose simulations remains lacking in the literature. One approach to accomplish this validation is calculation of stopping power using low-energy electron transport physics, as stopping power is a fundamental radiation transport quantity which must be simulated accurately for nearly any application. Here in this work, we use the Monte Carlo N-Particle (MCNP) radiation transport code with the single-event method for electron transport to calculate stopping powers of low-energy electrons (50 eV to 30 keV) in 41 elemental solids, 14 compound solids, and five rare gas solids, comparing simulation results to published semi-empirical stopping power calculations from optical measurements. In general, the simulations give good agreement (typically within ±10%) with semi-empirical stopping power values at higher energies: 300 eV and above for most elemental solids, 1 keV and above for compound solids, and 400 eV and above for rare gas solids. Agreement between MCNP and semi-empirical values is worse below these energies. The most significant source of error is the EPRDATA14 cross section data, which does not account for changes in electronic structure due to solid-state bonding, particularly in compound materials. The simplistic model of atomic excitation used to generate the EPRDATA14 cross sections is another key source of error. Additionally, the breakdown of the continuous slowing-down approximation introduces significant uncertainty at low energies, although this is a limitation of the calculation method and not of the simulation procedure. Accounting for these and other uncertainty sources, the single event method in MCNP is robust and able to give good accuracy for a variety of low-energy electron transport problems through diverse kinds of materials.