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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Advancing uncertainty characterization for understanding projected water scarcity in multi-sector, multi-actor river basins across scales.

Invited Talk at AGU Fall Meeting 2023 Modeling how human institutions and infrastructure interact with the water cycle is essential to better understand the vulnerability, and resilience of water resources systems from the local to the global scale. This is especially true when investigating multi-sector, multi-actor responses to the effects of long-term changes and short-term shocks. It is also well recognized that uncertainty present throughout the modeling cycle (e.g., in data, functional relations, and model coupling approaches) limits our ability to trace the interactive dynamics of human-water systems, as well as to quantify their implications for management and planning. Systems with large numbers of diverse stakeholders further compound this challenge, as uncertain drivers and complex dynamics might have very disparate effects on water users. The work presented is conducted through the Integrated Multisector Multiscale Modeling (IM3) Science Focus Area, which explores how human and natural systems co-evolve in response to change. Through this multi-year effort, we have developed exploratory modeling methods to better characterize how the uncertain human and natural drivers of water scarcity yield consequential vulnerabilities in institutionally complex, multi-sectoral systems. This talk will specifically present on a series of uncertainty characterization experiments performed in the Upper Colorado River Basin, a sub-basin of the Colorado with thousands of water users. These complementary and systematic experiments are aimed at understanding: How are various uncertain stressors (e.g., climate change, demand growth) affecting the diverse water users of this basin in terms of water shortage? What are the key drivers of this shortage for each user? Methods and results from this work are used to address additional questions on the ability of adaptation to modulate the effects of these uncertain drivers, and on their compounding effects across spatial scales and through sectoral interactions.

Hadjimichael, Antonia↗
DOE OSTI2026

Ligand Control of Ultrafast Hot-Carrier Cooling in CdSe Quantum Dots by a Coherent Nonadiabatic Mechanism

Using the results from global modeling of the broadband two-dimensional electronic spectrum and an analysis of the concurrent damping of excited-state vibronic coherences, we show herein that a coherent nonadiabatic mechanism converts the electronic excitation energy of the X3 (1P 3/2 –1P e ) exciton state to ligand vibrations on the <50 fs time scale in oleate- and hexadecylamine-capped CdSe quantum dots. A comparison of the rates for the two ligands suggests that this process is promoted by mid-frequency vibrations of the ligands due to modulation of their electron-donating tendency. An intramolecular vibrational redistribution process then follows on the ∼200 fs time scale with both ligands, which accompanies thermalization in the band-edge X1 (1S 3/2 –1S e ) state and dephasing of the spectator ligand vibrations. These findings suggest that charge-separated intermediates associated with photoinduced charge transfer or triplet–triplet excitation energy transfer will be produced with retention of phase coherence in the vibrations that modulate the charge-transfer character of surface-bound organic acceptors.

14 SOLAR ENERGY↗
DOE OSTI2021

Electrolyte-Guided Design of Electroreductive CO Coupling on Copper Surfaces

Engineering the interfacial distribution of electrolytic ions can aid in modulating the electrocatalyst performance and efficiency. Using a hybrid quantum-classical modeling approach, we describe how predictive tuning of the solution microenvironment on copper can enhance the efficiency of CO 2 reduction (CO 2 R) to C 2 products. We elucidate how competing electrolyte constituents in mixed electrolyte solutions stimulate restructuring of the electrochemical double layer (EDL) and stabilize the OCCO* dimer (* denotes surface adsorbed), with predictions validated in flow reactors using copper gas diffusion electrodes (Cu-GDEs). Furthermore, our findings highlight how molecular-scale electrolyte engineering with informed models of the EDL can be leveraged to tailor CO 2 R activity and selectivity toward C 2 products.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Mutual modulation between surface chemistry and bulk microstructure within secondary particles of nickel-rich layered oxides

Abstract Surface lattice reconstruction is commonly observed in nickel-rich layered oxide battery cathode materials, causing unsatisfactory high-voltage cycling performance. However, the interplay of the surface chemistry and the bulk microstructure remains largely unexplored due to the intrinsic structural complexity and the lack of integrated diagnostic tools for a thorough investigation at complementary length scales. Herein, by combining nano-resolution X-ray probes in both soft and hard X-ray regimes, we demonstrate correlative surface chemical mapping and bulk microstructure imaging over a single charged LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) secondary particle. We reveal that the sub-particle regions with more micro cracks are associated with more severe surface degradation. A mechanism of mutual modulation between the surface chemistry and the bulk microstructure is formulated based on our experimental observations and finite element modeling. Such a surface-to-bulk reaction coupling effect is fundamentally important for the design of the next generation battery cathode materials.

25 ENERGY STORAGE↗
DOE OSTI2023

Diurnal MCSs Precede the Genesis of Tropical Cyclone Mora (2017): The Role of Convectively Forced Gravity Waves

In this work, a novel high-resolution regional reanalysis is used to investigate the mesoscale processes that preceded the formation of Tropical Cyclone (TC) Mora (2017). Both satellite observations and the regional reanalysis show early morning mesoscale convective systems (MCSs) persistently initiated and organized in the downshear quadrant of the preexisting tropical disturbance a few days prior to the genesis of TC Mora. The diurnal MCSs gradually enhanced the meso-α-scale vortex near the center of the preexisting tropical disturbance through vortex stretching, providing a vorticity-rich and moist environment for the following burst of deep convection and enhancement of the meso-β-scale vortex. The regional reanalysis shows that the gravity waves that radiated from afternoon convection over the northern coast of the Bay of Bengal might play an important role in modulating the diurnal cycle of pregenesis MCSs. The diurnal convectively forced gravity waves increased the tropospheric stability, reduced the column saturation fraction, and suppressed deep convection within the preexisting tropical disturbance from noon to evening. A similar quasi-diurnal cycle of organized deep convection prior to TC genesis has also been observed over other basins. However, modeling studies are needed to conclusively demonstrate the relationships between the gravity waves and pregenesis diurnal MCSs. Also, whether diurnal gravity waves play a similar role in modulating the pregenesis deep convection in other TCs is worth future investigations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

De-Risking Large-Scale PV Systems Through Data Analytics

Large-scale photovoltaic system performance analysis is being conducted within the US Department of Energy's-sponsored PV Fleet Performance Data Initiative. This collaboration with commercial PV system owners collects and evaluates PV field performance data, and provides reports on aggregated results. Drawing on over 2200 sites across the US and over 24,000 separate PV inverters we have collected in excess of 8.3 gigawatts (GW) of performance data, representing 6-7% of the entire US installed PV capacity. A mixture of utility-scale and large commercial systems are represented, averaging 4.1 megawatts (MW) in size and 5 years in age. Initial results show average system degradation rates at -0.75% / year, which is slightly higher than historically reported module-level values of -0.5%/year. We also found that the availability of systems averaged 97.7%, which is lower than the typical 99% uptime assumed by many project economic forecasts. Given these results, we compared monthly performance with expected production values, based on satellite weather data and a simple PVWatts performance model. We found that systems were performing within 10% of monthly expectation over 90% of the time, with a fleet average vs expected monthly value of 0.994. We also evaluated the impact of extreme weather events on system performance, and found a range of short-term and longer-term performance effects ranging from grid outage, system downtime, module damage and accelerated long-term degradation rate.

analysis↗
DOE OSTI2025

Wind and Weather Variability within the Californian Offshore Wind Energy Areas

Weather variability over the Northeast Pacific (NEP) region and its influence on wind resources within the Californian offshore wind energy areas (WEAs) at Humboldt and Morro Bay are characterized using 20-years reanalysis model and satellite data. The hub-height (180 m) winds at both locations are predominantly northwesterly driven by the NEP high pressure system, with strong coastal gradients in surface pressure, fluxes, planetary boundary layer (PBL) depths and cloudiness. These sharp coastal gradients and strong annual cycles of temperature and moisture advections pose potential challenges in accurately modeling the local wind resource. Hub-height wind speeds and power capacity factors significantly vary for different regimes of PBL depths, surface fluxes and rain area fractions. This highlights the importance of studying the physical mechanisms driving these weather regimes, hence our analysis of how large-scale NEP weather variability drives the local meteorology at the WEAs. Furthermore, at both WEAs, PBL tops and cloud boundaries intersect the rotor layer (80-280 m) more than 30% and 20% of the time, respectively. While PBL depths significantly modulates hub-height winds and power, cloud boundaries do not have a similar impact, likely due to reanalysis errors in simulating cloud boundaries accurately. These findings underscore the challenges in deploying tall wind turbines in shallow cloudy boundary layers, where the interaction between clouds, precipitation, and atmospheric layers can impact turbine efficiency. As turbines grow taller and are deployed in more complex meteorological conditions, understanding these interactions is crucial for improving wind power forecasting and optimizing energy production in coastal regions.

17 WIND ENERGY↗
DOE OSTI2020

Thermal transport across nanoscale damage profile in sapphire irradiated by swift heavy ions

In this work, we studied the degradation of thermal conductivity in single crystal sapphire (α-Al 2 O 3 ) irradiated by 167 MeV Xe swift heavy ions (SHIs) over the multiple fluences in the range of 10 12 –10 14 ions/cm 2 . Thermal conductivity was measured primarily in the cross-plane direction using a noncontact ultrafast optical pump-probe technique called picosecond time domain thermoreflectance (TDTR). Multiple samples with variable ion fluences allowed us to probe distinct regions resulting from different regimes of microstructure evolution caused by electronic energy loss. By tuning the penetration depth of the thermal waves using different modulation frequencies, two regions with distinct conductivities were identified and the values of which were found to be consistent with phonon-mediated thermal transport models while the microstructure was confirmed by electron microscopy characterization. These damaged regions were determined to be a several micrometer thick ion track region and several tens of nanometer-thick amorphous layer present only above 5.0 × 10 13 ions/cm 2 . These results demonstrate the applicability of TDTR to resolve thermal transport behavior in SHI irradiated oxides having nonhomogeneous damage profile on a nanometer scale. The presented approach facilitates future studies aiming at resolving the impact of distinct damage resulting from electronic and nuclear stopping regimes under irradiation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

SA-GAT-SR: self-adaptable graph attention networks with symbolic regression for high-fidelity material property prediction

Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as powerful tools for high-throughput prediction of material properties, offering a compelling enhancement and alternative to traditional first-principles calculations. While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy, such approaches often lack physical interpretability and insights into materials behavior. Here, we introduce a novel computational paradigm—Self-Adaptable Graph Attention Networks integrated with Symbolic Regression (SA-GAT-SR)—that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression. Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintaining O(n) computational scaling. The integrated SR module subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships, achieving 23 × acceleration compared to conventional SR implementations that heavily rely on first-principle calculations-derived features as input. This work suggests a new framework in computational materials science, bridging the gap between predictive accuracy and physical interpretability, offering valuable physical insights into material behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multi-modular capacitive wireless power transfer circuit and related techniques

A high performance kilowatt-scale large air-gap multi-modular capacitive wireless power transfer (WPT) system is provided for electric vehicle (EV) charging. In one particular implementation, the multi-modular system achieves high power transfer while maintaining fringing electric fields within prescribed safety limits. The fringing fields are reduced using near-field phased-array field-focusing techniques, wherein the adjacent modules of the multi-modular system are out-phased with respect to one another. The inter-module interactions in this multi-modular system can be modeled, and an approach to eliminate these interactions in a practical EV charging environment is provided. To illustrate one example implementation, a prototype 1.2-kW 6.78-MHz 12-cm air-gap multi-modular capacitive WPT system comprising two 600-W modules is provided. This prototype system achieves 21.2 kW/m2 power transfer density and a peak efficiency of 89.8%. This multi-modular system also achieves a fringing field reduction of 50% compared to its individual modules.

Afridi, Khurram K.↗
DOE OSTI2021

UCGretina geant4 simulation of the GRETINA Gamma-Ray Energy Tracking Array

UCGretina, a geant4 simulation of the GRETINA gamma-ray tracking array of highly-segmented high-purity germanium detectors is described. We have developed a model of the array, in particular of the Quad Module and the capsules, that gives good agreement between simulated and measured photopeak efficiencies over a broad range of gamma-ray energies and reproduces the shape of the measured Compton continuum. Both of these features are needed in order to accurately extract gamma-ray yields from spectra collected in in-beam gamma-ray spectroscopy measurements with beams traveling at v / c ≳0.3 at the National Superconducting Cyclotron Laboratory and the Facility for Rare Isotope Beams. In the process of developing the model, we determined that millimeter-scale layers of passive germanium surrounding the active volumes of the simulated crystals must be included in order to reproduce measured photopeak efficiencies. In conclusion, we adopted a simple model of effective passive layers and developed heuristic methods of determining passive-layer thicknesses by comparison of simulations and measurements for a single crystal and for the full array. Prospects for future development of the model are discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Non-additive microbial community responses to environmental complexity

Environmental composition is a major, though poorly understood, determinant of microbiome dynamics. Here we ask whether general principles govern how microbial community growth yield and diversity scale with an increasing number of environmental molecules. By assembling hundreds of synthetic consortia in vitro, we find that growth yield can remain constant or increase in a non-additive manner with environmental complexity. Conversely, taxonomic diversity is often much lower than expected. To better understand these deviations, we formulate metrics for epistatic interactions between environments and use them to compare our results to communities simulated with experimentally-parametrized consumer resource models. We find that key metabolic and ecological factors, including species similarity, degree of specialization, and metabolic interactions, modulate the observed non-additivity and govern the response of communities to combinations of resource pools. Our results demonstrate that environmental complexity alone is not sufficient for maintaining community diversity, and provide practical guidance for designing and controlling microbial ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Jet classification using high-level features from anatomy of top jets

Recent advancements in deep learning models have significantly enhanced jet classification performance by analyzing low-level features (LLFs). However, this approach often leads to less interpretable models, emphasizing the need to understand the decision-making process and to identify the high-level features (HLFs) crucial for explaining jet classification. To address this, we consider the top jet tagging problems and introduce an analysis model (AM) that analyzes selected HLFs designed to capture important features of top jets. Our AM mainly consists of the following three modules: a relation network analyzing two-point energy correlations, mathematical morphology and Minkowski functionals for generalizing jet constituent multiplicities, and a recursive neural network analyzing subjet constituent multiplicity to enhance sensitivity to subjet color charges. We demonstrate that our AM achieves performance comparable to the Particle Transformer (ParT) while requiring fewer computational resources in a comparison of top jet tagging using jets simulated at the hadronic calorimeter angular resolution scale. Furthermore, as a more constrained architecture than ParT, the AM exhibits smaller training uncertainties because of the bias-variance tradeoff. We also compare the information content of AM and ParT by decorrelating the features already learned by AM. Lastly, we briefly comment on the results of AM with finer angular resolution inputs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Depth-resolved thermal conductivity and damage in swift heavy ion irradiated metal oxides

We investigated thermal transport in swift heavy ion (SHI) irradiated insulating single crystalline oxide materials: yttrium aluminum garnet- Y 3 Al 5 O 12 (YAG), sapphire (Al 2 O 3 ), zinc oxide (ZnO) and magnesium oxide (MgO) irradiated by 167 MeV Xe ions at 10 12 – 10 14 ions/cm 2 fluences. Depth profiling of the thermal transport on nano- and micro- meter scales was assessed by time-domain thermoreflectance (TDTR) and modulated thermoreflectance (MTR) methods, respectively. This combination allowed us to isolate the conductivities of different sub-surface damage-regions characterized by their distinct microstructure evolution regimes. Thermal conductivity degradation in SHI irradiated YAG and Al 2 O 3 is attributed to formation of ion tracks and subsequent amorphization, while in ZnO and MgO it is mostly due to point defects. Additionally, notably lower conductivity when probed by very low penetrating thermal waves is consistent with surface hillock formation. An analytical model based on Klemens-Callaway method for thermal conductivity coupled with a simplified microstructure evolution capturing saturation in defect concentration was used to obtain depth dependent damage across the ion impacted region. The studies showed that YAG has the highest damage profile resulting in the less dependence of thermal conductivity with the depth, while MgO on the contrary has the strongest dependence. The presented work sheds new light on how SHI induced defects affect thermal transport degradation and recovery of oxide ceramics as promising candidates for next generation nuclear reactor applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Guided Mode Evolution and Ionization Injection in Meter-Scale Multi-GeV Laser Wakefield Accelerators

Here, we show that multi-GeV laser wakefield electron accelerators in meter-scale, low density hydrodynamic plasma waveguides operate in a new nonlinear propagation regime dominated by sustained beating of lowest order modes of the ponderomotively modified channel; this occurs whether or not the injected pulse is linearly matched to the guide. For a continuously doped gas jet, this emergent mode beating effect leads to axially modulated enhancement of ionization injection and a multi-GeV energy spectrum of multiple quasimonoenergetic peaks; the same process in a locally doped jet produces single multi-GeV peaks with <10% energy spread. A three-stage model of drive laser pulse evolution and ionization injection characterizes the beating effect and explains our experimental results.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Pathways for Nucleation and Growth in Confined Spaces and at Interfaces

Mineral crystallization is central to myriad natural processes from the formation of snowflakes to stalagmites, but the molecular-scale mechanisms are often far more complex than models reflect. Feedbacks between the hydro-, bio-, and geo-spheres drive complex crystallization processes that challenge our ability to observe and quantify them, motivating an expansion of crystallization theories. Here, in this article, we discuss how the driving forces and timescales of nucleation are influenced by factors ranging from simple geometric confinement to distinct interfacial solution structures involving solvent organization, electrical double layers, and surface charging effects. Taken together, these ubiquitous natural phenomena can preserve metastable intermediates, drive precipitation of undersaturated phases, and modulate crystallization in time and space.

biosphere↗
DOE OSTI2020

Evaluating Energy Efficiency Opportunities from Connected and Automated Vehicle Deployments Coupled with Shared Mobility in California

Connected and Automated Vehicles (CAVs) can be considered to be a disruptive transportation technology, with the potential to significantly improve overall transportation system efficiency; however, CAVs may increase induce vehicle miles traveled (VMT) and bring on greater energy consumption. Further, shared mobility is another disruptive transportation event that is reshaping our travel patterns. The primary goal of this project was to extensively collect data from vehicles and associated infrastructure equipped with CAV technologies from both real-world experiments and simulation studies mainly deployed in California, and develop a comprehensive framework for evaluating energy efficiency opportunities from large-scale (e.g., statewide) introduction of CAVs and a wide deployment of shared mobility systems in a variety of scenarios. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance, and energy efficiency, a unique mesoscopic simulation-based model was developed for mobility and energy efficiency evaluation considering these disruptive transportation technologies. As a complement to existing studies on nationwide evaluation of CAVs’ energy impacts, this project was focused on data collection efforts and CAV applications under congested traffic environments that are frequently experienced on a massive scale across the major metropolitan areas in California. Extensive real-world data collection supplemented with simulation studies were conducted to cover a variety of CAV and shared mobility scenarios, particularly on scenarios less-explored in the existing research. Another key component of this project was to consider the interaction between different CAV technologies and shared mobility models, and the compound effect on energy efficiency. A comprehensive modeling suite was developed to quantify the impact of new mobility technologies on travel behavior and traffic performance. The developed modeling framework includes an energy intensity module, mode choice module and activity generation module that are integrated into an agent-based BEAM simulation platform to perform impact analysis based on a variety of scenarios. In addition, the RouteE model has been upgraded to incorporate the impact of CAVs on traffic flow, VMT and energy intensity, using micro-simulation data collected from both freeways and urban arterials. A novel fundamental influencing factor (FIF) mode choice model was developed to link CAV and shared mobility components with travel behaviors, and adapted into the BEAM-centered model framework. A statewide energy inventory was constructed under various CAV technology deployment scenarios by incorporating datasets and models for predicting vehicle market share and vehicle usage, which are tightly associated with the penetration of shared mobility systems. Based applying this modeling suite to a calibrated network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes. The statewide analysis based on the National Household Travel Survey (NHTS) sample data is consistent with the findings from the Riverside network and validate the developed clustering-prediction modeling methodology. The outcomes from this project will help close the knowledge gap on recognizing the potential performance and energy impacts of a broad deployment of CAV and shared mobility technologies across a wide range of roadway infrastructure with varying levels of congestion. Results from this project: 1) will support policymakers in steering CAV development and deployment towards an energy favorable direction; 2) reduce uncertainties in estimating energy saving opportunities from new mobility technologies and services; 3) increase the confidence of CAV technology investors both on the infrastructure side (i.e., transportation agencies) and on the vehicle side (i.e., OEMs); and 4) expedite the deployment of energy-efficient CAV and shared mobility applications.

33 ADVANCED PROPULSION SYSTEMS↗