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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Non-Equilibrium in a Dust-Forming Low-Temperature Plasma: A CARS Study

Dust-forming low-temperature plasmas are versatile systems for the production of nanoparticles with tunable functionalities. While attractive from a materials processing point of view, these systems are inherently complex, with several plasma-induced phenomena determining the properties of the produced materials. Here, we characterize a carbon nanoparticle-forming plasma using coherent anti-Stokes Raman spectroscopy (CARS), with the primary goal of measuring gas temperature. While gas temperature is typically assumed to be at or slightly above room temperature in these reactors, we measure gas temperatures exceeding 1000 K under typical process conditions. We find a correlation between the gas temperature and the nanoparticle yield, suggesting that the particle nucleation and growth process releases energy within the reaction volume, leading to significant gas heating. In addition, we find that the relaxation of vibrationally excited species at the particle surfaces is a major contributor to their heating. In conclusion, these results underscore the complexity of these systems and the need for their more in-depth characterization using advanced techniques such as CARS.

Basic Plasma Phenomena and Gas Discharges↗
DOE OSTI2026

Toward the origins of binding energy shifts and “satellites” formation during plasma-XPS measurements

In-plasma x-ray photoelectron spectroscopy (plasma-XPS) emerges as a powerful platform for real-time, in situ chemical analysis under conditions relevant to semiconductor processing and other plasma-enabled technologies. This study investigates the origins of binding energy (BE) shifts and the formation of “satellite” peaks observed during plasma-XPS measurements across conductive, dielectric, and gas-phase systems. Using a standard laboratory-based ambient pressure XPS apparatus coupled with an alternating current (AC)-driven capacitively coupled plasma source, we demonstrate that metastable surface species, such as transient Au oxides, can be detected during plasma exposure, revealing chemical states that are hardly accessible using conventional ultrahigh vacuum (UHV) XPS. In dielectric samples (e.g., undoped diamond, sapphire), we observe pressure- and plasma-type-dependent BE shifts of more than 50 eV, attributed to x-ray-induced and plasma-mediated surface charging. These shifts are mitigated at higher pressures/plasmas or in electronegative plasmas (e.g., O 2 ), the latter due to enhanced charge compensation mechanisms involving slow negative ions. For gas-phase species, AC-plasma excitation leads to spectral broadening and the emergence of “satellite” peaks with energy separations of a few electron volts, linked to oscillating local plasma potentials in the probing volume. Furthermore, these findings highlight the complex and important interplay between plasma parameters, surface charging, and local electric fields in shaping XPS spectra. Overall, plasma-XPS emerges as a critical metrological tool for probing transient surface chemistry, with implications for semiconductor processing, material synthesis, and plasma diagnostics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Facet-Dependent Adsorption of Pb(II) on Hematite (001), (116), and (104) Surfaces

Hematite’s common (001) and (012) facets are frequently used in model studies of lead (Pb) adsorption behavior, but there is a lack of research on the high-energy facets, e.g. (104), present in nature. Also, few studies have attempted to connect the molecular details of facet-specific Pb adsorption to macroscopic uptake behavior. Here, to address these knowledge gaps, we investigated Pb(II) adsorption behaviors on facet-engineered hematite nanoparticles dominated by (001), (104), and (116). Adsorption experiments revealed significant variations in Pb(II) uptake among the three samples, with (001) demonstrating the highest capacity and (116) showing the best adsorption efficiency when normalized to specific surface area. Adsorption kinetics followed the pseudo-second-order model, indicating the adsorption process is governed mostly by chemisorption. Adsorption isotherms were well fitted by the Langmuir model, indicating uptake proceeds until roughly monolayer adsorption. Detailed characterization revealed Pb(II) was adsorbed as single atoms with complex inner-sphere binding modes that varied across different facets, indicating adsorption is both structurally and energetically facet-dependent. Co-adsorption experiments further demonstrated Cu 2+ , Zn 2+ , and humic acid significantly promoted Pb(II) adsorption. This study advances the understanding of hematite surface reactivity in controlling macroscopic wet adsorption behaviors, providing valuable insights into the environmental fate of Pb(II).

Contaminant removal↗
DOE OSTI2020

First Principles Nonadiabatic Excited-State Molecular Dynamics in NWChem

Computational simulation of non-adiabatic molecular dynamics is an indispensable tool for understanding complex photoinduced processes such as internal conversion, energy transfer, charge separation, and spatial localization of excitons, to name a few. Here, we report an implementation of the fewest-switches surface hopping algorithm in the NWChem computational chemistry program. Here the surface hopping method is combined with linear-response time-dependent density functional theory calculations of adiabatic excited state potential energy surfaces. To treat quantum transitions between arbitrary electronic Born{Oppenheimer states, we have implemented both numerical and analytical differentiation schemes for derivative non-adiabatic couplings. A numerical approach for the time-derivative non-adiabatic couplings together with an analytical method for calculating non-adiabatic coupling vectors is an efficient combination for surface hopping approaches. Additionally, electronic decoherence schemes and a state reassigned unavoided crossings algorithm are also implemented to improve the accuracy of the simulated dynamics and to handle trivial unavoided crossings. We apply our code to study the ultrafast decay of photoexcited benzene, including a detailed analysis of the potential energy surface, population decay time scales, and vibrational coordinates coupled to the excitation dynamics. The development presented in this work is a baseline for future implementations of more sophisticated frameworks for simulating of non-adiabatic molecular dynamics in NWChem.

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

Aliovalent Doping of CeO 2 Improves the Stability of Atomically Dispersed Pt

Atomically dispersed supported catalysts hold considerable promise as catalytic materials. The ability to employ and stabilize them against aggregation in complex process environments remains a key challenge to the elusive goal of 100% atom utilization in catalysis. In this work, using a Gd-doped ceria support for atomically dispersed surface Pt atoms, we establish how the combined effects of aliovalent doping and oxygen vacancy generation provide dynamic mechanisms that serve to enhance the stability of supported single atom configurations. Using correlated, in situ X-ray absorption, photoelectron and vibrational spectroscopies for analysis of the samples on the two types of support (with and without Gd doping), we establish that the Pt atoms locate proximal to Gd dopants, forming a speciation that serves to enhance the thermal stability of Pt atoms against aggregation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Convection-Permitting Ensembles of an Isolated Mountain Thunderstorm during RELAMPAGO/CACTI

Abstract The north–south-oriented Sierras de Córdoba (SDC) ridge in central Argentina is noted for initiating thunderstorms that may grow into intense mesoscale convective systems (MCSs). It also initiates more isolated, shorter-lived cells under weaker synoptic forcing. These cells are less impactful than MCSs but may be difficult to predict in convective-scale numerical weather prediction (NWP) due to their strong sensitivities to subgrid and partially resolved processes. To study the mechanisms and predictability of such cells, convection-permitting ensemble simulations were conducted of an isolated, diurnally forced SDC thunderstorm during Cloud, Aerosol, and Complex Terrain Interactions (CACTI)/Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO). The rich observational data facilitated detailed ensemble verification, where dry biases in the surface energy balance and soil moisture were identified. These biases promoted rapid removal of convective inhibition and an early onset of precipitating cells over the SDC that were shallower and weaker than the observed cell. Correction, and then overcorrection, of the soil moisture bias in two successive ensembles was required to rectify the surface energy balance and improve the representation of the SDC cell. Nevertheless, substantial ensemble variability in convective precipitation was found, with some members producing more widespread convection than observed and others producing no deep convection at all. This variability was largely explained by a combination of thermodynamic and dynamic mechanisms, dominated by a positive sensitivity of convective precipitation to preconvective moist instability over the ridge. Secondary sensitivities were found to low-level upward mass flux and midlevel cross-barrier winds, the latter of which caused gravity waves with elevated downdrafts that tended to suppress incipient clouds.

Lopez, Andres [Department of Atmospheric and Ocean↗
DOE OSTI2024

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the “black-box” nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having “no change” (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Transformation of Ferrihydrite to Goethite and the Fate of Plutonium

Understanding the interactions between plutonium and iron (oxy)hydroxide minerals is necessary to gain a predictive understanding of plutonium environmental mobility and to evaluate long-term performance of nuclear waste repositories. We investigated the fate of plutonium during the formation of ferrihydrite and its subsequent transformation into goethite. Ferrihydrite was synthesized with varying quantities of Pu(IV) following either a sorption or coprecipitation process; the ferrihydrite was then aged hydrothermally to yield goethite. Additionally, the synthesized materials were characterized via extended X-ray absorption fine structure spectroscopy, transmission electron microscopy, and acid leaching to elucidate the nature of plutonium association with ferrihydrite and goethite. In samples prepared following the sorption method, plutonium was identified in two different forms: a PuO 2 precipitate and a surface-sorbed plutonium complex. For the samples prepared via coprecipitation, no PuO 2 formation occurs in the ferrihydrite precursor and in the goethite experiments where plutonium concentration is ≤1000 ppm (mg kg –1 ). In these coprecipitation products, plutonium is strongly bound to the minerals either via formation of an inner sphere complex, or via an incorporation process. In the coprecipitation experiments, PuO 2 formation only occurs at the highest plutonium concentration (3000 ppm), suggesting that during ferrihydrite transformation into goethite, part of the plutonium can be remobilized to form PuO 2 nanoparticles. Collectively, our results demonstrate that the nature of plutonium associated with the precursor ferrihydrite (adsorbed vs coprecipitated) will have a direct impact on the association of plutonium with its alteration product (goethite). Furthermore, the data illustrate that some properties of plutonium association with the precursor ferrihydrite are retained through the transformation into goethite. These findings show that plutonium strongly associates with iron (oxy)hydroxides formed through coprecipitation processes and in these materials, plutonium can be strongly retained by the iron minerals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Online thermal profile prediction for large format additive manufacturing: A hybrid CNN-LSTM based approach

Large format additive manufacturing (LFAM) is an advanced 3D printing technique that efficiently fabricates large-scale components through a layer-by-layer extrusion and deposition process. Accurate surface layer temperature monitoring is essential to prevent manufacturing failures and ensure final product quality. Traditional physics-based offline approaches for simulating thermal behavior are often inefficient and complex, posing challenges on real-time, in-situ monitoring. Here, to address this, we propose a data-driven hybrid CNN-LSTM model to predict sequential thermal images of arbitrary length using real-time infrared thermal imaging. In this approach, a Convolutional Neural Networks (CNN) is trained offline to capture spatial features, reduce dimensional complexity, and enhance time efficiency, while a stacked Long Short-Term Memory (LSTM) is applied online to capture temporal information for improved prediction of future thermal behavior in subsequent printing layers. Model performance is evaluated using MSE, SSIM, and PSNR metrics and is benchmarked against stacked LSTM and convolutional LSTM models, demonstrating superior accuracy and applicability. Additionally, to mitigate noise from moving extruders and gantry backgrounds in thermal images, a fine-tuned semantic segmentation model is implemented offline to extract printing geometry, enabling precise temperature tracking along the tool path for further thermal analysis. The frameworks developed in this study significantly advance temperature monitoring, thermal analysis, and in-situ manufacturing control for LFAM, bridging the gap between theoretical modeling and practical application.

Geometry extraction↗
DOE OSTI2020

Effect of Cr(III) Adsorption on the Dissolution of Boehmite Nanoparticles in Caustic Solution

Incorporation of relatively minor impurity metals onto metal (oxy)hydroxides can strongly impact solubility. In complex highly alkaline multicomponent tank waste such as that at the Hanford Nuclear Reservation, tests indicate that the surface area-normalized dissolution rate of boehmite ($γ$-AlOOH) nanomaterials is at least an order of magnitude lower than that predicted for the pure phase. In this work we examine the dissolution kinetics of boehmite coated by adsorbed Cr(III), which adheres at saturation coverages as sparse chemisorbed monolayer clusters. Using 40 nm boehmite nanoplates as a model system, temperature-dependent dissolution rates of pure versus Cr(III)-adsorbed boehmite showed that the initial rate for the latter is consistently several times lower, with an apparent activation energy 16 kJ·mol -1 higher. Although the surface coverage is only around 50%, solution analytics coupled to multimethod solids characterization reveals a phyicochemical armoring effect by adsorbed Cr(III) that substantially reduces the number of dissolution-active sites on particle surfaces. Such findings could help improve kinetics models of boehmite and/or metal ion adsorbed boehmite nanomaterials, ultimately providing a stronger foundation for the development of more robust complex radioactive liquid waste processing strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Fabrication of bulk alumina structures with humidity sensing capabilities using direct ink write technique

Purpose The purpose of this study is to evaluate different 3D structures for humidity sensing that will enable the fabrication of complex geometries with high moisture sensitivity. Design/methodology/approach Humidity sensors based on alumina ceramics were fabricated using direct ink write (DIW) technique. Different engineered surface area, polymer binder ratio and post-processing treatment were considered to increase moisture sensitivity. Findings It was found that the binder ratio plays an important role in controlling the rheology of the paste during printing and determining the pore size after post-processing treatment. The sensibility of the fabricated humidity sensor was investigated by measuring its capacitance response toward relative humidity (RH) varying from 40% to 90% RH at 25°C. It is shown that using 3D lattice design, printed alumina humidity sensor could improve sensitivity up to 31.6 pF/RH%, over an order of magnitude higher than solid alumina. Originality/value Most of the alumina humidity sensors available are films in nature because of manufacturing difficulties, which limited its potential of higher sensitivity, and thus broader applications. In this paper, a novel 3D alumina humidity sensor was fabricated using DIW 3D printing technology.

Engineering↗
DOE OSTI2023

A Novel Method for Diagnosing Land–Atmosphere Coupling Sensitivity in a Single-Column Model

The response of boundary layer properties and cloudiness to changes in surface evaporative fraction (EF) is investigated in a single-column model to quantify the locally coupled impact of subgrid surface variations on the atmosphere during summer. Sensitive coupling days are defined when the model atmosphere exhibits large variations across a range of EFs centered on the analyzed value. Coupling sensitivity exists as both positive feedback (cloudiness increases with EF) and negative feedback (clouds increase with decreasing EF) regimes. The positive regime manifests in shallow convection situations, which are capped by a strengthened inversion and subsidence, restricting the vertical extent of convection to just above the boundary layer. Surfaces with larger EF (greater surface latent heat flux) can inject more moisture into the vertically confined system, lowering the cloud base and an increasing cloud liquid water path (LWP). Negative feedback regimes tend to manifest when large-scale deep convection, such as from mesoscale convective systems and fronts, is advected through the domain, where convection strengthens over surfaces with a lower EF (greater surface sensible heat flux). The invigoration of these systems by the land surface leads to an increase in LWP through strengthened updrafts and stronger coupling between the boundary layer and the free atmosphere. These results apply in the absence of heterogeneity-induced mesoscale circulations, providing a one-dimensional dynamical perspective on the effect of surface heterogeneity. Finally, this study provides a framework of intermediate complexity, lying between parcel theory and high-resolution coupled land–atmosphere modeling, and therefore isolates the relevant first-order processes in land–atmosphere interactions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the “black-box” nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having “no change” (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

58 GEOSCIENCES↗
DOE OSTI2021

Data for: Spatial access and resource limitations control carbon mineralization in soils

This dataset contains data and code used for the paper "Spatial access and resource limitations control carbon mineralization in soils", https://doi.org/10.1016/j.soilbio.2021.108427. Core-scale soil carbon fluxes are ultimately regulated by pore-scale dynamics of substrate availability and microbial access. These are constrained by physicochemical and biochemical phenomena (e.g. spatial access and hydrologic connectivity, physical occlusion, adsorption-desorption with mineral surfaces, nutrient and resource limitations). We conducted an experiment to determine how spatial access and resource limitations influence core-scale water-soluble soil organic matter (SOM) mineralization, and how these are regulated by antecedent moisture conditions. Intact soil cores were incubated at field-moist vs. drought conditions, after which they were saturated from above (to simulate precipitation) or below (to simulate groundwater recharge). Soluble carbon (acetate) and nitrogen (nitrate) forms were added to some cores during the rewetting process to alleviate potential nutrient limitations. Soil respiration was measured during the incubation, after which pore water was extracted from the saturated soils and analyzed for water soluble organic carbon concentrations and characterization. Our results showed that carbon (C) amendments increased the cumulative carbon dioxide (CO2) evolved from the soil cores, suggesting that the soils were C-limited. Drought and rewetting increased soil respiration, and there was a greater abundance of complex aromatic molecules in pore waters sampled from these soils. This newly available substrate appeared to alleviate nutrient limitations on respiration, because there were no further respiration increases with subsequent C and N amendments. We had hypothesized that respiration would be influenced by wetting direction, as simulated precipitation would mobilize C from the surface. However, as a main effect, this response was seen only in the C-amended soils, indicating that surface-C may not have been bioavailable. At the pore scale (pore water samples), drought and the C, N amendments caused a net loss of identified molecules when the soils were rewet from below, whereas wetting from above caused a net increase in identified molecules, suggesting that fresh inputs stimulated the C-and N-limited microbial populations present deeper in the soil profile. Our experiment highlights the complex and interactive role of antecedent moisture conditions, wetting direction, and resource limitations in driving core-scale C fluxes.This dataset contains a compressed (.zip) archive of the data and R scripts used for this manuscript. The dataset includes files in .csv format, which can be accessed and processed using MS Excel or R. This archive can also be accessed on GitHub at https://github.com/kaizadp/TES_spatial_access_2021 (DOI: 10.5281/zenodo.5522938).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Responses of Boreal Forest Ecosystems and Permafrost to Climate Change and Disturbances: A Modeling Perspective

The north circumpolar region contains a large amount of carbon. This carbon storage is vulnerable due to permafrost degradation and wildfire disturbances under ongoing and projected climate change. Climate warming and wildfires change soil organic horizons gradually or abruptly, and modify permafrost thermal-hydrology and biogeochemistry, ecosystem structures, functions, and capability of sequestrating rising atmospheric CO2. Land models do not fully take accounts of these interactions and its complexity in the high latitude. This chapter describes a terrestrial ecosystem model with dynamic organic soil module (DOS-TEM) and its unique freezing-thawing algorithm, and presents key results of its applications mainly in boreal forests of Alaska. The DOS-TEM explicitly considers interactions of soil thermal and hydrological processes, permafrost degradation and the direct and indirect effects of wildfire disturbances, in addition to soil–plant C and N cycles. We first introduce four modules of DOS-TEM, focusing on its disturbance module and coupling with a dynamic organic soil module. Then we describe and validate DOS-TEM’s freezing-thawing algorithm and development based on two-directional Stefan algorithm (TDSA). Finally, we apply the DOS-TEM at site and region scales, with a focus on model ability to dynamically simulate soil organic thickness under warming and wildfires, and consequent impacts on permafrost in the Yukon River Basin. We conclude that land surface model development is urgently needed to include other critical landscape processes, such as thermalkarst and other disturbances, to synchronize thermal-hydrological-biogeochemical processes, and to incorporate an advanced understanding of biospheric feedbacks to atmosphere and ecosystems. Such a complexity of modeling scope is plausible with advancement of high performance computing.

Yi, Shuhua↗
DOE OSTI2023

Development and Characterization of Electrodes for Surface-Specific Attenuated Total Reflection Two-Dimensional Infrared Spectroelectrochemistry

Electrochemical interfaces still have remaining mysteries surrounding the interfacial region of the electrical double layer, despite being prevalent throughout the energy and water remediation industries. The electrical double layer is where many important dynamic processes such as catalysis and electron transfer occur. The goal of this work is to study the electrical double layer with two-dimensional infrared (2D IR) spectroscopy to experimentally access the details of the structural dynamics of this complex environment. However, there are several experimental challenges to applying 2D IR spectroscopy to this application, such as assuring the surface specificity of the spectrum, optimizing the signal strength while minimizing spectral distortions from dispersion and Fano line shapes, and selecting electrode materials that are both sufficiently IR compatible and conductive. Here we will discuss various considerations when designing 2D IR experiments of electrode interfaces utilizing several substrates and experimental configurations and demonstrate a robust method for 2D IR experiments of electrode interfaces under applied potential that combines nonconducting Si ATR wafers with conductive ITO and thin nanostructured films of plasmonically active Au functionalized with 3-mercapto-2-butanone (MCB). We show that layered electrodes on thin Si ATR wafers with MCB are sensitive to applied potential and that the distortions in the linear and 2D IR spectra are heavily dependent on the morphology of the Au surface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Exploring the impact of surface topography on Rayleigh-Bénard dry convection in the Pi cloud chamber using OpenFOAM: In cylindrical and rectangular geometries

The Pi convection-cloud chamber can generate steady-state turbulence in both rectangular and cylindrical shapes via Rayleigh-Bénard convection (RBC) by maintaining warm bottom and cold top surfaces. Although most experiments in the Pi chamber were conducted in cylindrical shapes, all previous Pi chamber simulations were conducted in a rectangular shape due to the limitations of those models to discretize a cylindrical domain when using the finite difference method therein. Here, we use OpenFOAM, an open-source finite-volume-based Computational Fluid Dynamics (CFD) software package, to conduct Large-Eddy Simulation (LES) of dry RBC in the Pi chamber at high Rayleigh numbers (10 8 to 10 9 ). Results show that large-scale circulation (LSC) direction varies in the chamber with a constant side wall temperature. Imposing a slight temperature imbalance at the side wall ranging from 0.1 to 0.7 degrees can lock the LSC, aligning better with Pi chamber observations, particularly at higher Rayleigh numbers. In addition, we examine the impact of surface topography on LSC and heat transfer in RBC systems within cylindrical and rectangular shapes under varying conditions. Results show that roughing top/bottom surfaces by adding bars of a few tens millimeters height can strengthen thermal plumes and enhance temperature fluctuations in the chamber. Furthermore, we observe that different bar height configurations lead to notable changes in LSC orientation and thermal stratification, highlighting the complex interactions between surface features and convection patterns. This finding highlights how surface topography and chamber geometry affect Rayleigh-Bénard convection, improving understanding of turbulent heat transfer and atmospheric boundary-layer processes. Direct Numerical Simulations (DNS) are also conducted to validate LES results. In conclusion, while LES effectively captures qualitative behaviors seen in DNS, it tends to underestimate velocity variances near walls, illustrating a trade-off between computational efficiency and accuracy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Structure of the Ecuadorian Upper Plate From a Joint Seismic‐Gravity Inversion

The Ecuadorian portion of the South American subduction zone presents an interesting case study in the structure and complex evolution of an upper plate. There are outstanding questions about its tectonic history, composition, and magmatic processes. While previous studies have employed ambient noise tomography to image the Ecuadorian upper plate, surface wave inversions alone often lack sensitivity at relevant shallow depths. This limitation can be overcome with an independent, complementary data set, such as gravity. We have jointly inverted Rayleigh wave phase velocities and Bouguer gravity anomalies to provide a more detailed seismic velocity model of the Ecuadorian upper plate. Our joint inversion has yielded several key improvements from previous models. First, we observe much shallower slow velocities beneath major basins (the Manabí, Progreso, and Gulf of Guayaquil), better aligning with expected basin structure. Second, we identify a high-velocity block beneath the entire forearc, corresponding to the Piñon Terrane, with velocities suggesting the presence of ultramafic material. Third, we highlight a new narrow swath of slow velocities beneath the Ecuadorian Andes, which closely follows the active volcanoes along the Eastern Cordillera. The extent of these slow velocities coincides with the termination of active arc volcanism and the predicted location of the subducted Carnegie Ridge. The predicted compositions for the mid to lower crust in the region preclude a purely compositional explanation for these velocities, suggesting that some level of partial melt is necessary.

Birkey, Andrew [Univ. of Delaware, Newark, DE (Uni↗