Search NASA⌕ Search

SEARCH · Search NASA

Results for “BOUNDARY VALUE”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Turbulence-Resolving Simulations of Hurricane Laura (2020): Insights Into Extreme Winds and Eyewall Turbulence

Extreme weather events pose significant risks to coastal and offshore energy infrastructure. In this work, we investigate the structure of mean winds and turbulence near the surface ( z < 300 m) that occurred during Hurricane Laura in 2020 on the US Gulf Coast. To this end, we perform turbulence-resolving simulations (..delta..x = 33.33 m) that encompass the entire eyewall of Hurricane Laura by introducing a novel method that we will refer to as Moving-Mesoscale to Static-LES Integrated Coupling (MOSAIC). The simulation results are validated against surface, boundary-layer, and mid-tropospheric observations. Our analysis of the extreme wind conditions near the surface indicates that the mean wind and turbulence profiles vary greatly at and near the eyewall, resulting in extreme values of mean wind speed (U > 50 m * s-1) and turbulence kinetic energy (k ~ 50 m2 * s-2) at altitudes above 50 m . To investigate in detail the nature of turbulence within the eyewall of the storm, we conduct higher-resolution simulations (..delta..x = 11.11 m) of a portion of the hurricane. We provide a comprehensive statistical description of turbulence in the eyewall region, highlighting the need to employ ..delta..x ~ 10 m grid spacing to capture the integral length-scales near the surface, second- and higher-order central moments (i.e., skewness and kurtosis), and spectral coherence in the flow accurately. These numerical simulations provide the most detailed description of mean winds and turbulent conditions within the eyewall of a historical tropical cyclone to date, illustrating how high-resolution simulations can enhance limited turbulence measurements in hurricanes and giving insight into the extreme conditions threatening offshore and coastal infrastructure.

17 WIND ENERGY↗
DOE OSTI2025

Factors influencing water quality in surface water and alluvial groundwaters downgradient of a reclaimed surface coal mine in the Powder River Basin of southeastern Montana, USA

Abstract Coal mining and reclamation can have a profound influence on hydrogeologic systems, with clear consequences for groundwater quality, yet their long-term influence on downgradient water quality over time following reclamation is less well documented. Geochemical trends were evaluated in water quality downgradient of a fully reclaimed landscape at the former Big Sky Mine in the Rosebud Creek watershed (southeastern Montana, USA), over a 3-year period (2020–2022), including bond release in 2022. Within 6 km downgradient from the reclaimed area, sulfate concentrations decreased from approximately 3500 to 1800 mg l −1 within the Miller Coulee alluvial aquifer. Major ions, δ 34 S SO4 values, and residence time tracers suggest that the observed decreases in sulfate concentration result from a combination of dilution by mixed-age inflows and incomplete transit of the high salinity plume from the mine boundary. Both bedrock and alluvial aquifers of the Rosebud Creek corridor contained contributions of millennia-old regional groundwater, which may serve to mitigate mine-derived high salinity waters. Rosebud Creek, which traverses the outflow zone of Miller Coulee in the study area, exhibited high sulfate concentrations during low flows and consistent downgradient increases in sulfate concentration. The possibility of plume dynamics in Miller Coulee suggests that the greatest water quality impacts may not yet have reached Rosebud Creek.

Keeshin, Skye I. (ORCID:0009000986449872)↗
DOE OSTI2026

Results of the second Ice Shelf–Ocean Model Intercomparison Project (ISOMIP+)

Ocean-driven basal melting of Antarctic ice shelves plays an important role in the mass loss of the Antarctic Ice Sheet. Ice shelf cavity-resolving ocean models are a valuable tool for understanding ice shelf-ocean interactions and for simulating projections of ice shelf and ocean states under future climate. Designed to assess the current state of ice shelf–ocean modelling, the second Ice Shelf–Ocean Model Intercomparison Project, ISOMIP+, consists of 12 ocean model configurations submitted with a common, idealised experimental setup. Here, we focus on the experiments Ocean0–2 (Asay-Davis et al., 2016), which are ocean models with idealised, static ice shelf geometries, but where the ocean reaches a balance with prescribed far-field ocean conditions. Different thermal transfer coefficient values (ranging from 0.011 to 0.2) are used for each model in the melting parameterisation to achieve a common, tuned melt rate since the models cover a range of types of vertical coordinates, ice–ocean boundary layer treatments, and numerical schemes. These model differences lead to spread in the resultant ocean properties, circulation, boundary-layer structure and spatial distribution of melting. We also highlight similarities between models, such as a shared linear relationship across most models between melt rate and overturning and barotropic streamfunctions during the spin-up and spin-down, demonstrating a robust relationship between melt and circulation across models and forcing conditions. The ISOMIP+ results provide a systematic comparison of ice shelf cavity-capable ocean models. However, we also demonstrate the need for realistic ice shelf–ocean model intercomparison projects (some already underway) to assess model biases and inter-model variation against sparse observations. Further research is needed to understand the differences between models and further improve our modelled representations of the ice–ocean boundary layer and ice shelf cavity circulation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Non-conformal interface-cohesive modeling with the shifted boundary method

The accurate simulation of boundary- and interface-dominated problems on complex geometries remains challenging when boundary- or interface-fitted meshes are difficult to generate, particularly for curved boundaries, polycrystalline microstructures, and dense interface networks. The Shifted Boundary Method (SBM) alleviates this meshing burden by shifting the enforcement of boundary conditions from the true boundary to a nearby surrogate boundary and recovering the effect of the true boundary through geometric correction terms, thereby enabling standard finite element spaces on non-boundary-fitted meshes. In this report, we develop a general shiftedboundary and shifted-interface framework within the open-source MOOSE framework. We first present a general SBM implementation for complex geometries on non-boundary-fitted meshes. We then adopt the Shifted Interface Method (SIM) for internal interfaces and develop a unified shifted-interface treatment in which the interface law is enforced on a surrogate interface and the effect of the true interface is recovered through shifted jumps, fluxes, and tractions. This perspective brings scalar thermal-contact and vector-valued cohesive-zone mechanics into a single framework, the latter realized as the Shifted Cohesive Zone Method (SCZM) and coupled with history-dependent constitutive models from NEML2. We further extend the MOOSE mesh infrastructure to support cohesive-zone calculations on distributed meshes. The framework is verified and demonstrated through three progressive studies: Poisson’s equation on a smoothed starshaped domain, a manufactured thermal-contact problem on a non-interface-fitted mesh, and a two-dimensional polycrystalline representative volume element combining crystal plasticity with cohesive grain-boundary interfaces. Across these studies, the shifted formulations reproduce boundary- and interface-fitted reference solutions with high fidelity, indicating that the proposed framework provides an accurate and efficient route to boundary- and interface-dominated simulations on arbitrary geometries without requiring fitted meshes.

Yang, Cheng-Hau↗
DOE OSTI2026

Investigating ultrafine aerosol turbulent fluxes during atmospheric new particle formation events

New particle formation (NPF) is an important source of atmospheric aerosols, yet quantitatively predicting its occurrence remains challenging, partly because ground-based measurements cannot capture NPF accurately if they occur in the upper atmosphere. While observations have confirmed the presence of new particles near the top of the planetary boundary layer (PBL), their origins and the direction of their vertical transport have remained ambiguous. Here, we propose and validate an analytical framework using airborne eddy-covariance (EC) and continuous wavelet transform (CWT) to directly quantify the vertical flux of newly formed particles and determine its dominant vertical transportation direction. Analyzing data from a dedicated airborne campaign over the Southern Great Plains, we observed a persistent and strong downward particle flux during NPF events, with a mean value of 133.8 cm?3 m s?¹ downward flux in the entrainment zone, whereas fluxes on non-NPF days were negligible. Our spectral analysis further confirms that these directional fluxes can be reliably captured using standard 1 Hz aerosol instrumentation. These findings suggest that new particle formation, driven by the entrainment of air from the overlying residual/stable layer during PBL growth, is a significant and potentially understudied source of boundary layer aerosols. The framework presented here provides a methodology to correctly attribute NPF events to specific altitudes, thereby improving the mechanistic understanding required for accurate atmospheric models.

Zhang, Ruoyu↗
DOE OSTI2025

Rapid spatial analysis of surrogate TRISO fuel particles using laser-induced breakdown spectroscopy image analysis

Laser-induced breakdown spectroscopy (LIBS) mapping enables rapid elemental and spatial analysis of solid samples. In this work, surrogate TRi-structural ISOtropic (TRISO) particles with various layers consisting of Zr, W, C, and Si were used to demonstrate the use of a new thickness measurement tool developed to analyze elemental images generated from LIBS maps. Zr particles with varying outer-layer thicknesses ranging from 16 to 32 μm were measured with scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS) and LIBS with both a complementary metal-oxide-semiconductor (CMOS) detector and an intensified charged coupled device (ICCD) detector. LIBS maps of particles were completed using CMOS and ICCD spectrometers with effective spatial resolutions of 4 and 2 μm, respectively. The novel thickness measurement tool identified layer regions within a LIBS map and then identified the locations of the boundaries between these layers. The tool then generated up to 1000 random profiles stemming radially from the center region, which were used to measure the layer thickness/radius. This image analysis tool demonstrated LIBS's ability to provide values comparable with SEM-EDS (3.7% relative difference) along with a 95% reduction in measurement time. Furthermore, the precision of these measurements was on par with the SEM-EDS measurements at <15% relative standard deviation. Following the analysis of the Zr particles, W particles with increased complexity (e.g., five versus three layers) were analyzed using both the CMOS and ICCD spectrometers simultaneously. The spatial dimensions were extracted with an average relative difference of 2.7% and an average relative standard deviation of 9%.

Andrews, Hunter B. [Oak Ridge National Laboratory ↗
DOE OSTI2025

Numerical case study of the aerosol–cloud interactions in warm boundary layer clouds over the eastern North Atlantic with an interactive chemistry module

The presence of warm boundary layer stratiform clouds over the eastern North Atlantic (ENA) region is commonly influenced by the Azores High, especially during the summer season. To investigate comprehensive aerosol–cloud interactions, this study employs the Weather Research and Forecasting model coupled with a chemistry component (WRF-Chem), incorporating aerosol chemical components that are relevant to the formation of cloud condensation nuclei (CCN) and accounting for aerosol spatiotemporal variation. This study focuses on aerosol indirect effects, particularly the long-range transport of aerosols, in the ENA region under three different weather regimes: a ridge with a surface high-pressure system, a post-trough with a surface high-pressure system, and a weak trough. The WRF-Chem simulations conducted at a near-large-eddy scale offer valuable insights into the model's performance, especially in terms of its ability to use high spatial resolution to capture mesoscale cloud features across various weather regimes. Our result shows that introducing 5 times more aerosols to either non-precipitating or precipitating clouds significantly increases ambient CCN numbers, resulting in, to varying degrees, higher liquid water path (LWP) values. The substantial aerosol–cloud interaction especially occurs in the precipitating clouds and demonstrates the susceptibility of the LWP to changes in CCN under different regimes. Conversely, thin, non-rain clouds at the edges of a cloud system are prone to evaporation, exhibiting an aerosol drying effect. The aerosols released during this process transition back to the accumulation mode, facilitating future activation. This dynamic behavior is not adequately represented in prescribed-aerosol simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Heuristic predictions of RMP configurations for ELM suppression in ITER burning plasmas and their impact on divertor performance

Abstract A subspace of resonant magnetic perturbation (RMP) configurations for edge localized mode (ELM) suppression is predicted for H-mode burning plasmas at 15 MA current and 5.3 T magnetic field in ITER. Perturbations to the core plasma can be reduced by a factor of 2 for equivalent edge stability proxies, while the perturbed plasma boundary geometry remains mostly resilient. The striated domain of perturbed field lines connecting from the main plasma (normalized poloidal flux < 1 ) to the divertor targets is found to be significantly larger than the expected heat load width in the absence of RMPs. This facilitates heat load spreading with peak values at an acceptable level below 10 MW m − 2 on the outer target already at moderate gas fueling and low Ne seeding for additional radiative dissipation of the 100 MW of power into the scrape-off layer (SOL). On the inner target, however, re-attachment is predicted away from the equilibrium strike point due to increased upstream heat flux, higher downstream temperature and less efficient impurity radiation.

3D plasma boundary modeling↗
DOE OSTI2025

Informed unsupervised machine learning analysis of dislocation microstructure from high-resolution differential aperture X-ray structural microscopy data

This study leverages high-resolution differential-aperture X-ray structural microscopy (DAXM) to probe the local dislocation structure in deformed 304L-stainless steel at small strain, by measuring the lattice rotation and deviatoric elastic strain with a sub-micron resolution. For a single grain in a polycrystalline specimen, the measured lattice rotation field over the measured volume exhibited a multimodal distribution while the deviatoric elastic strain showed a single-mode distribution. An unsupervised Cauchy mixture machine learning model was developed to resolve the multimodal distribution of the lattice rotation. By mapping the lattice rotation data associated with each Cauchy peak in the model back onto the measured volume, we identify contiguous regions of the crystal rotated near the average values corresponding to the peaks of the overall rotation distribution. These regions represent the grain subdivision in the microstructure. Finally, the dislocation density tensor was also computed and its norm was laid over the rotation field to detect the subgrain boundaries. This step provided a validation of the Cauchy mixture model for the analysis of the lattice rotation distribution. The current study highlights the integration of advanced X-ray microscopy techniques with data-driven analysis methods to uncover detailed microstructure scales in deformed crystals.

Machine learning; Lattice rotation; High-energy X-↗
DOE OSTI2024

A Modeling framework for flocculated cohesive sediment transport in the current bottom boundary layer

Cohesive sediment transport, where its settling velocity is controlled by the flocculation process, is a crucial component in determining biochemical cycles, fate of pollutants, and morphodynamics in many aquatic ecosystems. In this study, a modeling framework is presented to investigate how flocculation influences cohesive sediment transport in the current bottom boundary layer in dilute conditions, consistent with the calibration range of the flocculation model. From a local analysis of floc dynamics in homogenous turbulence, we identify that the floc size distribution is mainly controlled by floc cohesion and yield strength. The uncertainty in fractal dimension plays a minor role for the floc size but it influences the resulting floc density and settling velocity. The transport analysis in the current boundary layer shows that the flocculation process alters the vertical distribution of the settling velocity and hence the sediment concentration with a strong dependence on cohesion, floc yield strength, and floc structure. When the flocs are more susceptible to breaking, a well-mixed concentration profile is obtained. In contrast, for flocs with higher cohesion or yield strength, higher concentration with a sharp gradient is observed close to the bed. Overall, the settling velocity exhibits a low vertical variability within 20 % of the depth-averaged value except near the bed. Further, this suggests that using a depth-averaged settling velocity yields acceptable predictions of the sediment concentration profiles, especially for flocs with lower cohesion.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Extracted Water Induces Concentration Fluctuations in Model Ternary Liquid–Liquid Extraction System

Organic phase aggregation and phase splitting are two important (and connected) phenomena in liquid–liquid extraction of metal ions. Previously, we demonstrated how structure in binary mixtures of extractant and diluent originates from concentration fluctuations associated with the so-called “third-phase formation” liquid–liquid phase instability. For more complex systems, we sought to understand how extracted aqueous components affect these concentration fluctuations. Here, in this study, we use small-angle X-ray scattering and molecular dynamics simulations to investigate organic phase structural changes upon contact with water in a model ternary system of water/alkane/extractant. Critical exponents were determined from scattering with temperature variation and were consistent with mean field values. Then, by controlling the water content in the organic phase through direct dissolution of fixed quantities at constant temperature, we relate the observed increase in the fluctuation length scale to changes to the spinodal temperature of the third-phase formation phase boundary. We find a roughly linear relationship between water concentration and the spinodal temperature and a similar slope in that relationship for different linear alkane diluents. This suggests that the mechanism by which water extraction impacts the organic phase structure is to enhance concentration fluctuations through the introduction of additional polar extractant-water complexes, which increases the spinodal temperature and thereby decreases the reduced temperature, i.e., the distance to the instability. This explanation for how solutes induce aggregation in liquid–liquid extraction organic phases may extend to more complex systems, explaining the large increases in correlation lengths reported under high acid and metal loading.

Bonnett, Brittany L. [Argonne National Laboratory ↗
DOE OSTI2025

Quantifying Size Effects on Thermal Transport in CsPbBr 3 Nanocrystal Films

Colloidal lead halide perovskite nanocrystals (LHP NCs) are promising semiconductor materials for optoelectronic applications due to their strong quantum confinement, near-unity photoluminescence quantum yields, and tunable emission characteristics. However, their modest thermal stability remains a challenge, particularly at smaller core diameters due to enhanced phonon scattering at inorganic core-organic ligand interfaces. In this work, we directly quantify size-dependent thermal conductivity (κ) in lecithin-capped CsPbBr 3 NC thin films using a transducer-free, vibrational pump–visible probe (VPVP) spectroscopy technique. A mid-infrared pump thermally excites the ligand shell, while a broadband probe tracks transient reflectance change correlated to lattice temperature decay. Finite-element modeling of the decay dynamics yields κ values from 0.13 to 0.16 W·m –1 ·K –1 for NC films with sub-10 nm core diameter, significantly lower than those of its bulk counterpart. A steep κ suppression with decreasing NC size emphasizes the dominant role of ligand shells and boundary effects in thermal transport.

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

Convective shells in the interior of Cepheid variable stars: Overshooting models based on hydrodynamic simulations

Context. Because Cepheid variable stars have long been used as a cosmic benchmark for scaling distances in our Galaxy and beyond, the accuracy of stellar evolution models for Cepheids have wide-reaching effects. However, our understanding of the dynamics in the interiors of these physically complex stars is limited. Aims. Our goal is to provide a detailed multi-dimensional picture of hydrodynamic convection and convective boundary mixing in the interior of Cepheids. Methods. Using the Modules for Experiments in Stellar Astrophysics (MESA), we studied the structure of intermediate-mass stars that cross the instability strip. Then, we performed two-dimensional hydrodynamic simulations of six stars with the fully compressible Multidimensional Stellar Implicit Code (MUSIC). Our simulations did not model the radial pulsations but focused on the interior structure of this family of stars. We developed and applied a new statistical analysis to examine convection and convective boundary mixing in the interior of these stellar simulations. Results. Based on a grid of MESA models, we demonstrated that a common structure for intermediate mass Cepheids includes an interior convective shell as well as a thin outer convective envelope. Using the extreme value theory approach to analyze our MUSIC simulation data, we found that overshooting above the convective shell fills the space between these convectively unstable layers. We developed a new statistical analysis that provides a clearer picture of how overshooting fills this layer; it also allowed us to formulate a detailed comparison between overshooting above and below the convective shell. Our analysis effectively decomposes the overshooting layer into two layers: a weak overshooting layer and a strong overshooting layer. Statistically, this is accomplished by decomposing the strongly non-Gaussian probability density function into a mixture of gamma distributions. Using our mixture model, we showed that the ratio of overshooting lengths above and below the convective shell depends directly on the radial extent of the convective shell as well as its depth in the star. We proposed a new form for the diffusion coefficient that addresses the need for overlapping overshooting layers between convective shells. We introduced the idea of a “super-mixing layer” where overshooting from both the convective shell and the convective envelope results in efficient mixing and could be viewed as merging the two adjacent convective zones.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Causal fermion states in magnetic field in a relativistic rotating frame and electromagnetic radiation by a rapidly rotating charge

We consider the Dirac field uniformly rotating with angular velocity Ω and also subject to the constant magnetic field B directed along the rotation axis. The causal states are constrained to the interior of the light cylinder of radius c / Ω . When this radius is smaller than the system size, as in the quark-gluon plasma, the effect of the boundary on the fermion spectrum is critical. We derive the fermion spectrum and study its properties. We compute the intensity of the electromagnetic radiation emitted due to transitions between the fermion states. We study its dependence on energy and angular momentum for different values of the angular velocity and the magnetic field. Rotation has enormous impact on the electromagnetic radiation by the quark-gluon plasma with or without the magnetic field. Published by the American Physical Society 2024

Buzzegoli, Matteo (ORCID:0000000221145431)↗
DOE OSTI2026

CHESS 2025: Discrete-return LiDAR point clouds from NEON AOP surveys

This dataset provides Level 1 (L1) discrete-return light detection and ranging (LiDAR) point cloud data collected for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). These data were acquired to enable characterization of vegetation structure and other three-dimensional features of the land surface, and to evaluate structural changes that may have occurred between a prior LiDAR acquisition in 2018 and the 2025 overflight. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. LiDAR data were acquired using the Optech Galaxy Prime Airborne LiDAR Terrain Mapper onboard the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). These are the primary unclassified discrete-return LiDAR data delivered by NEON and are provided per flightline as LASzip (LAZ) 1.4 Format 6 files. Data were processed following the workflow described in the NEON L0-to-L1 Discrete Return LiDAR Algorithm Theoretical Basis Document (Krause and Goulden 2022). Each record in the unclassified point clouds represents a geolocated laser target/return recorded by the LiDAR system, with values for X, Y, Z position and return intensity. All point coordinates are provided in meters. Horizontal coordinates are referenced in Universal Transverse Mercator (UTM) zone 13N and the World Geodetic System (WGS) 1984 ensemble datum. Elevations are referenced to Geoid12A. Flight metadata describing flightline boundaries and positional uncertainty by point are also included. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2025

Advancing Aerosol Chemical Characterization and Vertical Profiling over the Southern Great Plains Using Uncrewed Aerial Sampling and Offline Aerosol Mass Spectrometry

Recent advancements in uncrewed aerial systems (UASs) and particulate matter (PM) analytical techniques have provided opportunities for atmospheric research. In this study, we deployed the Department of Energy’s fixed-wing ArcticShark UAS to examine PM 2.5 composition at varying altitudes─within and above the planetary boundary layer (PBL)─over the Southern Great Plains atmospheric observatory (SGP). A total of 22 flights were conducted across March, June, and August 2023. Composite filter samples were collected during each flight and analyzed with offline aerosol mass spectrometry (AMS), complemented by on-board real-time sensors and ground-based instrumentation, to provide a comprehensive view of regional aerosol characteristics. Results show clear vertical and seasonal differences in the aerosol composition. Relative to ground-level measurements, aloft samples exhibited shifts in the distribution of organic and inorganic PM, with the organic composition varying distinctly across seasons. Particulate organic nitrogen (ON) was elevated, with bulk compositions similar in March and June but strongly altered in August, likely driven by biomass burning and enhanced photochemical activity. Combined AMS and chemical ionization mass spectrometry analyses detected amines, amides, and amino acids. PM above the planetary boundary layer was enriched in oxidized organic aerosols, while ground-level PM contained higher nitrate and sulfate. Seasonal differences in aqueous-phase processing were also observed, which were strongest in March during persistent cloud cover and weaker in the drier August period, suggesting a shift from aqueous- to gas-phase SOA formation. In conclusion, these findings highlight the value of UAS in advancing PM measurements and vertical profiling of aerosol composition.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Characterizing Seasonal Variation of the Atmospheric Mixing Layer Height Using Machine Learning Approaches

As machine learning becomes more integrated into atmospheric science, XGBoost has gained popularity for its ability to assess the relative contributions of influencing factors in the atmospheric boundary layer height. To examine how these factors vary across seasons, a seasonal analysis is necessary. However, dividing data by season reduces the sample size, which can affect result reliability and complicate factor comparisons. To address these challenges, this study replaces default parameters with grid search optimization and incorporates cross-validation to mitigate dataset limitations. Using XGBoost with four years of data from the atmospheric radiation measurement (ARM) (Southern Great Plains (SGP) C1 site, cross-validation stabilizes correlation coefficient fluctuations from 0.3 to within 0.1. With optimized parameters, the R value can reach 0.81. Analysis of the C1 site reveals that the relative importance of different factors changes across seasons. Lower tropospheric stability (LTS, ~0.53) is the dominant factor at C1 throughout the year. However, during DJF, latent heat flux (LHF, 0.44) surpasses LTS (0.22). In SON, LTS (0.58) becomes more influential than LHF (0.18). Further comparisons among the four long-term SGP sites (C1, E32, E37, and E39) show seasonal variations in relative importance. Notably, during JJA, the differences in the relative importance of the three factors across all sites are lower than in other seasons. This suggests that boundary layer development in the summer is not dominated by a single factor, reflecting a more intricate process likely influenced by seasonal conditions such as enhanced convective activity, higher temperatures, and humidity, which collectively contribute to a balanced distribution of parameter impacts. Furthermore, the relative importance of LTS gradually increases from morning to noon, indicating that LTS becomes more significant as the boundary layer approaches its maximum height. Consequently, the LTS in the early morning in autumn exhibits greater relative importance compared to other seasons. This reflects a faster development of the mixing layer height (MLH) in autumn, suggesting that it is easier to retrieve the MLH from the previous day during this period. The findings enhance understanding of boundary layer evolution and contribute to improved boundary layer parameterization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Thermodynamics-guided machine learning model for predicting convective boundary layer height and its multi-site applicability

Accurate estimation of convective boundary layer height (CBLH) is vital for weather, climate, and air quality modeling. Machine learning (ML) shows promise in CBLH prediction, but input parameter selection often lacks physical grounding, limiting generalizability. This study introduces a novel ML framework for CBLH prediction, integrating thermodynamic constraints and the diurnal CBLH cycle as an implicit physical guide. Boundary layer growth is modeled as driven by surface heat fluxes and atmospheric heat absorption represented with the low tropospheric stability, using the diurnal cycle as input and output. TPOT and AutoKeras are employed to select optimal models, validated against Doppler lidar-derived CBLH data, achieving an R 2 of 0.84 across untrained years. Comparisons of eddy covariance (ECOR) and energy balance Bowen ratio (EBBR) flux measurements show the same prediction capability. Models trained on the ARM SGP C1 site with ECOR data and tested at E37 and E39 yield R 2 values of 0.79 and 0.81, respectively, demonstrating their adaptability. The ML model trained with all sites' data slightly enhances the performance compared with ML models trained over single-site data. The interquartile range for predicted CBLH is consistently narrower than that for DL-derived CBLH, reflecting lower variability in predicted CBLH compared to DL-derived CBLH, which is influenced by additional factors, which are not well represented with the model inputs. The model's generalizability across multiple sites at the ARM SGP site demonstrates its potential for transfer to greater distances, offering a scalable approach for enhancing boundary layer parameterization in atmospheric models.

Chu, Yufei [Stony Brook Univ., NY (United States)]↗