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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Identifying Neutrino Final States and Energies in MicroBooNE with New Deep-Learning Based LArTPC Reconstruction Frameworks

MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the $\nu_{\mu}$-dominated Booster Neutrino Beam at Fermilab, has been studying $\nu_{e}$ charged-current (CC) interaction rates to shed light on the MiniBooNE low energy excess. The LArTPC technology employed by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer vision and other machine learning techniques are promising tools for image processing that could boost efficiencies for selecting $\nu_{e}$-CC and other rare signals, reduce cosmic and beam-induced backgrounds, and improve the reconstruction of neutrino energies. The MicroBooNE experiment has been at the forefront of developing and testing such techniques for use in physics analyses. In this poster we overview deep-learning based reconstruction methods. We will showcase the use of a recurrent neural network to estimate neutrino energies and present a new reconstruction framework that uses convolutional neural networks to locate neutrino interaction vertices, tag pixels with track and shower labels, and perform particle identification on reconstructed clusters. We will present studies characterizing the performance of these new tools and demonstrate their effectiveness through their use in an inclusive $\nu_{e}$-CC event selection.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Mechanistic Insights into Molecular Copper Hydride Catalysis: the Kinetic Stability of CuH Monomers toward Aggregation is a Critical Parameter for Catalyst Performance

The activity of molecular copper hydride (CuH) complexes towards the selective insertion of unsaturated hydrocarbons under mild conditions has contributed significantly to versatile methodologies for upgrading these feedstocks. However, these catalysts are particularly susceptible to deleterious aggregation, leading to the depletion of active CuH species. Little is known about the mechanisms of CuH aggregation, how it influences overall catalyst performance, and how it can be controlled. We address these challenges with mechanistic studies on a model reaction of unactivated alkene hydroboration catalyzed by (IPr*CPh 3 )CuH (LCuH). Here, we report a comprehensive mechanistic investigation of this system, identifying an aggregation pathway that continuously depletes catalytically active LCuH to form inactive CuH clusters during turnover. Deactivation of LCuH is controlled primarily by the competition between the kinetics of the initial LCuH dimerization step and that of alkene insertion. We therefore propose that a more comprehensive understanding of CuH catalyst performance must account for the kinetics of the initial LCuH dimerization step, revising a previously explored thermodynamic understanding of CuH aggregation, where the concentration of active species is controlled by equilibria established between CuH dimers and monomers. With a series of (NHC)CuH congeners (NHC = N-heterocyclic carbene), we demonstrate that ostensibly minor structural modifications to the ligand peripheries can drastically affect the LCuH dimerization kinetics, while maintaining reactivity towards on–cycle alkene insertion. We employed a computational approach based on molecular dynamics simulations to provide an in-depth understanding of how specific structural ligand modifications can substantially increase the kinetic stability of monomeric CuH catalysts. Our combined experimental and computational studies suggest strategies for rational ligand design that can be broadly applied to molecular catalyst systems that are susceptible to deactivation via aggregation pathways.

Ryan, David E. [Pacific Northwest National Laborat↗
DOE OSTI2026

Group-Additivity–Embedded Multiscale Modeling for Electric Field-Enhanced Nanocatalysis

Elucidating structure-performance relationships remains a central challenge in field-enhanced catalysis, where nanoparticles exhibit nonuniform surface sites with site-dependent responses to electric fields. Low-coordination sites (edges, corners, and tips) are particularly electric field-sensitive (EF), leading to nonuniform charge distribution, adsorption energies, and catalytic activity. Here, using ammonia decomposition on a ruthenium cluster as a model system, we develop a transferable multiscale framework integrating density functional theory, group additivity (GA), Brønsted-Evans-Polanyi scaling, and microkinetic modeling to predict EF-dependent activity across nonuniform cluster sites. Across sites and fields, the nitrogen adsorption energy (E N ) emerges as the governing descriptor, yielding robust volcano relationships whose optimum shifts systematically with field: negative fields strengthen N binding via electron accumulation, while positive fields weaken N binding via charge depletion, moving the optimal E N toward weaker binding. Microkinetic analysis shows that N≡N bond formation remains the key kinetic bottleneck over most conditions; positive fields lower the effective barrier and, critically, increase the fraction of near-optimal active sites, leading to a net enhancement in overall activity relative to zero-field and negative-field cases. By capturing EF- and site-dependent energetics with high accuracy and low computational cost, this GA-embedded multi-scale simulation workflow provides a physically interpretable route to predict and design field-enhanced nanocatalysis.

ammonia decomposition↗
DOE OSTI2025

Statistical relationships across epigenomes using large-scale hierarchical clustering

Recent advances in genomics and sequencing platforms have revolutionized our ability to create immense data sets, particularly for studying epigenetic regulation of gene expression. However, the avalanche of epigenomic data is difficult to parse for biological interpretation given nonlinear complex patterns and relationships. This attractive challenge in epigenomic data lends itself to machine learning for discerning infectivity and susceptibility. In this study, we explore over 3000 epigenomes of uninfected individuals and provide a framework to characterize the relationships among epigenetic modifiers, their modifiers, genetic loci, and specific immune cell types across all chromosomes using hierarchical clustering. Hierarchical clustering of epigenomic data revealed consistent epigenetic patterns across chromosomes, demonstrating that variation due to epigenetic modifiers is greater than variation between cell types. Gene Ontology and KEGG pathway analyses indicated significant enrichment of genes involved in chromatin remodeling, mRNA splicing, immune responses, and the regulation of microRNAs and snoRNAs. Epigenetic modifiers frequently formed biologically relevant clusters, including the cohesin complex, RNA Polymerase II transcription factors, and PRC2 complex members. These clustering behaviors remained consistent across all chromosomes, supported by entropy analysis and high Adjusted Rand Index scores, indicating robust cross-chromosomal similarity. Co-occurrence analysis further revealed specific sets of modifiers that consistently appeared together within clusters, reflecting shared biological functions and interactions. Validation using another dataset confirmed the reproducibility of these clustering patterns and modifier co-occurrence relationships, underscoring the reliability and generalizability of the methodology.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Mitigation of DESI fiber assignment incompleteness effect on two-point clustering with small angular scale truncated estimators

We present a method to mitigate the effects of fiber assignment incompleteness in two-point power spectrum and correlation function measurements from galaxy spectroscopic surveys, by truncating small angular scales from estimators. We derive the corresponding modified correlation function and power spectrum windows to account for the small angular scale truncation in the theory prediction. We validate this approach on simulations reproducing the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) with and without fiber assignment. We show that we recover unbiased cosmological constraints using small angular scale truncated estimators from simulations with fiber assignment incompleteness, with respect to standard estimators from complete simulations. Additionally, we present an approach to remove the sensitivity of the fits to high k modes in the theoretical power spectrum, by applying a transformation to the data vector and window matrix. We find that our method efficiently mitigates the effect of fiber assignment incompleteness in two-point correlation function and power spectrum measurements, at low computational cost and with little statistical loss.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The Localized Active Space Method with Unitary Selective Coupled Cluster

Here, we introduce a hybrid quantum-classical algorithm, the localized active space unitary selective coupled cluster singles and doubles (LAS-USCCSD) method. Derived from the localized active space unitary coupled cluster (LAS-UCCSD) method, LAS-USCCSD first performs a classical LASSCF calculation, then selectively identifies the most important parameters (cluster amplitudes used to build the multireference UCC ansatz) for restoring interfragment interaction energy using this reduced set of parameters with the variational quantum eigensolver method. We benchmark LAS-USCCSD against LAS-UCCSD by calculating the total energies of (H 2 ) 2 , (H 2 ) 4 , and trans-butadiene, and the magnetic coupling constant for a bimetallic compound [Cr 2 (OH) 3 (NH 3 ) 6 ] 3+ . For these systems, we find that LAS-USCCSD reduces the number of required parameters and thus the circuit depth by at least 1 order of magnitude, an aspect which is important for the practical implementation of multireference hybrid quantum-classical algorithms like LAS-UCCSD on near-term quantum computers.

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

Accelerating Embedding Potential Optimization by Reconstructing the Pseudo-Valence Electron Density

Density functional embedding theory (DFET) enables use of electronic structure methods with higher accuracy than density functional theory in a local region, with applications thus far ranging from (photo/electro)catalysis to reactions in solution. DFET partitions a large collection of atoms into smaller groups that interact via a shared embedding (interaction) potential V emb , determined via functional optimization. The optimized effective potential (OEP) process used to optimize V emb is time-consuming and becomes a computational bottleneck due to sharp, oscillating features of V emb near nuclei. Here, similar to pseudopotential theory, by reconstructing electron densities used in the OEP process from smoother pseudo-valence-only (PVO) electron densities as proxies for total densities of the full system and subsystems, we can retain accuracy in the embedded electronic structure calculations while potentially reducing the overhead of V emb construction, within the projector augmented-wave (PAW) formalism. We explore three different chemical reactions as exemplars to test PVO–DFET, namely, H 2 dissociative adsorption on a Cu(111) surface, H 2 O adsorption on a Pt(111) surface, and aqueous [Ca 2+ –SO 4 2– ] ion-pair formation. The PVO approximation works well for all three systems with minimal loss of accuracy (∼10–70 meV error relative to the original exact-derivative (ED) approach) while accelerating V emb generation for the Cu and Pt systems respectively by 20× and 5×. Given proper numerical convergence parameters, the spatial distributions of differences between PVO- and ED-based V emb outside the core regions are small, explaining the exceptional agreement between the two approaches. Finally, we anticipate that this more efficient PVO–DFET approximation will be useful whenever computation of V emb is much more expensive than subsequent embedded high-level electron correlation calculations.

approximation↗
DOE OSTI2024

Graph Analytics on Jellyfish topology

Because large unstructured datasets is important for many science domains, distributed graph analytics is critical to many scientists. Unfortunately, obtaining scaling and performance for irregular communication is challenging because contemporary network interconnects are primarily designed to maximize bandwidths of fixed-neighborhoods large-message exchanges (e.g., stencils). Although there is no consensus on the “best” network topologies for irregular communication, unstructured graph-based interconnects can be more suitable. We analyze three popular graph workloads – clustering, pattern enumeration, and traversal — on comparable networks (in terms of resources and costs) constructed from Jellyfish Random Regular, Dragonfly and Fat tree topologies, varying the routing algorithms. Using packet-level simulations, we demonstrate up to 60% improvement in communication time with Jellyfish due to diversity of the short paths between arbitrary endpoints, which can reduce overall network stalls and congestion.

Graph Analytics, network topology, interconnect, H↗
DOE OSTI2024

Modeling of H2 Dispersion at ARIES

Hydrogen is a versatile and clean energy carrier that can be produced from various renewable sources such as wind, solar, and hydropower and help decarbonize electricity grids, industry, and transportation. Using the Hydrogen Research Facility under Advanced Research on Integrated Energy Systems (ARIES) at the National Renewable Energy Laboratory's (NREL) Flatirons campus as a test bench, the study examines the feasibility, useability, and value of using computational fluid dynamics (CFD) techniques to model hydrogen dispersion. The ARIES facility was chosen because controlled hydrogen releases can be performed at a rate of 27 kg-H2/hr. Site-specific atmospheric and weather condition data such as wind speed and temperature were used as inputs to the model. The results show statistical distributions and ranges of hydrogen concentrations at locations throughout the domain. Wind conditions are found to significantly impact the release behavior, including the hydrogen cloud's direction and concentrations. At low wind speeds (below 1 mph), hydrogen forms a cloud and at higher wind speeds (> 2-4 mph) hydrogen plume stretches in the direction of wind momentum. From >100 simulations for ARIES site-specific conditions, statistical quantities combined with a clustering algorithm were used to propose sensor location at various elevations from ground.

dispersion↗
DOE OSTI2025

Structural origin of disorder-induced ion conduction in NaFePO 4 cathode materials

Diffusion in NaFePO 4 can be enhanced through amorphization. Based on computations using DFT and machine learning potentials, we ascribe this phenomenon to the formation of less constrained Na-ion environments upon disordering. Most modern battery technologies depend on solid-state crystalline cathode materials. However, some of these materials are constrained by the low ionic conductivity of their most stable phases. An example of this is maricite (NaFePO 4 ). Interestingly, experiments have shown that maricite can improve its rate capability through disordering (amorphization). However, experimental characterization of amorphous cathode materials remains a major challenge, hindering a clear understanding of the structural origin of the disorder-induced improvement in sodium-ion mobility. To address this, we here employ molecular dynamics simulations by first training a machine learning potential for NaFePO 4 based on the atomic cluster expansion approach and a batch active learning potential parameterization scheme. This potential is then applied to explore the structural and dynamical properties of NaFePO 4 glasses as cathode materials. Specifically, we investigate the effect of glass structure on sodium-ion diffusion, revealing the relative influences of short-range and medium-range order features. We find significant heterogeneity in sodium-ion diffusivity in the glass, with fast-conducting ions residing in less constrained atomic environments with fewer P and Fe neighbors. These more mobile ions are also surrounded by larger ring-type structures. Overall, the results and developed approach present promising avenues for developing high-performance glassy cathodes for next-generation batteries.

Christensen, Rasmus↗
DOE OSTI2026

Maximizing machine learning interatomic potential transferability for the discovery of the novel stellated octadecagon Bi18-Pt24 cage structure

Achieving true transferability remains the central challenge for Machine Learning Interatomic Potentials (ML-IAPs) in modeling complex bimetallic nanoclusters across their vast potential energy surfaces. We systematically investigate data selection strategies to optimize the Chebyshev Interaction Model for Efficient Simulation (ChIMES) potential for the Bi-Pt nanoclusters by comparing three innovative sampling methods: Principal Component Analysis (PCA)/k-means (structural diversity), t-distributedStochasticNeighborEmbedding (t-SNE)/k-means (force-space diversity), and hierarchical clustering. Quantitatively, the PCA/k-means strategy proved most effective for global accuracy, yielding the lowest force errors and achieving energy root mean square errors (RMSE) values competitive with Density Functional Theory (DFT), demonstrating excellent accuracy (19.16meV/atom). Structural validation on 34 unique DFT-optimized isomers further confirmed the potential’s high fidelity, with the best model PCA/k-means reproducing structures with an average root mean square deviation (RMSD) of 0.10 Å. However, the t-SNE methods, by maximizing diversity in the force space, demonstrated superior extrapolative power, leading to the more precise prediction of a novel stellated octadecagon Bi18⁢Pt24 cage structure, demonstrating the potential for exploring previously unseen morphologies. Our results establish a clear methodology for strategic data sampling that successfully maximizes ML-IAP transferability, providing an accurate and computationally efficient tool that accelerates the theoretical discovery of complex bimetallic architectures.

Vangheluwe, Raphaël [Université Paris-Saclay, CNRS↗
DOE OSTI2026

Analysis of Fourth-, Fifth-, and Infinite-Order Triple Excitations in Unitary Coupled Cluster Theory

Here, in this work, we introduce a perturbative correction to the unitary coupled cluster method with single and double excitations (UCCSD) that incorporates the effects of missing triple excitations through fifth-order in many-body perturbation theory (MBPT). Referred to as UCCSD[T-5], this method is benchmarked alongside the previously developed UCCSD[T] to lend insight into the behavior of perturbative triples corrections relative to UCCSDT, which inherently provides an infinite-order treatment of triple excitations, as well as full configuration interaction (FCI). Two key findings emerge from this analysis. First, UCCSD[T] consistently yields ground-state energies in closest agreement with FCI, outperforming both UCCSD[T-5] and UCCSDT. Second, UCCSD[T-5] largely emulates the behavior of UCCSDT, suggesting a close relationship between fifth- and infinite-order triple-excitation contributions. Given the growing interest in UCC ansätze for quantum computing and the limitations of current quantum hardware, these results highlight the potential of classically computed perturbative corrections within UCC theory to capture triple-excitation effects without the additional quantum resources required by the UCCSDT ansatz.

Windom, Zachary W. [Oak Ridge National Laboratory ↗
DOE OSTI2024

High pressure suppression of plasticity due to an overabundance of shear embryo formation

Abstract High pressure shear band formation is a critical phenomenon in energetic materials due to its influence on both mechanical strength and mechanochemical activation. While shear banding is known to occur in a variety of these materials, the governing dynamics of the mechanisms are not well defined for molecular crystals. We conduct molecular dynamics simulations of shock wave induced shear band formation in the energetic material 1,3,5-trinitroperhydro-1,3,5-triazine (RDX) to assess shear band nucleation processes. We find, that at high pressures, the initial formation sites for shear bands, “embryos”, form in excess and rapidly lower deviatoric stresses prior to shear band formation and growth. This results in the suppression of plastic deformation. A local cluster analysis is used to quantify and contrast this mechanism with a more typical shear banding seen at lower pressures. These results demonstrate a mechanism that is reversible in nature and that supersedes shear band formation at increased pressures. We anticipate that these results will have a broad impact on the modeling and development of high-strain rate application materials such as those for high explosives and hypersonic systems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Visualization of Noisy and Less Noisy Computational Basis States in Quantum Computing

Quantum computing technology holds substantial promise as a reliable computational paradigm. However, current noisy intermediate scale quantum (NISQ) systems, are significantly impacted by noise originating from hardware inconsistencies. This noise causes errors and lowers output fidelity. So we must find which basis states cause errors. However, there are two main challenges in analyzing noise corresponding to basis states. First, the noise distribution data is high dimensional in nature, thereby making its analysis challenging. Second, although functional box plots have been used in the state of the art research to understand such a high dimensional data, they suffer from clutter and occlusion issues because of overplotting. In this study, we introduce an innovative visualization pipeline to address the aforementioned challenges to provide a clear depiction of noisy and less-noisy basis states. Specifically, our proposed visualization pipeline comprises three stages namely, low dimensional embedding, clustering, and violin plot visualization, to reduce visual clutter and effectively analyze high-dimensional noise distribution data. Our analysis uses quantum machine learning (QML) circuits as case study for drawing a distinction between noisy and less noisy basis states.

Senapati, Priyabrata [Kent State University]↗
DOE OSTI2025

Net Present Value Optimization of a Natural Gas Combined Cycle Plant with CO 2 Capture using a Water-Lean Solvent Considering Transient Electricity Price for Multiple Regions

Global CO 2 emissions are increasing at about a 1.5% rate per year. Fossil fuel-based plants are one of the main contributors to this rise. In the power generation industry, fossil fuel plants are dominant, and many plants are under development. In this study, a natural gas combined cycle (NGCC) power plant with postcombustion capture using a leading water-lean solvent is considered. For optimal design and operating schedule, large-scale dynamic optimization is undertaken for net present value (NPV) optimization. The first principle dynamic model of NGCC is developed, including a model of the highly efficient H-class gas turbines. For computational tractability of the dynamic optimization problem, a reduced-order model is developed by using the Hankel singular value decomposition. A waterlean solvent, N-(2-ethoxyethyl)-3-morpholinopropan-1-amine, is used for carbon capture. A model of the capture system is developed in Aspen Plus, which is used to develop a reduced-order model by using ALAMO, a machine learning software. In addition, a reduced model of the CO 2 compression system with a dehydration unit is also considered. The integrated system is used for NPV optimization by using the Python-based PYOMO platform. The PCC process is analyzed for three configurations-conventional packed bed, rotating packed bed (RPB), and a combination of RPB and direct contact cooler. The NPV optimization is performed for 14 regional markets by considering year-long clustered and continuous locational marginal price data with a 1 h interval. Optimization results show that the PCC can achieve 90% CO 2 capture with a positive NPV for six regions. Sensitivity studies conducted by using the PCC configurations indicate that the process is economically feasible for 9 regions out of 14 regional electricity markets with NPV values in the range of 33−540 $MM.

cabon capture↗
DOE OSTI2025

UnigeneFinder: An Automated Pipeline for Gene Calling From Transcriptome Assemblies Without a Reference Genome

ABSTRACT For most species, transcriptome data are much more readily available than genome data. Without a reference genome, gene calling is cumbersome and inaccurate because of the high degree of redundancy in de novo transcriptome assemblies. To simplify and increase the accuracy of de novo transcriptome assembly in the absence of a reference genome, we developed UnigeneFinder. Combining several clustering methods, UnigeneFinder substantially reduces the redundancy typical of raw transcriptome assemblies. This pipeline offers an effective solution to the problem of inflated transcript numbers, achieving a closer representation of the actual underlying genome. UnigeneFinder performs comparably or better, compared with existing tools, on plant species with varying genome complexities. UnigeneFinder is the only available transcriptome redundancy solution that fully automates the generation of primary transcript, coding region, and protein sequences, analogous to those available for high‐quality reference genomes. These features, coupled with the pipeline’s cross‐platform implementation, focus on automation, and an accessible, user‐friendly interface, make UnigeneFinder a useful tool for many downstream sequence‐based analyses in nonmodel organisms lacking a reference genome, including differential gene expression analysis, accurate ortholog identification, functional enrichments, and evolutionary analyses. UnigeneFinder also runs efficiently both on high‐performance computing (HPC) systems and personal computers, further reducing barriers to use.

Xue, Bo [Plant Resilience Institute Michigan State↗
DOE OSTI2025

The Three Hundred Project: Modeling baryon and hot-gas fraction evolution in simulated clusters

The baryon fraction of galaxy clusters, expressed as the ratio between the mass in baryons (including both stars and cold or hot gas) and the total mass, is a powerful tool to provide information on the cosmological parameters, while the hot-gas fraction provides indications on the physics of the intracluster plasma and its interplay with the processes that drive galaxy formation. Using cosmological hydrodynamical simulations of about 300 simulated massive galaxy clusters with a median mass M 500 ≈ 7 × 10 14 M ⊙ at z = 0, we model the relations between total mass and either baryon fraction or the hot gas fractions at overdensities Δ = 2500, 500, and 200 with respect to the cosmic critical density, and their evolution from z ∼ 0 to z ∼ 1.3. We utilized the simulated galaxy clusters from the Three Hundred project, which include star formation and feedback from both supernovae and active galactic nuclei. We fit the simulation results for such scaling relations against three analytic forms (linear, quadratic, and logarithmic in a logarithmic plane) and three forms for the redshift dependence, and we considered as a variable both the inverse of the cosmic scale factor, (1 + z), and the Hubble expansion rate, E(z). We show that power-law dependencies on cluster mass poorly describe the investigated relations. A power law fails to simultaneously capture the flattening of the total baryon and gas fractions at high masses, their drop at low masses, and the transition between these two regimes. The other two functional forms provide a more accurate description of the curvature in mass scaling. The fractions measured within smaller radii exhibit a stronger evolution than those measured within larger radii. From the analysis of these simulations, we evince that as long as we include systems in the mass range herein investigated, the baryon or gas fraction can be accurately related to the total mass through either a parabola or a logarithm in the logarithmic plane. The trends are common to all modern hydro simulations, although the amplitude of the drop at low masses might differ. Being able to observationally determine the gas fraction in groups will thus provide constraints on the baryonic physics.

galaxy clusters↗
DOE OSTI2024

Are Marine Low Cloud Droplet Concentrations Buffered by Entrained Aitken‐Mode Aerosol (Final Technical Report)

During the summertime, the high-latitude oceans come to life with green phytoplankton, which gain their energy from sunlight and are food for sea creatures small and large. When the phytoplankton are eaten or die, sulfur-rich gases are released under the ocean surface and mix into the air. Observations suggest that, over the Southern Ocean, frequent storms lift this air high into the atmosphere while raining out particulates like salt. As a result, the sulfur-rich air then spawns high concentrations of small ‘Aitken-mode’ aerosol particles. We hypothesize that these particles work their way down into the marine boundary layer, where they can replenish the supply of cloud-condensation nuclei scavenged by frequent precipitation. Further, this process maintains high concentrations of liquid cloud droplets in austral summer, promoting more sunlight to be reflected to space. We call this ‘Aitken buffering’. The primary objective of this project has been to document and test what role Aitken-mode aerosols play in clouds over the Southern Ocean and elsewhere. This effort has included three main components: 1) developing a computer model that realistically simulates the aerosol processes and the small-scale turbulent air motions that move aerosols around and create the clouds, 2) using that model to interpret and extend these observations for process understanding, by allowing different factors that contribute to the aerosol budget, such as surface wind speed, precipitation, surface gas exchange, etc. to be separated, and 3) studying Aitken-mode aerosol and its variability with a focus over the Southern Ocean and Antarctica, using data from Atmospheric Radiation Measurement (ARM) sites and other available observations. Initial computer studies in more idealized conditions found that elevated concentrations of Aitken-mode aerosols above the clouds could help prevent the breakup of those clouds by acting as cloud-condensation nuclei after they were entrained into the cloudy boundary layer. Simulations of a day during the ACE-ENA field campaign showed that Aitken-mode aerosols could also prevent cloud breakup under more realistic conditions. A new method that extracts information about Aitken-mode aerosols from measurements of aerosols onto which cloud droplets can form finds that Aitken-mode aerosols do vary seasonally over the Southern Ocean, with a peak in summertime, as described above. Other work during this project has focused on understanding how patterns of water vapor, clouds and precipitation are coupled within low-lying clouds over the oceans, and also on how cloud droplets cluster within clouds and how the distribution of cloud droplet sizes change as dry air is mixed into clouds, with the latter studies also using observations from ACE-ENA.

54 ENVIRONMENTAL SCIENCES↗