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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Machine learning from RANS and LES to inform coarse grid simulations

Nuclear system thermal hydraulic analysis has historically relied on computationally inexpensive 1D codes. However, such tools are unable to capture multiscale multidimensional effects in large nuclear reactor enclosures. On the other hand, simulations with higher fidelity can be too expensive for such purposes. One of the ways to reduce computational cost is to perform simulations on a coarse grid, which, unfortunately, introduces large discretization errors. In this paper, two high-to-low data-driven approaches are investigated: (1) a coarse grid turbulence model to predict eddy viscosity and (2) correction of errors in coarse grid velocity fields. The approaches aim to reduce grid- and turbulence model-induced errors in coarse grid Reynolds-averaged Navier–Stokes (RANS) simulations. Two sources of high-fidelity data, RANS and large eddy simulations (LES), are explored. To extract the eddy viscosity from the LES data, an inverse optimization problem is solved. However, the LES eddy viscosity is shown to be comparable to the RANS eddy viscosity in terms of error reduction. Therefore, the directly available RANS eddy viscosity was used to develop a coarse grid data-driven turbulence model. Additionally, error correction in velocity is used to reduce the remaining uncertainties and bring the results closer to reality. In conclusion, the performance of the frameworks is demonstrated for a scaled upper plenum of a gas-cooled reactor facility.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Stabilization of Charge-Transfer Excited States in Biological Systems: A Computational Focus on the Special Pair in Photosystem II Reaction Centers

Charge-transfer (CT) excited states play an important role in many biological processes. However, many computational approaches often inadequately address the equilibration effects of nuclear and environmental degrees of freedom on these states. One prominent example of systems in which CT states are of utmost importance is reaction centers (RC) in photosystems. Here we use a multiscale approach combined with time-dependent density functional theory to explore the lowest CT excited state of the special pair P D1 –P D2 in the Photosystem II-RC of a cyanobacterium. We find that the nonequilibrium CT excited state resides near the Soret band, making an exciton the lowest-energy excited state. However, accounting for nuclear and state-specific dielectric equilibration along the CT potential energy surface (PES), the CT state P D1 – –P D2 + stabilizes energetically below the excitonic state. Importantly, this underscores the crucial role of state-specific solvation in mapping the PES of CT states, as demonstrated in a simplified dimer model.

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

A Multiscale Simulation Study of Influence of Morphology on Ion Transport in Block Copolymeric Ionic Liquids

In this work, we present results of a multiscale simulation study involving coarse-graining and reverse-mapping steps probing at an atomistic resolution the influence of morphology on ion transport in block copolymer polymeric ionic liquids. We provide a detailed description of the multiscale simulation methodology and then describe the results in the context of four microphase-separated morphologies: two lamella systems, a cylinder, and a gyroid morphology. For the framework adopted in this study (in which the total number of ions was maintained fixed), morphology had little influence on the fraction of ions segregating to the interface and the coordination and hopping characteristics of such interfacially present ions. Such results manifested in anion mobilities being insensitive to the morphology once the dimensionality of the morphology was accounted for. Overall, our results are consistent with the hypothesis that the mobility of ions in such microphase-separated morphologies, after accounting for the dimensionality effects, can be roughly correlated to a linear superposition based on the fraction of bulk anions, which possess mobilities unperturbed from their values in the conducting homopolymers, and the interfacial anions, which exhibit much lower mobilities.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Molecular insights into CO 2 -to-bicarbonate transformation in functionalized anion exchange ionomers for electrochemical separations

Bipolar membrane (BPM) electrochemical processes are a promising platform for carbon dioxide (CO 2 ) separations, but the molecular level thermodynamic and kinetic understanding of CO 2 -to-bicarbonate (HCO 3 − ) transformation remain poorly understood. This study employs a multiscale computational approach to systematically explore the adsorption and reactive transformation of CO 2 in five anion exchange ionomer systems. Classical molecular dynamics (MD) simulation results demonstrate that polymers with imidazolium groups significantly reduce CO 2 diffusion and enhance (OH − )–CO 2 interactions due to stronger electrostatic and π-interactions. Compared to the commonly used quaternary ammonium ionomers, imidazolium-functionalized ionomers show improved CO 2 proximity and interaction strength. Ab initio MD and density functional theory (DFT) calculations reveal that the benzyl-substituted imidazolium (IM-Ben) substantially reduces the energy barrier for HCO 3 − formation (∼72 meV lower) compared to the alkyl-substituted IM-nBu, while also mitigating imidazolium deprotonation under moderate hydration conditions. Transition state analysis shows IM-Ben forms more extensive hydrogen-bonding networks, which stabilize the transition state structure and contribute to a lower energy barrier for bicarbonate formation. These findings highlight the advantage of the adjacent benzyl moiety in enabling efficient CO 2 -to-bicarbonate transformation via hydrated hydroxide ion counterions, offering mechanistic insights and clear molecular design principles for optimizing anion exchange ionomers at bipolar membrane interfaces for electrochemical CO 2 separation applications.

Bipolar membranes, Reactive transformation of CO2,↗
DOE OSTI2021

Building an AI-enhanced modeling framework to address multiscale predictability challenges

Focal Area(s): Build an AI-enhanced modeling framework that integrates the three focus areas in the solicitation. Science Challenge: The 4M-2N complexities (Multiscale, Multiphysics, Multibody, Multidimension, Non-linearity, and Non-Gaussianality) of atmospheric aerosol-cloud-precipitation-turbulence-radiation system poses physical and computational challenges to further advance predictive models; We plan to address the challenges by developing an AI-enhanced modeling framework that facilitates automated calibration and improvement of subgrid parameterizations, enhance data assimilation of measurements to improve initial and boundary conditions used to drive the physical model, and optimally blends data-driven and physics-based forecasting models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Large-eddy simulation of an atmospheric bore and associated gravity wave effects on wind farm performance in the southern Great Plains

Gravity waves are a common occurrence in the atmosphere, with a variety of generation mechanisms. Their impact on wind farms has only recently gained attention, with most studies focused on wind farm-induced gravity waves. In this study, the interaction between a wind farm and gravity waves generated by an atmospheric bore event is assessed using multiscale large-eddy simulations. The atmospheric bore is created by a thunderstorm downdraft from a nocturnal mesoscale convective system (MCS). The associated gravity waves impact the wind resource and power production at a nearby wind farm during the American Wake Experiment (AWAKEN) in the US southern Great Plains. A two-domain nested setup (Δx=300 and 20 m) is used in the Weather Research and Forecasting (WRF) model, forced with data from the High-Resolution Rapid Refresh model, to capture both the formation of the bore and its interaction with individual wind turbines. The MCS is resolved on the large outer domain, where the structure of the bore and the associated gravity waves are found to be especially sensitive to parameterized microphysics processes. On the finer inner domain, gravity wave interactions with individual wind turbines are resolved; wake dynamics are captured using a generalized actuator disk parameterization in WRF. The gravity waves are found to have a strong effect on the atmosphere above the wind farm; however, the effect of the waves is more nuanced closer to the surface where there is additional turbulence, both ambient and wake-generated. Notably, the gravity waves modulate the mesoscale environment by weakening and dissipating the preexisting low-level jet, which reduces hub-height wind speed and hence the simulated power output, which is confirmed by the observed supervisory control and data acquisition (SCADA) power data. Additionally, the gravity waves induce local wind direction variations correlated with fluctuations in pressure, which lead to fluctuations in the simulated power output as various turbines within the farm are subjected to waking from nearby turbines.

17 WIND ENERGY↗
DOE OSTI2023

Evaluation of present-day extreme precipitation over the United States: an inter-comparison of convection and dynamic permitting configurations of E3SMv1

Abstract Accurate simulation of the present-day characteristics of mean and extreme precipitation at regional scales remains a challenge for Earth system models, which is due in part to deficiencies in model physics such as convective parameterization (CP), and coarse resolution. High horizontal resolution (HR, ∼25 km) and multiscale modeling framework (MMF, i.e. replacing conventional CP with embedded km-scale cloud-resolving models) are two promising directions that could help improve the interaction between subgrid-scale physical processes and large-scale climate. Here, we evaluate simulated extreme precipitation over the United States (US) across three configurations (i.e. low-resolution [LR], HR, and MMF) of the Energy Exascale Earth System Model (E3SMv1) and intercompare them against two gridded observation datasets (climate prediction center daily US precipitation and integrated multi-satellite retrievals for global precipitation measurement). We assess the model’s ability to simulate very heavy seasonal precipitation (illustrated by the difference between the 99th and 90th percentile values) as well as the spatial distributions of several extreme precipitation indices defined by the expert team on climate change detection and indices. Our results show that both the dry (i.e. consecutive dry days (CDD)) and wet (i.e. consecutive wet days, maximum 5 day precipitation, and very wet days) extremes evaluated herein show some improvement as well as degradation with MMF and HR relative to LR. These results vary across seasons and US subregions. For instance, only the very heavy precipitation of winter is improved with MMF and HR. Both configurations alleviate the well-known drizzling bias evident in LR across both winter and summer in many parts of the US, largely due to the overall improvement in intensity and frequency of precipitation. Additionally, our results suggest that while E3SMv1-MMF has higher intensity rates when it does rain, it has too many CDD during the summer, contributing to a low mean precipitation bias.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Multiscale Thermal-hydraulic analysis of the MARVEL microreactor using a coupled SAM and SCM simulation. (PPTX)

This is .pptx document presenting a summary of the paper of the same name that has been submitted and accepted to NURETH-21: MARVEL is a natural-convection-cooled sodium-potassium microreactor that is anticipated to generate 85 kilowatts of thermal energy. It will operate within Idaho National Laboratory Transient Reactor Test Facility and is being developed by the DOE Microreactor Program. MARVEL will be used to test microreactor applications, evaluate systems for remote monitoring, and develop autonomous control technologies. A thermal-hydraulic computational model of this facility is a valuable tool to study important transients and calculate the safety limits of the micro-reactor design. For this purpose, the authors have chosen to use a multiscale coupled simulation: SCM for modeling the reactor core and SAM for the reactor's primary cooling system. SCM is MOOSE physics module for subchannel analysis, which was designed to model single-phase flows through liquid-metal cooled, wire-wrapped fuel pin sub-assemblies, ordered in a triangular lattice. The SCM code was modified to be able to model MARVEL?s unique geometry. SAM is a systems analysis module based on the MOOSE framework. It aims to provide fast-running, whole-plant transient analyses capability with improved-fidelity for various advanced reactor types. The coupling between the two SCM and SAM for MARVEL modeling is done implementing a domain over-lapping approach. The resulting coupled simulation can model transients such as reactor startup/shutdown and provide an intermediate fidelity picture of the temperature field and other variables, in the core. Results for the steady-state simulations are presented in the article as well as flow blockage transient.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2021

SISGR: Chemomechanics of Far-From-Equilibrium Interfaces (COFFEI)

Portable, reliable, and deployable devices for energy storage and conversion require fundamental changes in design of solid-state composites comprising ceramics and metals. These materials comprise the electrodes and electrolytes of next-generation solid-oxide fuel cells and all solid-state batteries, forming solid-state functional composites. The advent of solid-state batteries – which replace liquid electrolytes with solid electrolytes capable of lithium ion transport for reliable energy storage in portable batteries – and the increased demand space for all solid-state fuel cells capable of oxygen reduction at intermediate temperatures remain important challenges for improved material stability and decreased system cost. However, little is understood about three fundamental facets of materials that enable such solid-state energy applications. First, how do such materials deform, fracture, or delaminate under operando conditions? Second, how does such mechanical deformation limit or facilitate electronic and ionic transport within and across such material interfaces? Third, how we can predictably design interface-rich composites to engineer both structural and electrochemical stability? This COFFEI Group comprised expertise from Materials Science & Engineering and Nuclear Science & Engineering to integrate unique in situ experiments, simulations, and fabricated interfaces that address these fundamental questions in solid-state interfaces of nanoscale composites that will guide solid-state electrochemistry, transport kinetics, and mechanical deformation for nonstoichiometric materials that enable such applications. In particular, we built on COFFEI’s understanding of chemomechanical coupling among defect concentrations, ionic transport, electron transport, and stored elastic energy that is particularly acute in the far-from-equilibrium conditions typical of energy device applications. By tailoring our focus to solid-state interfaces, we addressed these important issues by (a) developing and applying advanced in situ and ex situ characterization tools to characterize model materials and interfaces synthesized with molecular-level control, under both laboratory-controlled and extreme environments representative of energy device operation; and (b) employing computational modeling and simulation frameworks to predict transport mechanisms, reactivity and stability of these model materials and interfaces under significant chemical strains typical of energy device operation. Recent progress provided insights to additional materials systems and electrochemomechanical fatigue and fracture that were not fully envisioned when the program was initiated. Specifically, in the final three years of COFFEI we pursued two integrated thrusts, with complementary focus. Thrust I focused on failure-resistant electrochemomechanical composites, while Thrust II focused on strain-modulated conductivity and reactivity across interfaces. In contrast to our initial COFFEI focus, these thrusts concentrated wholly on solid-state material interfacial interactions and included greater integration of multiscale visualization including in situ electron microscopy of strained structures/interactions and mesoscale simulations. Successful development of functionally superior and long-lived battery and fuel cell systems and stress adaptable oxides requires a deeper, fundamental understanding of the coupling among the historically important subfields of solid-state electrochemistry, transport kinetics, and mechanical deformation for nonstoichiometric metal oxide electrodes. In this program, the understanding and the application of chemomechanical coupling of defect concentrations, ionic transport, electro-catalytic activity and stored elastic energy, particularly acute in the far-from-equilibrium conditions typical of energy device applications, are being refined and implications for device operation clarified, including for miniaturized solid-state batteries and fuel cells.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Capturing, Analyzing, Maintaining, and Disseminating Shape Memory Material Data Between Information Management Systems

With an increased demand on reducing the time, cost, and effort to develop new materials, Integrated Computational Materials Engineering (ICME) has received widespread attention in various engineering disciplines as a catalyst for significantly reducing experimental testing during the material design process. An ICME approach to design can enable ‘fit-for-purpose’ materials to be realized in engineering applications by incorporating well-understood process-property-performance relationships between the various length and time scales in a material’s structure, enabling material optimization. However, such an approach requires validated multiscale models at the various length scales for a material, which in turn requires a large amount of data, a robust means of storing the data, and the ability to link data to developed material models. The NASA Vision 2040 [1] has identified nine key elements to enabling ICME approaches in system level design, with one being “Data, Information, and Visualization”, thus outlining the importance of a robust information management system for ICME. As the relationship between microstructure, properties, and material performance become better understood and incorporated into multiscale models that can be leveraged in application design, the emergence of new materials with application-driven properties can be realized. One such new material class that has seen growing attention are shape memory materials (SMM), in which a material can transition between a deformed and undeformed state via a reversible phase transformation when subject to a thermal, mechanical, or magnetic load [2]. SMMs have been used widely in aerospace and biomedical industries, including applications such as actuators, low-shock mechanisms, medical staples, braces, and stents [3, 4]. These materials exhibit unique behavior due to their ability to transition between phases, and thus the mechanisms that enable this transition must be captured in a data information management system and incorporated into SMM material models. At NASA Glenn Research Center, the Shape Memory Materials Database (SMMD) Tool has been developed to capture the necessary information that governs SMM material behavior and provide users the ability to select and visualize various SMMs for a specific application [5]. The database contains point-wise data for published SMM materials, along with the pedigree metadata for traceability necessary for a robust information management system. The database is also capable of storing in-house test data performed at NASA GRC by interacting with the developed Shape Memory Alloy (SMA) Analytics tool to extract the necessary point-wise values and populate the database. Although the SMMD Tool offers its users a single, authoritative source for SMM material data that is critical for model development and material design, the full material pedigree of the in-house test data for SMMs is not currently captured and is out of the scope for the SMMD tool. In this work, the schema for capturing SMM test data within the larger NASA GRC ICME Schema [6, 7, 8, 9] will be developed and implemented for thermomechanical tests conducted at NASA GRC. The developed schema will not only store the relevant data needed for the SMMD tool, but also the material pedigree (i.e., production of the bulk material, bulk material analysis, sample cut-out diagrams, sample fabrication procedure, etc.), test pedigree (i.e., test equipment used, measurement systems used, raw test data), and analysis pedigree (i.e., how the data in the SMMD tool is calculated). Furthermore, a Python-based framework will be developed to seamlessly interact between the SMA Analytics and SMMD tools, which will write the full dataset and associated metadata to the GRC Information Management System before passing the required point-wise data to the SMMD tool. Data informatics is a key element of the NASA Vision 2040, which requires not only that data is stored and maintained throughout the material lifecycle, but that the data is also accessible and reusable such that material development efforts can be minimized. Therefore, for an ICME design approach to be realized, a centralized information management system that drives the ICME process must be able to communicate with other databases. The work that will be presented in this presentation will therefore not only demonstrate the ability of NASA GRC’s information management system to capture SMM data, but also its ability to interact with pre-existing tools specialized for such materials.

Data management↗
DOE OSTI2023

Unexpected Kinetic Solvent Effects Enhance Activity and Selectivity in Biphasic Systems

Biphasic dehydration of fructose to 5-hydroxymethylfurfural (HMF) has shown unprecedented increases in productivity, but a mechanistic understanding is lacking. Herein, we couple fast experimental reaction kinetics, multiscale modeling (phase behavior, classical molecular dynamics(MD), and quantum mechanics/molecular mechanics MD), in situ sampling, and IR and 13 C-NMR spectroscopy to elucidate the complex effects of nonpolar extracting organic solvents on the kinetics of fructose dehydration. We show that these organic solvents can reach significant mutual solubility with water at reaction temperatures, enabling the partition of the sugar and catalyst into the extracting phase. In the organic-rich environment, the dehydration of fructose proceeds faster and more selectively than in water due to increased relative abundance of the reactive furanose isomer, enhanced water–catalyst–substrate interactions driven by nanophase separation, and higher product stability stemming from preferential solvation. Furthermore, we demonstrate that these solvent effects impact other critical biphasic reactions in biomass upgrading and provide qualitative principles for solvent selection.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2023

Multiscale Hybrid Modeling of the Impact Response of the Earth's Magnetotail to Ionospheric O+ Outflow

Ionospheric outflow plays an important role in coupling the ionosphere with the solar wind-magnetosphere system. Previous multi-fluid MHD studies explored the global influence of oxygen ions of ionospheric origin (O+) on magnetospheric dynamics. A detailed exploration of the interaction of ionospheric ions with the magnetotail requires kinetic treatment for ions. We perform a self-consistent investigation of these processes with a three-dimensional space-time adaptive hybrid code, HYPERS, powered by an intelligent Event-driven Multi-Agent Planning System (EMAPS). By comparing simulations with and without outflow we conclude that oxygen ions, flowing from the ionosphere through the lobes into the tail or directly entering the inner magnetosphere, are able to significantly modify the magnetotail configuration and induce X-points and current sheet structures at locations where magnetic reconnection does not occur in a simulation without outflow, potentially very close to the Earth. This finding may have implications for interpreting substorms and magnetotail reconnection events observed for southward magnetic field simultaneously with significant contents of oxygen ions of ionospheric origin.

multiscale↗
DOE OSTI2021

AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike’s full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Influence of microstructure on replacement and porosity generation during experimental dolomitization of limestones

Replacement reactions commonly alter the multiscale pore structures of rocks during fluid-rock interactions. Analysis of these processes in various model fluid-rock systems during controlled laboratory experiments provides insights into the origins of microstructures found in natural materials. This study focused on understanding the effects of initial starting material permeability and resultant differences in transport pathways on porosity and mineralogical changes during limestone dolomitization.A series of replacement experiments (32–317 days in duration) have been conducted in which 1.59 cm (5/8 in.) diameter cores of two different limestones were reacted with saturated MgCl2 solutions at 200 °C. The Texas Cream (Austin Chalk) is a high-porosity, high-permeability limestone, whereas both the porosity and permeability of the Carthage Marble (Burlington Limestone) are relatively low. Altered limestones were imaged using scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX), Time-of-Flight Secondary Ion Mass Spectrometry (ToF–SIMS) and electron microprobe analysis (EMPA). A representative grain boundary of the low-porosity limestone was targeted for a focused ion beam (FIB) lift-out and characterized using transmission electron microscopy (TEM). These results were coupled with analyses of radial changes in the porosity distribution of the core derived from X-ray and neutron small- and ultra-small angle scattering ((U)SANS/(U)SAXS).The high-porosity/permeability limestone showed a four times faster bulk replacement rate than the lower-porosity/permeability material, and a different mechanism of porosity development. For the low-porosity limestone, a two-stage replacement occurred, with the reacted region of the core consisting of an inner rim in which the limestone was replaced by two calcite-dolomite solid solutions, and an outer rim in which the dolomite was replaced by magnesite. Elongated pores formed along grain boundaries at the initial limestone/dolomite reaction interface, and additional nanometer-scale porosity was formed at the secondary magnesite replacement rim. Grain boundaries were identified as preferential pathways for transport leading to dolomitization and a grain boundary diffusion rate was calculated based on microstructural characterization. In contrast, replacement in the high-porosity limestone was accompanied by porosity generation through replacement of individual grains by dolomite throughout the sample and, in longer runs, magnesite in outer parts of the core. These observations emphasize that both the mechanisms of the replacement reaction and the microstructure and chemistry of the replaced product are contingent on the initial structure of the starting material.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Simulator for Hydrologic Unstructured Domains (SHUD v1.0): numerical modeling of watershed hydrology with the finite volume method

Abstract. Hydrologic modeling is an essential strategy for understanding and predicting natural flows, particularly where observations are lacking in either space or time or where complex terrain leads to a disconnect in the characteristic time and space scales of overland and groundwater flow. However, significant difficulties remain for the development of efficient and extensible modeling systems that operate robustly across complex regions. This paper introduces the Simulator for Hydrologic Unstructured Domains (SHUD), an integrated, multiprocess, multiscale, flexible-time-step model, in which hydrologic processes are fully coupled using the finite volume method. SHUD integrates overland flow, snow accumulation/melt, evapotranspiration, subsurface flow, groundwater flow, and river routing, thus allowing physical processes in general watersheds to be realistically captured. SHUD incorporates one-dimensional unsaturated flow, two-dimensional groundwater flow, and a fully connected river channel network with hillslopes supporting overland flow and baseflow. The paper introduces the design of SHUD, from the conceptual and mathematical description of hydrologic processes in a watershed to the model's computational structures. To demonstrate and validate the model performance, we employ three hydrologic experiments: the V-catchment experiment, Vauclin's experiment, and a model study of the Cache Creek Watershed in northern California. Ongoing applications of the SHUD model include hydrologic analyses of hillslope to regional scales (1 m2 to 106 km2), water resource and stormwater management, and interdisciplinary research for questions in limnology, agriculture, geochemistry, geomorphology, water quality, ecology, climate and land-use change. The strength of SHUD is its flexibility as a scientific and resource evaluation tool where modeling and simulation are required.

58 GEOSCIENCES↗
NASA NTRS1993

Session on coupled atmospheric/chemistry coupled models

The session on coupled atmospheric/chemistry coupled models is reviewed. Current model limitations, current issues and critical unknowns, and modeling activity are addressed. Specific recommendations and experimental strategies on the following are given: multiscale surface layer - planetary boundary layer - chemical flux measurements; Eulerian budget study; and Langrangian experiment. Nonprecipitating cloud studies, organized convective systems, and aerosols - heterogenous chemistry are also discussed.

Thompson, Anne↗
DOE OSTI2021

Multimodal imaging of oil-in-water bilgewater emulsion and biofilms

Bilgewater emulsions, consisting of oil and surfactant mixtures in water, affect many aquatic species. Thus development of methods and techniques to treat or mitigate the formation and undesired consequences of shipboard emulsions are urgently needed. This work presents new results of the evolution of the bilgewaer chemical and physical properties using in situ imaging. A Navy O/W emulsion consisting of three oils and a detergent mixture was used as the synthetic bilgewater model [1, 2]. Synthetic oil-in-water (O/W) emulsion droplets were seled in a vacuum compatible microfluidic sample holder and imaged using a suite of tools including scanning electron microscopy (SEM), time-of-flight secondary ion mass spectrometry (ToF-SIMS), confocal laser scanning microscopy, and optical microscopy, highlighting the advantage of multiscale analysis and its applications in waste reduction and treatment in the environment. The transferrable and vacuum compatible microfluidic interface, System for Analysis at the Liquid Vacuum Interface (SALVI), was used in this study. SALVI enabled surface analysis of liquids and liquid-solid interactions using ToF-SIMS and SEM [3, 4]. Its detection window is an aperture of 2 ?m in diameter open to vacuum, permitting direct detection of the liquid surface. Liquid is withheld by surface tension within the aperture. The interface is composed of a silicon nitride membrane and polydimethylsiloxane microchannel [3, 4]. A variety of samples including complex liquid mixtures, ionic liquids, single mammalian cells, live biofilms, and solid-electrolyte interface have been analyzed using in situ imaging [5, 6]. The feasibility of using SALVI for in situ SEM imaging of nanoparticles and colloids in liquid was recently demonstrated [7]. This paper shows new findings of multimodal imaging and analysis of synthesized emulsion particles in water. Figure 1 depicts a schematic of multimodal in situ imaging of bilgewater emulsion using SEM and ToF-SIMS. Furthermore, we study the interactions of microbes and bilgewater emulsion. Microbes play an important role in the ocean. We hypothesize that microbes release organics that can act as surfactants that affect bilgewater formation or weakening. We present the first systematic study of emulsions and biofilms and investigate the effects of biofilms on bilgewater emulsions. Pseudomonas was selected as a model strain. Biofilms were cultured in a microchannel to allow culture [8]. Once a thick layer of biofilms was formed, the medium solution was changed to a mixture consisting of 50 % bilgewater emulsion. Dispersed biofilms were collected at 24 hrs. and 48 hrs. after emulsions were introduced into the channel. Bilgewater emulsions, biofilms, and mixtures of bilgewater emulsions and biofilms were analyzed using multiple in situ and ex situ techniques as described earlier. Our findings indicate that biofilms change the chemical makeup of the emulsion surface compositions and emulsion droplet size distribution, confirming the hypothesis that extracellular polymeric substance (EPS) related components released from biofilms can function as surfactants and change the oil-in-water interfaces.

bilgewater, multimodal imaging, biofilms↗
DOE OSTI2022

Prediction of local concentration fields in porous media with chemical reaction using a multi scale convolutional neural network

The study of solute transport in porous media is of interest in many chemical engineering systems. Some example applications include packed bed catalytic reactors, filtration devices, and batteries. The pore scale modeling of these systems is time consuming and may require large computing resources, for this reason computational fluid dynamics (CFD) simulations are not practical if a large number of simulations is required, like in multiscale modeling, where a model at a large scale calls for pore scale simulations. It has been shown that neural networks can be trained with a dataset of flow simulations and then predict fields orders of magnitude faster, and with less computational resources, in new domains. However, it is crucial to provide the neural network with an effective description of the domain and the undergoing operating conditions to be able to train models that generalize accurately in unseen samples. Therefore, research is needed to employ neural networks in new complex systems. The appropriate training of a network for predicting coupled flow and solute transport processes is an outstanding problem due to the complex interplay between geometry and operating conditions. In this work, we train a multi scale convolutional neural network (MSNet) with a diverse dataset of simulations of transport and chemical reaction in porous media to predict the local concentration fields in images of porous media. Our dataset contains a wide diversity of sphere pack arrangements under different operating conditions (Péclet and Reynolds numbers). Further, we train a robust model by employing different input descriptors that represent the medium and the different operating conditions of each system. Our trained model is able to provide nearly instantaneous predictions, compared to around twenty hours of the CFD workflow, with less than 3.5% error on new geometries and transport conditions. Thus the model could be easily integrated in a multiscale workflow where fast response is needed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗