Search NASA⌕ Search

SEARCH · Search NASA

Results for “multiscale”

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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dynamics and lipid membrane coupling of the RAS-RAF complex revealed via multiscale simulations

To gain molecular and mechanistic insights into initiation of the RAS-RAF signaling cascade, we developed and used a combination of multiscale simulation and experimental approaches. The influence and impact of the membrane on RAS and RAF proteins is a factor we are just beginning to understand and appreciate in more detail. Molecular simulation is an ideal methodology to further study this complicated relationship between the membrane and associated proteins. Our previous work using Multiscale Machine-learned Modeling Infrastructure investigated different lipid compositions solely around the KRAS4b protein and the interplay between protein behavior and these membrane environments. Multiscale Machine-learned Modeling Infrastructure uses machine learning to couple adjacent simulation scales and has been efficiently scaled across some of the world’s largest high-performance computers. Recently, we have expanded this multiresolution framework to include the all-atom simulation scale and to incorporate the RAF RBDCRD domains. Here, we present the overall analysis results from this new simulation campaign comprising a mixture of RAS and RAF RBDCRD proteins. Approximately 35,000 coarse-grained and 10,000 all-atom molecular dynamics simulations were completed, sampled from a variety of protein/lipid composition configurations that were generated from a micron-scale continuum simulation containing hundreds of copies of the proteins. Our studies suggest that orientations of the RAS-RBDCRD complex on the membrane occupy distinct configurational states, and the spatial patterns of lipid arrangements around these different protein states are unique to each state. The extent and size of lipid “fingerprints” imposed on the membrane by the RAS-RBDCRD protein complex are significantly larger than observed for just the RAS protein on its own. These protein complexes strongly associate, but we do not observe statistically significant preferred protein-protein orientations. These observations indicate that spatial colocalization of RAS-RBDCRD proteins in the same vicinity may be assisted by specific membrane environments, acting to increase the probability of signaling complex formation.

Carpenter, Timothy S. [Lawrence Livermore National↗
DOE OSTI2026

Multiscale modeling of packed-bed microwave reactors and estimation of intrinsic materials' permittivity

Modeling of packed-bed microwave reactors relies on an accurate representation of particle size, shape, and distribution within the bed, as well as the particles' dielectric properties. The measured permittivity of microwave susceptors (powders or structured materials) depends on the geometric features of the particles and the porosity of the bed, as well as the specific form factor of a structured material. These are effective properties and cannot be used to analyze other reactor configurations unless the geometric effects are removed. Therefore, we introduce a methodology for extracting the intrinsic particle permittivity from experimentally measured effective permittivity by combining cavity-based measurements with multiscale simulations and machine learning. Further, we develop the first multiscale model of packed-bed microwave reactors that incorporate particle effects (geometric features, random packing, and particle contact). This approach bridges macroscopic observables with mesoscopic physics, enabling analysis of local hotspots, arcing, and contact effects that control reactor performance. Using polymer-based spherical activated carbon (PBSAC) and silicon carbide (SiC) as examples, we demonstrate that the inferred particle permittivity is consistent with independent experimental heating profiles we collect from microwave reactors without adjustable parameters. Finally, this methodology establishes a foundation for predictive, multiscale design of microwave packed-bed reactors that explicitly accounts for particle-scale effects, enabling the estimation of intrinsic permittivity for the first time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Uncertainty quantification in multiscale simulation of woven fiber composites

We report woven fiber composites have been increasingly employed as light-weight materials in aerospace, construction, and transportation industries due to their superior properties. These materials possess a hierarchical structure that necessitates the use of multiscale simulations in their modeling. To account for the inherent uncertainty in materials, such simulations must be integrated with statistical uncertainty quantification (UQ) and propagation (UP) methods. However, limited advancement has been made in this regard due to the significant computational costs and complexities in modeling spatially correlated structural variations coupled at different scales. In this work, a non-intrusive approach is proposed for multiscale UQ and UP to address these limitations. We introduce the top-down sampling method that allows to model non-stationary and continuous (but not differentiable) spatial variations of uncertainty sources by creating nested random fields (RFs) where the hyperparameters of an ensemble of RFs is characterized by yet another RF. We employ multi-response Gaussian RFs in top-down sampling and leverage statistical techniques (such as metamodeling and dimensionality reduction) to address the considerable computational costs of multiscale simulations. We apply our approach to quantify the uncertainty in a cured woven composite due to spatial variations of yarn angle, fiber volume fraction, and fiber misalignment angle. Finally, our results indicate that, even in linear analysis, the effect of uncertainty sources on the material’s response could be significant.

42 ENGINEERING↗
DOE OSTI2018

Exploring the effects of climatic variables on monthly precipitation variation using a continuous wavelet-based multiscale entropy approach

Highlights: • Developed a wavelet transform entropy approach to study precipitation. • Multiscale entropy values were the basis for precipitation regionalization. • Various entropy-based bands were introduced to segregate the CWME values for each cluster. • The relationship between changes of CWME and mean monthly precipitation were investigated. • Central rain gauges of each cluster were used for further analysis. Understanding precipitation on a regional basis is an important component of water resources planning and management. The present study outlines a methodology based on continuous wavelet transform (CWT) and multiscale entropy (CWME), combined with self-organizing map (SOM) and k-means clustering techniques, to measure and analyze the complexity of precipitation. Historical monthly precipitation data from 1960 to 2010 at 31 rain gauges across Iran were preprocessed by CWT. The multi-resolution CWT approach segregated the major features of the original precipitation series by unfolding the structure of the time series which was often ambiguous. The entropy concept was then applied to components obtained from CWT to measure dispersion, uncertainty, disorder, and diversification of subcomponents. Based on different validity indices, k-means clustering captured homogenous areas more accurately, and additional analysis was performed based on the outcome of this approach. The 31 rain gauges in this study were clustered into 6 groups, each one having a unique CWME pattern across different time scales. The results of clustering showed that hydrologic similarity (multiscale variation of precipitation) was not based on geographic contiguity. According to the pattern of entropy across the scales, each cluster was assigned an entropy signature that provided an estimation of the entropy pattern of precipitation data in each cluster. Based on the pattern of mean CWME for each cluster, a characteristic signature was assigned, which provided an estimation of the CWME of a cluster across scales of 1–2, 3–8, and 9–13 months relative to other stations. The validity of the homogeneous clusters demonstrated the usefulness of the proposed approach to regionalize precipitation. Further analysis based on wavelet coherence (WTC) was performed by selecting central rain gauges in each cluster and analyzing against temperature, wind, Multivariate ENSO index (MEI), and East Atlantic (EA) and North Atlantic Oscillation (NAO), indeces. The results revealed that all climatic features except NAO influenced precipitation in Iran during the 1960–2010 period.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Multiscale modeling of twinning and detwinning behavior of HCP polycrystals

A multiscale scheme is presented here to capture the twinning and detwinning mechanisms during cyclic loading of HCP polycrystals. In the current framework, the crystal plasticity finite element (CPFE) method is employed at the microstructural scale. The response of twinned regions at a material point is calculated using a novel homogenization scheme via a Taylor-type model. The homogenized behavior is then used in the CPFE to obtain the response of a polycrystal. An efficient rate-independent crystal plasticity model that has been incorporated into the open source PRISMS-Plasticity software is introduced here to capture the response of the twinned region. Accordingly, the corresponding algorithmic tangent modulus is derived. Finally, this multiscale scheme is validated against data available for the cyclic response of Mg alloy ZK60A. The results show that the multiscale model can capture the key elements of the sample response observed during the experiment.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Multiscale Characterization of Electrode-Induced Degradation in Perovskite Solar Cells

The stability of metal-halide-perovskite (MHP) solar cells must be understood and improved for the commercial viability of MHP technologies. Here, we apply multiscale characterization methods to study degradation modes, specifically electrode corrosion, for p-i-n MHP partial device stacks and full devices that are stored in the dark under an inert atmosphere. Our multiscale characterization approaches include full-device electro-optical performance using current-voltage (JV) curves and spatial imaging with electroluminescence (EL) and photoluminescence (PL). We further correlate interface properties using cross-sectional Kelvin probe force microscopy, which maps the nanoscale electric field properties, and electron microscopy, which demonstrates structural and chemical features. Devices stored as a full device stack degrade primarily by metal (Ag) electrode diffusion into the absorber, with formation of AgI byproducts and Ag accumulation near the indium tin oxide (ITO) contact. This causes decomposition of the perovskite absorber domains, loss of the potential drop at the electron transport layer (ETL)/perovskite interface near the metal contact, and increased equivalent resistance at the perovskite/hole transport layer (HTL) interface near the ITO contact. The devices stored without metal show a different degradation pathway dominated by corrosion of the ITO, creating voids at the ITO electrode surface with diffusion of In and Sn into the absorber. We conclude that metal electrode-induced degradation is the most severe degradation pathway under dark storage, but that ITO corrosion and absorber instability must also be mitigated. We further demonstrate mitigation of these degradation pathways by changes to the device stack, including a SnO x blocking layer at the ETL side and replacing ITO with FTO at the HTL side. These results provide a useful demonstration of specific dark degradation pathways at each electrode interface, as well as a unique multiscale example that links degradation of chemical, structural, and electrical interface properties to the full-device electro-optical characteristics.

14 SOLAR ENERGY↗
DOE OSTI2021

Unraveling the Ion Adsorption Kinetics in Microporous Carbon Electrodes: A Multiscale Quantum-Continuum Simulation and Experimental Approach

With the understanding that sorption in porous carbon electrodes is crucial to many environmental and energy technologies, such as capacitive deionization (CDI), supercapacitor energy storage, and activated carbon filters. In each of these examples, a practical model that can describe ion electrosorption kinetics is highly desirable for accelerating material design. Here, we proposed a multiscale model to study the ion electrosorption kinetics in porous carbon electrodes by combining quantum mechanical simulations with continuum approaches. Our model integrates the Butler-Volmer (BV) equation for sorption kinetics and a continuously stirred tank reactor (CSTR) formulation with atomistic calculations of ion hydration and ion-pore interactions based on density functional theory (DFT). We validated our model experimentally by using ion mixtures in a flow-through electrode CDI device and developed an in-line UV absorption system to provide unprecedented resolution of individual ions in the separation process. We showed that the multiscale model captures unexpected experimental phenomena that cannot be explained by the traditional ion electrosorption theory. The proposed multiscale framework provides a viable approach for modeling separation processes in systems where pore sizes and ion hydration effects strongly influence the sorption kinetics, which can be leveraged to explore possible strategies for improving carbon-based and, more broadly, pore-based technologies

36 MATERIALS SCIENCE↗
DOE OSTI2025

A neural master equation framework for multiscale modeling of molecular processes: application to atomic-scale plasma processes

Plasma-surface interactions (PSI) play a crucial role in microelectronics fabrication; however, their multiscale nature and array of complex, often unknown interactions make computational modeling of PSIs extremely difficult. To this end, we propose a general neural master equation (NME) framework that uses master equations to describe the dynamics of a molecular process, wherein neural networks learned from atomistic simulations represent unknown transitions between different system states. By leveraging the physics-based structure of master equations and data-driven state transitions, the NME framework promotes generalizability and physics interpretability, and can bridge disparate length and time scales. The framework is demonstrated for multiscale modeling of Si atomic layer etching and reactive ion etching, where the learned NME-based surface kinetic models exhibit good predictive and extrapolative capabilities for predicting experimentally relevant observables as a function of process parameters. The NME-based surface kinetic models obey physical constraints, which are violated in models based on neural ordinary differential equations. The proposed NME framework for multiscale modeling of molecular processes can pave the way for the discovery of new chemistries and materials in atomic-scale plasma processes.

Chemical engineering↗
DOE OSTI2022

Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems

Abstract A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained representations due to underlying highly nonlinear multiscale interactions. These closure models are common in many nonlinear spatiotemporal systems to account for losses due to reduced order representations, including many transport phenomena in fluids. Previous data-driven closure modeling efforts have mostly focused on supervised learning approaches using high fidelity simulation data. On the other hand, reinforcement learning (RL) is a powerful yet relatively uncharted method in spatiotemporally extended systems. In this study, we put forth a modular dynamic closure modeling and discovery framework to stabilize the Galerkin projection based reduced order models that may arise in many nonlinear spatiotemporal dynamical systems with quadratic nonlinearity. However, a key element in creating a robust RL agent is to introduce a feasible reward function, which can be constituted of any difference metrics between the RL model and high fidelity simulation data. First, we introduce a multi-modal RL to discover mode-dependant closure policies that utilize the high fidelity data in rewarding our RL agent. We then formulate a variational multiscale RL (VMRL) approach to discover closure models without requiring access to the high fidelity data in designing the reward function. Specifically, our chief innovation is to leverage variational multiscale formalism to quantify the difference between modal interactions in Galerkin systems. Our results in simulating the viscous Burgers equation indicate that the proposed VMRL method leads to robust and accurate closure parameterizations, and it may potentially be used to discover scale-aware closure models for complex dynamical systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

X-ray microscopy enables multiscale high-resolution 3D imaging of plant cells, tissues, and organs

Capturing complete internal anatomies of plant organs and tissues within their relevant morphological context remains a key challenge in plant science. While plant growth and development are inherently multiscale, conventional light, fluorescence, and electron microscopy platforms are typically limited to imaging of plant microstructure from small flat samples that lack a direct spatial context to, and represent only a small portion of, the relevant plant macrostructures. We demonstrate technical advances with a lab-based X-ray microscope (XRM) that bridge the imaging gap by providing multiscale high-resolution three-dimensional (3D) volumes of intact plant samples from the cell to the whole plant level. Serial imaging of a single sample is shown to provide sub-micron 3D volumes co-registered with lower magnification scans for explicit contextual reference. High-quality 3D volume data from our enhanced methods facilitate sophisticated and effective computational segmentation. Advances in sample preparation make multimodal correlative imaging workflows possible, where a single resin-embedded plant sample is scanned via XRM to generate a 3D cell-level map, and then used to identify and zoom in on sub-cellular regions of interest for high-resolution scanning electron microscopy. In total, we present the methodologies for use of XRM in the multiscale and multimodal analysis of 3D plant features using numerous economically and scientifically important plant systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Transition between inverse and direct energy cascades in multiscale optical turbulence

Transition between inverse and direct energy cascades in multiscale optical turbulence. Multiscale turbulence naturally develops and plays an important role in many fluid, gas, and plasma phenomena. Statistical models of multiscale turbulence usually employ Kolmogorov hypotheses of spectral locality of interactions (meaning that interactions primarily occur between pulsations of comparable scales) and scale-invariance of turbulent pulsations. However, optical turbulence described by the nonlinear Schrodinger equation exhibits breaking of both the Kolmogorov locality and scale-invariance. A weaker form of spectral locality that holds for multi-scale optical turbulence enables a derivation of simplified evolution equations that reduce the problem to a single scale modeling. We present the derivation of these equations for Kerr media with random inhomogeneities. Then, we find the analytical solution that exhibits a transition between inverse and direct energy cascades in optical turbulence.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Advancing material modeling in hydrocodes using a concurrent finite-element and molecular dynamics multiscale framework

We present a multiscale simulation framework that couples the finite-element method with molecular dynamics. Bypassing traditional equations of state (EOS) by using in-line atomistic simulations, the method offers the advantage of incorporating detailed microscale physics not easily represented with coarse-grained models. Coupling consistency with the continuum code is ensured through the use of lifting and restriction operators, in line with heterogeneous multiscale methods. The concurrent continuum-atomistic framework is validated through comparison with experimental results and conventional EOS models, and demonstrated in a shock-driven hydrodynamic flow simulation under extreme conditions. We further evaluate the framework's usability by comparing it to state-of-the-art EOS models of deuterium. A computational performance study reveals that the atomistic EOS evaluation is a feasible alternative to conventional approaches, and demonstrates a weak scaling of 99% efficiency. These results highlight the framework's potential for large-scale multiscale modeling across a broad range of materials and conditions.

Computer science↗
DOE OSTI2016

WE-H-206-01: Photoacoustic Tomography: Multiscale Imaging From Organelles to Patients by Ultrasonically Beating the Optical Diffusion Limit

Lihong V. Wang: Photoacoustic tomography (PAT), combining non-ionizing optical and ultrasonic waves via the photoacoustic effect, provides in vivo multiscale functional, metabolic, and molecular imaging. Broad applications include imaging of the breast, brain, skin, esophagus, colon, vascular system, and lymphatic system in humans or animals. Light offers rich contrast but does not penetrate biological tissue in straight paths as x-rays do. Consequently, high-resolution pure optical imaging (e.g., confocal microscopy, two-photon microscopy, and optical coherence tomography) is limited to penetration within the optical diffusion limit (∼1 mm in the skin). Ultrasonic imaging, on the contrary, provides fine spatial resolution but suffers from both poor contrast in early-stage tumors and strong speckle artifacts. In PAT, pulsed laser light penetrates tissue and generates a small but rapid temperature rise, which induces emission of ultrasonic waves due to thermoelastic expansion. The ultrasonic waves, orders of magnitude less scattering than optical waves, are then detected to form high-resolution images of optical absorption at depths up to 7 cm, conquering the optical diffusion limit. PAT is the only modality capable of imaging across the length scales of organelles, cells, tissues, and organs (up to whole-body small animals) with consistent contrast. This rapidly growing technology promises to enable multiscale biological research and accelerate translation from microscopic laboratory discoveries to macroscopic clinical practice. PAT may also hold the key to label-free early detection of cancer by in vivo quantification of hypermetabolism, the quintessential hallmark of malignancy. Learning Objectives: To understand the contrast mechanism of PAT To understand the multiscale applications of PAT Benjamin M. W. Tsui: Multi-modality molecular imaging instrumentation and techniques have been major developments in small animal imaging that has contributed significantly to biomedical research during the past decade. The initial development was an extension of clinical PET/CT and SPECT/CT from human to small animals and combine the unique functional information obtained from PET and SPECT with anatomical information provided by the CT in registered multi-modality images. The requirements to image a mouse whose size is an order of magnitude smaller than that of a human have spurred advances in new radiation detector technologies, novel imaging system designs and special image reconstruction and processing techniques. Examples are new detector materials and designs with high intrinsic resolution, multi-pinhole (MPH) collimator design for much improved resolution and detection efficiency compared to the conventional collimator designs in SPECT, 3D high-resolution and artifact-free MPH and sparse-view image reconstruction techniques, and iterative image reconstruction methods with system response modeling for resolution recovery and image noise reduction for much improved image quality. The spatial resolution of PET and SPECT has improved from ∼6–12 mm to ∼1 mm a few years ago to sub-millimeter today. A recent commercial small animal SPECT system has achieved a resolution of ∼0.25 mm which surpasses that of a state-of-art PET system whose resolution is limited by the positron range. More recently, multimodality SA PET/MRI and SPECT/MRI systems have been developed in research laboratories. Also, multi-modality SA imaging systems that include other imaging modalities such as optical and ultrasound are being actively pursued. In this presentation, we will provide a review of the development, recent advances and future outlook of multi-modality molecular imaging of small animals. Learning Objectives: To learn about the two major multi-modality molecular imaging techniques of small animals. To learn about the spatial resolution achievable by the molecular imaging systems for small animal today. To learn about the new multi-modality imaging instrumentation and techniques that are being developed. Sang Hyun Cho; X-ray fluorescence (XRF) imaging, such as x-ray fluorescence computed tomography (XFCT), offers unique capabilities for accurate identification and quantification of metals within the imaging objects. As a result, it has emerged as a promising quantitative imaging modality in recent years, especially in conjunction with metal-based imaging probes. This talk will familiarize the audience with the basic principles of XRF/XFCT imaging. It will also cover the latest development of benchtop XFCT technology. Additionally, the use of metallic nanoparticles such as gold nanoparticles, in conjunction with benchtop XFCT, will be discussed within the context of preclinical multimodal multiplexed molecular imaging. Learning Objectives: To learn the basic principles of XRF/XFCT imaging To learn the latest advances in benchtop XFCT development for preclinical imaging Funding support received from NIH and DOD; Funding support received from GE Healthcare; Funding support received from Siemens AX; Patent royalties received from GE Healthcare; L. Wang, Funding Support: NIH; COI: Microphotoacoustics; S. Cho, Yes: ;NIH/NCI grant R01CA155446 DOD/PCRP grant W81XWH-12-1-0198.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Meta Biome: a multiscale model integrating agent-based and metabolic networks to reveal spatial regulation in gut mucosal microbial communities

ABSTRACT Mucosal microbial communities (MMCs) are complex ecosystems near the mucosal layers of the gut essential for maintaining health and modulating disease states. Despite advances in high-throughput omics technologies, current methodologies struggle to capture the dynamic metabolic interactions and spatiotemporal variations within MMCs. In this work, we presentMetaBiome, a multiscale model integrating agent-based modeling (ABM), finite volume methods, and constraint-based models to explore the metabolic interactions within these communities. Integrating ABM allows for the detailed representation of individual microbial agents each governed by rules that dictate cell growth, division, and interactions with their surroundings. Through a layered approach—encompassing microenvironmental conditions, agent information, and metabolic pathways—we simulated different communities to showcase the potential of the model. Using ourin-silicoplatform, we explored the dynamics and spatiotemporal patterns of MMCs in the proximal small intestine and the cecum, simulating the physiological conditions of the two gut regions. Our findings revealed how specific microbes adapt their metabolic processes based on substrate availability and local environmental conditions, shedding light on spatial metabolite regulation and informing targeted therapies for localized gut diseases.MetaBiome provides a detailed representation of microbial agents and their interactions, surpassing the limitations of traditional grid-based systems. This work marks a significant advancement in microbial ecology, as it offers new insights into predicting and analyzing microbial communities. IMPORTANCE Our study presents a novel multiscale model that combines agent-based modeling, finite volume methods, and genome-scale metabolic models to simulate the complex dynamics of mucosal microbial communities in the gut. This integrated approach allows us to capture spatial and temporal variations in microbial interactions and metabolism that are difficult to study experimentally. Key findings from our model include the following: (i) prediction of metabolic cross-feeding and spatial organization in multi-species communities, (ii) insights into how oxygen gradients and nutrient availability shape community composition in different gut regions, and (iii) identification of spatiallyregulated metabolic pathways and enzymes inE. coli. We believe this work represents a significant advance in computational modeling of microbial communities and provides new insights into the spatial regulation of gut microbiome metabolism. The multiscale modeling approach we have developed could be broadly applicable for studying other complex microbial ecosystems.

Microbiology↗
DOE OSTI2021

Generalizable coordination of large multiscale workflows: challenges and learnings at scale

The advancement of machine learning techniques and the heterogeneous architectures of most current supercomputers are propelling the demand for large multiscale simulations that can automatically and autonomously couple diverse components and map them to relevant resources to solve complex problems at multiple scales. Nevertheless, despite the recent progress in workflow technologies, current capabilities are limited to coupling two scales. In the first-ever demonstration of using three scales of resolution, we present a scalable and generalizable framework that couples pairs of models using machine learning and in situ feedback. We expand upon the massively parallel Multiscale Machine-Learned Modeling Infrastructure (MuMMI), a recent, award-winning workflow, and generalize the framework beyond its original design. We discuss the challenges and learnings in executing a massive multiscale simulation campaign that utilized over 600,000 node hours on Summit and achieved more than 98% GPU occupancy for more than 83% of the time. We present innovations to enable several orders of magnitude scaling, including simultaneously coordinating 24,000 jobs, and managing several TBs of new data per day and over a billion files in total. Finally, we describe the generalizability of our framework and, with an upcoming open-source release, discuss how the presented framework may be used for new applications.

Bhatia, Harsh↗
DOE OSTI2022

Evaluation of Turbulence and Dispersion in Multiscale Atmospheric Simulations over Complex Urban Terrain during the Joint Urban 2003 Field Campaign

Abstract This paper evaluates the representation of turbulence and its effect on transport and dispersion within multiscale and microscale-only simulations in an urban environment. These simulations, run using the Weather Research and Forecasting Model with the addition of an immersed boundary method, predict transport and mixing during a controlled tracer release from the Joint Urban 2003 field campaign in Oklahoma City, Oklahoma. This work extends the results of a recent study through analysis of turbulence kinetic energy and turbulence spectra and their role in accurately simulating wind speed, direction, and tracer concentration. The significance and role of surface heat fluxes and use of the cell perturbation method in the numerical simulation setup are also examined. Our previous study detailed the model development necessary for our multiscale simulations, examined model skill at predicting wind speeds and tracer concentrations, and demonstrated that dynamic downscaling from mesoscale to microscale through a sequence of nested simulations can improve predictions of transport and dispersion relative to a microscale-only simulation forced by idealized meteorology. Here, predictions are compared with observations to assess qualitative agreement and statistical model skill at predicting wind speed, wind direction, tracer concentration, and turbulent kinetic energy at locations throughout the city. We also investigate the scale distribution of turbulence and the associated impact on model skill, particularly for predictions of transport and dispersion. Our results show that downscaled large-scale turbulence, which is unique to the multiscale simulations, significantly improves predictions of tracer concentrations in this complex urban environment. Significance Statement Simulations of atmospheric transport and mixing in urban environments have many applications, including pollution modeling for urban planning or informing emergency response following a hazardous release. These applications include phenomena with spatial scales spanning from millimeters to kilometers. Most simulations resolve flow only within the urban area of interest, omitting larger scales of turbulence and regional influences. This study examines a method that resolves both the small and large-scale flow features. We evaluate simulation accuracy by comparing predictions with observations from an experiment involving the release of a tracer gas in Oklahoma City, Oklahoma, with emphasis on correctly modeling turbulent fluctuations. Our results demonstrate the importance of resolving large-scale flow features when predicting transport and dispersion in urban environments.

42 ENGINEERING↗
DOE OSTI2016

MD/DPD Multiscale Framework for Predicting Morphology and Stresses of Red Blood Cells in Health and Disease

Healthy red blood cells (RBCs) have remarkable deformability, squeezing through narrow capillaries as small as 3 microns in diameter without any damage. However, in many hematological disorders the spectrin network and lipid bilayer of diseased RBCs may be significantly altered, leading to impaired functionality including loss of deformability. We employ a two-component whole-cell multiscale model to quantify the biomechanical characteristics of the healthy and diseased RBCs, including Plasmodium falciparum-infected RBCs (Pf-RBCs) and defective RBCs in hereditary disorders, such as spherocytosis and elliptocytosis. In particular, we develop a two-step multiscale framework based on coarse-grained molecular dynamics (CGMD) and dissipative particle dynamics (DPD) to predict the static and dynamic responses of RBCs subject to tensile forcing, using experimental information only on the structural defects in the lipid bilayer, cytoskeleton, and their interaction. We first employ CGMD on a small RBC patch to compute the shear modulus, bending stiffness, and network parameters, which are subsequently used as input to a whole-cell DPD model to predict the RBC shape and corresponding stress field. For Pf-RBCs at trophozoite and schizont stages, the presence of cytoadherent knobs elevates the shear response in the lipid bilayer and stiffens the RBC membrane. For RBCs in spherocytosis and elliptocytosis, the bilayer-cytoskeleton interaction is weakened, resulting in substantial increase of the tensile stress in the lipid bilayer. Furthermore, we investigate the transient behavior of stretching deformation and shape relaxation of the normal and defective RBCs. Different from the normal RBCs possessing high elasticity, our simulations reveal that the defective RBCs respond irreversibly, i.e., they lose their ability to recover the normal biconcave shape in successive loading cycles of stretching and relaxation. Our findings provide fundamental insights into the microstructure and biomechanics of RBCs, and demonstrate that the two-step multiscale framework presented here can be used effectively for in silico studies of hematological disorders based on first principles and patient-specific experimental input at the protein level.

Biochemistry & Molecular Biology↗
DOE OSTI2021

Multiscale Characterization of the Caney Shale — An Emerging Play in Oklahoma

From a hydrocarbon perspective, the Caney Shale has historically been evaluated as a sealing unit, which resulted in limited studies characterizing the rock properties of the Caney Shale and its suitability for hydraulic fracturing. The objective of our research is to help bridge the current knowledge gap through the integration of multiscale laboratory techniques and to characterize the macro- and microscale rock properties of the Caney Shale. We employed an integrated approach for the characterization of the Caney using 200 ft (61 m) of Caney core from a target well in southern Oklahoma. Core observation and petrographic analysis of thin sections were combined to characterize the general rock types and associated fabrics and textures. Mineralogical composition, pore system architecture, and rock fabric were analyzed using x-ray diffraction (XRD), scanning electron microscopy/energy dispersive x-ray spectroscopy (SEM/EDS), and focused ion beam (FIB)-SEM. In addition, rebound hardness and indentation testing were carried out to determine rock hardness (brittleness) and elasticity, respectively. With the integrated multiscale characterization, three mixed carbonate-siliciclastic rock types were identified — mudstone, calcareous siltstone, and silty carbonate — likely representing a spectrum of deposition from low to relatively high energy environments in the distal portions of a ramp system. Silty carbonate contains mostly interparticle pores. The calcareous siltstones and silty mudstones contain a combination of organic matter pores and interparticle pores. Each of the rock types shows unique mineralogical compositions based on XRD. The mudstone lithofacies has the highest clay content and the least carbonate content. Calcareous siltstones show moderate carbonate and clay content. Silty carbonate indicates the highest carbonate content with the least clay content. In an order of mudstone, calcareous siltstone, and silty carbonate, rebound hardness and Young’s modulus show an increasing trend. As a result of rock-fluid interactions, there are potential scaling reactions during completion and production that could ultimately affect permeability and production rates. Overall, the proposed multiscale integration approach is critical for the geologic characterization of most rocks. However, in shale reservoirs dominated by microporosity and microstructure where engineered fractures are expected to provide permeability at a reservoir scale, successful integration is essential. An optimized, integrated geological characterization of the Caney Shale that is well aligned with the engineering designs in drilling, completing, and producing wellbores will ultimately lead to optimal production while providing safe and environmentally responsible operations.

Wang, Yulun↗