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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Multiscale and multidimensional modeling of particle acceleration and transport in solar flares

Multi-messenger, multi-viewpoint, and time-resolved observations of solar flares are now providing unprecedented constraints on particle acceleration sites, energy conversion, and energy transport. The interpretation of current observations, including microwave imaging spectroscopy from EOVSA, hard x-ray (HXR) imaging from Solar Orbiter/STIX, gamma-ray diagnostics from Fermi, and in situ measurements from Parker Solar Probe and Solar Orbiter, collectively demands modeling frameworks that go beyond traditional spatially unresolved, one-zone models or single-mechanism descriptions. This review surveys multiscale and multidimensional modeling approaches, including kinetic, magnetohydrodynamic (MHD), and macroscopic particle models, that are being developed to meet the need. Kinetic simulations reveal that three-dimensional (3D) effects, including field-line chaos and self-generated turbulence, are essential for sustained power-law particle acceleration. MHD simulations now capture flux-rope eruptions, plasmoid-unstable current sheets, and turbulent flare regions in realistic magnetic topologies. Macroscopic models coupling MHD with energetic-particle models produce spatially resolved electron distributions and synthetic HXR and microwave emissions for direct comparison with observations. Despite these advances, outstanding challenges remain in bridging kinetic and global scales, improving MHD simulations and macroscopic particle models, and achieving quantitative model-observation closure.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Role of chemical production and depositional losses on formaldehyde in the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM)

Abstract. Formaldehyde (HCHO) is an important air pollutant with direct cancer risk and ozone-forming potential. HCHO sources are complex because HCHO is both directly emitted and produced from oxidation of most gas-phase reactive organic carbon. We update the secondary production of HCHO in the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM) in the Community Multiscale Air Quality (CMAQ) model. Production of HCHO from isoprene and monoterpenes is increased, correcting an underestimate in the current version. Simulated June–August surface HCHO during peak photochemical production (11:00–15:00 LT, local time) increased by 0.6 ppb (32 %) over the southeastern USA and by 0.2 ppb (13 %) over the contiguous USA. The increased HCHO compares more favorably with satellite-based observations from the TROPOspheric Monitoring Instrument (TROPOMI) and from aircraft-based observations. Evaluation against hourly surface observations indicates a missing nighttime sink that can be improved by increased nighttime deposition, which reduces June–August nocturnal (20:00–04:00 LT) surface HCHO by 1.1 ppb (36 %) over the southeastern USA and 0.5 ppb (29 %) over the contiguous USA. The ability of CRACMM to capture peak levels of HCHO at midday is improved, particularly at sites in the northeastern USA, while peak levels at sites in the southeastern USA are improved, although still lower than observed. Using established risk assessment methods, lifetime exposure of the population in the contiguous USA (∼ 320 million) to ambient HCHO levels predicted here may result in 6200 lifetime cancer cases, with 40 % from controllable anthropogenic emissions of nitrogen oxides and reactive organic compounds. Chemistry updates will be available in CRACMM version 2 (CRACMM2) in CMAQv5.5.

Skipper, T. Nash↗
DOE OSTI2022

A view on the current and future impact of research reactors

Full text of publication follows. The current fleet of nuclear research reactors worldwide is nearly 70 years old. These reactors have proven to be extremely valuable tools of nuclear science and engineering with a broad and interdisciplinary impact. To date, research reactors are utilized as tools for understanding the physics, operations, and safety of nuclear fission systems. In addition, they are used as intense sources of radiation in support of irradiation testing and nondestructive examination of materials. As this fleet of reactors ages, an urgent need exists to establish new facilities that can propel the benefit of these reactors into the 21. century. In fact, an opportunity exists to build research reactors based on technology concepts that are being considered for nuclear energy reactors. This may include high temperature gas cooled and/or molten salt based advanced and micro reactor concepts. Such future reactors should be designed to maintain the broad utility of current reactors in research and education. However, modern research reactors can be purposefully designed and instrumented to access neutronic and thermal hydraulic information that would support the development and validation of reactor multi-physics modeling and simulation techniques. In this case, the entire phenomenological paradigm of the reactor may be captured to understand the neutronic multiscale and its impact on operations and safety. Moreover, the generated data can be channeled to drive anticipatory examination of the state of the reactor. In general, a symbiotic relation may be envisioned between the modern research reactor and power reactor fleets, which could facilitate the safe and efficient implementation of clean nuclear energy. (author)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Continuum Modeling of Porous Electrodes for Electrochemical Synthesis

Electrochemical synthesis possesses substantial promise to utilize renewable energy sources to power the conversion of abundant feedstocks to value-added commodity chemicals and fuels. Of the potential system architectures for these processes, only systems employing 3-D structured porous electrodes have the capacity to achieve the high rates of conversion necessary for industrial scale. However, the phenomena and environments in these systems are not well understood and are challenging to probe experimentally. Fortunately, continuum modeling is well-suited to rationalize the observed behavior in electrochemical synthesis, as well as to ultimately provide recommendations for guiding the design of next-generation devices and components. In this review, we begin by presenting an historical review of modeling of porous electrode systems, with the aim of showing how past knowledge of macroscale modeling can contribute to the rising challenge of electrochemical synthesis. We then present a detailed overview of the governing physics and assumptions required to simulate porous electrode systems for electrochemical synthesis. Here, leveraging the developed understanding of porous-electrode theory, we survey and discuss the present literature reports on simulating multiscale phenomena in porous electrodes in order to demonstrate their relevance to understanding and improving the performance of devices for electrochemical synthesis. Lastly, we provide our perspectives regarding future directions in the development of models that can most accurately describe and predict the performance of such devices and discuss the best potential applications of future models.

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

Multiscale Simulation of Precipitation Over East Asia by Variable Resolution CAM-MPAS

This study evaluates the precipitation over East Asia simulated by the variable resolution CAM-MPAS featuring the Model for Prediction Across Scales (MPAS) atmospheric dynamical core coupled with the physics parameterizations of the Community Atmosphere Model (CAM) version 5.4. Two CAM-MPAS experiments, one with a global quasi-uniform resolution mesh of 120 km (MPAS-UR) and the other with a variable resolution mesh of 30 to 120 km refined over East Asia (MPAS-VR), are conducted from 1989 to 2005 following the Atmospheric Model Intercomparison Project protocol. Two regional climate model simulations at ~25 km resolution from the Coordinated Regional Downscaling Experiment East Asia second plan are also analyzed for comparison. Results show that CAM-MPAS performs better than the selected regional models in simulating the precipitation climatology over East Asia. Compared with MPAS-UR, MPAS-VR with refinement over East Asia better simulates the precipitation over and around the Tibetan Plateau (TP), the frequency distribution of light, moderate, and heavy precipitation in various regions of China, and the interannual precipitation pattern associated with the East Asian summer monsoon. The improvements in MPAS-VR relative to MPAS-UR largely come from the decreased grid spacing rather than the use of finer-scale terrain information. Particularly, decreased grid spacing is more essential for simulating the precipitation magnitude over TP and the location of peak precipitation south of TP, likely due to the better resolved physical and dynamical processes associated with orographic precipitation. The 30-120 km CAM-MPAS model is shown to be a promising tool for precipitation simulation over East Asia.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Ab initio generalized Langevin equation

We introduce a machine learning–based approach called ab initio generalized Langevin equation (AIGLE) to model the dynamics of slow collective variables (CVs) in materials and molecules. In this scheme, the parameters are learned from atomistic simulations based on ab initio quantum mechanical models. Force field, memory kernel, and noise generator are constructed in the context of the Mori–Zwanzig formalism, under the constraint of the fluctuation–dissipation theorem. Combined with deep potential molecular dynamics and electronic density functional theory, this approach opens the way to multiscale modeling in a variety of situations. Here, we demonstrate this capability with a study of two mesoscale processes in crystalline lead titanate, namely the field-driven dynamics of a planar ferroelectric domain wall, and the dynamics of an extensive lattice of coarse-grained electric dipoles. In the first case, AIGLE extends the reach of ab initio simulations to a regime of noise-driven motions not accessible to molecular dynamics. In the second case, AIGLE deals with an extensive set of CVs by adopting a local approximation for the memory kernel and retaining only short-range noise correlations. The scheme is computationally more efficient than molecular dynamics by several orders of magnitude and mimics the microscopic dynamics at low frequencies where it reproduces accurately the dominant far-infrared absorption frequency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Toward predictive permeabilities: Experimental measurements and multiscale simulation of methanol transport in Nafion

A polymer membrane's permeability to solutes determines its suitability for various applications: a permeability value is essential for predicting performance in diverse contexts. Using aqueous methanol permeation through Nafion as an example, we describe a methodology for determining membrane permeability that accounts for boundary layer effects and the possibility of swelling. For the materials and apparatus used herein, analysis of a permeance measurement and computational fluid dynamics simulations show that the mass transfer boundary layer is on the order of ones to tens of microns. Additionally, the data are used to develop and validate a multiscale model describing solute permeation through a hydrated membrane as a series of physical mechanistic steps: reversible adsorption from solution at the membrane interface, diffusion driven by a concentration gradient within the membrane, and reversible desorption into solution at the opposite membrane interface. The validated model is used to predict methanol transport across a solar-driven CO 2 reduction device and to assess the impact of polymer changes on the measured value. The approach of combining experimental data, computational fluid dynamics, and the mechanistic multiscale model is expected to provide more accurate analysis of membrane permeation data in cases with polymer swelling or unusual device geometries, among others.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Impact of the Earthquake Rupture on Ground-Motion Variability of the 24 August 2016 M w 6.2 Amatrice, Italy, Earthquake

Here, the devastating 24 August 2016 M w 6.2 earthquake that struck Amatrice, Italy, marked the beginning of a prolonged seismic sequence dominated by three subsequent M w ≥6.0 events in the central Apennines region. The earthquake destroyed Amatrice’s historic center, claiming the lives of 299 individuals and causing widespread damage in the neighboring villages. The severity of the ground shaking, with a recorded maximum acceleration of 850 cm/s 2 on the east–west component at the Amatrice station, was far greater than the predicted acceleration based on the Italian ground-motion model (GMM). As pointed out by several investigations, the observed ground-motion amplitude and its spatial variability during the earthquake can be linked to specific rupture characteristics, including slip distribution and rupture directivity effects revealed by the observed data (Tinti et al., 2016; Pischiutta et al., 2021). In this study, we conducted physics-based 3D numerical simulations of ground motion for the Amatrice earthquake for frequencies up to 3 Hz. We employed a series of kinematic rupture models and a well-constrained local 3D velocity model incorporating surface topography. The kinematic rupture realizations were generated using multiscale hybrid and fully stochastic models, following the technique proposed by Graves and Pitarka (2016). We focused on assessing the sensitivity of near-fault ground-motion amplitudes to earthquake rupture characteristics, in particular, the spatial slip pattern. To evaluate the quality of our simulations, we employed goodness-of-fit measurements performed in comparisons of simulated and recorded ground motions. The simulated ground motions compare well with the recorded data and predictions from GMMs for Italy, ITA18 (Lanzano et al., 2019). However, we found that the simulated interevent ground-motion variability (randomness in the source process) of peak ground velocity, σ (PGV) is higher than the constant σ (PGV) predicted by conventional GMMs. Our simulations using several rupture scenarios demonstrate that the near-fault ground-motion amplification pattern is directly related to the slip distribution pattern.

3D Ground Motions Simulations↗
DOE OSTI2022

Machine learning predictions for local electronic properties of disordered correlated electron systems

We present a scalable machine learning (ML) model to predict local electronic properties such as on-site electron number and double occupation for disordered correlated electron systems. Our approach is based on the locality principle, or the nearsightedness nature, of many-electron systems, which means local electronic properties depend mainly on the immediate environment. A ML model is developed to encode this complex dependence of local quantities on the neighborhood. We demonstrate our approach using the square-lattice Anderson-Hubbard model, which is a paradigmatic system for studying the interplay between Mott transition and Anderson localization. We develop a lattice descriptor based on the group-theoretical method to represent the on-site random potentials within a finite region. The resultant feature variables are used as input to a multilayer fully connected neural network, which is trained from data sets of variational Monte Carlo (VMC) simulations on small systems. We show that the ML predictions agree reasonably well with the VMC data. Our work underscores the promising potential of ML methods for multiscale modeling of correlated electron systems.

36 MATERIALS SCIENCE↗
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

AI‐Driven Defect Engineering for Advanced Thermoelectric Materials

Thermoelectric materials offer a promising pathway to directly convert waste heat to electricity. However, achieving high performance remains challenging due to intrinsic trade-offs between electrical conductivity, the Seebeck coefficient, and thermal conductivity, which are further complicated by the presence of defects. This review explores how artificial intelligence (AI) and machine learning (ML) are transforming thermoelectric materials design. Advanced ML approaches including deep neural networks, graph-based models, and transformer architectures, integrated with high-throughput simulations and growing databases, effectively capture structure-property relationships in a complex multiscale defect space and overcome the “curse of dimensionality”. This review discusses AI-enhanced defect engineering strategies such as composition optimization, entropy and dislocation engineering, and grain boundary design, along with emerging inverse design techniques for generating materials with targeted properties. Finally, it outlines future opportunities in novel physics mechanisms and sustainability, highlighting the critical role of AI in accelerating the discovery of thermoelectric materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Study of impacts of two types of cellular aging on the yeast bud morphogenesis

Understanding the mechanisms of the cellular aging processes is crucial for attempting to extend organismal lifespan and for studying age-related degenerative diseases. Yeast cells divide through budding, providing a classical biological model for studying cellular aging. With their powerful genetics, relatively short cell cycle, and well-established signaling pathways also found in animals, yeast cells offer valuable insights into the aging process. Recent experiments suggested the existence of two aging modes in yeast characterized by nucleolar and mitochondrial declines, respectively. By analyzing experimental data, this study shows that cells evolving into those two aging modes behave differently when they are young. While buds grow linearly in both modes, cells that consistently generate spherical buds throughout their lifespan demonstrate greater efficacy in controlling bud size and growth rate at young ages. A three-dimensional multiscale chemical-mechanical model was developed and used to suggest and test hypothesized impacts of aging on bud morphogenesis. Experimentally calibrated model simulations showed that during the early stage of budding, tubular bud shape in one aging mode could be generated by locally inserting new materials at the bud tip, a process guided by the polarized Cdc42 signal. Furthermore, the aspect ratio of the tubular bud could be stabilized during the late stage as observed in experiments in this work. The model simulation results suggest that the localization of new cell surface material insertion, regulated by chemical signal polarization, could be weakened due to cellular aging in yeast and other cell types, leading to the change and stabilization of the bud aspect ratio.

Tsai, Kevin↗
DOE OSTI2021

Operator learning for predicting multiscale bubble growth dynamics

We report simulating and predicting multiscale problems that couple multiple physics and dynamics across many orders of spatiotemporal scales is a great challenge that has not been investigated systematically by deep neural networks (DNNs). Herein, we develop a framework based on operator regression, the so-called deep operator network (DeepONet), with the long-term objective to simplify multiscale modeling by avoiding the fragile and time-consuming “hand-shaking” interface algorithms for stitching together heterogeneous descriptions of multiscale phenomena. To this end, as a first step, we investigate if a DeepONet can learn the dynamics of different scale regimes, one at the deterministic macroscale and the other at the stochastic microscale regime with inherent thermal fluctuations. Specifically, we test the effectiveness and accuracy of the DeepONet in predicting multirate bubble growth dynamics, which is described by a Rayleigh–Plesset (R–P) equation at the macroscale and modeled as a stochastic nucleation and cavitation process at the microscale by dissipative particle dynamics (DPD). First, we generate data using the R–P equation for multirate bubble growth dynamics caused by randomly time-varying liquid pressures drawn from Gaussian random fields (GRFs). Our results show that properly trained DeepONets can accurately predict the macroscale bubble growth dynamics and can outperform long short-term memory networks. We also demonstrate that the DeepONet can extrapolate accurately outside the input distribution using only very few new measurements. Subsequently, we train the DeepONet with DPD data corresponding to stochastic bubble growth dynamics. Although the DPD data are noisy and we only collect sparse data points on the trajectories, the trained DeepONet model is able to predict accurately the mean bubble dynamics for time-varying GRF pressures. Taken together, our findings demonstrate that DeepONets can be employed to unify the macroscale and microscale models of the multirate bubble growth problem, hence providing new insight into the role of operator regression via DNNs in tackling realistic multiscale problems and in simplifying modeling with heterogeneous descriptions.

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

Multiscale Concurrent Atomistic-Continuum (CAC) modeling of multicomponent alloys

We report strengthening in complex multicomponent systems such as solid solution alloys is controlled primarily by the dynamic interactions between dislocation lines and heterogeneously distributed solute species. Modeling of extended defect length scales in such multicomponent systems becomes prohibitively expensive, motivating the development of reduced order approaches. This work explores the application of the Concurrent Atomistic-Continuum (CAC) method to model dislocation mobility in random alloys at extended length scales. By employing recently developed average-atom interatomic potentials, the average “bulk” material response in coarse-grained regions interacts with true random solute species in the atomistic-scale domain. We demonstrate that spurious stresses in domain resolution transition regions are eliminated entirely due to the CAC formulation. Simultaneously, the key details of local stress fluctuation due to randomness in the dislocation core region are captured, and fluctuating stress smoothly decays to the long-range dislocation stress field response. Dislocation mobility calculations, for line lengths over 400 nm, are computed as a function of alloy composition in the model FeNiCr system and compared to full molecular dynamics (MD). The results capture the composition-dependent trends, while reducing degrees of freedom by nearly 40%. This approach can be readily extended to any system described by an EAM potential and facilitates the study of large-scale defect dynamics in complex solute environments to support computational alloy design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Hydrogen-induced degradation dynamics in silicon heterojunction solar cells via machine learning

Abstract Among silicon-based solar cells, heterojunction cells hold the world efficiency record. However, their market acceptance is hindered by an initial 0.5% per year degradation of their open circuit voltage which doubles the overall cell degradation rate. Here, we study the performance degradation of crystalline-Si/amorphous-Si:H heterojunction stacks. First, we experimentally measure the interface defect density over a year, the primary driver of the degradation. Second, we develop SolDeg, a multiscale, hierarchical simulator to analyze this degradation by combining Machine Learning, Molecular Dynamics, Density Functional Theory, and Nudged Elastic Band methods with analytical modeling. We discover that the chemical potential for mobile hydrogen develops a gradient, forcing the hydrogen to drift from the interface, leaving behind recombination-active defects. We find quantitative correspondence between the calculated and experimentally determined defect generation dynamics. Finally, we propose a reversed Si-density gradient architecture for the amorphous-Si:H layer that promises to reduce the initial open circuit voltage degradation from 0.5% per year to 0.1% per year.

14 SOLAR ENERGY↗
DOE OSTI2023

Modeling and Simulation of Advanced Manufacturing Techniques using MOOSE and MALAMUTE

Advanced manufacturing techniques offer increased geometry complexity, energy and material usage efficiency improvements, and an expanded palette of materials as compared to conventional manufacturing approaches. Advanced-manufacturing-produced parts can experience wide variations in the final microstructure, and these microstructure variations significantly impact the parts’ performance. In this chapter, we present recent code developments within Multiphysics Object-Oriented Simulation Environment (MOOSE) and in the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE). Here we demonstrate applying these modeling and simulation codes to two advanced manufacturing process types: advanced sintering techniques and laser-based additive manufacturing techniques. The multiphysics and multiscale capabilities of these codes enable prediction of the microstructure evolution resulting from variations in the Advanced manufacturing process parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Multiscale modeling for high burnup structure formation and associated pulverization

This report summarizes the lower length scale modeling work performed in the fiscal year 2023 under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to capture the microstrutural evolution and associated pulverization criteria in the high burnup regions. This year, the resolution mechanisms within the cluster dynamics code has been updated and it influence on the bubble growth in HBS regions at the mesoscale is studied. To improve the accuracy of phase-field models of fission gas bubble growth, a new Helmholtz free energy for high-density Xe gas is derived from a virial equation of state. Two previously used strategies for representing net vacancy production in phase-field models of fission gas bubble growth are compared with each other and with analytical models of bubble growth. The vacancy source only model is found to be more convenient to parameterize realistically compared with the vacancy source+sink model. Furthermore, the phase-field-fracture simulation have been performed using MD- informed failure stress values and realistic HBS structure obtained from the phase-field simulations. We also present the uncertainty bands on prediction of the critical stress to account for the effect of the lower length scale variabilities on the failure criteria at the mesoscale. It is observed that pulverization may occur in partially restructured regions with restructuring fraction as low as 17%. In light of this, an update to the BISON’s pulverization criteria is recommended.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Modeling Nanoconfinement Effects Using Active Learning

Predicting the spatial configuration of gas in nanopores of is relevant in applications such as fluid flow forecasting and hydrocarbon reserves estimation. For example, shale reservoirs have suffered from computationally intractable multiscale problems, since fluid properties such as viscosity, density, and adsorption must be calculated by using expensive molecular dynamics (MD) simulations within each nanopore, whereas flow through these connected nanopores must be simulated at the micrometer scale. We utilize machine learning techniques to quickly and accurately model nanoscale confinement effects as an important step toward bridging the nano and micro scales. Our workflow is based on building and training physics-based deep-neural-networks models by learning from a database of MD calculations. The model accounts for the adsorption phenomenon by predicting the statistical distribution of gas inside nanopores. Because large databases of MD calculations are expensive to create, we investigate active learning (AL) as a data set construction strategy. In this workflow, new data are selected based on the model uncertainty via the query-by-committee approach. We show that our workflow obtains accurate models that generalize to real scanning electron microscopy geometries with 1/10th of the number of MD calculations required vs random data set generation. Our method enables the possibility of modeling nanoconfinement effects at the mesoscale, where complex connected sets of nanopores affect flow.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗