High-resolution Computational Fluid Dynamics Research Articles

Coupling MATSim and the PALM Model System—Large Scale Traffic and Emission Modeling with High-Resolution Computational Fluid Dynamics Dispersion Modeling

To effectively mitigate anthropogenic air pollution, it is imperative to implement strategies aimed at reducing emissions from traffic-related sources. Achieving this objective can be facilitated by employing modeling techniques to elucidate the interplay between environmental impacts and traffic activities. This paper highlights the importance of combining traffic emission models with high-resolution turbulence and dispersion models in urban areas at street canyon level and presents the development and implementation of an interface between the mesoscopic traffic and emission model MATSim and PALM-4U, which is a set of urban climate application modules within the PALM model system. The proposed coupling mechanism converts MATSim output emissions into input emission flows for the PALM-4U chemistry module, which requires translating between the differing data models of both modeling systems. In an idealized case study, focusing on Berlin, the model successfully identified “hot spots” of pollutant concentrations near high-traffic roads and during rush hours. Results show good agreement between modeled and measured NOx concentrations, demonstrating the model’s capacity to accurately capture urban pollutant dispersion. Additionally, the presented coupling enables detailed assessments of traffic emissions but also offers potential for evaluating the effectiveness of traffic management policies and their impact on air quality in urban areas.

Data-driven approach for design and optimization of rotor–stator mixers for miscible fluids with different viscosities

Rotor–stator mixers (RSMs) are essential equipment used in the food, pharmaceutical, and cosmetic industries. The performance of RSMs is conventionally analyzed using three approaches based on empirical correlations and experimental and numerical analyses. This paper introduces a new approach based on a multifidelity data-driven framework for designing and optimizing RSMs that systematically combines elements of experimental and high-resolution computational fluid dynamics (CFD) models to train a machine learning model to predict RSM performance. It was subsequently coupled to an optimization solver based on a genetic algorithm. The developed framework was applied to a small-scale RSM with variable rotor blade radius, rotor speed, stator mesh width, and mass flow rate that is operated to mix two miscible fluids with different viscosities. The RSM was optimized at two regimes of low and high viscosities to achieve the best performance in terms of shaft power and mixing indices. The results demonstrate the successful application of the developed frameworks. The optimization of the mixing of high-viscosity fluids yielded a higher improvement than that for low-viscosity fluids. A decrease in shaft power of approximately 50% was obtained for the high-viscosity fluids, whereas the mixing indices increased up to 20% by the optimization of the geometrical input design parameters. The proposed framework can be advantageous for designers and operators at the industrial scale.

High-resolution regional climate–CFD integrated modelling to inform climate responsive design of northern buildings in a changing climate

In permafrost regions, where climate change poses significant challenges to infrastructure stability, understanding the thermal behaviour of buildings is crucial. This study conducts a detailed investigation into the thermal performance of elevated buildings in permafrost regions within the context of a changing climate. High-resolution regional climate simulation-informed computational fluid dynamics (CFD) models were developed for northern buildings. The findings indicate that the presence of elevated buildings can disrupt the permafrost’s natural thermal equilibrium in the future. This disturbance can extend vertically and horizontally, potentially leading to altered ground temperature gradients and increased air and ground temperatures by 4.25% and 3.85%, respectively. The research findings also highlight a 12.75% reduction in wind speed beneath the study building when transitioning from the local scale (i.e., single-building) to the neighbourhood scale (i.e. with surrounding buildings). These results underscore the critical significance of exploring the neighbourhood scale in building design and planning within permafrost regions, emphasizing the need for comprehensive assessment tools to inform effective strategies and decisions. The holistic approach adopted in this study, sets out a clear vision to guide northern adaptation initiatives that address some of the climate change issues in the buildings’ design by utilizing integrated climate system-built environment modelling.

Novel physics-informed optimization framework for complex multi-physics problems: Implementation for a sweeping gas membrane distillation module

The application of advanced machine learning algorithms such as deep learning is limited for the surrogate-based process optimization of complex multi-physics problems because high-quality experimental and numerical training samples required for deep-learning model training are expensive and scarce. To address this issue, in this study, a novel physics-informed process optimization (PIPO) framework was introduced. In the first step, surrogate models based on conventional neural networks (NN) were trained using a few available high-quality training samples. In the second step, the trained NN models were coupled to the process optimization solver in which the loss of physical laws was added to the optimizer’s objective function to find optimal design points that satisfy the laws of physics. As a result, the generalization performance of the framework was greatly improved for design targets outside the training range of NN models. PIPO is substantially different from the physics-informed neural networks where the loss of physics is added to the loss function used during NN model training. The PIPO framework was used to optimize a sweeping gas membrane distillation (SGMD) module. Eight input design variables, including process and geometrical parameters, were optimized for different challenging targets to achieve the best SGMD performance in terms of ammonia recovery ratio and concentration. It was shown that for noticeably few training samples of 68 experiments, the proposed framework was able to achieve the optimization targets within a reasonable computational cost. The optimum designs were verified and analyzed in detail by high-resolution computational fluid dynamics models.

Lead Fast Reactor Thermal-Hydraulic Testing Facilities in Support of the UK Advanced Modular Reactor Program

The Westinghouse lead-cooled fast reactor (LFR) is a next-generation nuclear power plant whose primary mission is to reduce front-end capital costs and generate flexible and cost-competitive electricity for global markets, while offering mission versatility and satisfying the highest safety and sustainability standards. The LFR is an economical choice for carbon-free power generation to meet the ambitious goals of reducing carbon emissions. The plant utilizes passive safety systems for high reliability and public safety. The LFR testing program aims to fill the technology gaps in the key materials, components, and systems of the LFR plant. It provides demonstrations of LFR engineering that reduce the uncertainty of the LFR design and experimental data for the verification and validation of the modeling and simulation computer codes. With the support of the LFR phenomena identification and ranking table, the phenomena important to plant safety with insufficient states of knowledge have been identified and a Westinghouse testing plan developed to address them. The United Kingdom Advanced Modular Reactor Program Phase 2 was purposed to advance the level of design maturity toward eventual regulatory approval and deployment. Westinghouse and its partners have developed eight state-of-the-art test facilities within the program. Among them, the Passive Heat Removal Facility, Versatile Lead Facility, Lead Water Interaction Facility, and the Lead Freezing Facility are thermal-hydraulic testing facilities, with the other four facilities dedicated to testing lead corrosion and material mechanical behavior in lead. The design, development, and testing of the four thermal-hydraulic facilities are presented in this paper. A variety of computer codes ranging from system-level analysis, component-level analysis, and high-resolution computational fluid dynamics analysis, are utilized to support the development of the facilities. The analyses provide operational and accidental conditions in the LFR to identify major testing conditions and inform the facility design and testing matrix. With a significant amount of experimental data being generated with these highly instrumented test facilities, the computer codes and models will be benchmarked against the experimental results to improve their validation and verification status.

Prediction of three-dimensional flow field inside realistic fibrous filter obtained from x-ray computed tomography images using deep convolutional neural networks

Deep-learning models garnered considerable attention in the field of fluid mechanics for physics discovery and approximation-model generation. This study aims to develop an approximation model to predict the flow field inside realistic fibrous filters based on an image-to-image approach to replace three-dimensional (3D) computational fluid dynamics (CFD) simulations, which are computationally expensive and difficult to apply to realistic fibrous filters. A data-driven framework is proposed using deep convolutional neural networks (CNNs) to provide a per-pixel prediction of the flow field. The model inputs are two-dimensional x-ray computed tomography images, whereas the outputs are the 3D distributions of the velocity vectors and pressure. High-resolution 3D CFD simulations are performed to create a database to train and test the CNN model. The model is applied to surgical and N95 face masks. The relative error of the CNN model over the test dataset is approximately 10% in regions with high velocity and pressure, and the model can provide a detailed high-resolution prediction of the flow field with a speedup of about three orders of magnitudes. A strict generalization test is conducted for completely unseen 3D segments with complex microstructures. The model generalizability still needs more improvements; however, the model can provide a low-resolution 3D flow field for those segments that can be used as the initial condition for CFD simulation to reduce the CFD computational time. This framework can be utilized for other types of filters and provides a basis for the design and optimization of fibrous filters.

Spatial Fluctuations of Optical Turbulence Strength in a Laboratory Turbulence Simulator

Controlled turbulence simulators in the laboratory have been extensively employed to investigate turbulence effects on light propagation in the atmosphere, driven by some advanced optical engineering such as remote sensing, energy-delivery systems, and free-space optical communication systems. Many studies have achieved rich results on the optical turbulence intensity, scintillation index, and power spectral density characteristics of the light propagation path in the center of a turbulence simulator, but a comprehensive analysis of the optical turbulence characteristics for different spatial locations is still lacking. We simulate turbulence with air as the medium in a classical convective Rayleigh–Bénard turbulence simulator through high-resolution computational fluid dynamics methods, the three-dimensional refractive index distribution is obtained, and the optical properties are analyzed comprehensively. It is found that the hot and cold plumes and the large-scale circulation strongly influence the inhomogeneity of Cn2 in the turbulence tank, making it weak in the middle and strong near the boundary. The refractive index power spectral density at different heights is centrally symmetric, with the slope gradually deviating from the −5/3 scaling power with increasing distance from the central region. Under the log-log plot, the variation of the refractive index variance with height exhibits a three-segmented feature, showing in order: a stable region, a logarithmic profile, and a power-law profile, in the region close to the boundary. These results will contribute to the construction of a suitable turbulence simulator for optical engineering applications.

LSTM Reconstruction of Turbulent Pressure Fluctuation Signals

This paper concerns the application of a long short-term memory model (LSTM) for high-resolution reconstruction of turbulent pressure fluctuation signals from sparse (reduced) data. The model’s training was performed using data from high-resolution computational fluid dynamics (CFD) simulations of high-speed turbulent boundary layers over a flat panel. During the preprocessing stage, we employed cubic spline functions to increase the fidelity of the sparse signals and subsequently fed them to the LSTM model for a precise reconstruction. We evaluated our reconstruction method with the root mean squared error (RMSE) metric and via inspection of power spectrum plots. Our study reveals that the model achieved a precise high-resolution reconstruction of the training signal and could be transferred to new unseen signals of a similar nature with extremely high success. The numerical simulations show promising results for complex turbulent signals, which may be experimentally or computationally produced.

Wave resistance of a ship moving in a lead between rigid ice sheets of finite thickness

This paper presents a combined theoretical and numerical analysis of a ship traveling in open water between rigid ice sheets of finite thickness with the objective to understand how the modeled ice influences the wave resistance and ship wave pattern. The first part of the analysis uses a mathematical model to evaluate the wave resistance in deep-water channels (or an ice sheet that reaches the sea floor). The model is able to separate the contributions of the transverse and divergent waves to the total wave resistance. Significant influence of both the ship speed and channel width is observed to both increase and decrease the wave resistance relative to the open water condition by as much as 50%. The second part of the analysis uses high-resolution computational fluid dynamics (CFD) on a contemporary ship that is traveling between two rigid ice sheets with finite thickness. The CFD simulations identify the critical ice thickness that corresponds to the condition in which the ice sheets function nearly as channel walls. It is found that the effect on the wave resistance is noticeable when the ice is 5% of the fundamental wavelength, and when the ice sheets are thicker than 20% of the fundamental wavelength, the resistance change due to the plates is nearly that of channel walls.

CFD Modeling of Phase Change during the Flashing-Induced Instability in a Natural Circulation Circuit

Flashing-induced instability (FII) has a significant impact on the safe operation of a natural circulation circuit, a phenomenon frequently encountered in the cooling systems of advanced light water reactors. While one-dimensional system codes are commonly used for the engineering design and safety analysis of FII, there is a strong academic interest in understanding the underlying physical mechanisms. To address this, high-resolution computational fluid dynamics (CFD) simulations serve as a valuable tool. However, the current state of CFD modeling for two-phase flows with phase change, which are particularly highly transient fluctuating flashing flows, is still in its early stages of development. In this study, we establish a CFD model that focuses on interphase heat transfer to analyze the phase change during FII. By incorporating experimental data from the literature, we investigate the transient flow field and thermodynamic behavior in the riser of the GENEVA test facility. The study provides valuable insights into the non-equilibrium and interfacial transfer phenomena during the phase change as well as the effect of high-frequency fluctuation. Additionally, we discuss in detail the challenges associated with FII modeling and the limitations of the current model. We also provide suggestions for potential improvements in future numerical studies. The results show that the thermal phase change and heat transfer coefficient model adopted for the simulation reasonably captures the evaporation and condensation process. However, it tends to under-predict the evaporation rate, which results in a larger pressure drop through the riser. The observation that the void fraction close to the wall is higher than that in the riser center evidences that the reliable modeling of bubble size distribution as well as the inclusion of non-drag forces are important for predicting the transverse void distribution. Furthermore, it reveals that both the temperature and pressure change in an FII, and their effects on phase change should be taken into account simultaneously.

Pix2Pix and Deep Neural Network-Based Deep Learning Technology for Predicting Vortical Flow Fields and Aerodynamic Performance of Airfoils

Traditional computational fluid dynamics (CFD) methods are usually used to obtain information about the flow field over an airfoil by solving the Navier–Stokes equations for the mesh with boundary conditions. These methods are usually costly and time-consuming. In this study, the pix2pix method, which utilizes conditional generative adversarial networks (cGANs) for image-to-image translation, and a deep neural network (DNN) method were used to predict the airfoil flow field and aerodynamic performance for a wind turbine blade with various shapes, Reynolds numbers, and angles of attack. Pix2pix is a universal solution to the image-to-image translation problem that utilizes cGANs. It was successfully implemented to predict the airfoil flow field using fully implicit high-resolution scheme-based compressible CFD codes with genetic algorithms. The results showed that the vortical flow fields of the thick airfoils could be predicted well using the pix2pix method as a result of deep learning.

The Biomechanical Characteristics of Swallowing in Tracheostomized Patients with Aspiration following Acquired Brain Injury: A Cross-Sectional Study.

Investigate the biomechanical characteristics in tracheostomized patients with aspiration following acquired brain injury (ABI) and further explore the relationship between the biomechanical characteristics and aspiration. This is a single-center cross-sectional study. The tracheostomized patients with aspiration following ABI and age-matched healthy controls were recruited. The biomechanical characteristics, including velopharynx (VP) maximal pressure, tongue base (TB) maximal pressure, upper esophageal sphincter (UES) residual pressure, UES relaxation duration, and subglottic pressure, were examined by high-resolution manometry and computational fluid dynamics simulation analysis. The penetration-aspiration scale (PAS) score was evaluated by a videofluoroscopic swallowing study. Fifteen healthy subjects and fifteen tracheostomized patients with aspiration following ABI were included. The decreased VP maximal pressure, increased UES residual pressure, and shortened UES relaxation duration were found in the patient group compared with the control group (p < 0.05). Furthermore, the subglottic pressure significantly decreased in patients (p < 0.05), while no significant difference was found in TB maximal pressure between groups (p > 0.05). In addition, in the patient group, VP maximal pressure (rs = -0.439; p = 0.015), UES relaxation duration (rs = -0.532; p = 0.002), and the subglottic pressure (rs = -0.775; p < 0.001) were negatively correlated with the PAS score, while UES residual pressure (rs = 0.807; p < 0.001) was positively correlated with the PAS score (p < 0.05), the correlation between TB maximal pressure and PAS score (rs = -0.315; p = 0.090) did not reach statistical significance. The biomechanical characteristics in tracheostomized patients with aspiration following ABI might manifest as decreased VP maximal pressure and subglottic pressure, increased UES residual pressure, and shortened UES relaxation duration, in which VP maximal pressure, UES relaxation duration, subglottic pressure, and UES residual pressure were correlated with aspiration.

BIM-GIS integration approach for high-fidelity wind hazard modeling at the community-level

Wind hazards often result in significant damage to the built environment cascading into impacts on the socio-economic systems within a community. The increasing frequency and intensity of hurricane hazards highlight the importance of developing high-resolution wind hazard models to better predict the consequences. Although previous studies have investigated hurricane-induced wind hazards in terms of hazard modeling and the subsequent vulnerability of buildings and infrastructure, these studies have not yet investigated applications of computational fluid dynamics (CFD) at the community-level. Therefore, in this study, a novel approach was developed to generate CFD models at the community-level by integrating building information modeling (BIM) and geographical information systems (GIS) to automate the generation of a high-resolution 3-D community model to be used as an input for a digital wind tunnel. This was done by harnessing the current advances in BIM and GIS applications and maximizing their capabilities by developing an algorithm that automates the 3-D geometry generation of communities with a detailed discretization of each building within the community. The 3-D community model was developed using the GIS shapefile of the buildings’ footprint and a parametric BIM model that uses a number of building parameters such as footprint dimensions, roof shape, foundation type, and the number of stories. Then, an algorithm was developed to automate the creation of the BIM model for each building within the community based on the prescribed building’s characteristics. The developed community model was used as an input for a numerical wind tunnel that uses CFD to account for the detailed wind pressure at each building after including the impacts of aerodynamics interference at the community-level. This novel BIM-GIS integration approach provides, for the first time, the next generation of high-resolution community-level CFD wind hazard modeling which aims to shift the current practice of wind hazard simulation at the community-level.

LSPIV analysis of ship-induced wave wash

Ship-induced wave wash affects the hydromorphological and ecological state of rivers through various mechanisms. The direct proximity of the riverbank is usually the most exposed, as the hydrodynamic stresses are the highest in these shallow water areas. Contrary to the steady and almost still, natural flow conditions (i.e., no waves of anthropogenic source), shoaling and breaking of ship waves increase the hydrodynamic stresses by orders of magnitudes, having notable ecological consequences, and resulting in bank erosion as well. Due to the shallow water depths and temporary drying, conventional measurement techniques are no longer applicable in these areas. In this study, large-scale particle image velocimetry (LSPIV) is used to quantify the prevailing flow conditions. In the absence of ground truth data in the wave breaking region, a high-resolution computational fluid dynamics model—verified with field pressure and acoustic Doppler velocimetry data—is used for the cross-validation of the LSPIV results. The results underline the applicability of LSPIV for the hydrodynamic analysis of wave velocities in this special riverine swash zone, which is of key importance from the aspect of ecology and bank erosion as well.Graphical abstract

Simulation of Traffic-Born Pollutant Dispersion and Personal Exposure Using High-Resolution Computational Fluid Dynamics

Road vehicles are a large contributor to nitrogen oxides (NOx) pollution. The routine roadside monitoring stations, however, may underrepresent the severity of personal exposure in urban areas because long-term average readings cannot capture the effects of momentary, high peaks of air pollution. While numerical modelling tools historically have been used to propose an improved distribution of monitoring stations, ultra-high resolution Computational Fluid Dynamics models can further assist the relevant stakeholders in understanding the important details of pollutant dispersion and exposure at a local level. This study deploys a 10-cm-resolution CFD model to evaluate actual high peaks of personal exposure to NOx from traffic by tracking the gases emitted from the tailpipe of moving vehicles being dispersed towards the roadside. The investigation shows that a set of four Euro 5-rated diesel vehicles travelling at a constant speed may generate momentary roadside concentrations of NOx as high as 1.25 mg/m3, with a 25% expected increase for doubling the number of vehicles and approximately 50% reduction when considering Euro 6-rated vehicles. The paper demonstrates how the numerical tool can be used to identify the impact of measures to reduce personal exposure, such as protective urban furniture, as traffic patterns and environmental conditions change.

Three-dimensional high-resolution image inversion and pore level CFD characterisation of effective thermal conductivity of replicated microcellular structures

Metallic-based microcellular structures are widely used in heat and mass transfer processes owing to their unique combination of high porosity, high surface area, fixed pore morphology and high Young modulus, enabling their suitability as heat pipes in oil and gas processing equipment, biomedical materials for bone repair and bone substitution, solar collectors, fuel cells, impact loading, soundproofing materials and metallurgical processing. Accurate representation of the effective thermal conductivity of these materials is imperative in understanding their heat transfer mechanisms leading to the design and optimisation of system performance. Due to the limited availability of experimental and predictive data on heat transport phenomena across the interstices of low-porosity microcellular structures - numerically simulated data of effective thermal conductivity for conduction heat transfer in metal foam-fluid systems have been compared for structures typified by near-circular pore walls and openings, i.e. “bottleneck-type” structures and foam porosity ranging between 0.65 and 0.78. A three-dimensional high-resolution image inversion and computational fluid dynamics modelling and simulation of conductive heat transfer for both the fluid and solid domains at pore level is used to estimate effective thermal conductivity for these structures. This approach is extended to structurally-adapted metal foam-fluid systems by broadening the pore volume fraction beyond 0.90 – resulting in the quantification of the fluid phase contribution for heat transfer enhancement and the proposition of empirical constants to support models developed by Calmidi & Mahajan [9]. Findings in this work offer strong support to the supposition that geometrical adaptions of microcellular structures can be used to modulate their effective thermal conductivity and that generalised values of empirical constants may be ambiguous to fully describe conduction heat transfer phenomena in microcellular structures. This approach may prove useful in the design of low-porosity metallic components for applications specific to conduction heat transfer.

An AI-based non-intrusive reduced-order model for extended domains applied to multiphase flow in pipes

The modeling of multiphase flow in a pipe presents a significant challenge for high-resolution computational fluid dynamics (CFD) models due to the high aspect ratio (length over diameter) of the domain. In subsea applications, the pipe length can be several hundreds of meters vs a pipe diameter of just a few inches. Approximating CFD models in a low-dimensional space, reduced-order models have been shown to produce accurate results with a speed-up of orders of magnitude. In this paper, we present a new AI-based non-intrusive reduced-order model within a domain decomposition framework (AI-DDNIROM), which is capable of making predictions for domains significantly larger than the domain used in training. This is achieved by (i) using a domain decomposition approach; (ii) using dimensionality reduction to obtain a low-dimensional space in which to approximate the CFD model; (iii) training a neural network to make predictions for a single subdomain; and (iv) using an iteration-by-subdomain technique to converge the solution over the whole domain. To find the low-dimensional space, we compare Proper Orthogonal Decomposition with several types of autoencoder networks, known for their ability to compress information accurately and compactly. The comparison is assessed with two advection-dominated problems: flow past a cylinder and slug flow in a pipe. To make predictions in time, we exploit an adversarial network, which aims to learn the distribution of the training data, in addition to learning the mapping between particular inputs and outputs. This type of network has shown the potential to produce visually realistic outputs. The whole framework is applied to multiphase slug flow in a horizontal pipe for which an AI-DDNIROM is trained on high-fidelity CFD simulations of a pipe of length 10 m with an aspect ratio of 13:1 and tested by simulating the flow for a pipe of length 98 m with an aspect ratio of almost 130:1. Inspection of the predicted liquid volume fractions shows a good match with the high fidelity model as shown in the results. Statistics of the flows obtained from the CFD simulations are compared to those of the AI-DDNIROM predictions to demonstrate the accuracy of our approach.

Atmospheric pollution from rockets

We address the impact of rocket exhaust gases on atmospheric pollution through high-resolution computational fluid dynamics simulations. We have modeled the exhaust gases and developing plume at several altitudes along a typical trajectory of a standard present-day rocket, as a prototypical example of a two-stage rocket to transport people and payloads into Earth's orbit and beyond. The modeled rocket uses RP-1 as the propellant and liquid oxygen as the oxidizer to generate ∼6806 kN of thrust via a total of nine nozzles, matching—as closely as possible based on available data—the specifications to the Thaicom 8 launch mission of the Falcon 9 rocket manufactured by SpaceX. We have used high-order discretization methods, 11th-order accurate, in conjunction with implicit large eddy simulations to model exhaust gas mixing, dispersion, and heat transfer into the atmosphere at altitudes up to 67 km. We show that pollution from rockets should not be underestimated as frequent future rocket launches could have a significant cumulative effect on climate. The production of thermal nitrogen oxides can remain considerable up to altitudes with an ambient atmospheric pressure below but of the same order of magnitude as the nozzles exit pressure. At the same time, the emitted mass of carbon dioxide in the mesosphere is equivalent to that contained in 26 km3 of atmospheric air at the same altitude.

Numerical Simulation of Conical and Linear-Shaped Charges Using an Eulerian Elasto-Plastic Multi-Material Multi-Phase Flow Model with Detonation.

This study developed a hydrocode to numerically simulate both conical and linear-shaped charges using an Eulerian multi-material and multi-phase flow model. Elasto-plastic solids and the detonation of a high explosive charge were modeled using a Johnson–Cook material model and the programmed burn model, respectively. Further, the plasticity of the solids was calculated using a radial return mapping algorithm. The model was solved using a high-resolution computational fluid dynamics (CFD) technique on Cartesian grids. Material interfaces were tracked using the level-set method, and the boundary conditions were imposed using the ghost fluid method. The developed hydrocode was validated using high-speed impact problems. Consequently, the developed hydrocode was used to successfully simulate the evolution and penetration of metal jets in shaped charges after a detonation.

High-Resolution Numerical Simulation of Microfiltration of Oil-in-Water Emulsion Permeating through a Realistic Membrane Microporous Structure Generated by Focused Ion Beam Scanning Electron Microscopy Images.

Owing to the limitations of visualization techniques in experimental studies and low-resolution numerical models based on computational fluid dynamics (CFD), the detailed behavior of oil droplets during microfiltration is not well understood. Hence, a high-resolution CFD model based on an in-house direct numerical simulation (DNS) code was constructed in this study to analyze the detailed dynamics of an oil-in-water (O/W) emulsion using a microfiltration membrane. The realistic microporous structure of commercial ceramic microfiltration membranes (mullite and α-alumina membranes) was obtained using an image processing technique based on focused ion beam scanning electron microscopy (FIB-SEM). Numerical simulations of microfiltration of O/W emulsions on the membrane microstructure obtained by FIB-SEM were performed, and the effects of different parameters, including contact angle, transmembrane pressure, and membrane microporous structure, on filtration performance were studied. Droplet deformation had a strong impact on filtration behavior because coalesced droplets with diameters larger than the pore diameter permeated the membrane pores. The permeability, oil hold-up fraction inside the pores, and rejection were considerably influenced by the contact angle, while the transmembrane pressure had a little impact on the permeability and oil hold-up fraction. The membrane structure, especially the pore size distribution, also had a significant effect on the microfiltration behavior and performance.