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"Flow simulation"
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Assessing Rheology Effects and Pore Space Complexity in Polymer Flow Through Porous Media: A Pore‐Scale Simulation Study
Non‐Newtonian fluid flow within porous media, exemplified by polymer remediation of contaminated groundwater/aquifer systems, presents complex challenges due to the fluids' complex rheological behavior within 3D tortuous pore structures. This paper introduces a pore‐scale flow simulator based on the OpenFOAM open‐source library, designed to model shear‐thinning flow within porous media. Leveraging this developed solver, extensive pore‐scale flow simulations were conducted on μ‐CT images of various real and synthetic porous media with varying complexity for both power‐law and Cross‐fluid models. We focused on the macroscale‐averaged deviation between bulk viscosity and the in‐situ viscosity, commonly denoted by a shift factor. We provided an in‐depth evaluation of the shift factor's dependency on the fluid's rheological attributes and the rock's pore space complexity. The least‐squares fitted values of the shift factor fell in the range of 1.6–9.5. Notably, the most pronounced shift factor emerged for extreme flow behavior indices. Our findings highlight not just the critical role of rheological parameters, but also demonstrate how the shift factor fluctuates based on tortuosity, characteristic pore length, and the cementation exponent. In particular, less porous/permeable systems with smaller characteristic pore lengths exhibited larger shift factors due to higher variations of shear rate and local viscosity in narrower flow paths. Additionally, the shift factor increased as rock became more tortuous and heterogeneous. The introduced pore‐scale simulation proves instrumental in determining the macroscopic averaged shift factor. This, in consequence, is vital for precise computations of viscosity and pressure drop when dealing with non‐Newtonian fluid flow in porous media. Key Points The pore‐scale flow simulation allowed an accurate evaluation of macroscopic deviation between bulk viscosity and the in‐situ viscosity The fluid's rheology exhibited stronger impacts on the dynamics of non‐Newtonian flow in porous media than pore structure descriptors The porous medium viscosity decreased by increasing the complexity of the pore space of the rock
Journal Article
Performance Comparison of Proton Exchange Membrane Water Electrolysis Cell Using Channel and PTL Flow Fields through Three-Dimensional Two-Phase Flow Simulation
Water electrolysis technology is required to overcome the intermittency of renewable energy sources. Among various water electrolysis methods, the proton exchange membrane water electrolysis (PEMWE) cell has the advantages of a fast response and high current density. However, high capital costs have hindered the commercialization of PEMWE; therefore, it is important to lower the price of bipolar plates, which make PEMWE expensive. In addition, since the flow field inscribed in the bipolar plate significantly influences the performance, it is necessary to design the enhanced pattern. A three-dimensional two-phase flow model was used to analyze the two-phase flow and electrochemical reactions of the PEMWE anode. In order to compare the experimental results with the simulation, experiments were conducted according to the flow rate, and the results were in good agreement. First, as a result of comparing the performance of the channel and PTL (porous transport layer) flow fields, the channel flow field showed better performance than the PTL flow field. For the channel flow field, the higher the ratio of the channel width-to-rib width and the permeability of PTL, the performance got better. In the case of the PTL flow field, with the increased capillary pressure, the performance improved even if the PTL permeability decreased. Next, the direction of gravity affected the performance only when the channel flow field was used, and the X+ and Z+ directions were optimal for the performance. Finally, increasing the inlet flow rate could reduce the difference in performance between the channel and PTL flow fields, but the pressure drop gradually increased.
Journal Article
Analysis of the Dispersive and Distributive Mixing Effect of Screw Elements on the Co-Rotating Twin-Screw Extruder with Particle Tracking
Compounding is an important step in processing base polymers and is used to incorporate various additives into a polymer. For this purpose, different screw elements are used for dispersive and distributive mixing on a co-rotating twin-screw extruder. Optimising the screw configuration requires precise knowledge of the screw elements’ mixing properties, which have not been thoroughly investigated. This study analyses the mixing behaviour of individual screw elements regarding dispersive and distributive mixing using 3D CFD flow simulations with subsequent particle tracking. For distributive mixing, the particle distribution behind the screw elements in the XY plane is analysed and the mixing index MQ, which relates the standard deviation and the mean value of the triangular areas between the particles, is calculated. For dispersive mixing, the maximum shear stress on the particle path and the integral of the shear stress over the residence time of each individual particle are determined. The results show that screw element geometry and rotation speed have a significant influence on dispersive and distributive mixing. In addition, better dispersive mixing is achievable with highly viscous materials. These findings enable the optimisation of the mixing zone of a co-rotating twin-screw extruder for the efficient mixing of mineral fillers.
Journal Article
Fluid–structure interaction in a flexible vegetation canopy in an open channel
Submerged flexible vegetation occupies the core of aquatic ecosystem research. Hydrodynamics of submerged flexible vegetation and its interaction with the flow in an open channel are of great significance in studying the mass and momentum transports in the flow. In this study, a numerical model for highly flexible vegetation based on large eddy simulation and the immersed boundary method was used to simulate the flow–vegetation interaction. It is recognised that alternate vortices with opposite sense rotations appear at the flow–vegetation interface. These vortices prompt the vegetation canopy to have wave-like coherent waving motion, commonly called the monami phenomena. The spatial scale and the spreading velocity in the streamwise direction of these vortices determine the wavelength, frequency and amplitude of the vegetation coherent waving motion. In this study, the fast Fourier transform method was applied to analyse the factors affecting the characteristics of the vegetation coherent waving motion. It is revealed that, as the flow velocity increases, the wavelength of the coherent waving motion decreases, while the frequency and amplitude increase. Besides, as the vegetation spacing increases, the wavelength and amplitude of the coherent waving motion increase, but the frequency decreases. Furthermore, an increase in the relative density of vegetation magnifies the amplitude of coherent waving motion without affecting the wavelength and frequency.
Journal Article
Integrating LiDAR, Photogrammetry, and Computational Fluid Dynamics for Wind Flow Simulations Around Existing Buildings
Integrating LiDAR and photogrammetry offers significant potential for ensuring the accuracy and completeness of the 3D models of existing structures, which are essential for several applications in the architectural, engineering, and construction (AEC) industry. This study has two primary objectives: the first is to demonstrate how LiDAR and photogrammetry complement each other, through the balance of LiDAR’s structural accuracy with photogrammetry’s rich texture data; the second is to validate the quality of the resulting mesh by using it for the CFD simulation of wind flow around a case study building. The integration method, though simple, is optimized to ensure high-quality point cloud registration, minimizing data quality impacts. To capitalize on the advantages of both manual and full point-cloud-based modeling methods, the study proposes a new hybrid approach. In the hybrid approach, the large-scale and simplified parts of the geometry are modeled manually, while the complex and detailed parts are reconstructed using high-resolution point cloud data from LiDAR and photogrammetry. Additionally, a novel region of constraints method (ROCM) is introduced to streamline wind flow simulations across varying scenarios without the need for multiple meshes. The results indicate that the integrated approach was able to capture the complete and detailed geometry of the case study building, including the complex window extrusions. The CFD simulations revealed differences in the wind flow patterns and pressure distributions when compared across different geometry modeling approaches. It was found that the hybrid approach is the best and balances efficiency, accuracy, and computational cost.
Journal Article
Efficient deep-learning-based surrogate model for reservoir production optimization using transfer learning and multi-fidelity data
In the realm of subsurface flow simulations, deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods, especially in addressing complex optimization problems. However, a significant challenge lies in the necessity of numerous high-fidelity training simulations to construct these deep-learning models, which limits their application to field-scale problems. To overcome this limitation, we introduce a training procedure that leverages transfer learning with multi-fidelity training data to construct surrogate models efficiently. The procedure begins with the pre-training of the surrogate model using a relatively larger amount of data that can be efficiently generated from upscaled coarse-scale models. Subsequently, the model parameters are fine-tuned with a much smaller set of high-fidelity simulation data. For the cases considered in this study, this method leads to about a 75% reduction in total computational cost, in comparison with the traditional training approach, without any sacrifice of prediction accuracy. In addition, a dedicated well-control embedding model is introduced to the traditional U-Net architecture to improve the surrogate model's prediction accuracy, which is shown to be particularly effective when dealing with large-scale reservoir models under time-varying well control parameters. Comprehensive results and analyses are presented for the prediction of well rates, pressure and saturation states of a 3D synthetic reservoir system. Finally, the proposed procedure is applied to a field-scale production optimization problem. The trained surrogate model is shown to provide excellent generalization capabilities during the optimization process, in which the final optimized net-present-value is much higher than those from the training data ranges.
Journal Article
A parametric geometry model of the aortic valve for subject-specific blood flow simulations using a resistive approach
Cardiac valves simulation is one of the most complex tasks in cardiovascular modeling. Fluid–structure interaction is not only highly computationally demanding but also requires knowledge of the mechanical properties of the tissue. Therefore, an alternative is to include valves as resistive flow obstacles, prescribing the geometry (and its possible changes) in a simple way, but, at the same time, with a geometry complex enough to reproduce both healthy and pathological configurations. In this work, we present a generalized parametric model of the aortic valve to obtain patient-specific geometries that can be included into blood flow simulations using a resistive immersed implicit surface (RIIS) approach. Numerical tests are presented for geometry generation and flow simulations in aortic stenosis patients whose parameters are extracted from ECG-gated CT images.
Journal Article
Detection of Flash Flood Inundated Areas Using Relative Difference in NDVI from Sentinel-2 Images: A Case Study of the August 2020 Event in Charikar, Afghanistan
On 26 August 2020, a devastating flash flood struck Charikar city, Parwan province, Afghanistan, causing building damage and killing hundreds of people. Rapid identification and frequent mapping of the flood-affected area are essential for post-disaster support and rapid response. In this study, we used Google Earth Engine to evaluate the performance of automatic detection of flood-inundated areas by using the spectral index technique based on the relative difference in the Normalized Difference Vegetation Index (rdNDVI) between pre- and post-event Sentinel-2 images. We found that rdNDVI was effective in detecting the land cover change from a flash flood event in a semi-arid region in Afghanistan and in providing a reasonable inundation map. The result of the rdNDVI-based flood detection was compared and assessed by visual interpretation of changes in the satellite images. The overall accuracy obtained from the confusion matrix was 88%, and the kappa coefficient was 0.75, indicating that the methodology is recommendable for rapid assessment and mapping of future flash flood events. We also evaluated the NDVIs’ changes over the course of two years after the event to monitor the recovery process of the affected area. Finally, we performed a digital elevation model-based flow simulation to discuss the applicability of the simulation in identifying hazardous areas for future flood events.
Journal Article
Tissue-growth-based synthetic tree generation and perfusion simulation
Biological tissues receive oxygen and nutrients from blood vessels by developing an indispensable supply and demand relationship with the blood vessels. We implemented a synthetic tree generation algorithm by considering the interactions between the tissues and blood vessels. We first segment major arteries using medical image data and synthetic trees are generated originating from these segmented arteries. They grow into extensive networks of small vessels to fill the supplied tissues and satisfy the metabolic demand of them. Further, the algorithm is optimized to be executed in parallel without affecting the generated tree volumes. The generated vascular trees are used to simulate blood perfusion in the tissues by performing multiscale blood flow simulations. One-dimensional blood flow equations were used to solve for blood flow and pressure in the generated vascular trees and Darcy flow equations were solved for blood perfusion in the tissues using a porous model assumption. Both equations are coupled at terminal segments explicitly. The proposed methods were applied to idealized models with different tree resolutions and metabolic demands for validation. The methods demonstrated that realistic synthetic trees were generated with significantly less computational expense compared to that of a constrained constructive optimization method. The methods were then applied to cerebrovascular arteries supplying a human brain and coronary arteries supplying the left and right ventricles to demonstrate the capabilities of the proposed methods. The proposed methods can be utilized to quantify tissue perfusion and predict areas prone to ischemia in patient-specific geometries.
Journal Article