Numerical Methods and Machine Learning Techniques for Complex Flows

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Fluid Science and Technology".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 4222

Special Issue Editors


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1. Department of Mechanical Engineering (Section of Mathematics), FEUP, University of Porto, 4200-465 Porto, Portugal
2. Center for Mathematics, University of Minho, 4710-057 Braga, Portugal
Interests: numerical analysis; integro-differential equations; mathematical modelling; viscoelastic flows; anomalous diffusion; machine learning
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Guest Editor
CEFT-Transport Phenomena Research Center, Department of Mechanical Engineering, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
Interests: theoretical and computational rheology; complex flows of complex fluids; electrokinetics; multiphase flow; micro-combustion
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Institute for Polymers and Composites, University of Minho, Campus de Azurém, 4800-058 Guimarães, Portugal
Interests: rheology; swelling; viscosity and viscoelasticity; polymers at interfaces and in confined spaces; numerical simulation; constitutive and multiscale modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue on “Numerical Methods and Machine Learning Techniques for Complex Flows” is intended to gather new developments in the numerical solution of the equations governing complex flows. These numerical methods can range from classical computational fluid dynamics to machine learning techniques.

All researchers working in these areas are encouraged to submit their work. All submissions will be subject to a rapid and thorough review.

Prof. Dr. Luís L. Ferrás
Dr. Alexandre M. Afonso
Dr. Célio Bruno Pinto Fernandes
Guest Editors

Manuscript Submission Information

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Keywords

  • Newtonian fluids
  • non-Newtonian fluids
  • rheology
  • numerical methods
  • finite element method
  • finite differences method
  • finite volume method
  • spectral methods
  • computational fluid dynamics
  • machine learning in fluid flows
  • simulation
  • modeling
  • constitutive equations
  • analysis

Published Papers (3 papers)

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Research

23 pages, 10653 KiB  
Article
Comprehensive Method for Obtaining Multi-Fidelity Surrogate Models for Design Space Approximation: Application to Multi-Dimensional Simulations of Condensation Due to Mixing Streams
by José Galindo, Roberto Navarro, Francisco Moya and Andrea Conchado
Appl. Sci. 2023, 13(11), 6361; https://doi.org/10.3390/app13116361 - 23 May 2023
Cited by 1 | Viewed by 876
Abstract
In engineering problems, design space approximation using accurate computational models may require conducting a simulation for each explored working point, which is often not feasible in computational terms. For problems with numerous parameters and computationally demanding simulations, the possibility of resorting to multi-fidelity [...] Read more.
In engineering problems, design space approximation using accurate computational models may require conducting a simulation for each explored working point, which is often not feasible in computational terms. For problems with numerous parameters and computationally demanding simulations, the possibility of resorting to multi-fidelity surrogates arises as a means to alleviate the effort by employing a reduced number of high-fidelity and expensive simulations and predicting a much cheaper low-fidelity model. A multi-fidelity approach for design space approximation is therefore proposed, requiring two different designs of experiments to assess the best combination of surrogate models and an intermediate meta-modeled variable. The strategy is applied to the prediction of condensation that occurs when two humid air streams are mixed in a three-way junction, which occurs when using low-pressure exhaust gas recirculation to reduce piston engine emissions. In this particular case, most of the assessed combinations of surrogate and intermediate variables provide a good agreement between observed and predicted values, resulting in the lowest normalized mean absolute error (3.4%) by constructing a polynomial response surface using a multi-fidelity additive scaling variable that calculates the difference between the low-fidelity and high-fidelity predictions of the condensation mass flow rate. Full article
(This article belongs to the Special Issue Numerical Methods and Machine Learning Techniques for Complex Flows)
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17 pages, 1904 KiB  
Article
A Primer on CFD-DEM for Polymer-Filled Suspensions
by Célio Fernandes, Luís L. Ferrás and Alexandre Afonso
Appl. Sci. 2023, 13(4), 2466; https://doi.org/10.3390/app13042466 - 14 Feb 2023
Cited by 2 | Viewed by 1079
Abstract
This work reports on an evaluation of the computational fluid dynamics–discrete element method (CFD-DEM) numerical approach to study the behavior of polymer-filled suspensions in a parallel-plate rheometer. For this purpose, an open-source CFD-DEM solver is used to model the behavior of such suspensions [...] Read more.
This work reports on an evaluation of the computational fluid dynamics–discrete element method (CFD-DEM) numerical approach to study the behavior of polymer-filled suspensions in a parallel-plate rheometer. For this purpose, an open-source CFD-DEM solver is used to model the behavior of such suspensions considering different particle volume fractions and different types of fluid rheology. We first validate the numerical approach for the single-phase flow of the continuum phase (fluid phase) by comparing the fluid’s azimuthal velocity and shear stress components obtained from the open-source solver against the analytical expressions given in cylindrical coordinates. In addition, we compare the numerical torque given by the numerical procedure with analytical expressions obtained for Newtonian and power law fluids. For both cases, there is a remarkable agreement between the numerical and analytical results. Subsequently, we investigated the effects of the particle volume fraction on the rheology of the suspension. The numerical results agree well with the experimentally measured ones and show a yield stress phenomenon with the increase of the particle volume fraction. Full article
(This article belongs to the Special Issue Numerical Methods and Machine Learning Techniques for Complex Flows)
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14 pages, 12270 KiB  
Article
Numerical Study of Lid-Driven Square Cavity Flow with Embedded Circular Obstacles Using Spectral/hp Element Methods
by Jianming Zhang, Bo Xiao and Wensheng Yang
Appl. Sci. 2022, 12(22), 11711; https://doi.org/10.3390/app122211711 - 17 Nov 2022
Cited by 2 | Viewed by 1601
Abstract
We show a successful numerical study of lid-driven square cavity flow with embedded circular obstacles based on the spectral/hp element methods. Various diameters of embedded two-dimensional circular obstacles inside the cavity and Reynolds numbers Re (from 100 to 5000) are considered. In order [...] Read more.
We show a successful numerical study of lid-driven square cavity flow with embedded circular obstacles based on the spectral/hp element methods. Various diameters of embedded two-dimensional circular obstacles inside the cavity and Reynolds numbers Re (from 100 to 5000) are considered. In order to verify the effectiveness and accuracy of the current methods, numerical results are investigated by comparing with those available in the literature obtained by the moving immersed boundary method (MIBM) and the lattice Boltzmann method (LBM). The present spectral/hp element methods have been not only successfully applied to study and visualize the primary and induced vortices but also capture new vortices on the lower right, upper left and upper right positions of the circular obstacle when Reynolds number Re = 100 and Re = 5000, which is not observed in the lattice Boltzmann method. The current data and figures are in good agreement with the published results. The results of the present study show that the spectral/hp element methods are effective and accurate in simulation of lid-driven cavity flow with embedded circular obstacles, and the present methods have the following advantages: less preprocesses required and high-resolution characteristics. Full article
(This article belongs to the Special Issue Numerical Methods and Machine Learning Techniques for Complex Flows)
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