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Mathematics
Special Issues
Computational Fluid Dynamics for Biological Applications and Bio-Inspired Designs
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Computational Fluid Dynamics for Biological Applications and Bio-Inspired Designs

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Dynamical Systems".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 1192

Special Issue Editor

Dr. Zheng Li

E-Mail Website
Guest Editor
Mechatronics Engineering Department, Morgan State University, Baltimore, MD 21251, USA
Interests: CFD; lattice Boltzmann method; smoothed-particle hydrodynamics; bio-inspired designs; CFD in biological applications

Special Issue Information

Dear Colleagues,

In recent years, the field of computational fluid dynamics (CFD) has grown rapidly while it has been applied to fundamental and applied applications. This Special Issue emphasizes three topics in CFD including, but not limited to: algorithm development in CFD; CFD for biological applications; and CFD for bio-inspired design. Submitted papers should cover CFD algorithms, methods and applications relevant to the current research and applied CFD challenges. CFD models at different scales, including the microscale, mesoscale and macroscale, are welcome in this Special Issue. Both high-fidelity and reduced-order CFD research will also be acceptable.

Dr. Zheng Li
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • CFD
  • lattice Boltzmann method
  • smoothed-particle hydrodynamics
  • bio-inspired designs
  • CFD in biological applications

Published Papers (1 paper)

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Research

20 pages, 5545 KiB  
Article
A Coupled Machine Learning and Lattice Boltzmann Method Approach for Immiscible Two-Phase Flows
by Peisheng Li, Hongsheng Zhou, Zhaoqing Ke, Shuting Zhao, Ying Zhang, Jiansheng Liu and Yuan Tian
Mathematics 2024, 12(1), 109; https://doi.org/10.3390/math12010109 - 28 Dec 2023
Viewed by 869
Abstract
An innovative coupling numerical algorithm is proposed in the current paper, the front-tracking method–lattice Boltzmann method–machine learning (FTM-LBM-ML) method, to precisely capture fluid flow phase interfaces at the mesoscale and accurately simulate dynamic processes. This method combines the distinctive abilities of the FTM [...] Read more.
An innovative coupling numerical algorithm is proposed in the current paper, the front-tracking method–lattice Boltzmann method–machine learning (FTM-LBM-ML) method, to precisely capture fluid flow phase interfaces at the mesoscale and accurately simulate dynamic processes. This method combines the distinctive abilities of the FTM to accurately capture phase interfaces and the advantages of the LBM for easy handling of mesoscopic multi-component flow fields. Taking a single vacuole rising as an example, the input and output sets of the machine learning model are constructed using the FTM’s flow field, such as the velocity and position data from phase interface markers. Such datasets are used to train the Bayesian-Regularized Back Propagation Neural Network (BRBPNN) machine learning model to establish the corresponding relationship between the phase interface velocity and the position. Finally, the trained BRBPNN neural network is utilized within the multi-relaxation LBM pseudo potential model flow field to predict the phase interface position, which is compared with the FTM simulation. It was observed that the BRBPNN-predicted interface within the LBM exhibits a high degree of consistency with the FTM-predicted interface position, showing that the BRBPNN model is feasible and satisfies the accuracy requirements of the FT-LB coupling model. Full article
(This article belongs to the Special Issue Computational Fluid Dynamics for Biological Applications and Bio-Inspired Designs)
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