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Machine learning-based CFD simulations: a review, models, open threats, and future tactics

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Abstract

This review targets various scenarios where CFD could be used and the logical parts needed for exemplary computations. The machine learning aspect with algorithms that have been implemented suggests design parameters to an algorithm that can be used for bodies in flights and different research-based algorithms that have been used and outlines the advantages, disadvantages, and tools used for computing the algorithm. Since fluid behavior is quite erratic, a single algorithm may not be versatile in every case. In some cases, multiple algorithms are combined for successful simulations. The uniqueness of the review lies in the combination of algorithms for every different case with theoretical analysis and disadvantages, which could be avoided by clubbing another algorithm that overcomes the problem. Since ML is not fully mature yet to provide high accuracy without bit preprocessing in the form of the numerical method, this is one of the heavy limitations that are briefly discussed.

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Abbreviations

ACOFIS:

Algorithm with the fuzzy inference system

AI:

Artificial inteligance

ANFIS:

Adaptive network-based fuzzy inference

BCR:

Bubble column reactor

CAD:

Computer-aided design

CFD:

Computational fluid dynamics

CT:

Computed tomography

DEFIS:

Differential evolution-based fuzzy inference system

DEM:

Discrete element method

FEA:

Finite element analysis

GAFIS:

Genetic algorithm combined with a fuzzy interface system

GPR:

Gaussian process regression

LLVM:

Low-dimensional ventilation model

LMA:

Levenberg–Marquardt algorithm

MLGA:

Machine learning genetic algorithm

RBFNN:

Radial basis function neural network

UAV:

Unmanned aerial vehicle

UQ:

Uncertainty quantification

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Authors and Affiliations

  1. School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Vellore,, Tamil Nadu, 632014, India

    Dhruvil Panchigar, Rhea Mary Mathew, Utkarsh Chadha & Senthil Kumaran Selvaraj

  2. School of Information Technology and Engineering (SITE), Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India

    Kunal Kar

  3. School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India

    Shashank Shukla

  4. Department of Materials Science and Engineering, Faculty of Applied Sciences and Engineering, School of Graduate Studies, University of Toronto, Toronto, ON, M5S 2Z9, Canada

    Utkarsh Chadha

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Panchigar, D., Kar, K., Shukla, S. et al. Machine learning-based CFD simulations: a review, models, open threats, and future tactics. Neural Comput & Applic 34, 21677–21700 (2022). https://doi.org/10.1007/s00521-022-07838-6

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