Air foil Shape Optimization Using Cfd And Parametrization Methods

Anitha, D; Shamili, G.K.; Kumar, Parth; Vihar, Rok

doi:10.1016/j.matpr.2017.12.122

Cited by 15 publications

(2 citation statements)

References 2 publications

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“…The parameterization of airfoil geometry, although not strictly required from the simulation perspective, reduces the required information needed to feed the machine learning algorithm [21]. Among the established parameterization algorithms, the cubic version of the CST [22] (class function/shape function transformation method) algorithm has been profusely adopted.…”

Section: Parameterizationmentioning

confidence: 99%

Airfoil Analysis and Optimization Using a Petrov–Galerkin Finite Element and Machine Learning

2023

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For the analysis of low-speed incompressible fluid dynamics with turbulence around airfoils, we developed a finite element formulation based on a stabilized pressure and velocity formulation. To shape the optimization of bidimensional airfoils, this formulation is applied using machine learning (TensorFlow) and public domain global optimization algorithms. The goal is to maximize the lift-over-drag ratio by using the class-shape function transformation (CST) parameterization technique and machine learning. Specifically, we propose equal-order stabilized three-node triangles for the flow problem, standard three-node triangles for the approximate distance function (ADF) required in the turbulence stage, and stabilized three-node triangles for the Spalart–Allmaras turbulence model. The backward Euler time integration was employed. An implicit time-integration algorithm was adopted, and a solution was obtained using the Newton–Raphson method. This was made possible in the symbolic form via Mathematica with the AceGen package. Three benchmarks are presented, with Reynolds numbers up to 1×107, demonstrating remarkable robustness. After the assessment of the new finite element, we used machine learning and global optimization for four angles of attack to calculate airfoil designs that maximized MMCL/CD.

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Section: Parameterizationmentioning

confidence: 99%

Airfoil Analysis and Optimization Using a Petrov–Galerkin Finite Element and Machine Learning

2023

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“…Anitha et al 12 used a genetic algorithm, particle swarm optimisation methods on NACA 4412 in a MATLAB environment. Airfoil parameterisation has been done using cubic spline, PARSEC and CST methods.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Mathematical Modeling, Design, and Optimization of Airfoils and Aircrafts Operating at Specific Reynolds Number

Kakade¹,

Seeni²

2022

ECS Trans.

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Although there are several airfoil designs available to choose from, for the application of aircraft design, the problem of finding the optimal design based on mathematical and physical modeling has not been attempted before. For that purpose, research on existing literature has been done to find the most efficient and optimal airfoil design and optimization technique to be further used for designing and optimizing airfoils. Both physical and mathematical modeling along with other computational algorithms will be used to generate optimal new airfoil designs for high lift and low drag for specific operational conditions.

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Adaptive multi-tracker optimization algorithm for global optimization problems: emphasis on applications in chemical engineering

Khosravi

Zakeri

Xie

et al. 2020

Engineering with Computers

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Air foil Shape Optimization Using Cfd And Parametrization Methods

Cited by 15 publications

References 2 publications

Airfoil Analysis and Optimization Using a Petrov–Galerkin Finite Element and Machine Learning

Airfoil Analysis and Optimization Using a Petrov–Galerkin Finite Element and Machine Learning

Mathematical Modeling, Design, and Optimization of Airfoils and Aircrafts Operating at Specific Reynolds Number

Adaptive multi-tracker optimization algorithm for global optimization problems: emphasis on applications in chemical engineering

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