Sensitivity Analysis of Lift and Drag Coefficients for Flow over Elliptical Cylinders of Arbitrary Aspect Ratio and Angle of Attack using Neural Network

Flow over bluff bodies has multiple engineering applications and thus, has been studied for decades. The lift and drag coefficients are practically important in the design of many components such as automobiles, aircrafts, buildings etc. These coefficients vary significantly with Reynolds number and...

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Bibliographic Details
Published in:arXiv.org 2021-10
Main Authors: Shahane, Shantanu, Kumar, Purushotam, Vanka, Surya Pratap
Format: Article
Language:English
Subjects:
Angle of attack
Artificial neural networks
Aspect ratio
Computational fluid dynamics
Design optimization
Drag coefficients
Fluid flow
Fluid mechanics
Laminar flow
Lift
Machine learning
Mathematical models
Multilayer perceptrons
Neural networks
Parameter sensitivity
Reynolds number
Steady flow
Virtual environments
Online Access:Get full text
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Summary:Flow over bluff bodies has multiple engineering applications and thus, has been studied for decades. The lift and drag coefficients are practically important in the design of many components such as automobiles, aircrafts, buildings etc. These coefficients vary significantly with Reynolds number and geometric parameters of the bluff body. In this study, we have analyzed the sensitivity of lift and drag coefficients on single and tandem elliptic cylinders to cylinder aspect ratios, angles of attack, cylinder separation, and flow Reynolds number. Sensitivity analysis with Monte-Carlo algorithm requires several function evaluations, which is infeasible with high-fidelity computational simulations. We have therefore trained multilayer perceptron neural networks (MLPNN) using computational fluid dynamics data to estimate the lift and drag coefficients efficiently. Line plots of the variations in lift and drag as functions of the governing parameters are also presented. The present approach is applicable to study of various other bluff body configurations.
ISSN:2331-8422