A Numerical Study of Response Surface Based Shape Optimization Using Neural Networks
American Journal of Mathematical and Computer Modelling
Volume 5, Issue 4, December 2020, Pages: 116-126
Received: Oct. 6, 2020;
Accepted: Oct. 24, 2020;
Published: Nov. 30, 2020
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Lawrence Munashe Mavima, Department of Mechanical Engineering, Saad Dahlab University, Blida, Algeria
Tanaka Mukoko, Department of Management and Engineering, Linköping University, Linköping, Sweden
Kudzanai Shinda Zhou, Department of Mechanical and Production Engineering, Harare Polytechnic, Harare, Zimbabwe
Industrial pipes that are used for fluid transport generally have to undergo many changes of shape to accommodate interfacing equipment related to plant operation, which results in flow maldistribution zones and higher pressure drops, and in turn leads to higher power consumption. In an attempt to redress this problem, ANSYS, a commercial Computational Fluid Dynamics (CFD) software, is used to perform numerical simulations based on a deterministic computational model of the internal fluid flow using the Reynolds Averaged Navier Stokes equations (RANS), a multi objective optimization study employing Response surface methodology and artificial neural networks. This numerical analysis has been performed on a galvanized steel duct for water recirculation. The focus of the paper is the study of the effect of a chosen set of several geometrical dimensions on the pressure drop and flow distribution inside the duct. Subsequently, a new set of designs with different geometrical parameters has been obtained to minimize the pressure drop and achieve a more uniform flow distribution by using artificial neural networks to generate a response surface and further employing Screening (Shifted-Hammersley sampling) as the optimization method that was used to select the best designs from amongst those that have been generated from the response surface.
Lawrence Munashe Mavima,
Kudzanai Shinda Zhou,
A Numerical Study of Response Surface Based Shape Optimization Using Neural Networks, American Journal of Mathematical and Computer Modelling.
Vol. 5, No. 4,
2020, pp. 116-126.
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