Abstract
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.
Highlights
The shape of a duct in fluid transport systems is not often designed from a fluid dynamic perspective
In recent years, optimization based on flow analysis is becoming increasingly popular in the field of engineering design but at the same time, manufacturers are increasingly looking for ways in which to reduce the time taken during product development
There are two types of Goal driven optimization (GDO) systems, namely: Direct optimization, wherein the “best” possible designs are selected from actual analysis results which have been solved and response surface optimization wherein the “best” possible designs are drawn from the generated response surface which provides a continuous variation of a chosen output/(s) with respect a given range of the inputs without having to solve for the entire design space
Summary
The shape of a duct in fluid transport systems is not often designed from a fluid dynamic perspective. In recent years, optimization based on flow analysis is becoming increasingly popular in the field of engineering design but at the same time, manufacturers are increasingly looking for ways in which to reduce the time taken during product development One such way is having the products developed in the form of virtual prototypes in which CAD software is used to model a prototype which can be used to predict the performance prior to constructing physical prototypes. The principal aim of this paper, to minimize the pressure drop and achieve a more uniform flow distribution within the duct, was achieved by drawing the best possible designs from the response surface given by the neural network by using the screening optimization method
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