Computational fluid dynamic based optimisation of an industrial axial fan for rapid prototyping
Doctoral Thesis
2017
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University of Cape Town
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Abstract
Axial air flow fans are widely used for air movement. In an increasingly international and competitive market, smaller fan companies find themselves in need of rapid preliminary design. This need is addressed in this study through the development of a first-revision, Computational Fluid Dynamics (CFD) based, optimisation tool which allows for rapid prototyping of a ducted axial fan. The result is an ElementalTM-based multi-disciplinary software tool, comprising 2D CFD, mesh movement, and constrained geometric optimisation. The analytical equation employed to represent the aerofoil significantly reduces the cost of the optimisation. A pseudo-3D fan model is generated by superimposing 2D CFD results. This is done without the general assumption of the free-vortex method, which is not a necessity for fan design and other velocity distributions may be used. For this purpose, an enhanced finite volume discretisation method was developed. A penalty function minimisation, by means of an unconstrained optimisation algorithm, is implemented thereafter. The primary objective is to deliver a specific fan static pressure rise, while optimising for fan static efficiency by means of altering the rotor blade geometry. The spherical quadratic steepest descent method is employed, which does not rely on any explicit line searches, as required by traditional steepest descent techniques. The rapid prototyping tool is finally applied to an under-performing base fan (Fan-D) which cannot meet a specified duty point. The resulting optimised fan (Fan-Optim) is manufactured and experimentally tested, in accordance with the ISO 5801 standard. The pseudo-3D model is proven to predict fan performance accurately at the target duty point, while capturing fan behaviour over a range of volumetric flow rates. The former is to within 13% of the fan static pressure rise and within 2.3% of fan static efficiency. While Fan-Optim meets the desired duty point within 2%, it offers a considerable improvement in fan static efficiency over Fan-D. Furthermore, an approximate 38% reduction in blade material is achieved as a secondary effect.
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Van Rooyen, J. 2017. Computational fluid dynamic based optimisation of an industrial axial fan for rapid prototyping. University of Cape Town.