Multi-objective Geometry Optimization of a Gas Cyclone Using Triple-Fidelity Co-Kriging Surrogate Models

Prashant Singh (), Ivo Couckuyt, Khairy Elsayed, Dirk Deschrijver and Tom Dhaene
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Prashant Singh: Uppsala Universitet
Ivo Couckuyt: Ghent University-imec
Khairy Elsayed: Helwan University
Dirk Deschrijver: Ghent University-imec
Tom Dhaene: Ghent University-imec

Journal of Optimization Theory and Applications, 2017, vol. 175, issue 1, No 9, 172-193

Abstract: Abstract Cyclone separators are widely used in a variety of industrial applications. A low-mass loading gas cyclone is characterized by two performance parameters, namely the Euler and Stokes numbers. These parameters are highly sensitive to the geometrical design parameters defining the cyclone. Optimizing the cyclone geometry therefore is a complex problem. Testing a large number of cyclone geometries is impractical due to time constraints. Experimental data and even computational fluid dynamics simulations are time-consuming to perform, with a single simulation or experiment taking several weeks. Simpler analytical models are therefore often used to expedite the design process. However, this comes at the cost of model accuracy. Existing techniques used for cyclone shape optimization in literature do not take multiple fidelities into account. This work combines cheap-to-evaluate well-known mathematical models of cyclones, available data from computational fluid dynamics simulations and experimental data to build a triple-fidelity recursive co-Kriging model. This model can be used as a surrogate with a multi-objective optimization algorithm to identify a Pareto set of a finite number of solutions. The proposed scheme is applied to optimize the cyclone geometry, parametrized by seven design variables.

Keywords: Cyclone separator; Surrogate-based optimization (SBO); Multi-fidelity data; Sparse Kriging; Recursive co-Kriging; 68T37; 68T05; 68U20; 97M10; 93A30; 49Q10; 80M50; 76Txx; 76Dxx; 68Uxx; 90-08 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10957-017-1114-3

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