Abstract
Open literature offers a wide canvas of techniques for surrogate-based multi-objective optimization. The large majority of works focus on methodological and theoretical aspects and are applied to simple mathematical functions. The present work aims at defining and assessing surrogate-based techniques used in complex optimization problems pertinent to the aerodynamics of reversible aerofoils. Specifically, it addresses the following questions: how meta-model techniques affect the results of the multi-objective optimization problem, and how these meta-models should be exploited in an optimization test-bed. The multi-objective optimization problem (MOOP) is solved using genetic optimization based on non-dominated sorting genetic algorithm (NSGA)-II. The paper explores the possibility to reduce the computational cost of multi-objective evolutionary algorithms (MOEA) using two different surrogate models (SM): a least square method (LSM), and an artificial neural network (ANN). SMs were tested in two optimization approaches with different levels of computational effort. In the end, the paper provides a critical analysis of the results obtained with the methodologies under scrutiny and the impact of SMs on MOEA. The results demonstrate how surrogate model incorporation into MOEAs influences the effectiveness of the optimization process itself, and establish a methodology for aerodynamic optimization tasks in the fan industry.
Highlights
Reversible single-stage axial fans are largely employed in tunnel and metro ventilation systems, to supply and extract air from the tunnels
evolutionary algorithms (EAs), the most popular belong to the family of genetic algorithms (GAs) and among these, we focused number of different EAs, the most popular belong to the family of genetic algorithms (GAs) and on non-dominated sorting genetic algorithm (NSGA)-II [15]
If the final objective is to compute a stable solution for the multi-objective optimization problem (MOOP), more accurate and computationally expensive fitness estimators need to be deployed into the multi-objective evolutionary algorithms (MOEA) framework, increasing the time to solution
Summary
Reversible single-stage axial fans are largely employed in tunnel and metro ventilation systems, to supply and extract air from the tunnels. Jin [19] and Manríquez [20] proposed a classification based on the way EAs or MOEAs incorporate the SMs (focusing on the optimization loop) This paper presents a study on the SM-based methodology to obtain a set of optimized aerofoil shapes for use in reversible fan blading. Designs 2018, 2, 19 boundary element method (BEM) can lead to non-optimal solutions, it is still a good replacement (mostly customary in design phase) of an expensive tool, such as computational fluid dynamic (CFD), to perform a preliminary study on this topic and to define guidelines to replicate the study in cascade configuration by means of CFD [22]. To mention just a few; the choice of the objective functions, the geometry (aerofoil) parameterization, and the optimization algorithm
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