Abstract

Finite element models of whole gas turbine engines, also known as whole engine models (WEMs), which consist of three-dimensional solid elements are not commonly used in design optimization studies due to the high computational cost of solving them for many designs. WEMs consisting of two-dimensional shell elements can be a suitable replacement for high-fidelity solid WEMs as they approximate the responses well while being significantly quicker to solve. However, in a surrogate-assisted optimization study, the accumulation of errors in the shell WEM evaluations can result in the construction of a surrogate model that can be somewhat misleading compared to the solid WEM response surface. Such a surrogate model could return promising designs that, when validated using solid WEMs, turn out to be suboptimal or infeasible. A novel approach which combines medial meshing and multi-fidelity surrogate modelling techniques is proposed to increase the feasibility of conducting whole engine optimization studies. We demonstrate the workflow for generating medial meshes on an engine intercasing geometry. The accuracy of medial mesh simulations with respect to solid mesh simulations is evaluated and discussed in the context of their suitability as a source of low-fidelity structural information for multi-fidelity surrogate models. The impact of this combination of techniques is subsequently illustrated using two case studies. The first case study is the optimization of an intermediate compressor casing for minimum mass with constraints on the casing stiffness. The results show that the multi-fidelity approach is able to find optimum designs that are equivalent to the expensive single-fidelity approach of using only solid mesh evaluations but at a significantly lower computational cost. The second case study is the optimization of a whole engine geometry. This case study serves to demonstrate the effectiveness of the multi-fidelity approach for solving realistic design problems.

Highlights

  • Due to advances in computational power and simulation efficiency, whole engine models (WEMs) have become a staple tool in the design process of gas turbine engines in industry (Voutchkov et al 2006; Arkhipov et al 2009; Toal et al 2014)

  • Within the range of interest, all main diagonal MAC values are near 1, while all off-diagonal MAC values are approximately zero. These results show that the medial mesh is a good replacement for the expensive solid mesh for dynamic analyses

  • For the intermediate compressor casing problem, the Co-Kriging approach is able to find feasible optimum designs that are on par with the single-fidelity Kriging approach which solely uses solid mesh evaluations

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Summary

Introduction

Due to advances in computational power and simulation efficiency, whole engine models (WEMs) have become a staple tool in the design process of gas turbine engines in industry (Voutchkov et al 2006; Arkhipov et al 2009; Toal et al 2014). The high cost of solving a WEM that is meshed using solid elements restricts the number of simulations that can be performed in the overall design process. Voutchkov et al (2006) demonstrated this geometry modification approach in a multi-objective optimization study on a whole jet engine which employed manually constructed shell meshes. Evaluations of whole engine designs using medial meshes can be used to construct a relatively inexpensive surrogate model of the overall structural behaviour. Toal et al (2014) demonstrated the use of Co-Kriging models for reducing the frequency of performing expensive whole engine transient thermo-mechanical simulations in the optimization of a high-pressure compressor for minimum specific fuel consumption.

The medial object
MANTLE and the medial mesh workflow
Validation of medial mesh simulations against solid mesh simulations
Static structural analysis
Free-free modal analysis
Kriging
Co-Kriging
Application of the multi-fidelity optimization with a medial mesh
Two-variable optimization of an engine intercasing
Eight-variable optimization of a whole engine
Conclusions
Findings
Replication of results
Full Text
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