Variable-Resolution Shape Optimization: Low-Fidelity Model Setup and Algorithm Scalability

2012 ◽  
Author(s):  
Leifur Leifsson ◽  
Slawomir Koziel
Keyword(s):  
Shape Optimization ◽  
Variable Resolution ◽  
Model Setup ◽  
Fidelity Model ◽  
Algorithm Scalability

Adaptive response prediction for aerodynamic shape optimization

2017 ◽  
Vol 34 (5) ◽  
pp. 1485-1500
Author(s):  
Leifur Leifsson ◽  
Slawomir Koziel
Keyword(s):  
Shape Optimization ◽  
Adaptive Response ◽  
Simulation Models ◽  
Response Prediction ◽  
High Fidelity ◽  
High Fidelity Simulation ◽  
Aerodynamic Shape Optimization ◽  
Content Type ◽  
Aerodynamic Shape ◽  
Fidelity Model

Purpose The purpose of this paper is to reduce the overall computational time of aerodynamic shape optimization that involves accurate high-fidelity simulation models. Design/methodology/approach The proposed approach is based on the surrogate-based optimization paradigm. In particular, multi-fidelity surrogate models are used in the optimization process in place of the computationally expensive high-fidelity model. The multi-fidelity surrogate is constructed using physics-based low-fidelity models and a proper correction. This work introduces a novel correction methodology – referred to as the adaptive response prediction (ARP). The ARP technique corrects the low-fidelity model response, represented by the airfoil pressure distribution, through suitable horizontal and vertical adjustments. Findings Numerical investigations show the feasibility of solving real-world problems involving optimization of transonic airfoil shapes and accurate computational fluid dynamics simulation models of such surfaces. The results show that the proposed approach outperforms traditional surrogate-based approaches. Originality/value The proposed aerodynamic design optimization algorithm is novel and holistic. In particular, the ARP correction technique is original. The algorithm is useful for fast design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search spaces, which is challenging using conventional methods because of excessive computational costs.

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