Variable-fidelity CFD models and co-Kriging for expedited multi-objective aerodynamic design optimization. Issue 8 (7th November 2016)

• Record Type: Journal Article
• Authors: Koziel, Slawomir; Tesfahunegn, Yonatan; Leifsson, Leifur
• Is Part Of: Engineering computations. Volume 33:Issue 8(2016)
• Page Start: 2320
• Page End: 2338
• Publication Date: 2016-11-07
• Subjects: Surrogate modelling -- Co-Kriging -- Aerodynamic shape optimization -- Design space reduction -- Multi-objective design -- Variable-fidelity CFD models; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-09-2015-0277
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: Strategies for accelerated multi-objective optimization of aerodynamic surfaces are investigated, including the possibility of exploiting surrogate modeling techniques for computational fluid dynamic (CFD)-driven design speedup of such surfaces. The purpose of this paper is to reduce the overall optimization time. Design/methodology/approach: An algorithmic framework is described that is composed of: a search space reduction, fast surrogate models constructed using variable-fidelity CFD models and co-Kriging, and Pareto front refinement. Numerical case studies are provided demonstrating the feasibility of solving real-world problems involving multi-objective optimization of transonic airfoil shapes and accurate CFD simulation models of such surfaces. Findings: It is possible, through appropriate combination of surrogate modeling techniques and variable-fidelity models, to identify a set of alternative designs representing the best possible trade-offs between conflicting design objectives in a realistic time frame corresponding to a few dozen of high-fidelity CFD simulations of the respective surfaces. Originality/value: The proposed aerodynamic design optimization algorithmic framework is novel and holistic. It proved useful for fast design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search space, which is extremely challenging when using conventional methods due to the excessive computational cost.