[논문]Surrogate Based Optimization Techniques for Aerodynamic Design of Turbomachinery

Samad, Abdus; Kim, Kwang-Yong

doi:10.5293/ijfms.2009.2.2.179

Surrogate Based Optimization Techniques for Aerodynamic Design of Turbomachinery 원문보기

International journal of fluid machinery and systems, v.2 no.2, 2009년, pp.179 - 188

Samad, Abdus (School of Engineering, University of Aberdeen) , Kim, Kwang-Yong (School of Mechanical Engineering, Inha University)

Abstract ▼ AI-Helper

Recent development of high speed computers and use of optimization techniques have given a big momentum of turbomachinery design replacing expensive experimental cost as well as trial and error approaches. The surrogate based optimization techniques being used for aerodynamic turbomachinery designs coupled with Reynolds-averaged Navier-Stokes equations analysis involve single- and multi-objective optimization methods. The objectives commonly tried to improve were adiabatic efficiency, pressure ratio, weight etc. Presently coupling the fluid flow and structural analysis is being tried to find better design in terms of weight, flutter and vibration, and turbine life. The present article reviews the surrogate based optimization techniques used recently in turbomachinery shape optimizations.

주제어

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문제 정의

The pioneer study on blade sweep in compressors has been done by Bliss [24]. The main objective in this study was to reduce the noise level induced by shock waves. Hah, et al.

가설 설정

b) Suitable guess of design space reduces the number of optimization iteration. Proper selection of design points in design is important to reduce uncertainty in prediction.
d) Multi-objective optimization methods can be used if the system contains multiple objectives. This gives simultaneously improvement of all the objectives.

제안 방법

They showed that the positive lean reduced a hub corner and tip clearance losses excepting near the mid-span region. The improvement in the compressor performance as well as the large reduction in the cost and time associated with a rig test was also obtained together with CFD calculations.
Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) [13, 14] developed by Sandia National Laboratory is used for surrogate analysis. The paper [13] presented and compared a number of algorithmic variations for surrogate-based optimization, including approximate subproblem formulations, merit function selections, iterate acceptance logic options, constraint relaxation approaches and convergence assessment techniques. Li and Padula [15] and Quipo et al.
It has been reported that the efficiency is increased due to movement of separation lines towards downstream direction reducing the separation vortex, end-wall losses, etc. These papers describe the blade shape optimization considering stacking line modification in terms of sweeping, leaning or skewing and airfoil shape modification in terms thickness, leading edge, trailing edge modification etc.

이론/모형

RSA method is applied to get the polynomial equations for each computed objective separately, and these equations are used for multi-objective Pareto optimal front generation. Hybrid MOEA which is used to find Pareto-optimal solutions uses real coded Non-dominated sorting of genetic algorithm (NSGA-II) with local search weighted sum method [22].
Weights are decided using the guideline that the weights should reflect our confidence in the surrogate model such that the surrogate which produces high error has low weight, and thus low contribution to the final weighted average surrogate, and viceversa. In this work, global weights are selection using generalized mean square cross-validation error (GMSE) or PRESS (in RSA terminology) that is a global data-based measure of goodness. Generalized mean square cross-validation error calculation procedure is given in the appendix.

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