Comparison of Several Optimization Strategies for Robust Turbine Blade Design

This paper addresses the problem of turbine blade shape optimization in the presence of geometric uncertainties. Several strategies are tested and compared on a two-dimensional compressor blade optimization process for which performance is assessed using a commercial Reynolds-averaged Navier-Stokes computational fluid dynamics code. In each case, a range of shape errors are considered that attempt to simulate foreign object damage, erosion damage, and manufacturing errors. These lead to stochastic performance measures that, in turn, are considered in a multi-objective optimization framework. Because of the long run times associated with Reynolds-averaged Navier-Stokes codes, use is also made of surrogate or response surface-based optimization methods to speed up the search processes. The paper shows that a range of technqiues can be used to tackle this problem, but that no one method is clearly best overall. The practitioner is therefore cautioned against favoring a single approach for such design problems. Further research may help clarify these issues

[1]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[2]  P. Bouillard,et al.  Hierarchical stochastic metamodels based on moving least squares and polynomial chaos expansion , 2011 .

[3]  Jerome Sacks,et al.  Computer Experiments for Quality Control by Parameter Design , 1990 .

[4]  Michael T. M. Emmerich,et al.  Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels , 2006, IEEE Transactions on Evolutionary Computation.

[5]  Andy J. Keane,et al.  Wing Optimization Using Design of Experiment, Response Surface, and Data Fusion Methods , 2003 .

[6]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[7]  Roman B. Statnikov,et al.  Multicriteria Optimization and Engineering , 1995 .

[8]  R. M. Hicks,et al.  Wing Design by Numerical Optimization , 1977 .

[9]  Apurva Kumar,et al.  Robust design methodologies: application to compressor blades , 2006 .

[10]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[11]  Joshua D. Knowles,et al.  Multiobjective Optimization on a Budget of 250 Evaluations , 2005, EMO.

[12]  Andy J. Keane,et al.  Multi-Objective Optimization Using Surrogates , 2010 .

[13]  Shigeyoshi Tsutsui,et al.  Genetic algorithms with a robust solution searching scheme , 1997, IEEE Trans. Evol. Comput..

[14]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[15]  Andy J. Keane,et al.  Robust-Optimal Design of a Lightweight Space Structure Using a Genetic Algorithm , 2003 .

[16]  T. Simpson,et al.  Efficient Pareto Frontier Exploration using Surrogate Approximations , 2000 .

[17]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[18]  Andy J. Keane,et al.  Robust Design of Compressor Fan Blades Against Erosion , 2006 .

[19]  Tiziano Ghisu,et al.  Robust design optimization of gas turbine compression systems , 2011 .

[20]  Andy J. Keane,et al.  Statistical Improvement Criteria for Use in Multiobjective Design Optimization , 2006 .

[21]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.