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Blade Shape Optimization of Transonic Axial Flow Fan in Terms of Sectional Profiles and Stacking Line

[+] Author Affiliations
Peng Song, Jinju Sun

Xi’an Jiaotong University, Xi’an, ChinaThe Collaborative Innovation Center for Advance Aero-Engine (CICAAE) of China, Beijing, China

Ke Wang

Xi’an Jiaotong University, Xi’an, China

Paper No. GT2014-25816, pp. V02BT39A016; 13 pages
  • ASME Turbo Expo 2014: Turbine Technical Conference and Exposition
  • Volume 2B: Turbomachinery
  • Düsseldorf, Germany, June 16–20, 2014
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-4561-5
  • Copyright © 2014 by ASME


Transonic axial flow fan has relatively high blade tip speed and produces higher pressure ratio than the subsonic. However, considerable losses are brought about by the shock waves close to blade tip and over part of span, leading to deteriorated overall efficiency and operating flow range. It is generally acknowledged that modifications of blade stacking line (axially sweep and tangentially lean) and sectional profiles can help to control spanwise distribution of blade loading, reduce shock loss and secondary flow, and extend the operating flow range.

The present study is to maximize the comprehensive benefits of simultaneously optimizing the sectional profiles and stack line by means of a global optimization method with reduced cost. In contrast with previous studies, it is of two distinguished features. First, in blade geometry parameterization, both sectional profiles and stacking line are varied to provide more flexible blade shape variation and subsequently permit more optimization performance gains. Secondly, with simultaneous variation of sectional profiles and stacking line, number of optimization variables and nonlinearity of optimization problem will increase largely. How to obtain a global optimal solution and also reduce the computation become the major concerns. For this purpose, a global optimization method proposed by us is used. It includes an improved CCEA (Cooperative Co-Evolution Algorithm) optimizer, adaptively updated kriging surrogate model, and one-stage Expected Improvement (EI) approach that permits adaptive sampling. At initial stage, a coarse surrogate model is constructed with small number of samples. During the optimization process, some new samples are identified, evaluated, and then used to refine the model and conduct further optimal searching. In the optimization process, the accuracy of the surrogate model is improved based on its own characteristics of optimization problem and this permits the optimizer to conduct the aim-oriented optimal searches. In such a manner, the surrogate model sustains high-level of accuracy while uses fewer samples, thus the blade optimization and computations are significantly reduced.

The optimization is conducted for NASA Rotor67 at design flow rate with a single workstation of DELL 7500. It is demonstrated that the optimized blade design produces significant performance gains at design condition (where the overall efficiency and pressure ratio are increased respectively by 1.27 and 6.53 points) and also at off-design conditions.

Copyright © 2014 by ASME



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