Adjoint-Based Geometrically Constrained Aerodynamic Optimization of a Transonic Compressor Stage

被引:0
作者
Xinrong Su
Can Ma
Xin Yuan
机构
[1] Tsinghua University,Key Laboratory for Thermal Science and Power Engineering of Ministry of Education
[2] Science and Technology on Thermal Energy and Power Laboratory,undefined
来源
Journal of Thermal Science | 2019年 / 28卷
关键词
aerodynamic optimization; compressor stage; discrete adjoint method; geometric constraint;
D O I
暂无
中图分类号
学科分类号
摘要
Efficient method to handle the geometric constraints in the optimization of turbomachinery blade profile is required. Without constraints on the blade thickness, optimal designs typically yield thinner blade to reduce the friction loss, however, at the risk of degraded strength and stiffness. This issue is seldom discussed and existing literature always treat the blade thickness constraint in an indirect manner. In this work, two different geometric constraints on the blade thickness are proposed and applied in the adjoint optimization: one is on the maximum blade thickness and the other is on the blade area. Methods to compute sensitivities of both constraints are proposed and they are integrated into an optimization system based on a finite volume code and a solver for the discrete adjoint equation. Adjoint optimization is conducted to minimize the entropy production in a transonic compressor stage. Results for the adjoint optimization without geometry constraint and two comparative cases are detailed. It is indicated that three cases yield similar performance improvement; however, if geometry constraints are properly handled, the optimal designs have almost the same maximum thickness as the original design, compared to a thinner blade profile with 14% reduction of maximum thickness in the case without geometry constraint. The cases considering geometry constraints also consume slightly reduced Central Processing Unit (CPU) cost. Result of this work verifies the effectiveness of the proposed method to treat geometric constraints in adjoint optimization.
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页码:850 / 861
页数:11
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