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Hybrid optimization of curvature continuous stacking line on the highly loaded diffuser cascade

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Abstract

The compressor is a critical component of aero-engines. In order to improve the performance, the compressor ratio of single-stage compressor is getting higher and higher, which will lead to high back pressure gradient and losses. To solve this problem, there are many techniques applied, such as cantilevered stator, tip clearance and slotted airfoils. However, traditional design methods are experience-dependent and time-consuming. This paper proposes a hybrid optimization method to optimize the stacking line of compressor cascade and reduce total pressure loss on both design and off-design conditions. The approach employs various surrogate models and a multi-infill strategy, outperforming traditional optimization methods using a single surrogate model and a single infilling strategy. The results show that compared to the original blade, the optimized blade has a 34.6\(\%\) lower mass-averaged total pressure loss at the design point, while the static pressure ratio increases by 2.43\(\%\). This paper innovatively combines deep learning-based surrogate models, the hybrid optimization algorithm, and the curvature-based blade shaping method to optimize the blade shape, shorten the blade design time, and ultimately reduce the losses significantly.

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Data availability

The data underlying this article will be shared on reasonable request to the corresponding author.

Abbreviations

\(\text {CDA}\) :

Controlled diffusion airfoil

\(\text {CFD}\) :

Computational fluid dynamics

\(n\) :

Order of Bezier curve

\(i\) :

Sequence number of the vertex of the characteristic polygon

\(J_{n,i} \) :

Bernstein polynomial

\(V_{i}\) :

Position vector of the control points

\(y\) :

Stacking line

\(K\) :

Curvature of stacking line

\(p\) :

Derivative of stacking line

\(\text {DOE}\) :

Design of experiment

\(\text {LHS}\) :

Latin hypercube sampling

\(\text {OLHS}\) :

Optimized Latin hypercube sampling

\(\text {MP}\) :

Minimizing the prediction

\(\text {EI}\) :

Expected improvement

\(\text {RSME}\) :

Root mean square error

\(\text {BPNN}\) :

Back propagation neural network

\(\text {ANN}\) :

Artificial neural network

\(\text {GA}\) :

Genetic algorithm

\(\text {SQP}\) :

Successive quadratic programming

\(\text {MDO}\) :

Multidisciplinary design optimization

\(\text {GIS}\) :

Geographic information system

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Acknowledgements

The constructive and helpful comments and suggestions of the reviewers are gratefully acknowledged.

Funding

This work was supported by the National Science and Technology Major Project (J2019-II-0017-0038) and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Ke Yao, Xingyi Zhang & Xiaoqing Qiang

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  1. Ke Yao
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  2. Xingyi Zhang
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  3. Xiaoqing Qiang
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Correspondence to Xiaoqing Qiang.

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Yao, K., Zhang, X. & Qiang, X. Hybrid optimization of curvature continuous stacking line on the highly loaded diffuser cascade. AS (2024). https://doi.org/10.1007/s42401-023-00265-y

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