关于压气机过渡段设计方法的探讨

doi:10.7527/S1000-6893.2013.0194

Abstract

Abstract:

To improve the flow losses of compressor intermediate duct and ensure rapidity of the design process and accuracy of the results, first, an optimization algorithm combined with different aerodynamic evaluation methods is proposed for the design of compressor annular strutted intermediate duct. The fast two-dimensional evaluation method is used for global optimization, and the three-dimensional viscous evaluation method by solving Reynolds averaged Navier-Stokes (RANS) equation is used for local optimization. This combination is effective and efficient for the design process, and a parameterization method beneficial to flow area control is formulated. Second, a sample is designed and loss analysis is performed. It is proved that the duct design result will suffer great deviation if the strut effect is ignored. And a flow area raise in the middle of the duct has a positive effect on reducing the total loss. This raise of area reduces the strong pressure gradient and reaccelerates the flow at the rear of the duct. The best rate of area raise exists which is affected by the profile of the strut and the ratio of the inlet to outlet area. Good agreement with similar design approaches is achieved, and it proves that the design method used in this text is valid.

Key words: S-shape annular duct, strut, design optimization, genetic algorithm, neural network

CLC Number:

V231.3

GAO Limin, FENG Xudong, CHEN Xuan, WU Ya'nan. Exploration About Compressor Intermediate Duct Design[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013, 34(5): 1057-1063.

References

[1] Ortiz D C, Miller R J, Hodson H P, et al. Effect of length on compressor inter-stage duct performance. ASME Paper, GT-2007-27752, 2007.

[2] Bailey D W. The aerodynamic performance of an annular S-shape duct. Leicestershire, UK: Department of Aeronautical and Automotive Engineering and Transport Studies, Loughborough University, 1997.

[3] Wallin F, Eriksson L. Response surface-based transition duct shape optimization. ASME Paper, GT-2006-90978, 2006.

[4] Tiziano G, Massimiliano M, Geoffrey T P, et al. Axial compressor intermediate duct design and optimization. AIAA-2007-1868, 2007.

[5] Naylor E M J, Dueas C O, Miller R J, et al. Optimisa-tion of non-axisymmetric endwalls in compressor S-shaped ducts. ASME Paper, GT-2008-50448, 2008.

[6] Bailey D W, Britchford K M, Carrote J F, et al. Performance assessment of an annular S-shaped duct. Journal of Turbomachinery, 1997, 199(1): 149-156.

[7] Clemen C, Albrecht P, Herzog S. Optimization of a turbofan bypass duct system. ASME Paper, GT-2012-68276, 2012.

[8] Que X B, Hou A P, Zhou S. S-shaped compressor transition duct design based on wall pressure gradient control. Acta Aeronautica et Astronautica Sinica, 2010, 31(3): 459-465. (in Chinese) 阙晓斌, 侯安平, 周盛. 基于壁面压力梯度控制的压气机S形过渡段设计. 航空学报, 2010, 31(3): 459-465.

[9] Les P, Wayne T. The NURBS book. 2nd ed. New York: Springer-Verlag, 1995.

[10] Zhou X H. Three-dimensional flow calculation method. Northwestern Polytechnical University Handouts, 1981. (in Chinese) 周新海. 三元气动计算方法初步. 西北工业大学讲义, 1981.

[11] Bornholdt S, Graudenz D. A general asymmetric neural networks and structure design by genetic algorithms. Neural Networks, 1992, 5(2): 327-334.

[12] Huppertz A, Flassig P M, Robert J F, et al. Knowledge-based 2D blade design using multi-objective aerodynamic optimization and a neural network. ASME Paper, GT-2007-28204, 2007.

[13] Rai M. Aerodynamic design using neural networks. AIAA Journal, 2000, 38(1): 173-182.

[14] Spalart P R, Allmaras S R. A one-equation turbulence model for aerodynamic flows. AIAA-1992-0439, 1992.

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Exploration About Compressor Intermediate Duct Design

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