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A novel optimization approach for axial turbine blade cascade via combination of a continuous-curvature parameterization method and genetic algorithm

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Abstract

A continuous-curvature parameterization method was coupled with the genetic algorithm and a RANS flow solver to optimize the cascade of VKI and Aachen turbine blades. The main advantage of the method is to generate blades with a continuous curvature distribution, which results in a smooth distribution of pressure and velocity on the blade surface. The geometry of the blade cascade was parameterized by 33 variables, and two objective functions were considered for the optimization. The first cost function was to reduce the total pressure loss with the constraints of mass flow rate, blade loading, and outlet flow angle. At the second cost function, the constraint of constant cross-sectional area was added to the previous constraints to preserve the structural strength of the turbine blade. The total pressure loss for the VKI blade decreased by 14.7 % and 10.6 % for the first and second objective functions, respectively. The total pressure loss for the Aachen blade was also reduced by 9.5 % and 7.5 % for the first and second objective functions, respectively. Due to the efficient geometry parameterization, the proposed method quickly converged to a high-efficiency blade at the early generations. The proposed method can be developed for optimizing the different blades of turbine, compressor, and airfoil types.

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Abbreviations

b :

Blade axial chord length (m)

c :

Blade chord length (m)

S :

Spacing between the blades

C l :

Lift coefficient

ṁ :

Mass flow rate (kg/s)

M :

Mach number

C(x) :

Curvature (1/m)

P :

Pressure (Pa)

A :

Cross-sectional area (m2)

LE :

Leading edge

TE :

Trailing edge

α :

Flow angle

β :

Blade angle

γ :

Stagger angle

ρ :

Density (kg/m3)

s :

Suction surface

p :

Pressure surface

1 :

Inlet

2 :

Outlet

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Acknowledgments

This study was supported by the Brain Pool Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2019H1D3A2A01061428). This work was also supported by the National Research Foundation of Korea (NRF) grant, which is funded by the Korean government (MSIT) (No. 2020R1A5A8018822).

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Authors and Affiliations

  1. Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran

    Mehrdad Nafar-Sefiddashti, Mahdi Nili-Ahmadabadi, Behnam Saeedi-Rizi & Ebrahim Shirani

  2. School of Mechanical Engineering, Pusan National University, Busan, 609-735, Korea

    Kyung Chun Kim

Authors
  1. Mehrdad Nafar-Sefiddashti
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  2. Mahdi Nili-Ahmadabadi
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  3. Behnam Saeedi-Rizi
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  4. Ebrahim Shirani
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  5. Kyung Chun Kim
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Corresponding authors

Correspondence to Mahdi Nili-Ahmadabadi or Kyung Chun Kim.

Additional information

Mahdi Nili-Ahmadabadi is an Associate Professor and the faculty member of Mechanical Engineering Department at Isfahan University of Technology. He received his Ph.D. degrees from Sharif University of Technology in 2010. His major research interests are inverse design, turbomachinery, experimental aerodynamics, and PIV measurement.

Kyung Chun Kim is a Distinguished Professor at the School of Mechanical Engineering of Pusan National University in Korea. He obtained his Ph.D. from the Korea Advanced Institute of Science and Technology (KAIST), Korea, in 1987. He was selected as a member of the National Academy of Engineering of Korea in 2004. His research interests include flow measurements based on PIV/LIF, POCT development, wind turbines, and organic Rankine cycle system.

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Nafar-Sefiddashti, M., Nili-Ahmadabadi, M., Saeedi-Rizi, B. et al. A novel optimization approach for axial turbine blade cascade via combination of a continuous-curvature parameterization method and genetic algorithm. J Mech Sci Technol 35, 3989–4000 (2021). https://doi.org/10.1007/s12206-021-0812-9

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  • DOI: https://doi.org/10.1007/s12206-021-0812-9

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