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Optimization of Discrete Cavities with Guide Vanes in A Centrifugal Compressor based on A Comparative Analysis of Optimization Techniques

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Abstract

Discrete cavities with guide vanes were developed and optimized to improve the operating stability of a centrifugal compressor. Various combinations of search algorithms and surrogate models were tested to find the best optimization methods. Aerodynamic analysis was performed using three-dimensional Reynolds-averaged Navier–Stokes equations. The numerical results obtained for the total pressure ratio and adiabatic efficiency were validated with experimental data for the centrifugal compressor with a smooth casing. The yaw and pitch angles of the guide vanes and axial distance between cavities were selected as design variables. The stall margin was used as an objective function for the design optimization. Latin hypercube sampling was used to select 27 sample points in the design space. The best combination was found by testing four surrogate models (response surface approximation, Kriging, radial basis neural network, and deep neural network models) and three searching algorithms (a genetic algorithm, particle swarm optimization, and hybrid PSO-GA). Hybrid PSO-GA with the DNN model showed the best overall results. The optimum design showed increments of 13.36% and 3.78% in the stall margin compared to compressors with a smooth casing and the reference cavity design, respectively.

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Abbreviations

ANN:

Artificial neural network

CFD:

Computational fluid dynamics

D :

Diameter of the impeller (mm)

DCGV:

Discrete cavities with guide vanes

DNN:

Deep neural network

DOE:

Design of experiments

GA:

Genetic algorithm

GCI:

Grid convergence index

KRG:

Kriging

L :

Axial length of the guide vanes (mm)

LB:

Lower bound

LHS:

Latin hypercube sampling

M :

Meridional coordinate

P :

Pressure (Pa) or Axial distance between cavities (mm)

PR:

Pressure ratio

PSO:

Particle swarm optimization

RANS:

Reynolds-averaged Navier–Stokes

RBNN:

Radial basis neural network

ReLu:

Rectified linear unit

RSM:

Root-mean-square

RSA:

Response surface approximation

SM:

Stall margin

SST:

Shear stress transport

T :

Temperature (K)

UB:

Upper bound

VIGV:

Variable inlet guide vane

x, θ, z :

Cylindrical coordinates

α Y :

Yaw angle of the guide vanes (°)

α P :

Pitch angle of the guide vanes (°)

γ :

Specific heat ratio

η :

Adiabatic efficiency

design:

Design condition of the centrifugal compressor

inlet:

Inlet of the centrifugal compressor

outlet:

Outlet of the centrifugal compressor

stall:

Near stall condition of the centrifugal compressor

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Funding

This work was supported by INHA UNIVERSITY Research Grant.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Inha University, 100 Inha-Ro, Michuhol-Gu, Incheon, 22212, Republic of Korea

    Sang-Bum Ma, Min-Su Roh & Kwang-Yong Kim

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  1. Sang-Bum Ma
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Correspondence to Kwang-Yong Kim.

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Ma, SB., Roh, MS. & Kim, KY. Optimization of Discrete Cavities with Guide Vanes in A Centrifugal Compressor based on A Comparative Analysis of Optimization Techniques. Int. J. Aeronaut. Space Sci. 22, 514–530 (2021). https://doi.org/10.1007/s42405-020-00341-z

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