4.5 Article

Design Optimization of a Dual-Bleeding Recirculation Channel to Enhance Operating Stability of a Transonic Axial Compressor

期刊

ENERGIES
卷 15, 期 1, 页码 -

出版社

MDPI
DOI: 10.3390/en15010159

关键词

axial compressor; recirculation channel; RANS analysis; optimization; stall margin; genetic algorithm

资金

  1. National Research Foundation of Korea (NRF) - Korean government (MSIT) [2019R1A2C1007657]
  2. National Research Foundation of Korea [2019R1A2C1007657] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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This study investigated the effects of a dual-bleeding port recirculation channel on the aerodynamic performance of a single-stage transonic axial compressor and optimized its configuration to enhance operating stability. The addition of the bleeding channel increased its stabilizing effect compared to a single-bleeding channel. Optimization resulted in a 51.36% increase in stall margin with only slight reductions in peak adiabatic efficiency and maximum pressure ratio.
The present work performed a comprehensive investigation to find the effects of a dual-bleeding port recirculation channel on the aerodynamic performance of a single-stage transonic axial compressor, NASA Stage 37, and optimized the channel's configuration to enhance the operating stability of the compressor. The compressor's performance was examined using three parameters: The stall margin, adiabatic efficiency, and pressure ratio. Steady-state three-dimensional Reynolds-averaged Navier-Stokes analyses were performed to find the flow field and aerodynamic performance. The results showed that the addition of a bleeding channel increased the recirculation channel's stabilizing effect compared to the single-bleeding channel. Three design variables were selected for optimization through a parametric study, which was carried out to examine the influences of six geometric parameters on the channel's effectiveness. Surrogate-based design optimization was performed using the particle swarm optimization algorithm coupled with a surrogate model based on the radial basis neural network. The optimal design was found to increase the stall margin by 51.36% compared to the case without the recirculation channel with only 0.55% and 0.28% reductions in the peak adiabatic efficiency and maximum pressure ratio, respectively.

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