Optimization design of radial inflow turbine combined with mean-line model and CFD analysis for geothermal power generation

Biao Li, Heping Xie, Licheng Sun, Jun Wang, Bowen Liu, Tianyi Gao, Entong Xia, Jvchang Ma and Xiting Long

Energy, 2024, vol. 291, issue C

Abstract: It is a considerable challenge to determine the key parameters affecting the efficiency and propose an accurate loss prediction model for radial flow turbine design. In current investigation, a one-dimensional mean-line model combined with particle swarm optimization algorithm and Kriging response surface surrogate model based on 3D numerical results were proposed to optimize radial flow turbines and evaluate performance. The loss model was carried out to analyze the relationship between geometric parameters, operating conditions and turbine performance. CFD numerical simulations were employed to revealed the mechanism of enhancing the turbine performance. The total-static efficiency was increased from 88.5 % to 91.7 %, and the clearance loss and passage loss were reduced by 18.4 % and 35.8 %, respectively,by optimized design. It is attributed to mitigate the curvature-induced boundary layer separation and vortex at the outlet, which reduces the secondary flow losses.The correlation between the predicted values of the Kriging response surface and the numerical results was up to 98.3 %. The results showed that the angle of attack was in the range of -10-0°, the blade has less influence on the flow loss. The current study effectively combined the flow path structure parameters and flow characteristics to realize the efficient optimization of ORC turbine.

Keywords: Radial inflow turbine; Optimization; Kriging response surface; Particle swarm algorithms; CFD analysis (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:291:y:2024:i:c:s0360544224002238

DOI: 10.1016/j.energy.2024.130452

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