CFD Performance Analysis of a Bulb Turbine Based on 3D-Scanning of the Prototype

This study presents a CFD-based performance evaluation of a bulb turbine reconstructed from high-resolution 3D scanning of the prototype runner in Namgang hydropower plant. Multi ple scans acquired under controlled blade-pitch and phase conditions were processed to capture key geometric features, including leading/trailing edges, the anti-cavitation fin and tip-clearance regions. Based on these data, a reverse-engineered model satisfying the IEC 60193 geometric-similarity tolerance was generated and scaled to model dimensions. Steady RANS simulations were carried out for maximum, rated and minimum head conditions consistent with model test conditions. The predicted hydraulic efficiencies agreed with the test results within ±3%, reflecting the combined uncertainties associated with scanning, reconstruction and CFD modeling. The results demonstrate that prototype-based geometry acquisition significantly enhances prediction reliability and provides a robust modeling approach for performance assessment. The established workflow offers a practical basis for future studies on cavitation behavior, operating-point optimization and digital-twin development for hydropower turbines.

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Publication Details

Journal
The KSFM Journal of Fluid Machinery
Published
2026-04-13
DOI
https://doi.org/10.5293/kfma.2026.29.2.071
Primary Topic
Wind Energy Research and Development
Type
article
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article

CFD Performance Analysis of a Bulb Turbine Based on 3D-Scanning of the Prototype

Soo-Hwang Ahn, Jong-Woong Choi, Yonggu Lee, Yong Cho
The KSFM Journal of Fluid Machinery
Wind Energy Research and Development
article

CFD Performance Analysis of a Bulb Turbine Based on 3D-Scanning of the Prototype

Soo-Hwang Ahn, Jong-Woong Choi, Yonggu Lee, Yong Cho
article en

Abstract

This study presents a CFD-based performance evaluation of a bulb turbine reconstructed from high-resolution 3D scanning of the prototype runner in Namgang hydropower plant. Multi ple scans acquired under controlled blade-pitch and phase conditions were processed to capture key geometric features, including leading/trailing edges, the anti-cavitation fin and tip-clearance regions. Based on these data, a reverse-engineered model satisfying the IEC 60193 geometric-similarity tolerance was generated and scaled to model dimensions. Steady RANS simulations were carried out for maximum, rated and minimum head conditions consistent with model test conditions. The predicted hydraulic efficiencies agreed with the test results within ±3%, reflecting the combined uncertainties associated with scanning, reconstruction and CFD modeling. The results demonstrate that prototype-based geometry acquisition significantly enhances prediction reliability and provides a robust modeling approach for performance assessment. The established workflow offers a practical basis for future studies on cavitation behavior, operating-point optimization and digital-twin development for hydropower turbines.

The KSFM Journal of Fluid MachineryVol. 29(2)
Affordable and clean energy
Openalex Percentile: Top 4%
Wind Energy Research and Development
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