Design Optimisation of Legacy Francis Turbine Using Inverse Design and CFD: A Case Study of Bérchules Hydropower Plant

Israel Enema Ohiemi and Aonghus McNabola ()
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Israel Enema Ohiemi: Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, D02 PN40 Dublin, Ireland
Aonghus McNabola: School of Engineering, RMIT University, 124 La Trobe Street, Melbourne, VIC 3000, Australia

Energies, 2025, vol. 18, issue 21, 1-27

Abstract: The lack of detailed design information in legacy hydropower plants creates challenges for modernising their ageing turbine components. This research advances a digitalisation approach which combines inverse design methodology (IDM) with multi-objective genetic algorithms (MOGA) and computational fluid dynamics (CFD) to digitally reconstruct and optimise the Bérchules Francis turbine runner and guide vane geometries using limited available legacy data, avoiding invasive techniques. A two-stage optimisation process was conducted. The first stage of runner blade optimisation achieved a 22.7% reduction in profile loss and a 16.8% decrease in secondary flow factor while raising minimum pressure from −877,325.5 Pa to −132,703.4 Pa. Guide vane optimisation during Stage 2 produced additional performance gains through a 9.3% reduction in profile loss and a 20% decrease in secondary flow factor and a minimum pressure increase to +247,452.1 Pa which represented an 183% improvement. The CFD validation results showed that the final turbine efficiency reached 93.7% while producing more power than the plant’s rated 942 kW. The sensitivity analysis revealed that leading edge loading at mid-span and normal chord proved to be the most significant design parameters affecting pressure loss and flow behaviour metrics. The research proves that legacy turbines can be digitally restored through hybrid optimisation and CFD workflows, which enables data-driven refurbishment design without needing complete component replacement.

Keywords: design optimisation; inverse design; hydropower; CFD; MOGA (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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