Optimization and reconstruction of pelton buckets based on statistical techniques, artificial neural networks and CFD modelling

Abstract

During operation Pelton turbines suffer from erosion and cavitation wear, which decreases the hydraulic efficiency. Welding repair is a cost-effective method for recovering the original bucket geometry but requires templates to ensure proper results. However, those templates may be unavailable for Pelton units with decades of operation. This is the case of one of the 2.8 MW Pelton units at Nima II, a small hydropower plant in Colombia that began operation in 1942. This paper presents the redesign and optimization of the bucket. First, the bucket is rebuilt following both, technical recommendations, and Design of Experiments (DOE) to verify the effects of the basic dimensions on the hydraulic efficiency. Experimental measurements on the eroded turbine validate the two-phase computational fluid dynamics (CFD) modelling to be applied in the optimization process. Second, the best inner surface is found by using a multi-objective optimization based on Artificial Neural Networks trained by the previous DOE and a genetic algorithm. At the end, the CFD results suggest a total hydraulic efficiency recovery of 8 %. Further experimental tests on the optimized runner showed a difference of 0.7 % from the predicted value.