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Methodology for the Optimization of a Novel Hydro Turbine with a Case Study

Research output: Contribution to Journal/MagazineJournal articlepeer-review

  • George Aggidis
  • Audrius Židonis
  • Luke Burtenshaw
  • Marc Dubois
  • Stephen Orritt
  • Dominic Pickston
  • George Prigov
  • Luke Wilmot
Article number7591
<mark>Journal publication date</mark>15/11/2023
Issue number22
Publication StatusPublished
<mark>Original language</mark>English


As the world strives towards its goal of net zero carbon emissions, it is vital that renewable energy sources be optimized to their full potential. A key source of renewable energy is hydropower, more specifically, the Pelton turbine—a highly efficient, widely used, and well-researched piece of turbomachinery. This review proposes a methodology that will aid future research on Pelton turbines and compares relevant literature to assess effective ways to improve upon the Pelton design. The methodology evaluates how both experimental and computational analysis can be utilized in parallel to accelerate the progress of research, giving an example of the adopted workflow presented in a case study. The literature study in this paper focuses on how a variety of bucket parameters can be optimized to improve the efficiency of a Pelton turbine and analyses the accuracy of CFD compared to experimental data from previous research involving Pelton and Turgo turbines. The findings revealed that a water exit angle of 169°–170° proved to be optimal, while modifications to the depth and internal geometry of the bucket seemed to have the greatest impact on the efficiency of Pelton turbines. A short discussion on the potential for utilizing the strengths of both Pelton and Turgo turbines is included to highlight the need for further research in this field. A combination of both simulation and experimental results running in parallel with each other during optimization is found to be beneficial due to advancements in rapid prototyping. By comparing experimental data with simulated data throughout the optimization process, mistakes can be realized early on in the process, reducing time in later stages. Having experimental data throughout the turbine’s development aids the computational process by highlighting issues that may have been missed when only using CFD.