Our research has been driven by an ambition to understand the hydraulic phenomena happening in hydraulic machines. We believe that this ambition can be achieved through experimental activities involving CFD simulations within our Virtual Hydraulic Laboratory (VHLab).

We have been steadily building our knowledge base and developing the methodology of designing better performing reaction turbines. SEi is continuously involved in research and development projects in collaboration with manufacturing and scientific entities, including Laval University (Canada) and Itajuba University (Brazil). We also explore new design and manufacturing methods for low-head hydro turbines.

Our primary focus is on improving the method of finding a runner blade shape that can extract the maximal amount of energy from the water flowing thought the turbine while satisfying certain hydrodynamic and mechanical parameters.

This complex design target is determined by the Multi-Objective Matrix (M-OM): a set of desired turbine performance parameters including hydraulic efficiencies, power, cavitation, and draft tube stability factor for operating points which are defined as the design target.

H [m] – Net head

Q [cms] – Flow

η [%] – hydraulic efficiency

σ [-] – cavitation coefficient (Thoma number)

ψ [-] – flow stability coefficient

index i = 1,2…n-1,n marks the number of the operating point

For that purpose, SEi employs the iterative rules of biological evolution as a tool. The runner shape parameters are subjected to manipulations following biology-inspired processes like:

• Selection: finding set of shape parameters that result in the best performances (closest to those represented by the Multi-Objective Matrix)
• Recombination: averaging of shape parameters among the selected ones that give results closest to those represented by the Multi-Objective Matrix
• Mutation: creating arbitrary variations in shape parameters values to generate the next generation of shapes

Dynamic parameterization, a unique SEi approach, is applied to facilitate the fine-tuning of the optimal shape. The complexity of the surface’s representation progresses along with the optimization procedure: the shape becomes more complex over the course of the design process.