<span lang="EN-GB">This study discusses the numerical optimisation and performance testing of the turbine runner profile for the designed gravitational water vortex turbine. The initial design of the turbine runner is optimised using a surface vorticity algorithm coded in MATLAB to obtain the optimal stagger angle. Design validation is carried out using computational fluid dynamics (CFD) Ansys CFX to determine the performance of the turbine runner with the turbulent shear stress transport model. The CFD analysis shows that by optimising the design, the water turbine efficiency increases by about 2.6%. The prototype of the vortex turbine runner is made using a 3D printing machine with resin material. It is later tested in a laboratory-scale experiment that measures the shaft power, shaft torque and turbine efficiency in correspondence with rotational speeds varying from 150 to 650 rpm. Experiment results validate that the optimised runner has an efficiency of 45.3% or about 14% greater than the initial design runner, which has an efficiency of 39.7%.</span>

This study explores the numerical optimization of water turbine runner profile performance using a surface vorticity model algorithm. The turbine is designed on a laboratory scale and operates at a net head of 0.09 m, 400 rpm, and a water flow rate of 0.003 m3/s. The initial design of the turbine runner was optimized to minimize losses in the hydrofoil. The optimization algorithm is coded in MATLAB software to obtain the optimal stagger angle that will be used in the water turbine design. Furthermore, design validation was performed using computational fluid dynamics analysis ANSYS CFX to determine the water turbine performance. The settings used in ANSYS CFX include the reference pressure of 1 atm, turbulence model shear stress transport, and inlet boundary conditions using total pressure and static pressure outlet boundary conditions. The computational fluid dynamics analysis reveals that by optimizing the design, the efficiency of the water turbine increases by approximately 2.6%. The surface vorticity model algorithm can be applied to optimize the design of the water turbine runner.