Optimization and experimental analysis of turbine airfoil of downhole generator based on genetic algorithm

Reliable power supply is essential for downhole measurement and intelligent drilling systems operating in deep and ultra-deep wells. This study develops an efficient hydraulic-airfoil optimization framework for a downhole turbine generator using the NACA66 profile. High-order Bezier curves with eight movable control points are used for parameterization, and a genetic algorithm (GA) coupled with MATLAB and XFOIL maximizes the sectional lift-to-drag ratio under geometric constraints. The principal optimization is performed at R e =3000 over an angle-of-attack range of 10 to 20°. The optimized airfoil shows favorable performance near 10° and achieves a lift-to-drag ratio 26.9 % higher than that of the original profile at 15°. Sensitivity analyses at R e =300, 3000, and 30 000 demonstrate stable hydrodynamic performance over the investigated range. CFD simulations at flow rates of 10 and 40 L s −1 further confirm improved pressure distribution and lift–drag characteristics. The turbine experiments conducted within different flow ranges showed that at the highest test flow rate, the torque increased from 0.997 to 1.297 N m (an increase of 30.2 %), and the rotational speed increased from 712–904 revolutions per minute (an increase of 27.1 %). The optimized turbine also exhibits higher tip-speed ratio and normalized power-performance characteristics across the tested operating range. These results verify that the proposed Bezier–GA–XFOIL framework effectively improves both sectional hydrodynamic performance and turbine-level mechanical response.