Blade optimization for hydrodynamic performance improvement of a horizontal axial river current turbine using discrete adjoint method

Utilizing hydrokinetic power efficiently has been an attractive topic in clean renewable energy. This paper proposes a discrete adjoint methodology by combining grid deformation techniques with gradient-based optimization, enabling the hydrodynamic optimization of a horizontal axial river current turbine (HARCT). The power coefficient is employed as the optimization target and hydrodynamic evaluation criteria to investigate the influence of five different initial conditions. Gradient-driven mechanism based on flow information operates adaptive blade morphing, which enhances hydrodynamic efficiency by improving pressure distribution near the leading edge and suppressing turbulent kinetic energy dissipation in the primary flow domain while maintaining coherent flow structures. Results demonstrated that the proposed method can achieve performance improvement while widening the high-efficiency operation range of the tip speed ratio (λTSR = 3.0–5.5). The initial conditions close to the design operation (λTSR = 4.0) is identified as the optimal, reaching an increase of 2.94 %∼15.40 % in efficiency, compared to the original model. It is also suggested that the optimal initial condition selection needs to consider specific operation conditions for maximizing efficiency, as the optimal initial TSR is not universal across operating ranges.