Support vector regression-based enhanced quasi-Monte Carlo simulation for efficient turbine runner reliability analysis

Purpose Accurate reliability evaluation of turbine runners under long-term and complex loads is essential, but reliability analysis of turbine runners often suffers from high computational cost, high input dimensionality and low failure probability. This paper aims to develop an accurate and efficient method for reliability analysis of turbine runners. Design/methodology/approach A support vector regression (SVR)-based enhanced quasi-Monte Carlo simulation (EQMCS) method is proposed for reliability analysis of turbine runners. An efficient scaling formula is also established to improve the robustness of failure probability assessment. The proposed method is validated through a high-dimensional numerical case and a turbine runner case. Findings The results show that the proposed method can effectively reduce computational cost while maintaining the accuracy of reliability analysis in high-dimensional problems and improve the efficiency and robustness of failure probability evaluation for complex structures. Originality/value This paper proposes a novel reliability analysis framework that integrates SVR, EQMCS and an efficient scaling strategy. The method can improve the accuracy, efficiency and robustness of reliability assessment for turbine runners.