Fatigue crack in hydraulic turbine runner: A review

【Background and Objective】Under large-scale new energy integration, hydraulic units often deviate from optimal working conditions, leading to deteriorated internal flow, uneven stress distribution, and increased risk of turbine blade fatigue crack. However, the internal and external factors causing turbine fatigue cracks under different working conditions remain unclear and comprehensive analysis of turbine fatigue damage is lacking. This study aims to unveil the underlying mechanism and find strategy to prevent turbine blade fatigue cracks under large-scale new energy integration. 【Method】Based on the internal and external factors responsible for runner blade cracks, the basic principle of turbine blade fatigue was elaborated by combining stress characteristics under different operating conditions. Current research methods, progress in blade fatigue studies, and emerging fatigue research approaches based on intelligent algorithms were systematically reviewed and analysed.【Result】Our study identified prominent bottlenecks in existing research in three areas: ①Theoretical analysis. The highly complex internal structure of turbines and the lag in model-prototype similarity rate theory are the main obstacles, leading to significant research challenges. ②Numerical simulation: Insufficient research depth (predominantly phenomenological descriptions lacking in-depth analysis), limited research scope (neglect of optimizing turbine start-stop rules), and low research efficiency (scarce application of single-channel pressure fluctuation analysis) hinder research advances. ③Field tests: Missing key dynamic stress data, inadequate in-depth analysis of test data, unevaluated impacts of strain gauge protection devices on data reliability, unbalanced focus on blades (ignoring guide vanes) limit research validity. Additionally, machine learning technology was found to be potential in addressing these challenges, promising a new paradigm for fatigue research.【Conclusion】Existing research on runner fatigue cracks, based on theoretical analysis, numerical simulation and experimental measurement, has yielded achievements but faces challenges. Future research is needed to integrate machine learning with traditional methods to develop more cost-effective and efficient fatigue analysis for turbine blades.