| 引用本文: | 李尚奇,周 叶,李晓超,等.水轮机转轮疲劳裂纹研究进展[J].灌溉排水学报,2026,45(1):67-82. |
| LI Shangqi,ZHOU Ye,LI Xiaochao,et al.水轮机转轮疲劳裂纹研究进展[J].灌溉排水学报,2026,45(1):67-82. |
|
| 摘要: |
| 【目的】揭示新能源规模化并网背景下水轮机叶片疲劳裂纹的诱发机理及防治策略。【方法】从转轮叶片裂纹产生的内、外因素出发，结合不同工况下应力特点阐明了水轮机叶片疲劳产生的基本原理，对当下研究叶片疲劳的方法及研究进展进行详细介绍，分析基于智能算法的疲劳研究。【结果】理论分析方面，水轮机内部结构高度复杂，模型与原型相似率理论发展相对滞后，共同构成了理论分析的根本瓶颈，导致其研究难度显著。数值模拟方面，研究深度不足，当前利用CFD技术开展不同工况转轮疲劳的研究虽有一定基础，但多停留于现象描述，缺乏深度剖析。特别是，针对特定工况下疲劳破坏的内、外因的系统性总结分析尚属空白。研究广度受限，鲜见研究从优化水轮机启停机规律的角度切入，分析其对水力机械疲劳破坏的影响机理，此方向亟待探索。研究效率尚待提高，单流道压力脉动分析作为一种能显著降低数值模拟工作量的有效手段，其应用目前较为匮乏，限制了研究效率的进一步提升。现场试验方面：关键数据缺失，动应力方向是揭示转轮裂纹发展趋势的关键指标，然而目前基于现场试验数据对其进行的深入分析严重不足。测量干扰待究，应变片防护装置对现场应力数据采集精度的影响尚未得到充分评估和论述，构成数据可靠性的潜在威胁。研究对象失衡，现有水轮机疲劳研究过度聚焦于叶片，对导叶在复杂流场中的受力状态及疲劳特性关注甚少，研究视角存在明显失衡。未来发展趋势：机器学习技术展现出巨大潜力，有望为突破上述瓶颈提供新范式。【结论】基于理论分析、数值模拟、实验测量对转轮疲劳裂纹的研究已取得一定成效，但也存在相应瓶颈，未来研究亟须将机器学习与传统方法深度融合，以期发展出更经济、高效的水轮机叶片疲劳分析方法。 |
| 关键词: 水轮机；动应力；动静干涉；疲劳分析；数值模拟；流固耦合；机器学习 |
| DOI：10.13522/j.cnki.ggps.2025168 |
| 分类号: |
| 基金项目: |
|
| Fatigue crack in hydraulic turbine runner: A review |
|
LI Shangqi, ZHOU Ye, LI Xiaochao, LU Zhiyang, LI Shangxin
|
|
1. China Institute of Water Resources and Hydropower Research, Beijing 100038, China;
2. Dalian Maritime University, Dalian 116026, China
|
| Abstract: |
| 【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. |
| Key words: water turbine; dynamic stress; static and dynamic interference; fatigue analysis; numerical simulation; fluid-solid coupling; machine learning |
