Numerical study on sediment erosion characteristics of Francis turbine runner

腐蚀 沉积物 岩土工程 涡轮机 混流式水轮机 地质学 环境科学 工程类 地貌学 机械工程
作者
Xin-Yu Wei,Junxian Pei,Wen-Quan Wang,Zhi-Feng Yu
出处
期刊:Engineering Failure Analysis [Elsevier BV]
卷期号:161: 108270-108270 被引量:1
标识
DOI:10.1016/j.engfailanal.2024.108270
摘要

Sediment erosion is a prevalent and significant challenge to hydro turbines in mountainous rivers. In order to gain an insight into the erosion characteristics of Francis turbines, this study considers the influence of actual sediment gradation and emphasizes the erosion mechanism from the perspective of flow structure. Numerical results reveal a significant correlation between erosion distributions and the location of inter-blade vortices, which is dependent on operating conditions. Specifically, at small or optimal openings, inter-blade vortices predominantly form on the suction side of blades, which coincidentally experiences the most severe sediment erosion. Conversely, at a large guide vane opening, sediment erosion and vortices are primarily distributed at outlet of pressure side, aligning closely with actual site observations at a hydropower station. With increase of operating heads, the average erosion rate of suction side decreases at all guide vane openings, while that of pressure side elevates significantly. In addition, the effect of particle size on sediment erosion was discussed and the maximum erosion rate is demonstrated to be proportional to sediment diameter. These findings would provide important engineering insights for operation optimization to reduce sediment erosion.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
汉堡包应助科研通管家采纳,获得10
刚刚
英俊的铭应助科研通管家采纳,获得10
刚刚
所所应助科研通管家采纳,获得10
刚刚
刚刚
yuanyuan发布了新的文献求助10
6秒前
9秒前
Qin应助yxl采纳,获得10
10秒前
11秒前
11秒前
斯文败类应助古芍昂采纳,获得10
12秒前
zz发布了新的文献求助10
13秒前
14秒前
15秒前
onetec完成签到,获得积分10
16秒前
小小的梦想完成签到,获得积分10
17秒前
17秒前
17秒前
Shopping完成签到,获得积分10
19秒前
天天快乐应助霜降采纳,获得10
19秒前
19秒前
昏睡的蟠桃应助zz采纳,获得80
20秒前
Hello应助zz采纳,获得10
20秒前
彭于晏应助zz采纳,获得10
20秒前
Akim应助zz采纳,获得10
20秒前
大模型应助zz采纳,获得10
20秒前
CodeCraft应助zz采纳,获得10
20秒前
CipherSage应助zz采纳,获得10
20秒前
小蘑菇应助zz采纳,获得10
21秒前
李健应助zz采纳,获得10
21秒前
21秒前
善学以致用应助Meng采纳,获得10
21秒前
21秒前
wxy发布了新的文献求助10
23秒前
天真的小珍完成签到,获得积分20
23秒前
26秒前
27秒前
慕青应助捏个小雪团采纳,获得10
27秒前
weixin112233发布了新的文献求助10
27秒前
zeus完成签到,获得积分10
27秒前
紫色水晶之恋完成签到,获得积分10
27秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Various Faces of Animal Metaphor in English and Polish 800
Signals, Systems, and Signal Processing 610
Photodetectors: From Ultraviolet to Infrared 500
Diagnostic Performance of Preoperative Imaging-based Radiomics Models for Predicting Liver Metastases in Colorectal Cancer: A Systematic Review and Meta-analysis 500
On the Dragon Seas, a sailor's adventures in the far east 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6347934
求助须知:如何正确求助?哪些是违规求助? 8162806
关于积分的说明 17171779
捐赠科研通 5404209
什么是DOI,文献DOI怎么找? 2861685
邀请新用户注册赠送积分活动 1839457
关于科研通互助平台的介绍 1688778