Capturing the Dynamic Stall in H-Type Darrieus Wind Turbines Using Different URANS Turbulence Models

失速(流体力学) 湍流 机械 翼型 叶尖速比 物理 边界层 层流-湍流转变 前沿 后缘 空气动力学
作者
Siddhant Jain,Ujjwal K. Saha
出处
期刊:Journal of Energy Resources Technology-transactions of The Asme [ASM International]
卷期号:142 (9) 被引量:10
标识
DOI:10.1115/1.4046730
摘要

Abstract The occurrence of dynamic stall phenomenon in an H-type Darrieus wind turbine with low tip speed ratio (TSR) has been numerically investigated on a single-bladed rotor with NACA 0012 airfoil. The Reynolds number (Re) ∼105 at TSR = 2 implicates complex turbulence environment around the blades of the turbine modeling which still remains a challenging problem. Thus, with a motivation to find out a suitable turbulence model to capture the dynamic stall, a comparative study is carried out between three unsteady Reynolds-averaged Navier–Stokes (URANS) models: Spalart–Allmaras (S-A), shear stress transport (SST) k–ω, and transition SST (TSST). It was found that the TSST model predicted the dynamic stall phenomenon the earliest, whereas, the S-A model predicted it the latest. The transitional phenomenon like formation and bursting of the laminar separation bubble (LSB) was best predicted by the TSST model. However, the TSST overpredicts the turbulent boundary layer (BL) roll up from the trailing edge (TE) toward the leading edge (LE). The percentage difference in the power coefficient (Cp) values with respect to the TSST accounted to 16.67% and 60% higher for SST k–ω and S-A models, respectively. The S-A model delays the torque coefficient (Ct) peak prediction by 5 deg and 11 deg azimuthal angle compared with SST k–ω and TSST models, respectively. Overall, it was found that the transitional aspect in TSST model is important in predicting the light stall regime; however, in the deep stall regime SST k–ω model performed well too.
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
刚刚
xxxx完成签到 ,获得积分10
1秒前
2秒前
冷傲威完成签到,获得积分10
3秒前
热木完成签到,获得积分10
3秒前
zzww完成签到,获得积分10
3秒前
路漫漫123完成签到,获得积分10
4秒前
nimo发布了新的文献求助10
5秒前
田様应助不问归期的风采纳,获得30
6秒前
surxwy发布了新的文献求助10
6秒前
标致小翠完成签到,获得积分10
7秒前
Zero完成签到,获得积分10
7秒前
完美世界应助潇洒紫萱采纳,获得10
8秒前
你说完成签到,获得积分10
9秒前
小不正经完成签到,获得积分10
9秒前
刻苦的剑心完成签到,获得积分10
12秒前
搜集达人应助曾小豪采纳,获得10
13秒前
路通完成签到,获得积分10
14秒前
甜甜甜圈完成签到 ,获得积分10
15秒前
15秒前
orixero应助优秀鸿涛采纳,获得10
15秒前
Sausage完成签到,获得积分10
16秒前
xinxin完成签到,获得积分10
17秒前
17秒前
18秒前
朴实寻真完成签到,获得积分10
19秒前
久别完成签到,获得积分10
20秒前
潇洒紫萱发布了新的文献求助10
21秒前
HXX完成签到,获得积分20
22秒前
高贵宛海完成签到,获得积分10
23秒前
23秒前
kk完成签到,获得积分10
23秒前
24秒前
24秒前
25秒前
26秒前
mmddlj发布了新的文献求助10
26秒前
情怀应助科研进化中采纳,获得10
27秒前
Mingyue123完成签到,获得积分10
27秒前
lianliyou发布了新的文献求助10
28秒前
高分求助中
Algorithmic Mathematics in Machine Learning 500
Handbook of Innovations in Political Psychology 400
Mapping the Stars: Celebrity, Metonymy, and the Networked Politics of Identity 400
引进保护装置的分析评价八七年国外进口线路等保护运行情况介绍 300
《続天台宗全書・史伝1 天台大師伝注釈類》 300
Visceral obesity is associated with clinical and inflammatory features of asthma: A prospective cohort study 300
Getting Published in SSCI Journals: 200+ Questions and Answers for Absolute Beginners 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3839884
求助须知:如何正确求助?哪些是违规求助? 3382134
关于积分的说明 10521516
捐赠科研通 3101562
什么是DOI,文献DOI怎么找? 1708143
邀请新用户注册赠送积分活动 822228
科研通“疑难数据库(出版商)”最低求助积分说明 773208