The Effect of Aspect Ratio on Compressor Performance

材料科学 物理 数学
作者
Ho-On To,RobertJ. Miller
出处
期刊:Journal of turbomachinery [ASME International]
卷期号:141 (8) 被引量:10
标识
DOI:10.1115/1.4043219
摘要

Abstract The optimum aspect ratio at which maximum efficiency occurs is relatively low, typically between 1 and 1.5. At these aspect ratios, inaccuracies inherently exist in the decomposition of the flow field into freestream and endwall components due to the absence of a discernible freestream. In this paper, a unique approach is taken: a “linear repeating stage” concept is used in conjunction with a novel way of defining the freestream flow. Through this approach, physically accurate decomposition of the flow field for aspect ratios as low as ∼0.5 can be achieved. This ability to accurately decompose the flow leads to several key findings. First, the endwall flow is found to be dependent on static pressure rise coefficient and endwall geometry, but independent of the aspect ratio. Second, the commonly accepted relationship that endwall loss coefficient varies inversely with the aspect ratio is shown to be physically inaccurate. Instead, a new term, which the authors refer to as the “effective aspect ratio,” should replace the term “aspect ratio.” Moreover, not doing so can result in efficiency errors of ∼0.6% at low aspect ratios. Finally, there exists a low aspect ratio limit below which the two endwall flows interact causing a large separation to occur along the span. From these findings, a low-order model is developed to model the effect of varying aspect ratio on compressor performance. The last section of the paper uses this low-order model and a simple analytical model to show that to a first order, the optimum aspect ratio is just a function of the loss generated by the endwalls at zero clearance and the rate of change in profile loss due to blade thickness. This means that once the endwall configuration has been selected, i.e., cantilever or shroud, the blade thickness sets the optimum aspect ratio.
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
Mars发布了新的文献求助10
刚刚
哈哈哈完成签到,获得积分10
刚刚
玛卡巴卡应助平常的毛豆采纳,获得100
1秒前
默默的青旋完成签到,获得积分10
2秒前
5秒前
搜集达人应助淡淡采白采纳,获得10
5秒前
高高代珊完成签到 ,获得积分10
6秒前
gmc发布了新的文献求助10
7秒前
7秒前
8秒前
善学以致用应助Mian采纳,获得10
8秒前
学科共进发布了新的文献求助60
9秒前
LWJ完成签到 ,获得积分10
9秒前
9秒前
缓慢的糖豆完成签到,获得积分10
10秒前
阉太狼完成签到,获得积分10
10秒前
11秒前
soory完成签到,获得积分10
12秒前
任性的傲柏完成签到,获得积分10
12秒前
lwk205完成签到,获得积分0
12秒前
13秒前
一一完成签到,获得积分10
13秒前
13秒前
13秒前
高中生完成签到,获得积分10
14秒前
14秒前
14秒前
希望天下0贩的0应助TT采纳,获得10
15秒前
xxegt完成签到 ,获得积分10
15秒前
16秒前
爱吃泡芙发布了新的文献求助10
16秒前
susu完成签到,获得积分10
18秒前
会神发布了新的文献求助10
18秒前
KK完成签到,获得积分10
19秒前
充电宝应助justin采纳,获得10
21秒前
22秒前
Ch完成签到 ,获得积分10
23秒前
25秒前
ajun完成签到,获得积分10
25秒前
25秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527961
求助须知:如何正确求助?哪些是违规求助? 3108159
关于积分的说明 9287825
捐赠科研通 2805882
什么是DOI,文献DOI怎么找? 1540070
邀请新用户注册赠送积分活动 716926
科研通“疑难数据库(出版商)”最低求助积分说明 709808