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首页> 外文会议>ASME Turbo Expo vol.6 pt.A; 20050606-09; Reno-Tahoe,NV(US) >AN EMPIRICAL PREDICTION METHOD FOR SECONDARY LOSSES IN TURBINES: PART Ⅱ - A NEW SECONDARY LOSS CORRELATION
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AN EMPIRICAL PREDICTION METHOD FOR SECONDARY LOSSES IN TURBINES: PART Ⅱ - A NEW SECONDARY LOSS CORRELATION

机译：涡轮次生损失的经验预测方法：第二部分-新的次生关联

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摘要

A new empirical prediction method for design and off-design secondary losses in turbines has been developed. The empirical prediction method is based on a new loss breakdown scheme, and as discussed in Part Ⅰ, the secondary loss definition in this new scheme differs from that in the conventional one. Therefore, a new secondary loss correlation for design and off-design incidence values has been developed. It is based on a database of linear cascade measurements from the present authors' experiments (Benner) as well as cases available in the open literature. The new correlation is based on correlating parameters that are similar to those used in existing correlations. This paper also focusses on providing physical insights into the relationship between these parameters and the loss generation mechanisms in the endwall region. To demonstrate the improvements achieved with the new prediction method, the measured cascade data are compared to predictions from the most recent design and off-design secondary loss correlations (Kacker and Okapuu, Moustapha et al. [3] using the conventional loss breakdown. The Kacker & Okapuu correlation is based on rotating-rig and engine data, and a scaling factor is needed to make their correlation predictions apply to the linear cascade environment. This suggests that there are additional and significant losses in the engine that are not present in the linear cascade environment.
机译：已经开发出一种新的经验预测方法，用于设计涡轮机和设计外的二次损失。经验预测方法基于一种新的损失细分方案，并且如第一部分所述，该新方案中的次级损失定义与传统的不同。因此，已经开发出用于设计和非设计入射值的新的二次损失相关性。它基于本作者实验（Benner）的线性级联测量数据库以及公开文献中提供的案例。新的相关性基于与现有相关性中使用的参数相似的相关性参数。本文还着重于提供有关这些参数与端壁区域中损耗产生机制之间关系的物理见解。为了证明使用新的预测方法所实现的改进，将测量的级联数据与最新设计和非设计次级损耗相关性（Kacker和Okapuu，Moustapha等人[3]使用常规损耗明细法进行的预测）进行比较。 Kacker＆Okapuu的相关性是基于旋转钻机和发动机数据，因此需要比例因子以使它们的相关性预测适用于线性级联环境，这表明发动机中存在额外的，显着的损失，这些损失并不存在。线性级联环境。

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