Simplified Approach to the Prediction and Analysis of Temperature Inhomogeneity in Rapid Compression Machines
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- 作者：Sajjad Yousefian ; Fran莽ois Gauthier ; Amadeo Mor谩n-Guerrero ; Robert R. Richardson ; Henry J. Curran ; Nathan J. Quinlan ; Rory F. D. Monaghan
- 刊名：Energy & Fuels
- 出版年：2015
- 出版时间：December 17, 2015
- 年：2015
- 卷：29
- 期：12
- 页码：8216-8225
- 全文大小：544K
- ISSN：1520-5029
文摘
Substantial progress has been made in understanding and reducing temperature inhomogeneity in rapid compression machines (RCMs) with the help of computational modeling. To date, however, it has not been possible to investigate and map the full range of possible RCM designs, working gases, and operating conditions. In this work, we present a framework which simplifies the task of comprehensive and general RCM performance prediction. A set of thermophysical and geometrical parameters has been defined to characterize the design and operating conditions of a general RCM. Dimensional analysis was applied to reduce the number of variables, and a sensitivity analysis, based on computational simulations, was used to rank the dimensionless parameters and eliminate unimportant ones. The results of this analysis show that Reynolds number, Prandtl number, aspect ratio, and crevice volume ratio are the most important parameters determining temperature inhomogeneity. A further set of computational simulations was conducted to predict postcompression temperature inhomogeneity over the full range of RCM design and operating parameters. These results are well-represented by a simple power-law equation that correlates a dimensionless temperature inhomogeneity parameter (mass-averaged over the main chamber) as a function of postcompression time with just three parameters鈥擯eclet number (the product of Reynolds and Prandtl numbers), aspect ratio, and crevice volume ratio. This equation can serve as a simple and general tool for RCM designers and users who wish to determine optimal configurations that minimize temperature inhomogeneity for combustion experiments.