Application of Large Eddy Simulation to a Torque Converter to Predict its Fluid Performance

2017-01-1116

03/28/2017

Features
Event
WCX™ 17: SAE World Congress Experience
Authors Abstract
Content
An automatic transmission torque converter is usually used as a power transmission element, which performs the function of the torque matching and the torque amplification of the engine power output. This is referred to as the fluid performance of the torque converter, which is determined by its blade shape. Therefore, it is necessary to predict the fluid performance of the torque converter at the design stage to determine the blade shape, to which computational fluid dynamics (CFD) analysis can be applied. At present, time-averaged turbulence models such as k-ε (called Reynolds-averaged Navier–Stokes—RANS—model) are often used in such CFD analysis for industrial purposes, and are not limited to torque converters because of its appropriate calculation time. However, major traditional RANS models are less reliable for applications to complex three-dimensional flows in the torque-converter than those to simple pipe, channel and boundary layer flows. Therefore, with respect to this issue, a large eddy simulation (LES), which can directly treat the unsteady phenomena of turbulence, has been applied to such complex flow fields not only for research but also for industrial purposes, though it still has a difficulty in the amount of calculation cost. This research applies an open-license software program “FrontFlow/red,” which is specially developed for high-performance computing. This enables the application of the LES turbulence model for a performance prediction of the torque converter design.
Meta TagsDetails
DOI
https://doi.org/10.4271/2017-01-1116
Pages
6
Citation
Tasaka, T., Oshima, N., Fujimoto, S., and Kishi, Y., "Application of Large Eddy Simulation to a Torque Converter to Predict its Fluid Performance," SAE Technical Paper 2017-01-1116, 2017, https://doi.org/10.4271/2017-01-1116.
Additional Details
Publisher
Published
Mar 28, 2017
Product Code
2017-01-1116
Content Type
Technical Paper
Language
English