Spatial Correlation and Length Scale Analysis of the Near-Wall Flow and Temperature Distribution of an Internal Combustion Engine

2020-01-1106

04/14/2020

Event
WCX SAE World Congress Experience
Authors Abstract
Content
Accurate predictions of in-cylinder heat transfer processes of internal combustion engines (ICEs) require a comprehensive understanding of the boundary layer development in the near-wall region (NWR). To add to the understanding of this NWR, this study uses experimental data of near-wall measurements collected in the transparent combustion chamber (TCC-III) engine via Particle Image Velocimetry (PIV) and toluene Planar Laser Induced Fluorescence (PLIF) thermometry. These near-wall flow and temperature distributions were compared with large-eddy simulations (LES) and 3-D conjugate heat transfer (CHT) modeling with a commercial CFD code (CONVERGE). The implementation of the conjugate heat transfer model enables capturing the variability in wall heat transfer as observed in the measurements. The results of this study is based on the analysis of ensemble averaged, standard deviation, PDFs of the fluctuating values, the spatial correlation of the velocity and temperature fluctuations, and their integral length scales. The LES CHT results do not compare well with the near-wall experimental velocity and temperature fields, which could be due to many factors including the wall models used in the study. The simulations are able to capture the trend of the spatial correlations, but not their magnitude. LES results show that length scales of velocity and temperature, and their spatial distribution, change significantly throughout the engine cycle, which directly affects the thermal gradients at the wall and therefore overall engine heat transfer.
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DOI
https://doi.org/10.4271/2020-01-1106
Pages
22
Citation
Wu, A., Alzuabi, M., and Sick, V., "Spatial Correlation and Length Scale Analysis of the Near-Wall Flow and Temperature Distribution of an Internal Combustion Engine," SAE Technical Paper 2020-01-1106, 2020, https://doi.org/10.4271/2020-01-1106.
Additional Details
Publisher
Published
Apr 14, 2020
Product Code
2020-01-1106
Content Type
Technical Paper
Language
English