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Prediction of Air Temperature Distribution around Rider on Idling Motorcycle by CFD Using DES Model
Published January 24, 2020 by Society of Automotive Engineers of Japan in Japan
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In this study, we investigated how to calculate and predict the air temperature distribution around the rider of a stationary motorcycle with the engine at idle. To analyze the air temperature distribution of an idling motorcycle, we needed to accurately predict the mixing of the forced convection air from the radiator fan and the natural convection air caused by the air temperature difference. For the calculation, we used two types of turbulent flow models: realizable k-ε (RKE) and detached eddy simulation (DES). First, in view of the mixing of the radiator exhaust with outside air, we made three-dimensional measurements of the air temperature distribution around the vehicle body to evaluate the accuracy of calculations made by the two models. We then used the models to predict the air temperature distribution around the rider for different air outlet duct configurations as well as for two motorcycles with different displacement values. The results showed that, although the RKE model effectively reproduced the qualitative trend of the air temperature distribution, it showed poorer prediction accuracy than the DES model. On the other hand, the DES model successfully reproduced the trends for different air outlet duct configurations, and the prediction error of air temperature around the rider was within 5°C of the actual measurements for the two different motorcycles. Although the DES computation time of 72 hours was seven times that of RKE, it was still considered practicable even for the model of the largest scale. Therefore, from the above, we can conclude that the DES model used in this study can effectively predict the air temperature distribution around the rider of an idling motorcycle.
CitationFujita, Y. and Tatsumi, H., "Prediction of Air Temperature Distribution around Rider on Idling Motorcycle by CFD Using DES Model," SAE Technical Paper 2019-32-0567, 2020.
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