Browse Publications Technical Papers 2005-01-1881
2005-04-11

Development of an Engine Oil Temperature Prediction Method Using 3D Model Simulation 2005-01-1881

Being able to plan each powertrain performance parameter with improved accuracy is an important factor for shortening the development period. In this regard, advance temperature predictions are particularly important because of the strong correlation between the thermal design and the vehicle layout, engine proper and other related parts.
At present, it is still difficult to make thermal performance predictions for the engine oil temperature. There are many examples of studies in which the oil temperature was predicted in a one-dimensional circuit. In those studies, either experimental data were used for the heat release from engine components to the oil or calculations were made with a model that was converted empirically from three-dimensions to one-dimension. Because the mechanisms of oil temperature changes inside the engine are not well understood in detail, it is not possible to examine how modifications of the engine structure might affect the oil temperature.
In this work, an effort was made to resolve such problems by making three-dimensional oil temperature predictions. While three-dimensional predictions present problems with respect to the calculation time and model size, those problems were overcome by performing a coupled thermal fluid-structural simulation using a general-purpose software code. Separate calculations were run to find the boundary conditions, including the flow rates of the circulating coolant and engine oil and the combustion gas condition. The calculated boundary conditions were then corrected using experimental data and theoretical considerations before inputting them into three-dimensional models of the cylinder head, block, water jacket and oil passages. This method made it possible to shorten the calculation time and reduce the model size. The results obtained with this method showed good agreement with the experimental data, making it possible to improve the accuracy of the thermal performance design.

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