Transient Thermal Analysis of CAD configured IC Engine Piston to Understand it’s Thermal Behavior

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Anjali Bal, Santosh R. Shekokar, Nitin A. Kharche, Yugesh A. Kharche

Abstract

Modern IC engine pistons are highly precise and work under heavy thermal pressure. They are designed to withstand under thermal stress circumstances. Metallurgy behind the piston manufacturing allows him to control his workability in engine running conditions. It is one of the engine components which always work under different stresses and forces while engine runs. Friction and higher working temperature always challenge piston to work smoothly without failing. Most of the pistons are made up of aluminium alloys and has very smooth surface to reduce friction and they are oil cooled. As it works under higher temperature, it may affect the workability of piston and thermal stresses could cause the issues like crack propagation, wear and tear etc. It is observed that, piston experiences lots of forc-es which are transient or continuous in the nature as per the vehicle running requirements. Hence it is obvious to understand the behav-iour of piston during such loading conditions. There are several methods available to check the stress and deformation of the piston. Out of them Finite Element Analysis (FEA) is one of the advanced and most popular method to predict the regions of stress and defor-mation concentration caused due to thermos mechanical stresses. This method is also capable to find the effect and behaviour of pis-ton by thermal loading, pressure applied by the hot flue gases and the friction. In this project Transient thermal analysis by FEA package (ANSYS) is used to find the piston behaviour under various loading conditions. CAD model of piston is prepared by using CATIA software Configuration technique is used for modelling of piston. Then imported to .iges file format to perform thermal analysis under thermos mechanical loading conditions. Results obtained are described to understand the behaviour of the piston which allows to predict the possible failure regions.

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