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This paper presents a method of calculating temperature field of the piston by using a wavelet neural network (WNN) to identify the unknown boundary conditions. Because of the complexity of the heat transfer and limitations of experimental conditions of heat transfer analysis of the piston in a diesel engine, boundary conditions of the piston temperature field were usually obtained empirically, and thus the result itself was uncertain. By employing the capability of resolution analysis from a wavelet neural network, the method obtains improved boundary heat transfer coefficients with a limited number of measured temperatures. Using FEA software iteratively, results show the proposed wavelet neural network analysis method improves the prediction of unknown boundary conditions and temperature distribution consistent with the experimental data with an acceptable error.

A patented VCC (variable combustion chamber) piston mechanism is presented, by which excess in-cylinder pressure would be able to be limited effectively based on each working cycle while a spark-ignition engine running with higher CR (compression ratio) of 12.0:1. A conventional engine can be converted to a VCC engine just by replacing its typical pistons with VCC pistons. Besides the benefits that VCR (Variable Compression Ratio) has been still pursued so far, there would be other advantages for VCC engine, such as excellent fuel economy at each load not only at light loads, and the improvement of cycle-by-cycle variation of in-cylinder pressure, and high reliability with simple structure. The innovative design of VCC piston is introduced. The main design features of VCC piston are a VCC mechanism assembled between the piston crown and the piston skirt, and a special reset cam assembled at the wrist-pin end of the connecting rod.