System Parameters Identifying and Performance Predicting of ICEs Combining Multidisciplinary Model With System Responding Data

For complex equipments, the phenomena of system declining such as wear and fatigue often takes place and spreads after a period of running. So it is important to identify the interior structural change of system during maintenance to avoid the system to be broken abruptly. Traditionally there are two methods to analyze and predict the structural change of system. One is from the monitoring data of equipments. Another is from the mechanism of structural changing and the interior working process of equipments. In this paper a combining method, which combining the advantages of the above two methods, is used to identify the structural change of internal combustion engines. The principle of the method is to firstly build an analytical system model, in which the system parameters stand for the structural parameters or constraints. Then the current value of system parameters in the model can be identified by comparing the calculating responding results and the detected responding data. From the varying of system parameters the structural change of system can be deduced. For internal combustion engines (ICEs), the most important CPSR (combustion Chamber-Piston-cylinder Sleeve-piston Rings) system is taken as the research object. A multidisciplinary model is built to simulate the interior working processes, especially the combustion process, the structural dynamics process, the tribology process and the coupling processes among them. Then the seeking-roots method (SRM) is used to identify the value of system parameters. A case study on a low power gasoline engine verifies the above method. In the case study, the blow by gap, which stands for the wear of piston rings and cylinder sleeve, is identified with the detected combustion pressure. The case study shows that the method of this paper can identify the structural change of complex equipments. It can provide accurate information for equipments maintenance as well as the residual life prediction.