Online prediction of the piston maximum temperature in dual-fuel engine

Abstract

Diesel–natural gas dual-fuel engine has become a hot research topic in recent years because of its excellent power and economy. However, the reliability of the dual-fuel engine does not meet the requirements of practical application. The piston maximum temperature of dual-fuel engine easily exceeds the security border. Toward this, this article presents a relational model to real-timely predict the piston maximum temperature of dual-fuel engine. Specifically, some easy-measured engine indirect signals, including NOx emission, excess air coefficient, and engine speed, are employed as the model inputs. The piston maximum temperatures, as the only output, are acquired offline by finite element analysis in ANSYS. Support vector regression is employed to solve the prediction model parameters. Cross-validation is introduced to determine some intermediate variables formed in the process of building model. Experiments revealed that the proposed model produces satisfying predictions with deviations less than 7°C. Thus, this study provides an effective method to monitor the piston maximum temperature state of dual-fuel engine in real time.