Evaluation of control-oriented flame propagation models for production control of a spark-assisted compression ignition engine

Abstract

The drive to improve internal combustion engines has led to efficiency objectives that exceed the capability of conventional combustion strategies. As a result, advanced combustion modes are more attractive for production. These advanced combustion strategies typically add sensors, actuators, and degrees of freedom to the combustion process. Spark-assisted compression ignition (SACI) is an efficient production-viable advanced combustion strategy characterized by spark-ignited flame propagation that triggers autoignition in the remaining unburned gas. Modeling this complex combustion process for control demands a careful selection of model structure to maximize predictive accuracy within computational constraints. This work comprehensively evaluates a physics-based and a data-driven model. The physics-based model produces a burn duration by computing laminar flame speed as a function of test point conditions. The crank-angle domain is intentionally excluded to reduce computational expense. The data-driven model is an artificial neural network (ANN). The candidate models are compared to a one-dimensional engine model validated to experimental SACI engine data. Though both models capture the trends in burn rates, the ANN model has a root-mean square error (RMSE) of 1.4 CAD, significantly lower than the 10.4 CAD RMSE of the physics-based model. The exclusion of the crank-angle domain results in insufficient detail for the physics-based model, while the ANN can tolerate this exclusion.

Modelling Study of Cycle-To-Cycle Variations (CCV) in Spark Ignition (SI)-Controlled Auto-Ignition (CAI) Hybrid Combustion Engine by Using Reynolds-Averaged Navier–Stokes (RANS) and Large Eddy Simulation (LES)

“…The developed model can well predict the heat release characteristics related to flame propagation and auto-ignition in SACI. Robertson and Prucka [9] developed a control-oriented data-driven model based on the artificial neural network (ANN) and found this model can accurately capture the nonlinear nature of flame propagation of SACI combustion with less computational expense. In particular, Mazda has applied SI-CAI hybrid combustion in the name of spark controlled compression ignition (SPCCI) in the Skyactiv-X engine, as the world's first mass produced gasoline compression ignition engine [10,11].…”

The Engine Combustion Phasing Prediction Based on the Support Vector Regression Method

“…The machine learning approach has been approved to be such a tool because it can eliminate the noise and effectively establish the relationship between engine operational parameters and engine responses [38,39]. In recent years, many researchers have combined machine learning methods to predict the combustion process [40,41] and to control the combustion phasing parameters of engines [42,43]. Edward et al [44] applied a clustering method based on fuzzy logic predicates to the combustion stage identification of internal combustion engines.…”