Augmentation of an Artificial Neural Network (ANN) Model with Expert Knowledge of Critical Combustion Features for Optimizing a Compression Ignition Engine Using Multiple Injections

The objective of this work was to identify methods of reliably predicting optimum operating conditions in an experimental compression ignition engine using multiple injections. Abstract modeling offered an efficient way to predict large volumes data, when compared with simulation, although the initial cost of constructing such models can be large. This work aims to reduce that initial cost by adding knowledge about the favorable network structures and training rules which are discovered. The data were gathered from a high pressure common rail direct injection turbocharged compression ignition engine utilizing a high EGR configuration. The range of design parameters were relatively large; 100 MPa - 240 MPa for fuel pressure, up to 62% EGR using a modified, long-route, low pressure EGR system, while the pilot timing, main timing, and pilot ratio were free within the safe operating window for the engine. The limits included restricting the dwell between injections, the upper and lower limits of main timing, and the pilot ratio which was limited to 50%. The outcomes of the research are expected to provide important insight for accelerating and augmenting engine testing by offering an alternative to exhaustive phenomenological modeling. Quantification of the models ability to represent the experimental data is done using cross validation and various metrics are used to prevent overtraining while also maintaining high computational performance. A brief overview of the relative performance of the methods is also provided.