Generalization performance of a deep learning based engine-out emissions model

Abstract

In our previous work [1], an ensemble of Convolutional Neural Network (CNN) models was used to predict engine-out emissions of Carbon Monoxide (CO), Unburned Hydrocarbons (HC) and Smoke directly from in-cylinder contours of multiple scalar fields - Equivalence Ratio, Temperature, Velocity and Turbulent Kinetic Energy (TKE) at Exhaust Valve Opening (EVO) generated by physics based Computational Fluid Dynamics (CFD) simulations and experimentally measured emissions data. A dataset of 600 CFD generated images, one for each engine operating condition, from a 1.6L light-duty diesel engine was used for training, validation and testing of the CNN ensemble model. In this short communication, several modifications were made to the image data generation and training procedure. The ensemble of CNN models was re-trained per the new procedure on the modified image dataset from the 1.6L engine. The generalization capability of this re-trained deep learning model was investigated by evaluating its prediction performance on a test dataset from a 3.0L diesel engine that was not used previously for training, validation or testing. The CNN ensemble model re-trained on the 1.6L engine dataset captured the qualitative trends for all three emissions species on the 3.0L engine test dataset, however, quantitatively its prediction performance was not as good. Future work with additional engines and input features is planned to improve the generalization performance of the CNN based emissions prediction model.