Analysis-driven pre-calibration of a modern automotive engine is extremely valuable in significantly reducing hardware investments and accelerating engine designs compliant with stricter emission regulations. Advanced modelling tools, such as a Virtual Engine Model (VEM) using Computational Fluid Dynamics (CFD), are often used within the framework of a Design of Experiments for Powertrain Engineering (DEPE) with the goal of streamlining significant portions of the calibration process. The success of the methodology largely relies on the accuracy of analytical predictions, especially engine-out emissions. Results show excellent agreements in engine performance parameters (with R2 > 98%) and good agreements in NOx and combustion noise (with R2 > 87%), while the Carbon Monoxide (CO), Unburned Hydrocarbons (HC) and Smoke emissions predictions remain a challenge even with a large n-heptane mechanism consisting of 144 species and 900 reactions and refined mesh resolution. In this study, a Machine Learning (ML) approach is presented to correlate in-cylinder images of Equivalence Ratio, Temperature, Velocity field and Turbulent Kinetic Energy (TKE) at Exhaust Valve Opening (EVO) to engine-out emissions of CO, HC and Smoke measured experimentally. The images generated from CFD simulations and experimentally measured emissions data were used to train a deep Convolutional Neural Network (CNN) based on the VGG16 architecture. The prediction performance of the trained model was evaluated on held-out data. The deep learning model led to a significant improvement in prediction of all emissions species and captured the qualitative trends as well. This model could be used as an emissions prediction sub-model in the Virtual Engine Model framework leading to significantly lower computational costs by avoiding the use of expensive chemistry solvers. Gradient-weighted Regression Activation Mapping (Grad-RAMS) was used to draw insights into image features that had a strong influence on the emissions predictions.
