Combining Optimization with Dymola to Calibrate a 2-zone Predictive Combustion Model

This document explores the development and calibration of a predictive 2-zone combustion model within Dymola and Modelica as part of the MORSE (Model-based Real-time Systems Engineering) project.

The research focuses on improving engine combustion prediction accuracy while maintaining simulation speeds suitable for Model-in-the-Loop (MiL), Software-in-the-Loop (SiL), and Hardware-in-the-Loop (HiL) applications. Using detailed thermodynamic, turbulence, ignition delay, and fuel entrainment modelling, the study combines Design of Experiments (DOE), optimisation workflows, and automated calibration techniques through VisualDoc and FMI-based integration tools to identify sensitive combustion parameters and tune the model against engine test data. The work demonstrates how predictive physical modelling and automated optimisation can support earlier-stage virtual calibration, drivability analysis, emissions development, and powertrain control validation while reducing reliance on costly physical prototyping and testing.