Yes / This paper introduces a hybrid dynamic modelling approach for the prediction of NOx emissions for a Diesel engine, based on a multi-physics simulation platform coupling a 1-D air path model (GT-Suite) with in-cylinder combustion model (CMCL Stochastic Reactor Model Engine Suite). The key motivation for this research was the requirement to establish a real time stochastic simulation capability for emissions predictions early in engine development, which required the replacement of the slow combustion chemistry solver (SRM) with an appropriate surrogate model. The novelty of the approach in this research is the introduction of a hybrid approach to metamodeling that combines dynamic experiments for the gas path model with a zonal optimal space-filling design of experiments (DoEs) for the combustion model. The dynamic experiments run on the virtual Diesel engine model (GT- Suite) was used to fit a dynamic model for the parameters required as input to the SRM. Optimal Latin Hypercubes (OLH) DoE run on the SRM model was used to fit a response surface model for the NOx emissions. This surrogate NOx model was then used to replace the computationally expensive SRM simulation, enabling real time simulations of transient drive cycles to be executed. The performance of the proposed approach was validated on a simulated NEDC drive cycle against experimental data collected for the engine case study, which proved the capability of methodology to capture the transient trends for the NOx emissions. The significance of this work is that it provided an efficient approach to the development of a global model with real time transient modelling capability based on the integration of dynamic and local DoE metamodeling experiments.

The data-driven models used for the design of powertrain controllers are typically based on the data obtained from steady-state experiments. However, they are only valid under stable conditions and do not provide any information on the dynamic behaviour of the system. In order to capture this behaviour, dynamic modelling techniques are intensively studied to generate alternative solutions for engine mapping and calibration problem, aiming to address the need to increase productivity (reduce development time) and to develop better models for the actual behaviour of the engine under real-world conditions. In this thesis, a dynamic modelling approach is presented undertaken for the prediction of NOx emissions for a 2.0 litre Diesel engine, based on a coupled pre-validated virtual Diesel engine model (GT- Suite ® 1-D air path model) and in-cylinder combustion model (CMCL ® Stochastic Reactor Model Engine Suite). In the context of the considered Engine Simulation Framework, GT Suite + Stochastic Reactor Model (SRM), one fundamental problem is to establish a real time stochastic simulation capability. This problem can be addressed by replacing the slow combustion chemistry solver (SRM) with an appropriate NOx surrogate model. The approach taken in this research for the development of this surrogate model was based on a combination of design of dynamic experiments run on the virtual diesel engine model (GT- Suite), with a dynamic model fitted for the parameters required as input to the SRM, with a zonal design of experiments (DoEs), using Optimal Latin Hypercubes (OLH), run on the SRM model. A response surface model was fitted on the predicted NOx from the SRM OLH DoE data. This surrogate NOx model was then used to replace the computationally expensive SRM simulation, enabling real-time simulations of transient drive cycles to be executed. The performance of the approach was validated on a simulated NEDC drive cycle, against experimental data collected for the engine case study. The capability of methodology to capture the transient trends of the system shows promising results and will be used for the development of global surrogate prediction models for engine-out emissions.