Phenomenological Heat Release Model for Multi-chamber Engines

To understand the detailed combustion trends of the internal combustion engine (ICE) for optimizing engine performance, accurate modeling of combustion phenomena is a crucial factor associated with ICE research. This thesis presents a phenomenological heat release rate (HRR) model for a pre-chamber combustion (PCC) engine to acquire insight into the combustion phenomena of the engine. The engine comprises two chambers which are PCC and main chamber combustion (MCC). The proposed model has been developed using a computationally efficient and flexible open-source Python framework that has been compared and validated against a 1-D simulated outcome of the GT-Power model. The thesis proposes an approach for the thermodynamic heat release analysis of PCC and MCC, considering the in-cylinder pressure measurements from experimental data of both chambers. Both chambers estimate the residual mass, temperature, excess air-ratio lambda (λ), and initial specific heat capacity ratio (γ). Then, based on the initial γ, the bulk gas temperature, and crank-angle-based γ trace have been determined. Moreover, the heat losses have been determined by relying on convective heat transfer coefficients in both chambers. The first law of thermodynamics has been employed to calculate the HRR for PCC and MCC, the PCC also considers the mass transfer rate and enthalpy flow. The proposed HRR model has been validated with the simulated output through comparison in three different loads (high, mid, and low). The various parameters of the proposed model including the temperature, heat transfer rate, specific heat capacity, heat loss, HRR, and cumulative HRR have been assessed by comparing the graphs of the developed model against the referenced model. Moreover, the model output has been validated using the mean absolute percentage error formula to ensure accuracy. The model accurately computes the HRR and delivers promising results in the validation. The proposed HRR model offers faster computation than the referenced model, flexible adjustments, and modification of different parameters, and a more cost-effective solution than the simulated model since the GT model requires a high amount of licensing expense. Additionally, the model offers easy modification of the combustion process allowing for accurate tuning and optimizing over GT-Suite three-pressure analysis. Overall, the developed model accurately estimates the different parameters of the MCC’s and PCC’s HRR with fewer exceptions.