Flame velocity is the main parameter for determination of combustion propagation in a spark-ignition engine. The first part of combustion that consists of flame initiation and flame kernel growth has laminar velocity. For a certain radius of kernel growth, transition to turbulent flame occurs; afterwards, the flame can be described as turbulent. This paper investigates the relationship between fuel properties and engine operation parameters, their influence on flame velocity and their ability to calculate the time delay from ignition to 50% mass fraction burned (MFB) that is used for adjusting the spark advance. The GT-Power software is employed to simulate the combustion process of a spark-ignition (SI) engine. The flame speed mean value model is applied to determine the laminar flame speed under different amounts of unburned mixture, temperatures and pressures. The results show that mixture with less than the stoichiometric ratio has the greatest laminar flame speed. At higher temperature, the difference between poor and rich mixture is significant for laminar flame speed. On the other hand, the relationship between turbulence intensity and engine speed is almost linear. The cylinder pattern used to create turbulence during the intake and compression strokes defines the slope between the engine speed and turbulent flame speed. The mean value flame speed model was capable of determining the combustion phasing and predicting spark ignition in advance.

A controlled auto-ignition (CAI) two-stroke cycle engine suggests an exceptional aspect and promising future for internal combustion engines (ICEs), such as a higher power-toweight ratio, higher combustion efficiency and lower exhaust gas emissions. Conventional two-stroke cycle engines emit higher exhaust gas emissions and offer lower fuel saving economy. Most of these drawbacks can be addressed if CAI combustion is associated with a two-stroke cycle engine. An experimental investigation is carried out based on a single-cylinder CAI two-stroke cycle engine using Internal and External Exhaust Gas Recirculation (In-EGR and Ex-EGR) and fuels with different octane numbers to investigate the exhaust emissions characteristics. The experimental results indicate a remarkable improvement in the engine's exhaust gas emissions. The concentration of uHC and CO emissions decreased with application of In/Ex-EGR. However, NOx emission increased with the use of In-EGR.

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The engine development process faces big challenges from new strict emission regulations in addition to the need for fuel efficiency improvements. The Software-in-the-Loop (SiL) and Hardware-in-the-Loop (HiL) environments decreases the required time during engine development, calibration, verification, and validation of the product. An accurate and easy to build dyno-engine model with real-time operational ability is required for this purpose. Artificial Neural Networks (ANN) have shown ability to model dynamic and complex systems like internal combustion engines. In this paper, the Group Method of Data Handling (GMDH) algorithm was utilized to build an ANN model of a heavy-duty diesel engine. One objective is to reduce the amount of manual labor on the results during the ANN model development process. The GMDH algorithm is a self-organizing process that will find the system laws and optimize the model structure automatically in one iteration. The GMDH model results were compared with a model developed by Levenberg-Marquardt Backpropagation (LM-BP) algorithm. The ANN models used actuator signals from an Engine Management System (EMS) to simulate the engine operation parameters. As revealed by the simulation results, the ANN models successfully predicted engine torque, fuel flow, and NOx concentration. The GMDH model as a self-organized model reduced lead time, had slightly higher transient cycle accuracy, had fewer inconsistent predictions, and demonstrated better extrapolation capability. The prediction accuracy for transient operation was improved by shifting the predicted value by calculating time delay and a decrease of 76.66% for fuel flow and 66.51% for NOX concentration in model accuracy were achieved. The GMDH dyno-engine model can be effectively applied as a virtual test cell instrument for testing, calibration, and optimization purposes.

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