Quasi-Dimensional Simulation of a Turbulent Jet Ignition Combustion in High-Performance High-Speed Motorcycle Engine

<div class="section abstract"><div class="htmlview paragraph">High-performance and high-revving Internal Combustion Engines (ICEs) are central to the motorbike driving experience. To achieve these peculiar characteristics, rich combustion strategies are often used to improve combustion performance, resulting in incomplete combustion leading to high unburned Hydro-Carbon (uHC) and Carbon Monoxide (CO) emissions. Turbulent Jet Ignition (TJI) combustion could replace standard Spark Ignition (SI) systems to improve combustion efficiency and reduce emissions with minimal modification to existing configurations. Extensive experimental and numerical analyses are needed to develop and understand the complex phenomena that characterize TJI combustion, using considerable time and resources. To address this, reduced-order models are used to accelerate the research and development processes by simplifying the analysis of complex combustion phenomena. This paper presents an enhanced reduced-order model initially developed for SI engines and later extended to include TJI combustion. Phenomenological sub-models were incorporated to capture the characteristics of TJI combustion, like flame development and enhanced turbulence. This model was applied to simulate high-speed, high-performance motorcycle ICEs at different engine speeds and loads and was validated against experimental and Three-Dimensional Computational Fluid Dynamics (3D CFD) results. Both SI and TJI conditions were analyzed, the latter characterized by a passive Pre-Chamber (PC). Using the same initial conditions as for 3D CFD simulations, the model demonstrated a high degree of accuracy in reproducing experimental and computational results, both quantitatively and qualitatively, across a wide range of operating conditions. A single set of tuning constants was applied successfully for all scenarios, highlighting the model’s robustness and versatility. This work underscores the potential of reduced-order modeling to advance the development of high-performance, low-emission combustion systems.</div></div>