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As known, reliable information about underlying turbulence intensity is a mandatory pre-requisite to predict the burning rate in quasi-dimensional combustion models. Based on 3D results reported in the companion part I paper, a quasi-dimensional turbulence model, embedded under the form of “user routine” in the GT-Power™ software, is here presented in detail. A deep discussion on the model concept is reported, compared to the alternative approaches available in the current literature. The model has the potential to estimate the impact of some geometrical parameters, such as the intake runner orientation, the compression ratio, or the bore-to-stroke ratio, thus opening the possibility to relate the burning rate to the engine architecture. Preliminarily, a well-assessed approach, embedded in GT-Power commercial software v.2016, is utilized to reproduce turbulence characteristics of a VVA engine.

Recently, a growing interest in the development of more accurate phenomenological turbulence models is observed, since this is a key pre-requisite to properly describe the burn rate in quasi-dimensional combustion models. The latter are increasingly utilized to predict engine performance in very different operating conditions, also including unconventional valve control strategies, such as EIVC or LIVC. Therefore, a reliable phenomenological turbulence model should be able to physically relate the actuated valve strategy to turbulence level during the engine cycle, with particular care in the angular phase when the combustion takes place.

In the recent years, the interest in heavy-duty engines fueled with Compressed Natural Gas (CNG) is increasing due to the necessity to comply with the stringent CO2 limitation imposed by national and international regulations. Indeed, the reduced number of carbon atoms of the NG molecule allows to reduce the CO2 emissions compared to a conventional fuel. The possibility to produce synthetic methane from renewable energy sources, or bio-methane from agricultural biomass and/or animal waste, contributes to support the switch from conventional liquid fuels to CNG. To drive the engine development and reduce the time-to-market, the employment of numerical analysis is mandatory. This requires a continuous improvement of the simulation models toward real predictive analyses able to reduce the experimental R&D efforts. In this framework, 1D numerical codes are fundamental tools for system design, energy management optimization, and so on.