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The increasingly demanding targets in terms of CO2 reduction lead to the adoption of engine technologies left so far for innovation. In diesel engines, some of the primary interests in adopting an advanced air management system, as Variable Valve Actuation (VVA), are related to Miller cycle enabling, and valve timing optimization. In this context, a numerical study was carried out in order to evaluate the impact of VVA on passenger car 4-cylinder diesel engine, 1.6 liters. The engine model, developed in GT-SUITE, features a predictive combustion model (DIPulse) and it is coupled with a fully predictive fuel injector model for the simulation of complex injection patterns. 3 different VVA techniques were evaluated, all targeting CO2 reduction: Late Exhaust Valve Opening (LEVO), Exhaust Phasing, and Late Inlet Valve Closure (LIVC) for enabling Miller cycle.

The need for achieving a fast warm up of the exhaust system has raised in the recent years a growing interest in the adoption of Variable Valve Actuation (VVA) technology for automotive diesel engines. As a matter of fact, different measures can be adopted through VVA to accelerate the warm up of the exhaust system, such as using hot internal Exhaust Gas Recirculation (iEGR) to heat the intake charge, especially at part load, or adopting early Exhaust Valve Opening (eEVO) timing during the expansion stroke, so to increase the exhaust gas temperature during blowdown. In this paper a simulation study is presented evaluating the impact of VVA on the exhaust temperature of a modern light duty 4-cylinder diesel engine, 1.6 liters, equipped with a Variable Geometry Turbine (VGT).

The predictive capabilities of an innovative multizone combustion model DIPulse, developed by Gamma Technologies, were assessed in this work for a last generation common rail automotive diesel engine. A detailed validation process, based on an extensive experimental data set, was carried out concerning the predicted heat release rate, the in-cylinder pressure trace, as well as NOx and soot emissions for several operating points including both part load and full load points. After a preliminary calibration of the model, the combustion model parameters were then optimized through a Latin Hypercube Design of Experiment (DoE), with the aim of minimizing the RMS error between the predicted and experimental burn rate of several engine operating points, thus achieving a satisfactory agreement between simulation and experimental engine combustion and emissions parameters.