Optimization via genetic algorithm of a variable-valve-actuation spark-ignition engine based on the integration between 1D/3D simulation codes and optimizer

In this work, a turbocharged spark ignition engine equipped with a variable valve actuation device is investigated to numerically optimize the Brake specific fuel consumption (BSFC) at different loads and speeds by employing a genetic algorithm. The engine is preliminary analyzed at the test bench under both part and full load operations and different valve strategies. A system schematization is realized in a 1D code. The developed model is integrated with user-defined sub-models for the description of the in-cylinder processes, and then is validated over the measurements. A 3D CFD model of a single cylinder is developed in a commercial code and validated against experimental mean in-cylinder pressure and combustion indicators. The validated 1D engine model is coupled to an external optimizer, to identify the optimal calibration, performing multi-variable and multi-objective optimizations with the adoption of the MOGA genetic algorithm. The latter aims at minimizing the BSFC in a BMEP sweep, at fixed speed, while controlling the load through the Inlet Valve Closure (IVC) at fully opened throttle valve. The optimization results show that an advanced control of the intake valve strategy allows a maximum BSFC advantage of 26% at medium/high loads and medium speeds, if compared to the manufacturer-advised engine calibration. The outcomes of the optimization process are also confirmed by the 3D CFD tool. The latter not only contributes to the tuning of the 1D model, but it also provides an in-depth on detailed 3D aspects, such as turbulence and knock, that could not be assessed via a simplified 1D approach. The presented methodology represents a valuable tool to refine the virtual calibration of VVA engines and to support the design phase, thus remarkably reducing the experimental efforts. Moreover, it is a promising example of integration between 1D and 3D CFD tools.