Despite the recent efforts devoted to develop alternative
technologies, it is likely that the internal combustion engine will
remain the dominant propulsion system for the next 30 years and
beyond. Also as a consequence of more and more stringent emissions
regulations established in the main industrialized countries,
strongly demanded are methods and technologies able to enhance the
internal combustion engines performance in terms of both efficiency
and environmental impact.
Present work focuses on the development of a numerical method
for the optimization of the control strategy of a diesel engine
equipped with a high pressure injection system, a variable geometry
turbocharger and an EGR circuit. A preliminary experimental
analysis is presented to characterize the considered six-cylinder
engine under various speeds, loads and EGR ratios. The fuel
injection system is separately tested on a dedicated test bench, to
determine the instantaneous fuel injection rate for different
injection strategies. The collected data are employed for tuning
proper numerical models, able to reproduce the engine behavior in
terms of performances (in-cylinder pressure, boost pressure,
air-flow rate, fuel consumption), noxious emissions (soot, NO) and
radiated noise. In particular, a 1D tool is developed with the aim
of characterizing the flow in the intake and exhaust systems and
predicting the engine-turbocharger matching conditions, by
including a short-route EGR circuit; a 3D model (AVL Fire™) is
assessed to reproduce into detail the in-cylinder thermo-fluid
dynamic processes, including mixture formation, combustion, and
main pollutants production; an in-house routine, also validated
against available data, is finally developed for the prediction of
the combustion noise, starting from in-cylinder pressure cycles.
Obviously, data exchange between the codes is previewed.
The overall numerical procedure is firstly checked with
reference to the experimentally analyzed operating points. The 1D,
3D and combustion noise models are then coupled to an external
optimizer (ModeFRONTIER™) in order to select the optimal
combination of the engine control parameters to improve the engine
performance and to contemporary minimize noise, emissions and fuel
consumption. Under the hypothesis of a pilot-main injection
strategy, a multi-objective optimization problem is solved through
the employment of a genetic algorithm. Eight degrees of freedom are
defined, namely start of injection, dwell time, energizing time of
pilot and main pulses, EGR valve opening, throttle valve opening,
swirl level, and turbine opening ratio. It is shown that
non-negligible improvements can be gained, also depending on the
importance given to the various objectives.