摘要
This work presents a methodology for using machine learning(ML)techniques in combination with 3D computa-tional fluid dynamics(CFD)modeling to optimize the cold-start fast-idle phase of a gasoline direct injection spark ignition(DISI)engine.The optimization process implies the identification of the range of operating parameters,that will ensure the following criteria under cold-start conditions:(1)a fixed IMEP of 2 bar(BMEP of 0 bar),(2)a stoichiometric exhaust equivalence ratio(based on carbon-to-oxygen atoms)to ensure the efficient operation of the after-treatment system,(3)enough exhaust heat flux to ensure a rapid light-offof the after-treatment system,and(4)reduced NOx and HC emissions.A total of six operating parameters will be identified as having a signifi-cant influence on cold-start engine performance.These parameters are associated with the fuel injection strategy(end of the second injection,injection pressure,and fuel mass);combustion strategy(spark timing,spark energy);and intake airflow(intake manifold pressure).Performing an optimization study exclusively using multi-cycle(at least 3 cycles)3D CFD simulations would be an arduous task.For example,to achieve an exhaust equivalence ratio of 1 and an IMEP of 2 bar,multiple iterations would be required for fuel mass(to account for film forma-tion),intake manifold pressure(to ensure enough air in-cylinder),and spark timing(to ensure the fixed load).This process would be more convoluted and expensive with the addition of constraints for exhaust heat flux and emissions.A promising approach to tackling such a complicated optimization process is to employ the concept of machine learning,which demands a database formed by the six operating parameters mentioned above.The current work will demonstrate a strategy of combining CFD modeling with advanced Gaussian Process Regres-sion(GPR)-based ML models to make predictions about DISI cold-start behavior with acceptable accuracy and a substantially reduced computational time.
