The GTPower software was used to establish onedimensional thermodynamic cycle simulation model of natural gas engine with a large bore. The accuracy of simulation model was verified after the calibration based on the engine bench test data. Then the effect of engine geometric compression ratio and Miller degree on engine performance was studied with the model. The neural network modeling and genetic algorithm were used to conduct the collaborative optimization of engine design and control parameters so as to improve the thermal efficiency and reduce the exhaust gas temperature. The results show that it is feasible to increase thermal efficiency, reduce exhaust gas temperature, and improve engine performance by the collaborative optimization of geometric compression ratio and Miller degree without loss of torque output.
CAO Jiale
,
LI Tie
,
YI Ping
,
HUANG Shuai
,
YANG Rundai
,
HUANG Yating
. Collaborative Optimization of Geometric Compression Ratio and Miller Degree of Large-Bore Natural Gas Engine[J]. Vehicle Engine, 2021
, 0(3)
: 26
-31
.
DOI: 10.3969/j.issn.1001-2222.2021.03.005
