The Lubrication Numerical Analysis And Parameter Optimization Of Crankshaft Main Bearings Based On AVL EXCITE

Internal combustion engine is the power machinery that converts chemical energy into mechanical energy.The level of its energy efficiency is not only related to the combustion conditions in the cylinder but also related to the working conditions of various friction pairs in the engine.The crankshaft main bearing is one of the most important friction pairs in the engine.Its lubricating perf ormance directly affects the fuel consumption and working life of the whole machine.The paper is based on the AVL EXCITE multi-body dynamics simulation software and combined with orthogonal experimental design and intelligence genetic algorithm.It analyzes the primary and secondary factors affecting the lubrication performance of the main bearing of crankshaft and optimizes the multi-objective based on a marine high-speed gasoline engine cooperation project.The main research work and innovation of this paper are as follows:(1)The marine high-speed gasoline engine AVL EXCITE multi-body dynamics model was built and the boundary condition of the model was obtained through bench test.Comparing the simulation results with the disassemble results after the b ench test,the abrasion position and area of the main bearing bush in the simulation were the same as those of the bearing bush after the bench test,which verifie d the correctness of the AVL EXCITE simulation model and increased the credibility of subsequent optimization analysis.(2)Orthogonal test was designed according to the factors affecting the lubrication characteristics of the main bearing,such as oil supply pressure,oil temperature,bearing width,radial clearance,bearing groove width,bush surface roughness and main journal surface roughness.And the primary and secondary factors that affect the lubrication performance of the main bearing and the optimal combination of parameters were obtained through range analysis.(3)Experimental design and sample data construction were conducted for the four most important factors affecting the lubricity of the main bearing with ModeFRONTIER software.An approximate model was established based on the RBFN neural network.It took the reliable range of the minimum oil film thickness and the maximum oil film pressure as the constraint condition and the total frictional power loss and the minimum oil flow rate as the objective functions.Finally,the multi-objective optimization of main bearing lubrication performance was carried out by NSGA-II with elitist strategy.The results showed that the lubrication results obtained by the RSM approximate model greatly improved the lubrication performance of the main bearing and reduced the frictional work loss,compared with the results of the original machine and the optimization results obtained by the orthogonal test.And through the bench test,the main bearing lubrication achieved a desired effect.