Numerical simulation and performance optimisation of in-cylinder friction of cylinder liner and piston ring in internal combustion engine based on deep neural network

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The difference of each factor and the overall score of each level

Name	Medium convex point offset	Effective lubrication length (mm)	Barrel height(μm)
Minimum oil film thickness
First level(i=1)	1.833	1.849	1.989
Second level(i=2)	1.881	1.874	1.892
Third level(i=3)	1.934	1.917	1.78
Aberration R	0.102	0.064	0.189
The friction loss of the top ring
First level(i=1)	0.332	0.311	0.306
Second level(i=2)	0.312	0.324	0.311
Third level(i=3)	0.286	0.32	0.303
Aberration R	0.038	0.001	0.004
Minimum oil film thickness
First level(i=1)	4	3	1
Second level(i=2)	3	2	2
Third level(i=3)	2	1	3
The evaluation index of the friction loss
First level(i=1)	4	1	2
Second level(i=2)	3	3	3
Third level(i=3)	2	2	1
Top ring type line parameter
First level(i=1)	7	4	3
Second level(i=2)	5	5	5
Third level(i=3)	3	3	4

Orthogonal optimization test factor level of piston ring type line parameter

Name	Medium convex point offset	Effective lubrication length (mm)	Barrel height(μm)
Level 1	10%	5	10
Level 2	20%	5.4	14
Level 3	30%	5.8	18

The original scheme is compared with the proposed scheme

Name	Medium convex point offset	Effective lubrication length (mm)	Barrel height(μm)	Minimum oil film thickness(μm)	Friction loss work(kW)
Original plan	Unbiased shift	2.6	18	1.431	0.295
After optimization	35%	4.6	10	2.051	0.282

Orthogonal optimization test results of piston ring line parameters

Name	Medium convex point offset	Effective lubrication length (mm)	Barrel height(μm)	Minimum oil film thickness(μm)	Friction loss work(kW)
Solution 1	10%	5	10	1.912	0.331
Solution 2	10%	5.4	14	1.829	0.325
Solution 3	10%	5.8	18	1.762	0.332
Solution 4	20%	5	14	1.859	0.312
Solution 5	20%	5.4	18	1.785	0.309
Solution 6	20%	5.8	10	2.015	0.310
Solution 7	30%	5	18	1.811	0.280
Solution 8	30%	5.4	10	2.021	0.289
Solution 9	30%	5.8	14	1.965	0.284