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Optimization Design of Crankshaft Bearing Combining Crankshaft Strength Factor

Gui Chang-lin

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Abstract

The crankshaft-bearing system of a four-stroke four-cylinder engine was taken as the research object,the optimization design of crankshaft bearing including the consideration of crankshaft strength was carried out.The necessity for the inclusion of crankshaft strength factor in the optimization design of crankshaft bearing was studied.The crankshaft strength was taken as one of restriction conditions when establishing the model of optimization design of crankshaft bearing.The particle swarm algorithm was used to optimize the crankshaft bearing.The results show that,comparing to the design of bearing for the original engine,14.21% reduction in total average power loss of bearing friction is presented by the optimized design of crankshaft bearing while meeting the crankshaft strength.Meanwhile,the optimization design of crankshaft bearing without taking into account the effect on crankshaft was analyzed.In this case,although total average power loss of bearing friction reduces obviously,the crankshaft strength can not meet the design demand.Therefore,the reasonable optimization design of crankshaft bearing should consider whole crankshaft-bearing system and the effect on crankshaft strength,and optimization solution in system demand can be obtained.

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What this paper is about

The crankshaft-bearing system of a four-stroke four-cylinder engine was taken as the research object,the optimization design of crankshaft bearing including the consideration of crankshaft strength was carried out.The necessity for the inclusion of crankshaft strength factor in the optimization design of crankshaft bearing was studied.The crankshaft strength was taken as one of restriction conditions when establishing the model of optimization design of crankshaft bearing.The particle swarm algorithm was used to optimize the crankshaft bearing.The results show that,comparing to the design of bearing for the original engine,14.21% reduction in total average power loss of bearing friction is presented by the optimized design of crankshaft bearing while meeting the crankshaft strength.Meanwhile,the optimization design of crankshaft bearing without taking into account the effect on crankshaft was analyzed.In this case,although total average power loss of bearing friction reduces obviously,the crankshaft strength can not meet the design demand.Therefore,the reasonable optimization design of crankshaft bearing should consider whole crankshaft-bearing system and the effect on crankshaft strength,and optimization solution in system demand can be obtained.

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Available abstract

The crankshaft-bearing system of a four-stroke four-cylinder engine was taken as the research object,the optimization design of crankshaft bearing including the consideration of crankshaft strength was carried out.The necessity for the inclusion of crankshaft strength factor in the optimization design of crankshaft bearing was studied.The crankshaft strength was taken as one of restriction conditions when establishing the model of optimization design of crankshaft bearing.The particle swarm algorithm was used to optimize the crankshaft bearing.The results show that,comparing to the design of bearing for the original engine,14.21% reduction in total average power loss of bearing friction is presented by the optimized design of crankshaft bearing while meeting the crankshaft strength.Meanwhile,the optimization design of crankshaft bearing without taking into account the effect on crankshaft was analyzed.In this case,although total average power loss of bearing friction reduces obviously,the crankshaft strength can not meet the design demand.Therefore,the reasonable optimization design of crankshaft bearing should consider whole crankshaft-bearing system and the effect on crankshaft strength,and optimization solution in system demand can be obtained.

Key concepts: Crankshaft, Bearing (navigation), Main bearing, Automotive engineering, Structural engineering, Engineering, Computer science, Mechanical engineering

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