Generation of Optimized Rotation of Counterweights from Balancing Device for Slider-Crank Mechanisms Wartsila Italia Spa

Format:

Book

Conference/Event

Author/Creator:

Destradi, Destradi, author.

Conference Name:

Automotive Technical Papers (2019-01-01 : Warrendale, Pennsylvania, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2019

Summary:

AbstractThe main purpose of this work was to minimize the total unbalanced inertial forces generated by slider-crank mechanism during steady-state regime, developing the concept behind the Lanchester balancer.The goal approach was based on a kinematic scheme, which realizes conversion of the input crankshaft steady speed into a prescribed output speed of counter-rotating weights. The needed adaptability of output rotation speed was obtained through theoretical model of noncircular gear transmission. The dynamic result consisted in generating balancing forces that should have acquired optimized instantaneous magnitudes.A mathematical model of the required device was built and analytical investigation was performed in order to obtain geometrical features of gears. This mathematical approach supplied an exact solution for potential employment into the kinematic scheme. At this point a review of feasibility of obtained results was done. The scarcity of practical utility of previously obtained results requested another strategy to get to the target. Consequently the main problem was reformulated as Evolutionary Multi-objective Optimization (EMO) problem. Optimization method was based on Genetic Algorithms (GA) and lead to the best compromise that produced a numerical result entered into Computer-Aided Design (CAD) routines, for modelling of noncircular gears constituting the needed system. The open-source evolutionary computation library MOEA Framework was set as the work environment. It supports a variety of Multi-objective Evolutionary Algorithms, including GA, Genetic Programming, and Particle Swarm Optimization. These tools were applied in different tests in order to gain the most efficient way of optimization. NSGA-II was revealed experimentally as most appropriate algorithm. At the end of this study we do a practical employ of NSGA-II together with CAD implementations for synthesis of solid models. The overall benefit of resultant balancing action was estimated as 89% reduction in peak-to-peak amplitude of dynamic load

Notes:

Vendor supplied data

Publisher Number:

2019-01-5020

Access Restriction:

Restricted for use by site license