Optimal motorcycle engine mount design parameter identification using robust optimization algorithms
| dc.contributor.author | Younis, Adel | |
| dc.contributor.author | AlKhatib, Fadi | |
| dc.contributor.author | Dong, Zuomin | |
| dc.date.accessioned | 2022-11-12T17:11:06Z | |
| dc.date.available | 2022-11-12T17:11:06Z | |
| dc.date.copyright | 2022 | en_US |
| dc.date.issued | 2022 | |
| dc.description.abstract | Mechanical vibrations have a significant impact on ride comfort; the driver is constantly distracted as a result. Volumetric engine inertial unbalances and road profile irregularities create mechanical vibrations. The purpose of this study is to employ optimization algorithms to identify structural elements that contribute to vibration propagation and to provide optimal solutions for reducing structural vibrations induced by engine unbalance and/or road abnormalities in a motorcycle. The powertrain assembly, swing-arm assembly, and vibration-isolating mounts make up the vibration-isolating system. Engine mounts are used to restrict transferred forces to the motorbike frame owing to engine shaking or road irregularities. Two 12-degree-of-freedom (DOF) powertrain motorcycle engine systems (PMS) were modeled and examined for design optimization in this study. The first model was used to compute engine mount parameters by reducing the transmitted load through the mounts while only considering shaking loads, whereas the second model considered both shaking and road bump loads. In both configurations, the frame is infinitely stiff. The mount stiffness, location, and orientation are considered to be the design parameters. The purpose of this study is to employ computational methods to minimize the loads induced by shaking forces. To continue the optimization process, Grey Wolf Optimizer (GWO), a meta-heuristic swarm intelligence optimization algorithm inspired by grey wolves in nature, was utilized. To demonstrate GWO’s superior performance in PMS, other optimization methods such as a Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) were used for comparison. To minimize the engine’s transmitted force, GWO was employed to determine the optimal mounting design parameters. The cost and constraint functions were formulated and optimized, and promising results were obtained and documented. The vibration modes due to shaking and road loads were decoupled for a smooth ride. | en_US |
| dc.description.reviewstatus | Reviewed | en_US |
| dc.description.scholarlevel | Faculty | en_US |
| dc.identifier.citation | Younis, A., AlKhatib, F., & Dong, Z. (2022). “Optimal motorcycle engine mount design parameter identification using robust optimization algorithms.” Algorithms, 15(8), 271. https://doi.org/10.3390/a15080271 | en_US |
| dc.identifier.uri | https://doi.org/10.3390/a15080271 | |
| dc.identifier.uri | http://hdl.handle.net/1828/14431 | |
| dc.language.iso | en | en_US |
| dc.publisher | Algorithms | en_US |
| dc.subject | engine mount system | en_US |
| dc.subject | mechanical vibrations | en_US |
| dc.subject | ride comfort | en_US |
| dc.subject | optimization | en_US |
| dc.subject | decoupling modes | en_US |
| dc.subject | computational mechanics | en_US |
| dc.title | Optimal motorcycle engine mount design parameter identification using robust optimization algorithms | en_US |
| dc.type | Article | en_US |