Abstract
AbstractThe increasing need for safe and comfortable high‐speed elevators due to the rise of super‐tall buildings has led to a focus on vibration reduction modelling and optimisation. This article selects factors that have a significant impact on the vibration of high‐speed elevator car systems through sensitivity evaluation to form a six‐dimensional parameter space and establishes a multi‐objective optimisation model for the car system. The Gibbis method and Radial Basis Function neural network are combined to sample and construct surrogate models, respectively. Meanwhile, a BA–EO algorithm that combines Bat algorithm and Extremal optimisation to adapt to a multidimensional parameter space is proposed here. In practical applications, the peak‐to‐peak value of vibration acceleration, which significantly affects human perception, is chosen as the objective function for vibration reduction optimisation. After optimisation, the vibrations of the car and car frame are decreased by 19% and 9%, respectively, which extend the service life of the high‐speed elevator and enhance safety and comfort for passengers.
Published Version
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