Optimization of Hydrodynamic Thrust Bearing Improved Teaching-Learning Algorithm
In order to minimize the power loss of Hydrodynamic thrust bearing during operation.An improved teaching and learn-ing algorithm(DWTLBO)is proposed to optimize the design of the thrust bearing.Compared with other classical intelligent opti-mization algorithms,such as particle swarm optimization(PSO)differential evolutionary algorithm(DE)and Teaching and Learning algorithm(TLBO),this algorithm introduces differential evolutionary operator in the learning stage to increase the crossover rate between groups,further improve the diversity of the algorithm and local search ability and avoid premature conver-gence.By establishing the thrust bearing model,four design variables including bearing step radius,oil groove recess radius,lubri-cating oil viscosity and lubricating oil flow were designed and the relevant parameters of the model were optimized by using the im-proved teaching and learning algorithm.The optimization results show that the proposed improved algorithm is better than the tra-ditional teaching and learning algorithm to obtain the optimal solution of the model,which is helpful to improve the design accu-racy of the model in the future engineering optimization.
NETL
NSTL
万方数据
维普
