Multidisciplinary design and optimization of journal bearing for high-power wind turbine speed increaser

With the increasing power of wind turbine generators (WTG), the failure rate of rolling bearings in wind turbines due to insufficient bearing capacity increases with the increase of bearing size. To overcome these challenges, this paper proposes a design optimization method for the rectangular groove elliptical sliding bearing (RGEB) of the WTG output shaft. This method can maximize the radial bearing capacity under the premise of ensuring that the oil film pressure is large, the end leakage and the temperature rise are small. The multidisciplinary design and modeling of RGEB are carried out according to the structure and working conditions of WTG. Based on computational fluid dynamics (CFD), the influence of the number of rectangular dynamic pressure grooves on the bearing performance is analyzed. The kriging model, BP neural network model, and SSA-BP model are constructed for each performance index of RGEB, to establish a high-precision combined surrogate model based on global error criterion. After establishing the optimization equation, the PSO and SQP combined optimization algorithm is used to optimize the optimal structural parameters. The results show that the bearing capacity of the optimized RGEB is 95.9% higher than that of the ordinary elliptical bearing (EB) without increasing the size and quality of the bearing. In addition, the combined surrogate model can effectively replace the expensive finite element model to deal with the WTG sliding-bearing optimization problem.