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Uniaxial fatigue tests of rubber dumbbell specimens under different mean and amplitude of strain are carried out. An Extreme Learning Machine (ELM) model optimized by Dragonfly Algorithm (DA) is proposed to predict the fatigue life of rubber based on measured rubber fatigue life data. Mean and amplitude of strain and measured rubber fatigue life are taken as input variables and output variables respectively in DA-ELM model. For comparison, genetic algorithm (GA) and particle swarm optimization (PSO) are used to optimize ELM parameters, and GA-ELM and PSO-ELM models are established. The comparison results show that DA-ELM model performs better in predicting the fatigue life of rubber with least dispersion. The coefficients of determination for the training set and test set are 99.47% and 99.12%, respectively. In addition, a life prediction model equivalent strain amplitude as damage parameter is introduced to further highlight the superiority of DA-ELM model.

The working principle and performance test method of the gerotor pump with multi-arc combined profile are introduced. According to the formation method of the rotor tooth profile, the calculation method of the inner rotor tooth profile is introduced, and the meshing characteristics of the inner and outer rotors are analyzed. On this basis, a calculation method for the displacement and instantaneous flow rate of the gerotor pump with multi-arc combined profile is proposed. In addition, a calculation model of the flow field characteristics of the gerotor pump with multi-arc combined profile is established, and the validity of the model is verified by experiments. Based on the model of traditional single-arc gerotor pump and the model of the gerotor pump with multi-arc combined profile, the flow rate, internal flow velocity, pressure distribution and gas volume fraction distribution under different working conditions are calculated respectively.