In order to reveal the characteristics of irregular coarse particle-wall collisions during deep-sea mining hydraulic transportation,a corresponding random rebound probability model was established,providing a foundational model for numerical analysis of coarse particle-wall interactions.Through coarse particle free-fall experiments and image recognition technology,experimental studies on the random rebound process of coarse particles under different incident velocities and incident angles were conducted.Particle motion parameters were automatically extracted and analyzed using custom MATLAB programs.Based on probability statistical analysis and nonlinear regression,analytical prediction models for the random rebound of coarse particles under different incident velocities and impact angles were established.Finally,numerical simulations were performed to obtain random rebound information of particles under specific impact conditions,and the predicted models were compared with those given by existing models.The study indicates that both normal distribution and generalized extreme value distribution can well describe the motion characteristics of coarse particle random rebound.Under low incident angle collisions,besides the rebound mode,coarse particles also exhibit a slip-deflection mode.With increasing incident angle,the mean of the random rebound angle first slightly decreases and then gradually increases,while the dispersion of rebound angles exponentially increases with the incident angle.Regarding the coefficient of restitution,as the incident angle increases,energy dissipation increases,leading to a decrease in the coefficient of restitution,with a slight increase in its dispersion.With increasing incident angle,the mean angular velocity change follows a semi-sinusoidal function,with the most significant rotation occurring at an incident angle of 45°,and the standard deviation of rotational velocity first increases and then decreases.Additionally,when numerically reconstructing,truncation values should be used to avoid unreasonable sample data.
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