Automatic calibration method for discrete element hertz contact parameters during geotechnical material simulation

Abstract

The invention discloses an automatic calibration method for discrete element hertz contact parameters during geotechnical material simulation, and the method comprises the following steps: substituting a target macroscopic parameter calibrated by a discrete element into an analytical formula to calculate a particle shear modulus and a Poisson's ratio, taking the particle shear modulus and the Poisson's ratio as initial estimation values of particle parameters, and establishing a numerical triaxial or biaxial test to obtain a macroscopic Young modulus and a Poisson's ratio; calculating the sizeof an error function, and judging whether particle parameters are updated or not; adopting different strategies according to different iteration times; during primary iteration, manually giving smalldisturbance in direct proportion to an initial estimated value; and adopting an adaptive moment estimation strategy to update parameters in multiple iterations. An embodiment shows that after finiteiteration of a single-particle-size or multi-particle-size discrete element sample, errors of calibration parameters are effectively controlled within a certain range. The calibration method has the advantages of automatic calibration capability, strong convergence capability and high calibration efficiency, and can effectively solve the calibration problem of the Hertz deformation parameter whenthe discrete element simulates the rock-soil body material.

Description

technical field [0001] The invention belongs to the field of geotechnical engineering. Specifically, the invention relates to an automatic calibration method of discrete element hertz contact parameters when simulating rock and soil materials. Background technique [0002] DEM is widely used in the analysis and research of geotechnical engineering problems such as foundation reinforcement and tunnel excavation. However, since the mesoscale parameters adopted by the discrete element algorithm are not easy to measure directly through physical tests, most of the current discrete element simulations for geotechnical engineering must first simulate certain types of conventional geotechnical tests (such as triaxial tests). By continuously adjusting the discrete element mesoscopic parameters, until the simulated object is basically consistent with the physical test in the target macroscopic index, the used simulation parameters are regarded as a set of reliable input parameters. T...

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Problems solved by technology

However, there are also the following shortcomings: (1) All mesoscopic parameters are trained at the same time, and each parameter interferes with each other in the process, which affects the calibration efficiency; (2) The update strategy comes from empirical assumptions, lacks strict mathematical foundation, and cannot guarantee sufficient convergence efficiency (3) The input mesoscopic initial value comes from the deformation relationship of particles with a single particle size and regular arrangement, which is quite different from the reality that the particle structure and particle size are randomly distributed in the actual geotechnical materials

The initial estimate of the macroscopic response that deviates too much from the actual bulk not only makes the training time too long, but also increases the risk of the material facing the local optimal trap during the training process; (4) The parameter update process ignores the macroscopic and microscopic Parameter relationship, for example: normal stiffness and tangential stiffness have an impact on the elastic modulus and Poisson's ratio in the macro parameters at the same time, according to the parameter update strategy used in this method, only the elastic modulus or Poisson's ratio is used to update the normal direction Stiffness or tangential stiffness are actually inappropriate; (5) do not distinguish between two-dimensional models and three-dimensional models in terms of parameter training strategies and processes

For example: using the same initial value estimation for 2D and 3D models, the model cannot achieve the best convergence efficiency

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