A Prediction Method of Macroscopic Stress Fluctuation in Granular System Based on Convolutional Neural Network

Abstract

The invention relates to a method for predicting macroscopic stress fluctuations of a granular system based on a convolutional neural network, which comprises the following steps: converting the microscopic plastic deformation of the granular system into regular The three-dimensional voxel matrix; establish the deep learning data set with the three-dimensional voxel matrix as the input and the actual macroscopic stress fluctuation of the granular system as the output; train the convolutional neural network based on the deep learning data set, and establish the microcosmic stress fluctuation of the granular system A cross-scale predictive model of the quantitative relationship between behavior and macroscopic responses. The present invention relates to a method for predicting macroscopic stress fluctuations of granular systems based on convolutional neural networks, which can establish the quantitative relationship of granular systems from microscopic plastic behavior to macroscopic mechanical responses, and is useful for macroscopic stress fluctuation prediction and macro-micro cross-scale research of granular materials. Provides a new and effective way.

Description

technical field [0001] The invention relates to the field of granular material mechanics research, in particular to a method for predicting macroscopic stress fluctuations of granular systems based on convolutional neural networks. Background technique [0002] Granular material is a complex system composed of a large number of discrete solid particles interacting with each other. It is ubiquitous in engineering construction, industrial production and all aspects of nature. Debris flows and dams widely exist in nature, and block stone materials used in civil engineering , Coal and sand in industrial production all involve the accumulation and movement of granular materials. Granular materials have characteristics ranging from discrete to continuous, from microscopic to macroscopic, from disordered to ordered, and from flow to clogging, and are one of the key research objects in basic disciplines such as physics, materials, and mechanics. [0003] In the related technology, ...

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Problems solved by technology

[0006] Embodiments of the present invention provide a method for predicting macroscopic stress fluctuations in granular systems based on convolutional neural networks to solve the problem that most of the macroscopic and microcosmic connections established in related technologies are qualitative, and it is impossible to quantitatively consider the dynamic behavior and macroscopic mechanical response of the system at the microscopic level relationship problems

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