Fatigue Failure Analysis of U75V Rail Material under Ⅰ+Ⅱ Mixed-Mode Loading: Characterization Using Peridynamics and Experimental Verification

Xiaochuan Ma,Weibin Yin,Yajie Wang,Linya Liu,Xianghe Wang,Yao Qian

INTERNATIONAL JOURNAL OF FATIGUE（2024）

State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure | Southwest Jiaotong Univ

Cited 0|Views20

Abstract

Fatigue failure behavior of U75V rail material under I +II mixed-mode loading is analyzed and characterized in this work. A fatigue crack propagation experiment was carried out on U75V rail material under mixed-mode loading for R = 0.1. Then, based on the Ordinary State-Based Peridynamic (OSB PD) theory, bond strain rate and bond energy release rate methods were used to characterize the fatigue failure behavior of the rail material. Finally, the simulation results were compared with experimental results in terms of crack propagation length, path and angle to evaluate the characterization effects of both methods. The conclusions were as follows: under single mode I loading, both methods could accurately characterize the fatigue failure behavior of the rail material. However, compared with the bond strain rate method, the bond energy release rate method could provide a more accurate characterization under I + II mixed-mode loading.

Translated text

Key words

Rail material,Fatigue failure,Mixed-mode,Peridynamics,Experiment

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper