WeChat Mini Program
Old Version Features

Thermomechanical In-Plane Dynamic Instability of Asymmetric Restrained Functionally Graded Graphene Reinforced Composite Arches Via Machine Learning-Based Models

Composite Structures(2023)

Zhongkai Univ Agr & Engn

Cited 32|Views63
Abstract
This paper studies the thermomechanical in-plane dynamic instability of asymmetric restrained functionally graded graphene reinforced composite (FG-GRC) arches, where graphene sheets with atom vacancy defects are distributed along the arch thickness according to a power law distribution. The temperature-dependent mechanical properties of the graphene reinforced composites are determined by a genetic programming (GP) assisted micromechanical model. The governing equations for the thermomechanical in-plane dynamic instability are derived by Hamilton’s principle and solved by differential quadrature method (DQM) in conjunction with Bolotin method. Comprehensive numerical studies are performed to examine the effects of vacancy defect and graded distribution of graphene, temperature variation, load position, as well as boundary conditions on the free vibration, elastic buckling, and dynamic instability behaviors of the FG-GRC arch. Numerical results show that the structural performance of the FG-GRC arch is weakened by graphene defect and temperature rise and is significantly influenced by both graphene distribution and boundary conditions.
More
Translated text
Key words
Defective graphene,Functionally graded arch,Asymmetric elastic constraint,Dynamic instability,Thermomechanical action,Genetic programming assisted micromechanical model
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
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined