基于Monte Carlo方法的磁驱动准等熵压缩实验不确定度量化评估
Explosion and Shock Waves(2023)
Abstract
磁驱动准等熵压缩实验是研究材料偏离Hugoniot状态高压物性和动力学行为的重要实验技术之一,开展不确定量化评估具有重要意义和价值.基于Monte Carlo原理,结合磁驱动准等熵压缩实验过程分析、Lagrange分析和特征线正向数据处理方法建立了适用于此类实验的Monte Carlo不确定度量化评估方法,实现利用磁驱动准等熵压缩实验获取材料声速、应力、应变等物理量以及状态方程和本构关系等物理模型的不确定度量化评估.利用建立的不确定度评估方法,对文献中已开展的钽、铜和NiTi合金的磁驱动准等熵压缩实验结果进行不确定度量化评估与分析.结果表明,基于本文中方法的评估结果与国外文献以相同原理得到的评估结果一致.对基于CQ-4装置开展的NiTi合金磁驱动准等熵压缩实验的评估结果表明,设计的磁驱动准等熵压缩实验是一种可靠的精密物理实验.在此基础上,深入讨论了磁驱动准等熵压缩实验的误差相关性和敏感性.结果表明:台阶样品厚度和粒子速度的测量是影响实验精度的主要因素.
MoreTranslated text
Key words
magnetically-driven quasi-isentropic compression,uncertainty,Monte Carlo method,quantitative evaluation
求助PDF
上传PDF
View via Publisher
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