基于多场耦合的电磁斥力机构运动参数研究
High Voltage Engineering(2022)
Abstract
为深入研究电磁斥力机构的多物理场耦合及其在产品设计中的影响,采用理论建模和仿真实验的方式,以温度场为媒介对典型电磁斥力机构运动参数进行多物理场耦合建模,统筹分析电场、磁场、温度场、应力场等多物理场的耦合效应.首先,通过实验验证仿真模型的准确性;其次,对比分析顺序耦合与直接耦合之间的差异,归纳出单次动作时多场耦合对电磁斥力机构运动参数的影响;最后,通过多场耦合的仿真研究分析不同环境、不同工况下电磁斥力机构各运动参数的变化情况.仿真结果表明:温度场对单次动作的影响可以忽略;温度场对复杂工况下斥力峰值、速度峰值等机构运动参数的影响超过5%;随着温度升高,机构安全系数降幅可达18%,温度超限对机构的寿命影响较大.该文研究对复杂工况下的机构运动参数及其寿命研究的准确可靠性有重要意义.
More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