Study on the Spatial Distribution of Argon Bubbles in a Steel Slab Continuous Casting Strand
Steel Research International(2021)SCI 2区SCI 3区
Yanshan Univ | Shougang Grp Co Ltd
Abstract
The spatial distribution of argon bubbles in an 1800 mm × 230 mm continuous casting strand under different casting speeds and argon flow rates are studied through the numerical simulation using the Euler–Euler‐multiple‐size‐group approach. The calculated fluid flow is validated with water modeling and nail board measurements of industrial trials. Argon bubbles with diameter of 2 mm are injected into the submerged entry nozzle (SEN). The speed of the molten steel near the SEN decreased with the casting speed increasing and increased with the argon flow rate increasing. Most of argon bubbles are larger than 4.5 mm inside the mold at the casting speed of 0.6 m min−1 and the argon flow rate of 18 NL min−1. The breakup rate of bubbles inside the mold increased with the increasing of the casting speed and the coalescence rate increased with the increasing of the argon flow rate. The coalescence and breakage of argon bubbles inside the mold mainly occurred during the rising of bubbles to the top surface and the moving of bubbles with the steel jet to the narrow face. The average diameter of bubbles decreased when the casting speed increased and the diameter increased when the argon flow rate increased.
MoreTranslated text
Key words
argon bubbles,multiple-size-group model,nail board measurement,water modeling
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
Related Papers
1996
被引用1023 | 浏览
2006
被引用66 | 浏览
2010
被引用44 | 浏览
2007
被引用150 | 浏览
2008
被引用156 | 浏览
1996
被引用122 | 浏览
2014
被引用35 | 浏览
2015
被引用14 | 浏览
2017
被引用17 | 浏览
2018
被引用54 | 浏览
2018
被引用24 | 浏览
2018
被引用23 | 浏览
2016
被引用37 | 浏览
2021
被引用28 | 浏览
2021
被引用28 | 浏览
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