CFD-based Modeling of Sarin Gas Dispersion in a Subway Station-A Hypothetical Scenario
Journal of Mining and Environment(2022)
Abstract
The main purpose of this work is modeling the dispersion of the sarin gas in a subway station in a hypothetical scenario. The dispersion is modeled using the CFD approach. In the analysis of the environmental conditions of the underground spaces, the only factor that draws a distinction between a subway station and other spaces is the train piston effect. Therefore, the present research work models the sarin dispersion in the two general cases of with and without a train in the subway system. About 0.5 L of sarin is assumed to be released through the main air handling unit (AHU) of the station. The results obtained show that in the case with no train service in the station, after 20 minutes of sarin release, the concentration and dose of sarin in the station will be 8.9 mg/m3 and 80 mg minute/m3, respectively, and these values are highly dangerous and lethal, and would have severely adverse effects on many individuals, and lead to death. This is highly important, especially when the effect of ventilation chambers at the ground level is taken into consideration. The results obtained also show that the train piston effect reduces the concentration and dose of sarin in the station so that when train arrival at and departure from the station, the sarin dose considerably reduces to 25 mg min/m3 after the release, and contributes to lower casualties. Finally, the results obtained show that time is a key factor to save lives in the management of such incidents.
MoreTranslated text
Key words
Sarin, Chemical attack, CFD, Subway station, Piston effect
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
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2023
被引用1
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
GPU is busy, summary generation fails
Rerequest