Identification of primary input parameters affecting evacuation in ventilated main control room through CFAST simulations and application of a machine learning algorithm to replace CFAST model-Reference-Cited by-同舟云学术

Identification of primary input parameters affecting evacuation in ventilated main control room through CFAST simulations and application of a machine learning algorithm to replace CFAST model

Published:2024-09 Issue:9 Volume:56 Page:3717-3729
ISSN:1738-5733
Container-title:Nuclear Engineering and Technology
language:en
Short-container-title:Nuclear Engineering and Technology

Author:

Singh Sumit Kumar^ORCID,Bae Jinsoo^ORCID,Zhang Yu,Lim Saerin,Heo Jongkook^ORCID,Kim Seoung Bum^ORCID,Shin Weon Gyu

Funder

National Research Foundation of Korea

Ministry of Science, ICT and Future Planning

Publisher

Elsevier BV

Reference24 articles.

1. Assessment of the habitability for a cabinet fire in the main control room of nuclear power plant using sensitivity analysis;Han;Fire Science and Engineering,2017

2. Effect of the HVAC conditions on the smoke ventilation performance and habitability for a main control room fire in nuclear power plant;Kim;Fire Science and Engineering,2016

3. Fire simulations for the abandonment risk assessment of main control room fire in domestic nuclear power plant;Kang;J. Korean Surg. Soc.,2014

4. Evaluation logic of main control board fire risk;Kang;Transactions of the Korean Nuclear Society Spring Meeting,2015

5. Electrical fire simulation in control room of an AGN reactor;Jyung;Nucl. Eng. Technol.,2021

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