A Model generation method and iteration algorithm for optimising fire protection thickness
With temperatures rising above 1000 °C within 5 min, hydrocarbon fire causes rapid strength degradation of structural steel members. It is among the most dangerous hazards, such as boiling liquid expanding vapour explosion (BLEVE) in the oil and gas industry. Intumescent coating as passive protectio...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-06-01
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| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016124000864 |
| _version_ | 1797258765323468800 |
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| author | Yang Li Zhuoran Feng Simon Thurlbeck Meini Su |
| author_facet | Yang Li Zhuoran Feng Simon Thurlbeck Meini Su |
| author_sort | Yang Li |
| collection | DOAJ |
| description | With temperatures rising above 1000 °C within 5 min, hydrocarbon fire causes rapid strength degradation of structural steel members. It is among the most dangerous hazards, such as boiling liquid expanding vapour explosion (BLEVE) in the oil and gas industry. Intumescent coating as passive protection is widely adopted to prevent the steel structure from material property reduction. However, when optimising fire protection with heat transfer simulation, repetitive modelling work and lacking recalculation principle hinder productivity improvement. This method is developed to generate steel beam models and provides an effective algorithm to optimise coating thickness considering the temperature of a specific region. The main functions of the method include: • Providing section dimensions, initial insulation thickness, target temperature and heating time, temperature allowance and mesh size as variables. • Automatically generating the Abaqus steel beam model under 3-side heating conditions. • Effective iteration algorithm to modify fire protection thickness: test containing 38 Universal beam sections with a 5 °C allowance below target shows that 55.2% were completed within five iterations and 76.3% were completed within eight iterations. |
| first_indexed | 2024-04-24T22:58:44Z |
| format | Article |
| id | doaj.art-e6031965ede14ce1a8063df46db9d144 |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-04-24T22:58:44Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-e6031965ede14ce1a8063df46db9d1442024-03-18T04:33:57ZengElsevierMethodsX2215-01612024-06-0112102632A Model generation method and iteration algorithm for optimising fire protection thicknessYang Li0Zhuoran Feng1Simon Thurlbeck2Meini Su3Corresponding author.; Department of Solids and Structures, The University of Manchester, Booth St E, Manchester M13, UKDepartment of Solids and Structures, The University of Manchester, Booth St E, Manchester M13, UKDepartment of Solids and Structures, The University of Manchester, Booth St E, Manchester M13, UKDepartment of Solids and Structures, The University of Manchester, Booth St E, Manchester M13, UKWith temperatures rising above 1000 °C within 5 min, hydrocarbon fire causes rapid strength degradation of structural steel members. It is among the most dangerous hazards, such as boiling liquid expanding vapour explosion (BLEVE) in the oil and gas industry. Intumescent coating as passive protection is widely adopted to prevent the steel structure from material property reduction. However, when optimising fire protection with heat transfer simulation, repetitive modelling work and lacking recalculation principle hinder productivity improvement. This method is developed to generate steel beam models and provides an effective algorithm to optimise coating thickness considering the temperature of a specific region. The main functions of the method include: • Providing section dimensions, initial insulation thickness, target temperature and heating time, temperature allowance and mesh size as variables. • Automatically generating the Abaqus steel beam model under 3-side heating conditions. • Effective iteration algorithm to modify fire protection thickness: test containing 38 Universal beam sections with a 5 °C allowance below target shows that 55.2% were completed within five iterations and 76.3% were completed within eight iterations.http://www.sciencedirect.com/science/article/pii/S2215016124000864Model generation method for composite model and Iteration algorithm for optimising fire protection thickness |
| spellingShingle | Yang Li Zhuoran Feng Simon Thurlbeck Meini Su A Model generation method and iteration algorithm for optimising fire protection thickness MethodsX Model generation method for composite model and Iteration algorithm for optimising fire protection thickness |
| title | A Model generation method and iteration algorithm for optimising fire protection thickness |
| title_full | A Model generation method and iteration algorithm for optimising fire protection thickness |
| title_fullStr | A Model generation method and iteration algorithm for optimising fire protection thickness |
| title_full_unstemmed | A Model generation method and iteration algorithm for optimising fire protection thickness |
| title_short | A Model generation method and iteration algorithm for optimising fire protection thickness |
| title_sort | model generation method and iteration algorithm for optimising fire protection thickness |
| topic | Model generation method for composite model and Iteration algorithm for optimising fire protection thickness |
| url | http://www.sciencedirect.com/science/article/pii/S2215016124000864 |
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