Vented gas explosion overpressure prediction of obstructed cubic chamber by Bayesian Regularization Artificial Neuron Network – Bauwens model
© 2018 Elsevier Ltd This study aims to develop an integrated model, namely Bauwens-BRANN model, to estimate the maximum overpressure of vented gas explosion. A series of experiments designed for cubic enclosures with and without obstacles are used in the development of Bauwens-BRANN model. Two impor...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Published: |
Elsevier
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/71091 |
| _version_ | 1848762387291701248 |
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| author | Shi, J. Li, J. Hao, Hong Pham, Thong Zhu, Y. Chen, G. |
| author_facet | Shi, J. Li, J. Hao, Hong Pham, Thong Zhu, Y. Chen, G. |
| author_sort | Shi, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2018 Elsevier Ltd This study aims to develop an integrated model, namely Bauwens-BRANN model, to estimate the maximum overpressure of vented gas explosion. A series of experiments designed for cubic enclosures with and without obstacles are used in the development of Bauwens-BRANN model. Two important parameters are modified to address the pre-existing issues of Bauwens model. By incorporating the Bayesian Regularization Artificial Neuron Network (BRANN) algorithm into the Bauwens model, the Bauwens-BRANN model is developed. Improved pressure estimation accuracy is seen for the Bauwens-BRANN model in comparison with the NFPA-68 2013 model. |
| first_indexed | 2025-11-14T10:46:45Z |
| format | Journal Article |
| id | curtin-20.500.11937-71091 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:46:45Z |
| publishDate | 2018 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-710912018-12-13T09:35:01Z Vented gas explosion overpressure prediction of obstructed cubic chamber by Bayesian Regularization Artificial Neuron Network – Bauwens model Shi, J. Li, J. Hao, Hong Pham, Thong Zhu, Y. Chen, G. © 2018 Elsevier Ltd This study aims to develop an integrated model, namely Bauwens-BRANN model, to estimate the maximum overpressure of vented gas explosion. A series of experiments designed for cubic enclosures with and without obstacles are used in the development of Bauwens-BRANN model. Two important parameters are modified to address the pre-existing issues of Bauwens model. By incorporating the Bayesian Regularization Artificial Neuron Network (BRANN) algorithm into the Bauwens model, the Bauwens-BRANN model is developed. Improved pressure estimation accuracy is seen for the Bauwens-BRANN model in comparison with the NFPA-68 2013 model. 2018 Journal Article http://hdl.handle.net/20.500.11937/71091 10.1016/j.jlp.2018.05.016 Elsevier restricted |
| spellingShingle | Shi, J. Li, J. Hao, Hong Pham, Thong Zhu, Y. Chen, G. Vented gas explosion overpressure prediction of obstructed cubic chamber by Bayesian Regularization Artificial Neuron Network – Bauwens model |
| title | Vented gas explosion overpressure prediction of obstructed cubic chamber by Bayesian Regularization Artificial Neuron Network – Bauwens model |
| title_full | Vented gas explosion overpressure prediction of obstructed cubic chamber by Bayesian Regularization Artificial Neuron Network – Bauwens model |
| title_fullStr | Vented gas explosion overpressure prediction of obstructed cubic chamber by Bayesian Regularization Artificial Neuron Network – Bauwens model |
| title_full_unstemmed | Vented gas explosion overpressure prediction of obstructed cubic chamber by Bayesian Regularization Artificial Neuron Network – Bauwens model |
| title_short | Vented gas explosion overpressure prediction of obstructed cubic chamber by Bayesian Regularization Artificial Neuron Network – Bauwens model |
| title_sort | vented gas explosion overpressure prediction of obstructed cubic chamber by bayesian regularization artificial neuron network – bauwens model |
| url | http://hdl.handle.net/20.500.11937/71091 |