Robust data-driven model to study dispersion of vapor cloud in offshore facility
Data driven models are increasingly used in engineering design and analysis. Bayesian Regularization Artificial Neural Network (BRANN) and Levenberg-Marquardt Artificial Neural Network (LMANN) are two widely used data-driven models. However, their application to study the dispersion in complex geome...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
Elsevier
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/74307 |
| _version_ | 1848763237670060032 |
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| author | Shi, J. Khan, F. Zhu, Y. Li, Jingde Chen, G. |
| author_facet | Shi, J. Khan, F. Zhu, Y. Li, Jingde Chen, G. |
| author_sort | Shi, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Data driven models are increasingly used in engineering design and analysis. Bayesian Regularization Artificial Neural Network (BRANN) and Levenberg-Marquardt Artificial Neural Network (LMANN) are two widely used data-driven models. However, their application to study the dispersion in complex geometry is not explored. This study aims to investigate the suitability of BRANN and LMANN in estimating dimension of flammable cloud in congested offshore platform. A large number of numerical simulations are conducted using FLACS. Part of these simulations results are used to training the network. The trained network is subsequently used to predict the vapor cloud dimension and compared against remaining simulation results. The predictive abilities of these network along with Response Surface Method and Frozen Cloud Approach (FCA) are studied. The comparative results indicate BRANN model with 20 hidden neurons is the most robust and precise. The developed BRANN would serve an effective and tool for quick Explosion Risk Analysis ERA. |
| first_indexed | 2025-11-14T11:00:16Z |
| format | Journal Article |
| id | curtin-20.500.11937-74307 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:00:16Z |
| publishDate | 2018 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-743072019-07-15T03:12:06Z Robust data-driven model to study dispersion of vapor cloud in offshore facility Shi, J. Khan, F. Zhu, Y. Li, Jingde Chen, G. Data driven models are increasingly used in engineering design and analysis. Bayesian Regularization Artificial Neural Network (BRANN) and Levenberg-Marquardt Artificial Neural Network (LMANN) are two widely used data-driven models. However, their application to study the dispersion in complex geometry is not explored. This study aims to investigate the suitability of BRANN and LMANN in estimating dimension of flammable cloud in congested offshore platform. A large number of numerical simulations are conducted using FLACS. Part of these simulations results are used to training the network. The trained network is subsequently used to predict the vapor cloud dimension and compared against remaining simulation results. The predictive abilities of these network along with Response Surface Method and Frozen Cloud Approach (FCA) are studied. The comparative results indicate BRANN model with 20 hidden neurons is the most robust and precise. The developed BRANN would serve an effective and tool for quick Explosion Risk Analysis ERA. 2018 Journal Article http://hdl.handle.net/20.500.11937/74307 10.1016/j.oceaneng.2018.04.098 Elsevier restricted |
| spellingShingle | Shi, J. Khan, F. Zhu, Y. Li, Jingde Chen, G. Robust data-driven model to study dispersion of vapor cloud in offshore facility |
| title | Robust data-driven model to study dispersion of vapor cloud in offshore facility |
| title_full | Robust data-driven model to study dispersion of vapor cloud in offshore facility |
| title_fullStr | Robust data-driven model to study dispersion of vapor cloud in offshore facility |
| title_full_unstemmed | Robust data-driven model to study dispersion of vapor cloud in offshore facility |
| title_short | Robust data-driven model to study dispersion of vapor cloud in offshore facility |
| title_sort | robust data-driven model to study dispersion of vapor cloud in offshore facility |
| url | http://hdl.handle.net/20.500.11937/74307 |