Application of Bayesian Regularization Artificial Neural Network in explosion risk analysis of fixed offshore platform. (January 2019)
- Record Type:
- Journal Article
- Title:
- Application of Bayesian Regularization Artificial Neural Network in explosion risk analysis of fixed offshore platform. (January 2019)
- Main Title:
- Application of Bayesian Regularization Artificial Neural Network in explosion risk analysis of fixed offshore platform
- Authors:
- Shi, Jihao
Zhu, Yuan
Khan, Faisal
Chen, Guoming - Abstract:
- Abstract: Computational Fluid Dynamics (CFD) is routinely used in Explosion Risk Analysis (ERA), as CFD-based ERA offers a good understanding of underlying physics accidental loads. Generally, simplifications were incorporated into CFD-based ERA to limit the number of simulations. Frozen Cloud Approach (FCA) is a frequently used simplification in the dispersion part of the CFD-based ERA procedure. However, its accuracy is questionable in the complex and congested environment such as offshore facility. Furthermore, in explosion part, some specific techniques, e.g. linear/double bin-interpolated techniques have been proposed while the corresponding accuracy is still unknown since the developers did not yet check their accuracy by considering the explosion computational data as the benchmark. This study presents a more accurate algorithm, namely Bayesian Regularization Artificial Neural Network (BRANN) and accordingly proposes the frameworks regarding BRANN-based models for the CFD-based ERA procedure. Firstly, the framework is proposed to develop the Transient-BRANN (TBRANN) model for transient dispersion study. In addition, the framework to determine the BRANN model for explosion study is developed. The proposed frameworks are explained by a case study of the fixed offshore platform. Consequently, this study confirms the more accuracy of the TBRANN model over FCA and the accuracy of BRANN model for CFD-based ERA. Highlights: Frameworks to develop Transient-BRANN(TBRANN) forAbstract: Computational Fluid Dynamics (CFD) is routinely used in Explosion Risk Analysis (ERA), as CFD-based ERA offers a good understanding of underlying physics accidental loads. Generally, simplifications were incorporated into CFD-based ERA to limit the number of simulations. Frozen Cloud Approach (FCA) is a frequently used simplification in the dispersion part of the CFD-based ERA procedure. However, its accuracy is questionable in the complex and congested environment such as offshore facility. Furthermore, in explosion part, some specific techniques, e.g. linear/double bin-interpolated techniques have been proposed while the corresponding accuracy is still unknown since the developers did not yet check their accuracy by considering the explosion computational data as the benchmark. This study presents a more accurate algorithm, namely Bayesian Regularization Artificial Neural Network (BRANN) and accordingly proposes the frameworks regarding BRANN-based models for the CFD-based ERA procedure. Firstly, the framework is proposed to develop the Transient-BRANN (TBRANN) model for transient dispersion study. In addition, the framework to determine the BRANN model for explosion study is developed. The proposed frameworks are explained by a case study of the fixed offshore platform. Consequently, this study confirms the more accuracy of the TBRANN model over FCA and the accuracy of BRANN model for CFD-based ERA. Highlights: Frameworks to develop Transient-BRANN(TBRANN) for transient dispersion study and BRANN for explosion study is proposed. A case study of fixed offshore platform is performed. Accuracy of BRANN model to study explosion. Better performance of TBRANN over Frozen Cloud Approach (FCA) for Explosion Risk Analysis (ERA). … (more)
- Is Part Of:
- Journal of loss prevention in the process industries. Volume 57(2019)
- Journal:
- Journal of loss prevention in the process industries
- Issue:
- Volume 57(2019)
- Issue Display:
- Volume 57, Issue 2019 (2019)
- Year:
- 2019
- Volume:
- 57
- Issue:
- 2019
- Issue Sort Value:
- 2019-0057-2019-0000
- Page Start:
- 131
- Page End:
- 141
- Publication Date:
- 2019-01
- Subjects:
- Explosion risk analysis -- Transient bayesian regularization artificial neuron network -- Frozen cloud approach -- Computational fluid dynamics
Chemical industries -- Safety measures -- Periodicals
660.2804 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09504230/ ↗
http://www.journals.elsevier.com/journal-of-loss-prevention-in-the-process-industries/ ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.jlp.2018.10.009 ↗
- Languages:
- English
- ISSNs:
- 0950-4230
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5010.562000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 9427.xml