Robust data-driven model to study dispersion of vapor cloud in offshore facility
MetadataShow full item record
© 2018 Elsevier Ltd 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.
Showing items related by title, author, creator and subject.
Alhamad, Mohammed (2011)Cloud computing has changed the strategy used for providing distributed services to many business and government agents. Cloud computing delivers scalable and on-demand services to most users in different domains. However, ...
Sun, L.; Dong, Hai; Hussain, F.; Hussain, O.; Chang, E. (2014)Cloud technology connects a network of virtualized computers that are dynamically provisioned as computing resources, based on negotiated agreements between service providers and users. It delivers information technology ...
Mostafa, Fahed. (2011)Market risk refers to the potential loss that can be incurred as a result of movements inmarket factors. Capturing and measuring these factors are crucial in understanding andevaluating the risk exposure associated with ...