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    Robust data-driven model to study dispersion of vapor cloud in offshore facility

    Access Status
    Fulltext not available
    Authors
    Shi, J.
    Khan, F.
    Zhu, Y.
    Li, Jingde
    Chen, G.
    Date
    2018
    Type
    Journal Article
    
    Metadata
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    Citation
    Shi, J. and Khan, F. and Zhu, Y. and Li, J. and Chen, G. 2018. Robust data-driven model to study dispersion of vapor cloud in offshore facility. Ocean Engineering. 161: pp. 98-110.
    Source Title
    Ocean Engineering
    DOI
    10.1016/j.oceaneng.2018.04.098
    ISSN
    0029-8018
    School
    School of Civil and Mechanical Engineering (CME)
    URI
    http://hdl.handle.net/20.500.11937/74307
    Collection
    • Curtin Research Publications
    Abstract

    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.

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