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

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.

• Publication year

  2018

• Venue

  Ocean Engineering

• Publication date

  2018-08-01

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1016/J.OCEANENG.2018.04.098
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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