Journal
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
Volume 57, Issue -, Pages 61-72Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jlp.2018.11.009
Keywords
NFPA-68 2018; BS EN 14994:2007; Bayesian Regularization Artificial Neuron Network; Integrated mathematical model; Turbulence-inducing obstacle effect; Vent area design
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Funding
- National Key R&D Program of China [2017YFC0804501]
- Fundamental Research Funds for the Central Universities [18CX05010A]
- Fundamental Research Funds for Innovation Program of Seventh-generation Ultra Deepwater Drilling Platform [2016[24]]
- National Basic Research Program of China (973 Program) [2015CB058000]
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This study aims to develop an integrated model - NFPA-68-BRANN model, which can be used to calculate the vent areas of cubic enclosures with obstacles. Seven experiments regarding vented explosion inside the obstructed enclosure are reviewed and applied to check the accuracy of two existing standards, i.e. the NFPA-68 2018 and the BS EN 14994:2007. Accordingly, the parameters to describe the flame development in the NFPA-68 2018 are amended by adopting the Bauwens model. Bayesian Regularization Artificial Neuron Network (BRANN) model presenting the non-linear relationship between the turbulent flame enhancement factor X and its affecting factors is subsequently developed. Eventually, the NFPA-68-BRANN model is generated by incorporating the BRANN model into the modified NFAP-68 2018. The accuracy of the NFPA-68-BRANN model is validated by using a series of the New Baker Test data.
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