A CFD-based empirical model for hazardous area extent prediction including wind effects

• Published in: Journal of loss prevention in the process industries 2021-07, Vol.71, p.104497, Article 104497
• Main Authors: Nascimento, Claudemi A., Luiz, Aurélio M., Barros, Paloma L., Neto, Antônio T.P., Alves, José J.N.
• Format: Article
• Language: English
• Subjects: CFD simulation; Empirical model; Gas emission; Hazardous area classification
• ISSN: 0950-4230
• DOI: 10.1016/j.jlp.2021.104497

Hazardous extent predictions that ensure process safety while avoiding overestimation have been a challenge for hazardous area classification. Specific leak scenarios can be addressed to build rapid empirical models to accurately determine hazardous extent considering several factors that are not included in general approaches. In view of that, this work aims to propose a novel CFD-based empirical model for gas emissions in open and unobstructed areas. It considers a wide range of variables such as storage temperature and pressure, orifice diameter, molecular weight, gas concentration, and wind velocity. A sensitivity analysis was performed to evaluate each variable's contribution to the gas cloud extent. The linear regression model resulting from the combination of all variables contribution was coupled with Ewan and Moddie's model to minimize the prediction errors due to the non-monotonic wind effects. The suggested algorithm accurately calculates the hazardous extent with a coefficient of determination equals to 0.9842 and a RMSE of 0.0151 for a dataset of 600 cases of generic gases release. The proposed model was also validated for 60 cases of hydrogen releases under different storage conditions, giving a coefficient of determination equal to 0.9829 and a RMSE of 0.0680, indicating a good agreement with the data. [Display omitted] •A novel model was built from accurate CFD results to predict hazardous extent.•Wind effect was included in the empirical model for hazardous extent prediction.•A refined algorithm was obtained for a specific hazardous scenario.•The proposed algorithm avoids errors due to non-monotonic wind behavior.•Validations revealed a good agreement of the proposed algorithm to the data.