Meta-modelling for fast analysis of CFD-simulated vapour cloud dispersion processes

• Meta-modelling is used with CFD simulation for fast prediction of vapour dispersion.
• PCT-PCA algorithm reduces the dimension of concentration field for meta-modelling.
• Meta-model-based uncertainty analysis is carried out to assess prediction variance.
• The method is demonstrated on a close-to-reality scenario of LNG dispersion process.

Released flammable chemicals can form an explosible vapour cloud, posing safety threat in both industrial and civilian environments. Due to the difficulty in conducting physical experiments, computational fluid dynamic (CFD) simulation is an important tool in this area. However, such simulation is computationally too slow for routine analysis. To address this issue, a meta-modelling approach is developed in this study; it uses a small number of simulations to build an empirical model, which can be used to predict the concentration field and the potential explosion region. The dimension of the concentration field is reduced from around 43,421,400 to 20 to allow meta-modelling, by using the segmented principal component transform-principal component analysis. Moreover, meta-modelling-based uncertainty analysis is explored to quantify the prediction variance, which is important for risk assessment. The effectiveness of the methodology has been demonstrated on CFD simulation of the dispersion of liquefied natural gas.