Large Scale Surrogate Modelling for Enhancement of Consequence Modelling of Industrial Fires

  Large Scale Surrogate Modelling for Enhancement of Consequence Modelling of Industrial Fires

Loy Yoke Yuan^a,b, Gade Pandu Rangaiah^a and Lakshminarayanan Samavedham^a

^aDepartment of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585

^bLloyd’s Register Global Technology Centre Pte Ltd, 1 Fusionopolis Place, #09-11 Galaxis, Singapore 138522

  Abstract

Quantitative Risk Assessment (QRA) traditionally employs empirical consequence models to calculate damage values resulting from a hazardous event, such as the radiation flux of industrial fires. With rapid advancements in computational technology, it is now possible to perform consequence modelling with computational fluid dynamics (CFD). CFD-based consequence modelling is arguably more accurate than traditional methodologies since it takes into account the effects of geometrical obstructions [1]. It also avoids some assumptions inherent with the use of empirical models for consequence modelling. However, the use of CFD is very costly due to the long simulation time, particularly when multiple events have to be modelled for the purpose of a QRA [2]. As a result, this becomes a barrier for CFD-based QRA to replace traditional QRA methods in the market. In this paper, it is proposed that the value of CFD-based consequence modelling for industrial fires can be increased with the use of surrogate models so as to enhance information gain and possibly reduce the number of simulations required to perform a full CFD-based QRA. This is a novel application of data analysis methodologies for QRA.

The present work investigates the accuracy of surrogate models generated using local linear interpolation (LLI) and support vector regression (SVR) algorithms, for industrial fires. A liquefied natural gas (LNG) satellite plant model [3] is used as the case study, with Fire Dynamics Simulator as the CFD software and SUMO toolbox [4] for the surrogate modelling. The study involves two input variables (wind speed and heat release rate per unit area of pool fire) and one output variable (time-averaged net radiation flux collected at ground level). The output variable is collected as a maximum time-average value throughout the simulation time, time-average value at 50 seconds and time-averaged value at 100 seconds.  Each surrogate model is trained by a fixed 100 input points designed with Latin Hypercube Sampling (LHS), and verified with 97 verification points selected differently from the LHS design. The accuracy of each surrogate model is measured with root mean square error (RMSE), and the overall accuracy of each algorithm (LLI and SVR) is presented. Our study will serve to highlight the potential of surrogate modelling for realistic consequence modelling of industrial fires as well as challenges faced in surrogate modelling of complex systems.

  References

[1] Hansen, O.R., Davis, S.G. and Gavelli, F. (2012). Benefits of CFD for onshore facility explosion studies. 8^th Global Congress on Process Safety, Houston Texas, 1-4 April 2012.

[2] Kajero, O.T., Thorpe, R.B., Chen, T., Wang, B. and Yao, Y. (2016). Kriging meta-model assisted calibration of computational fluid dynamics models. AIChE Journal. DOI 10.1002/aic.15352.

[3] Sun, B., Guo, K. and Pareek, V.K. (2014). Computational fluid dynamics simulation of LNG pool fire radiation for hazard analysis. Journal of Loss Prevention in the Process Industries (29), 92-102.

[4] Gorissen, D., Couckuyt, I., Demeester, P., Dhaene, T. and Crombecq, K. (2010). A surrogate modeling and adaptive sampling toolbox for computer based design. Journal of Machine Learning Research (11), 2051-2055.