Development of parametric reduced-order model for consequence estimation of rare events

Computational fluid dynamics (CFD) models have been widely used in the chemical process industry to analyze various aspects of high-consequence rare events. However, CFD models are computationally intensive in nature, and therefore, several developments have been made in building computationally efficient models. The existing computationally efficient models in the field of high-consequence rare events are temporally static in nature and do not represent system evolution with time, which is crucial for consequence modeling of rare events. Further, the consequences depend on various parameters in addition to inputs. Since it is not affordable to construct a new model for every parameter value, incorporation of inputs and parameters is an additional challenge in developing a consequence model. Hence, to address these challenges, this work proposes a k-nearest neighbor (kNN)based parametric reduced-order model (PROM) for consequence estimation of rare events to enhance numerical robustness with respect to parameter change. Specifically, local (with respect to parameters) ROMs are constructed based on multivariable output-error state space (MOESP) algorithm for a range of parameters, and they are linearly interpolated to estimate consequences at a new parameter value. The effectiveness of the proposed model is demonstrated through a case study of supercritical carbon dioxide release rare event. Published by Elsevier B.V. on behalf of Institution of Chemical Engineers.

Automated summary

Computational fluid dynamics (CFD) models are widely used in the chemical process industry to analyze high-consequence rare events. However, existing computationally efficient models are static and do not represent system evolution with time, posing a challenge for consequence modeling. To address this, a k-nearest neighbor (kNN) based parametric reduced-order model (PROM) is proposed to enhance numerical robustness with respect to parameter change.