A non-intrusive reduced-order modeling for uncertainty propagation of time-dependent problems using a B-splines Bezier elements-based method and proper orthogonal decomposition: Application to dam-break flows

A proper orthogonal decomposition-based B-splines Bezier elements method (POD-BSBEM) is proposed as a non-intrusive reduced-order model for uncertainty propagation analysis for stochastic time-dependent problems. The method uses a two-step proper orthogonal decomposition (POD) technique to extract the reduced basis from a collection of high-fidelity solutions called snapshots. A third POD level is then applied on the data of the projection coefficients associated with the reduced basis to separate the time-dependent modes from the stochastic parametrized coefficients. These are approximated in the stochastic parameter space using B-splines basis functions defined in the corresponding Bezier element. The accuracy and the efficiency of the proposed method are assessed using benchmark steady-state and time-dependent problems and compared to the reduced order model-based artificial neural network (POD-ANN) and to the full-order model-based polynomial chaos expansion (Full-PCE). The POD-BSBEM is then applied to analyze the uncertainty propagation through a flood wave flow stemming from a hypothetical dam-break in a river with a complex bathymetry. The results confirm the ability of the POD-BSBEM to accurately predict the statistical moments of the output quantities of interest with a substantial speed-up for both offline and online stages compared to other techniques.

Automated summary

The POD-BSBEM method is proposed as a non-intrusive reduced-order model for uncertainty propagation in stochastic time-dependent problems, utilizing proper orthogonal decomposition and B-splines approximation. Experimental results confirm its accuracy and efficiency in predicting statistical moments of output quantities of interest.