TY - JOUR
T1 - An enhanced algorithm for online Proper Orthogonal Decomposition and its parallelization for unsteady simulations
AU - Li, Xiaodong
AU - Hulshoff, Steven
AU - Hickel, Stefan
PY - 2022
Y1 - 2022
N2 - Proper Orthogonal Decomposition (POD) plays an important role in the analysis of complex nonlinear systems governed by partial differential equations (PDEs), since it can describe the full-order system in a simplified but representative way using a handful of dominant dynamic modes. However, determining a POD from the results of complex unsteady simulations is often impractical using traditional approaches due to the need to store a large number of high-dimensional solutions. As an alternative, the incremental Singular Value Decomposition (SVD) has been developed, which can be used to avoid the storage problem by performing the POD analysis on the fly using a single-pass updating algorithm. Nevertheless, the total computing cost of incremental SVD is more than traditional approaches. In order to reduce this total cost, we incorporate POD mode truncation into the incremental procedure, leading to an enhanced algorithm for incremental SVD. Two error estimators are formulated for this enhanced incremental SVD based on an aggregated expression of the snapshot solutions, equipping the proposed algorithm with criteria for choosing the truncation number. The effectiveness of these estimators and the parallel efficiency of the enhanced algorithm are demonstrated using transient solutions from representative model problems. Numerical results show that the enhanced algorithm can significantly improve the computing efficiency for different kinds of datasets, and that the proposed algorithm is scalable in both the strong and weak sense.
AB - Proper Orthogonal Decomposition (POD) plays an important role in the analysis of complex nonlinear systems governed by partial differential equations (PDEs), since it can describe the full-order system in a simplified but representative way using a handful of dominant dynamic modes. However, determining a POD from the results of complex unsteady simulations is often impractical using traditional approaches due to the need to store a large number of high-dimensional solutions. As an alternative, the incremental Singular Value Decomposition (SVD) has been developed, which can be used to avoid the storage problem by performing the POD analysis on the fly using a single-pass updating algorithm. Nevertheless, the total computing cost of incremental SVD is more than traditional approaches. In order to reduce this total cost, we incorporate POD mode truncation into the incremental procedure, leading to an enhanced algorithm for incremental SVD. Two error estimators are formulated for this enhanced incremental SVD based on an aggregated expression of the snapshot solutions, equipping the proposed algorithm with criteria for choosing the truncation number. The effectiveness of these estimators and the parallel efficiency of the enhanced algorithm are demonstrated using transient solutions from representative model problems. Numerical results show that the enhanced algorithm can significantly improve the computing efficiency for different kinds of datasets, and that the proposed algorithm is scalable in both the strong and weak sense.
KW - Incremental singular value decomposition
KW - Low-rank representation
KW - Order reduction
KW - Parallelization
KW - Proper Orthogonal Decomposition
KW - Unsteady simulations
UR - http://www.scopus.com/inward/record.url?scp=85138475831&partnerID=8YFLogxK
U2 - 10.1016/j.camwa.2022.09.007
DO - 10.1016/j.camwa.2022.09.007
M3 - Article
AN - SCOPUS:85138475831
SN - 0898-1221
VL - 126
SP - 43
EP - 59
JO - Computers and Mathematics with Applications
JF - Computers and Mathematics with Applications
ER -