Incremental Singular Value Decomposition Based Model Order Reduction of Scale Resolving Fluid Dynamic Simulations

TL;DR

This paper presents an incremental SVD-based model order reduction method for large-scale fluid dynamic simulations, enabling efficient data compression with minimal loss of accuracy, applicable to both academic and industrial flow scenarios.

Contribution

It introduces an adaptive incremental SVD approach for compressing scale-resolving flow data, optimizing the number of singular values retained without multiple simulation reruns.

Findings

Achieves up to 95% data reduction with 10% overhead.

Maintains flow data accuracy within 0.1%.

Applicable to both academic LES and industrial DES simulations.

Abstract

Scale-resolving flow simulations often feature several million [thousand] spatial [temporal] discrete degrees of freedom. When storing or re-using these data, e.g., to subsequently train some sort of data-based surrogate or compute consistent adjoint flow solutions, a brute-force storage approach is practically impossible. Therefore, -- mandatory incremental -- Reduced Order Modeling (ROM) approaches are an attractive alternative since only a specific time horizon is effectively stored. This bunched flow solution is then used to enhance the already computed ROM so that the allocated memory can be released and the procedure repeats. This paper utilizes an incremental truncated Singular Value Decomposition (itSVD) procedure to compress flow data resulting from scale-resolving flow simulations. To this end, two scenarios are considered, referring to an academic Large Eddy Simulation (LES) around a circular cylinder at Re=1.4E+05 as well as an industrial case that employs a hybrid filtered/averaged Detached Eddy Simulation (DES) on the flow around the superstructure of a full-scale feeder ship at Re=5E+08. The paper's central focus is on an aspect of practical relevance: how much information of the computed scale-resolving solution should be used by the ROM, i.e., how much redundancy occurs in the resolved turbulent fluctuations that favors ROM. In the course of the tSVD employed, this goes hand in hand with the question of "how many singular values of the snapshot-matrix should be neglected (or considered)" -- without (a) re-running the simulation several times in a try-and-error procedure and (b) still obtain compressed results below the model and discretization error. An adaptive strategy is utilized to obtain a fully adaptive data reduction of O(95) percent via a computational overhead of O(10) percent with a mean accuracy of reconstructed local and global flow data of O(0.1) percent.