Reduced-Order Modeling Based on Hybrid Snapshot Simulation

Abstract

This paper presents a hybrid snapshot simulation methodology to accelerate the generation of high-quality data for proper orthogonal decomposition (POD) and reduced-order model (ROM) development. The entire span of the snapshot simulation is divided into multiple intervals, each simulated by either high-fidelity full-order model (FOM) or fast local ROM. The simulation then alternates between FOM and local ROM to accelerate snapshot data generation while maintaining the data fidelity and representation. Model switch is determined on-the-fly by evaluating several criteria that monitor the dominance of leading POD modes and ROM trajectory. The incremental singular value decomposition (iSVD) is employed to continuously update ROMs for enhanced accuracy and utilization. A global ROM broadly applicable to various online simulation is immediately available at the end of the simulation. The hybrid snapshot simulation demonstrates excellent accuracy ([Formula: see text] error) and 2.09–2.6[Formula: see text]X speedup relative to its traditional counterpart. The constructed ROMs also preserve salient accuracy ([Formula: see text] error). The results prove feasibility of the proposed method for robust and efficient snapshot data generation and ROM development.