Mode interpretation and force prediction surrogate model of flow past twin cylinders via machine learning integrated with high-order dynamic mode decomposition

Understanding and modeling the flow field and force development over time for flow past twin tandem cylinders can promote insight into underlying physical laws and efficient engineering design. In this study, a new surrogate model, based on a convolutional neural network and higher-order dynamic mode decomposition (CNN-HODMD), is proposed to predict the unsteady fluid force time history specifically for twin tandem cylinders. Sampling data are selected from a two-dimensional direct numerical simulation flow solution over twin tandem cylinders at different aspect ratios (AR = 0.3-4), gap spacing (L* = 1-8), and Re = 150. To promote insight into underlying physical mechanisms and better understand the surrogate model, the HODMD analysis is further employed to decompose the flow field at selected typical flow regimes. Results indicate that CNN-HODMD performs well in discovering a suitable low-dimensional linear representation for nonlinear dynamic systems via dimensionality augment and reduction technique. Therefore, the CNN-HODMD surrogate model can efficiently predict the time history of lift force at various AR and L* within 5% error. Moreover, fluid forces, vorticity field, and power spectrum density of twin cylinders are investigated to explore the physical properties. It was found three flow regimes (i.e., overshoot, reattachment, and coshedding) and two wake vortex patterns (i.e., 2S and P). It was found the lift force of the upstream cylinder for AR < 1 is more sensitive to the gap increment, while the result is reversed for the downstream cylinder. It was found that the fluctuating component of the wake of cylinders decreases with increasing AR at L* = 1. Moreover, flow transition was observed at L* = 4.

Automated summary

In this study, a new surrogate model called CNN-HODMD is proposed to predict the unsteady fluid force time history for twin tandem cylinders. The results show that CNN-HODMD performs well in predicting the lift force at different aspect ratios and gap spacing within 5% error.