Temporal super-resolution prediction of wave field for trimaran with arbitrary layout based on dynamic mode decomposition- α method

As an important technology in ocean engineering and aerospace engineering fields, the development of flow field super-resolution reconstruction technology stems from the urgent need for high-fidelity flow field analysis. In order to avoid the randomness and the difficulty of parameter adjustment caused by machine-learning-based methods for flow field reconstruction, this paper uses the idea of dynamic mode decomposition (DMD), introduces the numerical method Schur–Padé for the real power of the matrix, and proposes a temporal super-resolution flow field prediction method DMD-α, which only uses matrix manipulation to realize the prediction of periodic flow field at any time. Taking the wave field formed by the periodic movement of a trimaran in regular waves as an example, a parameter selection strategy based on the DMD-α method is proposed to take reconstruction accuracy and efficiency into account. Furthermore, proper orthogonal decomposition and Kriging surrogate models are combined to realize the temporal super-resolution flow field prediction for a trimaran with arbitrary side-hull layout to validate the robustness of the DMD-α method. The results show that the proposed DMD-α method is stable, efficient, and can obtain high-fidelity flow prediction, which has great potential in the field of temporal super-resolution prediction of complex flow fields and optimization design based on fluid dynamic performances of various structures.