Wind field and pressure prediction of a ground-mounted rectangular cylinder based on Hankel-inspired reduced order model and temporal convolutional network-transformer

This study proposes a hybrid framework combining a Hankel-inspired reduced-order model (HROM) and a Temporal Convolutional Network (TCN)–Transformer network for predicting the wind field and surface pressure around a ground-mounted rectangular cylinder. Unlike purely data-driven approaches, the proposed method explicitly incorporates time-delay embedding to reconstruct an augmented dynamical phase space, enabling the reduced-order representation to better preserve the intrinsic temporal evolution of coherent flow structures. The HROM can effectively extract dominant large-scale modes associated with flow separation, shear layer, and wake dynamics, thereby providing a physically meaningful low-dimensional representation of the highly nonlinear wind field. On this basis, the TCN–Transformer network is employed to model the temporal evolution of modal coefficients, where the TCN captures local unsteady fluctuations induced by vortex dynamics, while the Transformer component accounts for long-range temporal dependencies associated with global flow organization. The results demonstrate that the proposed framework achieves accurate prediction of both velocity and surface pressure fields. In addition, increasing the delay embedding dimension in the HROM strengthens the energetic coherence of dominant flow modes, improves the physical interpretability of the reduced-order dynamics, and enhances overall prediction accuracy.