Predicting unsteady hydrodynamic performance of seaplanes based on diffusion models

Obtaining unsteady hydrodynamic performance is of great significance for seaplane design. Common methods for obtaining unsteady hydrodynamic performance data include tank test and Computational Fluid Dynamics (CFD) numerical simulation, which are costly and time-consuming. Therefore, it is necessary to obtain unsteady hydrodynamic performance in a low-cost and high-precision manner. Due to the strong nonlinearity, complex data distribution, and temporal characteristics of unsteady hydrodynamic performance, the prediction of it is challenging. This paper proposes a Temporal Convolutional Diffusion Model (TCDM) for predicting the unsteady hydrodynamic performance of seaplanes given design parameters. Under the framework of a classifier-free guided diffusion model, TCDM learns the distribution patterns of unsteady hydrodynamic performance data with the designed denoising module based on temporal convolutional network and captures the temporal features of unsteady hydrodynamic performance data. Using CFD simulation data, the proposed method is compared with the alternative methods to demonstrate its accuracy and generalization. This paper provides a method that enables the rapid and accurate prediction of unsteady hydrodynamic performance data, expecting to shorten the design cycle of seaplanes.