A ResNet with Coordinate Attention for Intelligent Prediction of the Strouhal Number of 2D Bluff Body Sections at Reynolds 20,000

Abstract

Accurate evaluation of the Strouhal number is important for wind-resistant design, but traditional aerodynamic assessments are computationally expensive. To support rapid schematic design, this paper proposes a deep learning framework to predict the Strouhal number for 2D bluff bodies. Arbitrary irregular sections are represented using a unified three-channel geometric tensor that combines distance and spatial coordinate fields. A Residual Network integrated with Coordinate Attention, termed ResCA-Net, is developed to focus on vortex-shedding determinants, particularly leading-edge separation points. The model was trained on 1000 randomly generated convex sections, with target St values obtained from 2D URANS simulations at Re = 2 × 104. On an independent test set, ResCA-Net achieves an R2 of 0.9129, an MAE of 0.0316, and a median absolute percentage error of 6.56%. Systematic benchmarking shows that the embedded Coordinate Attention outperforms SE, CBAM, and the baseline ResNet. A single inference takes only 43.56 milliseconds, which is over 160,000 times faster than transient CFD simulations. This end-to-end method provides an efficient and accurate surrogate model for rapid iterative screening of aerodynamic shapes.