A Complete Open‑Source CFD and Neural Surrogate Framework for Tandem Cylinder Flow Using OpenFOAM

The flow past two cylinders in tandem arrangement is of fundamental importance in engineering applications such as heat exchangers, offshore structures, and power transmission lines. This study presents a complete open‑source simulation pipeline using Gmsh for mesh generation and OpenFOAM for the finite‑volume solver, combined with a long short‑term memory (LSTM) neural network surrogate for fast predictions. A distance‑based refinement strategy resolves the flow accurately, with characteristic mesh sizes as low as around the cylinders. The methodology is validated against the classical Schäfer–Turek single‑cylinder benchmark at (Re=100), showing satisfactory agreement for force coefficients and Strouhal number. The main analysis focuses on a tandem configuration at \( Re=1.0\times10^5 \) with unequal diameters (\( D_1=0.1\;\mathrm{m} \), \( D_2=0.15\;\mathrm{m} \)) spaced \( 1.0\;\mathrm{m} \) centre‑to‑centre. The results reveal strong wake interaction: the downstream cylinder experiences higher mean drag \( (\overline{C}_D=0.997) \) and significantly larger lift fluctuations \( (C_L'=0.340) \) than the upstream cylinder \( (\overline{C}_D=0.947 \), \( C_L'=0.129 \)). Both cylinders shed vortices at the same frequency \( f=2.041\;\mathrm{Hz} \), yielding Strouhal numbers \( St_A=0.204 \) and \( St_B=0.306 \). An LSTM neural network trained on the force coefficient time series achieves near‑perfect predictions of the downstream lift and correctly reproduces the shedding frequency, providing a fast and accurate surrogate model. The fully reproducible open‑source workflow, including all CFD setup files and the neural network training code, is made available, enabling future studies on bluff‑body interactions and facilitating the adoption of data‑driven methods in fluid mechanics.