Benchmark dataset Turbulent Channel Flow for Chaos Meets Attention: Transformers for Large-Scale Dynamical Prediction

This dataset serves as a benchmark for 3D turbulent channel flows, based on simulations performed using a high-fidelity lattice Boltzmann method (LBM) solver, as described in Xue et al., Phys. Fluids, 34,5, 2022.It comprises 240 trajectories generated from 3D periodic turbulent channel flow simulations with a fixed relaxation time, $\tau = 0.5025$. We extract the central cross-section of the domain along the streamwise ($x$) direction with 3 coordinate components. The spatial resolution is $192 \times 192$, and the<b> friction Reynolds number</b> is set to $Re_{\tau} = 180$, equivalent to $Re = 3250$. The dataset is split into 192 training, 24 validation, and 24 test trajectories, all provided in <i>.npy</i> format.This dataset is designed to facilitate machine learning research in dynamical systems, especially in the challenging context of high-dimensional, turbulent flow regimes.