Abstract
Eddy-resolving turbulence simulations require stochastic inflow conditions that accurately replicate the complex, multiscale structures of turbulence. Traditional recycling-based methods rely on computationally expensive precursor simulations while existing synthetic inflow generators often fail to reproduce realistic coherent structures of turbulence. Recent advances in deep learning (DL) have opened new possibilities for inflow turbulence generation, yet many DL-based methods rely on deterministic, autoregressive frameworks prone to error accumulation, resulting in poor robustness for long-term predictions. In this work, we present CoNFiLD-inlet, a novel DL-based inflow turbulence generator that integrates diffusion models with a conditional neural field to produce realistic, stochastic inflow turbulence. By parametrizing inflow conditions using Reynolds numbers, CoNFiLD-inlet generalizes effectively across a wide range of Reynolds numbers ( Reτ between 10(3) and 10(4)) without requiring retraining or parameter tuning. Comprehensive validation through a priori and a posteriori tests in direct numerical simulation and wall-modeled large-eddy simulation demonstrates its high fidelity, robustness, and scalability, positioning it as an efficient and versatile solution for inflow turbulence synthesis.