JK-FLOW Japan-Korea Fluid Mechanics Online Workshop

Speaker: Mr. Taegeun Kim (Ph.D. student, POSTECH)

Abstract: Knudsen diffusion plays a central role in molecular transport through confined structures, where the mean free path of gas molecules becomes comparable to or larger than the characteristic length. Such transport is especially important in reactive systems including atomic layer deposition, nanoporous catalysis, and gas–surface reactions on microscale roughness, where diffusion and surface reaction jointly determine species distribution and reaction rate. Conventionally, Knudsen diffusion has been modeled using Fick’s law with a Knudsen diffusion coefficient at high Knudsen numbers or the Bosanquet relation at moderate Knudsen numbers. However, this local-gradient-based description neglects rarefaction effects that become dominant in the free-molecular regime. In this study, we show that Knudsen diffusion with surface reaction can exhibit Anti- Fick’s behavior. A kinetic-theory-based analytical model is developed for a confined trench flow with surface reaction and validated against DSMC simulations over various aspect ratios. The results reveal an Anti-Fick’s phenomenon, where the diffusive flux and mole fraction gradient have the same sign near the end-wall region. Using the analytical solution, we decompose the molecular contributions from the inlet, side walls, and end wall, and demonstrate that the Anti-Fick’s phenomenon originates from geometry-driven effect. These findings explain why a Fickian closure fails in Knudsen diffusion with surface reaction and show that Anti-Fick’s behavior is a key rarefaction effect that can alter species distribution and surface reaction rates in confined reactive flows.

Speaker: Mr. Tomoya Oura (Ph.D. student, Keio University)

Abstract: The aim of this work is to improve particle tracking simulations in filtered turbulent flow fields. Numerical simulations of fluid flow are essential for analyzing various phenomena, such as the transport of small particles. In engineering applications, a large eddy simulation is widely employed using filtered fields to reduce computational costs. However, particle tracking simulations in filtered fields often deteriorate the accuracy of particle motion. To address this issue, this study introduces a defiltering method based on machine-learning techniques to recover filtered velocities. The defiltering model is trained in a minimal turbulent channel flow and evaluated in a larger test domain, demonstrating excellent improvements in several particle statistics. Furthermore, the model successfully reconstructs statistics in curved turbulent channels even though it is trained in a minimal plane channel. However, the results also suggest that careful attention should be paid to the similarity of the model input for robust estimation. This talk highlights the remarkable performance, robustness, and limitations of the proposed defiltering method.

Speaker: Mr. Tianyang Fang (Graduate student, The University of Tokyo)

Abstract: Pulsed V-shaped fins are promising enhancement structures for compact heat exchangers, but their performance is limited by the trade-off between heat transfer and pressure loss. This study combined numerical simulations and Bayesian optimization to optimize pulsed V-shaped fin geometries under laminar flow conditions. Finite-volume simulations were performed in OpenFOAM at Reynolds number of 875 to evaluate the effects of wave height and oblique angle on secondary flow, heat transfer, and flow resistance. Larger wave heights and oblique angles enhanced boundary-layer disruption and heat transfer, but also increased pressure loss through stronger flow separation. Compared with conventional sinusoidal V-shaped protrusions, pulsed fins improved low-performance valley regions by promoting earlier flow reattachment. A periodic simulation framework was validated against theoretical solutions, reducing the mesh requirement to 1.6% of the full-domain model while maintaining trend between heat transfer performance and geometry. Bayesian optimization identified an optimal geometry near A/H = 0.1 and γ = 100°, balancing heat-transfer enhancement and friction penalties. The proposed framework enables efficient design of high-performance pulsed fin heat exchangers.