JK-FLOW Japan-Korea Fluid Mechanics Online Workshop

Speaker: Dr. Timothée Mouterde (Lecturer, The University of Tokyo) [GS]

Abstract: Droplets coated with hydrophobic particles, known as liquid marbles, exhibit ultralow friction as an air layer separates liquid from solid. This enables manipulation of small liquid volumes without losses, with applications in biomedical analysis, digital microfluidics, and chemistry. Yet, their capacity to carry hot liquids remains unexplored. This research examines the stability and static friction of hot liquid marbles placed on cooler substrates. We show that on hydrophilic surfaces, temperature differences cause rupture due to condensation bridging the core liquid with the substrate, while on hydrophobic surfaces, bridging increases static friction, shifting its nature from solid to liquid. Our model provides strategies to prevent rupture and friction, with larger particles, lower liquid volatility, or superhydrophobic substrates, broadening liquid marbles’ potential.

Speaker: Mr. Heesoo Shin (Ph.D. student, POSTECH) [GS]

Abstract: Predicting drag from surface roughness is a critical but costly challenge in fluid dynamics. My previous work (Shin et al., Phys. Fluids, 2024) utilized a Convolutional Neural Network (CNN) to predict the roughness function, ΔU+(= drag induced by rough surfaces), directly from raw surface topography, bypassing traditional parameterization. Critically, the model's feature maps revealed it had learned drag-inducing physics without any flow-field data, focusing on high elements and positive slopes correlated with pressure drag, thus resembling DNS drag maps. However, this predictive model's accuracy decreased for negative-skewness surfaces where pressure drag is not dominant, and it could not provide a low-dimensional representation suitable for analysis or generative design. To overcome this, our current work employs a drag-augmented autoencoder to discover a physically meaningful, low-dimensional manifold of these surfaces. By training the model to simultaneously reconstruct the surface and predict drag from its latent space, we force the representation to embed essential drag-relevant features. Initial results confirm this latent space successfully clusters and organizes surfaces by type and drag. Our ultimate goal is to leverage this structured manifold for the inverse design of novel, low-drag surfaces.

Speaker: Dr. Pierluigi Morra (Postdoc Research Associate, Johns Hopkins University) [GS]

Abstract: The performance of hypersonic vehicles is sensitive to environmental disturbances, especially in transitional flow. Accurate and efficient prediction of the flow state from limited sensors is critical in both fundamental studies and applications. Recent work has shown that assimilating scarce data into direct numerical simulations (Buchta et al., JFM, 947, R2, 2022) can reconstruct full flow fields, but at high computational cost. The computational burden of high fidelity simulations hinders broad adoption, particularly for large experimental campaigns or practical use. Here, we introduce a deep-learning approach that accelerates assimilation by two orders of magnitude in terms of experiments processed per unit time. We minimize simulations by optimally sampling the solution space, use a deep operator network (DeepONet) as a proxy for the compressible Navier–Stokes equations, and apply a gradient-free search to efficiently identify optimal solutions. The method is demonstrated on the assimilation of wind-tunnel measurements in Mach 6 boundary-layer flow over a 7-degree half-angle cone.

Speaker: Dr. Yutaro Motoori (Assistant Professor, The University of Osaka) [GS]

Abstract: It is well known that vortices of various sizes coexist in turbulence. However, when we visualize vortices using vorticity or the second invariant of the velocity gradient tensor, only the smallest-scale vortices are prominent. To identify vortices at arbitrary scales, it is therefore necessary to decompose turbulence into different scales. As shown in the visualization, the scale decomposition reveals that various-size vortices form hierarchical structures. In the present study, we conduct direct numerical simulations of wall turbulence, such as turbulent boundary layers and channel flows, to examine the hierarchy of coherent vortices. Based on the hierarchy of vortices, we discuss the sustaining mechanism of turbulent boundary layers and channel flows, and clarify both the universality and dissimilarity between these two turbulent flows.

Speaker: Mr. Jihoon Kim (Ph.D. student, Korea University) [GS]

Author list: Jihoon Kim[1], Jeonglae Kim[2], Jaiyoung Ryu[1]

1. Department of Mechanical Engineering, Korea University, Seoul, Republic of Korea

2. School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, USA

Abstract: Shock wave/turbulent boundary layer interactions (SWTBLI) in supersonic regimes are critical to the aerodynamic characteristics of high-speed aircraft. Detailed understanding of the interactions facilitates the development of future supersonic and hypersonic vehicles. Direct numerical simulations (DNS) are performed to solve the compressible Navier-Stokes equations for describing SWTBLI over a 24° compression ramp at a freestream Mach number of 2.9. Fully developed turbulent flows are imposed at the inflow using a recycling-rescaling method with a recycling distance of 10δ_in. The reference station is selected based on the distance from the compression corner, which is 7δ_in. Taylor microscales and Kolmogorov lengths, and Reynolds numbers are evaluated at the reference station for three different boundary-layer thicknesses. Simulation results are validated for mean velocity, density-scaled root-mean-square velocity fluctuations, and two-point correlations. From the turbulent kinetic energy budget, the mechanism of turbulence amplification influenced by the boundary-layer thickness is discussed.

Speaker: Mr. Soju Maejima (Ph.D. student, Tohoku University) [GS]

Abstract: The use of very coarse computational grids for large-eddy simulations (LES) causes the resolved turbulence to significantly deviate from the physically accurate turbulence. This deviation inhibits the training for a machine-learning-based sub-grid scale (SGS) model, where supervised training with the filtered direct numerical simulation (fDNS) solution as the proxy for the LES solution is often employed. This study proposes the unsupervised-supervised machine-learning pipeline as an SGS model for very-coarse LES (vLES). The key part of the pipeline is the unsupervised CycleGAN, which enables the super-resolution of the nonphysical vLES flowfields. The predicted high-wavenumber components are then extracted as the SGS stresses. The a posteriori test using the turbulent channel flow shows that the proposed method results in the accurate prediction of the near-wall Reynolds shear stress and the resulting mean velocity profile. The budget analyses of the Reynolds stresses reveal that the proposed SGS model predicts significant SGS backscatter in the spanwise normal stress component in the near-wall region, and that it is crucial for the accurate prediction of the mean velocity.