JK-FLOW Japan-Korea Fluid Mechanics Online Workshop

Speaker: Dr. Misa Ishimura (Assistant Professor, Yokohama National University)

Abstract: In a falling liquid film where surface waves induced by Kapitsa instability exist, it is known that heat/mass transfer are enhanced when a counter-current turbulent gas flow is applied, but on the other hand, the risk of flooding increases. We investigate the mechanism of flooding through experiments, 2D modeling, and linear stability analysis (LSA). One of the potential causes of flooding is absolute instability (AI). Using open-domain calculations with 2D model, we investigated the effects of AI, and we found that because the linear spatial growth rate of AI is unbounded, the absolute frequency is selected near the liquid inlet, and highly regular nonlinear surface waves are generated without causing flooding. In experimental studies, we reproduced a type of flooding called ripples, which are upward waves with wavelengths much shorter than typical downward long-waves (LW). Based on these experiments, we performed temporal LSA and identified three different instability modes: LW, new short-wave (SW) and new merged mode. In particular, the latter two instability modes showed negative velocities, suggesting that the ripples observed in the experiment were caused by SW mode.

Speaker: Dr. Ryungeun Song (Assistant Professor, Chungbuk National University)

Abstract: Electrohydrodynamic (EHD) jetting is a versatile technique for producing fine fibers or micro/nano-sized droplets, regardless of ink properties. The formation of a stable cone-jet, driven by the interaction between interfacial tension and electric forces, is central to its success. However, achieving this regime is challenging, as it requires measurement of various fluid properties, including viscosity, conductivity, permittivity, and surface tension. To address these challenges, we performed simulations based on the leaky-dielectric model to analyze cone-jet formation under various conditions. Our study reveals that cone-jet morphology is governed by key non-dimensional parameters, such as the Ohnesorge number (Oh), Weber number (We), electric capillary number (CaE), and relaxation parameter (α). This understanding allows us to predict how jet shapes respond to changes in these parameters and helps guide optimization toward a desired cone-jet regime. These findings support the development of a data-driven optimization system, such as Bayesian optimization. The simulation results enable automatic tuning of operating conditions based on observed jet shapes, even when the ink properties are unknown. This approach provides a foundation for more efficient and adaptive EHD jetting systems.

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.