Previous Talks

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.

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: 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. 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. Yelyn Ahn (Postdoctoral Research Associate, Seoul National University)

Abstract: Smoothed Particle Hydrodynamics (SPH) has emerged as a powerful mesh-free method for simulating complex multi-physics phenomena, including severe accident scenarios in nuclear power plants. However, the inherently high computational cost of SPH simulations has limited its practical application. To overcome this limitation, we have developed a dynamic load balancing algorithm for the multi-GPU parallelization of SPH simulations. This approach effectively distributes computational workloads across GPUs, addressing challenges from dynamically evolving, non-uniform particle distributions. In this work, we present the development and application of one-dimensional and two-dimensional dynamic load balancing techniques and demonstrate their effectiveness through large-scale simulations relevant to nuclear severe accident conditions, such as IVR-ERVC and corium spreading scenario. The proposed methods significantly improve computational efficiency and scalability, enabling high-resolution simulations within practical time frames. This advancement provides a crucial step toward realistic modeling of accident progression and mitigation strategies in nuclear safety research.

Speaker: Dr. Ming Liu (Project Research Associate, The University of Tokyo) [GS]

Abstract: Turbulent simulations with wall models are commonly used approaches to produce high fidelity flow fields with acceptable computational cost. However, most existing wall models are built under certain assumptions, which can affect their adaptivity to practical turbulent flows. To this end, we develop a novel wall model based on a deep neural network, namely, discriminator, which can discriminate instantaneous under-resolved and well-resolved flow fields. The fully developed velocity fields from direct numerical simulations (DNSs) on fine and coarse grids are performed and then adopted as the datasets to train the discriminator. Then, the well-trained discriminator is implemented into DNSs on coarse grids as a wall model. This dynamically updating the instantaneous velocity fields so as to make them indistinguishable from well-resolved ones through body force. The turbulent flow under bulk Reynolds number of 4600-40000 are investigated. As the discriminator-based wall model is introduced, the predicted wall shear stress, mean and rms velocity profiles are significantly improved compared with DNSs on coarse grids without a wall model.

Speaker: Dr. Ryo Araki (Assistant Professor, Tokyo University of Science)

Abstract: The small-scale universality of developed turbulence is often described as the small scales "forgetting" the macroscopic flow characteristics during the scale-local energy cascade. However, turbulence is inherently causal; for example, temporal fluctuations of small-scale quantities (such as the energy dissipation rate) exhibit a time-delayed correlation with large-scale quantities (such as the energy input rate). To reconcile this apparent paradox, we analysed high-Reynolds-number homogeneous and isotropic turbulence using information flux, a measure of how knowledge of a variable's current state reduces uncertainty about the future state of another variable in a dynamical system. Our analysis revealed a scale-local forward information transfer within the inertial range, accompanying the energy cascade. Furthermore, we examined the roles of different cascade mechanisms - vortex stretching (VS) and strain self-amplification (SSA) - in both energy and information transfer. Our findings indicate that these two transfers are governed by different mechanisms: the dominant energy cascade mechanism is not necessarily the most causal one, and vice versa.

Speaker: Mr. Jungjae Woo (Ph.D. student, Korea University)

Abstract: The integration of microbubble technology into fluid systems has opened new avenues for efficient and eco-friendly cleaning applications. This talk will present recent advancements in utilizing microbubble jets to enhance oil removal and energy efficiency in various cleaning applications. Microbubbles, characterized by their unique hydrodynamic properties and prolonged stability, have been shown to improve the removal of oil contaminants through enhanced jet instability, surface interactions, and turbulence intensification. To better understand the underlying mechanisms, the hydrodynamic characteristics of microbubble jets were analyzed, focusing on their influence on jet instability, velocity fields, and breakup dynamics. By incorporating microbubbles into conventional water jets, cleaning efficiency can be increased while reducing the reliance on chemical detergents and excessive water usage. These findings highlight the role of microbubble-driven jet instabilities in modifying flow behavior and their potential in developing next-generation eco-friendly cleaning technologies with maximized performance and minimized energy consumption.

Speaker: Dr. Mario Rüttgers (Researcher, Walter Benjamin Fellow, Inha University) [GS]

Abstract: With rising concerns regarding global warming and energy security, there is an increasing demand for renewable energy sources. Recently, meteorology-dependent renewable urban energy resources, i.e., urban wind turbines or solar energy devices, play a more and more important role in helping cities in shifting to an energy self-sufficient or energy positive status. This talk presents a tool that is developed for optimizing the utilization of urban wind turbines. The tool is realized with an optimization algorithm that combines and locates horizontal and vertical turbines in urban planning scenarios, while receiving feedback from urban flow predictions combined with the turbines’ power curves. The flow predictions are done by a graph convolutional neural network (GCNN) that is trained with data from computational fluid dynamics (CFD) simulations of randomly defined urban flows. The GCNN is fed with two types of inputs, i.e., information about the topology of the urban area, and wind conditions at the boundaries. The network is tested with cutouts of real cities and boundary conditions from publicly available meteorological data. The tool assists city planners in finding the perfect number and locations for urban wind turbines.

Speaker: Mr. Jiyeon Kim (Ph.D. student, Yonsei University) [GS]

Abstract: Recent advances in deep learning (DL) have highlighted the potential of generative models, which learn unknown data distributions to generate new samples from noise. By incorporating conditional inputs, these models enable various applications, including dynamics prediction, where past fields are used to predict future states. While generative adversarial network (GAN)-based models have dominated the field, challenges such as scalability and training instability persist. Recently, diffusion probabilistic models (DMs) have emerged, offering the robustness of likelihood-based approaches and achieving performance comparable to or surpassing GANs. However, their computational cost is significantly higher, often orders of magnitude greater than GANs with similar performance. This talk presents the application of DM to 2D turbulence prediction, along with a comprehensive performance evaluation against various DL models, including a conditional GAN. Our findings show that DMs outperform others at lead times shorter than the Eulerian integral time scale but experience significant performance degradation at longer lead times. Ongoing efforts to extend DMs to 3D turbulence prediction will also be discussed.

Speaker: Dr. Taeseok Kim (Assistant Professor, Jeju National University) [GS]

Abstract: The trend toward downsizing nuclear reactors is gaining significant traction due to enhanced safety and operational flexibility. The thermal components of reactors must also be reduced in size to meet compact design requirements. Among these components, the steam generator plays a critical role in nuclear reactor systems. Current small modular reactor (SMR) designs predominantly employ once-through helical steam generators; however, this approach faces limitations when adapting SMRs for maritime applications or low-power systems. The printed circuit heat exchanger (PCHE) has emerged as a promising alternative for steam generator applications due to its compact and efficient design. However, the characteristics of two-phase flow and boiling heat transfer in mini-channels, a key feature of PCHEs, remains insufficiently understood for direct application. In mini-channels, pressure drop and heat transfer coefficients significantly differ from those observed in conventional steam generator pipes. Two-phase flow parameters such as void fraction, flow regimes, and channel geometry can strongly influence these parameters. We are conducting research on the effects of PCHE design on two-phase flow pressure drops, heat transfer, and superheated steam generation.

Speaker: Mr. Yuta Iwatani (Ph.D. student, Tohoku University) [GS]

Abstract: Wall temperature can affect the dynamics of compressible flows through the dynamic viscosity and the nonlinear coupling of kinetic and internal energy, and the laminar-to-turbulent transition of the boundary layer (BL) is no exception. In this study, we investigate the effects of wall heat fluxes on the subharmonic transition of the boundary layer at Mach number 0.8 using direct numerical simulation (DNS), ultimately aiming to achieve drag reduction by controlling the BL in aircraft and other fluid machinery with wall temperature. The DNS results show that wall heating promotes the transition while cooling delays it. Notably, wall cooling impedes the growth of the two-dimensional linearly unstable mode (Mack’s first mode), while the subharmonic secondary instability of the first mode emerges, leading to flow behavior similar to oblique transition, distinct from the H-type transition observed in the heated and adiabatic cases. This shift in the predominant scenario, or the dominant modes, alters the nonlinear mode interactions in the transitional BL. We examine these nonlinear interactions of modes in the transitional BL using bispectral mode decomposition (BMD) and discuss the connection of the nonlinear mode interactions to the skin friction coefficient with the aid of the angular momentum integral analysis.

Speaker: Dr. Chungil Lee (Postdoctoral Research Associate, Tohoku University) [GS]

Abstract: Supersonic jets generated from the engine of the rocket and supersonic aircraft emit very strong noise. Screech tones are dominant noise source of jets and can cause structural fatigue in rockets and supersonic aircraft. Therefore, a precise understanding of screech dynamics is essential for both predicting and reducing screech tones. While many studies have been conducted to understand screech dynamics, the 3D unsteady dynamics of screech tones have not been experimentally reported due to the low temporal resolution of high-speed cameras. In the present work, we develop a 3D spatiotemporal super-resolution measurement technique to reconstruct time-resolved (TR) 3D flow fields from sensor data*. This approach simultaneously conducted the non-time-resolved 3D background oriented schlieren (3D-BOS) and TR microphone measurements. A linear regression model between 3D-BOS and microphone data is constructed to estimate TR 3D flow fields associated with screech tones from the microphone data. Using the proposed method, the intermittent events and azimuthal switching of flow structures associated with screech tones can be analyzed. (*Reference: Lee et al., Phys. Fluids, 2023)

Speaker: Dr. Shuji Otomo (Assistant Professor, Tokyo University of Agriculture and Technology) [GS]

Abstract: Accurate, non-intrusive force measurement remains challenging in many scenarios, particularly those involving animals or vehicles. Estimating forces from flowfields obtained via particle image velocimetry (PIV) has shown promise but remains highly complex. This talk presents the vortex force map (VFM) method as a solution for computing unsteady forces from PIV data. Beyond force computation, the VFM method also visualises the contribution of individual vortical structures to the overall force. The VFM method is applied to three kinematic cases: surging flat plates and pitching NACA 0018 aerofoils, at Reynolds numbers on the order of 10,000. These flows are characterised by massive separation, with coherent leading-edge and trailing-edge vortex shedding. In all cases, the VFM method demonstrates strong agreement with direct force measurements. Additionally, it proves robust to noise, a critical consideration in experimental fluid mechanics*. (* Reference: Otomo et al., Exp. Fluids, 2025, accepted)