List of Minisymposia
Each Minisymposium is expected to consist of at least one 2-hour session (6 presentations of 20 minutes
each). The number of sessions of each MS will be determined by the multiples of six papers submitted.
In each MS a Keynote lecture is allowed every two full sessions of the MS, where a keynote presentation normally comprises two presentation slots. This means you should have at least 10 confirmed presentations to schedule a KL.
The above scheduling includes time for questions and discussion.
Among other aspects related to computational fluid mechanics
Keywords:
Adaptive Mesh Refinement, Finite Difference, Finite Volume, High-Order, Lattice Boltzmann, Multiphysics, Computational Fluid Dynamics, GPU
Keywords:
Biological Swimming, Biological Flying, Computational Biomimetics, Computational Biomechanics
Keywords:
Computational Fluid Dynamics, Design optimization, Machine learning, Quantum annealing, Quantum computing, Quantum-inspired methods, Tensor network
Keywords:
Environmental and Health Applications, Euler-Lagrange, Particle-Laden Flows
Keywords:
Computational Fluid Dynamics, sports
Keywords:
Adaptive Mesh Refinement, computational fluid dynamics, finite element methods, Fluid Dynamics., High Performance Computing, Moving Boundaries, Shape Optimization
Keywords:
AI, Biological Flows, Mixed-dimensional Problems
Keywords:
AI, Computational Fluid Dynamics, High-Performance Computing, Industrial Flows
Keywords:
Computational Fluid Dynamics, Coupled Problems, Surrogate Modeling
Keywords:
Computational Fluid Dynamics, Computer Vision, Sensing Technologies
Keywords:
Multiphase Flow, Multiphysics, PDE models, Turbulent Flow
Compressible and incompressible flows
Keywords:
Coupled Problems, Error Control and Adaptivity, Multilevel Iterative Solvers, Navier-Stokes Equations, Space-Time Finite Element Methods
Keywords:
Stabilized Methods, computational fluid dynamics, Variational Multiscale (VMS) Method
Emerging techniques coming from genetic algorithms and artificial intelligence
Keywords:
Reduced-Order modelling, Scientific Machine Learning, Digital Twins
Keywords:
AI, CFD, data driven, Physics informed neural network
Keywords:
AI, Fluid Dynamics.
, Numerical Analysis, Scientific Computing, Machine Learning
Keywords:
AI, data driven, Machine Learning, Scientific Machine Learning;
Keywords:
AI, CFD, Numerical Modeling, Surrogate Modeling
Keywords:
data-driven modeling, scientific machine learning, computational fluid dynamics, Model reduction
Keywords:
Computational Fluid Dynamics, Model-Constrained Learning, Scientific Machine Learning, Artificial Intelligence, Discontinuous Galerkin, finite difference, finite element, finite volume
Machine learning (ML) and quantum computing have each begun to reshape computational fluid dynamics (CFD), promising speed-ups that could open up new application regimes. Yet using ML to tackle “big science” questions that are qualitatively out of reach for traditional simulation—such as uncovering underlying physical laws from limited observations—is still early-stage. Progress in quantum computing for CFD has arguably lagged further, because current hardware limitations have constrained large-scale numerical experimentation, keeping many capability assessments more theoretical and first-principled.
This workshop aims to bring together contributions and discussion on how ML and quantum methods might jointly address these frontier scientific questions, while also showcasing recent advances from each field individually. Tentative themes include:
1) Turbulence closures as inverse problems: addressing non-uniqueness, hidden variables, and multi-regime physics under sparse or biased data. Bayesian/probabilistic closures with physical constraints are a baseline, potentially complemented by quantum-enhanced sampling to explore complex posteriors.
2) Scientific inference for PDEs beyond physics-informed approaches: shifting focus from accelerating known models to inferring the governing physics in unknown regimes, including unknown forcings, constitutive laws, and boundary/initial conditions. Hybrid quantum–classical variational inference for PDE-constrained problems and physics-informed methods provide starting points, with open questions on what comes next.
3) Rare events in fluids (tails, transitions, extremes): using quantum-ML to predict and characterize low-probability but high-impact phenomena (e.g., laminar–turbulent transition, blow-up events, rogue-wave analogs, intermittency) that are poorly sampled in simulations yet can dominate real-world risk and undermine numerical reliability.
4) Multi-physics CFD as a grand inverse problem: treating condensation, phase change, reactive flows, and phase-transition kinetics as inference problems where governing physics is uncertain and validation data is indirect. Key directions include constrained constitutive modeling, posterior exploration in stiff kinetics/phase change, multi-fidelity/multi-scale data assimilation, and regime classification from sparse samples.
Keywords:
big science, machine learning, multi-physics , physics-informed, rare events, turbulence closures, quantum computing
Environmental flows, hydrology, coastal engineering, tsunamis modelling
MS010 – Computational Methods for Water Environmental Problems and Coastal/Flood Disaster Mitigation
Keywords:
Environmental Flow, Fluid-Structure Interaction, High-performance computing, Hydrological Disaster
Keywords:
Coastal Environment, Flood and Drought, Integrated Modeling, Climate Change
Keywords:
CFD-DEM coupling, fluid-granular interactions, geomechanics, LBM, MPM, multiscale modelling, particle-based methods, SPH
Keywords:
"Complex Flows", Computational Fluid Dynamics, Hydrological Disaster, Turbulent Flows
This minisymposium focuses on the development and application of Reduced Order Models (ROMs) for complex environmental systems, with particular emphasis on oceanic and atmospheric processes (e.g., circulation, waves, and wave-structure interactions). ROMs offer efficient alternatives to high-fidelity simulations by simplifying large-scale models while preserving critical system dynamics, making them highly valuable for real-time prediction and analysis.
Oceanic and atmospheric flows have significant societal impacts, but due to the massive physical scales involved, they pose severe computational challenges for operational forecasting. Machine learning and data-driven models—applying techniques such as dynamic mode decomposition (DMD) and its variants, physics-informed neural networks (PINNs), and generative AI approaches—are proving able to bridge this gap.
The minisymposium invites researchers and practitioners from diverse computational fields to discuss advancements in creating these reduced-order frameworks. Participants are encouraged to contribute to the ongoing dialogue on the advantages, limitations, and future directions of using hybrid or purely data-driven approaches to model complex physical processes that traditionally require prohibitive computational resources.
Keywords:
Coastal Environment, Data-Driven Methods, Environmental Flow, Ocean and Atmospheric Dynamics, Physics-Informed Machine Learning, Reduced-Order modelling
This minisymposium comprises the study of protective structures to mitigate natural hazards caused by environmental flows. These structures include, but are not limited to:
- Breakwaters, seawalls, groynes, etc. for coastal protection against tides, currents, water waves, and storm surges.
- Retaining walls, gabion meshes, and drainage systems to control erosion and prevent landslides and floods.
- Artificial windbreaks to protect soil from erosion and to keep snow from drifting.
The main focus of the minisymposium is on the design of relevant structural components, the simulation of complex fluid flows around or within the components, and the evaluation of the efficacy and the feasibility of the protective measures. Topics range from the reinforcement of existing structures to prevent floods and collapses, the dissipation and the redirection of flows using engineered obstacles and channels, e.g., by modifying their shape, up to procedures that
minimize the risk and the potential damage of catastrophic natural disasters, such as tsunami, earthquakes, and hurricanes.
Advances and novel approaches to obstacle flow problems in computational fluid mechanics, investigations of phenomena at complex interfaces between fluids and solid structures, as well as contributions to the mathematical modeling, treatment, and numerical simulation of physical processes that may lead to hazardous events are within the scope of this minisymposium.
Keywords:
Fluid-Structure Interaction, Natural Hazard Mitigation, Structural Optimization
Fluid-structure interactions
Keywords:
Computational Fluid Dynamics, Fluid-Structure Interaction, Multiphase Flow, Multiphysics
Keywords:
Biological Flows, Dispersed Flows, Capsule/particle/droplet, Ferrofluids
Keywords:
Applications , Fluid-Structure Interaction, High Performance Computing, Multiphysics, Numerical methods
Keywords:
computational fluid dynamics, smoothed particle hydrodynamics, finite element methods, fluid-structure interaction
Keywords:
Energy Harvesting, Fluid Dynamics, Flow-Induced Vibrations
Fluid–poroelastic structure interaction problems arise in many biomedical and engineering applications, including deformable porous materials, tissue perfusion, implantable bioartificial devices, and drug-eluting stent design [1, 2]. In this talk,we present a fully discrete second-order explicit Robin-Robin splitting scheme for the time-dependent Stokes–Biot problem on fixed domains. The method is based on a Robin reformulation of the fluid–poroelastic interface conditions and combines BDF2 time discretization in the Stokes and Biot subproblems with AB2 extrapolation of the interface data. This produces a fully explicit partitioned algorithm in which the fluid and poroelastic subproblems can be solved independently and in parallel at
each time step [4].
We also establish a discrete stability estimate for the fully discrete scheme using BDF2 energy identities, a decomposition of the extrapolated interface terms, and trace inequalities. The resulting stability bound is closed under a parabolic CFL condition linking the time step and mesh size. Besides, we develop an a priori error analysis in a discrete energy framework using a Fortin projection for the fluid variables and Ritz-type projections for the poroelastic variables. Under suitable regularity assumptions and second-order accurate initialization, we prove convergence in the natural energy norms, with total errors in fluid velocity, structure velocity, pore pressure, and elastic displacement bounded by 𝐶(ℎ𝑘 + Δ𝑡2). Numerical experiments based on manufactured solutions confirm the predicted convergence. This work extends earlier explicit splitting analysis for fluid–poroelastic interaction [3] and provides a justified,
second-order, parallelizable method for Stokes–Biot problem.
Keywords:
Fluid structure interaction, Numerical Analysis, Numerical methods
Free surfaces and moving interfaces
Keywords:
Adaptive mesh refinement, High-performance computing, Interface capturing, Surfactant transport, Multiphase flow, Phase Change
Keywords:
Data Assimilation, Granular and Multiphase Systems, High‑Performance Computing, Numerical Analysis Method, Numerical Modeling, Surrogate Modeling
Keywords:
Computation, Elastic Media, Fluids, Math, Waves
Keywords:
computational fluid dynamics, free surface flow, Numerical methods, Scientific Machine Learning
Keywords:
AI/ML, manufacturing flows, moving boundaries, solidification, Free surface flows, non-Newtonian
High performance computing applied to CFC
Keywords:
High-order Matrix-free Methods, hp-Multigrid, Mixed-Precision Arithmetic, Spectral Element Method, Extreme-scale CFD
Keywords:
GPU Computing, High-Performance Computing, In-Situ Visualization, Large-scale Applications, Performance Portability, Scientific Visualization, Computational Fluid Dynamics
Industrial applications: food industry, mining, cooper production, steel refinement, ventilation and refrigeration, energy generation
Keywords:
High-Fidelity, Multiphysics, Appplications, Computational Fluid Dynamics
Keywords:
CFD, Complex Flow Field, Modeling, Optimization, Turbomachinery
Laminar and turbulent flows
Keywords:
LES/RANS Modeling, Wall-Bounded Flows, "Complex Flows", Turbulence
Magnetohydrodynamic
Keywords:
Multiphase Flows, Multiphysics, Plasma, Turbulent Flows
Mathematical aspects of the formulation, modelling and simulation
Keywords:
" DSP cod method", " IEM technology ", "RK-IEM cod ", Digital twin "
Keywords:
high order derivatives, hyperbolic approximations
, PDE models, Fluid Dynamics
Nanofluidics and microfluidics
Keywords:
Experimental validation, Lab-on-a-chip, Microfluidics, Numerical methods, Organ-on-a-chip
Keywords:
Multiphase Flow, Porous Media Flow, Transport, Microscale Flow
Newtonian and No-Newtonian fluids
Keywords:
adaptive methods, finite element methods, learned rheology, multiscale methods, nonlinear solvers, viscoelasticity, viscoplasticity, Non-Newtonian flows, preconditioning
Numerical techniques: finite element, finite volume, finite differences, spectral and particle methods among others
Keywords:
Domain-preserving schemes, Structure-preserving methods, Compressible fluid flows
Keywords:
Moving Boundaries, Shape Optimization, Model Reduction, Non-Newtonian Fluids
Keywords:
Euler-Euler, Euler-Lagrange, Interface tracking, Numerical methods, Multi-phase, Non-Newtonian Fluids, Particles
Keywords:
High-Performance Computing, Machine Learning, Mesh Adaptivity, Shock Capturing, High-Order Methods
Keywords:
Compressible Flow, High Performance Computing, Kinetic Approaches, Multiphase Flow, Turbulent Flow, Lattice Boltzmann Method
Keywords:
Mixed-dimensional Problems, Discontinuous Galerkin, Finite Elements, Scientific Machine Learning;, Multiphysics
Keywords:
Computational Fluid Dynamics, Hyper-reduction, Model Reduction, Sampling and Collocation Techniques
Keywords:
Continuum Mechanics, Meshfree method, Multiphysics, Particle method
Keywords:
High-Order, hp-Adaptation, Large-Eddy Simulations, Shock Capturing
Sedimentation, material transportation
Keywords:
Geohazards, Multi-deformation levels
Statistical analyses, dimensional analysis, verification and validation techniques, uncertainties
Keywords:
dimension reduction, high-dimensional PDEs, uncertainty quantification
Keywords:
Engineering, Virtual Reality, Visualization
Thermo-fluid coupled problems
Thermal Energy Storage (TES) systems could play a key role in nuclear power
plants by reducing demand fluctuations and improving capacity factors.
Among available technologies, single-tank sensible heat storage using packed
beds of solid particles with air as the heat transfer fluid was selected as
the most suitable option for nuclear applications and for this study.
Several numerical studies have already been conducted on packed beds; however,
none involved a true CFD--DEM coupling. This work establishes such a coupling
between Ansys Fluent and Ansys Rocky, maintained throughout the entire
simulation. Previous studies used DEM solely to generate particle arrangements,
imported as static geometries into CFD with no coupling preserved at runtime.
Here, both solvers remain actively coupled, enabling more physically consistent
predictions of heat transfer and flow behaviour. The main objective was not
only to establish this methodology, but also to deepen the understanding of
packed beds by investigating the influence of particle characteristics.
The TES unit, based on the cylindrical geometry of
Rahjoo~et~al.~\cite{Rahjoo2023}, was filled with granite spheres chosen for
their availability in South Korea and low cost. Perfect and imperfect spheres
of varying diameters (10--40~mm), as well as heterogeneous fillings, were
investigated. Pressure drop will be considered in future work.
Results were evaluated based on outlet temperature evolution, thermocline
thickness, and agreement with established correlations~\cite{gunn1978}.
Smaller spheres yielded superior heat transfer performance. Non-spherical
particles enhanced heat transfer but increased thermocline thickness,
indicating a trade-off between thermal efficiency and stratification quality.
Heterogeneous fillings induced flow channelling among the largest spheres,
significantly reducing overall TES effectiveness. However, accurate modeling
of such systems remains challenging: reliance on historical correlations
inherently introduces uncertainties, and computational costs imposed meshing
compromises, adding further sources of error.
Keywords:
Fluid Dynamics, Multiphysics