Fluid Dynamics
arXiv:physics.flu-dyn
Turbulence, instabilities, control of fluid flows, computational fluid dynamics.
Trending in Fluid Dynamics
Can Transformers overcome the lack of data in the simulation of history-dependent flows?
It is well known that the lack of information about certain variables necessary for the description of a dynamical system leads to the introduction of historical dependence (lack of Markovian character of the model) and noise. Traditionally, scientists have made up for these shortcomings by designing phenomenological variables that take into account this historical dependence (typically, conformational tensors in fluids). Often, these phenomenological variables are not easily measurable experimentally. In this work, we study to what extent Transformer architectures are able to cope with the lack of experimental data on these variables. The methodology is evaluated on three benchmark problems: a cylinder flow with no history dependence, a viscoelastic Couette flow modeled via the Oldroyd-B formalism, and a non-linear polymeric fluid described by the FENE model. Our results show that the Transformer outperforms a thermodynamically consistent, structure-preserving neural network with metriplectic bias in systems with missing experimental data, providing lower errors even in low-dimensional latent spaces. In contrast, for systems whose state variables can be fully known, the metriplectic model achieves superior performance.
2512.163051
Dec 2025Fluid Dynamics
Particle Image Velocimetry Refinement via Consensus ADMM
Particle Image Velocimetry (PIV) is an imaging technique in experimental fluid dynamics that quantifies flow fields around bluff bodies by analyzing the displacement of neutrally buoyant tracer particles immersed in the fluid. Traditional PIV approaches typically depend on tuning parameters specific to the imaging setup, making the performance sensitive to variations in illumination, flow conditions, and seeding density. On the other hand, even state-of-the-art machine learning methods for flow quantification are fragile outside their training set. In our experiments, we observed that flow quantification would improve if different tunings (or algorithms) were applied to different regions of the same image pair. In this work, we parallelize the instantaneous flow quantification with multiple algorithms and adopt a consensus framework based on the alternating direction method of multipliers, seamlessly incorporating priors such as smoothness and incompressibility. We perform several numerical experiments to demonstrate the benefits of this approach. For instance, we achieve a decrease in end-point-error of up to 20% of a dense-inverse-search estimator at an inference rate of 60Hz, and we show how this performance boost can be increased further with outlier rejection. Our method is implemented in JAX, effectively exploiting hardware acceleration, and integrated in Flow Gym, enabling (i) reproducible comparisons with the state-of-the-art, (ii) testing different base algorithms, (iii) straightforward deployment for active fluids control applications.
2512.116951
Dec 2025Fluid Dynamics
Lazy Diffusion: Mitigating spectral collapse in generative diffusion-based stable autoregressive emulation of turbulent flows
Turbulent flows posses broadband, power-law spectra in which multiscale interactions couple high-wavenumber fluctuations to large-scale dynamics. Although diffusion-based generative models offer a principled probabilistic forecasting framework, we show that standard DDPMs induce a fundamental \emph{spectral collapse}: a Fourier-space analysis of the forward SDE reveals a closed-form, mode-wise signal-to-noise ratio (SNR) that decays monotonically in wavenumber, $|k|$ for spectra $S(k)\!\propto\!|k|^{-λ}$, rendering high-wavenumber modes indistinguishable from noise and producing an intrinsic spectral bias. We reinterpret the noise schedule as a spectral regularizer and introduce power-law schedules $β(τ)\!\propto\!τ^γ$ that preserve fine-scale structure deeper into diffusion time, along with \emph{Lazy Diffusion}, a one-step distillation method that leverages the learned score geometry to bypass long reverse-time trajectories and prevent high-$k$ degradation. Applied to high-Reynolds-number 2D Kolmogorov turbulence and $1/12^\circ$ Gulf of Mexico ocean reanalysis, these methods resolve spectral collapse, stabilize long-horizon autoregression, and restore physically realistic inertial-range scaling. Together, they show that naïve Gaussian scheduling is structurally incompatible with power-law physics and that physics-aware diffusion processes can yield accurate, efficient, and fully probabilistic surrogates for multiscale dynamical systems.
2512.095722
Dec 2025Fluid Dynamics
Settling dynamics of an oloid: experiments and simulations
This study presents a combined experimental and computational investigation of an oloid shaped particle settling in a quiescent fluid. The oloid, a unique convex shape with anisotropic geometry, provides a distinctive model for exploring how a particle's shape and orientation affect its settling dynamics. The settling oloids are tracked experimentally for Galileo numbers $48 \leq \text{Ga} \leq 5.4 \cdot 10^3$, using two particle sizes ($D_{\text{eq}}$ = 21.6 mm, and $D_{\text{eq}}$ = 10.8 mm). The density ratio between the particle and fluid $Γ$ = $\frac{ρ_p}{ρ_f}$ ranges from $1.11 \leq Γ\leq 1.30$ in the experiments. Computationally, the Galileo numbers $10 \leq \text{Ga} \leq 100$ are simulated, with $Γ= 2$. The experimental findings and numerical results are in good agreement, and give a consistent idea of the oloid settling dynamics. Our results indicate two distinct falling modes for the oloid, separated by Galileo number. The stable mode is characterised by a preferential orientation, with a rotation around the vertical axis, whereas the tumbling mode has randomly distributed orientation and rotation statistics. We characterise the falling velocity, orientation, and rotation dynamics of the oloids over a range of Galileo numbers. Additionally, the influence of the initial orientation is revealed to determine the rotation dynamics at low Galileo numbers.
2511.051371
Nov 2025Fluid Dynamics
Meshless data-driven decompositions with RBF-based inner products
Data-driven modal decompositions are useful tools for compressing data or identifying dominant structures. Popular ones like the dynamic mode decomposition (DMD) and the proper orthogonal decomposition (POD) are defined with continuous inner products. These are usually approximated with samples of data uniform in space and time. However, not every dataset fulfills this requirement. Numerical simulations with smoothed particle hydrodynamics or experiments with Lagrangian particle tracking velocimetry produce scattered data varying in time and space, rendering sample-based inner products impossible. In this work, we extend a previous approach that computes the modal decompositions with meshfree radial basis functions (RBFs). We regress the data and use the continuous representation of the RBFs to compute the required inner products. We choose our basis to be constant in time, greatly reducing the computational cost since the inner product of the data reduces to the inner product of the basis functions. We use this approach in the most popular decompositions, namely the POD, DMD, multi-scale POD, and the two versions of the spectral POD. For all decompositions, the RBFs give a mesh-free representation of the spatial structures. Two test cases are considered: particle image velocimetry measurements of an impinging jet and large eddy simulations of the flow past a transitional airfoil. In both cases, the RBF-based approach outperforms classical binning and better recovers relevant structures across all data densities.
2511.032641
Nov 2025Fluid Dynamics
Geometric Solution of Turbulence as Diffusion in Loop Space
Strongly nonlinear dynamics, from fluid turbulence to quantum chromodynamics, have long constituted some of the most challenging problems in theoretical physics. This review describes a unified theoretical framework, the loop space calculus, which offers an analytical approach to these problems. The central idea is a shift in perspective from pointwise fields to integrated loop observables, a transformation that recasts the governing nonlinear equations into a universal linear diffusion equation in the space of loops. This framework, supported by recent mathematical analysis, is analytically solvable and yields an exact, parameter-free solution for decaying hydrodynamic turbulence--the Euler ensemble--which is shown to be dual to a solvable string theory. The theory's predictions include: (i) the unification of spatial and temporal scaling laws, which are shown to be governed by two related, infinite spectra of intermittency and decay exponents, respectively, both derived from the nontrivial zeros of the Riemann zeta function; (ii) a first-order phase transition in magnetohydrodynamic (MHD) turbulence; and (iii) the formation of quantized, concentric shells in passive scalar mixing. The theory also predicts log-periodic oscillations in correlation functions, effects not described by standard phenomenology, for which there is now emerging evidence from high-precision turbulence experiments. The appearance of identical mathematical structures as solutions to the turbulent regime of Yang-Mills gradient flow points towards the broad applicability of this approach. The framework also yields a new type of minimal surface that solves the QCD loop equations in the limit of large loops.
2511.021651
Nov 2025Fluid Dynamics
Asymmetric behaviour of turbulence in the wake of wind farms caused by the Coriolis force
Large offshore wind farm wakes in shallow atmospheric boundary layers (ABL) exhibit often an asymmetric behaviour when observed through Synthetic-Aperture-Radar or simulated through Large-Eddy Simulations (LES). In previous LES of wind farms in the northern hemisphere, the asymmetry manifests as a streak at the left side of the wake, looking downstream, where the turbulence kinetic energy (TKE) is greater than the surrounding flow. This work aims at clarifying the physical mechanism that leads to the formation of such a phenomenon. Identifying the Coriolis force as one possible source of asymmetry in the resolved physics, we simulate a real wind farm located in the German Bight operating under different ABLs: one representative of the northern hemisphere; one of the southern hemisphere; and three fictitious ABLs where the Coriolis effects on the inflow and wake, i.e. veer and the wake deflecting force, are removed individually or altogether. Our results show that the TKE streak appears on the opposite side of the wake, i.e. the right one, in the southern hemisphere, and it is primarily caused by veer in the incoming flow, a result of the Coriolis force in a marine ABL. The process involves a larger TKE production which originates from a larger vertical shear promoted where the undisturbed veer profile converges towards the wake in the top part of the ABL. We find that the TKE streak improves the farm wake recovery modestly. Finally, we compare the asymmetry modelled by LES with those observed in several on-field measurements, finding striking similarities.
2510.263981
Oct 2025Fluid Dynamics
HybriNet-Hybrid Neural Network-based framework for Multi-Parametric Database Generation, Enhancement, and Forecasting
In this work, we introduce HybriNet an innovative and robust framework capable of enhancing spatial resolution, generating fluid dynamics databases for specific flow parameters, and predicting their temporal evolution. The methodology is based on the development of a reduced-order model (ROM) by integrating high-order singular value decomposition (HOSVD) with machine learning (ML) and deep learning (DL) techniques. The ROM enables the generation of multi-parametric fluid dynamics databases concerning varying flow conditions, increases the spatial resolution, and predicts the behaviour of the fluid dynamics problem in terms of time. This helps to accelerate numerical simulations and generate new data efficiently. The performance of the proposed approach has been validated using a collection of 30 two-dimensional laminar flow simulations over a square cylinder at different Reynolds numbers and angles of attack. The databases reconstructed using the proposed methodology exhibited a relative root mean square error below 2% when compared to ground-truth high-resolution data, demonstrating the robustness, accuracy, and efficiency of the proposed framework.
2510.256251
Oct 2025Fluid Dynamics
Influence of surfactant kinetics on rapid interface creation via microjet impact on liquid pools
We experimentally investigate the influence of surfactant adsorption kinetics on cavity dynamics during the rapid formation of interfaces. For this purpose, we use a submillimeter jet impacting onto a surfactant-laden liquid pool much larger than the jet dimensions. Cavity retraction and closure occur on a submillisecond timescale, posing a stringent test of the ability of surfactants to reduce surface tension dynamically. Our experiments reveal the difference between the effects of sodium dodecyl sulfate (SDS), a surfactant with moderately fast adsorption kinetics, and Surfynol 465, a surfactant with ultrafast adsorption kinetics. For SDS, the collapse pathway is nearly indistinguishable from that of pure water, suggesting negligible dynamic surface tension reduction. In contrast, Surfynol allows the emergence of deeper cavities that persist longer in the liquid pool. The harmonic oscillator model accurately captures the cavity retraction in the deep seal regime. The fitted values of the damping ratios are consistent with the dynamic surface tensions.
2510.254691
Oct 2025Fluid Dynamics
Conditional neural field for spatial dimension reduction of turbulence data: a comparison study
We investigate conditional neural fields (CNFs), mesh-agnostic, coordinate-based decoders conditioned on a low-dimensional latent, for spatial dimensionality reduction of turbulent flows. CNFs are benchmarked against Proper Orthogonal Decomposition and a convolutional autoencoder within a unified encoding-decoding framework and a common evaluation protocol that explicitly separates in-range (interpolative) from out-of-range (strict extrapolative) testing beyond the training horizon, with identical preprocessing, metrics, and fixed splits across all baselines. We examine three conditioning mechanisms: (i) activation-only modulation (often termed FiLM), (ii) low-rank weight and bias modulation (termed FP), and (iii) last-layer inner-product coupling, and introduce a novel domain-decomposed CNF that localizes complexities. Across representative turbulence datasets (WMLES channel inflow, DNS channel inflow, and wall pressure fluctuations over turbulent boundary layers), CNF-FP achieves the lowest training and in-range testing errors, while CNF-FiLM generalizes best for out-of-range scenarios once moderate latent capacity is available. Domain decomposition significantly improves out-of-range accuracy, especially for the more demanding datasets. The study provides a rigorous, physics-aware basis for selecting conditioning, capacity, and domain decomposition when using CNFs for turbulence compression and reconstruction.
2510.251351
Oct 2025Fluid Dynamics
Numerical Insights on Controlled Droplet Formation in a Microfluidic Flow-Focusing Device
In this article, we have developed a computational model to determine the droplet formation regime and its transition in a square microfluidic flow-focusing device that eventually dictate the droplet shape, size, and its formation frequency. We have methodically explored the influences of various physicochemical parameters on the droplet dynamics and flow regime transition, which are essential in the development of new methods for on-demand droplet generation. On the basis of the droplet formation mechanism, we have formulated flow maps for different liquid-liquid systems, and have also proposed a scaling law to predict the droplet length for a wide range of operating condition resulting from the variation of flow rates, and viscosities of the continuous phase as well as the interfacial tension. This work can effectively contribute in providing helpful guidelines on the design and operations of droplet-based flow-focusing microfluidic systems.
2510.2186872
Oct 2025Fluid Dynamics
Formation characteristics of Taylor bubbles in power-law liquids flowing through a microfluidic co-flow device
Formation and dynamics of Taylor bubble in power-law liquids flowing through a circular co-flow microchannel are numerically investigated using coupled level set and volume-of-fluid method. Aqueous solutions of polyacrylamide (PAAm) are used as power-law liquids. Influences of PAAm concentration, gas-liquid velocities, and surface tension on bubble characteristics are explored. Various mechanism of bubble breakup are observed in different concentration of PAAm. Based on the bubble length with respect to the channel diameter, two different flow regimes are identified. Flow pattern maps are constructed based on inlet velocities, and scaling laws are proposed to estimate the bubble length.
2510.1893030
Oct 2025Fluid Dynamics
CFD study on Taylor bubble characteristics in Carreau-Yasuda shear thinning liquids
In the present study, Taylor bubble formation in two-phase gas-non-Newtonian Carreau liquid flowing through a confined co-flow microchannel is investigated. Systematic analysis are carried out to explore the influences of rheological properties, inlet velocities, and surface tension on Taylor bubble length, shape, velocity and liquid film thickness. Aqueous solutions of carboxymethyl cellulose (CMC) with different mass concentrations are considered as the non-Newtonian liquids to understand the fundamentals of flow behaviour. With increasing solution viscosity and liquid phase inlet velocity, Taylor bubble formation frequency and velocity increased, however, the bubble length was found to decrease. Velocity profiles inside the Taylor bubble and liquid slug were analyzed, and distinct velocity distributions were found for different CMC concentrations. Flow pattern maps are constructed based on inlet velocities for Carreau liquids in co-flow microchannel. This study essentially provides useful guidelines in designing non-Newtonian microfluidic system for precise control and manipulation of Taylor bubbles.
2510.1892812
Oct 2025Fluid Dynamics
Smoothed Dissipative Particle Dynamics for Mesoscale Advection-Diffusion-Reaction Problems
Smoothed dissipative particle dynamics (SDPD) is a widely used particle-based method for modelling soft matter systems at mesoscopic and macroscopic scales, offering thermodynamic consistency and direct control over the fluid's transport properties. Here, we present an SDPD model that incorporates the transport of reactants on scales smaller than the discretising particles, including the evolution of compositional fields. The proposed methodology is well-suited for modelling complex systems governed by advection-diffusion-reaction (ADR) dynamics. Implemented in LAMMPS, the model is validated using a range of benchmark problems spanning diffusion-dominated, reaction-dominated, and coupled ADR regimes. Our simulation results demonstrate that the implemented SDPD model effectively captures complex behaviours, such as Turing pattern formation. The proposed model holds promise for applications across various fields, including biology, chemistry, materials science, and environmental engineering.
2510.184582
Oct 2025Fluid Dynamics
State estimation in homogeneous isotropic turbulence using super-resolution with a 4DVar training algorithm
Variational data assimilation and machine-learning based super-resolution are two alternative approaches to state estimation in turbulent flows. The former is an optimisation problem featuring a time series of coarse observations, the latter usually requires a library of high-resolution 'ground truth' data. We show that the classic '4DVar' data assimilation algorithm can be used to train neural networks for super-resolution in three-dimensional isotropic turbulence without the need for high-resolution reference data. To do this, we adapt a pseudo-spectral version of the fully-differentiable JAX-CFD solver (Kochkov et al, Proc. Nat. Acad. Sci. 118, 2021) to three-dimensional flows and combine it with a convolutional neural network for super-resolution. As a result we are able to include entire trajectories in our loss function which is minimised with gradient-based optimisation to define the neural network weights. We show that the resulting neural networks outperform 4DVar for state estimation at initial time over a wide variety of metrics, though 4DVar leads to more robust predictions towards the end of its assimilation window. We also present a hybrid approach in which the trained neural network output is used to initialise 4DVar. The resulting performance is more than twice as accurate as other state estimation strategies for all times and performs well even beyond known limiting lengthscales, all without requiring access to high-resolution measurements at any point.
2510.169041
Oct 2025Fluid Dynamics
On the effect of airfoil geometry on extreme vortex-gust encounters
Historically, investigations on gust encounters have been limited to thin airfoils. In this work, we examine vortex-gust encounters by a family of airfoils at a chord-based Reynolds number Re_c=100, which includes variations in the gust ratio, initial gust position, gust radius, angle of attack, airfoil thickness, and airfoil camber. We examine differences in the flow fields, lift-element distributions, and aerodynamic responses across several airfoil-gust interactions. We observe a large deviation of the flow fields and aerodynamic responses with respect to the baseline flows for increasing gust ratios and gust sizes. The initial position of the vortex gust influences the magnitude of the velocity gradients observed near the leading edge, effectively heightening or mitigating the amplitude of the lift response. Moreover, the lift fluctuation increases with the angle of attack until it flattens around $10^\circ$, reminiscent of an unsteady stall-like regime. Furthermore, we report a decrease in the amplitude of the gust-induced lift fluctuations for thicker airfoils, which we attribute to a decrease in the vorticity production levels from the leading edge. The exploration of a sensitive subset of the parameter space uncovers relevant trends, shedding light on regions that have received limited attention in past studies, with special focus on the influence of airfoil geometry.
2510.151541
Oct 2025Fluid Dynamics
The significance of two-way coupling in two-dimensional, dusty turbulence
The significance of small-scale forcing of particles on the carrier two-dimensional turbulent flow has been shown [Pandey, Perlekar, and Mitra, Phys. Rev. E, 100, 013114 (2019)] to influence the spectral scaling properties of the carrier fluid. We investigate possible consequences of such two-way coupling in a turbulent suspension of inertial particles in one and two-point Eulerian and Lagrangian statistics. In particular, we find signatures of a possibly enhanced intermittency in the vorticity distributions. We characterize the changes in the (small-scale) geometry of the flow via the Okubo-Weiss parameter. Finally, we examine the scaling properties of the second-order (vorticity) structure functions and find a non-trivial form of scale-invariance at finite mass-loading. This suggests that a dual-scale forcing mechanism on the two-dimensional Navier-Stokes equation may be an effective model to mimic the role of particle feedback in turbulence.
2510.104631
Oct 2025Fluid Dynamics
Enhanced accumulation of bitumen residue in a highly concentrated tailings flow by microbubbles from in-situ catalytic decomposition of hydrogen peroxide
The massive volume of oil sands tailings has been one of the most challenging environmental issues. In this work, we experimentally explore a simple and effective approach to bitumen residue separation from a highly concentrated slurry flow of the artificial oil sands tailings. By utilizing microbubbles from in-situ catalytic decomposition of H2O2 at low concentrations, bitumen aggregation is enhanced on the top part of the hydrotransport pipeline. The microscopic image analysis revealed the in-situ formation of microbubbles and confirmed that magnetic particles present in the slurries contributed to the fast release of the gas products and bubble formation from hydrogen peroxide decomposition. A high-speed camera was applied to capture images of the tailings flow in the pipeline through a transparent view window. A large number of tiny bubbles were identified post to the injection of H2O2 to the slurry flow. More than 70 % bitumen could be recovered from a lab-scale pipeline loop within 30 mins after injection. The bitumen recovery efficiency from the collected froth was quantitatively compared under seven conditions with varied dosages, the concentration of H2O2, and the amount of magnetic solids in the slurries. Our results confirmed that the total dosage of H2O2 is the dominant factor in in-situ microbubble formation for enhanced bitumen aggregation in the flow. Importantly, microbubbles were generated rapidly in the real mature fine tailings. The results from our study provide insights into the preferential distribution of oil residue in the flow during hydrotransport without the requirement for an additional device. Removal of oily residues from concentrated slurries may bring economical and environmental advantages.
2510.159587
Oct 2025Fluid Dynamics
Effects of Coal Particles on Microbubble-Enhanced Bitumen Separation in the Concentrated Slurry Flow of Oil Sands Tailings
Our study investigates the segregation of bitumen residues within the transport pipeline before disposal in the presence of coal particles in carriers and microbubbles. Coal particles decreased the bitumen recovery by 17% without the injection of microbubbles. In addition, the improvement in bitumen recovery efficiency by 6 mL of H2O2 is negligible due to a small number of bubbles formed from H2O2 decomposition in the flow. However, tremendous enhancement in the recovery efficiency was achieved with the simultaneous addition of coal particles and H2O2. Further increase in recovery was noted as a larger volume of H2O2 was injected to form more microbubbles. Computational fluid dynamics (CFD) simulations were conducted to help understand the effects of coal particles and microbubbles. The simulation results illustrated that the introduction of coal particles caused bitumen contents to accumulate in the middle of the pipe. Furthermore, an increased volume fraction of microbubbles contributed to a higher distribution of bitumen at the top of the pipe. This study not only offers valuable insights for developing an innovative strategy to enhance the efficiency of bitumen separation in hydrotransport processes but also contributes to a deeper understanding of the intricate interactions among bubbles, bitumen, and coal particles in a slurry flow.
2510.159574
Oct 2025Fluid Dynamics
Smart navigation of a gravity-driven glider with adjustable centre-of-mass
Artificial gliders are designed to disperse as they settle through a fluid, requiring precise navigation to reach target locations. We show that a compact glider settling in a viscous fluid can navigate by dynamically adjusting its centre-of-mass. Using fully resolved direct numerical simulations (DNS) and reinforcement learning, we find two optimal navigation strategies that allow the glider to reach its target location accurately. These strategies depend sensitively on how the glider interacts with the surrounding fluid. The nature of this interaction changes as the particle Reynolds number Re$_p$ changes. Our results explain how the optimal strategy depends on Re$_p$. At large Re$_p$, the glider learns to tumble rapidly by moving its centre-of-mass as its orientation changes. This generates a large horizontal inertial lift force, which allows the glider to travel far. At small Re$_p$, by contrast, high viscosity hinders tumbling. In this case, the glider learns to adjust its centre-of-mass so that it settles with a steady, inclined orientation that results in a horizontal viscous force. The horizontal range is much smaller than for large Re$_p$, because this viscous force is much smaller than the inertial lift force at large Re$_p$. *These authors contributed equally.
2510.092501
Oct 2025Fluid Dynamics
