In this study, we examine the mixing performance of thermally induced microfluidic swirlers, which are recently developed micromixers based on mixed thermal convection. In this configuration, a swirling flow motion is induced by the combination of natural convection and a pressure-driven Poiseuille flow. An experimental investigation was carried out on a microfluidic swirler composed of a glass capillary with a square cross-section of 800 $\times$ 800 $\unicode {x03BC}$m$^2$, measuring the three-dimensional flow fields in different operating conditions using the general defocusing particle tracking technique. Furthermore, a thorough numerical analysis was performed to characterise the mixing performance for different Reynolds numbers and microchannel dimensions. Our results show that thermally induced microfluidic swirlers have an optimal range of operation for microchannel with hydraulic diameters between 400 and 1600 $\unicode {x03BC}$m and Reynolds numbers around 1, where they show an increase of mixing efficiency up to 60 % with respect to the case of pure diffusion. The swirl is activated already at moderate temperature differences of 20–30 K, making this approach compatible with most chemical and biomedical applications.

In this study, the method of large-eddy simulation (LES) is applied to investigate the impact of patches of coarsened riverbed sediments on near-bed hydrodynamics and flow resistance. Six simulations are performed with riverbed coverage ratios of coarser particles (Ac/At, where Ac and At are the riverbed area covered by coarsened sediments and the total riverbed area, respectively) ranging from 0 % to 100 %. By ensuring identical crest heights for all particles, the influence of heterogeneous roughness height is eliminated, allowing for an isolated investigation of heterogeneous permeability effects. Results reveal distinct high- and low-flow streaks above coarsened and uncoarsened sediments, associated with elevated and reduced Reynolds shear stress, respectively. These streaky patterns are attributed to time-averaged secondary flows spanning the entire water depth, that converge toward coarsened sediments and diverge from uncoarsened areas. Elevated Reynolds shear stress, up to 1.9 times the reach-averaged bed shear stress, is observed in the interstitial spaces between coarser particles due to intensified hyporheic exchange at the sediment–water interface. Upwelling and downwelling flows occur upstream and downstream of coarsened sediments particles, respectively, driving dominant ejection and sweep events. At Ac/At = 16 %, ejections and sweeps contribute maximally to Reynold shear stress, increasing by up to 130 % and 110 %, respectively – approximately double their contributions in the uncoarsened case. The study identifies two mechanisms driving increased flow resistance over coarsened riverbeds: water-depth-scale secondary flows and grain-scale hyporheic exchanges. Consequently, the reach-averaged friction factor increases by 29.8 % from Ac/At = 0 % to 64 %, followed by a 15.8 % reduction in the fully coarsened scenario.

This article presents an experimentally validated computational fluid dynamics (CFD) model for localising and quantifying cavitation erosion in oil hydraulic valves using large eddy simulation (LES) turbulence modelling and the cavitation erosion indices by Nohmi. Cavitation erosion, a significant factor limiting the lifespan and performance of hydraulic valves and pumps, is challenging to simulate accurately due to factors like vapour-gas cavitation separation, cavitation model parameterisation for mineral oil and accounting for the influence of air. A test rig is shown that enables an adjustable air content, the separation of gas and vapour cavitation and optical access to the cavitating valve flow. The visualisation data from this rig was used to parametrise and validate the Zwart–Gerber–Belamri vapour cavitation model for mineral oil and to include the effect of free air, achieving excellent results. The model is used to quantify cavitation erosion load, with the cavitation indices accurately reflecting erosion location, shape and intensity as well as the damping effect of air. The simulation method is suitable for industrial use to reduce cavitation erosion in hydraulic components by optimising the flow path.

In this study, a novel smart surface-morphing technique is devised that dynamically optimises roughness parameter on a sphere with varying flow conditions to minimise drag. A comprehensive series of experiments are first performed to systematically study the effect of dimple depth ratios in the range of 0 ≤ k/d ≤ 2 × 10−2 across a Reynolds number range of 6 × 104 ≤ Re ≤ 1.3 × 105. It is observed that k/d significantly affects both the onset of the drag crisis and the minimum achievable drag. For a constant Re, drag monotonically reduces as k/d increases. However, there is a critical threshold beyond which drag starts to increase. Particle image velocimetry (PIV) reveals a delay in flow separation on the sphere’s surface with increasing k/d, causing the flow separation angle to shift downstream. This results in a smaller wake size and reduced drag. However, when k/d exceeds the critical threshold, flow separation moves upstream, causing an increase in drag. Using the experimental data, a predictive model is developed relating optimal k/d to Re for minimising drag. This control model is then implemented to demonstrate closed-loop drag control of a sphere. The results demonstrate up to a 50 % reduction in drag compared with a smooth sphere, across all Reynolds numbers tested.

Fish swimming together in schools interact via multiple sensory pathways, including vision, acoustics and hydrodynamics, to coordinate their movements. Disentangling the specific role of each sensory pathway is an open and important question. Here, we propose an information-theoretic approach to dissect interactions between swimming fish based on their movement and the flow velocity at selected measurement points in the environment. We test the approach in a controlled mechanical system constituted by an actively pitching airfoil and a compliant flag that simulates the behaviour of two fish swimming in line. The system consists of two distinct types of interactions – hydrodynamic and electromechanical. By using transfer entropy of the measured time series, we unveil a strong causal influence of the airfoil pitching on the flag undulation with an accurate estimate of the time delay between the two. By conditioning the computation on the flow-speed information, recorded by laser Doppler velocimetry, we discover a significant reduction in transfer entropy, correctly implying the presence of a hydrodynamic pathway of interaction. Similarly, the electromechanical pathway of interaction is identified accurately when present. The study supports the potential use of information-theoretic methods to decipher the existence of different pathways of interaction between schooling fish.

As a novel type of catalytic Janus micromotor (JM), a double-bubble-powered Janus micromotor has a distinct propulsion mechanism that is closely associated with the bubble coalescence in viscous liquids and corresponding flow physics. Based on high-speed camera and microscopic observation, we provide the first experimental results of the coalescence of two microbubbles near a JM. By performing experiments with a wide range of Ohnesorge numbers, we identify a universal scaling law of bubble coalescence, which shows a cross-over at dimensionless time $\tilde{t}$ = 1 from an inertially limited viscous regime with linear scaling to an inertial regime with 1/2 scaling. Due to the confinement from the nearby solid JM, we observe asymmetric neck growth and find the combined effect of the surface tension and viscosity. The bubble coalescence and detachment can result in a high propulsion speed of ∼0.25 m s−1 for the JM. We further characterise two contributions to the JM’s displacement propelled by the coalescing bubble: the counteraction from the liquid due to bubble deformation and the momentum transfer during bubble detachment. Our findings provide a better understanding of the flow dynamics and transport mechanism in micro- and nano-scale devices like the swimming microrobot and bubble-powered microrocket.

Recent developments of non-traditional machining techniques, like cavitating waterjet machining (CWJM), have gained attention for their simple operation and environment friendliness with zero carbon footprints. Cavitating waterjet machining leverages the erosive power of cavity bubbles combined with a waterjet to machine or modify a workpiece. For effective CWJM, proper positioning of the workpiece is crucial. The implosion of cavity bubbles generates microjets and shock waves, creating high temperatures and pressures for a few microseconds, impacting the workpiece. This study numerically and analytically investigates the cavitation phenomenon and their effects. Numerical simulation employs an implicit finite volume scheme with the Semi-Implicit Method for Pressure Linked Equations (SIMPLE) algorithm solving Reynolds-averaged Navier–Stokes equations. It also incorporates a discrete phase model (DPM) to analyse bubble distribution and size. An analytical model calculates the hydrodynamic impact load on the workpiece. The study measures hydrodynamic stress and microjet velocities from bubble implosions, using reverse engineering to assess cavitation impact on ductile materials (aluminium and chromium steel). The result reveals a linear relationship between pit deformation and hydrodynamic impact, with impacts ranging from 200 to 1000 MPa, and microjet velocities between 100 and 800 m s−1. Finally, this work accurately predicts the standoff distance and cavitation intensity in the downstream of flow domain.

Properties and functions of materials assembled from nanofibrils critically depend on alignment. A material with aligned nanofibrils is typically stiffer compared with a material with a less anisotropic orientation distribution. In this work, we investigate nanofibril alignment during flow focusing, a flow case used for spinning of filaments from nanofibril dispersions. In particular, we combine experimental measurements and simulations of the flow and fibril alignment to demonstrate how a numerical model can be used to investigate how the flow geometry affects and can be used to tailor the nanofibril alignment and filament shape. The confluence angle between sheath flow and core flow, the aspect ratio of the channel and the contractions in the sheath and/or core flow channels are varied. Successful spinning of stiff filaments requires: (i) detachment of the core flow from the top and bottom channel walls and (ii) a high and homogeneous fibril alignment. Somewhat expected, the results show that the confluence angle has a relatively small effect on alignment compared with contractions. Contractions in the sheath flow channels are seen to be beneficial for detachment, and contractions in the core flow channel are found to be an efficient way to increase and homogenise the degree of alignment.

In this work, three-dimensional numerical investigations into flow manipulation have been conducted using the principles of magnetohydrodynamics, followed by the analysis of the phenomenon of species mixing in a Carreau–Yasuda-type fluid. The flow control has been implemented by employing Lorentz forces to guide the conducting fluid along desired routes throughout a compact mixing chamber. The Lorentz forces were generated using electrode arrays placed in a magnetic field. We have demonstrated that different flow patterns can be created by using different electrode configurations with minor variation in the applied electrode potentials. Results show that the mixing performance of the device depends on the electrode configuration and rheology of the fluid – shear thinning, Newtonian or shear thickening. Effects of fluid rheology on different aspects of flow and mixing have been thoroughly investigated.

Many cardiovascular diseases occur due to an abnormal functioning of the heart. A diseased heart leads to severe complications and in some cases death of an individual. The medical community believes that early diagnosis and treatment of heart diseases can be controlled by referring to numerical simulations of image-based heart models. Computational fluid dynamics (CFD) is a commonly used tool for patient-specific simulations in cardiac flows, and it can be equipped to allow a better understanding of flow patterns. In this paper, we review the progress of CFD tools to understand the flow patterns in healthy and dilated cardiomyopathic (DCM) left ventricles (LVs). The formation of an asymmetric vortex in a healthy LV shows an efficient means of blood transport. The vortex pattern changes before any change in the geometry of LVs is noticeable. This flow change can be used as a marker of DCM progression. We can conclude that the vortex dynamics in LVs can be understood using the widely used vortex index, the vortex formation number (VFN). The VFN coupled with data-driven approaches can be used as an early diagnosis tool and leads to improvement in DCM treatment.

Kelvin wakes are fluid motions generated by a moving disturbance at a free surface. We present a machine learning-based framework for inferring the properties of such moving disturbances from the Kelvin-wake patterns. We perform phase-resolved simulations to establish a dataset of nearly half a million Kelvin wakes generated by disturbances of varying propagating speed, length scale and geometry. Trained with the augmented data, the neural network achieves accuracies of 99.7% and 92.4% in predicting the velocity and the length scale of the disturbance, respectively, even if a random noise has been added to the training data. The explainability of the neural network is demonstrated by quantifying the contribution of the input data to the prediction, which shows a strong connection with the diverging and transverse waves. The accuracy of the neural network in predicting the disturbance length scale is sensitive to wave nonlinearity.

In this study, we introduce a method, applied for the first time to manipulate human cells, by leveraging the controlled activation and deactivation of microbubble streaming – previously used for rigid polymer particles. This innovative technique enables automatic detection and non-destructive sorting of target cells within a microchannel, directing them into a collection chamber for further analysis or removal. A major focus was the quantification of shear stress distribution induced by the microbubble streaming, which confirmed the method’s biocompatibility. Even with prolonged exposure, no damage to live cells was observed, reinforcing the safety and viability of using microstreaming. These findings demonstrate the potential of microbubble streaming as a powerful tool for lab-on-a-chip systems and biomedical diagnostics.

Particle diffusometry (PD) is a technique of measuring the diffusion coefficient of a fluid sample by seeding it with tracer particles and observing their motion under a microscope. In microfluidic set-ups, the observed particles are often defocused and their motion is affected by factors such as fluid flow, which leads to high errors for conventional and deep learning-based PD (DPD) algorithms. This work improves the performance of DPD models by updating their architecture, avoiding temporal averaging in the input, and exploring the impact of various choices during training. These models provide state-of-the-art performance for generalised datasets regardless of particle shapes, concentration, flow or image noise and are called DPD-v2. These models provide a mean absolute error of 0.09$\unicode{x03BC}$m2s−1 for Gaussian particles and 0.07$\unicode{x03BC}$m2s−1 for defocused particles, which is 2x–4x lower errors as compared with the two following best methods. The performance of DPD-v2 models increases with crop size and the use of multiple stacks of images. The outputs of the DPD-v2 models were compared against the outputs from conventional algorithms on Gaussianised experimental no flow datasets, which provided < 0.5$\unicode{x03BC}$m2s−1 mean absolute difference. Hence, the DPD-v2 models can be used in real-world scenarios.

Efficient tools for predicting the drag of rough walls in turbulent flows would have a tremendous impact. However, accurate methods for drag prediction rely on experiments or numerical simulations which are costly and time consuming. Data-driven regression methods have the potential to provide a prediction that is accurate and fast. We assess the performance and limitations of linear regression, kernel methods and neural networks for drag prediction using a database of 1000 homogeneous rough surfaces. Model performance is evaluated using the roughness function obtained at a friction Reynolds number $Re_\tau$ of 500. With two trainable parameters, the kernel method can fully account for nonlinear relations between the roughness function $\Delta U^+$ and surface statistics (roughness height, effective slope, skewness, etc.). In contrast, linear regression cannot account for nonlinear correlations and displays large errors and high uncertainty. Multilayer perceptron and convolutional neural networks demonstrate performance on par with the kernel method but have orders of magnitude more trainable parameters. For the current database size, the networks’ capacity cannot be fully exploited, resulting in reduced generalizability and reliability. Our study provides insight into the appropriateness of different regression models for drag prediction. We also discuss the remaining steps before data-driven methods emerge as useful tools in applications.

Albeit laboratory experiments and numerical simulations have proven themselves successful in enhancing our understanding of long-living large-scale flow structures in horizontally extended Rayleigh–Bénard convection, some discrepancies with respect to their size and induced heat transfer remain. This study traces these discrepancies back to their origins. We start by generating a digital twin of one standard experimental set-up. This twin is subsequently simplified in steps to understand the effect of non-ideal thermal boundary conditions, and the experimental measurement procedure is mimicked using numerical data. Although this allows for explaining the increased observed size of the flow structures in the experiment relative to past numerical simulations, our data suggests that the vertical velocity magnitude has been underestimated in the experiments. A subsequent reassessment of the latter's original data reveals an incorrect calibration model. The reprocessed data show a relative increase in $u_{z}$ of roughly $24\,\%$, resolving the previously observed discrepancies. This digital twin of a laboratory experiment for thermal convection at Rayleigh numbers $Ra = \{ 2, 4, 7 \} \times 10^{5}$, a Prandtl number $Pr = 7.1$ and an aspect ratio $\varGamma = 25$ highlights the role of different thermal boundary conditions as well as a reliable calibration and measurement procedure.

An existing approach for deriving analytical expressions for slip-flow properties of Stokes flow is generalised and applied to a range of micro and nanoscale applications. The technique, which exploits the reciprocal theorem, can generate first-order predictions of the impact of Navier or Maxwell slip boundary conditions on surface moments of the traction force (e.g. on drag and torque). This article brings dedicated focus to the technique, generalises it to predict first-order slip effects on arbitrary moments of the surface traction, numerically verifies the technique on a number of cases and applies the method to a range of micro and nano-scale applications. Applications include predicting: the drag on translating spheres with varying slip length; the efficiency of a micro journal bearing; the speed of a self-propelled particle (a ‘squirmer’); and the pressure drop required to drive flow through long, straight micro/nano channels. Certain general results are also obtained. For example, for low-slip Stokes flow: any surface distribution of positive slip length will reduce the drag on any translating particle; and any perimetric distribution of positive slip length will reduce the pressure loss through a straight channel flow of arbitrary cross-section.

Marine propellers are studied in design and off-design modes of operation like crashback, where the propeller rotates in reverse while the vehicle is in forward motion. Past experiments (Jessup et al., Proceedings of the 25th Symposium on Naval Hydrodynamics, St John's, Canada, 2004; Proceedings of the 26th Symposium on Naval Hydrodynamics, Rome, Italy, 2006) studied the marine propeller David Taylor Model Basin 4381 in the open-jet test section of the 36-inch variable pressure water tunnel (VPWT). In crashback, a significant discrepancy with unclear sources exists between the mean propeller loads from the VPWT and open-water towing tank (OW) experiments (Ebert et al., 2007 ONR Propulsor S & T Program Review, October, 2007). We perform large-eddy simulation at $Re=561\,000$ and advance ratios $J=-0.50$ and $-0.82$ with the VPWT geometry included, contrasting to the unconfined (OW) case at those same $J$ and $Re=480\,000$. We identify and delineate the water tunnel interference effects responsible, and demonstrate that these effects resemble those of a symmetric solid model or bluff body. Solid blockage due to jet expansion and nozzle blockage due to proximity to the tunnel nozzle are identified as the primary interference effects. Their impact varies with the advance ratio $J$ and strengthens for higher magnitudes of $J$. The effective length scale to assess the severity of interference effects is found to be larger than the vortex ring diameter.