Active wake control (AWC) has emerged as a promising strategy for enhancing wind turbine wake recovery, but accurately modelling its underlying fluid mechanisms remains challenging. This study presents a computationally efficient wake model that provides end-to-end prediction capability from rotor actuation to wake recovery enhancement by capturing the coupled dynamics of wake meandering and mean flow modification, requiring only two inputs: a reference wake without control and a user-defined AWC strategy. The model combines physics-based resolvent modelling for large-scale coherent structures and an eddy viscosity modelling for small-scale turbulence. A Reynolds stress model is introduced to account for the influence of both coherent and incoherent wake fluctuations, so that the time-averaged wake recovery enhanced by the AWC can be quantitatively predicted. Validation against large-eddy simulations (LES) across various AWC approaches and actuating frequencies demonstrates the model’s predictive capability, accurately capturing AWC-specific and frequency-dependent mean wake recovery with less than 8 % error from LES while reducing computational time from thousands of central-processing-unit hours to minutes. The efficiency and accuracy of the model makes it a promising tool for practical AWC design and optimization of large-scale wind farms.

Though ubiquitous in many engineering applications, including drug delivery, the compound droplet hydrodynamics in confined geometries have been barely surveyed. For the first time, this study thoroughly investigates the hydrodynamics of a ferrofluid compound droplet (FCD) during its migration in a microchannel under the presence of a pressure-driven flow and a uniform external magnetic field (UEMF) to manipulate its morphology and retard its breakup. Finite difference and phase-field multiple-relaxation time lattice Boltzmann approaches are coupled to determine the magnetic field and ternary flow system, respectively. First, the influence of the magnetic Bond number (${Bo}_m$) on the FCD morphology is explored depending on whether the core or shell is ferrofluid when the UEMF is applied along $\alpha =0^\circ$ and $\alpha =90^\circ$ relative to the fluid flow. It is ascertained that imposing the UEMF at $\alpha =0^\circ$ when the shell is ferrofluid can postpone the breakup. Intriguingly, when the core is ferrofluid, strengthening the UEMF enlarges the shell deformation. Afterwards, the effects of the capillary number (${Ca}$), density ratio, viscosity ratio, radius ratio and surface tension coefficients are scrutinised on the FCD deformation and breakup. The results indicate that augmenting the core-to-shell viscosity and density ratios accelerates the breakup process. Additionally, surface tension between the core and shell suppresses the core deformation. Moreover, increasing the ${Ca}$ intensifies the viscous drag force exerted on the shell, flattening its rear side, which causes a triangular-like configuration. Ultimately, by varying ${Bo}_m$ and ${Ca}$, five distinct regimes are observed, whose regime map is established.

This paper presents a novel machine learning framework for reconstructing low-order gust-encounter flow field and lift coefficients from sparse, noisy surface pressure measurements. Our study thoroughly investigates the time-varying response of sensors to gust–airfoil interactions, uncovering valuable insights into optimal sensor placement. To address uncertainties in deep learning predictions, we implement probabilistic regression strategies to model both epistemic and aleatoric uncertainties. Epistemic uncertainty, reflecting the model’s confidence in its predictions, is modelled using Monte Carlo dropout – as an approximation to the variational inference in the Bayesian framework – treating the neural network as a stochastic entity. On the other hand, aleatoric uncertainty, arising from noisy input measurements, is captured via learned statistical parameters, and propagate measurement noise through the network into the final predictions. Our results showcase the efficacy of this dual uncertainty quantification strategy in accurately predicting aerodynamic behaviour under extreme conditions while maintaining computational efficiency, underscoring its potential to improve online sensor-based flow estimation in real-world applications.

We explored the dynamics of Taylor–Couette flows within square enclosures, focusing primarily on the turbulence regime and vortex behaviour at varying Reynolds numbers. Laboratory experiments were conducted using particle image velocimetry for Reynolds numbers $Re_{\varDelta }\in [0.23, 4.6]\times 10^3$ based on the minimum gap $\varDelta /d = 1/16$, $1/8$ and $1/4$, where $d$ is the cylinder diameter, or $Re\in [1.8, 9.8]\times 10^3$ based on $d/2$. At lower $Re$, the flow was dominated by well-defined Taylor and Görtler vortices, while higher $Re$ led to a turbulent state with distinct motions. Space–time radial velocity analysis revealed persistent Taylor vortices at lower $Re$, with larger gaps but increased turbulence, and irregular motions at higher $Re$, with smaller gaps. Velocity spectra reveal that the energy distribution is maintained at frequencies lower than the integral-type frequency $f_I$ across varying $\varDelta$ due to the dominance of large vortices. However, there is a monotonic increase in energy at higher frequencies beyond $f_I$. The reduced characteristic frequency $f_I\varDelta /\omega _ir_i \sim 1/10$ indicates that these motions scale linearly with angular velocity, and inversely with the gap. Proper orthogonal decomposition (POD) and spectral POD were used to distinguish between Taylor and Görtler vortices, showing the effects of gap size and the associated energy cascade. Linear stability analysis included as complementary support revealed primary instability of the Taylor vortex, which is similar to the circular enclosure, along with multiple corner modes that are unique to the geometry.

Long-duration and time-resolved particle image velocimetry measurements were conducted in rough-wall open channel flows (OCFs), with the friction Reynolds number ranging from 642 to 2034. The primary objective is to investigate the impacts of various turbulent motions at different scales on the mean wall-shear stress ($\langle \tau _w \rangle$). To achieve this aim, a physical decomposition of $\langle \tau _w \rangle$ was initially performed utilizing the double-averaged methodology proposed by Nikora et al. (2019 J. Fluid Mech. 872, 626–664). This method enabled the breakdown of $\langle \tau _w \rangle$ into three distinct constituents: viscous, turbulent and dispersive stress segments. The findings underscore the substantial roles that turbulent and dispersive stresses play, accounting for over 75 % and 9 % of $\langle \tau _w \rangle$, respectively. Subsequently, a scale decomposition was further applied to analyse the contributions of coherent motions at different scales to $\langle \tau _w \rangle$. Adopting typical cutoff streamwise wavelengths ($\lambda _x = 3h$ and $10h$), the contribution of large-scale motions (LSMs) and very large-scale motions (VLSMs) to the overall wall-shear stress was quantified. It was revealed that turbulent motions with $\lambda _x \gt 3h$ and $\lambda _x \gt 10h$ contribute more than 40 % and 18 % of $\langle \tau _w \rangle$, respectively. The scale decomposition of the wall-shear stress and the contribution from LSMs and VLSMs exhibit evident dependencies on the Reynolds number. The contribution of LSMs and VLSMs to $\langle \tau _w \rangle$ is lower in rough OCFs compared with those of smooth counterparts. Secondary currents induced by the rough wall are hypothesised to be responsible for the reduced strength of LSMs and VLSMs and decreases in their contribution to $\langle \tau _w \rangle$.

A linear stability model based on a phase-field method is established to study the formation of ripples on the ice surface. The pattern on horizontal ice surfaces, e.g. glaciers and frozen lakes, is found to be originating from a gravity-driven instability by studying ice–water–air flows with a range of water and ice thicknesses. Contrary to gravity, surface tension and viscosity act to suppress the instability. The results demonstrate that a larger value of either water thickness or ice thickness corresponds to a longer dominant wavelength of the pattern, and a favourable wavelength of 90 mm is predicted, in agreement with observations from nature. Furthermore, the profiles of the most unstable perturbations are found to be with two peaks at the ice–water and water–air interfaces whose ratio decreases exponentially with the water thickness and wavenumber.

In this work, a systematic study is carried out concerning the dynamic behaviour of finite-size spheroidal particles in non-isothermal shear flows between parallel plates. The simulations rely on a hybrid method combining the lattice Boltzmann method with a finite-difference solver. Fluid–particle and heat–particle interactions are accounted for by using the immersed boundary method. The effect of particle Reynolds number ($\textit{Re}_p=1{-}90$), Grashof number (${Gr}=0{-}200$), initial position and initial orientation of the particle are thoroughly examined. For the isothermal prolate particle, we observed that above a certain Reynolds number, the particle undergoes a pitchfork bifurcation; at an even higher Reynolds number, it returns to the centre position. In contrast, the hot particle behaves differently, with no pitchfork bifurcation. Instead, the Reynolds and Grashof numbers can induce oscillatory tumbling or log-rolling motions in either the lower or upper half of the channel. Heat transfer also plays an important role: at low Grashof numbers, the particle settles near the lower wall, while increasing the Grashof number shifts it towards the upper side. Moreover, the presence of thermal convection increases the rotational speed of the particle. Surprisingly, beyond the first critical Reynolds number, the equilibrium position of the thermal particle shifts closer to the centreline compared with that of a neutrally buoyant isothermal particle. Moreover, higher Grashof numbers can cause the particle to transition from tumbling to log-rolling or even a no-rotation mode. The initial orientation has a stronger influence at low Grashof numbers, while the initial position shows no strong effect in non-isothermal cases.

This study investigates noise generation from co-rotating rotors arranged in a side-by-side configuration. The analysis examines the effects of different phase delays and separation distances. A simple mathematical model is developed to provide insight into constructive and destructive noise interference. An experimental campaign was carried out to validate the proposed analytical model. Furthermore, the study introduces a space–time proper orthogonal decomposition technique to separate broadband and tonal components. Subsequently, wavelet analysis is applied to the tonal component, revealing a transition to chaos via intermittency, characterised by the local birth and decay of periodic oscillations. This phenomenon highlights the intricate and fascinating chaotic nature of interference transitions. The chaotic behaviour of the tonal component is related to the macro time scale of pressure fluctuations, and has been incorporated into the mathematical model. This model has several applications, including its potential use in the development of active control systems and the design of quieter distributed propulsion systems.