The sloping boundaries of stratified aquatic systems, such as lakes, are crucial environmental dynamic zones. While the role of sloping boundaries as energy dissipation hotspots is well established, their contribution to triggering large-scale motions has received less attention. This review delves into the development of thermally driven cross-shore flows on sloping boundaries under weak wind conditions. We specifically examine ‘thermal siphons’ (TS), a dynamical process that occurs when local free convection transforms into a horizontal circulation over sloping boundaries. Thermal siphons result from bathymetrically induced temperature (i.e. density) gradients when a lake experiences a uniform surface buoyancy flux, also known as differential cooling or heating. In the most common case of differential cooling of waters above the temperature of maximum density, TS lead to an overturning circulation characterised by a downslope density current and a surface return flow within a convective environment. Field observations, laboratory experiments and high-fidelity simulations of TS provide insights into their temporal occurrence, formation mechanisms, water transport dynamics and cross-shore pathways, addressing pivotal questions from an aquatic system perspective. Fluid mechanics is a fundamental tool in addressing such environmental questions and thereby serves as the central theme in this review.

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$Re=561\,000$ and advance ratios J=-0.50$J=-0.50$ and -0.82$-0.82$ with the VPWT geometry included, contrasting to the unconfined (OW) case at those same J$J$ and Re=480\,000$Re=480\,000$. 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$J$ and strengthens for higher magnitudes of J$J$. The effective length scale to assess the severity of interference effects is found to be larger than the vortex ring diameter.

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.

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}$u_{z}$ of roughly 24\,\%$24\,\%$, 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}$Ra = \{ 2, 4, 7 \} \times 10^{5}$, a Prandtl number Pr = 7.1$Pr = 7.1$ and an aspect ratio \varGamma = 25$\varGamma = 25$ highlights the role of different thermal boundary conditions as well as a reliable calibration and measurement procedure.

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$Re_\tau$ of 500. With two trainable parameters, the kernel method can fully account for nonlinear relations between the roughness function \Delta U^+$\Delta U^+$ 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.

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\mu$\mu$m2s−1 for Gaussian particles and 0.07\mu$\mu$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\mu$\mu$m2s−1 mean absolute difference. Hence, the DPD-v2 models can be used in real-world scenarios.