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Induced diffusion (ID), an important mechanism of spectral energy transfer due to interacting internal gravity waves (IGWs), plays a significant role in driving turbulent dissipation in the ocean interior. In this study, we revisit the ID mechanism to elucidate its directionality and role in ocean mixing under varying IGW spectral forms, with particular attention to deviations from the standard Garrett–Munk spectrum. The original interpretation of ID as an action diffusion process, as proposed by McComas et al., suggests that ID is inherently bidirectional, with its direction governed by the vertical-wavenumber spectral slope $\sigma$ of the IGW action spectrum, $n \propto m^\sigma$. However, through the direct evaluation of the wave kinetic equation, we reveal a more complete depiction of ID, comprising both a diffusive and a scale-separated transfer rooted in the energy conservation within wave triads. Although the action diffusion may reverse direction depending on the sign of $\sigma$ (i.e. red or blue spectra), the net transfer by ID consistently leads to a forward energy cascade at the dissipation scale, contributing positively to turbulent dissipation. This supports the viewpoint of ID as a dissipative mechanism in physical oceanography. This study presents a physically grounded overview of ID, and offers insights into the specific types of wave–wave interactions responsible for turbulent dissipation.
The term periglacial describes areas subject to repeated freezing and thawing and the processes associated with the growth of ice within soil and rock. Although originally referring to processes and climates adjacent to glaciers, “periglacial” now applies more broadly to cold-climate processes where frost action predominates. Earth’s cold, periglacial landscapes span both polar regions and many high elevation and mountainous areas. These landscapes are unlike any others, with ice-formed landforms such as pingos (Fig. 20.0) ice-wedge polygons, sorted circles, and rock glaciers found only in these cold landscapes.
We analyse the long-time dynamics of trajectories within the stability boundary between laminar and turbulent square duct flow. If not constrained to a symmetric subspace, the edge trajectories exhibit a chaotic dynamics characterised by a sequence of alternating quiescent phases and intense bursting episodes. The dynamics reflects the different stages of the well-known near-wall streak–vortex interaction. Most of the time, the edge states feature a single streak with a number of flanking vortices attached to one of the four surrounding walls. The initially straight streak undergoes a linear instability and eventually breaks in an intense bursting event. At the same time, the downstream vortices give rise to a new low-speed streak at one of the neighbouring walls, thereby causing the turbulent activity to ‘switch’ from one wall to the other. If the edge dynamics is restricted to a single or twofold mirror-symmetric subspace, the bursting and wall-switching episodes become self-recurrent in time, representing the first periodic orbits found in square duct flow. In contrast to the chaotic edge states in the non-symmetric case, the imposed symmetries enforce analogue bursting cycles to simultaneously appear at two parallel opposing walls in a mirror-symmetric configuration. Both the localisation of turbulent activity to one or two walls and the wall-switching dynamics are shown to be common phenomena in marginally turbulent duct flows. We argue that such episodes represent transient visits of marginally turbulent trajectories to some of the edge states detected here.
Symmetry-based analyses of multiscale velocity gradients highlight that strain self-amplification (SS) and vortex stretching (VS) drive forward energy transfer in turbulent flows. By contrast, a strain–vorticity covariance mechanism produces backscatter that contributes to the bottleneck effect in the subinertial range of the energy cascade. We extend these analyses by using a normality-based decomposition of filtered velocity gradients in forced isotropic turbulence to distinguish contributions from normal straining, pure shearing and rigid rotation at a given scale. Our analysis of direct numerical simulation (DNS) data illuminates the importance of shear layers in the inertial range and (especially) the subinertial range of the cascade. Shear layers contribute significantly to SS and VS and play a dominant role in the backscatter mechanism responsible for the bottleneck effect. Our concurrent analysis of large-eddy simulation (LES) data characterizes how different closure models affect the flow structure and energy transfer throughout the resolved scales. We thoroughly demonstrate that the multiscale flow features produced by a mixed model closely resemble those in a filtered DNS, whereas the features produced by an eddy viscosity model resemble those in an unfiltered DNS at a lower Reynolds number. This analysis helps explain how small-scale shear layers, whose imprint is mitigated upon filtering, amplify the artificial bottleneck effect produced by the eddy viscosity model in the inertial range of the cascade. Altogether, the present results provide a refined interpretation of the flow structures and mechanisms underlying the energy cascade and insight for designing and evaluating LES closure models.
From the Blue Ridge overlook in Shenandoah National Park, Virginia, USA, one can see the broad Shenandoah Valley, split by Massanutten Mountain, with more ridges and valleys in the distance (Fig. 9.1). This view of the Appalachian ridges and valleys provides a classic example of an eroded fold and thrust belt, where parallel ridges of hard, resistant rocks are separated by valleys underlain by comparatively softer rocks. Fold and thrust belt topography develops on folded bedrock structures called anticlines and synclines (Fig. 9.2). But this type of geologic structure is not without a long back-story. Most of the folded rocks underlying these mountains were originally deposited as flat-lying sediments, hundreds of millions of years ago. The folding occurred much later, driven by compressive forces associated with continental collision. Millions of years of subsequent erosion on these rocks were then required to give us the landscapes we see today.
Transonic buffet presents time-dependent aerodynamic characteristics associated with shock, turbulent boundary layer and their interactions. Despite strong nonlinearities and a large degree of freedom, there exists a dominant dynamic pattern of a buffet cycle, suggesting the low dimensionality of transonic buffet phenomena. This study seeks a low-dimensional representation of transonic airfoil buffet at a high Reynolds number with machine learning. Wall-modelled large-eddy simulations of flow over the OAT15A supercritical airfoil at two Mach numbers, $M_\infty = 0.715$ and 0.730, respectively producing non-buffet and buffet conditions, at a chord-based Reynolds number of ${Re} = 3\times 10^6$ are performed to generate the present datasets. We find that the low-dimensional nature of transonic airfoil buffet can be extracted as a sole three-dimensional latent representation through lift-augmented autoencoder compression. The current low-order representation not only describes the shock movement but also captures the moment when the separation occurs near the trailing edge in a low-order manner. We further show that it is possible to perform sensor-based reconstruction through the present low-dimensional expression while identifying the sensitivity with respect to aerodynamic responses. The present model trained at ${Re} = 3\times 10^6$ is lastly evaluated at the level of a real aircraft operation of ${Re} = 3\times 10^7$, exhibiting that the phase dynamics of lift is reasonably estimated from sparse sensors. The current study may provide a foundation towards data-driven real-time analysis of transonic buffet conditions under aircraft operation.
The adsorption of the antiseptic drug chlorhexidine acetate by halloysite was studied. It was shown that the adsorption kinetic curves obeyed a pseudo-second-order reaction equation. The Langmuir and Freundlich models described well the equilibrium adsorption of chlorhexidine acetate by halloysite. The pristine halloysite powder and the loaded clay were characterized using physicochemical methods such as dynamic light scattering, scanning electron microscopy, X-ray diffraction, nitrogen adsorption–desorption, Fourier-transform infrared spectroscopy and thermal analysis. It was found that the halloysite particles were in the form of cylindrical tubes with sizes in the range of 50–1500 nm. Analysis of the X-ray diffraction data showed that the process of chlorhexidine acetate loading did not change the crystal structure of halloysite. The values of the textural parameters of the materials under study were determined using Brunauer–Emmett–Teller, Barrett–Joyner–Halenda and density functional theory methods. The findings indicated that, by filling the pores of halloysite with chlorhexidine acetate, the volume of the pore space and the pore surface area decreased. In addition, it was found from the biological activity tests that halloysite loaded with chlorhexidine acetate demonstrates antimicrobial activity against Escherichia coli M-17 bacteria.
We explore the mechanisms and regimes of mixing in yield-stress fluids by simulating the stirring of an infinite, two-dimensional domain filled with a Bingham fluid. A cylindrical stirrer moves along a circular path at constant speed, with the path radius fixed at twice the stirrer diameter; the domain is initially quiescent and marked by a passive dye in the lower half. We first examine the mixing process in Newtonian fluids, identifying three key mechanisms: interface stretching and folding around the stirrer’s path, diffusion across streamlines and dye advection and interface stretching due to vortex shedding. Introducing yield stress leads to notable mixing localisation, manifesting through three mechanisms: advection of vortices within a finite distance of the stirrer, vortex entrapment near the stirrer and complete suppression of vortex shedding at high yield stresses. Based on these mechanisms, we classify three distinct mixing regimes: (i) regime SE, where shed vortices escape the central region, (ii) regime ST, where shed vortices remain trapped near the stirrer and (iii) regime NS, where no vortex shedding occurs. These regimes are quantitatively distinguished through spectral analysis of energy oscillations, revealing transitions and the critical Bingham and Reynolds numbers. The transitions are captured through effective Reynolds numbers, supporting the hypothesis that mixing regime transitions in yield-stress fluids share fundamental characteristics with bluff-body flow dynamics. The findings provide a mechanistic framework for understanding and predicting mixing behaviours in yield-stress fluids, suggesting that the localisation mechanisms and mixing regimes observed here are archetypal for stirred-tank applications.
We analyse the dynamics of a weakly elastic spherical particle translating parallel to a rigid wall in a quiescent Newtonian fluid in the Stokes limit. The particle motion is constrained parallel to the wall by applying a point force and a point torque at the centre of its undeformed shape. The particle is modelled using the Navier elasticity equations. The series solutions to the Navier and the Stokes equations are used to obtain the displacement and velocity fields in the solid and fluid, respectively. The point force and the point torque are calculated as series in small parameters $\alpha$ and $1/H$, using the domain perturbation method and the method of reflections. Here, $\alpha$ is the measure of elastic strain induced in the particle resulting from the fluid’s viscous stress and $H$ is the non-dimensional gap width, defined as the ratio of the distance of the particle centre from the wall to its radius. The results are presented up to $\textit {O}(1/H^3)$ and $\textit {O}(1/H^2)$, assuming $\alpha \sim 1/H$, for cases where gravity is aligned and non-aligned with the particle velocity, respectively. The deformed shape of the particle is determined by the force distribution acting on it. The hydrodynamic lift due to elastic effects (acting away from the wall) appears at $\textit {O}(\alpha /H^2)$ in the former case. In an unbounded domain, the elastic effects in the latter case generate a hydrodynamic torque at O($\alpha$) and a drag at O($\alpha ^2$). Conversely, in the former case, the torque is zero, while the drag still appears at O($\alpha ^2$).
Coastal environments are highly dynamic, making monitoring of suspended sediment concentration (SSC) both challenging and essential. SSC serves as an indicator of coastal processes, storm impact, water quality and ecosystem service delivery. However, direct measurement of SSC is costly, logistically difficult and spatially limited. Although remote sensing offers a promising alternative by estimating SSC from surface reflectance, it requires calibration and is often constrained by site-specific applicability. This study presents a machine learning framework for national-scale SSC estimation using Landsat-8 and Sentinel-2 imagery, calibrated with 147 in situ SSC samples. Several models were evaluated, with XGBoost yielding the best performance (R2 = 0.72, RMSE = 17 mg/L). SHapley Additive exPlanations values were used for model interpretability. Visible and infrared bands, along with geographic features, were identified as key predictors, reflecting the importance of coastal typology in shaping the SSC-reflectance relationship. The model’s value was demonstrated through a 10-year spatio-temporal analysis of SSC in Wexford Harbour. Seasonal patterns showed higher estuarine mixing in winter, while high SSC events coincided with rainfall and strong winds, indicating responsiveness to meteorological drivers. These findings highlight the potential of integrating remote sensing and machine learning for scalable, interpretable and cost-effective SSC monitoring.
Rivers and their valleys have long been a source of contemplation and wonder. They are not only key geomorphic agents, but they are also economically important, acting as transportation arteries, sources of irrigation water and food, and as generators of hydropower. We also use rivers for drinking, waste disposal, and for a variety of recreational activities. Many geomorphologists consider running water to be the most dominant and important geomorphic process – shaping landscapes everywhere. Even in deserts, running water is often the most important and widespread geomorphic agent.
Most valleys have a stream or a river at their bottom. In ancient days, it was thought that water simply “found” its way into preexisting valleys, forming rivers there. Geomorphologists now know that most valleys were formed by the rivers currently within them, which moved sediment out and carved the valley over time.
Arches, hoodoos, buttes, mesas … these are the picturesque landforms that most tourists and landscape-lovers know about, and which are the focus of many parks and recreation areas. All of these landforms are bedrock-controlled, with rock at or immediately beneath the surface. This chapter introduces a wide array of bedrock-controlled landforms. Most have formed on sedimentary rock, the most common rock in Earth’s upper crust. Thus, much of the focus in this chapter will be on landforms developed on flat-lying bedrock strata (layers) that have experienced minimal tectonic disturbance throughout their history. Chapters 9 and 10 focus on bedrock-controlled landforms formed on much more tectonically active landscapes.
Weathering is central to geomorphology; without it, landforms would not exist. Weathering sculpts rocks and landscapes at all scales, from producing tiny pits on rock surfaces to forming large valleys. It is everywhere.
However, weathering does not work alone. Instead, it operates alongside other surficial processes to produce the landscapes we see around us. Weathering is often defined as the in situ (meaning “in position”) breakdown of rocks and minerals. It is distinct from erosion, which involves the removal and transport of material, usually downslope. Often, weathering preconditions rocks for erosion by making them weaker and less coherent. Together, weathering and erosion operate to form landforms via denudation – the overall lowering of the land surface.
This study reports potassium (K) isotope compositions of diamondiferous kimberlites. Altered kimberlite samples exhibit δ41K values ranging from −1.293 ± 0.052 (2SD) to −0.114 ± 0.029 ‰, showing covariations with chemical indicators of alteration. This is consistent with the geochemical dynamics of K isotopes in hydrothermal fluid-related processes. In contrast, pristine kimberlite samples display restricted K isotope compositions, with δ41K values between −0.494 ± 0.057 and −0.270 ± 0.048 ‰. Notably, the δ41K values of these pristine kimberlite samples correlate well with K2O and Rb contents, suggesting that approximately ∼0.2 ‰ of K isotope fractionation is induced by phlogopite crystallization, as indicated by quantitative modelling. The estimated δ41K values of −0.458 ‰ for the primary kimberlite melt and of −0.414 ‰ for the kimberlite source imply a potential link to the bulk silicate Earth. These new measurements, along with literature data from various rocks, indicate that the K isotope composition in the deep mantle (>150 km) is more homogenous than in shallow regions, likely reflecting the efficiency of convection flow and K behaviour during subduction. In addition, the K isotope data reveal temporal variations in mantle-derived magmas from the Palaeozoic to the Cenozoic, highlighting the geological history and lithospheric destruction of the North China Craton. This study underscores the significance of K isotopes in enhancing our understanding of mantle dynamics, crustal recycling and the geochemical evolution of the Earth’s interior.
G. K. Gilbert is considered one of the founders of modern geomorphology (see Chapter 2). In his 1877 report on the geology of the Henry Mountains of Utah, he wrote that (p. 109).
How old is the Grand Canyon? When did the glaciers last retreat from this area? How long does it take to form an inch of topsoil? When did the earthquake occur that formed these rock scarps? These are the questions that geomorphologists ponder. This chapter will outline the tools and approaches we use to answer such questions.
Establishing how old a landform might be, that is, when it formed, has always occupied the mindset of geomorphologists. If we know how OLD a landform is, then we can begin to understand how it is evolving, how fast it might be changing, and how it formed in the first place. Fortunately, various dating principles and techniques now exist to address these issues. These techniques require the ability to measure change in a system or a landform over time, with the (usual) goal of establishing the age of a sediment package or a landform.