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As a step towards realising a skin-friction drag reduction technique that scales favourably with Reynolds number, the impact of a synthetic jet on a turbulent boundary layer was explored through a study combining wind-tunnel measurements and large eddy simulations. The jet was ejected in the wall-normal direction through a rectangular slot whose spanwise dimension matched that of dominant large-scale structures in the logarithmic region to target structures of that size and smaller simultaneously. Local skin-friction reduction was observed at both $x/\delta =2$ and $x/\delta =5$ downstream of the orifice centreline, where $\delta$ is the boundary-layer thickness. At $x/\delta =2$, the skin-friction reduction was observed to be due to the synthetic-jet velocity deficit intersecting the wall. At $x/\delta =5$, evidence from the simulations and wind-tunnel measurements suggests that a weakening of wall-coherent velocity scales is primarily responsible for the skin-friction reduction. Local skin-friction reduction which scales favourably with Reynolds number may be achievable with the synthetic jet employed in this study. However, there are many technical hurdles to overcome to achieve net skin-friction drag reduction over the entire region of influence. For instance, regions of skin-friction increase were observed close to the orifice ($x/\delta \lt 2$) and downstream of the orifice edge due to the induced motion of synthetic-jet vortical structures. Additionally, a recirculation region was seen to form during expulsion, which has implications for pressure drag on non-planar surfaces.
This chapter discusses the most basic frequentist method, the linear least squares (LLS) regression, for obtaining the optimal weights for the linear regression model that minimizes the squared error between the model prediction and observation. The chapter also considers how the goodness of its results can be evaluated quantitatively by the coefficient of determination (R-squared). The chapter then further discusses some variations of LLS, including ridge regression with an extra regularization term to make proper tradeoff between overfitting and underfitting, and linear method based on basis functions for nonlinear regression problems. Finally the last section of the chapter briefly discusses the Bayes methods that maximizes the likelihood and the posterior probability of the parameters of the linear regression model. This section serves to prepare the reader for discussion of various Bayesian learning algorithms in future chapters.
This chapter discusses the method of principal component analysis (PCA) for dimensionality reduction, by which the original high-dimensional feature space can be mapped into a much lower dimensional space still containing most of the separability information, based on either the total scatter matrix of the given dataset, or the within and/or between-class scatter matricies. This transformation from high to low dimensional space can be considered as a pre-processing stage before the main process for classification which can be carried out more efficiently and effectively in the low-dimensional space after the transformation.
The triadic interactions and nonlinear energy transfer are investigated in a subsonic turbulent jet at $Re = 450\,000$. The primary focus is on the role of these interactions in the formation and attenuation of streaky structures. To this end, we employ bispectral mode decomposition, a technique that extracts coherent structures associated with dominant triadic interactions. A strong triadic correlation is identified between Kelvin–Helmholtz (KH) wavepackets and streaks: interactions between counter-rotating KH waves generates streamwise vortices, which subsequently give rise to streaks through the lift-up mechanism. The most energetic streaks occur at azimuthal wavenumber $m = 2$, with the dominant contributing triad being $[m_1, m_2, m_3] = [1, 1, 2]$. The spectral energy budget reveals that the net effect of nonlinear triadic interactions is an energy loss from the streaks. As these streaks convect downstream, they engage in further nonlinear interactions with other frequencies, which drain their energy and ultimately lead to their attenuation. Further analysis identifies the dominant scales and direction of energy transfer across different spatial regions of the jet. While the turbulent jet exhibits a forward energy cascade in a global sense, the direction of energy transfer varies locally: in the shear layer near the nozzle exit, triadic interactions among smaller scales dominate, resulting in an inverse energy cascade, whereas farther downstream, beyond the end of the potential core, interactions among larger scales prevail, leading to a forward cascade.
This chapter is dedicated to the method of independent component analysis (ICA), which can be considered to be in parallel with PCA, as both methods are for the purpose of extracting some essential information, either the principal or independent components, from the given dataset, to be further processed. However, different from PCA, the ICA assumes the signals in the given data are linear combinations of a set of independent signal components (therefore also the name blind source separation or BSS), which can be recovered based on the fact that a linear combination of multiple random variables is more Gaussian than each of them individually. The ICA is therefore carried out based on the ICA is therefore carried out based on the principle of maximizing non-Gaussianity, often using measures such as kurtosis or negentropy to identify statistically independent components.
Secondary flows induced by spanwise heterogeneous surface roughness play a crucial role in determining engineering-relevant metrics such as surface drag, convective heat transfer and the transport of airborne scalars. While much of the existing literature has focused on idealized configurations with regularly spaced roughness elements, real-world surfaces often feature irregularities, clustering and topographic complexity for which the secondary flow response remains poorly understood. Motivated by this gap, we investigate multicolumn roughness configurations that serve as a regularized analogue of roughness clustering. Using large-eddy simulations, we systematically examine secondary flows across a controlled set of configurations in which cluster density and local arrangement are varied in an idealized manner, and observe that these variations give rise to distinct secondary flow polarities. Through a focused parameter study, we identify the spanwise gap between the edge-most roughness elements of adjacent columns, normalized by the channel half-height ($s_a/H$), as a key geometric factor governing this polarity. In addition to analysing the time-averaged structure, we investigate how variations in polarity affect the instantaneous dynamics of secondary flows. Here, we find that the regions of high- and low-momentum fluid created by the secondary flows alternate in a chaotic, non-periodic manner over time. Further analysis of the vertical velocity signal shows that variability in vertical momentum transport is a persistent and intrinsic feature of secondary flow dynamics. Taken together, these findings provide a comprehensive picture of how the geometric arrangement of roughness elements governs both the mean structure and temporal behaviour of secondary flows.
A fully resolved numerical study was performed to investigate interfacial heat and mass transfer enhanced by the fully developed Rayleigh–Bénard–Marangoni instability in a relatively deep domain. The instability was triggered by evaporative cooling modelled by a constant surface heat flux. The latter allowed for temperature-induced variations in surface tension giving rise to Marangoni forces reinforcing the Rayleigh instability. Simulations were performed at a fixed Rayleigh number (${\textit{Ra}}_h$) and a variety of Marangoni numbers (${\textit{Ma}}_h$). In each simulation, scalar transport equations for heat and mass concentration at various Schmidt numbers (${\textit{Sc}}=16{-}200$) were solved simultaneously. Due to the fixed (warm) temperature prescribed at the bottom of the computational domain, large buoyant plumes emerged quasi-periodically both at the top and bottom. With increasing Marangoni number a decrease in the average convection cell size at the surface was observed, with a simultaneous improvement in near-surface mixing. The presence of high aspect ratio rectangular convection cell footprints was found to be characteristic for Marangoni-dominated flows. Due to the promotion of interfacial mass transfer by Marangoni forces, the power in the scaling of the mass transfer velocity, $K_{\!L}\!\propto\! \textit{Sc}^{-n}$, was found to decrease from $n=0.50$ at ${\textit{Ma}}_h=0$ to $\approx 0.438$ at ${\textit{Ma}}_h=13.21\times 10^5$. Finally, the existence of a buoyancy-dominated and a Marangoni-dominated regime was investigated in the context of the interfacial heat and mass transfer scaling as a function of ${\textit{Ma}}_h+\varepsilon {\textit{Ra}}_h$, where $\varepsilon$ is a small number determined empirically.
Both experiments and direct numerical simulation (DNS) of hypersonic flow over a compression ramp show streamwise aligned streaks/vortices near the corner as the ramp angle is increased. The origin of this three-dimensional disturbance growth is not definitively known in the existing literature, but is typically connected to flow deceleration, centrifugal (Görtler) and/or baroclinic effects. In this work we consider the hypersonic problem with moderate wall cooling in the high Reynolds/Mach number, weak interaction limit. In the lower deck of the corresponding asymptotic triple-deck description we pose the linearised, three-dimensional, Görtler stability equations. This formulation allows computation of both receptivity and biglobal stability problems for linear spanwise-periodic disturbances with a spanwise wavelength of the same order as the lower-deck depth. In this framework the dominant response near the ramp surface is of constant density and temperature (at leading order) ruling out baroclinic mechanisms. Nevertheless, we show that there remains strong energy growth of upstream spanwise-varying perturbations and ultimately a bifurcation from two-dimensional to three-dimensional ramp flow. The unstable eigenmodes are localised to the separation region. The bifurcation points are obtained over a range of ramp angle, wall-cooling parameter and disturbance wavelength. Consistent with DNS results, the three-dimensional perturbations in this asymptotic formulation are streamwise aligned streaks/vortices, displaced above the separation region. In addition, the growth of upstream disturbances peaks near to the reattachment point, whilst the streaks persist beyond it, decaying relatively slowly downstream along the deflected ramp.
This chapter introduces a set of distances and scatter matrices of various kinds used to measure the difference or similarity between two sample points, one sample and one class/cluster, and two classes/clusters, and the within, between, and total scatteredness of classes/clusters, for the purpose of further measuring the separability of the classes/clusters in a subspace composed of features that are either selected or extracted from the original high dimensional feature space .
This chapter intruduces an important idea of kernel mapping, which can map the feature space to a much higher dimensional space where the class separability could be improved significantly for better classification results. Based on the assumption that all data samples only appear in the form of inner product in the algorithm, kernel mapping is actually carried out implicitly, in the sense that the mapping function never needs to be explicitly specified. The chapter then introduces the method of kernel PCA, as a variant of PCA, together with another variant probabilistic PCA. The chapter further considers the method of factor analysis based on two important concepts of latent variables and expectation maximization (EM), both playing some important roles in other learning algorithms to be discussed in future chapters. Finally the chapter moves on to discuss two additional methods, multidimensional scaling (MDS) and t-distributed stochastic neighbor embedding (t-SNE), for the same general purpose of dimensionality reduction.
This chapter discusses both supervised and unsupervised algorithms all to be carried out in a tree-like hierarchy, in which a classification or clustering problem is solved in a divide-and-conquer manner while traversing a binary tree. For supervised classification, the tree classifier is first constructed in the training phase, and then in the test phase, a set of classes are subdivided into two subsets at each node of the tree based on a subset of features specifically selected to best separate the two subsets. This operation is carried out along a path in the tree from the root node down to one of the leaf nodes representing one of the classes. For unsupervised clustering, the tree structure is constructed in either a top-down or ottom-up fashion. In the former case, the given dataset represented by root node is recursively split into two subsets represented by the two child nodes; while in the latter case, all samples each represented by one of the leaf nodes are merged sequentially until they form a single group at the root node. In either case, the splitting or merging is carried out based on certain distance previously considered. Such splitting or merging process can be truncated somewhere between the root and leaf nodes to obtain a set of clusters.
This chapter discusses nonlinear regression method based on gradient descent and its variations for obtaining the optimal parameters of any given nonlinear regression function.
This chapter is dedicated to the sole topic of support vector machine (SVM), a typical discriminative algorithm mostly for binary classification. The goal of the algorithm is to find a optimal hyperplane that separate the two classes (assumed to be linearly separable) in the feature space in such a way that the two classes are best separated, in the sense that the distances (called margin) between the plane and the samples closest to it (called support vectors) on either side of the plane are maximized. This is a constrained optimization problem which could be solved directly, but it is actually first converted to its dual problem and then solved by quadratis programming. The reason for solving the dual problem is due to the fact that all data points appear in the form of inner product, so that kernel method can be used to carry out the classification in a higher dimensional space in which the two classes become linearly separable even if they are not so in the original space. The chapter further considers some variants of SVM, such as sequential minimal optimization and generalized multiclass SVM.
Sea surface films significantly influence air–sea interaction. While their damping effect on gravity–capillary waves is well recognised, the detailed mechanisms by which surface films alter small-scale wave dynamics – particularly energy dissipation and near-surface flow patterns – remain insufficiently understood. This paper presents experimental observations focusing on small-scale wave profiles and surface-flow dynamics in the presence of surfactants, providing direct experimental evidence of underlying mechanisms such as Marangoni effects. The experiments demonstrate enhanced energy dissipation and significant alterations in near-surface flow caused by surfactants, including the transformation of typical circular motion into elliptical-like trajectories and the emergence of reverse surface drift.
Passive gust load alleviation systems have the potential to significantly reduce airframe mass without reliance on complex systems of sensors and actuators. Recent experimental work by the authors has shown that a passive, strain-actuated spoiler can rapidly reduce the lift coefficient of an aerofoil. In this work, we numerically investigate the efficacy of a strain-actuated spoiler in alleviating loads within the wider airframe. The airframe is represented by a beam model which is exposed to a series of One-Minus-Cosine gusts. The effect of the spoiler on the wing is captured by locally reducing lift when wingbox strains meet a triggering condition. The model spoiler is shown to be capable of reducing the sizing wing root bending moment by up to $17$% for the airframe and spoiler parameters considered. In addition, the sensitivity of this load alleviation to key spoiler design parameters is investigated. It is found that deploying the spoiler as early as possible in the gust provides the best load alleviation performance. In a few cases, the spoiler is found to induce a limit cycle oscillation in the wing by repeatedly deploying and stowing. This may be an artefact caused by the low fidelity structural model employed in this work. Nonetheless, two ways of preventing this behaviour are demonstrated. Our work demonstrates for the first time that a strain-actuated spoiler is capable of alleviating loads at the scale of a full aircraft.
Spectral analysis of the transport process of turbulence kinetic energy (TKE) in a channel roughened with spanwise-aligned circular-arc ribs is conducted based on direct numerical simulations (DNS). Test cases of varying pitch-to-height ratios ($P/H=3.0$, 5.0 and 7.5) and bulk Reynolds numbers (${\textit{Re}}_b=5600$ and 14 600) are compared. It is observed that the characteristic spanwise wavelength of the energy-containing eddies in the internal shear layer (ISL) increases as the value of $P/H$ increases, but decreases as the Reynolds number increases. In the ISL, the energy transport processes are dominated by turbulent production as the lead source term, but by turbulent diffusion and dissipation as the lead sink terms. It is found that regions with high production and dissipation rates of TKE in the ISL are associated with moderate and small wavelengths, respectively. The TKE production for sustaining moderate- and large-scale motions enhances gradually with an increasing value of $P/H$, while that for sustaining small-scale motions augments as the Reynolds number increases. It is interesting to observe that the interscale-transport term plays a critical role in draining TKE at moderate wavelengths as a sink and carries the drained TKE to small-scale eddies as a source. It is discovered that a higher pitch-to-height ratio leads to shortening of the characteristic spanwise wavelength of the dissipation process but prolongation of those of the production, interscale-transport and turbulent-diffusion processes in the ISL. By contrast, a higher Reynolds number results in reductions in the characteristic spanwise wavelengths of all spectral transport terms.