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Direct numerical simulations with two-way coupled Lagrangian tracking are carried out to study the bubble preferential concentration and the flow field modification. Simulations are conducted in an upward vertical turbulent channel driven by a constant pressure gradient, corresponding to a friction Reynolds number $Re_{\tau 0}=180$. Micro-sized bubbles with diameters ranging from 0.72 to 1.43 wall units are considered. Competition between lift force and wall-lift force in the wall-normal direction leads to significant near-wall bubble accumulation and directly results in distinct preferential concentration patterns across the channel. Below (above) the peak concentration height, the wall-lift (lift) force dominates, driving bubbles to accumulate in regions of high-speed sweep (low-speed ejection) events. In the vicinity of the wall, the wall-normal lift force exhibits a strong correlation with the local streamwise flow velocity, further reinforcing the preferential concentration of bubbles in high-speed regions. Additionally, bubbles show a strong preference for the low-enstrophy and high-dissipation nodal topologies. Furthermore, small bubbles primarily accumulate in the vicinity of the wall, reducing the work done on the flow and leading to a decrease in bulk velocity and turbulence statistics. In contrast, the turbulence statistics of large bubbles are nearly identical to those of the unladen flow. The impact of large bubbles on the flow field primarily manifests as an effective increase in the mean pressure gradient. These findings demonstrate that bubbles in the upward vertical channel flow exhibit strong preferential concentration behaviours, whereas their ability to modulate turbulence remains limited.
Exact mathematical expressions are derived to predict the exponent $p$ observed in non-equilibrium turbulence, where the classical dissipation law is replaced by a new dissipation scaling law $C_{\varepsilon } \sim \textit{Re}_{\lambda }^p$. Here, $ \textit{Re}_{\lambda }$ is the Taylor-based Reynolds number and $C_{\varepsilon } = \varepsilon L_{11} / u^{\prime 3}$ is the non-dimensional dissipation rate, defined by the viscous dissipation rate, $\varepsilon$, longitudinal integral scale, $L_{11}$, and root-mean-square of the velocity fluctuations $u^{\prime} = \sqrt {\overline {u^{\prime 2}}}$ (Vassilicos, Annu. Rev. Fluid Mech., vol. 47, 2015, pp. 95–114). Assuming homogeneous and isotropic turbulence, it is shown that the exact value of $p$ involves only first-order derivatives of these variables; however, at very high Reynolds numbers, and under particularly strong changes in the power input of the external forcing (without changing the shape of the forcing spectrum), the exact expression simplifies to $p = 3\pi / 4\alpha L_{110} - 5 / 2$, where $L_{110}$ is the initial value of the longitudinal integral scale and $\alpha$ represents an effective forcing wavenumber. Thus, the main finding is that only large-scale effects are involved in the imposition of the non-equilibrium dissipation scaling law. The results are compared with direct numerical simulation (DNS) results of isotropic turbulence under abruptly changing forcing conditions and with experimental data of non-equilibrium decaying isotropic turbulence, showing consistent results.
An oscillating body floating at the water surface produces a field of self-generated waves. When the oscillation induces a difference in fore–aft wave amplitude squared, these self-generated waves can be used as a mechanism to propel the body horizontally across the surface (Longuet-Higgins 1977 Proc. R. Soc. Lond. A, vol. 352, no. 1671, pp. 463–480). The optimisation of this wave-driven propulsion is the interest of this work. To study the conditions necessary to produce optimal thrust we will consider a shallow water set-up where a periodically oscillating pressure source acts as the body. In this framework, an expression for the thrust is derived by relation to the difference in fore–aft amplitude squared. The conditions on the source for maximal thrust are explored both analytically and numerically in two optimal control problems. The first case is where a bound is imposed on the norm of the control function to regularise it. Secondly, a more physically motivated case is studied where the power injected by the source is bounded. The body is permitted to have a drift velocity $U$. When scaled with the wave speed $c$, the dimensionless velocity $v=U/c$ divides the study into subcritical, critical and supercritical regimes and the optimal conditions are presented for each. The result in the bounded power case is then used to demonstrate how the modulation of power injected can slowly change the cruising velocity from rest to supercritical velocities.
Six types of shock wave interference resulting from the impingement of an incident shock on a bow shock are revisited by examining the sub-types that arise between the canonical types. Several new sub-types are predicted based on the theories of weak shock reflection and double-wedge shock interference, and verified via numerical simulations. Two additional types, Type IIw and Type IIs, are identified between Type II and Type III, whereas a Type Vw emerges between Type IV and Type V. These types originate from the transformation of the Mach reflection at the triple point, which evolves through weak shock reflections (von Neumann reflection, Vasilev reflection, Guderley reflection) before disappearing. The transition from Type III to Type IV is further shown to mirror sequences of double-wedge shock interference. Two additional types (Type IIIb and Type IVt) are found. Meanwhile, it is found that under large incoming flow Mach number ($M_0$ = 5), Types III, IV and their sub-types dominate, whereas Type II is absent; under small incoming flow Mach number ($M_0$ = 2.5), Types III and IV vanish and a modified Type Va emerges. This study adds seven new sub-types to the existing six types of shock interference. These work extend the classical six types of shock interferences into six-plus shock interference, a picture that shed new insight into shock interference.
The linear stability of nanofluid boundary-layer flow over a flat plate is investigated using a two-phase formulation that incorporates the Brinkman (1952 J. Chem. Phys., vol. 20, pp. 571–581) model for viscosity along with Brownian motion (BM) and thermophoresis (TP), building upon the earlier work of Buongiorno (2006 J. Heat Transfer, vol. 128, pp. 240–250). Solutions to the steady boundary-layer equations reveal a thin nanoparticle concentration layer near the plate surface, with a characteristic thickness of $O({\textit{Re}}^{-1/2}{\textit{Sc}}^{-1/3})$, for a Reynolds number ${\textit{Re}}$ and Schmidt number ${\textit{Sc}}$. When BM and TP are neglected, the governing equations reduce to the standard Blasius formulation for a single-phase fluid, and the nanoparticle concentration layer disappears, resulting in a uniform concentration across the boundary layer. Neutral stability curves and critical conditions for the onset of the Tollmien–Schlichting (TS) wave are computed for a range of nanoparticle materials and volume concentrations. Results indicate that while the effects of BM and TP are negligible, the impact of nanoparticle density is significant. Denser nanoparticles, such as silver and copper, destabilise the TS wave, whereas lighter nanoparticles, like aluminium and silicon, establish a small stabilising effect. Additionally, the viscosity model plays a crucial role, with alternative formulations leading to different stability behaviour. Finally, a high Reynolds number asymptotic analysis is undertaken for the lower branch of the neutral stability curve.
We present a mathematical model for tsunami and induced magnetic anomalies originating from a time-dependent seabed deformation in an otherwise quiescent ocean over a conductive seafloor. The deformation is assumed to be a slender fault, whose lateral extension is much larger than the longitudinal scale. Using a perturbative method with multiple time scales and Green’s function approach, we examine the slow evolution of the wave field and induced magnetic anomaly over transoceanic distances from the fault. The model is validated against deep-ocean observations from the 2011 Tōhoku-oki tsunami. Our study reveals that lateral propagation in two horizontal dimensions decreases the period of both the surface wave and induced magnetic signal compared with one-horizontal-dimension scenarios. Over time, initially longitudinal wave propagation alters as wave fronts bend and stretch, affecting the magnetic signal accordingly. Interestingly, the magnetic anomaly gradually separates from the leading tsunami wave and travels ahead of the tsunami by a distance proportional to the fault’s longitudinal scale. We show that increased lateral propagation reduces the detectability of magnetic anomalies. Finally, we derive an asymptotic formula valid for the long leading wave that travels ahead of the dispersive group over transoceanic distances. This formula holds promise for the rapid assessment of tsunami risk. These findings advance fundamental understanding and may inform the development of future tsunami early warning systems relying on magnetic field detection.
We study transverse profiles and time fluctuations of turbulence dissipation rate, turbulence kinetic energy and integral length scales by means of high-speed stereoscopic particle image velocimetry in the turbulent wake of a 6 : 1 prolate spheroid that has its principal axis aligned with the incoming non-turbulent flow. This turbulent wake of a slender body differs from turbulent bluff body wakes in terms of transverse non-homogeneity of turbulence dissipation rate and because it is not axisymmetric even though it nominally is. Even so, both transverse profiles and time fluctuations of turbulence dissipation rate coefficients (inverse ratio between the rate with which the large scales lose energy and the rate with which the small scales dissipate energy) and of the Taylor length-based Reynolds number (ratio between the turbulent kinetic energy mostly in the large scales and the turbulent kinetic energy at the smallest scales) obey self-regulating non-equilibrium, as previously found in various other turbulent flows. However, the power law relating the transverse variations and the time fluctuations of these two ratios differs from previously reported self-regulating non-equilibrium power law scalings in other turbulent flows.
The coupling between Rayleigh–Taylor (R–T) and Saffman–Taylor (S–T) instabilities, when a gas displaces a high-viscosity liquid, remains challenging to elucidate due to the unclear roles of density and viscosity contrasts. Counterintuitively, our radial Hele-Shaw cell experiments revealed that viscosity contrast – typically considered a damping factor – serves as the primary driver of instability. We observed that the glycerin–air interface, despite its higher viscosity, exhibits significantly greater instability than the water–air interface. This anomalous behaviour arises from the S–T mechanism, which accelerates the onset of nonlinearity and induces an early transition to fingering. We applied a unified model to decouple the competing influences of surface tension oscillation and viscous damping on R–T instability and the S–T destabilisation. Moreover, we proposed criteria for either mostly enhancing or completely freezing the instability. These findings offer valuable insights into manipulating hydrodynamic instabilities in contracting/expanding geometries through surface tension and viscosity.
High-intensity focused ultrasound (HIFU) is a non-invasive alternative to traditional surgery for detection and treatment. When HIFU targets a specific area, ultrasonic cavitation occurs with mechanical stress, causing tissue damage, a process that is significantly influenced by the surroundings. This paper presents a numerical study on the cavitation initiation and evolution mechanisms under focused ultrasonic waves considering the influence of a solid surface. Firstly, the dynamic property of focused ultrasonic waves and the generation of diffraction waves is explained based on the Huygens–Fresnel principle, and the prefocused phenomenon is analysed. Notably, the scenario considering the existence of a solid wall is discussed, with the corresponding cavitation clouds in a ‘tree-like’ pattern that can be generally divided into three or four subregions. The different initiation mechanisms of the near-wall cavitation clouds under a different relative distance between the theoretical focal point and the solid wall are discussed in detail. Finally, by considering the effects of the incident waves, scattered waves and their reflected waves on the solid wall, a wave superposition model is established that can clearly explain the distribution characteristics of the near-wall cavitation clouds with different modes. The understanding of the ultrasonic cavitation mechanism may support precise control in future HIFU applications.
This paper presents the first experimental measurement of the Prandtl–Meyer function in the non-ideal compressible flow regime. Planar contoured nozzle profiles expand the flow to the supersonic regime, providing a uniform parallel flow of siloxane MM (hexamethyldisiloxane, $\textrm{C}_{6}\textrm{H}_{18}\textrm{OSi}_{2}$). Prandtl–Meyer expansions are then generated at sharp convex corners, for discrete flow deflection angles from 5$^\circ$ to 30$^\circ$. Stagnation pressures and temperatures are measured in the settling chamber, immediately upstream of the test section, to estimate the level of non-ideality of the investigated flows, ranging from mild non-ideal conditions to dilute ideal-gas states. Non-ideal thermodynamic effects through the expansions are characterised by means of independent measurements of Mach number by schlieren visualisations, and static pressure. Experimental comparisons across different thermodynamic states confirm the role of the compressibility factor evaluated at total conditions as a similarity parameter for moderately high non-ideal flows. To extract values of the Prandtl–Meyer function from the measurements, a simplified analytical model for the Prandtl–Meyer function dependency on the Mach number is formulated. The recovered values agree with Prandtl–Meyer theory, complemented with state-of-the-art thermodynamic models, for all the examined operating conditions.
This paper presents the design and evaluation of reflectarray antennas at spherical surfaces aimed at achieving an optimal compromise between electrical performance and mechanical deployability for satellite antenna solutions based on offset reflector configurations. By implementing printed reflectarray elements on a spherical surface, the phase-shifting elements mitigate spherical aberration, leading to enhanced focusing capabilities comparable to those of parabolic reflectors. The spherical geometry inherently simplifies the folding mechanism due to the rotational symmetry of the reflecting surface and minimizes the differential spatial phase delay, improving the reflectarray in-band performance. Simulation results demonstrate that large-aperture spherical reflectarrays can replicate the gain and beam quality of parabolic reflectors with smoother phase distributions than flat or multifaceted reflectarrays. The integration of spherical reflectarrays in dual-antenna configurations is evaluated to realize compact and efficient antenna systems for next-generation satellites.
The effect of a smooth surface hump on laminar–turbulent transition over a swept wing is investigated using direct numerical simulation (DNS), and results are compared with wind tunnel measurements. When the amplitude of incoming crossflow (CF) perturbation is relatively low, transition in the reference (without hump) case occurs near $53\,\%$ chord, triggered by the breakdown of type I secondary instability. Under the same conditions, no transition is observed in the hump case within the DNS domain, which extends to $69\,\%$ chord. The analysis reveals a reversal in the CF velocity component downstream of the hump’s apex. Within this region, the structure and orientation of CF perturbations are linearly altered, particularly near the wall. These perturbations gradually recover their original state further downstream. During this recovery phase, the lift-up mechanism is weakened, reducing linear production, which stabilises the stationary CF perturbations and weakens spanwise gradients. Consequently, the neutral point of high-frequency secondary CF instability modes shifts downstream relative to the reference case, leading to laminar–turbulent transition delay in the presence of the surface hump. In contrast, when the amplitude of the incoming CF perturbation is relatively high, a pair of stationary counter-rotating vortices forms downstream of the hump. These vortices locally deform the boundary layer and generate regions of elevated spanwise shear. The growth of secondary instabilities in these high-shear regions leads to a rapid advancement of transition towards the hump, in agreement with experimental observations.
Artificial intelligence and the future of warfare offers an innovative and counter-intuitive study of how and why AI-infused weapon systems will affect the strategic stability between nuclear-armed states. The book demystifies the hype surrounding AI in the context of nuclear weapons and, more broadly, future warfare. It highlights the potential, multifaceted intersections of this and other disruptive technology – robotics and autonomy, cyber, drone swarming, big-data analytics, and quantum communications – with nuclear stability. Anticipating and preparing for the consequences of the AI-empowered weapon systems is, therefore, fast becoming a critical task for national security and statecraft. The book considers the impact of these trends on deterrence, military escalation, and strategic stability between nuclear-armed states – especially China and the US. Surprisingly little research considers how AI might affect nuclear-armed states’ perceptions of others’ intentions, rational choices, or strategic decision-making psychology. The book addresses these topics and more. It provides penetrating, nuanced, and valuable insights grounded in the latest multi-disciplinary research. The book draws on a wealth of political and cognitive science, strategic studies, and technical analysis to shed light on the coalescence of developments in AI and other disruptive emerging technologies. It sketches a clear picture of the potential impact of AI on the digitized battlefield and broadens our understanding of critical questions for international affairs. AI will profoundly change how wars are fought, and how decision-makers think about nuclear deterrence, escalation management, and strategic stability – but not for the reasons you might think.
How might AI-augmented drone swarming and hypersonic weapons complicate missile defense, undermine states’ nuclear deterrent forces, and increase the risk of escalation? How might AI-augmented unmanned systems effect escalation, deterrence, and conflict management, when fewer human lives are perceived to be at risk? The proliferation of a broad range of AI-augmented autonomous weapon systems – most notably drones used in swarming tactics – might have significant strategic implications for nuclear security and escalation in future warfare. Unmanned autonomous systems could be deployed in complex missions in hitherto inaccessible and cluttered environments, and aerial and underwater drones in swarms might eventually replace intercontinental ballistic missiles and nuclear-armed submarines for the delivery of nuclear weapons.
This book advances the case for narrow AI as a fundamentally destabilizing force, which could increase the risk of nuclear war. It explains how, left unchecked, the uncertainties created by the rapid proliferation and diffusion of AI into advanced weapons systems will become a significant source of future instability and great power (especially US–China) strategic competition. It conceptualizes recent technological developments in AI with the broader spectrum of emerging technologies – robotics and autonomy, cyberspace, hypersonic weapons, 5G networks, and quantum communications – and analyzes the impact of these trends for future warfare between nuclear states. Anticipating and preparing for the consequences of AI has already become a critical – yet underappreciated – task for international security, defense planning, and statecraft.
Will AI-augmented technology increase the risk of military escalation between great military rivals? This chapter argues that diverging Sino-American views on the escalation risks of co-mingling nuclear and non-nuclear military technologies – long-range precision strike missiles, missile defenses, cyber offense, hypersonic weapons, autonomous weapon systems, and nuclear command, control, and communications – will exacerbate the destabilizing effects caused by the fusion of these capabilities with AI applications.