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Quantum field theory predicts a nonlinear response of the vacuum to strong electromagnetic fields of macroscopic extent. This fundamental tenet has remained experimentally challenging and is yet to be tested in the laboratory. A particularly distinct signature of the resulting optical activity of the quantum vacuum is vacuum birefringence. This offers an excellent opportunity for a precision test of nonlinear quantum electrodynamics in an uncharted parameter regime. Recently, the operation of the high-intensity Relativistic Laser at the X-ray Free Electron Laser provided by the Helmholtz International Beamline for Extreme Fields has been inaugurated at the High Energy Density scientific instrument of the European X-ray Free Electron Laser. We make the case that this worldwide unique combination of an X-ray free-electron laser and an ultra-intense near-infrared laser together with recent advances in high-precision X-ray polarimetry, refinements of prospective discovery scenarios and progress in their accurate theoretical modelling have set the stage for performing an actual discovery experiment of quantum vacuum nonlinearity.
We investigate convection in a thin cylindrical gas layer with an imposed flux at the bottom and a fixed temperature along the side, using a combination of direct numerical simulations and laboratory experiments. The experimental approach allows us to extend by two orders of magnitude the explored range in terms of flux Rayleigh number. We identify a scaling law governing the root-mean-square horizontal velocity and explain it through a dimensional analysis based on heat transport in the turbulent regime. Using particle image velocimetry, we experimentally confirm, for the most turbulent regimes, the presence of a drifting persistent pattern consisting of radial branches, as identified by Rein et al. (2023, J. Fluid Mech.977, A26). We characterise the angular drift frequency and azimuthal wavenumber of this pattern as functions of the Rayleigh number. The system exhibits a wide distribution of heat flux across various time scales, with the longest fluctuations attributed to the branch pattern and the shortest to turbulent fluctuations. Consequently, the branch pattern must be considered to better forecast important wall heat flux fluctuations, a result of great relevance in the context of nuclear safety, the initial motivation for our study.
Direct numerical simulations in periodic plane channels are used to study turbulent flow over ‘patches’ of roughness distributed on otherwise smooth walls. Circular patches as well as those resembling natural bio-fouling roughness are considered. Roughnesses within the patches are statistically similar and formed by random distribution of roughness elements of truncated cone shape. The two main studied parameters are the characteristic length scale of the patches $\varLambda _P$ and roughness area coverage ratio (CR). To provide a reference, simulations of homogeneous roughness (i.e. with 100 % CR) are performed at different roughness element densities translated into different values of frontal solidity. Results show that when $\varLambda _P$ is of the order of channel half-height $\delta$, the global friction coefficient $C_f$ of patchy roughness is scattered around that of homogeneous roughness with similar ‘mean’ frontal solidity. As $\varLambda _P/\delta$ grows, asymptotic convergence towards an equilibrium value is identified. Considering the present data, a normalised $C_f$ can be satisfactorily correlated by $\varLambda _P/\delta$; the normalisation includes $C_f$ for a homogeneous roughness similar to the patch roughness at two limiting cases. This points towards the possibility to develop a universal heterogeneous roughness correlation based on a knowledge of existing homogeneous roughness correlations. Furthermore, local and global flow statistics are studied, which among others, indicate formation of secondary motions for regular patch arrangement at $\varLambda _P\approx \delta$ with implications on the outer layer similarity of global mean velocity and Reynolds stress profiles.
We consider the stability of Couette flow when travelling vibrations perturb one boundary. It is demonstrated that if the bounding surface profile takes the form of sinusoidal waves, then the otherwise stable shear flow becomes unstable for appropriately chosen values of vibration amplitude, phase speed and wavenumber. When instability arises, it is driven by centrifugal forces that are created by wave-imposed changes in the direction of fluid movement. Only subcritical waves, defined as vibrations with phase speed smaller than the maximum flow velocity, cause instability. As the fluid Reynolds number grows, the interval of vibration wavenumbers and phase speeds capable of flow destabilisation is enhanced. A range of parameters is identified for which the vibrations seem to play dual roles: they decrease the flow resistance while simultaneously generating streamwise vortices. This vibration class constitutes an energy-efficient control tool that may potentially intensify the mixing within a flow.
In this article, we investigate the behaviour of a cohesive granular material in a rotating drum. We use a model material with tuneable cohesion and vary the dimension of the drum in the radial and axial directions. The results show that the geometry of the drum may play a crucial role in the material dynamics, leading to significant changes in the surface morphology and flow regime. We attribute this behaviour to the fact that an increase in cohesion causes the grains to feel the sidewalls at a greater distance. Finally, we rationalize the results by introducing two dimensionless characteristic lengths, defined as the ratio of the drum dimensions to a cohesive length, which allow for the interpretation of the variation in the surface morphology and of the different flow regimes observed experimentally.
We present a novel technique to render objects invisible to incident waves in a water waveguide system with parallel walls at low frequencies. The invisibility of a waveguide defect, specifically a vertical surface-piercing circular cylinder, is achieved through local deformations of the waveguide walls in the immediate vicinity of the defect. Our method results in a reflection coefficient that is at least 20 times lower than in the case of a parallel waveguide. The effect is observed over a broad frequency range. Experimental results confirm the high efficiency of our approach, showing that backscattered energy is reduced by a factor of 100–5000 compared with the reference case within the considered frequency range.
Galaxy cluster X-ray cavities are inflated by relativistic jets that are ejected into the intracluster medium by active galactic nuclei (AGN). AGN jets prevent predicted cooling flow establishment at the cluster centre, and while this process is not well understood in existing studies, simulations have shown that the heating mechanism will depend on the type of gas that fills the cavities. Thermal and non-thermal distributions of electrons will produce different cavity Sunyaev Zel’dovich (SZ) effect signals, quantified by the ‘suppression factor’ f. This paper explores potential enhancements to prior constraints on the cavity gas type by simulating suppression factor observations with the Square Kilometre Array (SKA). Cluster cavities across different redshifts are observed to predict the optimum way of measuring f in future observations. We find that the SKA can constrain the suppression factor in the cavities of cluster MS 0735.6+7421 (MS0735) in as little as 4 h, with a smallest observable value of $f \approx 0.42$. Additionally, while the SKA may distinguish between possible thermal or non-thermal suppression factor values within the cavities of MS0735 if it observes for more than 8 h, determining the gas type of other clusters will likely require observations at multiple frequencies. The effect of cavity line of sight (LOS) position is also studied, and degeneracies between LOS position and the measured value of f are found. Finally, we find that for small cavities (radius < 80 kpc) at high redshift ($z \approx 1.5$), the proposed high frequencies of the SKA (23.75–37.5 GHz) will be optimal, and that including MeerKAT antennas will improve all observations of this type.
Intermittent swimming behaviour is commonly observed in larval zebrafish, often attributed to energy-saving mechanisms. In this study, we utilize a hybrid approach combining deep reinforcement learning and the immersed boundary–lattice Boltzmann method to train a larval zebrafish-like swimmer to reach a target with minimized energy expenditure. We find that when the tail-beat period is fixed, continuous swimming emerges as the optimal strategy. However, when the tail-beat period is allowed to vary, intermittent swimming proves superior in energy performance, achieved through reductions in tail-beat amplitude and frequency. Our detailed analysis reveals that intermittent swimmers employ rapid backward tail flicks to attain high speeds, coupled with slower forward tail flicks and coasting phases to conserve energy. Furthermore, we derive scaling laws governing the swimming performance of trained fish. These results offer valuable insights into the intermittent swimming patterns of fish, with implications for understanding bio-inspired locomotion and informing the design of energy-efficient aquatic systems.
The impact of intrinsic compressibility effects – changes in fluid volume due to pressure variations – on high-speed wall-bounded turbulence has often been overlooked or incorrectly attributed to mean property variations. To quantify these intrinsic compressibility effects unambiguously, we perform direct numerical simulations of compressible turbulent channel flows with nearly uniform mean properties. Our simulations reveal that intrinsic compressibility effects yield a significant upward shift in the logarithmic mean velocity profile that can be attributed to the reduction in the turbulent shear stress. This reduction stems from the weakening of the near-wall quasi-streamwise vortices. In turn, we attribute this weakening to the spontaneous opposition of sweeps and ejections from the near-wall expansions and contractions of the fluid, and provide a theoretical explanation for this mechanism. Our results also demonstrate that intrinsic compressibility effects play a crucial role in the increase in inner-scaled streamwise turbulence intensity in compressible flows, as compared with incompressible flows, which was previously regarded to be an effect of mean property variations alone.
Coherent structures over two distinct, organized wall perturbations – a transverse sinusoidal bump with and without small-scale longitudinal grooves – are studied using direct numerical simulations. Large-scale spanwise rollers (SRs) form via shear layer rollup past the bump peak, enveloping a large separation bubble (SB) for both a smooth wall (SW) and a grooved wall (GW). In a GW, small-scale alternatingly spinning jets emanating from the crests’ corners merge with the shear layer, altering the SRs compared with SRs in a SW. The underlying coherence of the highly turbulent SRs is educed via phase-locked ensemble averaging. Coherent vorticity contours of SRs are ellipses tilted downward, hence causing co-gradient Reynolds stress. The limited streamwise length of SB precludes SR tumbling, unlike in a free shear layer. The coherent field reveals minibubbles attached to the bump’s downstream wall with circulation opposite to that of the SB – they are larger, stronger and more numerous in GW than in SW – reducing skin friction. Compared with SW, the swirling jets in GW increase coherent production while decreasing incoherent production. Additionally, the jets push the SRs to travel faster and farther before reattachment. The SB experiences two different modes of oscillation due to high-frequency advection of the shear layer SR and low-frequency breathing of the SB, where the former dominates in GW and the latter in SW. Negative production is caused by counter-rotating vortex dipoles inducing flow ejections (for both SW and GW) and single vortices penetrating the grooves – both occurring in the region of flow acceleration.
This study investigates how the spatial configuration of submerged three-dimensional patches of vegetation impacts turbulence. Laboratory experiments were conducted in a channel with submerged patches of model vegetation configured with different patch area densities ($\phi _{p}$), representing the bed area fraction occupied by patches, ranging from 0.13 to 0.78, and different spatial patterns transitioning from two dimensional (channel-spanning patches) to three dimensional (laterally unconfined patches). These configurations produced a range of flow regimes within the canopy, from wake interference flow to skimming flow. At low area density ($\phi _{p}\lt0.5$), turbulence within the canopy increased with increasing $\phi _{p}$ regardless of spatial configuration, while at high area density ($\phi _{p}\gt0.5$), the relationship between turbulence and $\phi _{p}$ depended on the spatial configuration of the patches. For the same patch area density, the configuration with smaller lateral gaps generated stronger turbulence within the canopy. The relative contributions of wake and shear production also varied with the spatial configuration of the patches. At low area densities, wake production dominated over shear production, while at high area densities, shear production was more dominant due to an enhanced shear layer at the top of the canopy and reduced mean velocity within the canopy. A new predictive model for the channel-averaged turbulent kinetic energy (TKE) was developed as a function of channel-averaged velocity, canopy geometry, and patch area density, which showed good agreement with the measured TKE.
Deep reinforcement learning (DRL) is employed to develop control strategies for drag reduction in direct numerical simulations of turbulent channel flows at high Reynolds numbers. The DRL agent uses near-wall streamwise velocity fluctuations as input to modulate wall blowing and suction velocities. These DRL-based strategies achieve significant drag reduction, with maximum rates $35.6\,\%$ at $Re_{\tau }\thickapprox 180$, $30.4\,\%$ at $Re_{\tau }\thickapprox 550$, and $27.7\,\%$ at $Re_{\tau }\thickapprox 1000$, outperforming traditional opposition control methods. An expanded range of wall actions further enhances drag reduction, although effectiveness decreases at higher Reynolds numbers. The DRL models elevate the virtual wall through blowing and suction, aiding in drag reduction. However, at higher Reynolds numbers, the amplitude modulation of large-scale structures significantly increases the residual Reynolds stress on the virtual wall, diminishing the drag reduction. Analysis of budget equations provides a systematic understanding of the underlying drag reduction dynamics. The DRL models reduce skin friction by inhibiting the redistribution of wall-normal turbulent kinetic energy. This further suppresses the wall-normal velocity fluctuations, reducing the production of Reynolds stress, thereby decreasing skin friction. This study showcases the successful application of DRL in turbulence control at high Reynolds numbers, and elucidates the nonlinear control mechanisms underlying the observed drag reduction.
We study the effect of turbulence on collisions between a finite-size bubble and small inertial particles based on interface-resolved simulations. Our results show that the interaction with the flow field around the bubble remains the dominant effect. Nonlinear dependencies in this process can enhance the turbulent collision rate by up to 100 % compared to quiescent flow. Fluctuations in the bubble slip velocity during the interaction with the particle additionally increase the collision rate. We present a frozen-turbulence model that captures the relevant effects providing a physically consistent framework to model collisions of small inertial particles with finite-sized objects in turbulence.
The measurement problem has been a central puzzle of quantum theory since its inception, and understanding how the classical world emerges from our fundamentally quantum universe is key to its resolution. While the 'Copenhagen' and 'Many Worlds' interpretations have dominated discussion of this philosophically charged question, Zurek builds on the physics of decoherence and introduces the theory of 'Quantum Darwinism' to provide a novel account of the emergence of classical reality. Opening with a modern view of quantum theory, the book reconsiders the customary textbook account of quantum foundations, showing how the controversial axioms (including Born's rule) follow from the consistent core postulates. Part II discusses decoherence and explores its role in the quantum-to-classical transition. Part III introduces Quantum Darwinism, explaining how an information-theoretic perspective complements, elucidates, and reconciles the 'Copenhagen' and 'Many Worlds' interpretations. This insightful book is essential reading for any student or researcher interested in quantum physics.
A numerical study supplemented with theoretical analysis is made, to analyse the electrophoresis of highly charged soft particles in electrolytes with trivalent counterions. The electrokinetic model is devised under the continuum hypothesis, which incorporates the ion–ion electrostatic correlations, hydrodynamic steric interactions of finite sized ions and ion–solvent interactions. The governing equations for ion transport and electric field are derived from the volumetric free energy of the system, which includes the first-order correction for the non-local electrostatic correlations of interacting ions, excess electrochemical potential due to finite ion size as well as the Born energy difference of ions due to dielectric permittivity variation. The electrolyte viscosity is considered to be a function of the local volume fraction of finite-sized ions, which causes the diffusivity of ions to vary locally. The occurrence of mobility reversal of a soft particle having the same polarity of its core and soft shell charge and formation of a coion-dominated zone in the soft layer is elaborated through this study. This can explain the mechanisms for the attraction between like-charged soft particles, as seen in the condensation of DNAs. The impact of ion–ion correlations and ion–solvent interactions of finite-sized ions are analysed by comparing them with the results based on the standard model. At a higher range of the core charge density, the ion–ion correlations induce a condensed layer of counterions on the outer surface of the core, which draws coions in the electric double layer, leading to an inversion in polarity of the charge density and mobility reversal. The dielectric decrement and ion steric interactions create a saturation in ion distribution and hence, modify the condensed layer of counterions. The enhanced fixed charge density of the polyelectrolyte layer diminishes the ion correlations due to the stronger screening effects and prevents the formation of a coion dominated zone in the Debye layer. The impact of the counterion size and the mixture of monovalent and trivalent counterions on mobility is analysed.
We studied flow organization and heat transfer properties in mixed turbulent convection within Poiseuille–Rayleigh–Bénard channels subjected to temporally modulated sinusoidal wall temperatures. Three-dimensional direct numerical simulations were performed for Rayleigh numbers in the range $10^6 \leqslant Ra \leqslant 10^8$, a Prandtl number $Pr = 0.71$ and a bulk Reynolds number $Re_b \approx 5623$. We found that high-frequency wall temperature oscillations had minimal impact on flow structures, while low-frequency oscillations induced adaptive changes, forming stable stratified layers during cooling. Proper orthogonal decomposition (POD) analysis revealed a dominant streamwise unidirectional shear flow mode. Large-scale rolls oriented in the streamwise direction appeared as higher POD modes and were significantly influenced by lower-frequency wall temperature variations. Long-time-averaged statistics showed that the Nusselt number increased with decreasing frequency by up to 96 %, while the friction coefficient varied by less than 15 %. High-frequency modulation predominantly influenced near-wall regions, enhancing convective effects, whereas low frequencies reduced these effects via stable stratified layer formation. Phase-averaged statistics showed that high-frequency modulation resulted in phase-stable streamwise velocity and temperature profiles, while low-frequency modulation caused significant variations due to weakened turbulence. Turbulent kinetic energy (TKE) profiles remained high near the wall during both heating and cooling at high frequency, but decreased during cooling at low frequencies. A TKE budget analysis revealed that during heating, TKE production was dominated by shear near the wall and by buoyancy in the bulk region; while during cooling, the production, distribution and dissipation of TKE were all nearly zero.
The resonance mechanism in the initial of wind-wave generation proposed by Phillips (1957. J. Fluid Mech.2, 417–445) is a foundation of wind-wave generation theory, but a precise theoretical quantification of wave energy growth in this initial stage has not been obtained yet after more than six decades of research. In this study, we aim to address this knowledge gap by developing an analytical approach based on a novel complex analysis method to theoretically investigate the temporal evolution of the wave energy in the Phillips initial stage. We quantitatively derive and analyse the growth behaviour of the surface wave energy and obtain an analytical solution for its upper bound. Our result highlights the crucial effects of surface tension. Because the phase velocity of gravity–capillary waves has a minimal value at a critical wavenumber, gravity–capillary waves and gravity waves (which neglect surface tension) exhibit distinct resonance curve properties and wave energy growth behaviours. For gravity waves, the resonance curve extends indefinitely; for gravity–capillary waves, it either forms a finite-length curve or does not exist, depending on the wind speed. The leading-order term of the upper-bound solution of the energy of gravity waves increases linearly over time, while for gravity–capillary waves, the term increases linearly over time under strong wind conditions but remains finite under weak wind conditions. This theoretical study provides an analytical framework for the generation of wind-waves in the Phillips initial stage, which may inspire further theoretical, numerical and experimental research.
The transport process of a relativistic electron beam (REB) in high-density and degenerate plasmas holds significant importance for fast ignition. In this study, we have formulated a comprehensive theoretical model to address this issue, incorporating quantum degeneracy, charged particle collisions and the effects of electromagnetic (EB) fields. We model the fuel as a uniform density region and particularly focus on the effect of quantum degeneracy during the transport of the REB, which leads to the rapid growth of a self-generated EB field and a subsequently significant self-organized pinching of the REB. Through our newly developed hybrid particle-in-cell simulations, we have observed a two-fold enhancement of the heating efficiency of the REB compared with previous intuitive expectation. This finding provides a promising theoretical framework for exploring the degeneracy effect and the enhanced self-generated EB field in the dense plasma for fast ignition, and is also linked to a wide array of ultra-intense laser-based applications.
This study introduces a novel approach to investigate the Reynolds analogy in complex flow scenarios. It is shown that the total mechanical energy $\mathit {B}$, viz. the sum of kinetic energy and pressure work, and the field $\Gamma =\theta ^2/2$ (where $\theta$ is the transported passive scalar) are governed by two equations that are similar in form, when time-averaged for statistically stationary flows. For fully developed channel flows the integral energy balance links the mean bulk velocity and scalar with the volume averages of the respective dissipation rates, allowing the assessment of the Reynolds analogy in terms of the dissipation fields. This approach is tested on direct numerical simulation data of rough-wall turbulent channel flow at two different roughness Reynolds numbers, namely $k^+=15$ and $k^+=90$. For a unit Prandtl number, the same qualitative behaviour is observed for the mean wall-normal distributions of the budget-equation terms of $B$ and $\Gamma$, the latter being larger than the corresponding terms in the mechanical-energy budget. The Reynolds decomposition of the flow into temporal mean and stochastic parts reveals that roughness primarily affects the mean-flow dissipation. For the $k^+=90$ case, the analysis shows that attached-flow and high-shear regions dominate the integral mean scalar and momentum transfer and exhibit the greatest differences between the mean mechanical and scalar dissipation rates. In contrast, well-mixed regions, sheltered by large roughness elements, contribute similarly and minimally to the integral scalar and momentum transfer.
Machine learning has already shown promising potential in tiled-aperture coherent beam combining (CBC) to achieve versatile advanced applications. By sampling the spatially separated laser array before the combiner and detuning the optical path delays, deep learning techniques are incorporated into filled-aperture CBC to achieve single-step phase control. The neural network is trained with far-field diffractive patterns at the defocus plane to establish one-to-one phase-intensity mapping, and the phase prediction accuracy is significantly enhanced thanks to the strategies of sin-cos loss function and two-layer output of the phase vector that are adopted to resolve the phase discontinuity issue. The results indicate that the trained network can predict phases with improved accuracy, and phase-locking of nine-channel filled-aperture CBC has been numerically demonstrated in a single step with a residual phase of λ/70. To the best of our knowledge, this is the first time that machine learning has been made feasible in filled-aperture CBC laser systems.