Turbulent Channel Research Articles

Underwater Wavelength Attack on Discrete Modulated Continuous-Variable Quantum Key Distribution.

The wavelength attack utilizes the dependence of beam splitters (BSs) on wavelength to cause legitimate users Alice and Bob to underestimate their excess noise so that Eve can steal more secret keys without being detected. Recently, the wavelength attack on Gaussian-modulated continuous-variable quantum key distribution (CV-QKD) has been researched in both fiber and atmospheric channels. However, the wavelength attack may also pose a threat to the case of ocean turbulent channels, which are vital for the secure communication of both ocean sensor networks and submarines. In this work, we propose two wavelength attack schemes on underwater discrete modulated (DM) CV-QKD protocol, which is effective for the case with and without local oscillator (LO) intensity monitor, respectively. In terms of the transmittance properties of the fused biconical taper (FBT) BS, two sets of wavelengths are determined for Eve's pulse manipulation, which are all located in the so-called blue-green band. The derived successful criterion shows that both attack schemes can control the estimated excess noise of Alice and Bob close to zero by selecting the corresponding condition parameters based on channel transmittance. Additionally, our numerical analysis shows that Eve can steal more bits when the wavelength attack controls the value of the estimated excess noise closer to zero.

Development of an accurate central finite-difference scheme with a compact stencil for the simulation of unsteady incompressible flows on staggered orthogonal grids

In scale-resolving simulations such as Large Eddy Simulations (LES), the spatial discretization scheme of the convective term plays a crucial role in avoiding interference between the numerical errors and the subgrid-scale model. Accurate schemes lead to lower truncation errors and better predictions of turbulent flows without the need for an excessively refined grid. To this end, a new second-order finite-difference scheme (HCDS6) has been developed for incompressible flows and orthogonal staggered grids. Compared to the standard second-order scheme, the new scheme has significantly lower dispersion errors. Compared to existing high-order schemes, the numerical stencil of HCDS6 is more compact, which makes it easier to implement, especially considering boundary conditions around complex geometries using Immersed Boundary Methods (IBM). The HCDS6 scheme conserves the discrete momentum with limited production or dissipation of discrete kinetic energy, which guarantees its numerical stability. Its performance is evaluated using an open-source CFD package called REEF3D. Three benchmarks demonstrate the key properties and performance of the scheme: the convection of an isentropic vortex, the Taylor-Green vortex flow, and turbulent channel flow. Its relatively low dispersion errors, combined with ease of implementation, make the HCDS6 scheme a promising candidate for efficient scale-resolving simulations of turbulent flows.

Recognition and information transmission of multiplexed fractional orbital angular momentum

We propose an improved hologram with both phase and amplitude modulation to generate superimposed fractional optical vortices (SFOVs). The modulation of the optical field’s amplitude and phase is achieved through the utilization of controllable diffraction efficiency of the transmission function. The resulting interference fringes of an SFOV with four orbital angular momentum (OAM) modes exhibit a distinctive double-petal-like structure, serving as a distinguishable feature for the beam’s topological charges. Accurate demodulation of the multiplexed OAM modes of 256-ary SFOV is achieved using a residual next neural network based on machine learning. To showcase its practical utility, we employ the coherent OAM multiplexing system to transmit a Newton portrait with 0.01% error rate. Furthermore, the system robustly identifies beams propagating through computer-simulated oceanic turbulence channels to aid in the development of underwater optical communication. These promising results demonstrate the potential to further expand the range of modes and enhance the information processing capabilities in optical communication.

Performance enhancement of atmospheric turbulence channels in DWDM-FSO PON communication systems using M-ary hybrid DPPM-M-PAPM modulation schemes under pointing errors, ASE noise and interchannel crosstalk

Performance enhancement of atmospheric turbulence channels in DWDM-FSO PON communication systems using M-ary hybrid DPPM-M-PAPM modulation schemes under pointing errors, ASE noise and interchannel crosstalk

Influence of Coherent Vortex Rolls on Particle Dynamics in Unstably Stratified Turbulent Channel Flows

This work investigates the dynamics of heavy particles dispersed in turbulent channel flows under unstable thermal stratification conditions using point-particle direct numerical simulations (PP-DNS), to quantify the influence of large-scale coherent vortex rolls, arising from the combined effects of shear and buoyancy, on the spatial distribution and preferential sampling behavior of inertial particles. We examined three particle Stokes numbers (St+=0.6,60,120) and two friction Richardson numbers, Riτ=0.272 and Riτ=27.2, which exemplify the regimes below and above the critical condition for vortex roll formation, respectively. The results indicate that the flow reorganization into large-scale longitudinal vortices significantly alters the topological features of small scales in the near-wall region impinged by the thermal plumes, resulting in a prevalence of vorticity-dominated topologies. The interplay between this phenomenon and the tendency of particles to preferentially sample strain-dominated topologies leads to a distinctive asymmetric particle distribution in the near-wall planes. Inertial particles markedly accumulate in the strain-dominated regions where the coherent thermal plumes emerge from the walls, while avoiding the vorticity-dominated impingement zones. This peculiar particle response to the vortex rolls is most pronounced when the particle response time matches the characteristic timescale of the large-scale coherent motions in the cross-stream planes.

Super-resolution reconstruction of turbulent flows with a hybrid framework of attention

In a plethora of research endeavors concerning flow fields, acquiring high-resolution data is paramount. However, obtaining high-resolution turbulence data invariably requires substantial computational resources. Although super-resolution reconstruction of turbulent fields has emerged as a salient technique for detail extraction, conventional interpolation methods pose a significant challenge in reconstructing small-scale structures, often resulting in overly smooth outcomes. In this study, we propose a novel hybrid framework of spatially-adaptive feature attention (HSAFA) for the high-quality reconstruction of turbulent fields. This framework is characterized by the implementation of multidimensional feature fusion, which enhances the model's ability to capture details of turbulence. We rigorously applied the proposed model to datasets comprising laminar flow around a square cylinder and turbulent channel flows, with the reconstructed instantaneous velocity fields and statistics subjected to exhaustive and comparative analysis. Our findings demonstrate that HSAFA is capable of effectively reconstructing high-resolution turbulence fields from significantly low-resolution data, covering the range from laminar to turbulent flows.

Phase and Channel Estimation for High-Capacity Phase-Asynchronous Mode-Division Multiplexing Multiple-Input Multiple-Output Free-Space Optical Communications in Strong Turbulent Channels

Phase and Channel Estimation for High-Capacity Phase-Asynchronous Mode-Division Multiplexing Multiple-Input Multiple-Output Free-Space Optical Communications in Strong Turbulent Channels

Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements

Accurate prediction and measurement of wall-shear stress dynamics in fluid flows is crucial in domains as diverse as transportation, public utility infrastructure, energy technology and human health. However, we still lack adequate experimental methods that simultaneously capture the temporal and the spatial behaviour of the wall-shear stress. In this contribution, we present a holistic approach that derives these dynamics from particle-image velocimetry (PIV) measurements using a deep optical flow estimator with physical knowledge. While the experimental measurements resemble state-of-the-art PIV set-ups, the established particle image processing is replaced by a deep neural network specifically tailored to extract velocity and wall-shear stress information. Since this WSSflow framework operates at the original image resolution, it provides the respective flow field information at a much higher spatial resolution compared with state-of-the-art PIV processing. The results show that this per-pixel approach is essential for an accurate wall-shear stress estimation. The validity and physical correctness of the derived flow quantities are demonstrated with synthetic and real-world experimental data of a turbulent channel flow, a wavy turbulent channel flow and an elastic blood vessel flow. Where baseline data are available for comparison, the instantaneous and time-averaged wall-shear stress predictions accurately follow the ground truth data.

Direct numerical simulation of turbulent flow over irregular rough surfaces

Direct numerical simulations of turbulent channel flow at a shear Reynolds number of Re*=360 in smooth and rough channels have been performed. Made of irregular undulations, surface roughness was such that the ratio of the channel half-height to the root mean square roughness height is equal to 48, and the root mean square and the maximum crest and trough heights are equal to 7.5 and 23 wall units, respectively. The simulation results confirm that turbulence in the outer layer is not directly affected by the rough surface. The roughness effects on the turbulent stresses, the mean momentum balance, and the budget of turbulence kinetic energy are confined to the layer between 0 and 25 wall units; beyond which the profiles collapse with those for smooth channels. In the roughness sublayer, the peak value of the streamwise normal stress is reduced, while the spanwise and wall-normal components are increased. The largest increase is for the Reynolds shear stress, resulting in a significant increase in the turbulence production near the wall, even though the velocity gradient is decreased. The kinetic energy budget shows that turbulence production dominates the mean viscous diffusion of turbulence kinetic energy, and both mechanisms are balanced by turbulent dissipation. The friction factor using the Colebrook–White correlation calculated by specifying the sand–grain roughness equal to the root mean square of the roughness height predicts the friction velocity and the bulk velocity accurately. The streaky structures that exist near smooth walls were observed to be broken by the roughness elements, leading to a denser population of coherent structures near the wall, which increases the velocity fluctuations. The coherent structures developed in the roughness layer do not seem to penetrate into the outer layer.

Effect of the yaw angle on the riblet performance

Direct numerical simulations are performed to investigate the effect of the yaw angle (α) on the drag-reduction performance by riblets in a turbulent channel flow. The yaw angles of α=0° and 90° correspond to the riblets parallel and perpendicular to the free-stream direction, respectively. It is well known that maximum drag reduction occurs at α=0°, and the drag-reduction performance by riblets is degraded with increasing α. We obtain the critical yaw angle below which drag reduction can be achieved and investigate the corresponding mechanism. From a parametric study, drag reductions are achieved for α<20°. As α increases, the form drag increases more rapidly than the skin-friction drag decreases, resulting in the increase in the total drag. At drag reducing and increasing yaw angles, the mean velocity profiles in wall units shift upward and downward, respectively, and the latter profile suggests that the drag-increasing riblet is similar to a rough surface. With increasing α, the wall-normal and spanwise velocity fluctuations and Reynolds shear stress increase, and stronger near-wall vortices are generated. By applying a scaling analysis to the momentum equation in the riblet-perpendicular direction and the FIK (Fukagata–Iwamoto–Kasagi) identity [Fukagata et al., “Contribution of Reynolds stress distribution to the skin friction in wall-bounded flows,” Phys. Fluids 14, L73–L76 (2002); Peet and Sagaut, “Theoretical prediction of turbulent skin friction on geometrically complex surfaces,” Phys. Fluids 21, 105105 (2009); Bannier et al., “Riblet flow model based on an extended FIK identity,” Flow Turbul. Combust. 95, 351–376 (2015)] to the riblet-parallel direction, the total drag is expressed as a linear function of sin2α.

A posteriori study on wall modeling in large eddy simulation using a nonlocal data-driven approach

The feasibility of wall modeling in large eddy simulation (LES) using convolutional neural network (CNN) is investigated by embedding a data-driven wall model developed using CNN into the actual simulation. The training dataset for the data-driven wall model is provided by the direct numerical simulation of turbulent channel flow at Reτ=400. The data in the inner layer, excluding y+≤10, are used in the training process. The inputs of the CNN wall model are the velocity components, and the outputs of the wall model are the streamwise and spanwise components of the wall shear stress. An a priori test has already been carried out in our previous study to assess the potential of CNN in establishing a wall model, and the results have shown the reasonable accuracy of the CNN model in predicting the wall shear stress. In this study, the focus is on the a posteriori test, and the performance of the CNN wall model is investigated in the actual LES under various conditions. Initially, the model is used in a simulation with the same specifications as those used for obtaining the training dataset, and the effect of the wall-normal distance of the CNN model inputs is investigated. Then, the model is tested for coarser grid sizes and higher Reynolds number flows to check its generalizability. The performance of the model is also compared with one of the commonly used existing wall models, called ordinary differential equation (ODE)-based wall model. The results show that the CNN wall model has better accuracy in predicting the wall shear stress in the a posteriori test compared to the ODE-based wall model. Moreover, it is able to predict the flow statistics with reasonable accuracy for the wall-modeled LES under various conditions different from those of the training dataset.

Secondary motions in turbulent ribbed channel flows

Secondary motions in turbulent ribbed channel flows

Numerical investigation of the effect of air layer on drag reduction in channel flow over a superhydrophobic surface.

This study investigates the effects of an air layer on drag reduction and turbulence dynamics in channel flow over a superhydrophobic surface (SHS). Employing the OpenFOAM platform, direct numerical simulation was conducted to investigate turbulent channel flow with an air layer over an SHS. The simulations, which take into account the interaction between water and air, analyze various parameters such as velocity distribution, drag reduction (DR), Reynolds stress, turbulent kinetic energy (TKE), and coherent structures near the water-air interface. The presence of an air layer significantly alters the velocity distribution, leading to higher velocities at the interface compared to simulations without the air layer. Notably, the thickness of the air layer emerges as an important factor, with larger thicknesses resulting in increased velocities and drag reduction. This study underscores the substantial impact of the air layer on TKE near the superhydrophobic surface, emphasizing its role in understanding and optimizing drag reduction. Furthermore, the nonlinear relationship between slip velocity, Q contours, and coherent structures near the SHS are investigated.

Manchester Return-to-Zero On–Off Keying Modulation for Free-Space Optical Communication

This paper proposes a Manchester return-to-zero on–off keying (M-RZ-OOK) modulation for free-space optical (FSO) communication. M-RZ-OOK modulation is achieved by introducing Manchester coding into the RZ-OOK format. M-RZ-OOK has the features of phase-flipped impulse series in the spectrum. Therefore, normal and inversed channel state information (CSI) can be extracted by applying a local oscillator (LO) with the frequencies of impulses, respectively. These extracted CSIs can be applied to realize adaptive threshold decision (ATD) and adaptive power transmission (APT) in the forward and backward links simultaneously. The proposed M-RZ-OOK modulation was verified in simulations using various turbulence channels. The simulation results demonstrated that ATD and APT were effectively accomplished in the forward and backward links with the estimated normal and inversed CSIs.

BER Performance analysis of a convolutional coded non-Hermitian OFDM FSO system over strong atmospheric turbulent channel

BER Performance analysis of a convolutional coded non-Hermitian OFDM FSO system over strong atmospheric turbulent channel

An experimental investigation into the drag reduction performance of dimpled plates in a fully turbulent channel flow

Dimpled surfaces have gained increasing attention in recent years for their potential to reduce turbulent skin friction, a capability previously acknowledged for its beneficial implications on heat transfer. As a passive drag reduction method, dimpled surfaces offer significant advantages for marine applications due to their effectiveness and practical applicability. However, despite numerous studies, conflicting opinions and inconsistent drag reduction rates persist in the literature. This paper addresses these ambiguities and offers valuable insights into the effectiveness of dimpled surfaces for drag reduction in fully turbulent flows. We conducted an extensive experimental investigation involving various dimple configurations, including different depth-to-diameter ratios, diameters and orientations, utilising a specialised Fully Turbulent Flow Channel facility and a Particle Image Velocimetry system. Our findings demonstrated that circular dimple geometries, particularly those with low depth ratios, can achieve significant drag reduction of up to 27% as the Reynolds number increases. These results highlight the substantial potential of dimpled surfaces for improving energy efficiency in marine applications, where skin friction accounts for a significant portion of the total drag experienced by large vessels.

Boundary Layer Thickness and Friction Velocity by Symmetry Arguments.

We derive analytically for the first time the downstream evolution of the boundary layer thickness and the friction velocity of the zero-pressure-gradient turbulent boundary layer (ZPGTBL). Lie groups were used to derive the downstream evolution and to obtain the full set of the similarity variables and the leading-order similarity equations. An approximate leading-order solution was obtained using matched asymptotic expansions. The similarities and differences between ZPGTBL and turbulent channel flows in terms of the similarity equations are discussed to support the notion of leading-order universality of the near-wall layer.

Information propagation of focus wave mode localized waves in anisotropic turbulent seawater

The evolution of the information transfer capability of an optical system for underwater focused wave mode localized wave (FWMLW) in anisotropic weakly turbulent absorbing seawater is studied. By developing the probability distribution function as well as the detection probability of the vortex modes carried by the FWMLW and the average bit error rate of the FWMLW underwater system, the information capacity of the FWMLW system with a pointing error is modeled. Through a numerical analysis of the effects of turbulent seawater and optical system parameters on the built light intensity, the detection probability, and the information capacity models, we find that the FWMLW system has an optimal delay time determined by the spectrum bandwidth when the spectrum bandwidth is greater than 1. The information capacity of the FWMLW system is higher than that of the X localized wave system under the same turbulent seawater channel condition, and FWMLW is a better optical signal source for vortex mode division multiplexing underwater systems than a Bessel–Gaussian beam.

Identifying regions of importance in wall-bounded turbulence through explainable deep learning

Despite its great scientific and technological importance, wall-bounded turbulence is an unresolved problem in classical physics that requires new perspectives to be tackled. One of the key strategies has been to study interactions among the energy-containing coherent structures in the flow. Such interactions are explored in this study using an explainable deep-learning method. The instantaneous velocity field obtained from a turbulent channel flow simulation is used to predict the velocity field in time through a U-net architecture. Based on the predicted flow, we assess the importance of each structure for this prediction using the game-theoretic algorithm of SHapley Additive exPlanations (SHAP). This work provides results in agreement with previous observations in the literature and extends them by revealing that the most important structures in the flow are not necessarily the ones with the highest contribution to the Reynolds shear stress. We also apply the method to an experimental database, where we can identify structures based on their importance score. This framework has the potential to shed light on numerous fundamental phenomena of wall-bounded turbulence, including novel strategies for flow control.

Computationally Efficient and Interface Accurate Dual-Grid Phase-Field Simulation of Turbulent Drop-Laden Flows

Abstract In this work, we develop a dual-grid approach for the direct numerical simulations (DNS) of tur- bulent multiphase flows in the framework of the phase-field method (PFM). With the dual-grid approach, the solution of the Navier-Stokes equations (flow-field) and of the Cahn-Hilliard equa- tion (phase-field) are performed on two different computational grids. In particular, a base grid - fine enough to resolve the flow down to the Kolmogorov scale - is used for the solution of the Navier-Stokes equations, while a refined grid - required to improve the description of small interfacial structures - is used for the solution of the Cahn-Hilliard equation (phase-field method). The proposed approach is validated, and its computational efficiency is evaluated considering the deformation of a drop in a two-dimensional shear flow. Analyzing the computational time and memory usage, we observe a reduction between ≃30% and ≃40% (with respect to the single-grid approach), depending on the grid refinement factor employed for the phase-field variable. The applicability of the approach to a realistic three-dimensional case is also discussed, by focusing on the breakage of a thin liquid sheet inside a turbulent channel flow. Indications on the grid resolution representing a good compromise between accuracy and computational efficiency in drop-laden turbulence are also provided.