arXiv subjects

Chao Sun

Publications and source records attributed to Chao Sun.

At least 109 records · Page 6Linked to original sources

arXiv2022

Characterization of Coupled Turbulent Wind-wave Flows Based on Large Eddy Simulation

Wind-wave interaction involves wind forcing on wave surface and wave effects on the turbulent wind structures, which essentially influences the wind and wave loading on structures. Existing research on wind-wave interaction modeling ignores the inherent strong turbulences of wind. The present study aims to characterize the turbulent airflow over wave surfaces and wave dynamics under wind driving force. A high-fidelity two-phase model is developed to simulate highly turbulent wind-wave fields. Instead of using uniform wind, inherent wind turbulences are prescribed at the inlet boundary using the turbulent spot method. The developed model is validated by comparing the simulated wind-wave flow characteristics with experimental data. With the validated model, a numerical case study is conducted on a 10^2 m scale under extreme wind and wave conditions. The result shows that when inherent wind turbulences are considered, the resultant turbulence is strengthened and is the summation of the inherent turbulence and the wave-induced turbulence. In addition, the wave coherent velocities and shelter effect are enhanced because of the presence of wind inherent turbulence. The regions of intense turbulence depend on the relative speed between wind velocity and wave phase speed. Higher wind velocities induce greater turbulence intensities, which can be increased by up to 17%. The different relative speed between wind and wave can induce opposite positive-negative patterns of wave coherent velocities. The wave-coherent velocity is approximately proportional to the wind velocity, and the influenced region mainly depends on the wave heights.

physics.flu-dyn↗

arXiv2022

Decrypting material performance by wide-field femtosecond interferometric imaging of energy carrier evolution

Energy carrier evolution is crucial for material performance. Ultrafast microscopy has been widely applied to visualize the spatiotemporal evolution of energy carriers. However, direct imaging of small amounts of energy carriers on nanoscale remains difficult due to extremely weak transient signals. Here we present a method for ultrasensitive and high-throughput imaging of energy carrier evolution in space and time. This method combines femtosecond pump-probe techniques with interferometric scattering microscopy (iSCAT), named Femto-iSCAT. The interferometric principle and unique spatially-modulated contrast enhancement increase the transient image contrast by >2 orders of magnitude and enable the exploration of new science. We address three important and challenging problems: transport of different energy carriers at various interfaces, heterogeneous hot electron distribution and relaxation in single plasmonic resonators, and distinct structure-dependent edge state dynamics of carriers and excitons in optoelectronic semiconductors. Femto-iSCAT holds great potential as a universal tool for ultrasensitive imaging of energy carrier evolution in space and time.

physics.optics↗

arXiv2022

Micro-droplet nucleation through solvent exchange in a turbulent buoyant jet

Solvent exchange is a process involving mixing between a good solvent with dissolved solute and a poor solvent. The process creates local oversaturation which causes the nucleation of minute solute droplets. Such ternary systems on a macro-scale have remained unexplored in the turbulent regime. We experimentally study the solvent exchange process by injecting mixtures of ethanol and trans-anethole into water, forming a turbulent buoyant jet in upward direction. Locally, turbulent mixing causes oversaturation of the trans-anethole following turbulent entrainment. We optically measure the concentration of the nucleated droplets using a light attenuation technique and find that the radial concentration profile is sub-Gaussian. In contrast to the entrainment-based models, the spatial evolution of the oversaturation reveals continuous droplet nucleation downstream and radially across the jet, which we attribute to the limited mixing capacity of the jet. Though we are far from a full quantitative understanding, this work extends the knowledge on solvent exchange into the turbulent regime, and brings in a novel type of flow, broadening the scope of multicomponent, multiphase turbulent jets with phase transition.

physics.flu-dyn↗

arXiv2022

Accumulation and alignment of elongated gyrotactic swimmers in turbulence

We study the dynamics of gyrotactic swimmers in turbulence, whose orientation is governed by gravitational torque and local fluid velocity gradient. The gyrotaxis strength is measured by the ratio of the Kolmogorov time scale to the reorientation time scale due to gravity, and a large value of this ratio means the gyrotaxis is strong. By means of direct numerical simulations, we investigate the effects of swimming velocity and gyrotactic stability on spatial accumulation and alignment. Three-dimensional Vorono{\"ı} analysis is used to study the spatial distribution and time evolution of the particle concentration. We study spatial distribution by examing the overall preferential sampling and where clusters and voids (subsets of particles that have small and large Vorono{\"ı} volumes respectively) form. Compared with the ensemble particles, the preferential sampling of clusters and voids is found to be more pronounced. The clustering of fast swimmers lasts much longer than slower swimmers when the gyrotaxis is strong and intermediate, but an opposite trend emerges when the gyrotaxis is weak. In addition, we study the preferential alignment with the Lagrangian stretching direction, with which passive slender rods have been known to align. We show that the Lagrangian alignment is reduced by the swimming velocity when the gyrotaxis is weak, while the Lagrangian alignment is enhanced for the regime in which gyrotaxis is strong.

physics.flu-dyn↗

arXiv2022

Dynamics of finite-size spheroids in turbulent flow: the roles of flow structures and particle boundary layers

We study the translational and rotational dynamics of neutrally-buoyant finite-size spheroids in hydrodynamic turbulence by means of fully resolved numerical simulations. We examine axisymmetric shapes, from oblate to prolate, and the particle volume dependences. We show that the accelerations and rotations experienced by non-spherical inertial-scale particles result from volume filtered fluid forces and torques, similar to spherical particles. However, the particle orientations carry signatures of preferential alignments with the surrounding flow structures, which is reflected in distinct axial and lateral fluctuations for accelerations and rotation rates. The randomization of orientations does not occur even for particles with volume equivalent diameter size in the inertial range, here up to 60 $η$ at $Re_λ=120$. Additionally, we demonstrate that the role of fluid boundary layers around the particles cannot be neglected to reach a quantitative understanding of particle statistical dynamics, as they affect the intensities of angular velocities, and the relative importance of tumbling with respect to spinning rotations. This study brings to the fore the importance of inertial-scale flow structures in homogeneous and isotropic turbulence and their impacts on the transport of neutrally-buoyant bodies with size in the inertial range.

physics.flu-dyn↗

arXiv2022

Physical mechanisms for droplet size and effective viscosity asymmetries in turbulent emulsions

By varying the oil volume fraction, the microscopic droplet size and the macroscopic rheology of emulsions are investigated in a Taylor-Couette (TC) turbulent shear flow. Although here oil and water in the emulsions have almost the same physical properties (density and viscosity), unexpectedly, we find that oil-in-water (O/W) and water-in-oil (W/O) emulsions have very distinct hydrodynamic behaviors, i.e., the system is clearly asymmetric. By looking at the micro-scales, the average droplet diameter hardly changes with the oil volume fraction neither for O/W nor for W/O. However, for W/O it is about 50% larger than that of O/W. At the macro-scales, the effective viscosity of O/W is higher when compared to that of W/O. These asymmetric behaviors can be traced back to the presence of surface-active contaminants in the system. By introducing an oil-soluble surfactant at high concentration, remarkably, we recover the symmetry (droplet size and effective viscosity) between O/W and W/O emulsions. Based on this, we suggest a possible mechanism responsible for the initial asymmetry. Next, we discuss what sets the droplet size in turbulent emulsions. We uncover a -6/5 scaling dependence of the droplet size on the Reynolds number of the flow. Combining the scaling dependence and the droplet Weber number, we conclude that the droplet fragmentation, which determines the droplet size, occurs within the boundary layer and is controlled by the dynamic pressure caused by the gradient of the mean flow, as proposed by Levich (1962), instead of the dynamic pressure due to turbulent fluctuations, as proposed by Kolmogorov (1949). The present findings provide an understanding of both the microscopic droplet formation and the macroscopic rheological behaviors in dynamic emulsification, and connects them.

physics.flu-dyn↗

arXiv2022

Supergravitational turbulent thermal convection

High-Rayleigh number convective turbulence is ubiquitous in many natural phenomena and in industries, such as atmospheric circulations, oceanic flows, flows in the fluid core of planets, and energy generations. In this work, we present a novel approach to boost the Rayleigh number in thermal convection by exploiting centrifugal acceleration and rapidly rotating a cylindrical annulus to reach an effective gravity of 60 times Earth's gravity. We show that in the regime where the Coriolis effect is strong, the scaling exponent of Nusselt number versus Rayleigh number exceeds one-third once the Rayleigh number is large enough. The convective rolls revolve in prograde direction, signifying the emergence of zonal flow. The present findings open a new avenue on the exploration of high-Rayleigh number turbulent thermal convection and will improve the understanding of the flow dynamics and heat transfer processes in geophysical and astrophysical flows and other strongly rotating systems.

physics.flu-dyn↗

arXiv2021

How do the finite-size particles modify the drag in Taylor-Couette turbulent flow

We experimentally investigate the drag modification by neutrally buoyant finite-size particles with various aspect ratios in a Taylor-Couette (TC) turbulent flow. The current Reynolds number, $Re$, ranges from $6.5\times10^3$ to $2.6\times10^4$, and the particle volume fraction, $Φ$, is up to $10\%$. Particles with three kinds of aspect ratio, $λ$, are used: $λ=1/3$ (oblate), $λ=1$ (spherical) and $λ=3$ (prolate). Unlike the case of bubbly TC flow, we find that the suspended finite-size particles increase the drag of the TC system regardless of their aspect ratios. In addition, the normalized friction coefficient, $c_{f,Φ}/c_{f,Φ=0}$, decreases with increasing $Re$, the reason could be that in the current low volume fractions the turbulent stress becomes dominant at higher $Re$. As $Re$ increases, the particles distribute more evenly in the entire system, which results from both the greater turbulence intensity and the more pronounced finite-size effects of the particles at higher $Re$. Moreover, it is found that the variation of the particle aspect ratios leads to different particle collective effects. The suspended spherical particles, which tend to cluster near the walls and form a particle layer, significantly affect the boundary layer and result in maximum drag modification. The minimal drag modification is found in the oblate case, where the particles preferentially cluster in the bulk region, and thus the particle layer is absent. Based on the optical measurement results, it can be concluded that, in the low volume fraction ranges ($Φ=0.5\%$ and $Φ= 2\%$ here), the larger drag modification is connected to the near-wall particle clustering. The present findings suggest that the particle shape plays a significant role in drag modification.

physics.flu-dyn↗

arXiv2021

Machining Cycle Time Prediction: Data-driven Modelling of Machine Tool Feedrate Behavior with Neural Networks

Accurate prediction of machining cycle times is important in the manufacturing industry. Usually, Computer Aided Manufacturing (CAM) software estimates the machining times using the commanded feedrate from the toolpath file using basic kinematic settings. Typically, the methods do not account for toolpath geometry or toolpath tolerance and therefore under estimate the machining cycle times considerably. Removing the need for machine specific knowledge, this paper presents a data-driven feedrate and machining cycle time prediction method by building a neural network model for each machine tool axis. In this study, datasets composed of the commanded feedrate, nominal acceleration, toolpath geometry and the measured feedrate were used to train a neural network model. Validation trials using a representative industrial thin wall structure component on a commercial machining centre showed that this method estimated the machining time with more than 90% accuracy. This method showed that neural network models have the capability to learn the behavior of a complex machine tool system and predict cycle times. Further integration of the methods will be critical in the implantation of digital twins in Industry 4.0.

cs.LG↗

arXiv2021

PhysiNet: A Combination of Physics-based Model and Neural Network Model for Digital Twins

As the real-time digital counterpart of a physical system or process, digital twins are utilized for system simulation and optimization. Neural networks are one way to build a digital twins model by using data especially when a physics-based model is not accurate or even not available. However, for a newly designed system, it takes time to accumulate enough data for neural network model and only an approximate physics-based model is available. To take advantage of both models, this paper proposed a model that combines the physics-based model and the neural network model to improve the prediction accuracy for the whole life cycle of a system. The proposed hybrid model (PhysiNet) was able to automatically combine the models and boost their prediction performance. Experiments showed that the PhysiNet outperformed both the physics-based model and the neural network model.

cs.LG↗

arXiv2021

RelationRS: Relationship Representation Network for Object Detection in Aerial Images

Object detection is a basic and important task in the field of aerial image processing and has gained much attention in computer vision. However, previous aerial image object detection approaches have insufficient use of scene semantic information between different regions of large-scale aerial images. In addition, complex background and scale changes make it difficult to improve detection accuracy. To address these issues, we propose a relationship representation network for object detection in aerial images (RelationRS): 1) Firstly, multi-scale features are fused and enhanced by a dual relationship module (DRM) with conditional convolution. The dual relationship module learns the potential relationship between features of different scales and learns the relationship between different scenes from different patches in a same iteration. In addition, the dual relationship module dynamically generates parameters to guide the fusion of multi-scale features. 2) Secondly, The bridging visual representations module (BVR) is introduced into the field of aerial images to improve the object detection effect in images with complex backgrounds. Experiments with a publicly available object detection dataset for aerial images demonstrate that the proposed RelationRS achieves a state-of-the-art detection performance.

cs.CV↗

arXiv2021

Evidences for the existence of the ultimate regime in supergravitational turbulent thermal convection

What is the final state of turbulence when the driving parameter approaches to infinity? For thermal turbulence, in 1962, Kraichnan proposed a so-called ultimate scaling dependence of the heat transport (quantified by the Nusselt number $\text{Nu}$) on the Rayleigh number ($\text{Ra}$), which can be extrapolated to arbitrarily high $\text{Ra}$. The existence of Kraichnan ultimate scaling has been intensively debated in the past decades. In this work, using a supergravitational thermal convection system, with an effective gravity up to 100 times the Earth's gravity, both Rayleigh number and shear Reynolds number can be boosted due to the increase of the buoyancy driving and the additional Coriolis forces. Over a decade of $\text{Ra}$ range, we demonstrate the existence of Kraichnan-like ultimate regime with four direct evidences: the ultimate scaling dependence of $\text{Nu}$ versus $\text{Ra}$; the appearance of turbulent velocity boundary layer profile; the enhanced strength of the shear Reynolds number; the new statistical properties of local temperature fluctuations. The present findings will greatly improve the understanding of the flow dynamics in geophysical and astrophysical flows.

physics.flu-dyn↗

arXiv2021

Distributed Generalized Nash Equilibrium Seeking of N-Coalition Games with Full and Distributive Constraints

In this work, we investigate the distributed generalized Nash equilibrium (GNE) seeking problems for $N$-coalition games with inequality constraints. First, we study the scenario where each agent in a coalition has full information of all the constraints. A finite-time average consensus-based approach is proposed by using full decision information. It is proven that the algorithm converges to a GNE (specifically, a variational equilibrium) of the game. Then, based on the finite-time consensus tracking, we propose a distributed algorithm by using partial decision information only, where only neighboring action information is needed for each agent. Furthermore, we investigate the scenario where only distributive constraint information is available for the agents. The terminology ``distributive" here refers to the scenario where the constraint information acquisition and processing are conducted across multiple agents in a coalition, and each agent has access to only a subset of constraints, instead of the scenario where each agent has full information of all the constraints. In this case, the multiplier computation and the constraint information required for each individual are reduced. A full decision information algorithm and a partial decision information algorithm for this scenario are proposed, respectively. Numerical examples are given to verify the proposed algorithms.

math.OC↗

arXiv2021

Ice front shaping by upward convective current

The extent and the morphology of ice forming in a differentially heated cavity filled with water is studied by means of experiments and numerical simulations. We show that the main mechanism responsible for the ice shaping is the existence of a cold upward convective current in the system. Such a current is ascribed to the peculiar equation of state of water, i.e., the non-monotonous dependence of density with temperature. The precise form of the ice front depends on several factors, first the temperature difference across the cell which drives the convection, second the wall inclination with respect to the vertical, both of which are here explored. We propose a boundary-layer model and a buoyancy-intensity model which account for the main features of the ice morphology.

physics.flu-dyn↗

arXiv2021

Equilibrium states of the ice-water front in a differentially heated rectangular cell

We study the conductive and convective states of phase-change of pure water in a rectangular container where two opposite walls are kept respectively at temperatures below and above the freezing point and all the other boundaries are thermally insulating. The global ice content at the equilibrium and the corresponding shape of the ice-water interface are examined, extending the available experimental measurements and numerical simulations. We first address the effect of the initial condition, either fully liquid or fully frozen, on the system evolution. Secondly, we explore the influence of the aspect ratio of the cell, both in the configurations where the background thermal-gradient is antiparallel to the gravity, namely the Rayleigh-Bénard (RB) setting, and when they are perpendicular, i.e., vertical convection (VC). We find that for a set of well-identified conditions the system in the RB configuration displays multiple equilibrium states, either conductive rather than convective, or convective but with different ice front patterns. The shape of the ice front appears to be always determined by the large scale circulation in the system. In RB, the precise shape depends on the degree of lateral confinement. In the VC case the ice front morphology is more robust, due to the presence of two vertically stacked counter-rotating convective rolls for all the studied cell aspect-ratios.

physics.flu-dyn↗

arXiv2021

TBNet:Two-Stream Boundary-aware Network for Generic Image Manipulation Localization

Finding tampered regions in images is a hot research topic in machine learning and computer vision. Although many image manipulation location algorithms have been proposed, most of them only focus on the RGB images with different color spaces, and the frequency information that contains the potential tampering clues is often ignored. In this work, a novel end-to-end two-stream boundary-aware network (abbreviated as TBNet) is proposed for generic image manipulation localization in which the RGB stream, the frequency stream, and the boundary artifact location are explored in a unified framework. Specifically, we first design an adaptive frequency selection module (AFS) to adaptively select the appropriate frequency to mine inconsistent statistics and eliminate the interference of redundant statistics. Then, an adaptive cross-attention fusion module (ACF) is proposed to adaptively fuse the RGB feature and the frequency feature. Finally, the boundary artifact location network (BAL) is designed to locate the boundary artifacts for which the parameters are jointly updated by the outputs of the ACF, and its results are further fed into the decoder. Thus, the parameters of the RGB stream, the frequency stream, and the boundary artifact location network are jointly optimized, and their latent complementary relationships are fully mined. The results of extensive experiments performed on four public benchmarks of the image manipulation localization task, namely, CASIA1.0, COVER, Carvalho, and In-The-Wild, demonstrate that the proposed TBNet can significantly outperform state-of-the-art generic image manipulation localization methods in terms of both MCC and F1.

cs.CV↗

arXiv2021

Lagrangian dynamics and heat transfer in porous media convection

We report a numerical study of Rayleigh--Bénard convection through random porous media using pore-scale modelling, focusing on the Lagrangian dynamics of fluid particles and heat transfer for varied porosities $ϕ$. Due to the interaction between the porous medium and the coherent flow structures, the flow is found to be highly heterogeneous, consisting of convection channels with strong flow strength and stagnant regions with low velocities. The modifications of flow field due to porous structure have a significant influence on the dynamics of fluid particles. Evaluation of the particle displacement along the trajectory reveals the emergence of anomalous transport for long times as $ϕ$ is decreased, which is associated with the long-time correlation of Lagrangian velocity of the fluid. As porosity is decreased, the cross-correlation between the vertical velocity and temperature fluctuation is enhanced, which reveals a mechanism to enhance the heat transfer in porous media convection.

physics.flu-dyn↗

arXiv2021

Effects of radius ratio on annular centrifugal Rayleigh-Bénard convection

We report on a three-dimensional direct numerical simulation study of flow structure and heat transport in the annular centrifugal Rayleigh-Bénard convection (ACRBC) system, with cold inner and hot outer cylinders corotating axially, for the Rayleigh number range Ra $ \in [{10^6},{10^8}]$ and radius ratio range $η= {R_i}/{R_o} \in [0.3,0.9]$. This study focuses on the dependence of flow properties on the radius ratio $η$. The temperature and velocity fields reveal that different curvatures of the inner and outer cylinders of the ACRBC system lead to asymmetric movements of hot and cold plumes under the action of Coriolis force, resulting in the formation of zonal flow. The physical mechanism of zonal flow is verified by the dependence of the drift frequency of the large-scale circulation rolls and the space- and time-averaged azimuthal velocity on $η$. We find that the larger $η$ is, the weaker the zonal flow becomes. We show that the heat transport efficiency increases with $η$. It is also found that the bulk temperature deviates from the arithmetic mean temperature and the deviation increases as $η$ decreases. This effect can be explained by a simple model that accounts for the curvature effects and the radially-dependent centrifugal force in ACRBC.

physics.flu-dyn↗