Random Rough Surfaces Research Articles

A numerical investigation of droplet bouncing behaviors on the superhydrophobic surfaces with different micro-structures

Better understanding of the droplet bouncing behaviors and microscopic mechanisms on superhydrophobic surfaces is of significance for a large number of energy applications. In this study, the bouncing process of droplets on superhydrophobic surfaces was investigated by lattice Boltzmann method (LBM). Four types of micro-structures for superhydrophobic surface were investigated: perfectly smooth surface, perfectly regular surface, randomly regular surface, and randomly rough surface. Random rough surfaces were described by skewness (−0.9 to 0.9), kurtosis (1.0–5.0) and standard deviation (0.3–1.1). The basic morphology and roughness parameters of the randomly rough surfaces were obtained by atomic force microscopy (AFM) and was developed by numerical reconstruction method. After the experimental verification, the droplet bouncing characteristics on the superhydrophobic surface with different micro-structures are compared and summarized. The maximum spreading length, the contact time and post-bounce morphology of droplet bouncing process are analyzed. It was found that the maximum spreading length of the droplet is reduced with the increasing of surface skewness, kurtosis and standard deviation. In the random regular surface, the increase of parameters from minimum to maximum reduced the maximum spreading length by 8.22%. The total contact time increased by 16.61%. When the number of surface sharp edges are smaller or the groove-like structures are wider, the contact time of droplet bouncing process can be obviously reduces. The droplet has the best bouncing performance on superhydrophobic surface that satisfy a skewness of 0.3–0.5, kurtosis of 3.0 and standard deviation of 0.7–0.9. The dynamic processes study of the droplets on surfaces with four gradations of roughness has not been done before. The gradual roughness can reflect the change process from theoretical superhydrophobic surfaces to practically prepared superhydrophobic surfaces with droplet bouncing. We have discovered a new law of droplet bouncing from progressively more complex surface microstructures. The main contribution of this work is to give simulation data of droplet bouncing on four surfaces simultaneously. The most suitable surfaces for bouncing are compared and conclusions are drawn. The influence of different surface micro-structures on the droplet bouncing behaviors obtained in this paper may be useful for the optimal design of superhydrophobic surface.

Two-photon polymerization for random rough surface preparation

A typical tool to characterize diffuse materials is the determination of the scattering and absorption coefficients. Most of these evaluation methods consider optically smooth surfaces between the investigated and the surrounding medium. However, realistic surfaces generally show a distinct roughness, which influences the reflected and transmitted light of the investigated material. Hence, it is necessary to understand the optical behavior of these rough surfaces. We present a method for the preparation of such optically rough surfaces using two-photon polymerization. The properties of these rough surfaces can be precisely adjusted within a range of a few hundred nanometers. Additionally, an easy-to-use preparation method is shown to compensate for reflections from the backside of the used substrate. Hence, these surfaces can be used for measuring the reflectance leading to a better understanding of the scattering behavior of rough surfaces and their influence on the determination of the optical properties of turbid media.

A Priori Bounds for Elastic Scattering by Deterministic and Random Unbounded Rough Surfaces

A Priori Bounds for Elastic Scattering by Deterministic and Random Unbounded Rough Surfaces

Flexible and Accurate Prior Model Construction Based on Deep Learning for 2-D Magnetotelluric Data Inversion

The conventional magnetotelluric (MT) data inversion methods, such as the nonlinear conjugate gradient method, quasi-Newton method, and Gauss–Newton method and so on, can converge robustly, but their results are easily affected by the initial model and regularization term. Although supervised learning can break through the resolution limitation by directly learning the nonlinear relationship between the model and the data, it cannot guarantee the data fitting without considering physical constraints. Here, we propose a novel prior model generation method using deep learning for conventional inversion to jointly take advantages of the two techniques. We first combine Gaussian random rough surface scheme and random polygon generation algorithm to construct practical 2-D geoelectric models, in which the prior information on the geoelectric structure can be flexibly integrated. Then, a fast 2-D MT forward modeling method is applied to calculate the forward responses and establish the training set. Finally, we use the training set to complete the parameter optimization of U-shaped network (U-NET) and run the conventional inversion with prior model generated by the trained U-NET. Numerical experiments with synthetic data show that the proposed method can effectively integrate the advantages of conventional inversion and supervised learning, and remarkably improve the resolution in the inversion if proper training sets are used. The inversions of the USArray data also prove that our method can retain high-resolution structures predicted by the U-NET in the final inversion results with a data fitting as good as the traditional Gauss–Newton method.

Numerical Analysis of Mueller Matrix for Random Rough Surfaces

Numerical Analysis of Mueller Matrix for Random Rough Surfaces

A Stochastic Gradient Descent Method for Computational Design of Random Rough Surfaces in Solar Cells

A Stochastic Gradient Descent Method for Computational Design of Random Rough Surfaces in Solar Cells

Thermal Balance and Water Ice Sublimation on the Surface of Hyperactive Comet 103P/Hartley 2

AbstractHyperactive comets have attracted attention due to their high water production rate with an unclear mechanism, though some hypotheses have been proposed to explain it. Based on the thermal theories of the comet nuclei, this paper studied a comet surface thermal model considering the sublimation of H2O. In this paper, a method for solving the sublimation rate of water ice by infrared spectroscopy is proposed. The method adopts the assumption of subpixel temperature nonuniformity on the comet nucleus surface and uses the numerical solution of the Fredholm equation. We use the HRI‐IR spectrum (1.05–4.8 μm) data by EPOXI to analyze the pixel‐by‐pixel water sublimation rate of hyperactive comet 103P/Hartley 2. The results show that sublimation exists in most areas of the surface with or without surface roughness, and most of the water production rate (70%–90%) may come from the comet nucleus when Gauss random surface roughness is adopted. According to the sublimation law, it is estimated that the sublimation temperature of water ice on 103P is above 180 K. If the dust‐to‐ice volume ratio is 3:1, the sublimation temperature is about 200–210 K, which indicates that the water ice may sublimate underneath. This may explain why exposed water ice on the surface can hardly be observed while the active fraction of this comet is up to 100%.

Multiscale in-situ quantification of the role of surface roughness and contact area using a novel Mica-PVS triboelectric nanogenerator

Triboelectric nanogenerators (TENGs) are energy harvesters generating electricity via the triboelectric effect and electrostatic induction. However, the influence of interface mechanics on TENG performance requires attention. Here, we study the effect of random multiscale surface roughness on TENG performance using a novel in-situ optical technique to directly visualise the contact interface. To achieve this, a new type of TENG is developed based on transparent mica in contact with polyvinyl siloxane (PVS). A wide range of surface roughness instances were created on the PVS surface (Sq from 1.5 to 82.5 µm) by replicating 3D-printed masters developed from numerically generated rough surfaces. TENG output was found to be highly sensitive to surface roughness over a wide range of forces and frequencies. The dependence of real contact area on roughness was identified as the underlying cause. In this work, electrical output (and contact area) decreased significantly with increasing roughness. The highest output (smoothest PVS surface) gave open circuit voltage 222.8 V, short-circuit current density 53 mA/m2 and peak-power density 4256 mW/m2: a competitive output given the rapid and simple fabrication, low cost and long durability demonstrated. The new Mica-PVS TENG, the direct technique for TENG interface visualisation and the insights on the role of topography and contact area will be invaluable for future TENG design.

A gate leakage current-powered loadless 4T SRAM with immunity against random dopant fluctuation and surface roughness in silicon-silicon dioxide interface

The sensitivities of the data hold, read and write performances for the gate leakage-powered loadless 4T SRAM cell on the variations in the MOSFETs’ gate leakage currents are examined by SPICE Monte Carlo simulations in 32 nm technology. The standard deviations in the gate leakage current variations are taken from the device simulation results in which the variations are caused by the random dopant fluctuation and the surface roughness in silicon-silicon dioxide interface. It is shown that the static noise margin (SNM) in the data hold state is 60 mV at 1 V power supply voltage for the −5σ cell with the MOSFETs’ threshold voltage variations taken into consideration. The read SNM and the write SNM are shown to be 160 mV and 440 mV for the −5σ cell, respectively, which are better than the 6TSRAM cell.

A numerical study of the droplet impact dynamics on a two-dimensional random rough surface

Considerable efforts had been devoted to investigating numerically the droplet impact dynamics on a superhydrophobic surface, whereas most of these numerical simulations were restricted to the two-dimensional (2D) axisymmetric coordinate system with the one-dimensional (1D) substrate surface. In this work, a three-dimensional (3D) computational fluid dynamics (CFD) model, which intergrew a 2D random rough surface, was proposed to investigate the droplet impact dynamics, and the multi-phase flow issue was solved by the Navier–Stokes equations. It is remarkable that the 3D CFD model revealed several significant dynamic details that were not easily captured in a 2D axisymmetric coordinate system or practical experiments. For instance, the 3D CFD model provided a unique perspective to understand the varying dynamic behaviors of impinged droplet in terms of the velocity streamline and dynamic viscosity analyses. Herein, the dynamic viscosity diagram revealed that the sprawl droplet on the 2D random rough surface was classified as the Cassie state, while as the Wenzel state for the smooth surface, which also explained the better bouncing behaviors of the droplet from the random rough surface. Accordingly, we suggested a visual way to evaluate the solid–liquid contact area surrounded by the triple-phase contact line. The effects of finger protrusion and central cavity growth from the sprawl droplet on the vortex generation were further analyzed on the ground of the velocity amplitude distribution and streamline data. The present work can provide early guidance to inquire into the impact dynamics of droplets on the random rough surface.

Random rough surface effects on the performance of near-field thermophotovoltaic system

Surfaces roughness exists inevitably during manufacturing process, while how it affects the performance of the near-field thermophotovoltaic (NF-TPV) system is still unclear. In this work, we investigate the effect of rough surfaces on the performance of NF-TPV systems theoretically. Two typical NF-TPV systems with the same plasmonic emitter (Ga-doped ZnO, GZO) and different TPV cells (GaSb and InAs) are considered. The effective multilayer approach and analytical approximation model are applied to analyze near-field radiative heat transfer and the performance of the NF-TPV system. Near-field radiative heat transfer between the ideal GZO and TPV cell with smooth surfaces is through the TR-SPPs modes (interaction between the evanescent waves generated by total reflection and the surface plasmon polaritons), while the surface roughness excites the red-shift and the coupling of the TR-SPPs modes. The influence of surface roughness on the performance of the NF-TPV system depends on the cooperation between the resonance frequency of the emitter and the bandgap of TPV cell. Generally, roughness increases the power output for a given average emitter-cell distance, while the efficiency can be improved or reduced. This work will help the understanding and evaluation of the performance of NF-TPV systems with rough surfaces.

Investigation of Contact Clusters Between Rough Surfaces

We investigate the persistence of micro-contacts between two elastic random rough surfaces by means of a simple model for quasi-static sliding. Contact clusters are calculated with the Boundary Element Method, then surfaces are repeatedly displaced to study the evolution of the original contact area. While the real contact area remains constant due to the rejuvenation of micro-contacts, the original contact clusters are progressively erased and replaced by new ones. We find an approximate exponential decrease of the original real contact area with a characteristic length that is influenced both by statistics of the contact cluster distribution and physical parameters. This study aims to shine light on the microscopic origins of phenomenological rate-and-state friction laws and the memory effects observed in frictional sliding.

CNN-Based Deep Learning Architecture for Electromagnetic Imaging of Rough Surface Profiles

A convolutional neural network (CNN)-based deep learning (DL) technique for electromagnetic (EM) imaging of rough surfaces separating two dielectric media is presented. The direct scattering problem is formulated through the conventional integral equations, and the synthetic scattered field data are produced by a fast numerical solution technique, which is based on method of moments (MoM). Two different special CNN architectures are designed and implemented for the solution of the inverse rough surface imaging problem, wherein both random and deterministic rough surface profiles can be imaged. It is shown by a comprehensive numerical analysis that the proposed DL inversion scheme is very effective and robust.

Dynamics study of integrable turbulence with fourth-order nonlinear Schrödinger equation.

In this paper, we focus on the fourth-order nonlinear Schrödinger equation, which can describe the optical system and the Heisenberg spin system. We consider a continuous wave perturbed by the one-dimensional random rough surface as the initial condition. First, we numerically resolve the eigenvalues under different control parameters utilizing the Fourier collocation method. Then, we simulate the evolution of this equation under the above initial conditions via the symmetrical split-step Fourier method. Moreover, we investigate the "steady" chaotic state by evolving a large number of initial conditions for the same control parameters. We find that the control parameters of the initial condition affect the number and intensity of rogue waves (RWs) in integrable turbulence. In particular, we locate the inflection point where the control parameter affects the velocities of solitons and the inconsistency within the parameter of the contribution to the generation of RWs. We further verify that the collision between breathers, solitons, and breathers and solitons can generate RWs. These results will enable us to understand the turbulent state and the formation mechanism of RWs.

Contact angle hysteresis and lateral adhesion strength on random rough surfaces

Based on the classical Robbins–Joanny model of contact angle hysteresis, a new dimensionless parameter (q0/q2) sensitive to roughness power spectrum is proposed to be linearly related to (1) contact angle hysteresis caused by roughness heterogeneity and (2) lateral adhesion strength of a sliding water bridge. Both hypotheses were validated using liquid bridge sliding experiments on random rough surfaces. q0/q2 qualitatively reflects the decay rate of any two points on the surface from mutually independent to correlated with diminishing separation distance.

A phenomenological study and comparison of the characteristics of droplet impact liquid film dynamics on randomly rough surfaces

The fouling of aero-engine blades is the main cause of degradation of engine performance and online washing is one of the most effective methods for restoring engine performance. The flow characteristics of the washing fluid after it impinges on the blade surface are critical to the process. The liquid film flow becomes complicated after being impacted by a droplet, because the fouling blade is a random rough surface. The purpose of this study is to evaluate the dynamical characteristics of droplets after they impact the liquid film, focusing on the diameter, the height of the coronal water bloom, and the near-wall flow. We establish a random rough surface to simulate the droplet impacting the liquid film on the fouling surface and analyze the morphological evolution of the corona during the droplet impact process. The results show that an increase in the particle size has a greater impact on the coronal diameter than the coronal height. In addition, a higher droplet impact velocity and thicker liquid film are conducive to the secondary atomization of droplets and improve the transport rate of the cleaning solution. However, the flowability of the liquid film at the impact point is best when the droplet impacts the thin liquid film. Increasing the thickness of the liquid film gradually helps to improve its overall fluidity and results in a better cleaning effect.

A General Load–Displacement Relationship Between Random Rough Surfaces in Elastic, Non-adhesive Contact, with Application in Metal Additive Manufacturing

A General Load–Displacement Relationship Between Random Rough Surfaces in Elastic, Non-adhesive Contact, with Application in Metal Additive Manufacturing

Unconditionally stable FDTD-based approach for scattering from an object above random rough surface

For investigating scattering characteristics of an object located above a rough random surface relating to radar applications, we proposed an unconditionally stable finite-difference time-domain (FDTD)-based solver with novel lattice arrangement approach to simulate the scattering from the object/rough surface composite model. The accuracy and performance of the proposed method are thoroughly assessed for 3-D object above a 2-D random rough surface benchmarked against conventional Yee’s FDTD-based method for various polarization, angles of incidence and truncated rough surface sizes. Simulation results demonstrate that valid scattering characteristics of the object and its underlying rough surface can be obtained by the proposed solver with a smaller problem space and shorter simulation time. By presenting this time-domain, full-wave, unconditionally stable, efficient solver for scattering from objects and rough terrain surfaces, this work aims to facilitate access, analysis and understanding of radar targets and environment’s scattering characteristics for the applications of detection, recognition, remote sensing, etc.

Regression analysis of wetting characteristics for different random surface roughness of polydimethylsiloxane using sandpapers

This paper studies various wetting characteristics on different surfaces of Polydimethylsiloxane (PDMS) polymer. The random roughness of the surface is engineered by using sandpapers to introduce different order of hydrophobic properties to understand the temporal evolution of drying droplets. We develop statistical models to predict temporal evolution of the base diameter, height, surface, and contact angle of drying droplets with varying grit size or surface roughness. Five different robust polynomial regression models have been compared for the prediction of three dependent variables - base diameter, height, and surface of drying droplets for random rough surfaces. In a nutshell, we here identify the best statistical model to capture the dynamics of drying droplets on hydrophobic surfaces of random roughness characteristics.

The impact of non-Gaussian height distributions on the statistics of isotropic random rough surfaces

The impact of non-Gaussianity on the statistical geometry of isotropic random rough surfaces is analysed in this contribution. The non-Gaussian height distribution is modeled using the Weibull probability distribution. The summits height distribution, expected mean curvature and density of summits are obtained and compared with analytical solutions given by Nayak’s theory for the Gaussian case. A significant deviation from the Gaussian cases is observed. The mean curvature of negatively-skewed surfaces is found to decrease with the increase of height, contradicting the trend registered for the Gaussian case. Nayak’s parameter globally dominates the impact of the spectral properties. However, the wavelength ratio and the Hurst exponent are required to characterize the effect of the spectrum on the rough surfaces statistics.