Roughness Amplitude Research Articles

In Situ STM Study of Roughening of Au(111) Single-Crystal Electrode in Sulfuric Acid Solution during Oxidation-Reduction Cycles.

Oxidation-reduction cycles (ORCs) on Au(111) in 0.1 M sulfuric acid solution change the electrode morphology due to the formation of many new nanosized islands. With increasing the cycle number, the roughness of the surface increases due to the formation of multiatomic-step adatom islands and pits. The final roughness value is a function of the applied potential window, number of ORCs, scan rate, electrolyte concentration, and any applied delay time. In a first experiment, the roughening was tracked by recording the STM images in 11 steps during 200 ORCs. The results show the formation of pyramidal islands and a linear correlation between the roughness amplitude and the cycle number. In a second experiment, the 200 cycles were studied in 38 steps, while after each step, two images were recorded with a 3 min delay by holding the potential in the double-layer window. This leads to a lower roughness increase due to the high surface mobility of the Au surface atoms, which smoothens the surface during the delay time. Finally, the oxidation-reduction charge density per cycle shows an inverse correlation with surface roughness due to the (111) terrace showing a higher surface oxidation charge than the other sites and facets. Each delay causes a strong increase in the oxidation charge which is a consequence of surface smoothening during the delays leading to an enhancement of the (111) related oxidation charge.

Characterization of sustainable biocompatible materials based on chitosan: cellulose composites containing sporopollenin exine capsules

In this work, photothermal beam deflection spectrometric technique (BDS) is applied for non-contact and non-destructive characterization of chitosan (CS): cellulose (CEL) biocomposites with incorporated sporopollenin exine capsules (SEC). The objective was to determine the structural and thermal properties of synthesized CS:CEL:SEC composites with varying amounts of SEC, and to validate the BDS by photopyroelectric calorimetry (PPE) as an independent technique. It was found that CS:CEL biocomposites without SEC exhibit low porosities, which are on the order of 0.005 %, but can be increased by augmenting the content of CEL in the composite and/or by incorporation of SEC. By increasing the SEC content of CS:CEL composites to 50 % (w/w), the porosity increased up to 0.17 %. SEC also increases the surface roughness of biocomposite by over 2000-times to reach the roughness amplitude of 6 μm in composites with 50 % SEC. The thermal conductivities of investigated biocomposites were in the range of 40–80 mWm−1 K−1, while the thermal diffusivities were on the order of fractions of mm2s−1. With first validation of BDS results for thermal properties of CS:CEL-based composites, which show agreement with PPE results to within 5 %, this study confirms BDS technique as a perspectives tool for non-destructive characterization of CS:CEL:SEC biocomposites.

LightGBM hybrid model based DEM correction for forested areas.

The accuracy of digital elevation models (DEMs) in forested areas plays a crucial role in canopy height monitoring and ecological sensitivity analysis. Despite extensive research on DEMs in recent years, significant errors still exist in forested areas due to factors such as canopy occlusion, terrain complexity, and limited penetration, posing challenges for subsequent analyses based on DEMs. Therefore, a CNN-LightGBM hybrid model is proposed in this paper, with four different types of forests (tropical rainforest, coniferous forest, mixed coniferous and broad-leaved forest, and broad-leaved forest) selected as study sites to validate the performance of the hybrid model in correcting COP30DEM in different forest area DEMs. In the hybrid model of this paper, the choice was made to use the Densenet architecture of CNN models with LightGBM as the primary model. This choice is based on LightGBM's leaf-growth strategy and histogram linking methods, which are effective in reducing the data's memory footprint and utilising more of the data without sacrificing speed. The study uses elevation values from ICESat-2 as ground truth, covering several parameters including COP30DEM, canopy height, forest coverage, slope, terrain roughness and relief amplitude. To validate the superiority of the CNN-LightGBM hybrid model in DEMs correction compared to other models, a test of LightGBM model, CNN-SVR model, and SVR model is conducted within the same sample space. To prevent issues such as overfitting or underfitting during model training, although common meta-heuristic optimisation algorithms can alleviate these problems to a certain extent, they still have some shortcomings. To overcome these shortcomings, this paper cites an improved SSA search algorithm that incorporates the ingestion strategy of the FA algorithm to increase the diversity of solutions and global search capability, the Firefly Algorithm-based Sparrow Search Optimization Algorithm (FA-SSA algorithm) is introduced. By comparing multiple models and validating the data with an airborne LiDAR reference dataset, the results show that the R2 (R-Square) of the CNN-LightGBM model improves by more than 0.05 compared to the other models, and performs better in the experiments. The FA-SSA-CNN-LightGBM model has the highest accuracy, with an RMSE of 1.09 meters, and a reduction of more than 30% of the RMSE when compared to the LightGBM and other hybrid models. Compared to other forested area DEMs (such as FABDEM and GEDI), its accuracy is improved by more than 50%, and the performance is significantly better than other commonly used DEMs in forested areas, indicating the feasibility of this method in correcting elevation errors in forested area DEMs and its significant importance in advancing global topographic mapping.

Low-Reynolds-number droplet motion in shear flow micro-confined by a rough substrate

A three-dimensional spectral boundary element method has been employed to compute for the dynamics of the droplet motion driven by shear flow near a single solid substrate with a rough surface. The droplet size is comparable with the surface features of the substrate. This is a problem that has barely been explored but with applications in biomedical research and heat management. This work numerically investigated the influences of surface roughness features, such as the roughness amplitude and wavelength, on the droplet deformation and velocities. We observe that a greater amplitude or wavelength leads to larger variations in droplet velocity perpendicular to the substrate. The droplet velocity along the substrate increases when the amplitude is reduced or when the wavelength increases. The effects of capillary number and viscosity ratios have also been studied. The droplet deformation and its velocity increases as we increase the capillary number, while the viscosity ratio shows a non-monotonic influence on the droplet behavior. The predicted droplet behaviors, including deformation, velocities, and trajectories, can provide physical insight, help to understand the droplet behavior in microfluidic devices without a perfectly smooth surface, and contribute in the design and operation of those devices.

Thermomechanical and Structural Analysis of Manufactured Composite Based on Polyamide and Aluminum Recycled Material.

The paper presents an analysis of the filler's effect on the machining process and on changes in the thermomechanical properties of polymer composites based on aluminum chips. Composite research samples with a polymer matrix in the form of polyamide 6 were made by the pressing method. Comparative studies were carried out on the changes in thermomechanical properties and structure of the obtained molders with different filler contents and different fractions after the machining process. In order to determine the changes in thermal and mechanical properties, analysis was carried out using the differential scanning calorimetry (DSC) method, thermal analysis of dynamic mechanical properties (DMTA) and a detailed stereometric analysis of the surface. After mechanical processing, roughness amplitude parameters and volumetric functional parameters were determined. In order to analyze the structure, tomographic examinations of the manufactured composite were conducted. In relation to the polymer matrix, a significant increase in the storage modulus of the composites was noted in the entire temperature range of the study. An increase in the enthalpy of melting of the matrix was noted in composites with a lower filler content and a shift in the melting range of the crystalline phase. Significant differences were noted in the study of the composite surfaces in the case of using fillers obtained after machining with different fractions. The dependencies of the functional and amplitude parameters of the surfaces after machining of composite samples prove the change in the functional properties of the surface. The use of aluminum chips in the composite significantly changed the surface geometry.

On the surface roughness properties of fly ash-based geopolymer mortars with teff straw ash from the image analysis viewpoint

Fly ash (FA)-based geopolymer mortar is being considered for sustainability since it enjoys peculiar properties such as high mechanical properties and environmental benefits. However, the high curing temperatures of FA-based geopolymer mortar play a complex role in its outstanding mechanical behaviour thereby requiring other precursors, such as teff straw ash (TSA). In this study, we develop surface roughness properties of FA-based geopolymer mortars with TSA, where the ambient temperature is used for curing. The surface roughness characteristics of the FA-based geopolymer mortars with TSA exploit the surface roughness simulations from Gwyddion and the approach is demonstrated here in the contexts of spatial, hybrid and amplitude roughness parameters. The results show that some roughness parameters, including Ra and Rq values for FA-based geopolymer mortars with TSA, exhibit significant decreasing trends as the TSA contents increase. However, in comparison with the CA specimens (ambient temperature curing) and CE specimens (elevated temperature curing), the trends among the majority of the roughness parameters for FA-based geopolymer mortars with TSA turn out not to be very good probably due to changes in the internal structures of the mortars. Moreover, without the kurtosis values (Rku) of less than 3, it is easy to demonstrate that all the profiles of the investigated specimens reflect sharp valleys and peaks. The findings of surface roughness properties allow one to grasp the roughness parameters of FA-based geopolymer mortars with TSA. The approach for generating surface roughness properties of FA-based geopolymer mortars with TSA offers significant quantitative and qualitative information required for the bonding of mortar layers.

Fractal theory-based contact analysis of surface microtextured involute spline coupling

Accurate estimation of the contact stress of the aviation involute spline coupling is the basis of accurate estimation of its wear. In view of this, in this work, a fractal contact coefficient for involute spline coupling with surface texture based on the gear fractal contact model and M-B fractal contact model was established. Then, the relationship between fractal dimension and actual contact area, as well as the relationship between actual contact area and load were derived. And also, a fractal contact model for involute spline coupling with surface texture was established. Finally, the influence of geometric parameter on the contact coefficient and fractal contact model was analyzed. The results indicate that: the fractal dimension (D), roughness amplitude parameter (G), and material property parameter (ϕ) have important effects on the fractal contact model of involute spline coupling on the tooth surface; There must be an appropriate fractal dimension (D) value that makes the contact state optimal; As the roughness amplitude parameter (G) value increases, the surface contact condition deteriorates and the surface contact strength decreases. Conversely, as the material property parameter (ϕ) value increases, the surface contact condition improves and the surface contact strength increases. It is a preliminary exploration for accurately calculating the contact stress of involute spline coupling, laying a solid theoretical foundation for accurately predicting the wear of involute spline coupling with surface texture.

Research on the correlation between roughness parameters and contact stress on tooth surfaces and its dominant characteristics

The contact performance of the tooth surface is a crucial factor affecting the operational lifespan of gears. There is a significant correlation between tooth surface topography and contact stress, as well as fatigue performance. To investigate the influence of different topography parameters on tooth surface contact stress, this paper performs analytical calculations on asperity contacts of rough tooth surfaces based on reconstruction modeling. Through efficient calculations, sufficient correlation analysis data is obtained. The neural network analysis method is used to measure the degree of influence of roughness parameters on tooth surface contact performance. Research on the correlation between micro-topography parameters and maximum contact stress on tooth surfaces, as well as regression analysis, is conducted. The results show that: (1) Considering rough surfaces, the tooth surface contact stress field exhibits two stress peaks in the near-surface layer and sub-surface layer, with a maximum Mises stress increase of up to 30 % compared to a smoother surface. (2) In the case of low roughness (Sa < 0.2 μm), the amplitude of the maximum Mises stress peak in the near-surface layer is close to that in the sub-surface layer. However, as the roughness amplitude increases, the amplitude of the near-surface stress peak increases sharply. (3) Ranking the roughness parameters based on their influence on contact stress, the main parameters characterizing contact performance are identified as Spk, Vmp, and Sq. The peak characteristics of the surface are the decisive factors affecting fatigue extremes, which are stronger than the traditional surface roughness parameter Sq. This paper establishes a direct correlation model between tooth surface topography parameters and contact stress, providing a foundation for simplifying stress calculations considering complex topographies and offering new insights for fatigue-resistant design of tooth surfaces.

In-situ measurements of contact evolution for fractal rough surfaces under normal compression

The contact interface plays a key role in the overall functionality and stability of structures. Understanding the evolution of the contact interface over time and its dependency on materials and load is crucial for functional integrity and operational safety assessment. In this study, we employ in-situ three-dimensional X-ray computed tomography (3DXRCT) to examine the creep behavior of 3D-printed surfaces exhibiting various roughness under constant normal compression. We observe that the overall contact area enlargement during the contact creep decreases with roughness amplitude and fractal dimension. The variation of interfacial separation distance is found to increase with roughness amplitude and decrease with fractal dimension. Correlation analysis reveals that the microcontact size played a more important role than the asperity shape in determining the microcontact enlargement. By examining the calculated interfacial strains extracted from XRCT measurements, significant deformations are found to occur at the non-contacting zones, indicating strong asperity interactions. This study offers high-resolution experimental measurements and unravels the asperity micromechanics for contact creep on rough surfaces, providing insights into understanding and optimizing the performance of rough interfaces.

Experimental and DEM analysis for the shear characteristics of interface between sand and irregular concrete under dynamic normal loading

To explore the dynamic shear property of sand–irregular concrete interface, a series of direct shear tests were conducted under dynamic normal loading. These tests were designed with various joint roughness coefficients (JRC), amplitudes and frequencies of dynamic normal loading. DEM models were established to analyze the microscopic mechanical behavior. The research results indicate that a larger amplitude and frequency of the dynamic normal loading would lead to a decrease in the interface shear stress and dilation. As the JRC increased, the interface shear strength first increased and then decreased, and the dilation increased. A rougher surface would reduce the phase shift between peak shear and normal stress. The phase shift between normal stress and friction coefficient was always half a normal loading cycle. A greater JRC would activate a more violent particle motion and a thicker shear band. The increase in effective roughness would lead to an increase in the deviation angle of the anisotropic direction. The evolution of force chains was observed in three-dimensional space. Weak force chains were screened out during the shearing process. As the shearing developed, the number of force chains decreased, the average length and strength of force chains continued to increase.

Exploring nonlinearity in quarter car models with an experimental approach to formulating fractional order form and its dynamic analysis.

This study explores the inherent nonlinearity of quarter car models by employing an experimental and numerical approach. The dynamics of vehicular suspension systems are pivotal for ensuring passenger comfort, vehicle stability, and overall ride quality. In this paper we assessed the impact of various parameters and components on suspension performance, enabled the optimization of ride comfort, stability, and handling characteristics. Firstly, experimental analysis allowed for the investigation of factors that are challenging to model theoretically, such as stiffness nonlinearity and damping characteristics, which may vary under different operating conditions. Time domain and frequency response diagram of the model has been obtained. Secondly, a quarter-car with single degree-of-freedom presented and investigated in fractional order form. Fractional order dynamics emphasize nonlinearities in quarter car models, capturing real-world dynamics effectively. The proposed fractional-order nonlinear quarter car model employed Caputo derivative. For numerical analysis of fractional order system, the Adam-Bashforth-Moulton method is used and the disturbance of road assumed to be stochastic. Results show that the dynamic response of the vehicle can be chaotic. Influence of road roughness amplitude and frequency on vehicle vibration is investigated.

The evolution of stresses and shear resistance on rough faults at large slip

The deviation of natural faults from planarity results in geometric asperities and a heterogeneous stress field, affecting the shear resistance and slip behavior. This study numerically examines the effects of macro-scale roughness, local friction, and wear on the evolution of the fault stresses and shear resistance with fault slip. In contrast to previous analytical and numerical studies, our quasistatic simulations allow for slip at the scale of roughness wavelengths, large roughness levels, and fault opening. The simulations indicate that analytical solutions, which predict a linear increase in shear resistance with slip, significantly overestimate the additional shear resistance from roughness, i.e., the roughness drag, at slips larger than several percent of the minimum roughness wavelength. Starting with mated fault surfaces, the average shear resistance on the fault initially rises rapidly, but the rate of increase diminishes significantly as slip increases and the faults open. While initially showing a larger increase in shear resistance than self-similar faults, at large slip, self-affine faults exhibit significantly smaller shear resistance, and the background stresses are bounded without the need for off-fault inelastic deformation. Although smaller than predicted by analytical solutions, the simulation results show that the additional resistance from macro-scale geometrical irregularities is important, enabling rough faults to operate under shear resistance significantly larger than that from local friction only. The impact of macro-scale roughness increases with the roughness amplitude and the friction coefficient, while wear reduces fault stresses and average shear resistance. The combined effect of local friction and roughness is nonlinear, involving a direct contribution to shear resistance from roughness, as observed in prior studies, and an additional contribution from the increasing average normal stress with slip, which amplifies the resistance from friction and becomes more pronounced at large slip.

The study of ideal/defected graphene nanosheet roughness after atomic deposition process: Molecular dynamics simulation

In this work, molecular dynamics (MD) approach was performed to study the surface roughness of ideal/defected graphene nanosheet after carbon atoms deposition at various temperatures and pressures. In our calculations, the atomic interactions of nanostructures are based on TERSOFF and Lennard-Jones potential functions. The results show that the temperature of simulated structure is an important parameter in atomic deposition process and initial temperature enlarges, intensifies atomic deposition ratio. Numerically, by temperature increasing to 15 K, the surface roughness amplitude increase to 0.98 Å/0.83 Å after atomic deposition in ideal/defected structure. The roughness power in MD simulations converges to 0.64/0.55 in ideal/defected sample at maximum temperature. Furthermore, the pressure effects on dynamical behavior of simulated samples were reported in our study. We conclude that, by increasing initial pressure from 0 to 2 bar, the surface roughness amplitude in ideal/defected atomic arrangement increases to 1.01 Å/0.84 Å after deposition process and the roughness power of simulated structures reaches to larger value. Numerically, by initial pressure setting at 2 bar, the roughness power value converged to 0.72/0.56 in ideal/defected graphene. Reported numeric results in various temperature and pressures predicted the initial condition can be manipulated the atomic deposition process in ideal/defected graphene nanostructures.

A comprehensive study on the effects of surface post-processing on fatigue performance of additively manufactured AlSi10Mg: An augmented machine learning perspective on experimental observations

In this study, the efficacy of 21 distinct surface post-processing methods on the fatigue behavior of an additively manufactured (AM) material was investigated. Various treatments, encompassing single-step processes like sand blasting (SB), conventional shot peening (CSP), severe shot peening (SSP), gradient severe shot peening (GSSP), ultrasonic shot peening (USP), severe vibratory peening (SVP), ultrasonic nanocrystal surface modification (UNSM), laser shock peening (LSP), laser polishing (LP), tumble finishing (TF), chemical polishing (CP), electro-chemical polishing (ECP), and machining (M), as well as hybrid treatments, were systematically investigated for their impact on the fatigue behavior of hourglass laser powder based fused (LB-PBF) AlSi10Mg specimens. A comprehensive series of experimental tests were carried out, covering surface texture analyses, microstructural characterizations, porosity measurements, hardness, residual stresses (RS), monotonic tensile tests, and rotating bending fatigue tests at four stress levels. Following the experimental investigations, the acquired data were leveraged to develop a machine learning (ML)-based model to correlate the fatigue life with the influencing factors and conduct parametric and sensitivity analyses. The model utilized a deep learning approach to predict the fatigue response of LB-PBF AlSi10Mg, incorporating various artificial neural networks (ANNs) and considering input parameters such as yield strength, ultimate tensile strength, elongation to fracture, porosity, depth of pore closure, surface hardness, depth of hardness variation, surface RS, maximum compressive residual stresses (CRS), depth of the CRS field, top surface grain size, surface layer mean grain size, surface roughness, and stress amplitude. Depending on the life regime, the factors influencing fatigue behavior can vary significantly. In higher stresses, dominated by plastic strains, surface residual stress emerges as the primary factor. Conversely, in lower stresses, dominated by elastic strains, the significance of surface roughness becomes more pronounced, followed by surface residual stress, surface hardness and other factors. These findings underscore the contextual importance of different influencing factors for enhancing fatigue resistance based on varying loading conditions.

A fatigue life model accounting for the combined effect of surface roughness and microstructure: Application to SLM fabricated Hastelloy-X

This study introduces a microstructure-sensitive fatigue life prediction framework based on CP-FFT which includes the effect of surface roughness. The model is applied to flat Hastelloy-X specimens fabricated using Selective Laser Melting. The surface roughness measured is incorporated introducing a free surface with sinusoidal profile. The framework can accurately predict the fatigue life reduction of rough flat specimens using the fatigue parameters of polished bulk specimens and introducing the actual microstructure and roughness. A parametric study shows a monotonic reduction of life with the roughness amplitude but a minor effect of the surface waviness.

Fabric handling evaluation of woven shirting fabrics by the fabric touch tester

A fabric touch tester is a novel instrument with fabric handling properties. Its principal advantage is that the device has integrated modules for compression, surface friction, and thermal and bending properties. This module integration simplifies the testing process, and provides an efficient measurement method, and a comprehensive physical index. This study focused on woven fabrics for dress shirts. The fabric samples comprised three groups with various yarn compositions – that is, cotton, polyester, and wool. Sample group 1 was composed of cotton, polyester, and blended yarns; sample group 2 mainly compared the effect of twist yarns on the fabric touch; and sample group 3 was composed of wool and blended yarns with polyester. The handling properties were assessed by compression work and the compression recovery rate for the compression attributes. The results revealed that fabric (T100p1) with high twist-level yarns had a higher value of compression work (242.56 gf*mm2), and the texture type may affect the compression characteristics more significantly than the blending ratio. The fabric touch tester can also distinguish small changes in the compression properties of samples (0.43–0.71), the maximum heat flux and surface roughness amplitude for the thermal and surface roughness properties. The results revealed that the maximum heat flux value of all the samples in this study was 1109 Wm−2, the sample C100 using pure cotton yarn had the highest maximum heat flux value (1270 Wm−2). Moreover, the sample W100 with 100% wool fiber yarn had the highest surface roughness amplitude in the warp direction (81 μm) and surface roughness amplitude in the weft direction (67 μm). Finally, the bending average rigidity was used to assess the bending performance of the fabric samples. These fabric touch tester indicators were applied to analyze the fabric handling characteristics of woven shirting fabrics, and perform cross-analysis among samples.

Fracture Morphology Influencing Supersonic CO2 Transport: Application in Geologic CO2 Sequestration

AbstractGeologic carbon sequestration requires CO2 injection into the storage formation at high‐injecting pressure. Such high pressure could induce choked flow accompanying huge variations in thermodynamic properties of CO2 at a converging‐diverging (CD) fractures in the storage formation. In this study, high‐velocity CO2 transport through CD fractures was investigated to quantify the effect of fracture morphology on occurrence of both choked flow and shockwave that constrain the mass flow rate of fluid. In addition, variations in thermodynamic CO2 properties including Mach Number (Ma), defined by the ratio of fluid velocity to sound speed, and shock properties were investigated. The morphological characteristics of CD fractures were determined by nine properties, such as throat diameter, throat length, inlet diameter, outlet diameter, throat diameters of two‐connected CD fractures, fracture wall curvature, roughness amplitude, and frequency. As a result, the throat diameter crucially affected choked flow occurrence and maximum Ma. When the inlet and outlet diameters varied, the profiles for Ma variation were consistent in the converging and diverging segments, respectively. In addition, regardless of change in the throat length, the position of maximum Ma was nearly constant with showing the position length ratio of 0.13–0.14. However, roughness of fracture wall significantly influenced the Ma variation and occurrence of shock. In particular, backflows segregated from the main CO2 flow were observed near the wall roughness.

Prediction of contact resistance of electrical contact wear using different machine learning algorithms

H62 brass material is one of the important materials in the process of electrical energy transmission and signal transmission, and has excellent performance in all aspects. Since the wear behavior of electrical contact pairs is particularly complex when they are in service, we evaluated the effects of load, sliding velocity, displacement amplitude, current intensity, and surface roughness on the changes in contact resistance. Machine learning (ML) algorithms were used to predict the electrical contact performance of different factors after wear to determine the correlation between different factors and contact resistance. Random forest (RF), support vector regression (SVR) and BP neural network (BPNN) algorithms were used to establish RF, SVR and BPNN models, respectively, and the experimental data were trained and tested. It was proved that BP neural network model could better predict the stable mean resistance of H62 brass alloy after wear. Characteristic analysis shows that the load and current have great influence on the predicted electrical contact properties. The wear behavior of electrical contacts is influenced by factors such as load, sliding speed, displacement amplitude, current intensity, and surface roughness during operation. Machine learning algorithms can predict the electrical contact performance after wear caused by these factors. Experimental results indicate that an increase in load, current, and surface roughness leads to a decrease in stable mean resistance, while an increase in displacement amplitude and frequency results in an increase in stable mean resistance, leading to a decline in electrical contact performance. To reduce testing time and costs and quickly obtain the electrical contact performance of H62 brass alloy after wear caused by different factors, three algorithms (random forest (RF), support vector regression (SVR), and BP neural network (BPNN)) were used to train and test experimental results, resulting in a machine learning model suitable for predicting the stable mean resistance of H62 brass alloy after wear. The prediction results showed that the BPNN model performed better in predicting the electrical contact performance compared to the RF and SVR models.

Deformation-dependent gel surface topography due to the elastocapillary and osmocapillary effects.

Actively tuning surface topography is crucial for the design of smart surfaces with stimuli-responsive friction, wetting, and adhesion properties. This paper studies how elastocapillary deformation and osmocapillary phase separation can lead to rich deformation-dependent surface topography in polymeric gels. In a purely elastic material, stretching always flattens the surface due to the Poisson effect. We show that stretching can roughen the surface due to the elastocapillary and osmocapillary effects. The roughening can be tuned by the gel stiffness, the gel osmotic pressure, the deformation mode, and the initial amplitude of surface roughness. The rich deformation-dependent behavior of gel surface topography points to a new direction in designing smart surfaces.

Ground-Motion Variability for Ruptures on Rough Faults

ABSTRACT Fault roughness influences earthquake rupture dynamics, seismic energy radiation, and, hence, resulting ground motion and its variability. Using 3D dynamic rupture simulations considering a range of rough-fault realizations, we investigate the effects of rupture complexity caused by fault roughness on ground-motion variability, that is, the variability of peak ground acceleration (PGA) and velocity (PGV) as a function of distance. In our analysis, we vary hypocenter locations (leading to unilateral and bilateral ruptures) and fault roughness amplitude to generate a set of magnitude M ≈ 7 strike-slip dynamic rupture simulations. Synthetic seismic waveforms computed on a dense set of surface sites (maximum resolved frequency 5.75 Hz) form our database for detailed statistical analyses. For unilateral ruptures, our simulations reveal that ground-shaking variability (in terms of PGA and PGV) remains nearly constant with increasing distance from the fault. In contrast, bilateral ruptures lead to slowly decreasing ground-motion variability with increasing distance in the near field (less than 20 km). The variability becomes almost constant at large fault distances. We also find that low-amplitude fault roughness leads to ruptures that are likely to generate higher PGA variability than events on faults with high-amplitude roughness. Increasing fault roughness distorts the radiation pattern, thereby reducing directivity effects and, hence, potentially lowering ground-motion variability. The average PGV variability from our rough-fault rupture models is consistent with estimates from empirical ground-motion models (GMMs). However, the average PGA variability exceeds the variability encoded in empirical GMMs by nearly 20%. Hence, our findings have implications for near-source ground-motion prediction in seismic hazard studies, because ground-motion variability depends on details of the earthquake rupture process and is larger than GMM estimates.