Types Of Roughness Research Articles

Using the Degree r ∈ [0, 1] in Defining Rough Fuzzy Sets

This paper introduced a new definition of rough fuzzy sets based on a fuzzy ideal defined on a fuzzy approximation space (X, R). This definition considered the degree r ∈ [0, 1] in defining the after sets and the before sets of the arbitrary fuzzy relation R on the finite set of objects X. It is shown that this new type of roughness is a generalization to many of previous definitions in the fuzzy case and also in the ordinary case. As an application, it is given a rough fuzzy‐connected set notion depending on some degree r ∈ [0, 1].

Extending Copper Interconnects and Epoxy Dielectrics to Multi-GHz Frequencies.

Future multichip packages require Die-to-Die (D2D) interconnects operating at frequencies above 10 GHz; however, the extension of copper interconnects and epoxy dielectrics presents a trade-off between performance and reliability. This paper explores insertion losses and adhesion as a function of interface roughness at frequencies up to 18 GHz. We probe epoxy surface chemistry as a function of curing time and use wet etching to modulate surface roughness. The morphology is quantified by atomic force microscopy (AFM) and two-dimensional fast Fourier transform (2D FFT). Peel test and vector network analysis are used to examine the impacts of both type and level of roughness. The trade-offs between power efficiency and reliability are presented and discussed.

Influence of Fines Content and Pile Surface Characteristics on the Pullout Resistance Performance of Piles.

In this study, the direct shear test and model pullout test results are presented to assess the impact of soil fines content and shear resistance characteristics of the pile-soil interface on the pullout resistance of drilled shafts. The direct shear test on the soil-pile interface was conducted based on the pile surface simulated using sandpaper with three roughness types (#24, #40, and #400) and varying fines content. The direct shear test results of soil showed that the internal friction angle decreased by about 29% and the cohesion increased by about 110% when the fine powder content increased from 5% to 30%. Specifically, in the case of soil-sandpaper (#24), the interface friction angle decreased by about 31%, and the adhesion increased by about 16%. The sandpaper with a roughness of #40 and #400 also showed a similar trend. Normalizing the shear strength parameters from the direct shear test demonstrated an intersection between the normalized curves of the friction angle and cohesion (or adhesion) within a specific fines content range. This suggests that shear strength parameters play a significant role based on fines content. Analyzing the normalized index using model pullout test results indicated the necessity to evaluate the contribution of friction angle and cohesion (or adhesion) of the shear surface, taking into account the fines content of the soil for predicting pile pullout resistance.

I-scan combined with laryngovideostroboscopy for predicting malignancy in vocal fold leukoplakia.

This study evaluated vocal fold leukoplakia using i-scan combined with laryngovideostroboscopy for risk assessment prediction. A total of 141 patients with 218 lesions were enrolled in this study. Morphological characteristics of leukoplakia, assessment of the vascular pattern using i-scan, and vocal fold vibratory function were analyzed. The number of patients with no, mild, moderate, severe dysplasia, and invasive carcinoma were 68, 40, 17, 46 and 47, respectively. The sensitivity of morphological characteristic, vascular pattern, vibratory function and predictive model were 77.4%, 72%, 69.9%, and 82.8%, respectively. Receiver operating characteristic curve analysis of morphological characteristic, vascular pattern, vibratory function and predictive model were 0.771, 0.824, 0.769, and 0.923, respectively. The results of logistic regression analysis showed that rough morphological types, perpendicular vascular pattern, severe decrease and absence of mucosal waves increased the risk of malignancy (OR = 5.531, 4.973, and 16.992, respectively; P < 0.001). I-scan combined with laryngovideostroboscopy can improve the differential diagnosis of low-risk and high-risk vocal fold leukoplakia.

Machine learning approaches for adequate prediction of flow resistance in alluvial channels with bedforms.

In natural rivers, flow conditions are mainly dependent on flow resistance and type of roughness. The interactions among flow and bedforms are complex in nature as bedform dynamics primarily regulate the flow resistance. Manning's equation is the most frequently used equation for this purpose. Therefore, there is a need to develop alternate reliable techniques for adequate prediction of Manning's roughness coefficient (n) in alluvial channels with bedforms. Thus, the main objective of this study is to utilize machine learning (ML) models for predicting 'n' based on the six input features. The performance of ML models was assessed using Pearson's coefficient (R2), sensitivity analysis, Taylor's diagram, box plots, and K-fold method has been used for the cross-validation. Based on the output of the current work, models such as random forest, extra trees regression, and extreme gradient boosting performed extremely well (R2 ≥ 0.99), whereas, Lasso Regression models showed moderate efficiency in predicting roughness. The sensitivity analysis indicated that the energy grade line has a significant impact in predicting the roughness as compared to the other parameters. The alternate approach utilized in the present study provides insights into riverbed characteristics, enhancing the understanding of the complex relationship between roughness and other independent parameters.

Physical and Chemical Properties of High-Temperature Silicone-Based Polymer Coatings Applied on Different Surface Roughnesses

High-temperature coatings play a crucial role in protecting surfaces exposed to extreme temperatures, corrosion, and other harsh environments. This paper focuses on the physical and chemical properties of solvent-borne and water-borne high-temperature silicone-based polymer coatings applied on two types of surface roughness of carbon steel plates. The corrosion protection performance of the coatings was characterized using a salt spray chamber, humidity chamber, electrochemical impedance spectroscopy (EIS) measurements, and differential scanning calorimetry (DSC). The physical properties of high-temperature coatings were determined using the Shore D hardness method and cross-cut adhesion tests. This study investigates the effects of different surface preparation methods on coating adhesion, which is considered to be a crucial property of organic coatings for corrosion protection durability. The thermal stability of the coating was tested using furnace cyclic testing. The results show that high-temperature coatings in general exhibit excellent thermal stability, high adhesion strength, and good resistance to warm and humid environments, except in the conditions of a salty atmosphere. This study reveals that coating performance is affected by the composition and surface preparation method. This study can be useful for coating manufacturers and researchers interested in understanding the physical and chemical properties of high-temperature coatings and their applications in various environments.

Prediction of equivalent sand-grain size and identification of drag-relevant scales of roughness – a data-driven approach

Despite decades of research, a universal method for prediction of roughness-induced skin friction in a turbulent flow over an arbitrary rough surface is still elusive. The purpose of the present work is to examine two possibilities; first, predicting equivalent sand-grain roughness size $k_s$ based on the roughness height probability density function and power spectrum (PS) leveraging machine learning as a regression tool; and second, extracting information about relevance of different roughness scales to skin-friction drag by interpreting the output of the trained data-driven model. The model is an ensemble neural network (ENN) consisting of 50 deep neural networks. The data for the training of the model are obtained from direct numerical simulations (DNS) of turbulent flow in plane channels over 85 irregular multi-scale roughness samples at friction Reynolds number $Re_\tau =800$ . The 85 roughness samples are selected from a repository of 4200 samples, covering a wide parameter space, through an active learning (AL) framework. The selection is made in several iterations, based on the informativeness of samples in the repository, quantified by the variance of ENN predictions. This AL framework aims to maximize the generalizability of the predictions with a certain amount of data. This is examined using three different testing data sets with different types of roughness, including 21 surfaces from the literature. The model yields overall mean error 5 %–10 % on different testing data sets. Subsequently, a data interpretation technique, known as layer-wise relevance propagation, is applied to measure the contributions of different roughness wavelengths to the predicted $k_s$ . High-pass filtering is then applied to the roughness PS to exclude the wavenumbers identified as drag-irrelevant. The filtered rough surfaces are investigated using DNS, and it is demonstrated that despite significant impact of filtering on the roughness topographical appearance and statistics, the skin-friction coefficient of the original roughness is preserved successfully.

Direct numerical simulation of turbulent flow in pipes with realistic large roughness at the wall

We carry out direct numerical simulation (DNS) of turbulent flow in rough pipes. Two types of irregular roughness are investigated, namely a grit-blasted and a graphite surface. A wide range of Reynolds numbers is tested, from the laminar up to the fully rough regime, attempting to replicate Nikuradse's pioneering study. Despite the large relative roughness, outer-layer similarity is achieved at high Reynolds number as hypothesised by Townsend, with deviations from the smooth wall case of 4 % for the grit-blasted surface and 13 % for the graphite surface. This makes it possible to define a roughness function and the equivalent sand-grain roughness. The results are compared with those obtained in plane channels, with small differences pointing to the residual influence of the duct cross-sectional shape in the presence of relatively large roughness. The computed friction factors behave similar to those Nikuradse's chart, with differences in terms of the friction factor in the laminar region and of the critical Reynolds number, which are partly absorbed by using the hydraulic radius as reference length scale. The distributions of the velocity fluctuations intensities point to a isotropisation of turbulence in the near-wall region resulting from the roughness, with influence of the roughness geometry. Comparison of the computed equivalent sand-grain roughness height suggest that existing correlations suffer from poor predictive power, at least for surfaces with large relative roughness.

Data-driven prediction of the equivalent sand-grain roughness

Surface roughness affects the near-wall fluid velocity profile and surface drag, and is commonly quantified by the equivalent sand-grain roughness {k}_{s}. It is essential to estimate {k}_{s} for accurate fluid dynamic problem modeling. While numerous roughness correlation formulas have been proposed to predict {k}_{s} in the fully rough regime, most of them are restricted to certain roughness types, with various geometric parameters considered in each case, leading to ongoing disagreements regarding its parameterization and lack of universality. In this study, a Particle Swarm Optimized Backpropagation (PSO-BP) method is proposed to predict {k}_{s} based on the selected surface parameters from previous DNS, LES, and experimental results for flow behavior over various surface roughness. The PSO-BP model’s ability to predict {k}_{s} in the fully rough region is evaluated and compared with both the existing roughness correction formulas as well as the traditional BP model. An optimized polynomial function is also proposed to serve as a ‘white box’ for predicting {k}_{s}. It turns out that the PSO-BP method has better performance in the evaluation metrics compared to other methods, yielding a Mean Absolute Error (MAE) of 0.0390, a Mean Squared Error (MSE) of 0.0026 and a Mean Absolute Percentage Error (MAPE) of 28.12%. This novel approach for estimating {k}_{s} has practical applicability and holds promise for improving the precision and efficiency of calculations related to equivalent sand-grain roughness, and thus provides more accurate and effective solutions for CFD and other engineering applications.

The FABDEM Outperforms the Global DEMs in Representing Bare Terrain Heights

Many remote sensing and geoscience applications require a high-precision terrain model. In 2022, the Forest And Buildings removed Copernicus digital elevation model (FABDEM) was released, in which trees and buildings were removed at a 30 m resolution. Therefore, it was necessary to make a comprehensive evaluation of this model. This research aims to perform a qualitative and quantitative analysis of fabdem in comparison with the commonly used global dems. We investigated the effect of the terrain slope, aspect, roughness, and land cover types in causing errors in the topographic representation of all dems. The fabdem had the highest overall vertical accuracy of 5.56 m. It was the best dem in representing the terrain roughness. The fabdem and Copernicus dem were equally influenced by the slopes more than the other models and had the worst accuracy of slope representation. In the tree, built, and flooded vegetation areas of the fabdem, the mean errors in elevation have been reduced by approximately 3.34 m, 1.26 m and 1.55 m, respectively. Based on Welch's t-test, there was no significant difference between fabdem and Copernicus dem elevations. However, the slight improvements in the fabdem make it the best filtered dem to represent the terrain heights over different land cover types.

Influence of wall roughness on pressure distribution and performance of centrifugal dredge pumps

Wall roughness is an important factor that affects pump head and efficiency. Based on the open source program OpenFOAM, the wall function method and the equivalent sand roughness model were used to study the influence of wall roughness on the performance of centrifugal dredge pumps. Then, the steady state of the dredge pump under 10 different types of wall roughness was calculated using the k-omega SST turbulence model. The pressure distribution under different flow rates and rotation speeds was also analyzed. The results reveal that the relationship between the increase of wall roughness and the performance of the dredge pump can be roughly divided into three stages, which may be related to the link between roughness and the viscous layer. In particular, the pump performance decreases significantly, and the shaft power increases linearly when it is in the third stage (complete rough region). Finally, different degrees of roughness have minimal effects on the pressure distribution of the impeller.

Can 100% Pasture-Based Livestock Farming Produce Enough Ruminant Meat to Meet the Current Consumption Demand in the UK?

Grassland is grouped into temporary, permanent, and rough grazing types in the United Kingdom (UK), making up more than 60% of the national agricultural land area. It provides avenues for grazed fodder or ensiled forage contributing a large proportion of the diets consumed by cattle and sheep. The official agricultural census data in 2011 to 2020 showed that, on average, UK cattle and sheep farming can produce meat to satisfy 83.3 and 100.8% of domestic cattle beef and sheep meat consumption levels, respectively. Out of the large agricultural census datasets, we used the populations of cattle and sheep, as well as the UK definition of a standard livestock unit (SLU), to normalise the respective herd populations into a total standard livestock unit (TSLU). We then used the annual domestic meat production in dressed carcass weight to calculate cattle and sheep meat productivity per SLU. Using the potential herbal dry matter yields per year and areas of the different grassland types across the UK, the potential total available pasture feed was calculated. This potential production of herbal biomass was translated into the potential carrying capacity expressed in a TSLU. This total potential carrying capacity was partitioned into cattle and sheep sectors so that the routes of pasture-based-only options with which to produce ruminant meat to meet the current UK domestic consumption demands were assessed. The estimated mean potential annual pasture forage feed in 2011–2020 was approximately 82.0 million (M) metric tonnes (t), which can be translated into a potential carrying capacity of 17.9 M SLUs compared with the current mean 9.36 M SLUs in the survey data of the UK. With the ratio of sheep to cattle at 8.2:25 in the national TSLU, the UK national demands at present consumption levels of cattle and sheep meat can be arithmetically met with pasture grass utilisation rates at or above 65% and 50% by cattle and sheep farming systems, respectively.

Superposition of populations in multi-objective evolutionary optimization of car suspensions

The design of the suspension model of road cars is extremely important for both driver comfort and safety. Road traffic injuries are currently the eighth leading cause of death worldwide and are predicted to become the seventh leading cause of death by 2030, according to the World Health Organization (WHO). Among other factors, road infrastructure is one of the most important factors to consider when designing the suspension model, especially as autonomous driving becomes more popular. In addition, there is a growing need to test and study the impact of road roughness and pavement type on autonomous vehicles (AVs). This work provides the following contributions: a platform for suspension system optimization–flexible, highly parameterizable and rich in applications. Our solution implements, tests and optimises the 2, 3 and 4 degrees of freedom (DOF) quarter-car models (QCM) with linear seat suspension and the 5 DOF half-car model (HCM), respectively, based on random road profiles generated according to ISO 8608 standards. In addition, many multi-objective optimization (MOO) algorithms have been implemented and applied in isolation to see their comparative performance (in terms of Pareto fronts, hypervolume, coverage, spread, and epsilon), but most importantly we have also introduced a meta-optimization technique by super-positioning Non-dominated Sorting Genetic Algorithm II and Strength Pareto Evolutionary Algorithm 2, where each of the algorithms, running concurrently, produces a percentage of the total number of individuals in a generation, percentage that is calculated according to the solution quality of the individuals in the previous generation. The experimental results were obtained by simulating the optimal configurations generated by the MOO heuristic algorithms on different categories of cars (small/medium). Finally, the Taguchi method and regression analysis were used to evaluate the effects of specific parameters on system responses such as sprung mass acceleration and displacement. The Taguchi simulation was performed to experimentally validate the results obtained by metaheuristic optimization methods.

Study on the influence of surface roughness on the diffraction efficiency of two-dimensional gratings.

This study investigates the effect of surface roughness on the diffraction efficiency of two-dimensional gratings. Firstly, a roughness model was constructed using FDTD, followed by a significant analysis of the ridge roughness, groove roughness, and sidewall roughness on diffraction efficiency. Then, the impact of each roughness type on diffraction efficiency was studied separately. Results indicate that ridge roughness has a negative impact on diffraction efficiency, whereas groove roughness and sidewall roughness have a positive impact on the diffraction efficiency of two-dimensional gratings. When ridge, groove, and sidewall roughness coexist, diffraction efficiency decreases with an increase in roughness, consistent with previous research. However, under conditions of minimal roughness, diffraction efficiency actually increases. Finally, an experiment was conducted to verify the conclusions. The results of this study have significant reference value for the application and development of precision measurement techniques for gratings.

Supercritical Froude Number Flow through Ducts with Statistically Roughened Walls

High-speed fluid flows over roughened surfaces occur in many engineering applications; one important application involves high velocity water flows in pipelines with roughened interior walls where the wall roughness affects head loss estimates necessary for engineering design purposes. The present analysis provides an analytical solution of the fluid physics underlying the induced static pressure profile resulting from high Froude number supercritical velocity through duct with random wall roughness. The analytic solution of the hyperbolic governing small perturbation velocity potential equation subject to high Froude number flows brings forward characteristic wave solutions that determine the static pressure profile in a duct with random height wall roughness. While current engineering practice utilizes semi-empirical engineering equations employing test data to determine the friction factor, velocity and static pressure profiles and head loss for different roughness types in different sized ducts as a function of Reynolds number (as summarized in a later section of the paper), the present analysis provides a new analytical method to determine the fluid physics involved in the static pressure change induced by wall random roughness in ducts subject to high Froude number supercritical flows.

Ultrastructure of receptive stigma and transmitting tissue at anthesis in two pear species

The ultrastructure of stigmatic and stylar secretory tissues was studied in one cultivar of &lt;i&gt;Pyrus communis&lt;/i&gt; and six cultivars of &lt;i&gt;Pyrus pyrifolia&lt;/i&gt; var. &lt;i&gt;culta&lt;/i&gt; (the so-called Nashi pear) using standard light, scanning, and transmission electron microscopy methods. Although both tissues secreted an extracellular fluid necessary for the development of male gametophyte, they differed markedly in the ultrastructure of the extracellular matrix and in the distribution and ultrastructure of organelles. The difference was most evident in regard to the endoplasmic reticulum, which represented the rough, smooth, and vesicular type in stigmatic papillate epidermis and distal stigmatoid tissue cells and occurred mainly as the rough type in form of expanded cisternae filled with fine-fibrillar content in the transmitting tissue of the style.

Public Database of Cracks Images in Mortar Coating with Different Types of Surface Finishes

The use of technology, such as artificial intelligence (AI), in production processes has been optimizing several industrial realities. In civil construction, AI can be used in different applications, one of which is building inspection. One of the difficulties in developing this type of study is the low number of public image databases that represent more general aspects of building wear. In view of this, the main objective of this research was to set up a public database of images of cracks in mortar coating, considering different types of surface finish—smooth type, scrapped type, and rough type. A database was created with 33,088 images that went through a systematic labeling process based on classes defined in the study. Network training was carried out through transfer learning using the VGG16 in different groupings of finishes. It was found that the training accuracy varies according to surface finish and data balancing. The finish of the scrapped type was the one that presented the lowest accuracy. The database presented several types of noise and was unbalanced in all categories defined in the labeling. In this way, it was possible to create a database that represented possible situations to be found in real inspections.

Boundedness for commutators of rough p-adic fractional Hardy type operators

In this paper, we introduce the rough p-adic Hardy type operator and obtain the boundedness for its commutator generated by the p-adic CMO function on p-adic Herz-type spaces. Moreover, we establish the λ-central BMO estimates for the commutator of the rough p-adic fractional Hardy operator on p-adic central Morrey spaces.

Flow Regimes and Types of Solid Obstacle Surface Roughness in Turbulent Heat Transfer Inside Periodic Porous Media

Flow Regimes and Types of Solid Obstacle Surface Roughness in Turbulent Heat Transfer Inside Periodic Porous Media

Numerical Study of the Effects of Roughness Coupled with Inclination on a Turbulent Flow around an Obstacle

In this study, we simulate the cooling of a microprocessor by thermal convection in three different shapes: a square, a trapezoidal, and a triangular shape. The latter is improved by a variety of types of roughness, including square roughness, triangular roughness Type 1, triangular roughness Type 2, and triangular roughness Type 3. The microprocessors are kept at a constant temperature, the air flow is constant, and the geometry is fixed. The physical phenomenon is simulated by the ANSYS software. The numerical results reported in this study cover the ranges of the obstacle’s angle of inclination, 0°≤θ≤45°, (square obstacles, θ=0°, trapezoidal obstacles, 0°&lt;θ&lt;45°, triangular obstacles, θ=45°) and Reynolds number, 2500≤Re≤10,000. The findings relate to streamlines, dynamic pressure (max), mean velocity, temperature field, mean Nusselt number (Nu/Nu0) profiles, local coefficient of friction (Cf/f0), mean coefficient of friction (f/f0) profiles, mean velocity field with roughness, and fluid temperature field with roughness. The aim of the study is to show the interaction between the roughness parameter and the obstacle geometry. In the case of a triangular obstacle, the contact between the cold air and the obstacle is significant downstream of the obstacle, which gives us good cooling, and the Nusselt number has an important value because the agitation of the flow increases convective heat transfer, and the coefficient of friction is low because the air flow is uniform.