Rough Model Research Articles

The role of the areal parameters on turbulent flow over 2D Gaussian roughness

In the past decades, numerous efforts have been dedicated to establishing a direct correlation between a geometric parameter that represents wall roughness and the corresponding velocity reduction, known as the Roughness Function ΔU+. This reduction is influenced by various statistical measures of roughness height, including average roughness height, peak-to-valley roughness distance, roughness root mean square, and Effective Slope, among others. It has been demonstrated that a singular measure of roughness height cannot sufficiently predict the Roughness Function across all turbulent regimes. Consequently, many studies have concentrated on identifying a universal correlation between roughness geometry and the downward shift of the mean velocity profile. In this study, we investigated the correlation between various geometrical parameters and the Roughness Function using Large Eddy Simulation (LES) techniques in channel flows at a friction Reynolds number of Reτ=400. Given the complexity introduced by the random nature of irregular roughness, we explored specific aspects of the relationship between wall irregularities and the roughness function by studying 2D geometries. This approach allowed us to systematically investigate the impact of geometrical properties on the roughness function and isolate the effects of roughness density and coverage area. Several irregular rough surfaces, characterized by different average oscillations height, different distributions and different densities, were designed throughout 2D Gaussian functions. With the aim to find a universal correlation between the roughness geometry and the Roughness Function, new geometrical quantities were investigated, based on the global area occupied by the roughness A∗. The prediction of the roughness function can be thus obtained using apriori data. To predict the roughness function, we introduced a parameter called Effective Area (EA), which is derived from the correlation between the Effective Slope (ES) and the roughness area A∗. Our findings indicate that a single geometric parameter, whether ES or Area A, is insufficient to predict the roughness effect. Conversely, combining these two parameters enhances predictive accuracy, at least for the proposed roughness model. This improvement can be attributed to the ability of ES to interpret the roughness distribution and height, while the coverage area is effective in predicting roughness density over a flat plate.

Anisotropic 3D rough surface reconstruction model based on fractal method and its application in contact characteristics

Anisotropic 3D rough surface reconstruction model based on fractal method and its application in contact characteristics

Characterization and reconstruction of rough fractures based on vector statistics

The characteristics of fracture networks are of great significance for unconventional geo-energy exploitation such as shale oil, shale gas and geo-thermal extraction. Natural fractures and induced fractures after reservoir stimulation control the mechanical properties and fluid flow of the reservoirs. The quantitative characterization and reconstruction of rough fractures are essential for studying the fluid flow and mechanical properties of the reservoirs with complicated fracture networks. Based on vector statistics, a vector statistical method (VSM) for quantitative characterization of rough fractures is proposed and tested using 10 standard joint profiles. Furthermore, the growth vector counting method (counting method) and growth vector probability method (probability method) for single rough fractures in two-dimensional (2D) and three-dimensional (3D) models are proposed. Afterward, a morphology comparison and quantitative evaluation of single rough fractures before and after reconstruction are conducted. When the counting and probability methods are used to reconstruct single rough fractures, the difference in the tortuosity and fractal dimensions of the original and reconstructed fractures are less than 5% and 2.5%, respectively. It implies the counting and probability methods proposed herein can reconstruct rough fractures with approximate statistical characteristics and quantitative characterization parameters. Subsequently, the counting and probability methods for single rough fractures are further developed for 2D and 3D modeling of conventional and rough discrete fracture networks. The results indicate that the growth vector counting and probability methods proposed in this study have significant potential for rough discrete fracture network modeling. In addition, the merits and limitations of the proposed algorithms in modeling discrete fracture network models are discussed.

Study on the surface formation mechanism and theoretical model of brittle surface roughness in turning machinable ceramics

Study on the surface formation mechanism and theoretical model of brittle surface roughness in turning machinable ceramics

Deepwater 3D Measurements with a Novel Sensor System

A novel 3D sensor system for underwater application is presented, primarily designed to carry out inspections on industrial facilities such as piping systems, offshore wind farm foundations, anchor chains, and other structures at deep depths of up to 1000 m. The 3D sensor system enables high-resolution 3D capture at a measuring volume of approximately 1 m3, as well as the simultaneous capture of color data using active stereo scanning with structured lighting, producing highly accurate and detailed 3D images for close-range inspection. Furthermore, the system uses visual inertial odometry to map the seafloor and create a rough 3D overall model of the environment via Simultaneous Localization and Mapping (SLAM). For this reason, the system is also suitable for geological, biological, or archaeological applications in underwater areas. This article describes the overall system and data processing, as well as initial results regarding the measurement accuracy and applicability from tests of the sensor system in a water basin and offshore with a Remotely Operating Vehicle (ROV) in the Baltic Sea.

A 3D urban scene reconstruction enhancement approach based on adaptive viewpoint selection of panoramic videos

AbstractThe widely used unmanned aerial vehicle oblique photogrammetry often suffers from information loss in complex urban environments, leading to geometric and textural defects in the resulting models. In this study, a close‐range panoramic optimal viewpoint selection assisted 3D urban scene reconstruction enhancement method is proposed for areas prone to defects. We first introduce the ground panoramic data acquisition equipment and strategy, which are different from those of the single‐lens supplementary photography method. Data acquisition is accomplished through a single and continuous surround‐style collection approach. The full coverage of the panoramic video in the space–time dimension enables the acquisition of texture details without considering camera station planning. Then, a panoramic multiview image generation approach is proposed. Adaptive viewpoint selection is achieved using unbiased sampling points from the rough scene model, and viewpoint optimisation is adopted to ensure sufficient image overlap and intersection effects, thus improving the scene reconstructability. Finally, the 3D model is generated by photogrammetric processing of the panoramic multiview images, resulting in an enhanced modelling effect. To validate the proposed method, we conducted experiments using real data from Qingdao, China. Both the qualitative and quantitative results demonstrate a significant improvement in the quality of geometric and textural reconstruction. The tie‐point reprojection errors are less than 1 pixel, and the registration accuracy with the model from oblique photogrammetry is comparable to that of optimised‐view photography. By eliminating the need for on‐site camera station planning or manual flight operations and effectively minimising the redundancy of panoramic videos, our approach significantly reduces the photography and computation costs associated with reconstruction enhancement. Thus, it presents a feasible technical solution for the generation of urban 3D fine models.

Deterministic Simulation of Surface Textures for the Piston Ring/Cylinder Liner System in a Free Piston Linear Engine

The use of synthesis gas (SYNGAS) from waste gasification has been pointed out as a key strategy to help the energy transition. However, SYNGAS’ low calorific power is considered a difficult obstacle to its technological use in internal combustion engines. To overcome this, a novel free-piston linear motor has been proposed to pave the way for the use of SYNGAS in the mobility sector. Surface texturing has vast potential to reduce friction losses in this system. This study utilizes a deterministic numerical model to investigate the mixed lubrication performance of a textured piston ring/cylinder liner conjunction in a free piston engine. The model considers the simultaneous solution of the lubrication and asperity contact problems at the roughness scale, including texturing features on the cylinder surface. The numerical model employs the Reynolds equation with mass-conserving cavitation to calculate the inter-asperity fluid pressure. The rough contact model utilizes the Hertz theory for elastic contact to calculate the contact pressure at each asperity between the piston liner surface and the admitted smooth and rigid ring surface. Surface texturing demonstrated remarkable effectiveness, particularly in the hydrodynamic lubrication regime, with a maximum friction reduction of 38.5% observed for an area coverage of 50%. This was accompanied by a notable shift in the transition from the boundary to the mixed lubrication regime. The textured surfaces exhibited consistent efficiency in reducing fluid pressure and shear stress as the coverage of the textured areas increased. The incorporation of dimples on these surfaces played a crucial role by augmenting the lubricant storage capacity while concurrently reducing the real shear and contact areas. This study offers valuable insights into the nuanced friction-reducing mechanisms of surface textures, illuminating their influence on the coefficient of friction and the formation of lubricant films across various lubrication regimes.

Study of Effect of Graphite Based Magnetorheological Fluids on Braking Performance and Rotor Surface of Magnetorheological Disk Brake Using Full-Scale Brake Inertia Dynamometer

The performance of magnetorheological (MR) brakes depends on the rheological and tribological properties of MR fluid. An additive-free MR fluid does not offer better braking performance, which limits the commercial application of MR brake. In view of these issues, MR fluids were prepared in this present work employing graphite flakes as an additive. The required rheological properties of these MR fluids were measured on the magneto rheometer, and their braking performances were measured as per braking standard IS13453 using a fabricated MR disk brake on a 2/3-wheeler brake inertia dynamometer. Due to the interaction of MR fluid with the rotor and housing surface, the MR gap (i.e., another performance parameter) varies. Therefore, the effect of the interaction of prepared MR fluids on the rotor surface properties of the MR disk brake was also investigated. The constituents of MR fluids generate roughness on the rotor and housing surface. As a result, a mathematical model for surface roughness was presented and verified with experimental results. Based on the study, it is found that 0.5% (w/w) of graphite flake is better than additive-free MR fluid.

Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model

Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model

On predictive models for the equivalent sand grain roughness for wall-bounded turbulent flows

One of the long-standing goals of rough wall fluid dynamics research is to determine the drag penalty of surfaces based solely on their topographical parameters. The most important length scale or roughness parameter that best describes a surface in relation to friction drag has not been agreed upon yet, despite the many studies that, over the years, have attempted to identify the most appropriate surface parameter. The concept of an equivalent sand-grain roughness (ks) was introduced to standardize different types of roughness in wall-bounded turbulence, serving as an input parameter for predicting the roughness function ΔU+. To anticipate ΔU+ generated by a rough surface under turbulent flow conditions, experts use roughness correlations that establish a correspondence between the topographical characteristics of the surface and ks. Therefore, a chronological compilation of roughness correlations is presented, detailing the parameter ranges and types of roughness used in their development. This study evaluates the effectiveness of predictive correlation functions and aims to formulate a universal function by exploring a comprehensive assortment of three-dimensional (3D) surface textures available in the literature. The results suggest that a correlation based on surface height skewness (ksk) and streamwise effective slope (ESx) can predict the ratio (ks/kq), where kq is the root mean square roughness height for 3D roughness in the fully rough regime. Despite the fact that the correlation is restricted to 3D surface roughness, which is a more realistic representation, the model demonstrated a high level of accuracy in predicting ks for over 120 distinct rough surfaces.

Surface Roughness Prediction in Hard Turning (Finishing) of 16MnCr5 Using a Model Ensemble Approach

This paper investigates the predictive quality in finishing turning processes of 16MnCr5, specifically focusing on the use of model ensemble methods to predict surface roughness. Surface roughness plays a critical role in determining the quality and functionality of machined components. The objective of this study is to develop an accurate and robust predictive model for surface roughness after finishing turning operations. The proposed approach employs a model ensemble method, which combines multiple predictive models to enhance the accuracy and reliability of the predictions. The experimental setup includes collecting data using various types of in-situ sensors along with corresponding surface roughness measurements. The collected dataset is used to train and test the ensemble model, which integrates different machine learning algorithms and statistical techniques. The results demonstrate that the model ensemble method yields superior predictive performance compared to individual models, effectively capturing the complex relationships between process signals and surface roughness.

Emulsions in microfluidic channels with asymmetric boundary conditions and directional surface roughness: stress and rheology.

The flow of emulsions in confined microfluidic channels is affected by surface roughness. Directional roughness effects have recently been reported in channels with asymmetric boundary conditions featuring a flat wall, and a wall textured with directional roughness, the latter promoting a change in the velocity profiles when the flow direction of emulsions is inverted [D. Filippi et al., Adv. Mater. Technol., 2023, 8, 2201748]. An operative protocol is needed to reconstruct the stress profile inside the channel from velocity data to shed light on the trigger of the directional response. To this aim, we performed lattice Boltzmann numerical simulations of the flow of model emulsions with a minimalist model of directional roughness in two dimensions: a confined microfluidic channel with one flat wall and the other patterned by right-angle triangular-shaped posts. Simulations are essential to develop a protocol based on mechanical arguments to reconstruct stress profiles. Hence, one can analyze data to relate directional effects in velocity profiles to different rheological responses close to the rough walls associated with opposite flow directions. We finally show the universality of this protocol by applying it to other realizations of directional roughness by considering experimental data on emulsions in a microfluidic channel featuring a flat wall and a wall textured by herringbone-shaped roughness.

Improving the Accuracy of the Surface Roughness Model in Grinding Through Square Root Transformation

Surface roughness is a crucial parameter for mechanical products. To achieve small surface roughness, the grinding method is often chosen as the final machining process. The regression model of surface roughness forms the basis for controlling the grinding process and predicting surface roughness under specific conditions. The effectiveness of process control and the accuracy of predicted surface roughness depend on the precision of the surface roughness regression model. This study aims to enhance the accuracy of the surface roughness regression model by employing square root transformation. An experimental process was conducted with a total of eighteen experiments. In each experiment, three cutting parameters, including workpiece speed, tool feed rate, and cutting depth, were varied. Surface roughness was measured in each experiment. After conducting experiments, a surface roughness regression model was established, denoted as Model (1), without using any data transformation. The square root transformation was applied to convert the surface roughness dataset into another set of data. From this dataset, another surface roughness model, referred to as Model (2), was developed. Both models were used to predict surface roughness, and the predicted results were compared with the actual surface roughness in the experiments. Four parameters were used to compare Models (1) and (2), including the coefficient of determination (R-Sq), adjusted coefficient of determination (R-Sq(adj)), mean absolute error percentage (%MAE), and mean squared error (%MSE). All four parameters for Model (2) were superior to those for Model (1). The results confirmed that the square root transformation successfully improved the accuracy of the surface roughness regression model in grinding applications.

A NEW ROUGH FRACTURE PERMEABILITY MODEL OF COAL WITH INJECTED WATER BASED ON DAMAGED TREE-LIKE BRANCHING NETWORK

The fracture network structure of coal is very complex, and it has always been a hot issue to characterize the fracture network structure of coal by using a tree-like branching network. In this paper, a new rough fracture permeability model of water injection coal based on a damaged tree-like branching network is proposed. In this model, fractal theory and sine wave model are used to characterize the rough characteristics of fracture structure. In addition, the applicability of the model is verified by the self-developed experimental system, and the sensitivity analysis and weight analysis of the influencing factors in the model are carried out. The results show that the factors affecting the permeability of the established permeability mathematical model are mainly the amplitude factors of sinusoidal fluctuation, length ratio, maximum branch series, number of damaged fractures, opening ratio, opening fractal dimension and initial fracture length. Among them, the sinusoidal fluctuation amplitude factor has the greatest influence on the permeability and the other two are inversely proportional. Therefore, with pulsating water injection, the frequency of changes in the water pressure was altered to improve the water injection efficiency in coal fractures with sinusoidal distribution to increase permeability. The research results provide a theoretical basis for further improving the theoretical models for seepage in porous media and fluid flow analysis of damaged tree-like branching networks.

Cardinality rough neighborhoods with applications

<p>Rough set theory serves as an effective method for managing complicated real-world data. Through rough approximation operators, it discerns both confirmed and possible data attainable through subsets. Earlier studies have presented several rough approximation models, drawing inspiration from neighborhood systems aimed at enhancing accuracy degree and satisfying the axioms of traditional approximation spaces (TAS) that were initiated by Pawlak. This article proposes an easy method to deal with information systems in most cases, wherein it introduces a new forming of generalized approximation spaces, namely, cardinality rough neighborhoods. It is defined depending on the cardinal number of the $ \mathcal{N}_\sigma $-neighborhoods of elements that are established under an arbitrary relation. Their main features are investigated and the connections between them, as well as their relationships with the preceding kinds of neighborhood systems, are uncovered with the aid of some examples. Then, novel rough set paradigms induced by cardinality rough neighborhoods are displayed that satisfy most properties of Pawlak's paradigm. Next, a topological method to study these paradigms is provided, wherein this method produces approximation operators similar to the given paradigms in six cases that are proved. Additionally, both paradigms in a practical example concerning books and the authors who authored them or participated in their authorship are applied. To illuminate the need for the current concepts, we elaborate on their advantages from different views. Finally, a summary of the obtained results and relationships and suggestions for some forthcoming work are offered.</p>

RELATIVE PERMEABILITY MODEL OF TWO-PHASE FLOW IN ROUGH CAPILLARY ROCK MEDIA BASED ON FRACTAL THEORY

In this paper, the gas-water two-phase flow characteristics of rock media are studied based on fractal theory and the relative roughness model, and the analytical model of gas-water relative permeability of rock pores with relative roughness is derived. Through numerical simulation, it is found that the maximum flow velocity in the rough microchannel is greater than the maximum flow velocity in the smooth microchannel, but the average flow velocity in the rough microchannel is less than the average flow velocity in the smooth microchannel. The theoretical model results show that the dimensionless permeability of gas-water two-phase flow is inversely proportional to the fractal dimension of relative roughness and tortuosity, and the relative permeability of gas-water two-phase flow is inversely proportional to the relative roughness. The correctness of the model is verified by comparing it with the relevant experimental data.

Evaluation method of e-government audit information based on big data analysis

Abstract In order to improve the problems of low efficiency of intelligent auditing and long approval cycles in the evaluation of e-government audit information, this paper proposes a model for the evaluation of e-government audit information. The model covers the acquisition of audit information, evaluation rules, and e-government auditing. The intuitionistic fuzzy roughness model is applied to determine the distinguishing relationship between objects based on the intuitionistic fuzzy values of different objects and to determine the evaluation rules for audit information. An e-government auditing approach based on big data analysis is also proposed, and a genetic algorithm is used to optimize the relevant parameters of a convolutional neural network to enhance the stability of the e-government auditing model. Audit information evaluation activities are carried out at Changchun Housing Provident Fund Center in Jilin Province, China, whose shared approval information data failure records in 2023 accounted for as high as 70.54% of all documents, with low stability. The number of cases and closure of the provident fund center accepting the extranet from 2017 to 2023 only accounted for 4.57% and 3.99% of the whole. In the audit information evaluation indicators, nine secondary indicators, such as credit management and indirect income, have an evaluation value of more than 0.5 in the “medium” evaluation level, and many indicators have an evaluation value of more than 0 in the “poor” evaluation level, so there are many shortcomings in the business.

Integrating Taguchi Method and Support Vector Machine for Enhanced Surface Roughness Modeling and Optimization

Integrating Taguchi Method and Support Vector Machine for Enhanced Surface Roughness Modeling and Optimization

Chezy’s Resistance Coefficient in a Round-cornered Rectangle Channel

Abstract The determination of resistance coefficients, such as Chezy’s or Manning’s coefficients, requires a great deal of sensible thought in order to express these coefficients better and more extensively in free-surface channels and aqueducts. This can be achieved if the expression of the resistance coefficient is well stated and takes into account the maximum number of parameters for governing flows in channels. However, in most practical cases, if these coefficients are not expressed by implicit models, they are generally taken as constant and arbitrary. To this end and in a rational manner, the dimensioning and design of channels requires the expression of the resistance coefficient in an easily and explicit form by adopting numerous flow parameters, namely the roughness of the walls, the aspect ratio, the slope of the channels and essentially the viscosity of the liquid. To achieve this aim, the Chezy’s resistance coefficient C is identified using the rough model method (RMM), which gives the discharge under uniform flow conditions appropriate to a round-cornered rectangle channel.

An intuitionistic fuzzy rough model for maritime transportation chains under disaster effects: the case of COVID-19

The outbreak of COVID-19 has caused disruptions in port transport infrastructure and shipping, resulting in higher shipping rates in 2022. This article aims to assess the extent of COVID-19 risks on the capacity and concentration of traffic flows, and evaluate the degree of imbalance in the transport system during 2020-2021 compared to the previous period of 2018-2019. The data is analyzed using the Intuitionistic Fuzzy Rough set, which allows the evaluation of multiple heterogeneous measures to produce an aggregate outcome. The model is based on the intensity of non-stationary incoming and outgoing traffic, as well as the intervals between separate ship calls. The data obtained from the study shows the dynamics of changes in traffic flows under the influence of COVID-19. The results indicate a minimal degree of its influence, which did not lead to any failures or modifications in the composition of traffic flows. The method can also allow for the development of complex simulation models to assess the limit of potential deviations, after which, with a high degree of probability, deformation of traffic flows is possible.