Rough Ground Research Articles

A 3D SPH–FE coupling for FSI problems and its application to tire hydroplaning simulations on rough ground

A 3D fluid–structure coupling between Smoothed Particle Hydrodynamics (SPH) and Finite Element (FE) methods is proposed in this paper, with its application to complex tire hydroplaning simulations on rough ground. The purpose of this work is to analyze the SPH–FE coupling capabilities for modeling efficiently such a complex phenomenon. On the fluid side, the SPH method is able to handle the three complex interfaces of the hydroplaning phenomenon: free-surface, ground/fluid and fluid/tire interfaces. On the solid side, the FE method is used for its ability to treat tire–ground contact. A new algorithm dedicated to such SPH–FE coupling strategies is proposed to optimize the computational efficiency through the use of differed time steps between fluid and solid solvers. This way, the number of calls to the FE solver is minimized while maintaining the accuracy and stability of the coupling. The ratio between these respective time steps relies on a control procedure based on pressure loading. The present 3D SPH–FE model is first validated with different academic test cases and experimental data before considering the complex problem of the 3D hydroplaning simulations. Hydroplaning simulations are performed and analyzed on 3D configurations involving both smooth and rough grounds.

AUTOMATIC DETECTION OF ROAD EDGES FROM AERIAL LASER SCANNING DATA

Abstract. When aerial laser scanning (ALS) is deployed with targeted flight path planning, urban scenes can be captured in points clouds with both high vertical and horizontal densities to support a new generation of urban analysis and applications. As an example, this paper proposes a hierarchical method to automatically extract data points describing road edges, which are then used for reconstructing road edges and identifying accessible passage areas. The proposed approach is a cell-based method consisting of 3 main steps: (1) filtering rough ground points, (2) extracting cells containing data points of the road curb, and (3) eliminating incorrect road curb segments. The method was tested on a pair of 100 m × 100 m tiles of ALS data of Dublin Ireland’s city center with a horizontal point density of about 325 points/m2. Results showed the data points of the road edges to be extracted properly for locations appearing as the road edges with the average distance errors of 0.07 m and the ratio between the extracted road edges and the ground truth by 73.2%.

Dynamic obstacle-surmounting analysis of a bilateral-wheeled cable-climbing robot for cable-stayed bridges

PurposeField robots can surmount or avoid some obstacles when operating on rough ground. However, cable-climbing robots can only surmount obstacles because their moving path is completely restricted along the cables. This paper aims to analyse the dynamic obstacle-surmounting models for the driving and driven wheels of the climbing mechanism, and design a mechanical structure for a bilateral-wheeled cable-climbing robot to improve the obstacle crossing capability.Design/methodology/approachA mechanical structure of the bilateral-wheeled cable-climbing robot is designed in this paper. Then, the kinematic and dynamic obstacle-surmounting of the driven and driving wheels are investigated through static-dynamic analysis and Lagrangian mechanical analysis, respectively. The climbing and obstacle-surmounting experiments are carried out to improve the obstacle crossing capability. The required motion curve, speed and driving moment of the robot during obstacle-surmounting are generated from the experiments results.FindingsThe presented method offers a solution for dynamic obstacle-surmounting analysis of a bilateral-wheeled cable-climbing robot. The simulation, laboratory testing and field experimental results prove that the climbing capability of the robot is near-constant on cables with diameters between 60 and 205 mm.Originality/valueThe dynamic analysis method presented in this paper is found to be applicable to rod structures with large obstacles and improved the stability of the robot at high altitude. Simulations and experiments are also conducted for performance evaluation.

Soil Moisture Monitoring with Dual-Incidence-Angle RISAT-1 Data: A Pilot Study from Vidarbha Region

Soil moisture is a crucial parameter in the estimation of crop yield, drought forecast, hydrological and climatic studies. Soil moisture retrieval from synthetic aperture radar data is yet to be operational due to difficulty in removing the influence of vegetation and roughness in the backscattering signal. This limitation is addressed in this study by a combination of lower and higher incidence angle RISAT-1 SAR data. Roughness was derived using a linear model prepared by correlating σ°high HH–σ°low HH with root mean square height of the ground roughness component. A new attempt is made to remove the influence of vegetation using a model prepared by integrating the radar vegetation index and normalized differential vegetation index. The derived soil moisture was validated with the ground-truth data with the R2 value of 0.62 and with the error of 5.4% (volumetrically).

Real-Time Modeling of Forward-Looking Synthetic Aperture Ground Penetrating Radar Scattering From Rough Terrain

Ground penetrating radar (GPR) is a viable tool for fast and high fidelity detection of concealed explosive threats. The radar effectiveness is limited by scattering from rough terrain which considerably obscures the buried target response. To calculate the rough ground scattering, a 3-D full-wave algorithm such as finite-difference frequency-domain (FDFD) method is required but is often prohibitive for multiple frames when the GPR antennas are distant from the target region. This paper presents a real-time 3-D modeling of a moving platform forward-looking GPR scattering from rough terrain located at great electrical distances from the GPR antenna. For a synthetic aperture, the computational domain of the focal region is reduced to a very small subset of the entire observed volume, and the surface clutter is computed via a mere multiplication of a precomputed impulse response matrix of the rough ground with the matrix characterizing the GPR transmitting signal. For a vehicle-mounted GPR detection system, this results in a significant reduction of complexity and saving of computation resources. The effectiveness of the algorithm is evaluated through an implementation of 3-D Monte Carlo simulation for various rough surface parameters. Our developed model compares well with the direct FDFD results, and can be used for lossy and frequency-dispersive soils.

GPR Antipersonnel Mine Detection Based on Tensor Robust Principal Analysis

The ground Penetrating Radar (GPR) is a promising remote sensing modality for Antipersonnel Mine (APM) detection. However, detection of the buried APMs are impaired by strong clutter, especially the reflection caused by rough ground surfaces. In this paper, we propose a novel clutter suppression method taking advantage of the low-rank and sparse structure in multidimensional data, based on which an efficient target detection can be accomplished. We firstly created a multidimensional image tensor using sub-band GPR images that are computed from the band-pass filtered GPR signals, such that differences of the target response between sub-bands can be captured. Then, exploiting the low-rank and sparse property of the image tensor, we use the recently proposed Tensor Robust Principal Analysis to remove clutter by decomposing the image tensor into three components: a low-rank component containing clutter, a sparse component capturing target response, and noise. Finally, target detection is accomplished by applying thresholds to the extracted target image. Numerical simulations and experiments with different GPR systems are conducted. The results show that the proposed method effectively improves signal-to-clutter ratio by more than 20 dB and yields satisfactory results with high probability of detection and low false alarm rates.

Soil roughness retrieval from TerraSar-X data using neural network and fractal method

The purpose of this study is to estimate the surface roughness (rms) using TerraSar-X data in HH polarization. Simulation of data is carried out at a wide range of moisture and roughness using the Integral Equation Model (IEM). The inversion method is based on Multi-Layer Perceptron neural network. Inversion technique is performed in two steps. In the first step, the neural network is trained using synthetic data. The inputs of the first neural network are the backscattering coefficient and incidence angle, and the moisture is the output. In the next step, three neural networks are built based on a prior and without prior information on roughness. The inputs of three neural network are backscattering coefficient, estimated moisture in the first step and incidence angle and the roughness is output. The validation of the proposed methods is carried out based on synthetic and real data. Ground roughness measurements are extracted from Digital Terrain Model (DTM) using the fractal method. The accuracy of moisture from synthetic data is 6.1 vol% without prior information on moisture and roughness. The roughness (rms) accuracy of synthetic datasets is 0. 61 cm without prior information and is 0.31 cm and 0.38 cm for rms lower than 2 cm and rms between 2 and 4 cm, with prior information on roughness. The result's analysis of the simulated data showed that the prior information on roughness strongly improves the accuracy of roughness and moisture estimates. The accuracy of rms estimates for the TerraSar-X image in the HH polarization is about 0.9 cm in the case of no prior information on roughness. The accuracy improves to 0.57 cm for rms lower than 2 cm and 0.54 cm for rms between 2 and 4 cm with prior information on roughness. An overestimation of rms for rms lower than 2 cm and an underestimation of rms for rms higher than 2 cm are observed. The results of the accuracy of the synthetic and real data showed that the X band in HH polarization has a very good potential to estimate the soil roughness.

Performance investigation of magnetorheological finishing of rolls surface in cold rolling process

Cold rolling is one of the most vital manufacturing operation in the sheet metal industries. It produces controlled size and accurate thinner gauge components with a smooth surface. In cold rolling operation, the rolls are the most important component. If rolls surface gets finished at a greater level, a smooth surface on the final products can be achieved. This may improve functional performance for their practical use. To accomplish this requirement, the magnetorheological (MR) finishing process using rectangular-rotating tool core tip has been used to finish the external surface of the rolls. The process parameters such as roll workpiece rotation, tool feed, tool rotation, working gap and current intensity of the electromagnet and composition of MR polishing fluid are considered to investigate the performance of cold rolling rolls using the rectangular-rotating core tip-based MR finishing process. For effectively performing the MR finishing operation, the optimum process parameters are predicted by using the response surface methodology. These optimum parameters have been used to perform the MR finishing on the external surface of the industrial H13 die steel rolls as used in cold rolling process. The surface roughness gets reduced to 18 nm from the initial ground roughness value of 200 nm. After the MR finishing operation, the cold rolling rolls are tested its functional performance in the industry. The performance of the MR finished rolls surface is investigated as compared with the ground rolls surface. Experimentally and theoretically analyse the roll force, roll torque, and power consumption on use of before and after MR finished rolls surface in cold rolling operation. Almost all the performance parameters with MR finished rolls surface are found in favour of improvement in functional operation during its actual application in the cold rolling process.

Electromagnetic Scattering of Rough Ground Surface Covered by Multilayers Vegetation

A microwave scattering model is proposed for the ground surface covered with multilayers vegetation. The vegetation leaves are simulated with the discrete elliptical particles in layers, which are randomly distributed above the randomly rough surface. The finite element method is applied to solve the scattering magnetic field equation based on the Dirichlet boundary, and the relationship between the radar cross section and bidirectional reflectance distribution function is deduced with the coherent scattering field. The internal mechanism of microwave scattering of multilayers vegetation is explored with the numerical results, and the relationship of vegetation growing parameter with scattering characteristic is established.

Back to the Rough Ground: “Phronesis” and “Techne” in Modern Philosophy and in Aristotle by Joseph Dunne

Research Article| April 01 2019 Back to the Rough Ground: “Phronesis” and “Techne” in Modern Philosophy and in Aristotle by Joseph Dunne Dunne, Joseph, Back to the Rough Ground: “Phronesis” and “Techne” in Modern Philosophy and in Aristotle (Notre Dame: University of Notre Dame Press, 1993), 492 pp. Albert R. Jonsen Albert R. Jonsen Search for other works by this author on: This Site Google Common Knowledge (2019) 25 (1-3): 422. https://doi.org/10.1215/0961754X-7312309 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Albert R. Jonsen; Back to the Rough Ground: “Phronesis” and “Techne” in Modern Philosophy and in Aristotle by Joseph Dunne. Common Knowledge 1 April 2019; 25 (1-3): 422. doi: https://doi.org/10.1215/0961754X-7312309 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search Books & JournalsAll JournalsCommon Knowledge Search Advanced Search The text of this article is only available as a PDF. Copyright © 2019 Duke University Press2019 Article PDF first page preview Close Modal You do not currently have access to this content.

Modelling jumping in Locusta migratoria and the influence of substrate roughness

Locusts are widely recognized for their jumping performances. Jumping allows locusts to avoid predators and initiate flight. In this study, jumping performances of Locusta migratoria were modelled with a lumped-parameter theoretical model. Results showed that L. migratoria can reach a maximum take-off velocity of 1.83 m/s and maximum acceleration of 66.72 m/s2, with take-off angle of about 36°. While carrying out such a high performance, locusts are prone to slip when taking off from a smooth ground, especially for locusts with only one hind leg. Experiments have been therefore conducted to test the influence of ground roughness on jumping performance. There was no significant relation between ground roughness and jumping success, and this was due to combined action of rigid claws and adhesive pads ensuring static contact between tarsus and ground. It was noted that large take-off angles might help locusts to jump successfully with only one hind leg left, as well as to achieve better performance on smooth surfaces. The excellent contact ability on surfaces with different roughness gives meaningful insights for jumping robot design.

Development of Rough Terrain Mobile Robot Control Strategy Capable of Position and Direction Estimation by a Single Marker

In the present study, a mobile robot control strategy of rough terrain capable of position and direction estimation by a single marker was proposed, and the effect was performed. To realize wheel / caterpillar mobile robot movement on the rough terrain such as house farm, two major problems (1) the precise position estimation and (2) the direction estimation are important before the development of the control theory. Generally, the position / direction have been estimated by the wheel rotation (odometry) and gyro or acceleration sensor’s output integration, but there are many problems such as many impacts from the ground and the wheel slipping on the rough ground to the precise estimation. The aim of this study is to develop a method (algorithm) to estimate the mobile robot direction using a single marker placed at the top of the robot by changing the front movement and the target direction movement periodically. The reason using a single marker is that it is difficult process to find and analyze positions of the many markers attached on the robot in the camera image from a long distance, and the positions of many markers have been unstable by the tilting of the robot, agricultural product’s shadow, getting dirty of the body and so on.Our proposed method would be effective in the difficult situation to find or analyze the marker position of the robot surfaces by the reason of camera position and the environment, and it could realize wheels / caterpillar type mobile robot movement on the muddy situation.

Design and Material Analysis of a Suspension System in Scooter by using Finite Element Analysis Method

The principle part for a vehicle suspension is the compression spring system, which is fabricated for arresting shock impulse. Suspension system work on the principle of fluid transformation between compression and expansion cycle. Mostly the suspension system is used in motorcycles for providing better handling, prompt braking, safety, and comfort by keeping the passengers isolated from road noise, bumps, and vibration. The shock absorbers duty is to absorb or dissipate energy. In a vehicle, it reduces the effect of traveling over rough ground, leading to improved ride quality, and increase in comfort due to the substantially reduced amplitude of disturbances. In most of the cases, the spring which is used in the scooter suspension system is made up of beryllium copper, phosphor bronze and titanium, but in this research paper a scooter suspension system is designed and the designed suspension system is analyzed using ansys with different material properties. The composite material gives less deformation because of the high stiffness to load carrying capacity. So the Composite suspension system is recommended for two and three-wheeled vehicles.

Study of the flow fields over simplified topographies with different roughness conditions using large eddy simulations

The parameters influencing the wind turbine fatigue load calculations, such as two-point correlations Ruu, power spectrum density Su, turbulent length scale Lu, skewness Sku, and kurtosis Kuu of the wind are examined. Four simplified topographies, i.e., a 3D hill with smooth ground (3Ds), a 3D hill with rough ground (3Dr), a 2D ridge with smooth ground (2Ds), and a 2D ridge with rough ground (2Dr) are considered to investigate the influence from the shape of the topography and the ground roughness conditions. Ruu was found to vary considerably for different hill shapes and ground roughness conditions. Sku and Kuu peaked in the shear layer region in the smooth cases, but not in the rough cases. Su exhibited concentration in the wake in the 3Ds, 3Dr, and 2Ds cases, but not in the 2Dr case. In addition, a prominent increase in Lux was observed just above the summit of the smooth 3D hill. The flow fields were further visualized using the enstrophy and Q-criteria. Coherent turbulent structures were observed to exist in the wake in the 3Ds, 3Dr, and 2Ds cases, whereas the flow was highly mixed in the wake in the 2Dr case.

Suggestion of Rhombus Four Wheel Type Rover with Active Link

The surface of Mars and the moon is a rocky area with rocks and a rough ground, where soft sand exists. A future rover is required to have high driving performance for exploration of the crater interior etc. In soft ground travel, the reaction force generated by the deformation of the travel surface is small, and the surface may be excavated due to the rotation of the wheels, making it impossible to travel. In this paper, we focus on the travel of the exploration rover on soft ground, and experimentally verify the effective travel system for soft ground travel.

Walking Behavior Optimization of Biped Robot on Rough Ground Focusing on Spine Mechanism

Walking Behavior Optimization of Biped Robot on Rough Ground Focusing on Spine Mechanism

Numerical Prediction of Strong Wind Induced by Topographic Effect

This paper describes the numerical study of nonstratified airflow over a real complex terrain. Attention is focused on the mechanism of a local strong wind induced by a topographic effect. In order to clarify the mechanism of the occurrence of strong winds accompanied by the effects of terrain, the use of a numerical simulation is very effective, in which conditions can be set without the influence of ground roughness and temperature distribution. As a result, airflow converged to a small basin of mountain terrain in the upper stream, and local strong wind was generated leeward along the slope of the mountain terrain. Furthermore, the influence of the reproduction accuracy of geographical features, that is, horizontal grid resolution, was examined. Consequently, to reproduce the above-mentioned local strong wind, it was shown that horizontal grid resolution from 50 m to about 100 m was necessary.

A two-dimensional model based on the expansion of physical wake boundary for wind-turbine wakes

Two-dimensional (2D) wake models with a self-similar Gaussian shape of velocity deficit are highly accurate in predicting wind-turbine wakes. To better leverage their advantages for large-scale engineering applications, an approximation of the physical wake boundary (rw) is proposed based on data from both numerical simulations and experiments, i.e. rw=2σ, where σ is the standard deviation of the Gaussian-like profile. In addition, instead of defining the wake expansion corresponding to σ, a physically more intuitive expansion rate k that corresponds to the physical wake boundary is introduced. Then, a linear expansion law is employed to characterize the evolution of the wake behind the wind rotor, which leaves k as the only parameter to be determined in the 2D wake model. Data from large eddy simulation, physical experiments and field observations shows that the present analytical model can predict the wake of a wind turbine with high accuracy. The cases considered in the present study indicate that k=0.075 recommended by the one-dimensional (1D) wake model (Jensen model) for onshore wind farms also works well in the present 2D model under moderate ground roughness for a turbine generally operating in regime II. Because of its simplicity, good accuracy and low cost, the present 2D model is appealing to large-scale engineering applications.

Resource-Area-Dependence Analysis: Inferring animal resource needs from home-range and mapping data.

An animal’s home-range can be expected to encompass the resources it requires for surviving or reproducing. Thus, animals inhabiting a heterogeneous landscape, where resource patches vary in size, shape and distribution, will naturally have home-ranges of varied sizes, so that each home-range encompasses a minimum required amount of a resource. Home-range size can be estimated from telemetry data, and often key resources, or proxies for them such as the areas of important habitat types, can be mapped. We propose a new method, Resource-Area-Dependence Analysis (RADA), which uses a sample of tracked animals and a categorical map to i) infer in which map categories important resources are accessible, ii) within which home range cores they are found, and iii) estimate the mean minimum areas of these map categories required for such resource provision. We provide three examples of applying RADA to datasets of radio-tracked animals from southern England: 15 red squirrels Sciurus vulgaris, 17 gray squirrels S. carolinensis and 114 common buzzards Buteo buteo. The analyses showed that each red squirrel required a mean (95% CL) of 0.48 ha (0.24–-0.97) of pine wood within the outermost home-range, each gray squirrel needed 0.34 ha (0.11–1.12) ha of mature deciduous woodland and 0.035–0.046 ha of wheat, also within the outermost home-range, while each buzzard required 0.54 ha (0.35–0.82) of rough ground close to the home-range center and 14 ha (11–17) of meadow within an intermediate core, with 52% of them also relying on 0.41 ha (0.29–0.59) of suburban land near the home-range center. RADA thus provides a useful tool to infer key animal resource requirements during studies of animal movement and habitat use.

Effects of ground roughness on near-surface flow field of a tornado-like vortex

Roughness of a terrain is an important parameter that influences the near-ground flow field of tornadoes. In this study, both stationary and translating tornado-like vortices were simulated in a laboratory to investigate the influence of ground roughness on their flow fields. The near-ground flow fields, over the smooth and rough terrains, were studied using a mini version (1:3-scale) of the ISU tornado simulator and a 2D-PIV system for a stationary tornado and using the ISU tornado simulator and a pressure-based anemometer for a translating tornado. Main goal of this study is to examine the influence of ground roughness on tornado flow field. The results show that, for the stationary tornado, the flow regime transitioned from a multi-celled core structure over a smooth terrain at a higher swirl ratio to a single or dual-celled core structure over the rough terrain. A significant decrease in tangential velocity and core radius, and an increase in vertical and radial velocities and turbulent kinetic energy near the center of the tornado were also observed. For the translating tornado, ground roughness reduced the core radius, increased the radial velocity toward the center of rotation, and moved the location of the maximum horizontal velocity toward the front side of the tornado center close to the ground. There was a decrease in local or corner-flow swirl ratio in both stationary and translating tornadoes for the rough terrains compared with the smooth one.