Fractal dimensions of terrain profiles

Analysing terrain profiles of fields, roads, and other terrains, it was determined that terrain profiles are random and non-periodical. Mandelbrot has defined non-scaling, self-similar figures as fractals, and many investigators have tried to characterize natural forms and structures using fractal geometry. The work here investigates whether terrain profiles can be defined as fractals. Fractal dimensions of profiles were calculated. These were compared with a locus of Brownian motion further to investigate characteristics of terrain profiles. Fractals are defined to be self-similar and irregular. Measuring and analysing terrain profiles, it was established that the statistical characteristics of any part of a terrain profile are similar and that the statistical characteristics of profiles of any kind of terrain are similar irrespective of roughness. This means that terrain profiles are self-similar, and irregular. From these results, it was determined that terrain profiles are fractals. The fractal dimensions were calculated with a coarse-graining method and by Power Spectral Densities (PSD), and fractal dimensions by Scaling were between 1.1 and 1.8 and by PSD between 1.3 and 1.5. Using the locus of Brownian motion, fractal dimensions were 1.5 or slightly larger than those of the terrain profiles. Fractal dimensions for the locus of smoothed Brownian motion were nearly equal to terrain profiles. Therefore terrain profiles could be artificially generated from the locus of smoothed Brownian motion. It appears that terrain roughness is formed by random and non-periodical force.     

Calculating fractal parameters from low-resolution terrain profiles

Driver comfort on rough terrain is an important factor in the off-road performance of wheeled and tracked ground vehicles. The roughness of a terrain has typically been quantified by the U.S. Army as the root-mean-square elevation deviation (RMS) of the terrain profile. Although RMS is an important input into many mobility calculations, it is not scale invariant, making it difficult to estimate RMS from low resolution terrain profiles. Fractal parameters are another measure of roughness that are scale invariant, making them a convenient proxy for RMS. While previous work found an empirical relationship between fractal dimension and RMS, this work will show that, by including the cutoff length, an analytic relationship between fractal properties and RMS can be employed. The relationship has no free parameters and agrees very well with experimental data - thus providing a powerful predictive tool for future analyses and a reliable way to calculate surface roughness from low-resolution terrain data in a way that is scale invariant. In addition, we show that this method applies to both man-made ride courses and natural terrain profiles.     

粗糙表面的分形特征与分形表达研究

得用触针轮廓仪和数据采集系统对磨削和车削表面的粗糙轮廓曲线进行了测量，并就粗糙表面的分形特征作了分析与讨论，同时还提出了粗糙表面的特征粗糙度概念及其定义，并将其用表面粗糙度水平的表述。     

Evaluation of fractal dimension of soft terrain surface

By using a laser profiler, the roughness of sowed and plowed surfaces was obtained. Through evaluation of the precision of fractal dimensions based on the Weierstrass–Mandelbort (W–M) function, we found the rescaled (R/S) analysis method and the structure spectral method were not suitable for the calculation of the fractal dimension on a soft terrain surface. Therefore, the fractal dimension, non-scale range and correlation coefficient for each kind of terrain were analyzed using the following fractal computational methods – (i) variate-difference method, (ii) power spectral density method, and (iii) root mean square method. The results showed that: fractal dimension of plowed terrain was large with small fluctuations, while its internal structure was complex. The power spectral density method was not robust enough to compute the fractal dimension of a soft terrain surface. The fractal dimension computed using the root mean square method was found to be more accurate for the soft terrain surface. Moreover, the correlation coefficient of linear regression when using the root mean square method was good and the range of non-scale variation was small.     

岩石节理粗糙面的分形几何特性与剪切强度关系的实验研究

自然形成的岩石节理面的表面形状具有分形特性，利用分形理论来描述岩石节理的粗糙特性，并确立与岩石节理面的剪切力学特性间的关系对于指导岩体工程设计，和管理具有重要一研究利用开发设计的三维激光测距仪在精确测定岩石节理面的表面形状４ 基础上，依据变量图法建立了 岩石节理面的     

引入特征粗糙度参数的Stribeck曲线试验研究

为探讨表面粗糙度对Stribeck曲线的影响,对不同初始表面的不锈钢销试件与45#钢盘试件在浸油润滑条件下进行摩擦磨损试验,研究摩擦系数的变化规律.结果表明:摩擦副表面越粗糙,对应Stribeck曲线上混合润滑区域面积越大,曲线斜率越小,使得不同表面粗糙度下的摩擦系数试验模型不具有唯一性.因此,将由分形参数导出的能客观表征粗糙表面的"特征粗糙度"参数引入Stribeck动压参数,从而提出新的动压参数.在新的动压参数下,具有不同表面粗糙度摩擦副的Stribeck曲线具有较好的一致性,继而可建立与粗糙度无关的摩擦系数试验模型.     

粗糙表面法向接触刚度的分形模型

提出了以往有关粗糙表面法向接触刚度理论研究工作的缺陷与不足,并在一定的前提假设下,基于球体与平面的接触理论和粗糙表面的分形接触理论,从理论上给出了具有尺度独立性的粗糙表面法向接触刚度分形模型,并进行了数字仿真研究。     

基于自适应格网数字水深模型的水下地形匹配定位算法

为了解决当前的水下地形匹配定位算法未考虑格网大小随海底地形变化而自动调整的问题,提出了一种基于自适应格网数字水深模型的水下地形匹配定位算法。首先,引入基于四叉树的自适应格网模型,改进其局部格网的相似性评估指标与构网约束方法;然后,设计匹配区的确定及待匹配航迹的选取策略,给出待匹配航迹的水深值的计算方法,构建出目标匹配定位的地形相关组合算子。实验结果表明:提出的算法相比于基于等距离的规则格网模型的算法有以下优势:1)在地形特征丰富区域的定位精度明显提高;2)可以避免出现地形特征越明显匹配定位精度越低的情况;3)能一定程度地克服水深系统误差对定位精度的影响。     

Fractals — An overview of potential applications to transport in porous media

We present an overview of the potential applicability of fractal concepts to various aspects of transport phenomena in heterogeneous porous media. Three examples of phenomena where a fractal approach should prove illuminating are presented. In the first example we consider pore level heterogeneities as typified by pore surface roughness. We suggest that roughness may be usefully modelled by fractal curves and surfaces and also cite experimental evidence for regarding pores as fractals. In the second example we consider a fractal network approach to modelling large-scale heterogeneities. The presence of features on all length scales in simple fractal models should capture the essential role played by the presence of heterogeneities on many scales in natural reservoirs. Studies of transport phenomena in such models may yield valuable insights into the problems of macroscopic dispersion. The final example concerns dispersion in multiphase flow. Here the fractal character is attributed to the distribution of the fluid phases rather than the porous medium itself. Again studies of transport phenomena in simple fractal models should help to clarify various problems associated with the corresponding phenomena in real reservoirs.     

Fractal Prediction Model of Spontaneous Imbibition Rate

By utilizing fractal dimension as one of the parameters to characterize rocks, a mathematical model was derived to predict the production rate by spontaneous imbibition. This fractal production model predicts a power law relationship between spontaneous imbibition rate and time. Fractal dimension can be estimated from the fractal production model using the experimental data of spontaneous imbibition in porous media. The experimental data of recovery in gas-water-rock and oil–water–rock systems were used to test the fractal production model. The rocks (Berea sandstone, chalk, and The Geysers graywacke) in which the spontaneous water imbibition experiments were conducted had different permeabilities ranging from 0.5 to over 1000 md. The results demonstrate that the fractal production model can match the experimental data satisfactorily in the cases studied. The fractal dimension data inferred from the model match were approximately equal to the values of fractal dimension measured using a different technique (mercury-intrusion capillary pressure) in Berea sandstone.     

水库设计的三维可视化研究

为了提高水库设计的自动化水平,实现模型的三维可视化,本文研究了三维地形的建模方法及其在水库设计中的应用。采用了地性特征点和地性线参与三维建模的方法,提高了数字高程模型DEM的地形逼真度。在此基础上通过一种综合了水平线和覆盖消隐算法的合成消隐算法实现了彻底的消隐以及图像的快速生成。通过三维水库模型,不仅可以得到水库的容量、库水的淹没边界和淹没面积,而且还可以生成地形剖面图。如果地形剖面图与地质图、水文图、构造图等进行叠加,可以形成综合剖面图。文章最后采用三维可视化方法模拟了某水库的部分设计工作,结果表明,通过水库的三维地形模型可以简单直观的完成坝址的选择、水库容量以及蓄水的水位线的确定等方面的工作。     

A slip-line plasticity analysis of sliding friction of rough surfaces exhibiting self-affine (fractal) behavior

A plasticity analysis of sliding friction of rough (fractal) surfaces sliding against smooth surfaces was developed based on a slip-line model of a rigid spherical asperity (wear particle) plowing and cutting through a soft semi-infinite medium. Solutions of the fraction of fully plastic asperity microcontacts responsible for the evolution of friction and energy dissipation were obtained in terms of the total normal load (global interference), interfacial adhesion characteristics, topography (fractal) parameters of the hard surface, and elastic–plastic material properties of the soft surface. This was accomplished by incorporating the slip-line model of a single microcontact into a friction analysis of sliding surfaces demonstrating multi-scale roughness. Numerical results provide insight into the effects of global interference (normal load), fractal parameters (surface roughness) of the hard surface, interfacial shear strength (adhesion), and material properties of the soft surface on plastic deformation at the microcontact level, global coefficient of friction, and frictional energy dissipated during sliding.     

考虑摩擦系数和微凸体相互作用的粗糙表面接触热导分形模型

基于三维分形理论,建立了考虑摩擦系数和微凸体相互作用的粗糙表面接触热导分形模型,并且考虑了微凸体的弹性变形、弹塑性变形和完全塑性变形. 通过该模型,分析了摩擦系数、分形维数、分形粗糙度和接触载荷对热接触热导的影响. 研究结果表明:接触热导随着摩擦系数和分形粗糙度的增大而减小,随着分形维数和接触载荷的增大而增大. 该研究为开展接合面的热传递提供了一定的理论基础.      

A novel nonlinear contact stiffness model of concrete–steel joint based on the fractal contact theory

The heavy-duty machine tool is usually assumed in the concrete foundation, in which the machine tool-foundation joints have a critical effect on the working accuracy and life of heavy-duty machine tool. This paper proposed a novel contact stiffness model of concrete–steel joint based on the fractal theory. The topography of contact surface exist in concrete–steel joint has a fractal feature and can be described by fractal parameters. Asperities are considered as elastic, plastic deformation in micro-scale. However, the asperities of concrete surface will be crushed when the stress is larger than their yield limit. Then, the force balance of contact surfaces will be broken. Here, an iteration model is proposed to describe the contact state of concrete–steel joint. Because the contact asperities cover a very small proportion (less than 1%), the load on crushed asperities is assumed carried by other no contact asperities. This process will be repeated again and again until the crushed asperities are not being produced under external load. After that, the real contact area, contact stiffness of the concrete–steel joint can be calculated by integrating the asperities of contact surfaces. Nonlinear relationships between contact stiffness and load, fractal roughness parameter G, fractal dimension D can be revealed based on the presented model. An experimental setup with concrete–steel test-specimens is designed to validate the proposed model. Results indicate that the theoretical vibration mode shapes agree well with the experimental variation mode shapes. The errors between theoretical and experimental natural frequencies are less than 4.18%. The presented model can be used to predict the contact stiffness of concrete–steel joint, which will provide a theoretical basis for optimizing the connection characteristic of machine tool-concrete foundation.     

岩石分形节理粗糙性对应力场影响的光弹模拟研究

节理的粗糙性是影响岩体力学特性的重要因素。现有的众多的研究结果表明：不规则的节理面具有很好的自相似特征，可以用分形几何去描述，节理面的分形维数是表征节理面粗糙度（ＪＲＣ）恰当的统计量。基于以上观点，为了能够精确地控制节理面的粗糙性，本文以Ｍａｎｄｅｌｂｒｏｔｗｅｉｅｒｓｔｒａｓｓ函数生成不同分维的分形曲线来模拟实际的粗糙节理面，制成光弹实验试样进行多组的单压和压剪实验，通过光弹条纹和接触点的变化来研究不规则的节理对于岩体变形，应力场的影响，得出了一些有益的结论．     

脆性材料裂纹扩展的分形运动学

大量实验结果表明脆性材料裂纹扩展路径是弯弯曲曲的，不规则的，断裂表面粗糙不平，表现出分形特征。本文应用分形几何推导出脆性材料裂纹扩展的分形运动学公式，得到了动、静态断裂韧性与分形裂纹扩展速度、裂纹长度、微结构特征量δ＿ｃ以及分形维数之间的关系；推导出了裂纹扩展速度的计算公式，得出宏观量测的裂纹扩展速度Ｖ＿０应小于分形裂纹扩展速度Ｖ；并推导出直接根据材料动、静态断裂韧性和裂纹扩展速度Ｖ＿０估算材料裂纹扩展路径的分维计算公式．本文理论分析结果与实验值在定性定量上达到了较好的一致。     

岩石节理(断裂)表面的多重分形性质

提出了新的投影覆盖法———ＰｒｏｊｅｃｔｉｖｅＣｏｖｅｒｉｎｇＭｅｔｈｏｄ（ＰＣＭ），并结合无接触激光表面测量技术直接测定出断裂表面分维值Ｄ∈［２，３）．建立了表面粗糙性投影覆盖概率函数，分析了节理（断裂）表面的多重分形性质．     

Efficient generation of accurate mobility maps using machine learning algorithms

U.S Army’s mission is to develop, integrate, and sustain the right technology solutions for all manned and unmanned ground vehicles, and mobility is a key requirement for all ground vehicles. Mobility focuses on ground vehicles’ capabilities that enable them to be deployable worldwide, operationally mobile in all environments, and protected from symmetrical and asymmetrical threats. In order for military ground vehicles to operate in any combat zone, the planners require a mobility map that gives the maximum predicted speeds on these off-road terrains. In the past, empirical and semi-empirical techniques (Ahlvin and Haley, 1992; Haley et al., 1979) were used to predict vehicle mobility on off-road terrains such as the NATO Reference Mobility Model (NRMM). Because of its empirical nature, the NRMM method cannot be extrapolated to new vehicle designs containing advanced technologies, nor can it be applied to lightweight robotic vehicles.The mobility map is a function of different parameters such as terrain topology and profile, soil type (mud, snow, sand, etc.), vegetation, obstacles, weather conditions, and vehicle type and characteristics.A physics-based method such as the discrete element method (DEM) (Dasch et al., 2016) was identified by the NATO Next Generation NRMM Team as a potential high fidelity method to model the soil. This method allows the capture of the soil deformation as well as its non-linear behavior. Hence it allows the simulation of the vehicle on any off-road terrain and have an accurate mobility map generated. The drawback of the DEM method is the required simulation time. It takes several weeks to generate the mobility map because of the large number of soil particles (millions) even while utilizing high performance computing.One approach to reduce the computational time is to use machine learning algorithms to predict the mobility map. Machine learning (Boutell et al., 2004; Burges, 1998; Barber et al., 1997) can lead to very accurate mobility predictions over a wide range of terrains. Machine learning is divided into two categories: the supervised and the unsupervised learning. Supervised learning requires the training data to be labeled into predetermined classes, while the unsupervised learning does not require the training data to be labeled. Machine learning can help generate mobility maps using trained models created from a minimum number of simulation runs. In this study different supervised machine learning algorithms such as the support vector machine (SVM), the nearest neighbor classifier (k-NN), decision trees, and boosting methods were used to create trained models labeled as 2 classes for the ‘go/no-go’ map, 5 classes for the 5-speed map, and 7 classes for the 7-speed map. The trained models were created from the physics-based simulation runs of a nominal wheeled vehicle traversing on a cohesive soil.     

关联表面分形特性的润滑模型

在分配有面粗糙度与材料表面分形特性关系的基础上，将分形特性引入润滑方程，提出了关联表面分形特性的润滑模型，并分析了润滑模型中压力流量因子和剪切流量因子与表面分形维数之间关系。计算结果表明：在相同分形维数（D）下，随着微突体纵横比v的增大，压力流量因子和剪切流量因子相应增大，且其随分形维数D的变化同随油膜粗糙度比H的变化相比呈现出更强的不规则特性，出现局部最大和最小值，并且分辨率较高。