Fuzzy knowledge-based model for prediction of soil loosening and draft efficiency in tillage

A knowledge-based system for assessing soil loosening and draft efficiency in tillage is presented. The knowledge-based system was built through expert opinion elicitation and available scientific data using fuzzy logic. It is expected that such a non-linear relationship includes some uncertainties. A fuzzy inference system employing fuzzy If-Then rules has an ability to deal with ill-defined and uncertain systems. Compared with traditional approaches, fuzzy logic is more efficient in linking the multiple inputs to a single output in a non-linear domain. The main purpose of this study is to investigate the relationship between cultivator shares working parameters to soil loosening and draft efficiency, and to illustrate how fuzzy expert system might play an important role in prediction of these. Experimental values were taken in soil bin. The trials were conducted in different working depths and forward velocities of cultivator shares. In this paper, a sophisticated intelligent model, based on Mamdani approach fuzzy modeling principles, was developed to predict the changes in soil loosening and draft efficiency of tool. The fuzzy model consists of 25 rules. In this research, a Mamdani max-min inference for inference mechanism and the center of gravity (Centroid) defuzzifier formula method for defuzzification were used as these operators assure a linear interpolation of the output between the rules. The verification of the proposed model is achieved via various numerical error criterias. For all parameters, the relative error of predicted values was found to be less than the acceptable limits (10%).     

Evaluation of an empirical traction equation for forestry tires

Variable load test data were used to evaluate the applicability of an existing forestry tire traction model for a new forestry tire and a worn tire of the same size with and without tire chains in a range of soil conditions. The clay and sandy soils ranged in moisture content from 17 to 28%. Soil bulk density varied between 1.1 and 1.4g cm⁻³ with cone index values between 297 and 1418 kPa for a depth of 140 mm. Two of the clay soils had surface cover or vegetation, the other clay soil and the sandy soil had no surface cover. Tractive performance data were collected in soil bins using a single tire test vehicle with the tire running at 20% slip. A non-linear curve fitting technique was used to optimize the model by fitting it to collected input torque data by modifying the coefficients of the traction model equations. Generally, this procedure resulted in improved prediction of input torque, gross traction ratio and net traction ratio. The predicted tractive performance using the optimized coefficients showed that the model worked reasonably well on bare, uniform soils with the new tire. The model was flexible and could be modified to predict tractive performance of the worn tire with and without chains on the bare homogeneous soils. The model was not adequate for predicting tractive performance on less uniform soils with a surface cover for any of the tire treatments.     

Fuzzy evaluation for an intelligent air-cushion tracked vehicle performance investigation

This paper presents the fuzzy logic expert system (FLES) for an intelligent air-cushion tracked vehicle performance investigation operating on swamp peat terrain. Compared with traditional logic model, fuzzy logic is more efficient in linking the multiple units to a single output and is invaluable supplements to classical hard computing techniques. Therefore, the main purpose of this study is to investigate the relationship between vehicle working parameters and performance characteristics, and to evaluate how fuzzy logic expert system plays an important role in prediction of vehicle performance. Experimental values are taken in the swamp peat terrain for vehicle performance investigation. In this paper, a fuzzy logic expert system model, based on Mamdani approach, is developed to predict the tractive efficiency and power consumption. Verification of the developed fuzzy logic model is carried out through various numerical error criteria. For all parameters, the relative error of predicted values are found to be less than the acceptable limits (10%) and goodness of fit of the predicted values are found to be close to 1.0 as expected and hence shows the good performance of the developed system.     

Hybrid soft soil tire model (HSSTM). Part III: Model parameterization and validation

Part I describes the tire structure model; part II the contact detection and contact interface models for rigid and deformable terrains; part III the model parameterization and validation. Model parameters are estimated using non-linear least-square optimization to minimize the error between the Hybrid Soft Soil Tire Model (HSSTM) predictions and experimental data. The parameterization routines’ initial conditions are estimated from modal analysis in radial and circumferential directions. The preliminary parameterized model is incorporated in the optimization routine to find tire sidewall and belt parameters in the radial direction using quasi-static cleat loading test data. The vertical force at the spindle and tire contact patch are used to study the model accuracy in the radial direction. FlatTrac tire longitudinal and lateral force test data are employed to estimate the parameters in these directions. The tire shear force and moment at the spindle are validated against experimental data for lateral dynamics performance.     

Predicting soil fragmentation during tillage operation using fuzzy logic approach

One of the main characteristics of the soil structure, which affects the plant growth and its yield, is its aggregates size. Correct tillage operations leads to prevention from soil degradation and help to maintain and improve its physical, chemical, and biological characteristics. In this paper, a model based on fuzzy logic approach was used to describe the soil fragmentation for seedbed preparation in the composition of primary and secondary tillage implements of subsoiler, moldboard plow and disk harrow as conventional tillage composition in the region. Field experiments were carried out at educational and research farms of faculty of agriculture, University of Mohaghegh Ardabili. In this paper, an intelligent model, based on Mamdani approach fuzzy modeling principles, was developed to predict soil fragmentation during tillage operation. The model inputs included soil moisture content, tractor forward speed and soil sampling depth. The fuzzy model consisted of 50 rules, in which three parameters of root mean square error (RMSE), relative error (ɛ), and coefficient of determination (R²) were used to evaluate the fuzzy model. These parameters were calculated 0.167%, 3.95%, and 0.988%, respectively. According to the results of this research, the fuzzy model can be introduced as one of the methods for predicting soil fragmentation during the tillage operation with high accuracy.     

Laboratory experimental study of tire tractive performance on soft soil: Towing mode,traction mode,and multi-pass effect

Tire tractive performance, soil behavior under the traffic, and multi-pass effect are among the key topics in the research of vehicle off-road dynamics. As an extension of the study (He et al., 2019a), this paper documents the testing of a tire moving on soft soil in the traction mode or towing mode, with a single pass or multiple passes, and presents the testing results mainly from the aspects of tire tractive performance parameters, soil behavior parameters, and multi-pass effect on these parameters. The influence of tire inflation pressure, initial soil compaction, tire normal load, or the number of passes on the test data has been analyzed; for some of the tests, the analysis was completed statistically. A multi-pass effect phenomenon, different from any phenomenon recorded in the available existing literature, was discovered and related to the ripple formation and soil failure. The research results of this paper can be considered groundwork for tire off-road dynamics and the development of traction controllers for vehicles on soft soil.     

Nomograms for the estimation of soil strength from indentation tests

The paper presents a selection of graphical techniques for the rapid analysis of the complex non-linear equations describing the penetration performance of drop-indenters in terms of the Mohr-Coulomb strength parameters of a soil. Charts are presented for the evaluation of these parameters from two simple identation measurements. The strength prediction performance of these charts is not particularly good when compared with values obtained off established and more elaborate laboratory methods. However, the attractive feature of the proposed technique is its extreme simplicity and speed of operation. In the solution of many practical soil-machine mechanics problems an order of magnitude estimate of soil strength is usually adequate. It would therefore appear that further refinement of the methods described would be warranted for use in such applications.     

Tire and soil effects on power loss: Measurement and comparison with finite element model results

In the present study, the effect of vertical load, tire inflation pressure and soil moisture content on power loss in tire under controlled soil bin conditions were investigated. Also a finite element model of tire-soil interaction in order to achieve a suitable model for predicting power loss in tire was created. Increasing the vertical load on the tire had a noteworthy impact on increasing the tire contact volume with the soil, reducing the percentage of slip, and increasing the rolling resistance; although, reducing the load on the tire had the opposite effect. At a constant inflation pressure, by increasing the vertical load on the tire, the amount of power loss due to the rolling resistance and the total power loss in the tire increased. Increase in soil moisture content increased the power loss caused by slip. Increasing the inflation pressure at a constant vertical load, also increasing the soil moisture content, led to an increase in the power loss caused by rolling resistance, and increase total power loss. The obtained error for estimating power loss of rolling resistance and total power loss was satisfactory and confirmed the acceptability of the model for power loss estimation.     

The prediction of tractive performance on soil surfaces

A new approach to the traction prediction equation is described. The proposed equation uses the soil deformation modulus and physical properties of agricultural tyres as parameters. The novel features of this approach include the assumption of a non-linear shear stress distribution and change in the value of soil deformation modulus with the normal stress. A model which suggests a relationship between the contact patch area and the soil deformation modulus is also introduced. The prediction equation was compared with the widely used Wismer and Luth equation and measured data obtained by Wittig. The proposed approach results in an improvement over Wismer and Luth in the prediction of traction and it also involves minimal testing.     

Identification of tire forces using Dual Unscented Kalman Filter algorithm

Nowadays, application of active control systems in vehicles has been developed in order to increase safety and steerability. In these systems, using an appropriate dynamic model can be very effective in increasing the accuracy of simulations and analysis. Tire-road forces are crucial in vehicle dynamics and control since they are the only forces that a vehicle experiences from the ground and have maximum uncertainty on vehicle dynamic model. In order to simulate the non-linear regimes of vehicle motion, the ‘Pacejka’ tire model is being utilized. In this paper, a dynamic model with Dual Unscented Kalman Filter algorithm has been utilized to identify the lateral forces, side slip angle, and normal forces of tires. In order to solve the non-linear least squares problem, these parameters were given as input to the hybrid Levenberg–Marquardt and quasi Newton algorithm to find the Pacejka tire model coefficients in the offline mode. Four degrees of freedom vehicle model combined with Pacejka tire model are used for simulation in various maneuvers. Results show appropriate compatibility with CarSim software.     

Algorithm and implementation of soil–tire contact analysis code based on dynamic FE–DE method

One of the fundamental problems in terramechanics is soil–tire system. Past achievements on this topic can be observed in various literatures. Fast development on CPU power of PC system enables us to apply numerical methods to this basic subject. Among others, finite element method (FEM) has been applied to simple problems of soil–tire system not only in 2D but also in 3D approach. However, it is noted that the current FEM technology cannot handle “singular” boundary conditions with sufficient accuracy of analysis. Typical example of this limitation can be seen in an application to traction tire–soil contact problems, where the contact point of tire lug tip behaves as the singular point of stress field. On the other hand, distinct or discrete element method (DEM) has in essence the capability of analyze microscopic deformation (or flow) of soil as many researchers have already been demonstrated. It is noted that DEM suffers large calculation time that is consumed not only at contact check between particulate elements but also at incremental time step. In our present study, we try to combine both merit of FEM and DEM together in order to analyze the soil–tire system interaction, where, for example, a tire and deep soil layer are modeled as FEM and soil surface layer as DEM. We propose simple algorithm of this FE–DE coupled method and sample program is developed that can solve some basic terramechanics problems in order to verify our idea. The obtained result shows qualitatively sufficient accuracy.     

SOME VALIDATION OF STANDARD,MODIFIED AND NON-LINEAR k–ε TURBULENCE MODELS

Standard, modified and non-linear k–ε: turbulence models are validated against three axisymmetric flow problems—flow through a pipe expansion, flow through a pipe constriction and an impinging jet problem—to underpin knowledge about the solution quality obtained from two-equation turbulence models. The extended models improve the prediction of turbulence as a flow approaches a stagnation point and the non-linear model allows for the prediction of anisotropic turbulence. Significantly different values for the non-linear model coefficients are proposed in comparison with values found in the literature. Nevertheless, current turbulence models are still unable to accurately predict the spreading rate of shear layers. © 1997 by John Wiley & Sons, Ltd. Int. j. numer. methods fluids, 24: 965–986, 1997.     

Finite element modeling of tire/terrain interaction: Application to predicting soil compaction and tire mobility

Tire/terrain interaction has been an important research topic in terramechanics. For off-road vehicle design, good tire mobility and little compaction on terrain are always strongly desired. These two issues were always investigated based on empirical approaches or testing methods. Finite element modeling of tire/terrain interaction seems a good approach, but the capability of the finite element has not well demonstrated. In this paper, the fundamental formulations on modeling soil compaction and tire mobility issues are further introduced. The Drucker-Prager/Cap model implemented in ABAQUS is used to model the soil compaction. A user subroutine for finite strain hyperelasticity model is developed to model nearly incompressible rubber material for tire. In order to predict transient spatial density, large deformation finite element formulation is used to capture the configuration change, which combines with soil elastoplastic model to calculate the transient spatial density due to tire compaction on terrain. Representative simulations are provided to demonstrate how the tire/terrain interaction model can be used to predict soil compaction and tire mobility in the field of terramechanics.     

基于WOA-BP神经网络的超低温冻土抗压强度预测模型研究

为获得超低温冻土抗压强度预测模型, 探究超低温状态下冻土的物理性质及力学性质的变化, 对含水率19%, 22%, 25%和28%的低液限黏土土样进行?180 °C ~ ?10 °C的单轴压缩强度试验, 并测量?80 °C ~ ?10 °C土样的未冻水含量, 建立基于WOA-BP神经网络和BP神经网络的预测模型, 探究含水率、温度、未冻水含量与超低温冻土抗压强度关系. 预测结果表明: 含水率、温度、未冻水含量与超低温冻土抗压强度存在复杂的非线性关系, 特别是在?180 °C ~ ?80 °C区间内, 现有的线性拟合公式已无法准确预测该区间内冻土抗压强度; 基于WOA-BP神经网络预测模型的整体预测效果较好, 其绝对误差平均值为1.167 MPa, 相对误差平均值为7.62%, BP神经网络预测模型的绝对误差平均值为8.462 MPa, 相对误差平均值为47.99%. 基于鲸鱼优化算法的BP神经网络预测模型预测误差明显小于BP神经网络预测模型及线性拟合值, 更接近实测值. 该预测模型具有较高精确度, 能有效解决超低温冻土抗压强度与其影响因素间复杂的非线性关系, 可为人工冻结技术在地层应急工程中的应用提供参考.      

Stochastic modeling of tire–snow interaction using a polynomial chaos approach

Developing accurate models to simulate the interaction between pneumatic tires and unprepared terrain is a demanding task. Such tire–terrain contact models are often used to analyze the mobility of a wheeled vehicle on a given type of soil, or to predict the vehicle performance under specified operational conditions (as related to the vehicle and tires, as well as to the running support). Due to the complex nature of the interaction between a tire and off-road environment, one usually needs to make simplifying assumptions when modeling such an interaction. It is often assumed that the tire–terrain interaction can be captured using a deterministic approach, which means that one assumes fixed values for several vehicle or tire parameters, and expects exact responses from the system. While this is rarely the case in real life, it is nevertheless a necessary step in the modeling process of a deterministic framework. In reality, the external excitations affecting the system, as well as the values of the vehicle and terrain parameters, do not have fixed values, but vary in time or space. Thus, although a deterministic model may capture the response of the system given one set of deterministic values for the system parameters, inputs, etc., this is in fact only one possible realization of the multitude of responses that could occur in reality. The goal of our study is to develop a mathematically sound methodology to improve the prediction of the tire–snow interaction by considering the variability of snow depth and snow density, which will lead to a significantly better understanding and a more realistic representation of tire–snow interaction. We constructed stochastic snow models using a polynomial chaos approach developed at Virginia Tech, to account for the variability of snow depth and of snow density. The stochastic tire–snow models developed are based on the extension of two representative deterministic tire–snow interaction models developed at the University of Alaska, including the pressure–stress deterministic model and the hybrid (on-road extended for off-road) deterministic model. Case studies of a select combination of uncertainties were conducted to quantify the uncertainties of the interfacial forces, sinkage, entry angle, and the friction ellipses as a function of wheel load, longitudinal slip, and slip angle. The simulation results of the stochastic pressure–stress model and the stochastic hybrid model are compared and analyzed to identify the most convenient tire design stage for which they are more suitable. The computational efficiency of the two models is also discussed.     

Off-road tire modeling and the multi-pass effect for vehicle dynamics simulation

Off-road operations are critical in many fields and the complexity of the tire-terrain interaction deeply affects vehicle performance. In this paper, a semi-empirical off-road tire model is discussed. The efforts of several researchers are brought together into a single model able to predict the main features of a tire operating in off-road scenarios by computing drawbar pull, driving torque, lateral force, slip-sinkage phenomenon and the multi-pass behavior. The approach is principally based on works by Wong, Reece, Chan, and Sandu and it is extended in order to catch into a single model the fundamental features of a tire running on soft soil. A thorough discussion of the methodology is conducted in order to highlight strengths and weakness of different implementations. The study considers rigid wheels and flexible tires and analyzes the longitudinal and the lateral dynamics. Being computationally inexpensive a semi-empirical model is attractive for real time vehicle dynamics simulations. To the best knowledge of the authors, current vehicle dynamics codes poorly account for off-road operations where tire-terrain interaction dominates vehicle performance. In this paper two soils are considered: a loose sandy terrain and a firmer loam. Results show that the model realistically predicts longitudinal and lateral forces providing at the same time good estimates of the slip-sinkage behavior and tire parameters sensitivity.     

APPLICATIONS OF NONLINEAR OPTIMIZATION METHOD TO NUMERICAL STUDIES OF ATMOSPHERIC AND OCEANIC SCIENCES

IntroductionNumericalpredictionofweatherandclimateessentiallyconsistsofsolvingasetofpartialdifferentialequations,whichisusuallyreferredasa“model”inourscientificliterature ,withproperinitialandboundaryvalues .Duetothegreatdifficultiesforthetheoreticalstudyposedbythecomplexityandnonlinearityofatmosphericandoceanicmotions,numericalmodelinghasbecomeacommonapproachintheworld .Consequently ,numericalmodelshowsitssuperiorityinscientificbackgroundforthetaskofnumericalweatherandclimateprediction .But…     

Statistical modeling and comparison with experimental data of tire–soil interaction for combined longitudinal and lateral slip

The interaction of a tire with a soft terrain has multiple sources of uncertainties such as the mechanical properties of the terrain, and the interfacial properties between the tire and the terrain. These uncertainties are best characterized using statistical methods such as the development of stochastic models of tire–soil interaction. The quality of the models can be assessed via statistical validation measures or metrics. Although validation of stochastic tire–soil interaction models has recently been reported with good results, it involves longitudinal slip only without considering lateral slip which can occur simultaneously with longitudinal motion. This paper presents results of the validation of a simple stochastic tire–soil interaction model for the more complicated case of combined slip. The statistical methods used for validation include the development of a Gaussian process metamodel, the calibration of model parameters using the approach of the maximum likelihood estimate in conjunction with new test data. The validation of the calibrated model, when compared with test data, is obtained using four validation metrics with good results.     

Effect of non-linearities in the identification and fault detection of gear-pair systems

This study investigates issues related to parametric identification and health monitoring of dynamical systems with non-linear characteristics. In the first part, a gear-pair system supported on bearings with rolling elements is selected as an example mechanical model and the corresponding equations of motion are set up. This model possesses strongly non-linear characteristics, accounting for gear backlash and bearing stiffness non-linearities. Then, the basic steps of the parametric identification and fault detection procedure employed are outlined briefly. In particular, a Bayesian statistical framework is adopted in order to estimate the optimal values of the gear and bearing model parameters. This is achieved by combining experimental information from vibration measurements with theoretical information built into a parametric mathematical model of the system. In the second part of the study, characteristic numerical results are presented. First, based on the effect of the system parameters on its dynamics, a solid basis is created for explaining some of the peculiar results obtained by applying classical gradient-based optimization methodologies for the strongly non-linear system examined. Some serious difficulties, associated with the existence of irregular response or the coexistence of multiple motions, are first pointed out. A solution to some of these problems, through the application of a suitable genetic algorithm, is then presented. Special problems, related to more classical identification issues associated with the presence of measurement noise and model error, are also investigated.     

Semi-empirical traction prediction equations based on relevant soil parameters

Extensive single-wheel traction tests were conducted in the vicinity of UC Davis campus using four different radial ply tires, two soil types, four soil conditions, three levels of inflation pressures, and three axle loads. Soil sinkage and shear characteristics were also obtained using an instrumented soil test device in every test condition. These field data were analyzed to obtain semi-empirical traction prediction equations for radial ply tires. In general, these prediction equations were able to predict traction equation parameters with less than 25% error.