This study presents a coupled modelling approach that integrates soil deformation prediction and bulldozing force estimation during continuous soil cutting. Two soil mass transfer models are developed: a semi-empirical model and a neural network trained on Discrete Element Method (DEM) simulation data. The semi-empirical model provides a physically interpretable formulation in which soil mass transfer depends solely on cutting depth and bulldozing distance. In contrast, the neural network captures complex deformation behaviours by incorporating five input features. Based on the predicted mass transfer, soil deformation at different locations can be estimated. The semi-empirical model is applicable to both cohesive and noncohesive soils, whereas the current neural network is limited to noncohesive soils as an initial test. The influence of several factors on soil mass transfer is thoroughly investigated, including cohesion, bulldozing velocity, internal friction, and others. A coupling method is proposed that integrates the soil mass-transfer prediction models with a semi-empirical bulldozing-force model. Validation against DEM simulation results shows that both proposed models accurately predict soil mass transfer and surface deformation. The coupling framework also provides accurate and consistent predictions of bulldozing force across a wide range of cutting conditions and varying gravity levels.
Accurate prediction of vehicle driving behavior on deformable terrain requires consideration of both vehicle dynamics and soil deformation. In this study, an MBD–DEM coupled simulation framework was developed for full-vehicle driving analysis on deformable terrain. Tire dragging experiments were first conducted to measure sinkage and berm formation profiles, and DEM contact parameters were identified using sensitivity analysis, parameter sweep, and normalized root mean square error (NRMSE)-based evaluation. A full-vehicle multibody dynamics (MBD) model was then constructed and experimentally validated through asphalt bump-driving tests. The validated vehicle model was coupled with the DEM-based soil model to perform acceleration–deceleration and bump-driving simulations on deformable terrain. Simulation results were compared with experiments in terms of longitudinal acceleration, vertical acceleration, and pitch rate responses. The coupled simulations reproduced the experimental vehicle dynamic responses within approximately 15% NRMSE under all tested driving conditions. The results demonstrate that DEM contact parameters identified through single-tire experiments can be effectively applied to full-vehicle MBD–DEM coupled simulations for deformable terrain analysis.
Rotary tillage is widely used in pre-sowing preparation; however, existing tilling designs do not always provide the required quality of loosening with limited capacity of small-sized units. The purpose of the study was to analyze the interaction of a rotary tiller with the soil and to assess the energy intensity of processing when using L-shaped blades with additional wedge-shaped teeth. An analytical model of rotary tillage based on the energy balance has been developed, taking into account the power costs for rolling the unit, cutting the soil and discarding the destroyed particles. Field experiments have been conducted by varying the translational velocity of the unit and the angle of installation of the blades. It is established that the geometry of the blades significantly affects the process of soil loosening and the energy intensity of the work. The wedge-shaped teeth provide additional local soil loosening and contribute to the formation of a fine-grained structure. Increasing the speed of movement reduces the traction resistance per blade due to a reduction in the time of local contact of the tillage tool with the soil. The experimental results are consistent with the calculated dependencies and confirm the adequacy of the model.
In this work, an attempt was made to determine the tensile stress in tilled sandy and loamy soil with volumetric moisture contents of 0.06 and 0.24 cm3 cm−3, resulting from the load of an agricultural tractor’s wheel. A 3D tire-soil interface was mapped using the photogrammetric method, in which cracks typical of soil tension were identified. Based on the geometric dimensions of the cracks, the value of the tensile stress was determined, assuming that the soil is a brittle material in the meaning of the theory of limit stresses. The obtained results were used to calculate the drawbar pull, the values of which were compared with calculations according to the Coulomb equation and with the values measured in the field experiment. The comparison showed very small differences between the analyzed values of the drawbar pull, this trend was valid for both soils and both moisture contents. The obtained results do not justify questioning the validity of the Coulomb model. They rather constitute an incentive for further research for various soils and moisture contents in order to better understand the soil tensile stress behavior and its actual contribution to the mechanism of generating traction force.
The numerical simulations are extensively used in predicting tire performance on a deformable terrain because of their ability to capture the complex non-linear behavior of the soil. The modeling of non-cohesive (dry sand) soil is challenging as it undergoes fragmentation, separation, and significant particle rearrangement during tire-soil interaction. The study focuses on developing a methodology for performing numerical simulations of tire-soil interaction using meshed (CEL) and meshless (SPH) methods and their validation. Further, the computational efficiency and accuracy of both numerical methods are also compared. The soil material model parameters are identified and validated through numerical simulations of in-situ tests (cone penetrometer test). Later, the numerical simulations of tire-soil interaction are performed. The stress-strain plots, contact area, and tire sinkage predicted by both methods are compared. The numerical net traction force obtained using both numerical methods is correlated with the experimental data. The SPH method has better accuracy, while the CEL method has higher computational efficiency for tire-non cohesive soil interaction simulations.
The driving speed increase is an important development direction and engineering problem of unmanned planetary rovers. In order to study the influence of driving speed increase on Mars rover driving state when crossing obstacles, a test system was developed and the ground simulation tests of Mars rover prototype were carried out. Aiming at the Mars cruise scene, an experimental scheme including obstacle type and driving speed was proposed. When Mars rover speed increases by about 10 times (30 mm/s-300 mm/s, average results under various experimental conditions), the current and power increase by about 210%, and the energy consumption is reduced by about 60%. With the increase of driving speed, the Mars rover body sway and bump are more obvious. The angular velocity of the body increases by about 470%, and the acceleration increases by about 260%. The peak value of wheel-obstacle force increases by about 140%. The quasi-static model at slow speed and the multi-body dynamics model at fast speed are analyzed. How to optimize high-speed driving and obstacle-crossing state of planetary rover is discussed. This study can provide valuable reference for researchers engaged in design and controller development of high-speed planetary rovers.
The Bekker-Wong model remains a cornerstone for predicting wheel-soil interactions, relying on Bevameter tests to characterize soil behavior via plate sinkage and in-situ shear tests. However, accurately modeling these tests and the underlying physics remains challenging. Empirical Bekker parameters do not represent intrinsic soil properties and do not reliably generalize across test setups or boundary conditions. Prior physics-based approaches were likewise limited, as conventional formulations do not explicitly account for evolving soil state. To advance beyond empirical characterization, this study adopts a theoretically grounded critical-state soil mechanics framework, utilizing the state-dependent NorSand model implemented with an implicit large-strain finite element solver. This achieves forward-only predictions of Bevameter tests derived exclusively from laboratory-calibrated triaxial data. Calibration used a novel automated surrogate-based Bayesian optimization scheme, with NorSand further augmented with data-driven meta-models for shear modulus G and hardening rate H. The model is validated against field data and reproduces in-situ shear (6%-15% relative error), capturing density-dependent shear strength. Plate sinkage predictions match early sinkage (16% error) but diverge at large settlements due to numerical instability. The multi-scale Cullinan Sand dataset (6 bender-element, 18 triaxial, 52 Bevameter) and Bayesian calibration code are made openly available at https: //doi.org/10.5281/zenodo.17584463 and https://github.com/raykruger/BayesNorSand.
Understanding wheel-soil interaction on deformable terrain is essential for interpreting traction behavior and supporting mobility assessment in planetary exploration. This study presents an image-assisted measurement framework for estimating wheel torque during single-wheel tracking tests on Korean Lunar Simulant (KLS-1). A camera mounted behind the wheel captures the imprint trace, representing soil deformation and contact patterns generated by wheel passage, while a torque/force sensor records the corresponding mechanical response. The visual and mechanical signals are processed using Gabor wavelet filtering and principal component analysis to extract compact descriptors of the wheel-soil interaction state. A support vector machine is used to classify imprint trace patterns (lugged and smooth wheel conditions), and the resulting features are combined with synchronized torque measurements to train a long short-term memory (LSTM) model for one-step-ahead torque estimation. Experimental results show reliable imprint trace classification (97.3% accuracy) and accurate torque prediction (MAE = 0.0219 N center dot m, RMSE = 0.0272 N center dot m, R2 = 0.937) under controlled conditions. The proposed framework illustrates how image-derived information can complement conventional terramechanics measurements by providing additional insight into soil deformation behavior. While the present study is limited to controlled laboratory conditions, the approach provides a basis for data-driven extensions to wheel-soil interaction analysis.
Considering the importance of soil compaction management for maintaining agricultural fertility and optimizing off-road vehicle performance, this study aimed to determine the relative contributions of key parameters and to identify an optimal combination of vertical load and number of passes that simultaneously minimizes soil normal stress and maximizes dynamic contact length in tire-soil interaction. Experiments were conducted in a soil bin using a single agricultural tire at three vertical loading levels and four traffic levels under constant speed conditions. Normal stress was measured using buried load cells, and dynamic contact length was calculated from the relationship between contact duration and wheel speed. Sensitivity analysis within the investigated experimental range indicated that dynamic contact length exerted the dominant influence on normal stress variation (71.17%), followed by vertical load (16.89%) and number of passes (11.40%). Multi-objective optimization based on the time-resolved contact-stress dataset identified that a vertical load of 2 kN at the third pass provided the most favorable trade-off, corresponding to a stress of 47.4 kPa and a dynamic contact length of 156.1 mm. These findings provide a quantitative framework for improving vehicle performance while mitigating soil compaction under controlled operating conditions.
The cultivation scale of root crops has been increasing annually with the growing global demand. However, issues such as high digging resistance and significant soil accumulation during the mechanical harvesting of root crops still need to be addressed. This paper proposes a novel composite biomimetic digging shovel inspired by the exceptional digging characteristics of moles and mole crickets. The shovel features a mole claw structure as the principal part and a mole cricket claw structure as the shovel surface. Firstly, by fitting the contour curves of mole and mole cricket claws, a biomimetic digging shovel blade is modelled and its feasibility is analyzed. Subsequently, taking potato harvesting as an example, a novel composite biomimetic digging shovel is designed. Based on the physical properties of potatoes and soil, a potato-soil discrete element model is constructed, and a digging simulation process is conducted. The results indicate that the proposed digging shovel can improve soil flowability compared to the flat digging shovel, with a drag-reduction rate of 10.96%. Finally, the results of the soil bin test are consistent with the simulation results, and the resistance during the operation decreased by 11.1%, further validating the evident drag-reduction effect of the proposed digging shovel.
Soft lunar regolith hampers rover mobility through slippage, sinkage, and poor maneuverability. Wheel grousers shear the regolith to generate thrust, so their geometry is crucial on complex terrain for self-extrication, obstacle negotiation, and stable travel. However, under manned-rover loads and realistic regolith properties, systematic comparisons of how different grouser shapes affect coupled traction-vibration behavior remain limited. Here, we design a 350 mm-radius metal elastic wheel with four grouser configurations-straight, interrupted straight, Vshaped, and interrupted V-shaped-build a soil-bin testbed, and apply a time-frequency analysis workflow with traction benchmarking. Experiments at 450 N and 750 N evaluate slip-traction responses and vibration characteristics. Straight grousers deliver the largest slip-traction integral and the lowest low-frequency vibrations at both loads, followed by the interrupted straight design; V-shaped variants perform worse. Relative to other geometries, straight grousers improve stability by 9.45%-37.29%. Increasing load amplifies configurationdependent differences, with V-shaped RMS vibrations exceeding those of straight grousers by 30.96%- 87.46%. Discrepancies between predicted and measured traction and sinkage remain within acceptable bounds. These results inform grouser selection across load cases and surface conditions, supporting optimization of tractive efficiency, energy consumption, and stability.
Returning straw to the fields can not only reduce carbon emissions, but also enhance soil fertility. However, in loam soil environments containing straw, plant protection vehicles are prone to sinking and slipping when operating in the field. In response to this issue, this study conducts the soil hardness test, triaxial compression test, and plate compaction test to deeply explore the influence of key parameters of straw-containing loam soil on its mechanical properties. Firstly, the natural bulk density and moisture content of the field loam are determined, and the hardness of soil with different straw contents at different depths is measured. Then, the failure shear stress and mechanical parameters of straw-containing soil under different straw contents are obtained using the soil static triaxial apparatus. Finally, based on the results of the plate compaction test and Bekker’s pressure-settlement model, the influence of straw content and moisture content on the pressure-settlement relationship of straw-containing loam soil is analyzed in detail. The research results indicate that an increase in soil straw content and moisture content can cause a decrease in mechanical parameters such as hardness, failure shear stress, deformation index, and equivalent deformation modulus, which is meaningful for guiding the design of plant protection vehicles.
This paper investigates the interactions between wheels and soft soil terrain, focusing on the impact of soil strain rate, which is related to the forward velocity of the wheel, on the sinkage of wheels. An improved Drucker-Prager model, incorporating strain rate effects on the cohesive (c), elastic modulus (E), and strain-hardening/strainsoftening behaviors, is proposed and validated against triaxial tests in this study. Using finite element methods (FEM) with the Coupled Euler Lagrange (CEL) method, this study simulates dynamic wheel-soil interaction and analyzes the influence of soil parameters (i.e., including internal friction angle phi, cohesion c, elastic modulus E, axle load, and forward velocity) on wheel sinkage. The results show that: (i) Strain rate affects the c and E, with negligible impact on the phi; the enhanced D-P model effectively captures these characteristics; (ii) The sensitivity of the wheel sinkage to strain rate is pronounced for soils with low c and phi but diminishes in high-strength soils; (iii) Wheel sinkage nonlinearly decreases as the c, phi, E, and velocity increase while increases as the axle load increases. The sensitivity of wheel sinkage to velocity is as significant as that to c, phi, E, and axle load, with a CAM of 0.52.
Soil shear behaviour and particle detachment at the soil surface level play a critical role in soil stability and resistance to wind erosion, yet their dependence on soil water content and loading conditions remains insufficiently understood. This study measured soil surface properties (surface shear strength, adhesion, and interface friction angle) of sandy loam and clay soils under three soil water contents (low, medium, high) using a soil surface shear apparatus. Discrete Element Models (DEM) were developed to replicate the surface soil shear tests, calibrated and validated using the measurements. The models were applied to assess wind erosion through simulations of surface soil particle displacement. Test results showed that interface friction angle and adhesion increased with soil water content. The DEM models replicated the measured shear responses across all the soil types and water contents, with relative errors in the range of 2.1-18.4 % across all soil conditions. Simulation results indicated decreasing particle displacement with higher soil water content, reflecting enhanced aggregate stability and reduced detachment of the soil particles. Horizontal particle displacement was dominant in sandy loam, while clay showed greater vertical displacement at higher water content. This numerical approach provides a reliable method for predicting soil stability for erosion prevention.
The amphibious screw-propelled vehicle has significant potential for military and civilian applications due to its adaptability to diverse road conditions and amphibious capabilities. This study proposes a parameter calibration scheme using the discrete element method (DEM) and response surface method (RSM) to establish a discrete element model for multi-body dynamics under sandy loam conditions. The actual accumulation angle was determined through soil accumulation tests, while key parameters were identified using the Plackett-Burman test. Parameter ranges were established through slope-climbing tests, and a second-order regression model was developed using the Box-Behnken test. With an actual accumulation angle of 34.79 degrees as a benchmark, the optimal parameters were identified: a soil-soil collision recovery coefficient of 0.3, a rolling friction coefficient of 0.112, and a soil-vehicle collision recovery coefficient of 0.385. Simulation experiments showed an average pile angle of 34.814 degrees , with a relative error of only 0.78%, validating the calibration method. Additionally, a multibody dynamics and discrete element coupling approach was used to construct the vehicle-soil interaction system. Results revealed that the vehicle maintains good handling stability, the driving wheels provide sufficient traction, and the screw propulsion system exhibits excellent performance.
Soil moisture content significantly affects the longitudinal force-longitudinal slip ratio characteristics of off-road tire. This article proposes an off-road Burckhardt tire model which can predict the longitudinal force-slip ratio curve under different soil moisture content. Soil mechanics parameters for six soil moisture content levels were calibrated through soil test, where soil moisture content ranges from dry to the liquid limit. The virtual Flat-Trac test bench is developed by Project Chrono, which can simulate off-road tire rolling on wet soil based on soil mechanics parameters. The characteristics of tire longitudinal force-slip ratio under different moisture content are analyzed, which mainly include the anti-symmetry and the non-zero crossing in the static state. A tire parameter identification method based on weighted mean of vectors is proposed to address the poor identification accuracy resulting from the wide parameter identification range. Adapted Burckhardt tire model parameters under various soil moisture content are identified. The relationship between each parameter and moisture content is analyzed. Off-road Burckhardt tire model considering soil moisture content variation is introduced which can be applied to real-time longitudinal control of vehicles across wet soil.
Tillage breaks the hardpan and loosens the soil, creating a fine, uniform seedbed that ultimately improves crop yield. This study aimed to develop a soil clod detection model and soil clod distribution maps after primary tillage, based on three clod-size categories: small (d < 100 mm), medium (100 mm <= d <= 250 mm), and large (d > 250 mm). An experiment was conducted at the research field at ICAR-CIAE with implemented geometry (IG) at three levels IG I (MB Plough), IG II (duck foot cultivator), and IG III (chisel cultivator), and two levels of moisture content (13.2 and 18.6%). GPS-tagged images were collected and annotated in ImageJ to determine clod parameters. The state-of-the-art deep learning-based object detection models (YOLOv7, YOLOv8, and YOLOv11) were used for automated clod classification. The models attained mean average precision values of 81.20%, 97.42%, and 95.44% respectively. The results revealed that clod size after primary tillage depends on the implement geometry and soil moisture. Computer vision-based clod-size distribution mapping can support IDSS frameworks by providing data for optimising secondary tillage operations, such as adjusting the u/v ratio of a rotavator to enhance soil pulverisation efficiency and tillage performance.
Soil compaction is a major challenge in modern agriculture. Multiple wheel passes with varying wheel types, sizes and loads are common, and the resulting impact on compaction remains unclear. This study aims to develop and evaluate a simple method to aggregate total wheel loads from multiple wheel passes to quantify their effect on soil compaction, disregarding other factors such as wheel size or type. From 2016 to 2021, 20 field trials were conducted on a silty loam site in northwest Germany, involving 1 to 8 wheel passes. For each wheel pass, static mass and vertical soil displacement at three depths were determined. Different aggregation schemes to calculate one effective load (Leff) for all wheels were analyzed. A linear statistical model was applied to link vertical soil displacement to Leff, soil depth, initial bulk density and water content. The best-performing scheme for Leff closely matched the simple cumulative wheel loads (RMSE: 0.147 cm; r2: 0.59), while variants resembling maximum wheel load performed poorly (RMSE: 0.174 cm; r2: 0.43). All other variants lay between these two special cases. Our results suggest that for multiple wheel passes, the simple cumulative load is a reliable predictor for use in the assessment of soil compaction.
The increasing use of small- to medium-sized tires in off-road equipment exposes a limitation in existing tire testing protocols designed mainly for larger tires. A Mobile Tire Testing Device (MTTD) was developed that evaluates rolling resistance under controlled soil-bin testing environments. It investigates how forward velocity at 1, 2 and 3 km/h together with vertical load at 885, 1275, 1766 and 2060 N and cone index of 600, 1000 and 1400 kPa affect rolling resistance. Forward speed was adjusted using motor frequency control and gear shifting, while soil conditions were modified through tillage and controlled weight addition. The results demonstrated that rolling resistance increased substantially with increasing load. At a fixed cone index of 600 kPa, rolling resistance increased from 61.3 N at 885 N to 167.2 N at 2060 N, whereas at a higher cone index of 1400 kPa it ranged from 48.5 N to 137.4 N over the same load range. Forward speed produced changes that remained within measurement uncertainty and were therefore not physically meaningful. This study fills an essential testing requirement for tires with diameters under 70 cm and widths under 50 cm by developing a functional testing system to boost tire performance and operational effectiveness in agricultural and construction equipment.
