Discrete Element Modelling (DEM) For Earthmoving Equipment Design and Analysis: Opportunities and Challenges

Simulation of granular materials (soil, rocks) interaction with earthmoving machines provides opportunities to accelerate new equipment design and improve efficiency of earthmoving machine performances. Discrete Element Modelling (DEM) has a strong potential to model soil and rocks bulk behavior in response to forces applied through interaction with machinery. Numerical representation of granular materials and methodology to validate and verify constitutive micro-mechanical models in DEM will be presented. In addition, how DEM codes can be integrated to CAE tools such as multibody dynamics will also be discussed. A case study of tillage bar-soil interaction was modeled in EDEM to predict tillage draft force and soil failure zone in front of tool moving at 2.68-m/sec and depth of 102-mm. The draft force and soil failure zone was predicted at 10% and 20% error from laboratory measured data

Discrete Element Modeling (DEM) of Cone Penetration Testing on Soil With Varying Relative Soil Density

Modeling soil-tool interaction is essential for equipment design and performance evaluation on soil behavior responses under loading. Computational tools based on particle-based mechanics such as Discrete Element Modeling (DEM) and Smoothed Particle Hydrodynamics (SPH) have potential in modeling large strain soil dynamic behaviors from soil-tool interaction. The objective of this study is to validate the accuracy and robustness of DEM calibration methodology as it relates to soil deformation during cone penetration on varying initial soil relative density. The influence of factors such as DEM material properties and cone to particle size ratio on DEM cone penetration simulation will be investigated. The paper presents a comparison of DEM predicted cone penetration resistance and laboratory measured penetration data on Norfolk sandy loam. Soil mechanical behavior was modeled with Hertz-Mindlin (HM) contact stiffness model and a new coupled frictional law for static and rolling resistance coefficients. The DEM material properties were calibrated using residual strength from direct shear test. DEM simulations were performed using LIGGGHTS, open source DEM code. Cone penetrometer experiments using anÂASABE standard cone with 12.53 mm cone base diameter and 30-degree cone tip were used to validate the calibrated DEM model. DEM prediction of cone penetration resistance trend and steady state values were in close agreement with the laboratory measured data for relative density range from 5 to 30%. At higher dense states (relative density of 90%), DEM calibration requires further improvement

Evaluation of Low Inflation Tire Technologies on Soil Compaction

Evaluation of recent advances in tire technologies such as advanced deflection agricultural tires (Firestone IF and VF) and precision tire inflation technologies on soil compaction, traction, fuel economy and crop yield responses are important. The purpose of the study was to investigate the effects of field and transport (road) tire inflation pressure settings of row-crop agricultural tractor and planter tires on soil compaction. A randomized complete block design experiment was conducted at the Iowa State University farm at Boone, Iowa for two tire inflation pressure levels on Dual Front (Firestone IF 420/85R34) and Dual Rear (Firestone IF 480/80R50) tires on a John Deere 8310R MFWD tractor, and transport tires (Super single 445/50R22.5) on a John Deere DB60 planter. Soil compaction was measured using Stress State Transducers (SSTs) buried at 15-cm and 30-cm depths beneath the untrafficked soil surface. The soil cone index depth profile was measured at tire-centerline, tire-edge and 20 cm laterally outboard of the tire edge before and after tractor-planter tire passes. Peak Octahedral Normal Stress (ONS) and the corresponding Octahedral Shear Stress (OSS) values in soil were calculated from the SST data. The peak ONS and corresponding OSS values from the road tire inflation pressure settings were statistically higher (p-value \u3c 0.05) than the field tire inflation pressure settings. The maximum ONS was observed at 15 cm soil depth from the road tire inflation pressure setting of the rear tractor tires (179 kPa tire inflation pressure and 33 kN load per tire). The ONS from the front tractor tires (138 kPa tire inflation pressure and 17 kN load per tire) and planter transportation tires (620 kPa tire inflation pressure and 16.5 kN load per tire) were similar. Cone index data also showed significant differences, comparing before and after tires passes, at the tire-centerline. The peak cone index values for the 0 to 100 mm soil depth range were 1.3 MPa and 1.2 MPa from the road and field tire inflation pressure settings, respectively

Discrete element modeling of cultivator sweep-to-soil interaction: Worn and hardened edges effects on soil-tool forces and soil flow

Simulation of tool-to-soil interaction provides opportunities to accelerate new equipment design and evaluate performance of tillage tools. Simulation based evaluation of worn tillage tools performance on soil flow has not been done. Discrete Element Modelling (DEM) has a potential to simulate worn tool to soil interaction problems, where worn tools CAD can be generated using 3D scanning. The DEM parameters of Hertz-Mindlin with Parallel Bond model were calibrated to match draft force and soil failure zone measured from a tool bar moving at 0.22 m/s and 38 mm cutting depth. The draft force and soil forward failure zone were predicted at 7% and 24% relative errors compared to measured values, respectively. Using the optimized DEM soil model, the interaction of three 3D reconstructed sweeps (new sweep, carbide treated-worn, untreated-worn) with soil were simulated to compare their geometric wear dimensional loss, performance on soil forces and soil flow. Results showed that the carbide treated-worn sweep had similar soil draft force and soil forward failure distance as the new sweep. The untreated-worn sweep showed lower vertical force (less suction) and its wing induced soil failure zone (front and lateral) showed poor soil tilth quality compared with the carbide treated-worn sweep and the new sweep

Discrete Element Model Calibration Using Multi-Responses and Simulation of Corn Flow in a Commercial Grain Auger

Grain augers are primary grain conveying equipment in agriculture. Quantitative prediction of dynamic grain flow in grain augers using discrete element modeling (DEM) has potential to support simulation-based engineering design of grain handling equipment. The objective of this study was to develop a DEM corn model using a multi-response calibration methodology and validation of combine-harvested corn flow in a commercial grain auger. Using a Latin hypercube design of experiment (DOE) sampling from four particle interaction DEM parameters values, 27 DEM simulations were generated for four DEM corn shape approximations (1-sphere, 2-spheres, 5-spheres, and 13-spheres) to create virtual DEM experiments of bucket-discharged and anchor-lifted angle of repose (AOR) tests. A surface meta-model was developed using the DEM interaction parameters as predictor variables, and normalized AOR expressed as a mean square error (MSE), i.e., the sum of square differences between DEM simulations and laboratory-measured AOR. Analysis of the MSE percentiles with lower error differences between DEM simulations and laboratory AOR and the computational effort required per simulation (h per simulation) showed that the 2-spheres DEM model had better performance than the 1-sphere, 5-spheres, and 13-spheres models. Using the best stepwise linear regression models of bucket AOR MSE (R2 of 0.9423 and RMSE of 94.56) and anchor AOR MSE (R2 of 0.5412 and RMSE of 78.02) and a surface profiler optimization technique, an optimized 2-spheres DEM corn model was generated. The DEM predicted AOR with relative errors of 8.5% for bucket AOR and 7.0% for anchor AOR. A DEM grain auger simulation used as a validation step also showed good agreement with the laboratory-measured steady-state mass flow rate (kg s-1) and static AOR (degrees) of corn piled on a flat surface, with DEM prediction relative error ranging from 2.8% to 9.6% and from 8.55% to 1.26%, respectively

Calibration and Validation of a Discrete Element Model of Corn Using Grain Flow Simulation in a Commercial Screw Grain Auger

Screw augers are primary grain conveying equipment in the agriculture industry. Quantitative prediction of grain conveyance using screw augers requires better understanding and measurement of bulk particle-particle and particlerigid- body interactions. Discrete element modeling (DEM) has potential to simulate particle dynamics and flow within a screw auger and thus provide simulation-based guidance for auger design and operating parameters. The objective of this study was to develop a DEM corn model calibration methodology and validation for combine-harvested corn flow in a commercial screw auger. The methodology used a virtual design of experiment (DOE) varying DEM corn parameters and calibration to match grain pile formation expressed in a normalized angle of repose (AOR). DEM corn particle shape was approximated using 1-sphere and clumped spheres (2-sphere, 5-sphere, and 13-sphere) matching the measured physical parameters of equivalent geometrical diameter, 2D axial dimensions, 3D axial dimensions, and detailed CAD-approximated corn dimensions, respectively. For each DEM corn shape approximation, a virtual DOE using Latin square hypercube design with four independent DEM Hertz-Mindlin contact model interaction coefficients was developed. The DEM assembly of particles matching the initial conditions of the AOR test was created in EDEM 2.7. From the quasi-static AOR of corn flow in the AOR tests and EDEM simulations, the mean square error (MSE), a sum of square difference in grain heights in the AOR tests and EDEM simulations, was used as a bulk material dependent response for the calibration process. The DEM 2-sphere corn shape model and the material interaction coefficients showed the minimum MSE (5.31 mm) compared to the 1-sphere, 5-sphere, and 13-sphere models. With the best DEM corn shape model (2-sphere) and DEM model parameters with the minimum MSE, validation of the DEM in predicting corn flow in a commercial screw auger in laboratory tests at two rotational speeds (250 and 450 rpm) was performed and showed good prediction (within 5% relative error) in matching the change in mass flow rate with the change in auger rotational speed

Modeling Ground Penetrating Radar (GPR) Technology for Seed Planting Depth Detection using Numerical Scheme based on Finite Difference Time Domain (FDTD) Method

Ground Penetrating Radar (GPR) is an electromagnetic (EM) signal based technology, commonly used as a non-destructive technique to explore subsurface features and identify different depth profiles in materials. The overall goals of this work is to evaluate GPR for non-destructive mapping of seed planting depth. Soils are inherently complex materials and numerous factors affect GPR behavior. The fundamental factors affecting GPR response are dielectric permittivity, magnetic permeability, and electrical conductivity, which are influenced by soil bulk density, texture, salinity, organic matter, volumetric water content, seed properties and physical geometry. To successfully optimize GPR’s ability to detect seed planting depth, the influence of these factors must be evaluated. This paper describes the development of a single dimensional GPR simulation model, based on finite difference time domain (FDTD) method, to evaluate the use of GPR sensing of seed planting depth. The simulation results shows that the EM signal is highly sensitive to high values of the electrical conductivity. High permittivity values decrease the EM signal velocity, wavelength and strength. A combination of these two properties leads to a significant EM signal attenuation ranging from 0 to ~ 800 dBm-1 as the signal traverses through the soil and seed. The lack of sufficient dielectric contrast between soil and seed presents a challenge on the detectability of the reflected signal by the radar receiver, therefore a sufficient dielectric contrast between the soil and seed has to be present to allow the GPR to be a viable tool to map the seed planting depth

Modeling Soil Forces on a Rotating Tine Mechanism in Artificial Soil

Understanding soil-tool interaction can enable better maneuvering of weeding tools to achieve higher weeding efficacy. The interaction between vertical tine of a rotating tine mechanism and soil was investigated using a mathematical model that estimated forces on a tine of a rotating tine mechanism operating at different linear and rotational velocities. The kinematics associated with linear and rotational velocities of a rotating tine mechanism were modelled, and the magnitude of shearing and inertial forces were estimated. A soil bin experiment was conducted using artificial soil with one tine to estimate the shear and inertial force coefficient values. Experimental conditions were the same for both the sets of tests. Experimental factors were longitudinal velocity at three levels (0.09 m/s, 0.29 m/s and 0.5 m/s) and speed ratio, the ratio of longitudinal velocity to peripheral velocity of the tines, at three levels (1, 1.5 and 2). Horizontal draft force and torque on the tine mechanism were measured. The nonlinear least squares method was used to estimate model parameters from experimental data, resulting in the shear force coefficient ranging from 2.96 to 37.5 N and the inertial force coefficient ranging from 16.6 to 528 N-s2 -m-2 . These variations in shear and inertial forces on the tine were due to differences soil failure patterns across the treatment

Investigating effects of interaction of single tine and rotating tine mechanism with soil on weeding performance using simulated weeds

Mechanical weeding augmented with automation technology should results in highly effective weeding systems. However, the interaction between controlled weeding mechanisms and soil and weeding performance is not well understood. Moreover, soil is highly variable and makes studying this interaction challenging. The main objective of this research was to develop a method to investigate the effects of mechanical tool-soil interaction on weeding performance for different operating conditions in a controlled environment. Experiments were conducted in an indoor soil bin with loam soil, and the weeding performance was studied using small wooden cylinders as simulated weed plants. The investigations featured a single cylindrical tine and a rotating tine mechanism, vertically-oriented and inserted into the soil. Total width of soil disturbance and potential weeding rate were evaluated for the single cylindrical tine at different levels of operational factors namely: tine diameter (6.35 mm, 7.94 mm and 9.53 mm), working soil depth (25.4 mm, 50.8 mm and 76.2 mm) and tine speed (0.23 m/s and 0.45 m/s). Potential weeding rate was examined for the rotating tine mechanism across two operational factors: working soil depth (25.4 mm and 76.2 mm) and rotational speed (25, 50 and 100 rpm). Statistical analysis was performed using ANOVA at p \u3c 0.05. A simulation of the rotating tine mechanism was developed which estimated disturbed area. For the single tine, soil disturbance width was independent of the test speeds; however, diameter and depth had significant effects as the width increased with increased levels of these two parameters. All three parameters had significant effects on potential weeding rate of the single tine, and the rates were observed to increase for higher levels of the operating parameters. For the rotating tine mechanism, both depth and rotational speed were significant. The potential weeding rate for the mechanism was found to increase for higher levels of these parameters. The results showed that although the width of soil disturbance due to a cylindrical tine are affected by tine diameter and working soil depth, operating parameters such as increased longitudinal and rotational speeds also affect plant disturbance. The percentage of disturbed soil area in simulation followed similar patterns as the percentage disturbed plants observed in the experiments

Using gprMax to Model Ground-Penetrating Radar (GPR) to Locate Corn Seed as an Attempt to Measure Planting Depth

Planting depth (PD) plays an essential role in crop production by substantially impacting germination rates and yield potential. However, techniques to measure PD nondestructively have not been developed. A two-dimensional gprMax simulation study was conducted to investigate the effects of soil electromagnetic properties on ground-penetrating radar (GPR) waves. The primary objective was to examine the possibility of using GPR as a nondestructive sensor to detect subsurface corn seeds with the goal of measuring PD. A conventional fixed-offset gprMax antenna in contact with the soil surface was used in the simulations. Corn seed models of different materials and sizes were simulated, with properties of natural and synthetic (metal) corn seeds. The seed models were spherical, with radial dimensions of 0.006 and 0.024 m to simulate small and large corn seeds, respectively. Corn seed models were embedded in three homogeneous soil models (sandy loam, loam, and clay), and 1.6 and 2.6 GHz antenna models were used as excitation frequencies. A-scans and B-scans were obtained from the simulations. The A-scans showed that all targets (small natural corn and metal corn models, and large natural corm and metal corn models) successfully provided response amplitudes proportional to their dielectric properties in sandy loam and loam, but not in clay. In high bulk density soils, GPR waves failed to penetrate the soil models, and the targets were not detected. The 2.6 GHz antenna provided better response amplitudes from the targets. In the driest soil models (2.5%, and 5%), no response amplitude signatures were observed. In dry and relatively dry soil models (15%), the simulation times were much shorter to obtain a response amplitude from the targets (with feeble response amplitudes) compared to relatively wetter soils. To validate these models, laboratory experiments were conducted with three treatment factors (soil type, planting depth, and moisture content). In dry soils, corn seeds could be detected using a 2.6 GHz GPR antenna; however, the detection varied substantially within replicates of the same moisture group. Further research is necessary to understand the effects of soil moisture on the detection variability of buried corn seeds