Vehicle–soil interaction: Testing, modeling, calibration and validation

“…By using the simulated model they studied on the performance of the wheeled vehicle on deformable terrains tried to construct an applied framework for the complex problem of soiltire interaction. They used most of the developed models to evaluate the material properties and simulation input parameters (Taheri et al, 2015;Ivanov, 2010;Li et al, 2007;Lee, 2015;Xia and Yang, 2012;Grecenko and Prikner, 2014;Hildebrand et al, 2008;Lee and Gard, 2014). The correlation between wheel of vehicle and terrain were investigated from different aspects.…”

Section: Vehicle-terrain Interaction and Modeling Of Pavement Compactionmentioning

confidence: 99%

Multi-faceted investigation and modeling of compaction parameters for road construction

Özgan

Serin

Vural

2015

Journal of Terramechanics

View full text Add to dashboard Cite

“…e parameters signi cantly in uencing the performance of drive wheels include the tire in ation pressures, wheel slip, and vertical wheel loads [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Application of Soft Computing Techniques for the Analysis of Tractive Properties of a Low-Power Agricultural Tractor under Various Soil Conditions

2020

View full text Add to dashboard Cite

Considering the fuel consumption and soil compaction, optimization of the performance of tractors is crucial for modern agricultural practices. The tractive performance is influenced by many factors, making it difficult to be modeled. In this work, the traction force and tractive efficiency of a low-power tractor, as affected by soil coefficient, vertical load, horizontal deformation, soil compaction, and soil moisture, were studied. The optimal work of a tractor is a compromise between the maximum traction force and the maximum tractive efficiency. Optimizing these factors is complex and requires accurate models. To this end, the performances of soft computing approaches, including neural networks, genetic algorithms, and adaptive network fuzzy inference system, were evaluated. The optimal performance was realized by neural networks trained by backpropagation as well as backpropagation combined with a genetic algorithm, with a coefficient of determination of 0.955 for the traction force and 0.954 for the tractive efficiency. Based on models with the best accuracy, a sensitivity analysis was performed. The results showed that the traction performance is mainly influenced by the soil type; nevertheless, the vertical load and soil moisture also exhibited a relatively strong influence.

show abstract

Vehicle–soil interaction: Testing, modeling, calibration and validation

Cited by 23 publications

References 26 publications

Differences in tractor performance parameters between single-wheel 4WD and dual-wheel 2WD driving systems

Differences in tractor performance parameters between single-wheel 4WD and dual-wheel 2WD driving systems

Multi-faceted investigation and modeling of compaction parameters for road construction

Application of Soft Computing Techniques for the Analysis of Tractive Properties of a Low-Power Agricultural Tractor under Various Soil Conditions

Contact Info

Product

Resources

About