Investigation and modeling of the tractive performance of radial tires using off-road vehicles

Ekinci, Şerafettin; Çarman, Kazım; Kahramanlı, Humar

doi:10.1016/j.energy.2015.10.070

Cited by 21 publications

(13 citation statements)

References 28 publications

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“…Snow on grass was the most variable case but it is more or less comparable to heavy snow on concrete. This data is well in line with what is to be expected from literature, particularly the results listed by Rajamani [14] and [30], where the same behaviour was found for various ground conditions. It should be noted that these slip-friction curves are not intended to match the actual system performance exactly.…”

Section: Ax Bu Y CX X Ax Bu K Y Cxsupporting

confidence: 82%

Longitudinal vehicle dynamics model for construction machines with experimental validation

Alexander¹,

Vacca²

2017

Int. J. Automot. Mech. Eng.

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Construction machines represent a particular set of difficulties when modelling their system dynamics. Due to their generally low velocities and unorthodox operating conditions, the standard modelling equations used to simulate the behaviour of highway vehicles can have a poor behaviour for these systems. This paper sets forth a vehicle model which is suitable for construction machines, which travel at low velocities and encounter significant external forces in daily operation. It then shows the work done in validating the machine model with experimental data. First, the overall vehicle dynamics are developed, including a model for the machine behaviour when pushing against a resistive force. Then, a wheel force generation model suitable for low-velocity systems is discussed. Finally, pertinent experimental results are presented. Two different model validation tests were run. Both tests generated results which were matched well by the simulation model. In fact, the model matches experimental data reasonably well for both roading and pushing conditions. This indicates that the modelling methods described in this work are appropriate for the modelling of low-velocity systems such as wheel loaders and other construction machinery.

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Section: Ax Bu Y CX X Ax Bu K Y Cxsupporting

confidence: 82%

Longitudinal vehicle dynamics model for construction machines with experimental validation

Alexander¹,

Vacca²

2017

Int. J. Automot. Mech. Eng.

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“…The training of the network was continued until the test error reached the determined tolerance value. After the successful training of the network, the network was tested by the test data (Ekinci et al, 2015). In addition, the prediction of the model was made according to the traditional methods of the wind erosion rate (ỹ) by using the programme Statistica, version 5.…”

Section: Methodsmentioning

confidence: 99%

“…A typical ANN is structured using three neural layers: an input layer, a hidden layer (sometimes more than one layer is necessary), and an output layer. Information flows from the input layer to the output layer through the hidden layers (Martí et al, 2013;Ekinci et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Measurements and modelling of wind erosion rate in different tillage practices using a portable wind erosion tunnel

Çarman

Marakoğlu

Taner

et al. 2016

Zemdirbyste-Agriculture

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Artificial intelligence systems are widely accepted as a technology providing an alternative method to solve complex and ill-defined problems. Artificial neural network (ANN) is a technique with a flexible mathematical structure, which is capable of identifying a complex nonlinear relationship between the input and output data. The objective of this study was to investigate the relationship between dust concentration and wind erosion rate, and to illustrate how ANN might play an important role in the prediction of wind erosion rate. Data were recorded via field experiments by using a portable field wind tunnel. The experiments were carried out for eight different tillage applications that include the conventional, six different reduced tillage and the direct seeding practices. Particulate matter (PM) concentration generally decreased with a decrease in number or intensity of tillage operations. Direct seeding resulted in the lowest PM 10 concentration. After tillage applications, wind erosion rate varied between 113 and 1365 g m -2 h -1 . Results showed that wind erosion rate was lower in direct seeding than in conventional and reduced tillage applications. In this paper, a sophisticated intelligent model, based on a 1-(8-5)-1 ANN model with a back-propagation learning algorithm, was developed to predict the changes in the wind erosion rate due to dust concentration occurring during tillage. In addition, the prediction of the model was made according to traditional methods of wind erosion rate by using the programme Statistica, version 5. The verification of the proposed model was carried out by applying various numerical error criteria. The ANN model consistently provided better predictions compared with the nonlinear regression-based model. The relative error of the predicted values was found to be less than the acceptable limits (10%). Based on the results of this study, ANN appears to be a promising technique for predicting wind erosion rate.

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“…Bietresato et al (7) investigated the predictive capability of several configurations of ANNs for estimating indirectly the performance (torque, brake specific fuel consumption (BSFC)) of diesel engines used in agricultural tractors. Ekinci et al (11) assessed ANN and two types of Support Vector Regression (SVR) models to predict the tractive efficiency. The results illustrated that the ANN approach trained using Levenberge Marquardt algorithm provided more accurate results.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network and Stepwise Approach for Predicting Tractive Efficiency of the Tractor (Case Jx75t)

Almaliki¹

2019

IQ J Agr Scs

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The aim of this study is to develop and predict models of tractive efficiency using the artificial neural network and stepwise approach. The tractive efficiency of tractor (CASE JX75T) was measured experimentally. Experiments were conducted in the site of Basrah University. Which had silty clay soil texture. The field conditions included effect of two level of cone index (550 and 980 kPa), two level of moisture content (8 and 21%), three forward speeds (0.54, 0.83 and 1.53 m/s) and four level of tillage depths (10, 15, 20 and 25 cm). The results illustrated that both developed models (stepwise approach and ANN technique) had acceptable performance for predicting tractive efficiency of tractor under various field conditions. However, ANN model outperformed stepwise model, where 4-7-1 topology showed the best power for predicting tractive efficiency with R-squared of 0.97 and MSE of 0.0074 with Levenberg-Marquardt training algorithm. The analysis of variance demonstrated that the studied parameters had single significant effect on tractive efficiency. The most parameter influential on tractive efficiency was tillage depth followed forward speed, cone index and moisture content.

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Investigation and modeling of the tractive performance of radial tires using off-road vehicles

Cited by 21 publications

References 28 publications

Longitudinal vehicle dynamics model for construction machines with experimental validation

Longitudinal vehicle dynamics model for construction machines with experimental validation

Measurements and modelling of wind erosion rate in different tillage practices using a portable wind erosion tunnel

Artificial Neural Network and Stepwise Approach for Predicting Tractive Efficiency of the Tractor (Case Jx75t)

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