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Evaluating the effect of tire parameters on required drawbar pull energy model using adaptive neuro-fuzzy inference system

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  • Taghavifar, Hamid
  • Mardani, Aref

Abstract

Determination of the required energy for drawbar pull of agricultural tractors plays a significant role in the characterization of the quality of tractors during different operations. Assessment of the effect of some tire parameters on drawbar pull energy was performed utilizing a single-wheel tester in a soil bin facility. To this aim, the potential of a global searching soft computing approach (i.e. adaptive neuro-fuzzy inference system) with various membership functions was evaluated. The tire parameters of velocity at three levels of 0.8, 1 and 1.2 m/s, wheel load at three levels of 2, 3 and 4 kN and slippage at three levels of 8, 12 and 15% were applied to single-wheel tester while four installed load cells were responsible for the measurement of drawbar pull. It was concluded that drawbar pull energy is a direct function of wheel load, velocity and slippage. Hence, the greatest value of 1.056 kJ corresponded to the wheel load of kN, slippage of 15% and velocity of 1.2 m/s. The outperforming model yielded mean square error and coefficient of determination values of 0.00236 and 0.995, respectively.

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  • Taghavifar, Hamid & Mardani, Aref, 2015. "Evaluating the effect of tire parameters on required drawbar pull energy model using adaptive neuro-fuzzy inference system," Energy, Elsevier, vol. 85(C), pages 586-593.
    • Handle: RePEc:eee:energy:v:85:y:2015:i:c:p:586-593
    DOI: 10.1016/j.energy.2015.03.072
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    References listed on IDEAS

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    1. Taghavifar, Hamid & Mardani, Aref & Karim-Maslak, Haleh, 2014. "Multi-criteria optimization model to investigate the energy waste of off-road vehicles utilizing soil bin facility," Energy, Elsevier, vol. 73(C), pages 762-770.
    2. Taghavifar, Hamid & Mardani, Aref, 2014. "A comparative trend in forecasting ability of artificial neural networks and regressive support vector machine methodologies for energy dissipation modeling of off-road vehicles," Energy, Elsevier, vol. 66(C), pages 569-576.
    3. Taghavifar, Hamid & Mardani, Aref, 2014. "Applying a supervised ANN (artificial neural network) approach to the prognostication of driven wheel energy efficiency indices," Energy, Elsevier, vol. 68(C), pages 651-657.
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    Cited by:

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    3. Chetan Badgujar & Sanjoy Das & Dania Martinez Figueroa & Daniel Flippo, 2023. "Application of Computational Intelligence Methods in Agricultural Soil–Machine Interaction: A Review," Agriculture, MDPI, vol. 13(2), pages 1-39, January.
    4. Moinfar, AbdolMajid & Shahgholi, Gholamhossein & Gilandeh, Yousef Abbaspour & Gundoshmian, Tarahom Mesri, 2020. "The effect of the tractor driving system on its performance and fuel consumption," Energy, Elsevier, vol. 202(C).
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    6. Janulevičius, Algirdas & Damanauskas, Vidas, 2015. "How to select air pressures in the tires of MFWD (mechanical front-wheel drive) tractor to minimize fuel consumption for the case of reasonable wheel slip," Energy, Elsevier, vol. 90(P1), pages 691-700.

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