 Evaluating the effect of tire parameters on required drawbar pull energy model using adaptive neuro-fuzzy inference systemHamid Taghavifar and Aref Mardani Energy, 2015, vol. 85, issue C, 586-593 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. Keywords: Artificial intelligence; ANFIS; Energy; Drawbar pull; Soil bin (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:85:y:2015:i:c:p:586-593 DOI: 10.1016/j.energy.2015.03.072 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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