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Experimental analysis of the dissipated energy through tire-obstacle collision dynamics

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  • Taghavifar, Hamid
  • Mardani, Aref
  • Hosseinloo, Ashkan Haji

Abstract

Wheeled vehicles are responsible for a substantial portion of dissipated energy while the share of tire-ground interface is among the most role playing elements in this regard. The vibrations and kinetics of a traversing wheel over an obstacle is a paradigm that can serve as a functional example for energy dissipation of wheeled vehicles. This paper communicates the analysis of the dissipated energy for a traveling wheel at collision time with obstacles while a controlled laboratory condition of the soil bin facility equipped with a single wheel-tester rig was utilized to carry out the experiments. The tests were conducted as affected by wheel load, obstacle height, obstacle geometry, slippage and speed. It was inferred that the increment of collision speed, obstacle height and tire slippage lead to the increase of the dissipated energy; however, the complexity lies in the contradictory effect of wheel load. This can be attributed to the nonlinear wheel dynamics and the vibration attenuation process. It has to be emphasized that the outcome of this study would serve as a functional catalyst for the extensive researches concerned with the machine design industry and the heavy vehicle trafficking management.

Suggested Citation

  • Taghavifar, Hamid & Mardani, Aref & Hosseinloo, Ashkan Haji, 2015. "Experimental analysis of the dissipated energy through tire-obstacle collision dynamics," Energy, Elsevier, vol. 91(C), pages 573-578.
    • Handle: RePEc:eee:energy:v:91:y:2015:i:c:p:573-578
    DOI: 10.1016/j.energy.2015.08.050
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    References listed on IDEAS

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    1. Taghavifar, Hamid & Mardani, Aref, 2014. "Analyses of energy dissipation of run-off-road wheeled vehicles utilizing controlled soil bin facility environment," Energy, Elsevier, vol. 66(C), pages 973-980.
    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.
    Full references (including those not matched with items on IDEAS)

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