• Title/Summary/Keyword: tire dynamics

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Effects of Rear Diffuser Size on the Driving Performance of a Passenger Car (자동차의 주행 성능에 미치는 리어 디퓨저 크기의 영향)

  • Lee, Gyo Woo
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.20 no.2
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    • pp.655-661
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    • 2019
  • This study examined the change in driving performance according to the starting position of the rear diffuser of a vehicle. To accomplish this, the CATIA 3D design program was used to model the vehicle with reference to a commercial SUV vehicle and design the rear diffuser to start from 300, 400, and 500 mm from the rear tire. The flow and drag change were analyzed and the change in air flow was confirmed using Fluent, a flow analysis program at a vehicle traveling speed of 60, 100, and 140 km/h. The rear diffuser reduced the lift and drag forces compared to no diffuser regardless of the starting position. This is because if there is a rear diffuser, it will reduce the vortex phenomenon by suppressing the flow separation that occurs when air is drawn out from the rear portion of the vehicle. In this study, the starting point SP 400 was determined to be the optimal condition because the lift force was the smallest at SP 400 and the lift reduction effect was the best.

Development of simulation model of an electric all-wheel-drive vehicle for agricultural work

  • Min Jong Park;Hyeon Ho Jeon;Seung Yun Baek;Seung Min Baek;Dong Il Kang;Seung Jin Ma;Yong Joo Kim
    • Korean Journal of Agricultural Science
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    • v.51 no.3
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    • pp.315-329
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    • 2024
  • This study was conducted for simulation model development of an electric all-wheel-drive vehicle to adapt the agricultural machinery. Data measurement system was installed on a four-wheel electric driven vehicle using proximity sensor, torque-meter, global positioning system (GPS) and data acquisition (DAQ) device. Axle torque and rotational speed were measured using a torque-meter and a proximity sensor. Driving test was performed on an upland field at a speed of 7 km·h-1. Simulation model was developed using a multi-body dynamics software, and tire properties were measured and calculated to reflect the similar road conditions. Measured and simulated data were compared to validate the developed simulation model performance, and axle rotational speed was selected as simulation input data and axle torque and power were selected as simulation output data. As a result of driving performance, an average axle rotational speed was 115 rpm for each wheel. Average axle torque and power were 4.50, 4.21, 4.04, and 3.22 Nm and 53.42, 50.56, 47.34, and 38.07 W on front left, front right, rear left, and rear right wheel, respectively. As a result of simulation driving, average axle torque and power were 4.51, 3.9, 4.16, and 3.32 Nm and 55.79, 48.11, 51.62, and 41.2 W on front left, front right, rear left, and rear right wheel, respectively. Absolute error of axle torque was calculated as 0.22, 7.36, 2.97, and 3.11% on front left, front right, rear left, rear right wheel, respectively, and absolute error of axle power was calculated as 4.44, 4.85, 9.04, and 8.22% on front left, front right, rear left, and rear right wheel, respectively. As a result of absolute error, it was shown that developed simulation model can be used for driving performance prediction of electric driven vehicle. Only straight driving was considered in this study, and various road and driving conditions would be considered in future study.