کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
801487 | 1467300 | 2013 | 14 صفحه PDF | دانلود رایگان |

• Gaussian process models were used for calibration and validation.
• Interval-based metrics were used to assess the quality of validation.
• Physical and statistical models performed well at a global level.
We address the challenge of the validation of models for a vehicle interacting with a natural snowy terrain by applying a rigorous statistical framework. Gaussian process-based stochastic metamodels were used to fit noisy test data in drawbar pull and traction as a function of slip, and to transform the deterministic physically-based tire–snow interaction model into a stochastic one. Important parameters such as the mechanical properties of snow, the coefficient of friction between the tire and snow, and the depth of snow were obtained using a Gaussian maximum likelihood method. The uncertainties of parameters, and prediction using calibrated parameters for front and rear wheels were quantified and assessed using interval-based local and global validation metrics between models and test data. Overall agreement between models and test data is good.
Journal: Journal of Terramechanics - Volume 50, Issues 5–6, October–December 2013, Pages 289–302