首页> 外文会议>NATO Advanced Study Institute on Advanced Autonomous Vehicle Design for Severe Environments >6.1. Treatment of Uncertainties in Multibody Dynamic Systems using a Generalized Polynomial Chaos Approach; Case Study on a Full Vehicle
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6.1. Treatment of Uncertainties in Multibody Dynamic Systems using a Generalized Polynomial Chaos Approach; Case Study on a Full Vehicle

机译:6.1。使用广义多项式混沌方法处理多体动力系统的不确定性;案例研究全车辆

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In this study we look into the treatment of parametric uncertainties and the response of a vehicle system with several degrees of freedom, using the polynomial chaos approach. The research aims at investigating the accuracy of the method compared with traditional Monte Carlo simulations, and the change in computational efficiency as the number of uncertain parameters and stochastic external excitations increase. A 7 degree-of-freedom full-car dynamic model has been developed. Key parameters of the system have been assumed to be stochastic, with large uncertainties. In addition, the vehicle runs over rough (and undeformable) terrain, with uncertain terrain height. The polynomial chaos expansion and the Galerkin approach are used to quantify the uncertainties and to determine the time evolution of the stochastic system under sole or combined sources of uncertainties.
机译:在这项研究中,我们研究了使用多项式混沌方法的参数化不确定性的处理和车辆系统的响应。该研究旨在调查与传统蒙特卡罗模拟相比的方法的准确性,以及计算效率的变化作为不确定参数的数量和随机外部激励的增加。已经开发出7自由度的全车动态模型。系统的关键参数被认为是随机的,具有大的不确定性。此外,车辆运行粗糙(和可靠)地形,具有不确定的地形高度。多项式混沌扩展和Galerkin方法用于量化不确定性,并确定唯一或组合的不确定来源下随机系统的时间演变。

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