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Research Papers

Stochastic Modeling in Multibody Dynamics: Aerodynamic Loads on Ground Vehicles

[+] Author and Article Information
Christian Wetzel

Institut für Technische Mechanik, Universität Karlsruhe, Kaiserstrasse 10, 76131 Karlsruhe, Germanywetzel@itm.uka.de

Carsten Proppe1

Institut für Technische Mechanik, Universität Karlsruhe, Kaiserstrasse 10, 76131 Karlsruhe, Germanyproppe@itm.uka.de

1

Corresponding author.

J. Comput. Nonlinear Dynam 5(3), 031009 (May 18, 2010) (9 pages) doi:10.1115/1.4001376 History: Received March 13, 2009; Revised October 02, 2009; Published May 18, 2010; Online May 18, 2010

The influence of wind loads on the driving behavior of ground vehicles is an important economical, safety, and comfort issue. The crosswind performance is of great interest, as violent lateral winds can cause major accidents or will at least make the driver and the passengers feel very uncomfortable and insecure. In this paper, a sampling based methodology for the analysis of stochastic ground vehicle systems is presented. Starting from the well known Monte Carlo method more sophisticated reliability methods with higher efficiency are introduced and their advantages and drawbacks are critically reviewed. Furthermore, probabilistic sensitivity analyses are presented, which can be used to quantify the importance of the random variables on the response of the vehicle system. The influence of the parameters of the probability density functions is investigated by means of a novel response surface method. The mentioned approach is applied to a nonlinear road vehicle model under strong crosswind excitation for which the failure probabilities and the sensitivities are computed.

Copyright © 2010 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Sketch of the failure (F) and safe (S) domains with corresponding limit state function g(z)=0 and the MPP

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Figure 2

Schematic two-dimensional sketch of the line sampling procedure

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Figure 3

Characteristic random walks on a two-dimensional grid

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Figure 4

Top, side, and front views of the assembled half-car model

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Figure 5

Sketch of the wind velocity vres and angle of attack βw

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Figure 6

Applied gust characteristic with and without superposed turbulent fluctuations

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Figure 7

Correlation coefficients computed for u0=16 m/s, v0=100 km/h, and dry surface

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Figure 8

Morris sensitivity coefficients computed for u0=16 m/s, v0=100 km/h, and dry surface

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Figure 9

Morris sensitivity coefficients computed for u0=16 m/s, v0=100 km/h, and icy surface

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Figure 10

Estimated important direction from the subset simulation for u0=12 m/s, v0=80 km/h, and dry surface

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Figure 11

Failure probability PG computed by line sampling and subset simulation for v0=100 km/h and dry surface

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Figure 12

Failure probability PG computed by line sampling with and without superposed turbulence for v0=100 km/h and dry surface

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Figure 13

Failure probability PG computed by line sampling for v0=60,80,100 km/h and dry surface

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Figure 14

Failure probability PG computed by line sampling for αw=0,30 deg and v0=80 km/h and dry surface

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Figure 15

Failure probability PG computed by line sampling for v0=100 km/h and icy surface

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Figure 16

Comparison of the normal and extreme value probability density functions of the random variable mA

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Figure 17

Variation of the failure probability of mA for u0=16 m/s and v0=100 km/h, computed by MLSQ-IS

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