Rigid multibody system dynamics with uncertain rigid bodies

This paper is devoted to the construction of a probabilistic model of uncertain rigid bodies for multibody system dynamics. We first construct a stochastic model of an uncertain rigid body by replacing the mass, the center of mass, and the tensor of inertia by random variables. The prior probability distributions of the stochastic model are constructed using the maximum entropy principle under the constraints defined by the available information. The generators of independent realizations corresponding to the prior probability distribution of these random quantities are further developed. Then several uncertain rigid bodies can be linked to each other in order to calculate the random response of a multibody dynamical system. An application is proposed to illustrate the theoretical development.

[1]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[2]  Marc P. Mignolet,et al.  Nonparametric Stochastic Modeling of Uncertainty in Rotordynamics , 2009 .

[3]  I. Babuska,et al.  Solving elliptic boundary value problems with uncertain coefficients by the finite element method: the stochastic formulation , 2005 .

[4]  Christian Soize,et al.  Experimental identification of turbulent fluid forces applied to fuel assemblies using an uncertain model and fretting-wear estimation , 2009 .

[5]  Mihai Anitescu,et al.  Efficient sampling for spatial uncertainty quantification in multibody system dynamics applications , 2009 .

[6]  Christian Soize A nonparametric model of random uncertainties for reduced matrix models in structural dynamics , 2000 .

[7]  Adrian Sandu,et al.  Modeling Multibody Dynamic Systems With Uncertainties . Part I : Theoretical and Computational Aspects , 2004 .

[8]  J. Baumgarte Stabilization of constraints and integrals of motion in dynamical systems , 1972 .

[9]  Christian Soize,et al.  Structural-acoustic modeling of automotive vehicles in presence of uncertainties and experimental identification and validation. , 2008, The Journal of the Acoustical Society of America.

[10]  Christian Soize,et al.  Probabilistic model identification of uncertainties in computational models for dynamical systems and experimental validation , 2008 .

[11]  E. Jaynes Information Theory and Statistical Mechanics , 1957 .

[12]  Christian Soize,et al.  Experimental validation of a nonparametric probabilistic model of nonhomogeneous uncertainties for dynamical systems. , 2004, The Journal of the Acoustical Society of America.

[13]  Daya K. Nagar,et al.  Matrix-variate Kummer-Beta distribution , 2002, Journal of the Australian Mathematical Society.

[14]  Werner Schiehlen,et al.  Multibody Systems Handbook , 2012 .

[15]  Lin Li,et al.  On the impact of cargo weight, vehicle parameters, and terrain characteristics on the prediction of traction for off-road vehicles , 2007 .

[16]  Christian Soize Random matrix theory for modeling uncertainties in computational mechanics , 2005 .

[17]  Paolo Mantegazza,et al.  Multistep Integration of Ordinary, Stiff and Differential-Algebraic Problems for Multibody Dinamics Applications , 2001 .

[18]  Christian Soize,et al.  Probabilistic approach for model and data uncertainties and its experimental identification in structural dynamics: Case of composite sandwich panels , 2006 .

[19]  Adrian Sandu,et al.  Modeling Multibody Systems with Uncertainties. Part I: Theoretical and Computational Aspects , 2006 .

[20]  Werner Schiehlen,et al.  Multibody System Dynamics: Roots and Perspectives , 1997 .

[21]  C Soize,et al.  Maximum entropy approach for modeling random uncertainties in transient elastodynamics. , 2001, The Journal of the Acoustical Society of America.

[22]  Adrian Sandu,et al.  Modeling multibody systems with uncertainties. Part II: Numerical applications , 2006 .

[23]  Antonio Carrarini,et al.  Reliability Based Analysis of the Crosswind Stability of Railway Vehicles , 2006 .

[24]  E. J. Haug,et al.  Computer aided kinematics and dynamics of mechanical systems. Vol. 1: basic methods , 1989 .

[25]  R. Serfling Approximation Theorems of Mathematical Statistics , 1980 .

[26]  E. Somersalo,et al.  Statistical and computational inverse problems , 2004 .

[27]  J. Hammersley SIMULATION AND THE MONTE CARLO METHOD , 1982 .

[28]  Dan Negrut,et al.  A Framework for Uncertainty Quantification in Nonlinear Multi-Body System Dynamics , 2009 .

[29]  Marc P. Mignolet,et al.  Nonparametric Stochastic Modeling of Uncertainty in Rotordynamics—Part II: Applications , 2010 .

[30]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[31]  Christian Soize,et al.  A probabilistic model for bounded elasticity tensor random fields with application to polycrystalline microstructures , 2011 .

[32]  Adrian Sandu,et al.  Modeling Multibody Dynamic Systems With Uncertainties . Part II : Numerical Applications , 2004 .

[33]  Roger G. Ghanem,et al.  A Bounded Random Matrix Approach for Stochastic Upscaling , 2009, Multiscale Model. Simul..

[34]  Christian Soize,et al.  Construction of probability distributions in high dimension using the maximum entropy principle: Applications to stochastic processes, random fields and random matrices , 2008 .