Journal ArticleDOI
A polynomial chaos approach to the analysis of vehicle dynamics under uncertainty
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TLDR
In this article, polynomial chaos approaches that explicitly consider parametric uncertainty during modelling of vehicle dynamics are presented, and experimental results compared with the simulation results performed on ANVEL (a vehicle simulator) indicate that the method can be used to predict vehicle motion in realistic scenarios.Abstract:
The ability of ground vehicles to quickly and accurately analyse their dynamic response to a given input is critical to their safety and efficient autonomous operation. In field conditions, significant uncertainty is associated with terrain and/or vehicle parameter estimates, and this uncertainty must be considered in the analysis of vehicle motion dynamics. Here, polynomial chaos approaches that explicitly consider parametric uncertainty during modelling of vehicle dynamics are presented. They are shown to be computationally more efficient than the standard Monte Carlo scheme, and experimental results compared with the simulation results performed on ANVEL (a vehicle simulator) indicate that the method can be utilised for efficient and accurate prediction of vehicle motion in realistic scenarios.read more
Citations
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Journal ArticleDOI
A new uncertain analysis method and its application in vehicle dynamics
TL;DR: In this article, the Polynomial-Chaos-Chebyshev-Interval (PCCI) method is proposed for vehicle dynamics involving hybrid uncertainty parameters. But the PCCI method is non-intrusive, because it does not require the amendment of the original solver for different and complicated dynamics problems.
Journal ArticleDOI
Propagating Uncertainty in Power System Dynamic Simulations Using Polynomial Chaos
TL;DR: Two uncertainty quantification approaches using polynomial-chaos-based methods are proposed and investigated and reveal that they are able to produce comparable accuracy as the Monte Carlo based method while achieving significantly improved computational efficiency for both stable and unstable power system operating conditions.
Journal ArticleDOI
Efficient computational techniques for mistuning analysis of bladed discs: A review
Jie Yuan,Fabrizio Scarpa,Giuliano Allegri,Branislav Titurus,Sophoclis Patsias,Ramesh Rajasekaran +5 more
TL;DR: In this article, a review of the relevant literature about mistuning problems in bladed disc systems, and their implications for the uncertainty propagation associated to the dynamics of aeroengine systems is presented.
Journal ArticleDOI
A PCM-based stochastic hydrological model for uncertainty quantification in watershed systems
TL;DR: In this paper, an uncertainty quantification framework is proposed for hydrologic models based on probabilistic collocation method (PCM), which first uses polynomial chaos expansion (PCE) to approximate the hydrological outputs in terms of a set of standard Gaussian random variables, and then estimates the unknown coefficients in the PCE through collocation.
Journal ArticleDOI
A coupled ensemble filtering and probabilistic collocation approach for uncertainty quantification of hydrological models
TL;DR: The results indicate that the proposed EFPC approach can effectively quantify the uncertainty of hydrologic models.
References
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Book
Stochastic Finite Elements: A Spectral Approach
Roger Ghanem,Pol D. Spanos +1 more
TL;DR: In this article, a representation of stochastic processes and response statistics are represented by finite element method and response representation, respectively, and numerical examples are provided for each of them.
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The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
TL;DR: This work represents the stochastic processes with an optimum trial basis from the Askey family of orthogonal polynomials that reduces the dimensionality of the system and leads to exponential convergence of the error.
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Monte Carlo methods
TL;DR: The general nature of Monte Carlo methods can be found in this paper, where a short resume of statistical terms is given, including random, pseudorandom, and quasirandom numbers.
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Simulation and the Monte Carlo Method
TL;DR: This book provides the first simultaneous coverage of the statistical aspects of simulation and Monte Carlo methods, their commonalities and their differences for the solution of a wide spectrum of engineering and scientific problems.