Probabilistic collocation for period-1 limit cycle oscillations
TL;DR: In this article, a probabilistic collocation for limit cycle oscillations (PCLCO) is proposed, which is a non-intrusive approach to compute the polynomial chaos description of uncertainty numerically.
About: This article is published in Journal of Sound and Vibration.The article was published on 2008-03-18. It has received 55 citations till now. The article focuses on the topics: Polynomial chaos & Orthogonal collocation.
Citations
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TL;DR: The generalized polynomial chaos expansion (GPC) as mentioned in this paper is a nonsampling method which represents the uncertain quantities as an expansion including the decomposition of deterministic coefficients and random orthogonal bases.
Abstract: In recent years, extensive research has been reported about a method which is called the generalized polynomial chaos expansion. In contrast to the sampling methods, e.g., Monte Carlo simulations, polynomial chaos expansion is a nonsampling method which represents the uncertain quantities as an expansion including the decomposition of deterministic coefficients and random orthogonal bases. The generalized polynomial chaos expansion uses more orthogonal polynomials as the expansion bases in various random spaces which are not necessarily Gaussian. A general review of uncertainty quantification methods, the theory, the construction method, and various convergence criteria of the polynomial chaos expansion are presented. We apply it to identify the uncertain parameters with predefined probability density functions. The new concepts of optimal and nonoptimal expansions are defined and it demonstrated how we can develop these expansions for random variables belonging to the various random spaces. The calculation of the polynomial coefficients for uncertain parameters by using various procedures, e.g., Galerkin projection, collocation method, and moment method is presented. A comprehensive error and accuracy analysis of the polynomial chaos method is discussed for various random variables and random processes and results are compared with the exact solution or/and Monte Carlo simulations. The method is employed for the basic stochastic differential equation and, as practical application, to solve the stochastic modal analysis of the microsensor quartz fork. We emphasize the accuracy in results and time efficiency of this nonsampling procedure for uncertainty quantification of stochastic systems in comparison with sampling techniques, e.g., Monte Carlo simulation.
122 citations
TL;DR: In this paper, the authors presented the theory and application of the generalized polynomial chaos expansion for the stochastic free vibration of orthotropic plates under the uncertainties in elasticity moduli.
74 citations
Cites methods from "Probabilistic collocation for perio..."
...The method has been also applied for stochastic simulation of various vibration problems, see [11,12,30,31] and references therein....
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TL;DR: In this paper, an adaptive stochastic finite elements approach with Newton-Cotes quadrature and simplex elements is developed for resolving the effect of random parameters in flow problems.
67 citations
TL;DR: In this paper, the Stochastic Harmonic Balance Method (Stochastic-HBM) was applied to a flexible non-linear rotor system, with random parameters modeled as random fields.
Abstract: The effects of uncertainties on the non-linear dynamics response remain misunderstood and most of the classical stochastic methods used in the linear case fail to deal with a non-linear problem. So we propose to take into account of uncertainties into non-linear models, by coupling the Harmonic Balance Method (HBM) and the Polynomial Chaos Expansion (PCE). The proposed method called the Stochastic Harmonic Balance Method (Stochastic-HBM) is based on a new formulation of the non-linear dynamic problem in which not only the approximated non-linear responses but also the non-linear forces and the excitation pulsation are considered as stochastic parameters. Expansions on the PCE basis are performed by passing via an Alternate Frequency Time method with Probabilistic Collocation (AFTPC) for estimating the stochastic non-linear forces in the stochastic domain and the frequency domain. In the present paper, the Stochastic Harmonic Balance Method (Stochastic-HBM) that is applied to a flexible non-linear rotor system, with random parameters modeled as random fields, is presented. The Stochastic-HBM combined with an Alternate Frequency-Time method with Probabilistic Collocation (AFTPC) allows us to solve dynamical problems with non-regular non-linearities in presence of uncertainties. In this study, the procedure is developed for the estimation of stochastic non-linear responses of the rotor system with different regular and non-regular non-linearities. The finite element rotor system is composed of a shaft with two disks and two flexible bearing supports where the non-linearities are due to a radial clearance or a cubic stiffness. A numerical analysis is performed to analyze the effect of uncertainties on the non-linear behavior of this rotor system by using the Stochastic-HBM. Furthermore, the results are compared with those obtained by applying a classical Monte-Carlo simulation to demonstrate the efficiency of the proposed methodology.
49 citations
TL;DR: The quasi-periodic stochastic dynamic response is evaluated considering uncertainties in linear and nonlinear parts of the mechanical system and it is found that the results agreed very well whilst requiring significantly less computation.
45 citations
Cites methods from "Probabilistic collocation for perio..."
...For various numerical tests, it is found that the results agreed very well whilst requiring significantly less computation....
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References
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Book•
20 Dec 1990TL;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.
Abstract: Representation of stochastic processes stochastic finite element method - response representation stochastic finite element method - response statistics numerical examples.
5,495 citations
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.
Abstract: We present a new method for solving stochastic differential equations based on Galerkin projections and extensions of Wiener's polynomial chaos Specifically, we represent 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 Several continuous and discrete processes are treated, and numerical examples show substantial speed-up compared to Monte Carlo simulations for low dimensional stochastic inputs
4,473 citations
Book•
01 Feb 1986
TL;DR: In this article, Navier-Stokes et al. discuss the fundamental principles of Inviscid, Incompressible Flow over airfoils and their application in nonlinear Supersonic Flow.
Abstract: TABLE OF CONTENTS Preface to the Fifth Edition Part 1: Fundamental Principles 1. Aerodynamics: Some Introductory Thoughts 2. Aerodynamics: Some Fundamental Principles and Equations Part 2: Inviscid, Incompressible Flow 3. Fundamentals of Inviscid, Incompressible Flow 4. Incompressible Flow Over Airfoils 5. Incompressible Flow Over Finite Wings 6. Three-Dimensional Incompressible Flow Part 3: Inviscid, Compressible Flow 7. Compressible Flow: Some Preliminary Aspects 8. Normal Shock Waves and Related Topics 9. Oblique Shock and Expansion Waves 10. Compressible Flow Through Nozzles, Diffusers and Wind Tunnels 11. Subsonic Compressible Flow Over Airfoils: Linear Theory 12. Linearized Supersonic Flow 13. Introduction to Numerical Techniques for Nonlinear Supersonic Flow 14. Elements of Hypersonic Flow Part 4: Viscous Flow 15. Introduction to the Fundamental Principles and Equations of Viscous Flow 16. A Special Case: Couette Flow 17. Introduction to Boundary Layers 18. Laminar Boundary Layers 19. Turbulent Boundary Layers 20. Navier-Stokes Solutions: Some Examples Appendix A: Isentropic Flow Properties Appendix B: Normal Shock Properties Appendix C: Prandtl-Meyer Function and Mach Angle Appendix D: Standard Atmosphere Bibliography Index
3,113 citations
2,718 citations
Book•
15 Feb 2002
TL;DR: In this paper, the authors present a broad overview of nonlinear phenomena point attractors in autonomous systems, including limit cycles in autonomous system, and chaotic behaviour of one-and two-dimensional maps.
Abstract: Preface. Preface to the First Edition. Acknowledgements from the First Edition. Introduction PART I: BASIC CONCEPTS OF NONLINEAR DYNAMICS An overview of nonlinear phenomena Point attractors in autonomous systems Limit cycles in autonomous systems Periodic attractors in driven oscillators Chaotic attractors in forced oscillators Stability and bifurcations of equilibria and cycles PART II ITERATED MAPS AS DYNAMICAL SYSTEMS Stability and bifurcation of maps Chaotic behaviour of one--and two--dimensional maps PART III FLOWS, OUTSTRUCTURES AND CHAOS The Geometry of Recurrence The Lorenz system Rosslers band Geometry of bifurcations PART IV APPLICATIONS IN THE PHYSICAL SCIENCES Subharmonic resonances of an offshore structure Chaotic motions of an impacting system Escape from a potential well Appendix. Illustrated Glossary. Bibliography. Online Resource. Index.
1,731 citations