Results 151 to 160 of about 3,846 (183)

Application of non-intrusive polynomial chaos theory to real time simulation

2013 IEEE Grenoble Conference, 2013
Simulation tools play a critical role in the design and test of power systems. In particular, real time simulation is now reliable and constitutes the basis for Hardware in the Loop and Power hardware in the Loop testing techniques. The application of real time simulation and related techniques to power systems is made particularly challenging as it ...
Junjie Tang, Fei Ni, Kanali Togawa, Ferdinanda Ponci, Antonello Monti   +4 more
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Improved non-intrusive polynomial chaos for reliability analysis under hybrid uncertainty

2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE), 2013
With the increasing of systems' scale and complexity, reliability analysis faces more challenges which mainly include hybrid uncertainty, implicit limit state function and numerous uncertain input variables. Non-intrusive polynomial chaos (NIPC) is a promising technology for uncertainty quantification with high efficiency and accuracy.
Yao Wang, Shengkui Zeng, Jianbin Guo
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Non-Intrusive Polynomial Chaos Methods for Uncertainty Quantification in Fluid Dynamics

48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, 2010
This paper examines uncertainty quantification in computational fluid dynamics (CFD) with non-intrusive polynomial chaos (NIPC) methods which require no modification to the existing deterministic models. The NIPC methods have been increasingly used for uncertainty propagation in high-fidelity CFD simulations due to their non-intrusive nature and strong
Serhat Hosder, Robert Walters
openaire   +1 more source

A non‐Gaussian Bayesian filter for sequential data assimilation with non‐intrusive polynomial chaos expansion

International Journal for Numerical Methods in Engineering, 2021
AbstractNon‐Gaussian data assimilation is vital for several applications with nonlinear dynamical systems, including geosciences, socio‐economics, infectious disease modeling, and autonomous navigation. Widespread adoption of non‐Gaussian data assimilation requires easy‐to‐implement schemes.
Srikanth Avasarala, Deepak Subramani
openaire   +1 more source

A Non-Intrusive Polynomial Chaos Method For Uncertainty Propagation in CFD Simulations

44th AIAA Aerospace Sciences Meeting and Exhibit, 2006
In this extended abstract, we present a Non-Intrusive Polynomial Chaos (PC) method for the propagation of input uncertainty in Computational Fluid Dynamics (CFD) simulations. By the “non-intrusive” term, we specify a method which does not modify the original deterministic code used in the simulations.
Serhat Hosder, Robert Walters, Rafael Perez   +2 more
openaire   +1 more source

Non-intrusive polynomial chaos for efficient uncertainty analysis in parametric roll simulations

Journal of Marine Science and Technology, 2015
Monte Carlo analyses are generally considered the standard for uncertainty analysis. While accurate, these analyses can be expensive computationally. Recently, polynomial chaos has been proposed as an alternative approach to the estimation of uncertainty distributions (Hosder et al. A non-intrusive polynomial chaos method for uncertainty propagation in
M. D. Cooper, W. Wu, L. S. McCue
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Non-intrusive Polynomial Chaos Expansion Based Uncertainty Analysis of Bioethanol Production Process

2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 2019
Ethanol production has been a topic of great interest since the world found the significance of renewable biofuels. Process efficiency and sustainability are main points of focus for ethanol production. Uncertainty in input parameters and its effect on the process outcome, i.e., ethanol production, has been a challenge in realizing efficient operations
Iftikhar Ahmad, Uzair Ibrahim, Zumrud Imdad, Gulsayyar Ali   +3 more
openaire   +1 more source

Comparison of Non-Intrusive Polynomial Chaos and Stochastic Collocation Methods for Uncertainty Quantification

47th AIAA Aerospace Sciences Meeting including The New Horizons Forum and Aerospace Exposition, 2009
Non-intrusive polynomial chaos expansion (PCE) and stochastic collocation (SC) methods are attractive techniques for uncertainty quantification (UQ) due to their strong mathematical basis and ability to produce functional representations of stochastic variability. PCE estimates coefficients for known orthogonal polynomial basis functions based on a set
Michael Eldred, John Burkardt
openaire   +1 more source

Uncertainty quantification for criticality problems using non-intrusive and adaptive Polynomial Chaos techniques

Annals of Nuclear Energy, 2013
Abstract In this paper we present the implementation and the application of non-intrusive spectral techniques for uncertainty analysis of criticality problems. Spectral techniques can be used to reconstruct stochastic quantities of interest by means of a Fourier-like expansion. Their application to uncertainty propagation problems can be performed in
L. Gilli, D. Lathouwers, J.L. Kloosterman, T.H.J.J. van der Hagen, A.J. Koning, D. Rochman   +5 more
openaire   +1 more source

Efficient Sampling for Non-Intrusive Polynomial Chaos Applications with Multiple Uncertain Input Variables

48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 2007
The accuracy and the computational e!ciency of a Point-Collocation Non-Intrusive Polynomial Chaos (NIPC) method applied to stochastic problems with multiple uncertain input variables has been investigated. Two stochastic model problems with multiple uniform random variables were studied to determine the e"ect of di"erent sampling methods (Random, Latin
Serhat Hosder, Robert Walters, Michael Balch   +2 more
openaire   +1 more source