3,592 research outputs found
Global sensitivity analysis of model uncertainty in aeroelastic wind turbine models
A framework is presented for performing global sensitivity analysis of model parameters associated with the Blade Element Momentum (BEM) models. Sobol indices based on adaptive sparse polynomial expansions are used as a measure of global sensitivities. The sensitivity analysis workflow is developed using the uncertainty quantification toolbox UQLab that is integrated with TNO's Aero-Module aeroelastic code. Uncertainties in chord, twist, and lift- and drag-coefficients have been parametrized through the use of NURBS curves. Sensitivity studies are performed on the NM80 wind turbine model from the DanAero project, for a case with 19 uncertainties in both model and geometry. The combination of parametrization and sparse adaptive polynomial chaos yields a new efficient framework for global sensitivity analysis of aeroelastic wind turbine models, paving the way to effective model calibration
Computing derivative-based global sensitivity measures using polynomial chaos expansions
In the field of computer experiments sensitivity analysis aims at quantifying
the relative importance of each input parameter (or combinations thereof) of a
computational model with respect to the model output uncertainty. Variance
decomposition methods leading to the well-known Sobol' indices are recognized
as accurate techniques, at a rather high computational cost though. The use of
polynomial chaos expansions (PCE) to compute Sobol' indices has allowed to
alleviate the computational burden though. However, when dealing with large
dimensional input vectors, it is good practice to first use screening methods
in order to discard unimportant variables. The {\em derivative-based global
sensitivity measures} (DGSM) have been developed recently in this respect. In
this paper we show how polynomial chaos expansions may be used to compute
analytically DGSMs as a mere post-processing. This requires the analytical
derivation of derivatives of the orthonormal polynomials which enter PC
expansions. The efficiency of the approach is illustrated on two well-known
benchmark problems in sensitivity analysis
Propagation of epistemic uncertainty in queueing models with unreliable server using chaos expansions
In this paper, we develop a numerical approach based on Chaos expansions to
analyze the sensitivity and the propagation of epistemic uncertainty through a
queueing systems with breakdowns. Here, the quantity of interest is the
stationary distribution of the model, which is a function of uncertain
parameters. Polynomial chaos provide an efficient alternative to more
traditional Monte Carlo simulations for modelling the propagation of
uncertainty arising from those parameters. Furthermore, Polynomial chaos
expansion affords a natural framework for computing Sobol' indices. Such
indices give reliable information on the relative importance of each uncertain
entry parameters. Numerical results show the benefit of using Polynomial Chaos
over standard Monte-Carlo simulations, when considering statistical moments and
Sobol' indices as output quantities
Polynomial-Chaos-based Kriging
Computer simulation has become the standard tool in many engineering fields
for designing and optimizing systems, as well as for assessing their
reliability. To cope with demanding analysis such as optimization and
reliability, surrogate models (a.k.a meta-models) have been increasingly
investigated in the last decade. Polynomial Chaos Expansions (PCE) and Kriging
are two popular non-intrusive meta-modelling techniques. PCE surrogates the
computational model with a series of orthonormal polynomials in the input
variables where polynomials are chosen in coherency with the probability
distributions of those input variables. On the other hand, Kriging assumes that
the computer model behaves as a realization of a Gaussian random process whose
parameters are estimated from the available computer runs, i.e. input vectors
and response values. These two techniques have been developed more or less in
parallel so far with little interaction between the researchers in the two
fields. In this paper, PC-Kriging is derived as a new non-intrusive
meta-modeling approach combining PCE and Kriging. A sparse set of orthonormal
polynomials (PCE) approximates the global behavior of the computational model
whereas Kriging manages the local variability of the model output. An adaptive
algorithm similar to the least angle regression algorithm determines the
optimal sparse set of polynomials. PC-Kriging is validated on various benchmark
analytical functions which are easy to sample for reference results. From the
numerical investigations it is concluded that PC-Kriging performs better than
or at least as good as the two distinct meta-modeling techniques. A larger gain
in accuracy is obtained when the experimental design has a limited size, which
is an asset when dealing with demanding computational models
- …