Benchmarking Uncertainty Quantification Methods Using the NACA 2412 Airfoil with Geometrical and Operational Uncertainties

Quagliarella, Domenico; Serani, Andrea; Diez, Matteo; Pisaroni, Michele; Leyland, Pénélope; Montagliani, Luca; Iemma, Umberto; Gaul, Nicholas J.; Shin, Jaekwan; Wünsch, Dirk; Hirsch, Charles; Stern, Frederick

doi:10.2514/6.2019-3555

Cited by 10 publications

(7 citation statements)

References 28 publications

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“…The efficiency of the UQ methods is, in general, problem dependent and has to be carefully assessed. As an example, several UQ methods were compared for an airfoil benchmark problem in [8].…”

Section: Introductionmentioning

confidence: 99%

Comparing multi-index stochastic collocation and multi-fidelity stochastic radial basis functions for forward uncertainty quantification of ship resistance

Piazzola

Tamellini

Pellegrini

et al. 2022

Engineering with Computers

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This paper presents a comparison of two multi-fidelity methods for the forward uncertainty quantification of a naval engineering problem. Specifically, we consider the problem of quantifying the uncertainty of the hydrodynamic resistance of a roll-on/roll-off passenger ferry advancing in calm water and subject to two operational uncertainties (ship speed and payload). The first four statistical moments (mean, variance, skewness, and kurtosis), and the probability density function for such quantity of interest (QoI) are computed with two multi-fidelity methods, i.e., the Multi-Index Stochastic Collocation (MISC) and an adaptive multi-fidelity Stochastic Radial Basis Functions (SRBF). The QoI is evaluated via computational fluid dynamics simulations, which are performed with the in-house unsteady Reynolds-Averaged Navier–Stokes (RANS) multi-grid solver $$\chi$$ χ navis. The different fidelities employed by both methods are obtained by stopping the RANS solver at different grid levels of the multi-grid cycle. The performance of both methods are presented and discussed: in a nutshell, the findings suggest that, at least for the current implementation of both methods, MISC could be preferred whenever a limited computational budget is available, whereas for a larger computational budget SRBF seems to be preferable, thanks to its robustness to the numerical noise in the evaluations of the QoI.

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Section: Introductionmentioning

confidence: 99%

Comparing multi-index stochastic collocation and multi-fidelity stochastic radial basis functions for forward uncertainty quantification of ship resistance

Piazzola

Tamellini

Pellegrini

et al. 2022

Engineering with Computers

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show abstract

“…For instance, in [1,2,3] the uncertainty quantification (UQ) of a high-speed catamaran in irregular head waves was performed via both CFD computations and towing-tank experiments, while the application of several UQ methods to an airfoil benchmark problem were discussed in e.g. [4].…”

Section: Introductionmentioning

confidence: 99%

Comparing Multi-Index Stochastic Collocation and Multi-Fidelity Stochastic Radial Basis Functions for Forward Uncertainty Quantification of Ship Resistance

Piazzola¹,

Tamellini²,

Pellegrini³

et al. 2021

Preprint

Self Cite

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This paper presents a comparison of two multi-fidelity methods for the forward uncertainty quantification of a naval engineering problem. Specifically, we consider the problem of quantifying the uncertainty of the hydrodynamic resistance of a roll-on/roll-off passengers ferry advancing in calm water and subject to two operational uncertainties (ship speed and payload). The first four statistical moments (mean, variance, skewness, kurtosis), and the probability density function for such quantity of interest (QoI) are computed with two multi-fidelity methods, i.e., the Multi-Index Stochastic Collocation (MISC) method and an adaptive multi-fidelity Stochastic Radial Basis Functions (SRBF) algorithm. The QoI is evaluated via computational fluid dynamics simulations, which are performed with the in-house unsteady Reynolds-Averaged Navier-Stokes (RANS) multi-grid solver χnavis. The different fidelities employed by both methods are obtained by stopping the RANS solver at different grid levels of the multi-grid cycle. The performance of both methods are presented and discussed: in a nutshell, the findings suggest that, at least for the current implementations of both algorithms, MISC could be preferred whenever a limited computational budget is available, whereas for a larger computational budget SRBFs seem to be preferable, thanks to its robustness to the numerical noise in the evaluations of the QoI.

show abstract

“…There is a large literature on numerical methods to face problems with uncertain input parameters. We may find engineering applications of these methodologies exploring random material properties, random boundary conditions and/or random domain topology: [1,2,3,4,5,6,7,8,9,10,11,12,13,14] just to mention some.…”

Section: Introductionmentioning

confidence: 99%

“…When geometric uncertainty analysis is carried out, care should be taken using traditional mesh-based numerical methods to solve the deterministic problem, because it is well known that the numerical simulation can be meshdependent and the results are sensitive to the quality of the remeshing operation. Examples of CFD problems with geometric uncertainties, solved by non-intrusive techniques with remeshing of geometry (body-fitted approach) can be found in [6,7,14].…”

Section: Introductionmentioning

confidence: 99%

Analysis of geometric uncertainties in CFD problems solved by RBF-FD meshless method

Zamolo

Parussini

2020

Journal of Computational Physics

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Benchmarking Uncertainty Quantification Methods Using the NACA 2412 Airfoil with Geometrical and Operational Uncertainties

Cited by 10 publications

References 28 publications

Comparing multi-index stochastic collocation and multi-fidelity stochastic radial basis functions for forward uncertainty quantification of ship resistance

Comparing multi-index stochastic collocation and multi-fidelity stochastic radial basis functions for forward uncertainty quantification of ship resistance

Comparing Multi-Index Stochastic Collocation and Multi-Fidelity Stochastic Radial Basis Functions for Forward Uncertainty Quantification of Ship Resistance

Analysis of geometric uncertainties in CFD problems solved by RBF-FD meshless method

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