Non-intrusive Probabilistic Collocation Method for Operational, Geometrical, and Manufacturing Uncertainties in Engineering Practice

Wünsch, Dirk; Nigro, Rémy; Coussement, Grégory; Hirsch, Charles

doi:10.1007/978-3-319-77767-2_9

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Figure 13 shows that the isentropic efficiency is most sensitive to the 3rd eigenmode. This is in alignment of earlier findings by Wunsch et al (2019) that the modes retaining the largest variability of the geometry are not necessarily the modes with the most influence on output quantities.…”

Section: Simulation Setupsupporting

confidence: 91%

Characterization of manufacturing uncertainties with applications to uncertainty quantification and robust design optimization

Wünsch¹,

Hirsch

2021

J. Glob. Power Propuls. Soc.

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Methodologies to quantify the impact of manufacturing uncertainties in 3D CFD based design strategies have become increasingly available over the past years as well as optimization under uncertainties, aiming at reducing the systems sensitivity to manufacturing uncertainties. This type of non-deterministic simulation depends however strongly on a correct characterization of the manufacturing variability. Experimental data to characterize this variability is not always available or in many cases cannot be sampled in sufficiently high numbers. Principal Component Analysis (PCA) is applied to the sampled geometries and the influence of tolerances classes, sample size and number of retained deformation modes are discussed. It is shown that the geometrical reconstruction accuracy of the deformation modes and reconstruction accuracy of the CFD predictions are not linearly related, which has important implications on the total geometrical variance that needs to be retained. In a second application the characterization of manufacturing uncertainties to a marine propeller is discussed. It is shown that uncertainty quantification and robust design optimization of the marine propeller can successfully be performed on the basis of the derived uncertainties. This leads to a propeller shape that is less sensitive to the manufacturing variability and therefore to a more robust design.

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Section: Simulation Setupsupporting

confidence: 91%

Characterization of manufacturing uncertainties with applications to uncertainty quantification and robust design optimization

Wünsch¹,

Hirsch

2021

J. Glob. Power Propuls. Soc.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The collaborative EU research project UMRIDA [16] (Uncertainty Management for Robust Design in Aeronautics, 2013-2016), tackled robust design under a large number of independent uncertainties. In this project, problems with more than 10 to 20 independent uncertain parameters were considered high dimensional and treated by dimensionality reduction techniques such as Karhunen-Loeve expansions [17,18], and sparse Polynomial Chaos [19,20] expansions. The construction of surrogates in high dimensions can also benefit from the gradients, estimated, for instance, by adjoint methods [21].…”

Section: Introductionmentioning

confidence: 99%

A Bayesian approach for quantile optimization problems with high-dimensional uncertainty sources

Sabater

Maître

Congedo

et al. 2021

Computer Methods in Applied Mechanics and Engineering

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Robust aerodynamic optimization and design exploration of a wide-chord transonic fan under geometric and operational uncertainties

Cheng

Zhou

et al. 2023

Energy

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Non-intrusive Probabilistic Collocation Method for Operational, Geometrical, and Manufacturing Uncertainties in Engineering Practice

Cited by 5 publications

References 6 publications

Characterization of manufacturing uncertainties with applications to uncertainty quantification and robust design optimization

Characterization of manufacturing uncertainties with applications to uncertainty quantification and robust design optimization

A Bayesian approach for quantile optimization problems with high-dimensional uncertainty sources

Robust aerodynamic optimization and design exploration of a wide-chord transonic fan under geometric and operational uncertainties

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