Uncertainty Quantification in Internal Flows

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

doi:10.2514/6.2017-1200

Cited by 6 publications

(5 citation statements)

References 2 publications

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“…An example of the rotor flow field is shown in Figure 6 from a deterministic RANS simulation 8 . The results show a shock-induced boundary layer separation at approximately mid chord of the blade as well as an interaction of the transonic shock from a leading blade with the leading-edge flow about a following blade.…”

Section: B Internal Aerodynamicsmentioning

confidence: 99%

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Overview of the AVT-191 Project to Assess Sensitivity Analysis and Uncertainty Quantification Methods for Military Vehicle Design

Benek

Luckring

2017

55th AIAA Aerospace Sciences Meeting

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Section: B Internal Aerodynamicsmentioning

confidence: 99%

“…Team members include C. Dineseu of Numeca International, S. Smrnov and C. Lacor of the University of Brussels. Summary results in these AIAA special sessions are provided by Nigro et al 8…”

Section: B Internal Aerodynamicsmentioning

confidence: 99%

Overview of the AVT-191 Project to Assess Sensitivity Analysis and Uncertainty Quantification Methods for Military Vehicle Design

Benek

Luckring

2017

55th AIAA Aerospace Sciences Meeting

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“…The internal aerodynamics team's experience with the NASA Rotor 37 configuration 8 (See, Figure 4) were reported in these AIAA special sessions by Nigro et al 9 , and their methods and findings are summarized in Table 2. The team used high-fidelity, physicsbased flow models (i.e., CFD) on which to apply the uncertainty methods.…”

Section: B Internal Aerodynamicsmentioning

confidence: 99%

Summary Findings from the AVT-191 Project to Assess Sensitivity Analysis and Uncertainty Quantification Methods for Military Vehicle Design

Benek

Luckring

2017

55th AIAA Aerospace Sciences Meeting

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A NATO symposium held in Greece in 2008 identified many promising sensitivity analysis and uncertainty quantification technologies, but the maturity and suitability of these methods for realistic applications was not clear. The NATO Science and Technology Organization, Task Group AVT-191 was established to evaluate the maturity and suitability of various sensitivity analysis and uncertainty quantification methods for application to realistic vehicle development problems. The program ran from 2011 to 2015, and the work was organized into four discipline-centric teams: external aerodynamics, internal aerodynamics, aeroelasticity, and hydrodynamics. This paper summarizes findings and lessons learned from the task group.

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“…The Normal distribution was most often assumed to represent geometric or operation variability in the field of turbomachinery and wing airfoils. [27][28][29][30][31] The assumption is often justified by referring to the Central Limit Theorem. Previous studies have showed that the statistical characteristics of the surface deviations of the compressor blade accord with Normal distribution.…”

Section: Introductionmentioning

confidence: 99%

Stochastic aerodynamic analysis for compressor blades with manufacturing variability based on a mathematical dimensionality reduction method

Guo

Chu

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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It is essential for engineering manufacture and robust design to evaluate the impact of manufacturing variability on the aerodynamics of compressor blades efficiently and accurately. In the paper, a novel quadratic curve approximation method based on the scanning points of blade design profiles was introduced and combined with Karhunen–Loève expansion, a mathematical dimensionality reduction method for modeling manufacturing variability as truncated Normal process was proposed. Subsequently, Sparse Approximation of Moment-based Arbitrary Polynomial Chaos (SAMBA PC) and computational fluid dynamics (CFD) were applied to build a computational framework for stochastic aerodynamic analysis considering manufacturing variability. Finally, the framework was adopted to evaluate the aerodynamic variations of a high subsonic compressor cascade under the design incidence. The results illustrate that the SAMBA PC method is more efficient than the traditional methods such as Monte Carlo simulation (MCS) for stochastic aerodynamic analysis. Through uncertainty quantification, the impact of manufacturing variability on the global aerodynamic performance is primarily reflected in the fluctuation of aerodynamic losses, and the fluctuation of the total losses is mainly contributed by the fluctuation of the separation loss after the suction peak (a negative pressure spike near the leading edge (LE)) and the boundary-layer loss on the suction surface (SS). With sensitivity analysis, the most important geometric modes to aerodynamics can be revealed, which provides a useful reference for manufacturing inspection process and helps reduce computational cost in robust design.

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Uncertainty Quantification in Internal Flows

Cited by 6 publications

References 2 publications

Overview of the AVT-191 Project to Assess Sensitivity Analysis and Uncertainty Quantification Methods for Military Vehicle Design

Overview of the AVT-191 Project to Assess Sensitivity Analysis and Uncertainty Quantification Methods for Military Vehicle Design

Summary Findings from the AVT-191 Project to Assess Sensitivity Analysis and Uncertainty Quantification Methods for Military Vehicle Design

Stochastic aerodynamic analysis for compressor blades with manufacturing variability based on a mathematical dimensionality reduction method

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