Surrogate Modeling of Manufacturing Variation Effects on Unsteady Interactions in a Transonic Turbine

Brown, Jerram L.; Beck, Jeff; Kaszynski, Alex A.; Clark, John P.

doi:10.1115/1.4041314

Cited by 3 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PyVista is used extensively by the Air Force Research Labs (AFRL) for data visualization and plotting in research articles including figures visualizing 3D tessellated models generated from structured light optical scanner and results from finite element analysis. AFRL publications leveraging PyVista for 3D visualization include: (D. Gillaugh, Kaszynski, Brown, Johnston, & Slater, 2017), (Brown, Beck, Kaszynski, & Clark, 2018a), (Brown, Beck, Kaszynski, & Clark, 2018b), (J. A.…”

Section: Mentionsmentioning

confidence: 99%

PyVista: 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK)

Sullivan¹,

Kaszynski²

2019

JOSS

Self Cite

327

173

View full text Add to dashboard Cite

show abstract

Section: Mentionsmentioning

confidence: 99%

PyVista: 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK)

Sullivan¹,

Kaszynski²

2019

JOSS

Self Cite

327

173

View full text Add to dashboard Cite

show abstract

Gaussian Stochastic Process Modeling of Blend Repaired Airfoil Modal Response Using Reduced Basis Mode Shape Approach

Brown

Kaszynski

Gillaugh

et al. 2022

Journal of Engineering for Gas Turbines and Power

View full text Add to dashboard Cite

A machine learning (ML) approach is developed to predict effects of blend repairs on airfoil frequency, modal assurance criterion (MAC), and modal displacement. The method is demonstrated on a transonic compressor rotor airfoil. A parametric blend geometry is developed that encompassing a large range of blend geometries. This repair geometry is used to modify the airfoil surface definition and a mesh morphing process transforms the nominal finite element model to the repaired configuration. A multi-level full factorial sampling of the blend repair design space provides training data to a Guassian stochastic process (GSP) regressor. Frequency and MAC results create a vector of training data for GSP calibration, but the airfoil mode shapes require further mathematical manipulation to avoid GSP models for each nodal displacement. This paper develops a method to reduce blended airfoil mode shape emulation cost by transforming the mode shape data into a reduced basis space using principal component analysis (PCA). The coefficients of this reduced basis train a GSP that predicts results for new blended airfoils. The emulated coefficients are used with the reduced basis vectors in a reconstruction of blended airfoil mode shape. Validation data is computed at a full-factorial design that maximizes the distance from training points. It is found that large variations in modal properties from large blend repairs can be accurately emulated with a reasonable number of training points. The reduced basis approach of mode shape variation is found to more accurately predict MAC variation when compared to direct MAC emulation.

show abstract

On the Probabilistic Endurance Prediction Approach for Turbomachinery Blades and Vanes

Kulkarni

2023

Journal of Turbomachinery

View full text Add to dashboard Cite

The design and aeromechanical assessment of turbomachinery blades and vanes comprises a wide range of complex processes that tend to be based on conventional deterministic methods. These processes often provide a ‘snapshot’ evaluation of the new component designs at the nominal operating conditions. While the deterministic methods can predict the high cycle fatigue (HCF) endurance with reasonable accuracy; they assume that the conservative safety factors applied to cover for the parametric variations, uncertainties and unknowns will not change during the product life cycle. This approach is intended to be conservative and in some cases may overlook the lack of robustness. The present paper proposes a robust design analysis approach based on probabilistic methodology for the aeromechanical assessment of rotor blades and stator vanes of turbomachinery. The robust design approach can account explicitly for the effects of design and manufacturing variability. This methodology can reduce the levels of conservatism in the deterministic approach and can provide a more thorough risk assessment. This paper offers a generalised aeromechanical analysis formulation based on probabilistic methods to evaluate the HCF capability of turbomachinery components. Herein, this methodology is demonstrated by using a typical stator vane of an aero engine compressor and it is based on Monte-Carlo and DOE simulations. The methodology consists of parametric sensitivity studies and identification of the most influential parameters that control the HCF endurance. Future ideas and roadmap of the aeromechanical probabilistic analysis capability development are also discussed.

show abstract

Surrogate Modeling of Manufacturing Variation Effects on Unsteady Interactions in a Transonic Turbine

Cited by 3 publications

References 27 publications

PyVista: 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK)

PyVista: 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK)

Gaussian Stochastic Process Modeling of Blend Repaired Airfoil Modal Response Using Reduced Basis Mode Shape Approach

On the Probabilistic Endurance Prediction Approach for Turbomachinery Blades and Vanes

Contact Info

Product

Resources

About