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Review of Geometric Uncertainty Quantification in Gas Turbines
11 references·10 citing·36 second hop
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this work references cited by second hop

References

  • 1996Response Surface Methodology: Process and Product Optimization Using Designed Experiments9,540 cited
  • 1991Stochastic Finite Elements: A Spectral Approach5,753 cited
  • 2002The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations4,821 cited
  • 2008Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics769 cited
  • 2010Spectral methods for uncertainty quantification : with applications to computational fluid dynamics572 cited
  • 2004Numerical Challenges in the Use of Polynomial Chaos Representations for Stochastic Processes451 cited
  • 1990Polynomial Chaos in Stochastic Finite Elements416 cited
  • 2014Active Subspace Methods in Theory and Practice: Applications to Kriging Surfaces267 cited
  • 2002Uncertainty analysis for fluid mechanics with applications143 cited
  • 2001Approach for uncertainty propagation and robust design in CFD using sensitivity derivatives100 cited
  • 2011DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis.99 cited

Cited by

  • 2021Nonlinear uncertainty quantification of the impact of geometric variability on compressor performance using an adjoint method41 cited
  • 2023A data-driven robust design optimization method and its application in compressor blade25 cited

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

  • 2023Investigation on high-dimensional uncertainty quantification and reliability analysis of aero-engine24 cited
  • 2024Reliability-based robust optimization design for tolerance of aerospace thin-walled components based on surrogate model22 cited
  • 2023Multidisciplinary robust optimization approach of fan rotors under structural constraints with blade curvature16 cited
  • 2021Stochastic aerodynamic analysis for compressor blades with manufacturing variability based on a mathematical dimensionality reduction method16 cited
  • 2024Performance prediction of gas turbine blade with multi-source random factors using active learning-based neural network15 cited
  • 2024A dimensionality reduction method for uncertainty study of geometric variations of turbomachinery blades13 cited
  • 2023Uncertainty Quantification of Compressor Map Using the Monte Carlo Approach Accelerated by an Adjoint-Based Nonlinear Method9 cited
  • 2022An Efficient Quantification Method Based on Feature Selection for High-Dimensional Uncertainties of Multistage Compressors8 cited