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Found 6 papers, 5 papers with code
Density reconstruction from schlieren images through Bayesian nonparametric models
This study proposes a radically alternate approach for extracting quantitative information from schlieren images.
Automatic Borescope Damage Assessments for Gas Turbine Blades via Deep Learning
Building upon state-of-the-art methods from computer vision, we show that damage statistics can be presented for each blade in a blade row separately, and demonstrate the workflow on two borescope videos.
Blade Envelopes Part II: Multiple Objectives and Inverse Design
In Part I of this two-part paper, a workflow for the formulation of blade envelopes is described and demonstrated.
Computational Engineering, Finance, and Science
Blade Envelopes Part I: Concept and Methodology
Blades manufactured through flank and point milling will likely exhibit geometric variability.
Dimensionality Reduction
Computational Engineering, Finance, and Science
Applications
Uncertainty Quantification for Data-driven Turbulence Modelling with Mondrian Forests
The MF predictive uncertainty is also found to be better calibrated and less computationally costly than the uncertainty estimated from applying jackknifing to random forest predictions.
Fluid Dynamics Computational Physics
Supporting Multi-point Fan Design with Dimension Reduction
Our results using polynomial variable projection clearly demonstrate that one can accurately identify dimension reducing subspaces for turbomachinery functionals at a fraction of the cost associated with prior methods.
