23 citations · 23 across the 2 of their papers we have counts for
11 papers
Prior-informed Uncertainty Modelling with Bayesian Polynomial Approximations
Chun Yui Wong, Pranay Seshadri, Andrew B. Duncan +2
Orthogonal polynomial approximations form the foundation to a set of well-established methods for uncertainty quantification known as polynomial chaos. These approximations deliver…
Automatic Borescope Damage Assessments for Gas Turbine Blades via Deep Learning
Chun Yui Wong, Pranay Seshadri, Geoffrey T. Parks
To maximise fuel economy, bladed components in aero-engines operate close to material limits. The severe operating environment leads to in-service damage on compressor and turbine…
Optimization by moving ridge functions: Derivative-free optimization for computationally intensive functions
James C. Gross, Geoffrey T. Parks
A novel derivative-free algorithm, optimization by moving ridge functions (OMoRF), for unconstrained and bound-constrained optimization is presented. This algorithm couples trust r…
Supporting Multi-point Fan Design with Dimension Reduction
Pranay Seshadri, Shaowu Yuchi, Shahrokh Shahpar +1
Motivated by the idea of turbomachinery active subspace performance maps, this paper studies dimension reduction in turbomachinery 3D CFD simulations. First, we show that these sub…
Spatial Flow-Field Approximation Using Few Thermodynamic Measurements Part I: Formulation and Area Averaging
Pranay Seshadri, Duncan Simpson, George Thorne +2
Our investigation raises an important question that is of relevance to the wider turbomachinery community: how do we estimate the spatial average of a flow quantity given finite (a…
Spatial Flow-Field Approximation Using Few Thermodynamic Measurements Part II: Uncertainty Assessments
Pranay Seshadri, Andrew Duncan, Duncan Simpson +2
In this second part of our two-part paper, we provide a detailed, frequentist framework for propagating uncertainties within our multivariate linear least squares model. This permi…