28 citations · 58 across the 12 of their papers we have counts for
17 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…
Blade Envelopes Part II: Multiple Objectives and Inverse Design
Chun Yui Wong, Pranay Seshadri, Ashley Scillitoe +3
Blade envelopes offer a set of data-driven tolerance guidelines for manufactured components based on aerodynamic analysis. In Part I of this two-part paper, a workflow for the form…
Bayesian Assessments of Aeroengine Performance with Transfer Learning
Pranay Seshadri, Andrew Duncan, George Thorne +3
Aeroengine performance is determined by temperature and pressure profiles along various axial stations within an engine. Given limited sensor measurements both along and between ax…
Blade Envelopes Part I: Concept and Methodology
Chun Yui Wong, Pranay Seshadri, Ashley Scillitoe +2
Blades manufactured through flank and point milling will likely exhibit geometric variability. Gauging the aerodynamic repercussions of such variability, prior to manufacturing a c…
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…
Extremum Sensitivity Analysis with Polynomial Monte Carlo Filtering
Chun Yui Wong, Pranay Seshadri, Geoffrey T. Parks
Global sensitivity analysis is a powerful set of ideas and heuristics for understanding the importance and interplay between uncertain parameters in a computational model. Such a m…
Embedded Ridge Approximations
Chun Yui Wong, Pranay Seshadri, Geoffrey Parks +1
Many quantities of interest (qois) arising from differential-equation-centric models can be resolved into functions of scalar fields. Examples of such qois include the lift over an…
Numerical Evaluation of Entropy Generation in Isolated Airfoils and Wells Turbines
Tiziano Ghisu, Francesco Cambuli, Pierpaolo Puddu +3
In recent years, a number of authors have studied entropy generation in Wells turbines. This is potentially a very interesting topic, as it can provide important insights into the…
Dimension Reduction via Gaussian Ridge Functions
Pranay Seshadri, Shaowu Yuchi, Geoffrey T. Parks
Ridge functions have recently emerged as a powerful set of ideas for subspace-based dimension reduction. In this paper we begin by drawing parallels between ridge subspaces, suffic…
An Aerodynamic Analysis of a Robustly Redesigned Modern Aero-Engine Fan
Pranay Seshadri, Geoffrey Parks, Shahrokh Shahpar
This paper documents results from a recent computational study aimed at de-sensitizing fan stage aerodynamics---in a modern, high bypass ratio aero-engine---to the effects of rear-…
Density-Matching for Turbomachinery Optimization Under Uncertainty
Pranay Seshadri, Geoffrey Parks, Shahrokh Shahpar
A monotonic, non-kernel density variant of the density-matching technique for optimization under uncertainty is developed. The approach is suited for turbomachinery problems which,…
Efficient optimisation of structures using tabu search
Andy M. Connor, Keith A. Seffen, Geoffrey T. Parks +1
This paper presents a novel approach to the optimisation of structures using a Tabu search (TS) method. TS is a metaheuristic which is used to guide local search methods towards a…
A density-matching approach for optimization under uncertainty
Pranay Seshadri, Paul Constantine, Gianluca Iaccarino +1
Modern computers enable methods for design optimization that account for uncertainty in the system---so-called optimization under uncertainty. We propose a metric for OUU that meas…