23 citations · 25 across the 4 of their papers we have counts for
13 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…
Jarvis for Aeroengine Analytics: A Speech Enhanced Virtual Reality Demonstrator Based on Mining Knowledge Databases
Sławomir Konrad Tadeja, Krzysztof Kutt, Yupu Lu +3
In this paper, we present a Virtual Reality (VR) based environment where the engineer interacts with incoming data from a fleet of aeroengines. This data takes the form of 3D compu…
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…
Uncertainty Quantification for Data-driven Turbulence Modelling with Mondrian Forests
Ashley Scillitoe, Pranay Seshadri, Mark Girolami
Data-driven turbulence modelling approaches are gaining increasing interest from the CFD community. However, the introduction of a machine learning (ML) model introduces a new sour…
AeroVR: Immersive Visualization System for Aerospace Design
Slawomir Konrad Tadeja, Pranay Seshadri, Per Ola Kristensson
One of today's most propitious immersive technologies is virtual reality (VR). This term is colloquially associated with headsets that transport users to a bespoke, built-for-purpo…
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…