Summary
I am currently working on the EPSRC-funded "RELIANT project" (Risk EvaLuatIon fAst iNtelligent Tool for COVID19), which aims to identify solutions for the management of people and spaces in the current pandemic and during the easing of restrictions.
The two figures below show results from an AI-based model of multiphase flow in a pipe (slug flow), taken from Wolffs, Z., Heaney, C.E., Srinil, N., Pain, C.C. et al (2021) An AI-based Reduced-Order Model with Domain Decomposition, Autoencoders and Adversarial Networks Applied to Fluid Flow Problems (in preparation).
Figure 1: Slug flow – the original CFD snapshot (above), the approximation from the AI-based model, in this case, a convolutional autoencoder in combination with a predictive adversarial network (below).
Figure 2: Instabilities or slugs advecting downstream – snapshots taken from the original CFD model (above), snapshots taken beyond 70 metres from the AI-based model (below), which was trained on snapshots from a 10 metre domain.
Prior to RELIANT, I have worked on:
• the "MUFFINS project" (MUltiphase Flow-induced Fluid-flexible structure InteractioN in Subsea applications), focusing on reduced order modelling of slug flow in pipes;
• the Newton-funded "Smart GeoWells" project, of which the aim was the numerical prediction of optimal drilling strategies for extracting geo-thermal energy;
• the "Fast Reactor Physics Modelling" project funded by HMS Sultan, which investigated the application of reduced order modelling to problems in reactor physics.
My research interests span several areas including:
(1) reduced order modelling;
(2) calculating error bounds for parameters derived from computational homogenisation;
(3) investigating summation rules for the quasi-continuum method;
(4) error estimation and adaptive mesh refinement;
(5) meshless methods in geomechanics.
Selected Publications
Journal Articles
Heaney C, Liu X, Go H, et al., 2022, Extending the capabilities of data-driven reduced-order models to make predictions for unseen scenarios: applied to flow around buildings, Frontiers in Physics, Vol:10, ISSN:2296-424X, Pages:1-16
Heaney CE, Wolffs Z, Tómasson JA, et al., 2022, An AI-based non-intrusive reduced-order model for extended domains applied to multiphase flow in pipes, Physics of Fluids, Vol:34, ISSN:1070-6631, Pages:1-22
Obeysekara A, Salinas P, Heaney CE, et al., 2021, Prediction of multiphase flows with sharp interfaces using anisotropic mesh optimisation, Advances in Engineering Software, Vol:160, ISSN:0965-9978, Pages:1-16
Heaney CE, Buchan AG, Pain CC, et al., 2021, Reduced-order modelling applied to the multigroup neutron diffusion equation using a nonlinear interpolation method for control-rod movement, Energies, ISSN:1996-1073
Quilodrán-Casas C, Silva VS, Arcucci R, et al., 2021, Digital twins based on bidirectional LSTM and GAN for modelling COVID-19
Heaney CE, Li Y, Matar OK, et al., 2020, Applying Convolutional Neural Networks to Data on Unstructured Meshes with Space-Filling Curves, Arxiv