Imperial College London

Dr Chris Cantwell

Faculty of EngineeringDepartment of Aeronautics

Senior Lecturer in Aeronautics
 
 
 
//

Contact

 

+44 (0)20 7594 5050c.cantwell Website

 
 
//

Location

 

Department of Aeronautics, Room 219City and Guilds BuildingSouth Kensington Campus

//

Summary

 

Publications

Publication Type
Year
to

79 results found

Green MD, Kirilov KS, Turner M, Marcon J, Eichstädt J, Laughton E, Cantwell CD, Sherwin SJ, Peiró J, Moxey Det al., 2024, NekMesh: An open-source high-order mesh generation framework, Computer Physics Communications, Vol: 298, ISSN: 0010-4655

High-order spectral element simulations are now becoming increasingly popular within the computational modelling community, as they offer the potential to deliver increased accuracy at reduced cost compared to traditional low-order codes. However, to support accurate, high-fidelity simulations in complex industrial applications, there is a need to generate curvilinear meshes which robustly and accurately conform to geometrical features. This is, at present, a key challenge within the mesh generation community, with only a few open-source tools able to generate curvilinear meshes for complex geometries. We present NekMesh: an open-source mesh generation package which is designed to enable the generation of valid, high-quality curvilinear meshes of complex, three-dimensional geometries for performing high-order simulations. We outline the software architecture adopted in NekMesh, which uses a pipeline of processing modules to provide a flexible, CAD-independent high-order mesh processing tool, capable of both generating meshes for a wide range of use cases, as well as post-processing linear meshes from a range of input formats for use with high-order simulations. A number of examples in various application areas are presented, with a particular emphasis on challenging aeronautical and fluid dynamics test cases. Program summary: Program title: NekMesh (version 5.4.0) CPC Library link to program files: https://doi.org/10.17632/d82hjm4v6r.1 Licensing provisions: MIT Programming language: C++ External routines/libraries: Boost, TinyXML, OpenCASCADE, Triangle, TetGen, HDF5 Nature of problem: NekMesh is a high-order mesh generation framework with the goal of providing a robust framework to automate the process of generating valid meshes for complex three-dimensional CAD geometries. Solution method: Energy minimisation, solid body models and other techniques based around high-order finite element methods. Additional comments including restrictions and unusual features: The s

Journal article

Ntagiantas K, Pignatelli E, Peters NS, Cantwell CD, Chowdhury RA, Bharath AAet al., 2024, Estimation of fibre architecture and scar in myocardial tissue using electrograms: An in-silico study, Biomedical Signal Processing and Control, Vol: 89, ISSN: 1746-8094

Atrial Fibrillation (AF) is characterized by disorganized electrical activity in the atria and is known to be sustained by the presence of regions of fibrosis (scars) or functional cellular remodelling, both of which may lead to areas of slow conduction. Estimating the effective conductivity of the myocardium and identifying regions of abnormal propagation is therefore crucial for the effective treatment of AF. We hypothesize that the spatial distribution of tissue conductivity can be directly inferred from an array of concurrently acquired contact electrograms (EGMs). We generate a dataset of simulated cardiac AP propagation using randomized scar distributions and a phenomenological cardiac model and calculate contact EGMs at various positions on the field. EGMs are enriched with noise extracted from biological data acquired in the lab. A deep neural network, based on a modified U-net architecture, is trained to estimate the location of the scar and quantify conductivity of the tissue with a Jaccard index of 91%. We adapt a wavelet-based surrogate testing analysis to confirm that the inferred conductivity distribution is an accurate representation of the ground truth input to the model. We find that the root mean square error (RMSE) between the ground truth and our predictions is significantly smaller (pval<0.01) than the RMSE between the ground truth and surrogate samples.

Journal article

Gao AK, Cantwell CD, Son O, Sherwin SJet al., 2023, Three-dimensional transition and force characteristics of low-Reynolds-number flows past a plunging airfoil, Journal of Fluid Mechanics, Vol: 973, ISSN: 0022-1120

The three-dimensional (3-D) transition of the leading-edge vortex (LEV) and the force characteristics of the plunging airfoil are investigated in the chord-based Strouhal number Stc range of 0.10 to 1.0 by means of experimental measurements, numerical simulations and linear stability analysis in order to understand the spanwise instabilities and the effects on the force. We find that the interaction pattern of the LEV, the LEV from a previous cycle (pLEV) and the trailing-edge vortex (TEV) is the primary mechanism that affects the 3-D transition and associated force characteristics. For Stc ≤ 0.16, the 3-D transition is dominated by the LEV-TEV interaction. For 0.16 < Stc ≤ 0.44, the TEV lies in the middle of the LEV and the pLEV and therefore vortex interaction between them is relatively weak; as a result, the LEV remains two-dimensional up to a relatively high Reynolds number of Re = 4000 at Stc = 0.32. For 0.44 < Stc ≤ 0.54, and at relatively low Reynolds numbers, the pLEV and the TEV tend to form a clockwise vortex pair, which is beneficial for the high lift and stability of the LEV. For 0.49 ≤ Stc, the pLEV and TEV tend to form an anticlockwise vortex pair, which is detrimental to the lift and flow stability. In the last Stc range, vortex interaction involving the LEV, the TEV and the pLEV results in an unstable period-doubling mode which has a wavelength of about two chord-lengths and the 3-D transition enhances the lift.

Journal article

Lino M, Fotiadis S, Bharath AA, Cantwell CDet al., 2023, Current and emerging deep-learning methods for the simulation of fluid dynamics, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 479, Pages: 1-39, ISSN: 1364-5021

Over the last decade, deep learning (DL), a branch of machine learning, has experienced rapid progress. Powerful tools for tasks that have been traditionally complex to automate have been developed, such as image synthesis and natural language processing. In the context of simulating fluid dynamics, this has led to a series of novel DL methods for replacing or augmenting conventional numerical solvers. We broadly classify these methods into physics- and data-driven methods. Physics-driven methods, generally, tune a DL model to provide an analytical and differentiable solution to a given fluid dynamics problem by minimizing the residuals of the governing partial differential equations. Data-driven methods provide a fast and approximate solution to any fluid dynamics problem that shares some physical properties with the observations used when tuning the DL model’s parameters. Meanwhile, the symbiosis of numerical solvers and DL has led to promising results in turbulence modelling and accelerating iterative solvers. However, these methods present some challenges. Exclusively data-driven flow simulators often suffer from poor extrapolation, error accumulation in time-dependent simulations, as well as difficulties in training against turbulent flows. Substantial effort is, therefore, being invested into approaches that may improve the current state of the art.

Journal article

Kim MY, Nesbitt J, Koutsoftidis S, Brook J, Pitcher D, Cantwell C, Handa B, Jenkins C, Houston C, Rothery S, Jothidasan A, Perkins J, Bristow P, Linton N, Drakakis E, Peters N, Chowdhury R, Kanagaratnam P, Ng FSet al., 2023, Immunohistochemical characteristics of local sites that trigger atrial arrhythmias in response to high frequency stimulation, EP Europace, Vol: 25, Pages: 726-738, ISSN: 1099-5129

Introduction: The response to high frequency stimulation (HFS) is used to locate putative sites of ganglionated plexuses (GPs), which are implicated in triggering atrial fibrillation (AF). Objective: To identify topological and immunohistochemical characteristics of presumed GP sites functionally identified by HFS. Methods: 63 atrial sites were tested with HFS in 4 Langendorff-perfused porcine hearts. A 3.5mm tip quadripolar ablation catheter was used to stimulate and deliver HFS to the left and right atrial epicardium, within the local atrial refractory period. Tissue samples from sites triggering atrial ectopy/AF (ET) sites and non-ET sites were stained with choline acetyl transferase (ChAT) and tyrosine hydroxylase (TH), for quantification of parasympathetic and sympathetic nerves, respectively. The average cross-sectional area (CSA) of nerves was also calculated.Results: Histomorphometry of 6 ET sites (9.5%) identified by HFS evoking at least a single atrial ectopic was compared with non-ET sites. All ET sites contained ChAT-immunoreactive (ChAT-IR) and/or TH-immunoreactive nerves (TH-IR). Nerve density was greater in ET sites compared to non-ET sites (nerves/cm2: 162.3 ±110.9 vs 69.65 ±72.48; p=0.047). Overall, TH-IR nerves had larger CSA than ChAT-IR nerves (µm2: 11,196 ± 35,141 vs 2,070 ± 5,841; p<0.0001), but in ET sites, TH-IR nerves were smaller than in non-ET sites (µm2: 6,021±14,586 vs 25,254 ± 61,499; p<0.001).Conclusions: ET sites identified by HFS contained higher density of smaller nerves than non-ET sites. Majority of these nerves were within the atrial myocardium. This has important clinical implications on devising an effective therapeutic strategy for targeting autonomic triggers of AF.

Journal article

Lindblad D, Sherwin SJ, Cantwell C, Lawrence J, Proenca A, Moragues Ginard Met al., 2023, Large Eddy simulations of isolated and installed jet noise using the high-order discontinuous Galerkin method, AIAA SCITECH 2023 Forum, Publisher: American Institute of Aeronautics and Astronautics, Pages: 1-21

A recently developed computational framework for jet noise is used to compute the noise generated by an isolated and installed jet. The framework consists of two parts. In the first part, the spectral/hp element framework Nektar++ is used to compute the near-field flow. Nektar++ solves the unfiltered Navier-Stokes equations on unstructured grids using the high-order discontinuous Galerkin method. The discrete equations are integrated in time using an implicit scheme based on the matrix-free Newton-GMRES method. In the second part, the Antares library is used to compute the far-field noise. Antares solves the Ffowcs Williams - Hawkings equation for a permeable integration surface in the time domain using a source-time dominant algorithm. The simulations are validated against experimental data obtained in the Doak Laboratory Flight Jet Rig, located at the University of Southampton. For the isolated jet, good agreement is achieved, both in terms of the flow statistics and the far-field noise. The discrepancies observed for the isolated jet are believed to be caused by an under-resolved boundary layer in the simulations. For the installed jet, the flow statistics are also well predicted. In the far-field, very good agreement is achieved for downstream observers. For upstream observers, some discrepancies are observed for very high and very low frequencies.

Conference paper

Fotiadis S, Lino M, Hu S, Garasto S, Cantwell CD, Bharath AAet al., 2023, Disentangled Generative Models for Robust Prediction of System Dynamics, Pages: 10222-10248

The use of deep neural networks for modelling system dynamics is increasingly popular, but long-term prediction accuracy and out-of-distribution generalization still present challenges. In this study, we address these challenges by considering the parameters of dynamical systems as factors of variation of the data and leverage their ground-truth values to disentangle the representations learned by generative models. Our experimental results in phase-space and observation-space dynamics, demonstrate the effectiveness of latent-space supervision in producing disentangled representations, leading to improved long-term prediction accuracy and out-of-distribution robustness.

Conference paper

Li S, Agha-Jaffar D, Panagopoulos D, Ntagiantas K, Hawkins AJF, Wang L, Kanagaratnam P, Chowdhury RA, Cantwell CDet al., 2023, Comparison of a Discrete-Cell and Dontinuum Model of Two-Dimensional Ventricular Tissues Under Modulation of Cx43, ISSN: 2325-8861

Cardiac arrhythmias are growing causes of morbidity and mortality across the world. To aid the discovery of the mechanisms driving these arrhythmias, in-silico mod-els have been developed to simulate signal propagation in cardiac tissues. Continuum models such as monodomain and bidomain approaches are the most common methods to represent multicellular electrical activity. These approaches have been successfully applied on the whole heart and large-scale tissues with acceptable approximation. However, they may not be appropriate for microscopic scale simulations. It is known that cellular remodelling plays a role in arrhythmogenesis. However, in patients and laboratory models, the direct effect and importance of single factors are difficult to determine.Discrete-cell models represent action potential generation and propagation in individual cells and provide a more accurate cell-level simulation, but at much greater computational cost. In this study, 2D simulations of ventricular tissues with a range of gap junction distributions, informed by biological staining experiments, are performed. Both continuum and discrete-cell models are applied to the same tissues and their propagation patterns are compared. While the continuum model accurately captures propagation with uniform Cx43 distribution, the discrete-cell model provides better accuracy with heterogeneous distributions.

Conference paper

Coveney S, Cantwell C, Roney C, 2022, Atrial conduction velocity mapping: clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate, MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, Vol: 60, Pages: 2463-2478, ISSN: 0140-0118

Journal article

Lino M, Fotiadis S, Bharath AA, Cantwell CDet al., 2022, Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics, PHYSICS OF FLUIDS, Vol: 34, ISSN: 1070-6631

Journal article

Lino M, Cantwell C, Fotiadis S, Bharath AAet al., 2022, REMuS-GNN: A rotation-equivariant model for simulating continuum dynamics, Algebraic and Geometric Learning Workshops 2022, Publisher: ML Research Press, Pages: 226-236

Numerical simulation is an essential tool in many areas of science and engineering, but its performance often limits application in practice or when used to explore large parameter spaces. On the other hand, surrogate deep learning models, while accelerating simulations, often exhibit poor accuracy and ability to generalise. In order to improve these two factors, we introduce REMuS-GNN, a rotation-equivariant multi-scale model for simulating continuum dynamical systems encompassing a range of length scales. REMuS-GNN is designed to predict an output vector field from an input vector field on a physical domain discretised into an unstructured set of nodes. Equivariance to rotations of the domain is a desirable inductive bias that allows the network to learn the underlying physics more efficiently, leading to improved accuracy and generalisation compared with similar architectures that lack such symmetry. We demonstrate and evaluate this method on the incompressible flow around elliptical cylinders.

Conference paper

Lindblad D, Sherwin S, Cantwell C, Lawrence J, Proenca A, Moragues Ginard Met al., 2022, Aeroacoustic analysis of a subsonic jet using the discontinuous Galerkin method, 28th AIAA/CEAS Aeroacoustics 2022 Conference, Publisher: American Institute of Aeronautics and Astronautics, Pages: 1-21

In this work, the open-source spectral/hp element framework Nektar++ is coupled with the Antares library to predict noise from a subsonic jet. Nektar++ uses the high-order discontinuous Galerkin method to solve the compressible Navier-Stokes equations on unstructured grids. Unresolved turbulent scales are modeled using an implicit Large Eddy Simulation approach. In this approach, the favourable dissipation properties of the discontinuous Galerkin method are used to remove the highest resolved wavenumbers from the solution. For time-integration, an implicit, matrix-free, Newton-Krylov method is used. To compute the far-field noise, Antares solves the Ffowcs Williams - Hawkings equation for a permeable integration surface in the time-domain using a source-time dominant algorithm. The simulation results are validated against experimental data obtained in the Doak Laboratory Flight Jet Rig, located at the University of Southampton.

Conference paper

Son O, Gao A, Gursul I, Cantwell C, Wang Z, Sherwin Set al., 2022, Leading-edge vortex dynamics on plunging airfoils and wings, Journal of Fluid Mechanics, Vol: 940, Pages: 1-30, ISSN: 0022-1120

The vortex dynamics of leading-edge vortices on plunging high-aspect-ratio (AR = 10) wings and airfoils were investigated by means of volumetric velocity measurements, numerical simulations, and stability analysis in order to understand the deformation of the leading-edge vortex filament and spanwise instabilities. The vortex filaments on both the wing and airfoilexhibit spanwise waves, but with different origins. The presence of a wing tip causes the leg of the vortex to remain attached to the wing upper surface, while the initial deformation of the filament near the wing-tip resembles a helical vortex. The essential features can be modelled as the deformation of initially L-shaped semi-infinite vortex column. In contrast, the instabilityof the vortices is well captured by the instability of counter-rotating vortex pairs, which are formed either by the trailing-edge vortices or the secondary vortices rolled-up from the wing surface. The wavelengths observed in the experiments and simulations are in agreement with the stability analysis of counter-rotating vortex pairs of unequal strength.

Journal article

Lino M, Fotiadis S, Bharath AA, Cantwell Cet al., 2022, Towards fast simulation of environmental fluid mechanics with multi-scale graph neural networks, AI for Earth and Space Science, Publisher: ICLR, Pages: 1-11

Numerical simulators are essential tools in the study of naturalfluid-systems, but their performance often limits application in practice.Recent machine-learning approaches have demonstrated their ability toaccelerate spatio-temporal predictions, although, with only moderate accuracyin comparison. Here we introduce MultiScaleGNN, a novel multi-scale graphneural network model for learning to infer unsteady continuum mechanics inproblems encompassing a range of length scales and complex boundary geometries.We demonstrate this method on advection problems and incompressible fluiddynamics, both fundamental phenomena in oceanic and atmospheric processes. Ourresults show good extrapolation to new domain geometries and parameters forlong-term temporal simulations. Simulations obtained with MultiScaleGNN arebetween two and four orders of magnitude faster than those on which it wastrained.

Conference paper

Ntagiantas K, Panagopoulos D, Poon WM, Mahendra Kumar JL, Agha-Jaffar D, Peters NS, Cantwell CD, Bharat AA, Chowdhury RAet al., 2022, Electrogram-based Estimation of Myocardial Conduction Using Deep Neural Networks, ISSN: 2325-8861

Contact electrograms (EGMs) can be used to guide catheter ablation in the treatment of atrial fibrillation. However, our understanding of the link between electrophysiology (EP) and the underlying myocardial substrate is limited. We use neural networks and EGMs to estimate the amount of collagen within the field of view of the recording electrodes. EGMs were recorded from rat ventricular slices (n=15), Samples were imaged using second harmonic generation (SHG) microscopy, allowing for quantification of collagen. A convolutional neural network (1D-ResNet) was trained to estimate collagen distribution from the recorded EGMs. Each electrogram, recorded for one cycle length, was paired with a collagen index for the corresponding electrode. The total number of samples was 91,239. We successfully estimated collagen index in the testing set, with an absolute error of 0.022± 0.024, and a correlation coefficient of R=0.81 between the predicted and true collagen amount. The network identifies main morphological features of the EGMs as useful features for quantifying collagen underneath the electrode. This work provides a framework and proof of concept that location of scar can be predicted from EGMS using neural networks.

Conference paper

Letchumy MJ, Brook J, Ntagiantas K, Panagopoulos D, Agha-Jaffar D, Peters NS, Qureshi N, Chowdhury RA, Cantwell CDet al., 2022, The Effects of Electrode Configuration on Omnipolar Electrograms: An In-Silico Approach, ISSN: 2325-8861

Atrial Fibrillation (AF) is the most common cardiac ar-rhythmia, involving pathological triggers and substrate in the atria. In the clinical catheter laboratory, contact electrograms are an essential tool to characterise AF. Omnipolar electrograms (OE), derived from three or more neighbouring electrodes, are thought to be superior compared to traditional unipolar and bipolar electrograms by eliminating far-field effects and correcting for wavefront incidence angle. We sought to understand the changes in OE morphology under different electrode configurations using 2D simulations of healthy tissue and scarred tissue. Virtual unipolar electrograms (UE) were generated from single electrodes which were used to predict the local electric field and subsequently calculate OEs in cliques of 3, 4, and 6 electrodes at different inter-electrode spacings. Five features were identified on each OE to measure changes in OE morphology under different clique configurations. Additionally, the morphology of the OE signals in the presence of fibrosis was examined. OE signals obtained from scarred tissue are more fractionated compared to healthy tissue. The most appropriate inter-electrode distance for interpreting the OE signals was found to be 2-3mm, using either three or four electrodes.

Conference paper

Liu B, Cantwell CD, Moxey D, Green M, Sherwin SJet al., 2022, VECTORISED SPECTRAL/HP ELEMENT MATRIX-FREE OPERATOR FOR ANISOTROPIC HEAT TRANSPORT IN TOKAMAK EDGE PLASMA

A highly efficient matrix-free Helmholtz operator with single-instruction multipledata (SIMD) vectorisation is implemented in Nektar++ [1] and applied to the simulation of anisotropic heat transport in tokamak edge plasma. A tokamak is currently the leading candidate for a practical fusion reactor using the magnetic confinement approach to produce electricity through controlled thermonuclear fusion. Predicting the transport of heat in magnetized plasma is important to designing a safe tokamak design. Due to the ionized nature of plasma, the heat conduction of the magnetized plasma is highly anisotropic along the magnetic field lines. In this study, a variational form is proposed to simulate the anisotropic heat transport in magnetized plasma and the details of its mathematical derivation and implementation are presented. To accurately approximate the thermal load of plasma deposition on the wall of tokamak chamber, highly scalable and efficient algorithms are crucial. To achieve this, a matrix-free Helmholtz operator is implemented in the Nektar++ framework, utilising sum-factorisation to reduce the operation count and increase arithmetic intensity, and leveraging SIMD vectorisation to accelerate the computation on modern hardware. The performance of the implementation is assessed by measuring throughput and speed-up of the operators using deformed and regular quadrilateral and triangular elements.

Conference paper

Gao AK, Sherwin SJ, Cantwell CD, 2022, THREE-DIMENSIONAL TRANSITION OF A LOW REYNOLDS NUMBER FLOW PAST A PLUNGING NACA 0012 AIRFOIL AT POST-STALL ANGLE OF ATTACK

The two-dimensional to three-dimensional transition of a flow past a plunging NACA 0012 airfoil at a Reynolds number of Re = 400, based on the chord length c, and an angle of attack of 15 degrees was investigated using global linear stability analysis and spanwise-homogeneous direct numerical simulation (DNS). The peak-to-peak plunging amplitude was fixed at A/c = 0.5 and the Strouhal number was varied from Stc = 0.10 to Stc = 1.00. This parameter regime encompasses flow phenomena of leading-edge vortex (LEV) dominated flow (0.10 ≤ Stc ≤ 0.19), almost vanishing LEV-trailing-edge vortex (TEV) interaction (0.22 ≤ Stc < 0.5), strong previous cycle LEV-TEV interaction (0.49 ≤ Stc ≤ 0.95) and aperiodic flow (Stc ≥ 0.99). For the periodic baseflow, Floquet stability analysis was conducted. Below a Strouhal number of 0.5, the Floquet multiplier is smaller than the static airfoil which indicates the plunging motion stabilises the two-dimensional baseflow. For higher frequencies, a period-doubling mode appears, which has a peak Floquet multiplier around a spanwise wavelength of 2c. This unstable mode also dominates in three-dimensional direct numerical simulations (DNS). Finally, a short-wave mode becomes unstable at Stc = 0.95, which generates more small-scale vorticies in the DNS result.

Conference paper

Hossain MZ, Cantwell CD, Sherwin SJ, 2021, A spectral/hp element method for thermal convection, International Journal for Numerical Methods in Fluids, Vol: 93, Pages: 2380-2395, ISSN: 0271-2091

We report on a high‐fidelity, spectral/hp element algorithm developed for the direct numerical simulation of thermal convection problems. We consider the incompressible Navier–Stokes (NS) and advection–diffusion equations coupled through a thermal body‐forcing term. The flow is driven by a prescribed flowrate forcing with explicit treatment of the nonlinear advection terms. The explicit treatment of the body‐force term also decouples both the NS and the advection–diffusion equations. The problem is then temporally discretized using an implicit–explicit scheme in conjunction with a velocity‐correction splitting scheme to decouple the velocity and pressure fields in the momentum equation. Although not unique, this type of discretization has not been widely applied to thermal convection problems and we therefore provide a comprehensive overview of the algorithm and implementation which is available through the open‐source package Nektar++. After verifying the algorithm on a number of illustrative problems we then apply the code to investigate flow in a channel with uniform or streamwise sinusoidal lower wall, in addition to a patterned sinusoidal heating. We verify the solver against previously published two‐dimensional results. Finally, for the first time we consider a three‐dimensional problem with a streamwise sinusoidal lower wall and sinusoidal heating which, for the chosen parameter, leads to the unusual dynamics of an initially unsteady two‐dimensional instability leading to a steady three‐dimensional nonlinear saturated state.

Journal article

Benacchio T, Bonaventura L, Altenbernd M, Cantwell CD, Duben PD, Gillard M, Giraud L, Goeddeke D, Raffin E, Teranishi K, Wedi Net al., 2021, Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction, International Journal of High Performance Computing Applications, Vol: 35, Pages: 285-311, ISSN: 1094-3420

Progress in numerical weather and climate prediction accuracy greatly depends on the growth of the available computing power. As the number of cores in top computing facilities pushes into the millions, increased average frequency of hardware and software failures forces users to review their algorithms and systems in order to protect simulations from breakdown. This report surveys hardware, application-level and algorithm-level resilience approaches of particular relevance to time-critical numerical weather and climate prediction systems. A selection of applicable existing strategies is analysed, featuring interpolation-restart and compressed checkpointing for the numerical schemes, in-memory checkpointing, user-level failure mitigation and backup-based methods for the systems. Numerical examples showcase the performance of the techniques in addressing faults, with particular emphasis on iterative solvers for linear systems, a staple of atmospheric fluid flow solvers. The potential impact of these strategies is discussed in relation to current development of numerical weather prediction algorithms and systems towards the exascale. Trade-offs between performance, efficiency and effectiveness of resiliency strategies are analysed and some recommendations outlined for future developments.

Journal article

Lino M, Cantwell C, Fotiadis S, Pignatelli E, Bharath Aet al., 2020, Simulating surface wave dynamics with convolutional networks, Publisher: arXiv

We investigate the performance of fully convolutional networks to simulatethe motion and interaction of surface waves in open and closed complexgeometries. We focus on a U-Net architecture and analyse how well itgeneralises to geometric configurations not seen during training. Wedemonstrate that a modified U-Net architecture is capable of accuratelypredicting the height distribution of waves on a liquid surface within curvedand multi-faceted open and closed geometries, when only simple box andright-angled corner geometries were seen during training. We also consider aseparate and independent 3D CNN for performing time-interpolation on thepredictions produced by our U-Net. This allows generating simulations with asmaller time-step size than the one the U-Net has been trained for.

Working paper

Ali R, Qureshi N, Liverani S, Roney C, Kim S, Lim P, Tweedy J, Cantwell C, Peters Net al., 2020, Left atrial enhancement correlates with myocardial conduction velocity in patients with persistent atrial fibrillation, Frontiers in Physiology, Vol: 11, ISSN: 1664-042X

Background: Conduction velocity (CV) heterogeneity and myocardial fibrosis both promote re-entry, but the relationship between fibrosis as determined by left atrial (LA) late-gadolinium enhanced cardiac magnetic resonance imaging (LGE-CMRI) and CV remains uncertain.Objective: Although average CV has been shown to correlate with regional LGE-CMRI in patients with persistent AF, we test the hypothesis that a localized relationship exists to underpin LGE-CMRI as a minimally invasive tool to map myocardial conduction properties for risk stratification and treatment guidance.Method: 3D LA electroanatomic maps during LA pacing were acquired from eight patients with persistent AF following electrical cardioversion. Local CVs were computed using triads of concurrently acquired electrograms and were co-registered to allow correlation with LA wall intensities obtained from LGE-CMRI, quantified using normalized intensity (NI) and image intensity ratio (IIR). Association was evaluated using multilevel linear regression.Results: An association between CV and LGE-CMRI intensity was observed at scales comparable to the size of a mapping electrode: −0.11 m/s per unit increase in NI (P < 0.001) and −0.96 m/s per unit increase in IIR (P < 0.001). The magnitude of this change decreased with larger measurement area. Reproducibility of the association was observed with NI, but not with IIR.Conclusion: At clinically relevant spatial scales, comparable to area of a mapping catheter electrode, LGE-CMRI correlates with CV. Measurement scale is important in accurately quantifying the association of CV and LGE-CMRI intensity. Importantly, NI, but not IIR, accounts for changes in the dynamic range of CMRI and enables quantitative reproducibility of the association.

Journal article

Kim M-Y, Sandler B, Sikkel MB, Cantwell CD, Leong KM, Luther V, Malcolme-Lawes L, Koa-Wing M, Ng FS, Qureshi N, Sohaib A, Whinnett ZI, Fudge M, Lim E, Todd M, Wright I, Peters NS, Lim PB, Linton NWF, Kanagaratnam Pet al., 2020, The ectopy-triggering ganglionated plexuses in atrial fibrillation, Autonomic Neuroscience, Vol: 228, ISSN: 1566-0702

BackgroundEpicardial ganglionated plexus (GP) have an important role in the pathogenesis of atrial fibrillation (AF). The relationship between anatomical, histological and functional effects of GP is not well known. We previously described atrioventricular (AV) dissociating GP (AVD-GP) locations. In this study, we hypothesised that “ET-GP” are upstream triggers of atrial ectopy/AF and have different anatomical distribution to AVD-GP.ObjectivesWe mapped and characterised ET-GP to understand their neural mechanism in AF and anatomical distribution in the left atrium (LA).Methods26 patients with paroxysmal AF were recruited. All were paced in the LA with an ablation catheter. HFS (80 ms) was synchronised to each paced stimulus (after 20 ms delay) for delivery within the local atrial refractory period. HFS responses were tagged onto CARTO™ 3D LA geometry. All geometries were transformed onto one reference LA shell. A probability distribution atlas of ET-GP was created. This identified high/low ET-GP probability regions.Results2302 sites were tested with HFS, identifying 579 (25%) ET-GP. 464 ET-GP were characterised, where 74 (16%) triggered ≥30s AF/AT. Median 97 (IQR 55) sites were tested, identifying 19 (20%) ET-GP per patient. >30% of ET-GP were in the roof, mid-anterior wall, around all PV ostia except in the right inferior PV (RIPV) in the posterior wall.ConclusionET-GP can be identified by endocardial stimulation and their anatomical distribution, in contrast to AVD-GP, would be more likely to be affected by wide antral circumferential ablation. This may contribute to AF ablation outcomes.

Journal article

Brook J, Kim M-Y, Koutsoftidis S, Pitcher D, Agha-Jaffar D, Sufi A, Jenkins C, Tzortzis K, Ma S, Jabbour R, Houston C, Handa B, Li X, Chow J-J, Jothidasan A, Bristow P, Perkins J, Harding S, Bharath A, Ng FS, Peters N, Cantwell C, Chowdhury Ret al., 2020, Development of a pro-arrhythmic ex vivo intact human and porcine model: cardiac electrophysiological changes associated with cellular uncoupling, Pflügers Archiv European Journal of Physiology, Vol: 472, Pages: 1435-1446, ISSN: 0031-6768

We describe a human and large animal Langendorff experimental apparatus for live electrophysiological studies and measure the electrophysiological changes due to gap-junction uncoupling in human and porcine hearts. The resultant ex vivo intact human and porcine model can bridge the translational gap between smaller simple laboratory models and clinical research. In particular, electrophysiological models would benefit from the greater myocardial mass of a large heart due to its effects on far field signal, electrode contact issues and motion artefacts, consequently more closely mimicking the clinical setting Porcine (n=9) and human (n=4) donor hearts were perfused on a custom-designed Langendorff apparatus. Epicardial electrograms were collected at 16 sites across the left atrium and left ventricle. 1mM of carbenoxolone was administered at 5ml/min to induce cellular uncoupling, and then recordings were repeated at the same sites. Changes in electrogram characteristics were analysed.We demonstrate the viability of a controlled ex vivo model of intact porcine and human hearts for electrophysiology with pharmacological modulation. Carbenoxolone reduces cellular coupling and changes contact electrogram features. The time from stimulus artefact to (-dV/dt)max increased between baseline and carbenoxolone (47.9±4.1ms to 67.2±2.7ms) indicating conduction slowing. The features with the largest percentage change between baseline to Carbenoxolone were Fractionation +185.3%, Endpoint amplitude -106.9%, S-Endpoint Gradient +54.9%, S Point, -39.4%, RS Ratio +38.6% and (-dV/dt)max -20.9%.The physiological relevance of this methodological tool is that it provides a model to further investigate pharmacologically-induced proarrhythmic substrates.

Journal article

Kim M-Y, Sandler B, Sikkel MB, Cantwell CD, Leong KM, Luther V, Malcolme-Lawes L, Koa-Wing M, Ng FS, Qureshi N, Sohaib A, Whinnett ZI, Fudge M, Lim E, Todd M, Wright I, Peters NS, Lim PB, Linton NWF, Kanagaratnam Pet al., 2020, The anatomical distribution of the ectopy-triggering ganglionated plexus in patients with atrial fibrillation, Circulation: Arrhythmia and Electrophysiology, Vol: 13, Pages: 1045-1047, ISSN: 1941-3084

Journal article

Lei CL, Ghosh S, Whittaker DG, Aboelkassem Y, Beattie KA, Cantwell CD, Delhaas T, Houston C, Novaes GM, Panfilov AV, Pathmanathan P, Riabiz M, Dos Santos RW, Walmsley J, Worden K, Mirams GR, Wilkinson RDet al., 2020, Considering discrepancy when calibrating a mechanistic electrophysiology model., Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 378, Pages: 1-23, ISSN: 1364-503X

Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterize uncertainty in model inputs and how that propagates through to outputs or predictions; examples of this can be seen in the papers of this issue. In this review and perspective piece, we draw attention to an important and under-addressed source of uncertainty in our predictions-that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here, we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes and autoregressive-moving-average models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

Journal article

Niederer SA, Aboelkassem Y, Cantwell CD, Corrado C, Coveney S, Cherry EM, Delhaas T, Fenton FH, Panfilov AV, Pathmanathan P, Plank G, Riabiz M, Roney CH, Dos Santos RW, Wang Let al., 2020, Creation and application of virtual patient cohorts of heart models., Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 378, Pages: 1-20, ISSN: 1364-503X

Patient-specific cardiac models are now being used to guide therapies. The increased use of patient-specific cardiac simulations in clinical care will give rise to the development of virtual cohorts of cardiac models. These cohorts will allow cardiac simulations to capture and quantify inter-patient variability. However, the development of virtual cohorts of cardiac models will require the transformation of cardiac modelling from small numbers of bespoke models to robust and rapid workflows that can create large numbers of models. In this review, we describe the state of the art in virtual cohorts of cardiac models, the process of creating virtual cohorts of cardiac models, and how to generate the individual cohort member models, followed by a discussion of the potential and future applications of virtual cohorts of cardiac models. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

Journal article

Clayton RH, Aboelkassem Y, Cantwell CD, Corrado C, Delhaas T, Huberts W, Lei CL, Ni H, Panfilov AV, Roney C, Dos Santos RWet al., 2020, An audit of uncertainty in multi-scale cardiac electrophysiology models., Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 378, Pages: 1-21, ISSN: 1364-503X

Models of electrical activation and recovery in cardiac cells and tissue have become valuable research tools, and are beginning to be used in safety-critical applications including guidance for clinical procedures and for drug safety assessment. As a consequence, there is an urgent need for a more detailed and quantitative understanding of the ways that uncertainty and variability influence model predictions. In this paper, we review the sources of uncertainty in these models at different spatial scales, discuss how uncertainties are communicated across scales, and begin to assess their relative importance. We conclude by highlighting important challenges that continue to face the cardiac modelling community, identifying open questions, and making recommendations for future studies. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

Journal article

Houston C, Marchand B, Engelbert L, Cantwell CDet al., 2020, Reducing complexity and unidentifiability when modelling human atrial cells, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 378, Pages: 1-17, ISSN: 1364-503X

Mathematical models of a cellular action potential in cardiac modelling have become increasingly complex, particularly in gating kinetics which control the opening and closing of individual ion channel currents. As cardiac models advance towards use in personalised medicine to inform clinical decision-making, it is critical to understand the uncertainty hidden in parameter estimates from their calibration to experimental data. This study applies approximate Bayesian computation to re-calibrate the gating kinetics of four ion channels in two existing human atrial cell models to their original datasets, providing a measure of uncertainty and indication of potential issues with selecting a single unique value given the available experimental data. Two approaches are investigated to reduce the uncertainty present: re-calibrating the models to a more complete dataset and using a less complex formulation with fewer parameters to constrain. The re-calibrated models are inserted back into the full cell model to study the overall effect on the action potential. The use of more complete datasets does not eliminate uncertainty present in parameter estimates. The less complex model, particularly for the fast sodium current, gave a better fit to experimental data alongside lower parameter uncertainty and improved computational speed.

Journal article

Fotiadis S, Pignatelli E, Valencia ML, Cantwell C, Storkey A, Bharath AAet al., 2020, Comparing recurrent and convolutional neural networks for predicting wave propagation, Publisher: arXiv

Dynamical systems can be modelled by partial differential equations andnumerical computations are used everywhere in science and engineering. In thiswork, we investigate the performance of recurrent and convolutional deep neuralnetwork architectures to predict the surface waves. The system is governed bythe Saint-Venant equations. We improve on the long-term prediction overprevious methods while keeping the inference time at a fraction of numericalsimulations. We also show that convolutional networks perform at least as wellas recurrent networks in this task. Finally, we assess the generalisationcapability of each network by extrapolating in longer time-frames and indifferent physical settings.

Working paper

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: http://wlsprd.imperial.ac.uk:80/respub/WEB-INF/jsp/search-html.jsp Request URI: /respub/WEB-INF/jsp/search-html.jsp Query String: respub-action=search.html&id=00633253&limit=30&person=true