Chatpattanasiri, Chotirawee;
Franzetti, Gaia;
Bonfanti, Mirko;
Diaz-Zuccarini, Vanessa;
Balabani, Stavroula;
(2023)
Towards Reduced Order Models via Robust Proper Orthogonal Decomposition to capture personalised aortic haemodynamics.
Journal of Biomechanics
, Article 111759. 10.1016/j.jbiomech.2023.111759.
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Abstract
Data driven, reduced order modelling has shown promise in tackling the challenges associated with computational and experimental hemodynamic models. In this work, we focus on the use of Reduced Order Models (ROMs) to reconstruct velocity fields in a patient-specific dissected aorta, with the objective being to compare the ROMs obtained from Robust Proper Orthogonal Decomposition (RPOD) to those obtained from the traditional Proper Orthogonal Decomposition (POD). POD and RPOD are applied to in vitro, hemodynamic data acquired by Particle Image Velocimetry and compare the decomposed flows to those derived from Computational Fluid Dynamics (CFD) data for the same geometry and flow conditions. In this work, PIV and CFD results act as surrogates for clinical haemodynamic data eg. MR, helping to demonstrate the potential use of ROMS in real clinical scenarios. The flow is reconstructed using different numbers of POD modes and the flow features obtained throughout the cardiac cycle are compared to the original Full Order Models (FOMs). Robust Principal Component Analysis (RPCA), the first step of RPOD, has been found to enhance the quality of PIV data, allowing POD to capture most of the kinetic energy of the flow in just two modes similar to the numerical data that are free from measurement noise. The reconstruction errors differ along the cardiac cycle with diastolic flows requiring more modes for accurate reconstruction. In general, modes 1-10 are found sufficient to represent the flow field. The results demonstrate that the coherent structures that characterise this aortic dissection flow are described by the first few POD modes suggesting that it is possible to represent the macroscale behaviour of aortic flow in a low-dimensional space; thus significantly simplifying the problem, and allowing for more computationally efficient flow simulations or machine learning based flow predictions that can pave the way for translation of such models to the clinic.
| Type: | Article |
|---|---|
| Title: | Towards Reduced Order Models via Robust Proper Orthogonal Decomposition to capture personalised aortic haemodynamics |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.jbiomech.2023.111759 |
| Publisher version: | https://doi.org/10.1016/j.jbiomech.2023.111759 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Aortic Haemodynamics, Particle Image Velocimetry, Computational Fluid Dynamics, Reduced Order Model, Proper Orthogonal Decomposition, Robust Principle Component Analysis, Patient-specific modelling |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10175712 |
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