Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots
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Computational fluid dynamics is a common tool in cardiovascular science and engineering to simulate, predict and study hemodynamics in arteries. However, owing to the complexity and scale of cardiovascular flow problems, the evaluation of the model could be computationally expensive, especially in those cases where a large number of evaluations are required, such as uncertainty quantification and design optimisation. In such scenarios, the model may have to be repeatedly evaluated due to the changes or distinctions of simulation domains. In this work, a data-driven surrogate model is proposed for the efficient prediction of blood flow simulations on similar but distinct domains. The proposed surrogate model leverages surface registration to parameterise those similar but distinct shapes and formulate corresponding hemodynamics information into geometry-informed snapshots by the diffeomorphism constructed between the reference domain and target domain. A non-intrusive reduced-order model for geometrical parameters is subsequently constructed using proper orthogonal decomposition, and a radial basis function interpolator is trained for predicting the reduced coefficients of the reduced-order model based on reduced coefficients of geometrical parameters of the shape. Two examples of blood flowing through a stenosis and a bifurcation are presented and analysed. The proposed surrogate model demonstrates its accuracy and efficiency in hemodynamics prediction and shows its potential application toward real-time simulation or uncertainty quantification for complex patient-specific scenarios.
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