Blood is a multiphase fluid, constituted of a plasma phase and a red blood cell (RBC) phase. Predicting the distribution of the RBC phase has applications in terms of medical device design, and for the characterisation of the risk of thrombus formation where atherosclerosis is present on coronary arteries. Computational fluid dynamics (CFD) can be used to simulate the multiphase flow of blood, but is time-consuming and requires a high level of technical expertise. This study evaluates the use of artificial neural networks (ANNs), as an alternative to CFD, to predict RBC distribution as part of blood flow through a coronary artery bifurcation model, both including and excluding stenosis. ANNs were trained on a dataset of 80 simulations generated using steady-state multiphase CFD. The initial data-driven ANNs encountered issues with overfitting and high errors in velocity component predictions. A physics-informed neural network (PINN) was employed, using a reduced order model (ROM), to enhance velocity component predictions, achieving average percentage error (APE) within 8.5% of CFD. These improved predictions were integrated into a hybrid model combining the PINN and the data-driven ANN to predict RBC distribution more effectively. The hybrid model achieved APEs ranging from 0.04% to 0.05%. Moreover, the hybrid model’s predictions were 14 times faster than CFD transient runs, demonstrating potential for translation into clinical use. In conclusion, a combined ROM-PINN and data-driven approach enables fast high-accuracy predictions of flow for multiphase fluids such as blood when compared to CFD.
Sharma et al. (Thu,) studied this question.