Chan Wei Xuan, Ding Wenhao, Li Binghuan, Wong Hong Shen, Yap Choon Hwai
Department of Bioengineering, Imperial College London, Exhibition Road, London, SW7 2AZ, United Kingdom.
Department of Chemical Engineering, Imperial College London, Exhibition Road, London, SW7 2AZ, United Kingdom.
Comput Biol Med. 2025 May;190:110074. doi: 10.1016/j.compbiomed.2025.110074. Epub 2025 Mar 26.
Numerical simulations of fluid dynamics in tube-like structures are important to biomedical research to model flow in blood vessels and airways. It is further useful to some clinical applications, such as predicting arterial fractional flow reserves, and assessing vascular flow wall shear stresses to predict atherosclerosis disease progression. Traditionally, they are conducted via computational fluid dynamics (CFD) simulations, which, despite optimization, still take substantial time, limiting clinical adoption. To improve efficiency, we investigate the use of the multi-case Neural Network (NN) to enable real-time predictions of fluid dynamics (both steady and pulsatile flows) in a 3D curved tube (with a narrowing in the middle mimicking a stenosis) of any shape within a geometric range, using only geometric parameters and boundary conditions. We compare the unsupervised approach guided by physics governing equations (physics informed neural network or PINN) to the supervised approach of using mass CFD simulations to train the network (supervised network or SN). We find that multi-case PINN can generate accurate velocity, pressure and wall shear stress (WSS) results under steady flow (spatially maximum error < 2-5 %), but this requires a specific enhancement strategies: (1) estimating the curvilinear coordinate parameters via a secondary NN to use as inputs into PINN, (2) imposing no-slip wall boundary condition as a hard constraint, and (3) advanced strategy to better spatially propagate effects of boundary conditions. However, we cannot achieve reasonable accuracy for pulsatile flow with PINN. Conversely, SN provides very accurate velocity, pressure, and WSS predictions under both steady and pulsatile flow scenarios (spatially and/or temporally maximum error averaged over all geometries <1 %), and is much less computationally expensive to train. To achieve this, strategies (1) and (2) above and a spectral encoding strategy for pulsatile flow are necessary. Thus, interestingly, the use of physics constraints is not effective in our application.
管状结构中流体动力学的数值模拟对生物医学研究非常重要，可用于模拟血管和气道中的血流。它对一些临床应用也很有用，比如预测动脉血流储备分数，以及评估血管壁切应力以预测动脉粥样硬化疾病的进展。传统上，这些模拟是通过计算流体动力学（CFD）进行的，尽管经过了优化，但仍然需要大量时间，限制了其在临床上的应用。为了提高效率，我们研究了使用多案例神经网络（NN）来实时预测几何范围内任意形状的三维弯曲管（中间有狭窄模拟狭窄）中的流体动力学（稳定流和脉动流），仅使用几何参数和边界条件。我们将由物理控制方程引导的无监督方法（物理信息神经网络或PINN）与使用大量CFD模拟训练网络的监督方法（监督网络或SN）进行了比较。我们发现，多案例PINN在稳定流条件下可以生成准确的速度、压力和壁面切应力（WSS）结果（空间最大误差<2-5%），但这需要特定的增强策略：（1）通过辅助神经网络估计曲线坐标参数作为PINN的输入；（2）将无滑移壁边界条件作为硬约束施加；（3）采用先进策略以更好地在空间上传播边界条件的影响。然而，对于脉动流，我们无法通过PINN获得合理的精度。相反，SN在稳定流和脉动流情况下都能提供非常准确的速度、压力和WSS预测（所有几何形状在空间和/或时间上的最大误差平均<1%），并且训练的计算成本要低得多。为了实现这一点，上述策略（1）和（2）以及脉动流的频谱编码策略是必要的。因此，有趣的是，在我们的应用中，物理约束的使用并不有效。
