An algorithm for coupling multibranch in vitro experiment to numerical physiology simulation for a hybrid cardiovascular model
An algorithm for coupling multibranch in vitro experiment to numerical physiology simulation for a hybrid cardiovascular model
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DOI：
10.1002/cnm.3289
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发表时间：
2019-12-09
影响因子：
2.1
通讯作者：
Kung, Ethan
中科院分区：
文献类型：
--
作者：
Mirzaei, Ehsan;Farahmand, Masoud;Kung, Ethan
混合心血管建模方法将体外实验与计算集总参数模拟相结合，实现了在闭环生理学背景下对医疗器械进行直接物理测试。体外和计算域之间的接口对于正确捕获两者的动态相互作用是必不可少的。为此，我们开发了一种迭代算法，能够将包含多个分支的体外实验耦合到集总参数生理学模拟。该算法使用迭代的布洛登方法确定实验中每个分支的唯一流波形解。为了进行算法测试，我们首先使用数学代物来表示体外实验，并演示了体外代物与Fontan患者的计算生理耦合的五种场景。这种测试方法可以验证耦合结果的准确性，因为数学代理可以直接集成到计算模拟中，以获得耦合系统的“真正解”。我们的算法成功识别了所有测试场景下的解流波形，结果与真实解匹配，精度高。在所有的测试用例中，实现期望的收敛标准的迭代次数少于130次。为了模拟实际的体外实验，其中噪声污染了测量，我们通过添加随机噪声来干扰代理模型。在所有情况下，耦合算法可达到的收敛容差都低于附加噪声的大小。最后，我们使用该算法将物理实验与计算生理学模型相结合，以证明其在现实世界中的适用性。
The hybrid cardiovascular modeling approach integrates an in vitro experiment with a computational lumped-parameter simulation, enabling direct physical testing of medical devices in the context of closed-loop physiology. The interface between the in vitro and computational domains is essential for properly capturing the dynamic interactions of the two. To this end, we developed an iterative algorithm capable of coupling an in vitro experiment containing multiple branches to a lumped-parameter physiology simulation. This algorithm identifies the unique flow waveform solution for each branch of the experiment using an iterative Broyden's approach. For the purpose of algorithm testing, we first used mathematical surrogates to represent the in vitro experiments and demonstrated five scenarios where the in vitro surrogates are coupled to the computational physiology of a Fontan patient. This testing approach allows validation of the coupling result accuracy as the mathematical surrogates can be directly integrated into the computational simulation to obtain the "true solution" of the coupled system. Our algorithm successfully identified the solution flow waveforms in all test scenarios with results matching the true solutions with high accuracy. In all test cases, the number of iterations to achieve the desired convergence criteria was less than 130. To emulate realistic in vitro experiments in which noise contaminates the measurements, we perturbed the surrogate models by adding random noise. The convergence tolerance achievable with the coupling algorithm remained below the magnitudes of the added noise in all cases. Finally, we used this algorithm to couple a physical experiment to the computational physiology model to demonstrate its real-world applicability.