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Physically consistent data assimilation method based on feedback control for patient‐specific blood flow analysis
Author(s) -
Ii Satoshi,
Adib Mohd Azrul Hisham Mohd,
Watanabe Yoshiyuki,
Wada Shigeo
Publication year - 2018
Publication title -
international journal for numerical methods in biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.741
H-Index - 63
eISSN - 2040-7947
pISSN - 2040-7939
DOI - 10.1002/cnm.2910
Subject(s) - robustness (evolution) , data assimilation , control theory (sociology) , scalar (mathematics) , scalar field , poisson's equation , poisson distribution , mathematics , computer science , mathematical optimization , mathematical analysis , statistics , physics , artificial intelligence , geometry , control (management) , biochemistry , chemistry , meteorology , mathematical physics , gene
This paper presents a novel data assimilation method for patient‐specific blood flow analysis based on feedback control theory called the physically consistent feedback control‐based data assimilation (PFC‐DA) method. In the PFC‐DA method, the signal, which is the residual error term of the velocity when comparing the numerical and reference measurement data, is cast as a source term in a Poisson equation for the scalar potential field that induces flow in a closed system. The pressure values at the inlet and outlet boundaries are recursively calculated by this scalar potential field. Hence, the flow field is physically consistent because it is driven by the calculated inlet and outlet pressures, without any artificial body forces. As compared with existing variational approaches, although this PFC‐DA method does not guarantee the optimal solution, only one additional Poisson equation for the scalar potential field is required, providing a remarkable improvement for such a small additional computational cost at every iteration. Through numerical examples for 2D and 3D exact flow fields, with both noise‐free and noisy reference data as well as a blood flow analysis on a cerebral aneurysm using actual patient data, the robustness and accuracy of this approach is shown. Moreover, the feasibility of a patient‐specific practical blood flow analysis is demonstrated.