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J Biomech Eng. 2017 Dec 1;139(12). doi: 10.1115/1.4037792.

Methodology for Computational Fluid Dynamic Validation for Medical Use: Application to Intracranial Aneurysm.

Author information

1
Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY 14260.
2
Toshiba Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY 14203.
3
Department of Biomedical Engineering, University at Buffalo, Buffalo, NY 14260.
4
Toshiba Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY 14260.
5
Department of Neurosurgery, University at Buffalo, Buffalo, NY 14226.
6
Department of Mechanical and Aerospace Engineering, University at Buffalo, 324 Jarvis Hall, Buffalo, NY 14260.
7
Department of Neurosurgery, University at Buffalo, Buffalo, NY 14226 e-mail: .

Abstract

Computational fluid dynamics (CFD) is a promising tool to aid in clinical diagnoses of cardiovascular diseases. However, it uses assumptions that simplify the complexities of the real cardiovascular flow. Due to high-stakes in the clinical setting, it is critical to calculate the effect of these assumptions in the CFD simulation results. However, existing CFD validation approaches do not quantify error in the simulation results due to the CFD solver's modeling assumptions. Instead, they directly compare CFD simulation results against validation data. Thus, to quantify the accuracy of a CFD solver, we developed a validation methodology that calculates the CFD model error (arising from modeling assumptions). Our methodology identifies independent error sources in CFD and validation experiments, and calculates the model error by parsing out other sources of error inherent in simulation and experiments. To demonstrate the method, we simulated the flow field of a patient-specific intracranial aneurysm (IA) in the commercial CFD software star-ccm+. Particle image velocimetry (PIV) provided validation datasets for the flow field on two orthogonal planes. The average model error in the star-ccm+ solver was 5.63 ± 5.49% along the intersecting validation line of the orthogonal planes. Furthermore, we demonstrated that our validation method is superior to existing validation approaches by applying three representative existing validation techniques to our CFD and experimental dataset, and comparing the validation results. Our validation methodology offers a streamlined workflow to extract the "true" accuracy of a CFD solver.

PMID:
28857116
PMCID:
PMC5686786
DOI:
10.1115/1.4037792
[Indexed for MEDLINE]
Free PMC Article

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