Korean Circ J.  2011 Aug;41(8):423-430. 10.4070/kcj.2011.41.8.423.

Computational Fluid Dynamics in Cardiovascular Disease

• Lee BK

• KMID: 1826347
• DOI: http://doi.org/10.4070/kcj.2011.41.8.423

Abstract

Computational fluid dynamics (CFD) is a mechanical engineering field for analyzing fluid flow, heat transfer, and associated phenomena, using computer-based simulation. CFD is a widely adopted methodology for solving complex problems in many modern engineering fields. The merit of CFD is developing new and improved devices and system designs, and optimization is conducted on existing equipment through computational simulations, resulting in enhanced efficiency and lower operating costs. However, in the biomedical field, CFD is still emerging. The main reason why CFD in the biomedical field has lagged behind is the tremendous complexity of human body fluid behavior. Recently, CFD biomedical research is more accessible, because high performance hardware and software are easily available with advances in computer science. All CFD processes contain three main components to provide useful information, such as pre-processing, solving mathematical equations, and post-processing. Initial accurate geometric modeling and boundary conditions are essential to achieve adequate results. Medical imaging, such as ultrasound imaging, computed tomography, and magnetic resonance imaging can be used for modeling, and Doppler ultrasound, pressure wire, and non-invasive pressure measurements are used for flow velocity and pressure as a boundary condition. Many simulations and clinical results have been used to study congenital heart disease, heart failure, ventricle function, aortic disease, and carotid and intra-cranial cerebrovascular diseases. With decreasing hardware costs and rapid computing times, researchers and medical scientists may increasingly use this reliable CFD tool to deliver accurate results. A realistic, multidisciplinary approach is essential to accomplish these tasks. Indefinite collaborations between mechanical engineers and clinical and medical scientists are essential. CFD may be an important methodology to understand the pathophysiology of the development and progression of disease and for establishing and creating treatment modalities in the cardiovascular field.

Keyword

Hydrodynamics; Viscosity; Cardiovascular diseases

MeSH Terms

Aortic Diseases
Cardiovascular Diseases
Cooperative Behavior
Diagnostic Imaging
Heart Diseases
Heart Failure
Hot Temperature
Human Body
Hydrodynamics
Magnetic Resonance Imaging
Viscosity

Figure

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  Fig. 1 Viscosity of Newtonian and non-Newtonian fluids according to shear rate.

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  Fig. 2 Pressure and velocity of the coronary artery and aorta as a boundary condition in the bifurcation model (Lee et al.). A: left coronary artery. B: abdominal aorta.

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  Fig. 3 An example of CFD in left coronary artery. Finite volume method, adapting Rhie-Chow algorithm, computed with ANSYS CFX package program (Anflux, Seoul, Korea) in SUN SPARC station 20 (Sun Korea Co., Seoul, Korea) were used. At first, a mesh or grid of region of interest is generated from the coronary extract images of computerized tomogram. All the digitalized data velocity, pressure information according as cardiac cycle as a boundary condition was selected to put into an appropriate algebraic solution. And, the next step is mathematic solving process by the computer. At this process, mechanical engineers and medical scientists should discuss about all the clinical situations for selecting an appropriate algebraic solution. Final step is visualization process for user. There are so many representative processing results, such as pressure profiles, velocity profiles, particle tracing, time-averaged wall shear stress (TAWSS), oscillating shear index (OSI), etc. This figure shows high TAWSS, OSI at bifurcation.

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  Fig. 4 The velocity vectors (upper) and distribution of wall shear stress (lower) in the coronary artery model. Prominent abrupt changes in velocity and wall shear stress at the outer wall around the branched site are noted during the deceleration period.

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  Fig. 5 Maximal velocity profile at inspiration (A) and expiration (B) in the supine position and inspiration (C) and expiration (D) in the standing position at an model of Fontan circulation. Tube structures represent cross-shaped reconstructed model of superior vena cava, inferior vena cava, right pulmonary artery, and left pulmonary artery at upper, lower, left and right tube.

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  Fig. 6 Modified Windkessel model for the human arterial system (Qin, Q1, and Q1 are defined as the flow rate exiting from the left ventricle during systole, the flow rate passing through the peripheral system, and the flow rate passing through the distal system, respectively. Similarly, p1 and p2 are the pressures measured at the proximal and distal locations, respectively. C1 and C2 are proximal and distal compliances where L corresponds to the inertia of blood.

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  Fig. 7 Proximal (Q1) and distal (Q2) flow rates in the left ventricle calculated with the Herschel-Bulkley equation.

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