One of the three 2014 Overset Grid Symposium Invited Speakers were Dr. Ajit Yoganathan, the head of the Cardiovascular Fluid Mechanics laboratory at Georgia Institute of Technology. The presentation can be found on the symposium website. The following is the abstract I wrote for Dr. Yoganathan to summarize the talk.

Computational Challenges in Cardiovascular Fluid Mechanics

Department of Biomedical Engineering, Georgia Institute of Technology, Technology Enterprise Park, Suite 200, 387 Technology Circle, Atlanta GA, 30313-2412, U.S.A.


Abstract

Computational challenges in cardiovascular fluid mechanics address a cross-section of challenging problems in using the computational fluid dynamics to simulate cardiovascular related situations, i.e. blood flow with cardiovascular devices, blood damage, surgical planning, inverse characterization of soft tissue properties, and other applications. Each of these applications begets complex physical and mathematical models of such difficulty that, even with the current most powerful supercomputers at hand, it is still convenient to simplify, to a certain extent, the geometries and algorithms used. The input data required by the models are of high complexity and uncertainty and the numerical solutions require algorithms that are not only accurate but also computationally efficient as human lives may ultimately depend on their accuracy and computing efficiency.

Some aspects of cardiovascular fluid mechanics, being related to a complex moving human body, can only be studied through computer simulations. However, the lack of realistic human models limits the ability to simulate realistic boundary conditions and approximations are used to replace them. The vast majority of new devices are not tested in a human environment before approval, which results in a significant number of recalls. The cost, when utilizing human testing, is too high. Hence, yet an additional need for more accurate and stable computational models to overcome these obstacles.

Five phases need to be completed to reach and display the computational solutions. Firstly, a, preferably human, subject has to be scanned in order to obtain the images of the area and volume in question. Secondly, image processing has to be performed on the results of the first phase to create a 3D model. Thirdly, high quality robust mesh is to be generated of the 3D model from the second phase. Then, fourthly, the numerical algorithms are to be employed to produce solutions, often spanning multiple space-time scales. And, lastly, the results are to be post-processed. Each of these five phases offers opportunities for further enhancements. Scanning can be more accurate and faster; image processing can be more automated, i.e. less laborious, and more precise; mesh generation can be more efficient and of higher quality, e.g. hexahedral elements using automated algorithms only; numerical methods can be computationally more efficient and accurate; and post-processing can be more user friendly and, again, automated.

The challenges in the area of cardiovascular fluid mechanics are of higher complexity and necessity than in most other areas. Yet, there still exists large room for continued development and the quality of human lives is to be improved by investing more time, effort and resources in order to get to the point when surgeons get to routinely rely on computational results.

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