Numerical study on dynamic mechanism of brain volume and shear deformation under blast loading

Li, Zhijie; Du, Z.; You, Xiaoqing; Liu, Zhanli; Cheng, Jinkun; Luo, Chengcheng; Chu, Delin; Ning, Shaowu; Kang, Yuetong; Yang, Chen; Zhuang, Zhuo

doi:10.1007/s10409-019-00875-w

Cited by 11 publications

(1 citation statement)

References 34 publications

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Order By: Relevance

“…[5] to different problems and different model reduction techniques. Finally, we are interested in improving the full order model, by considering turbulence effects (see, e.g., [31,32]), as well as coupling the fluid model with an elasticity model to simulate fluid-structure interaction (FSI) (see, e.g., [18,[33][34][35]). This would make the presented pipeline more complete and versatile.…”

Section: Discussionmentioning

confidence: 99%

Non-intrusive data-driven ROM framework for hemodynamics problems

et al. 2021

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Reduced order modeling (ROM) techniques are numerical methods that approximate the solution of parametric partial differential equation (PED) by properly combining the high-fidelity solutions of the problem obtained for several configurations, i.e. for several properly chosen values of the physical/geometrical parameters characterizing the problem. By starting from a database of high-fidelity solutions related to a certain values of the parameters, we apply the proper orthogonal decomposition with interpolation (PODI) and then reconstruct the variables of interest for new values of the parameters, i.e. different values from the ones included in the database. Furthermore, we present a preliminary web application through which one can run the ROM with a very user-friendly approach, without the need of having expertise in the numerical analysis and scientific computing field. The case study we have chosen to test the efficiency of our algorithm is represented by the aortic blood flow pattern in presence of a left ventricular (LVAD) assist device when varying the pump flow rate.

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