The aortic valve is responsible for allowing blood flow from the heart left ventricle into the aorta during the systolic phase of the cardiac cycle and for preventing backflow during the diastolic phase. The aortic valve is composed of three leaflets attached to the aortic root in proximity of three aortic dilations named sinuses of Valsalva. Leaflets open and close as a result of transvalvular pressure drop. Specifically, when the ventricular pressure is higher than the aortic pressure the leaflets open, whilst they close the valve orifice when the aortic pressure is higher than the ventricular pressure. Valve functionality may be impaired due to several conditions, such as aortic valve stenosis and aortic valve regurgitation, with a consequent increase in the risk of left ventricle hypertrophy and cardiac failure [1]. Among treatment options for aortic valve disease, a major role is played by the surgical replacement of the native valve with a prosthetic device.
Seat-related discomfort and health problems, which occur especially during long rowing tours or training sessions, can be reduced by rowing seats with a surface geometry that is ergonomically optimized for the particular rower. This seat optimization can be done by analyzing measured pressure distributions and modifying the standard seat surface geometry for a specific person based on these results using CAD tools. The project presented here focuses on the purely virtual development of the optimal geometry for specific rowers. FE simulations were performed using Human Body Models (HBMs) to define seat geometries for specific individuals.
Every year road traffic accidents are responsible of approximately 1.3 million deaths in the world, resulting in one of the main causes of mortality. According to the World Health Organization (WHO), by the 2020s road traffic accidents will be the leading cause of premature death. Moreover, between 20 and 50 million people involved in incidents suffer non-fatal injuries, most of them leading to disabilities [1]. These injuries considerably affect individuals, their families, and nations from both social and economic points of view. Over the last 60 years, experimental activities focused on the impact behavior of the human body were carried out with crash dummies and human cadavers, expanding the available injury database, exposing the most common injury scenarios and allowing the development of effective predictive criteria. The most frequently injured body regions resulted to be head and lower limbs; however severe to fatal injuries (Abbreviate Injury Scale values AIS 3+), are more commonly related to head impacts, as shown in Figure 1 [2].
Physics-based computer simulations of the heart are gaining rising interest for optimizing the design of medical devices and for its treatment prediction and planning. LS-DYNA offers a powerful framework for modeling cardiac electrophysiology, mechanics, and fluid dynamics, as well as the coupling between the three physics. However, its wider adoption is hindered by several requirements among which: knowledge in cardiac function in health and pathology, expertise in numerical simulation, appropriate right modeling choices for the target application, availability of realistic heart geometries. In this paper, we present a free to use python package that allows for the generation of physiologically accurate heart models in an automatic and modular fashion. The architecture is organized in an abstract form that allows users to easily choose between the different physics, anatomical chambers of interest and parameters of interest and export the LS-DYNA keyword files ready for simulation. We also introduce the relevant heart modeling features that are available in LS-DYNA and present two exemplary models generated by the package: a full electrophysiology heart model and a bi-ventricular mechanical model.
Understanding the corneal mechanical properties has great importance in the study of corneal pathologies and the prediction of refractive surgery outcomes. Non-Contact Tonometry (NCT) is a non-invasive diagnostic tool intended to characterize the corneal tissue response in vivo by applying a defined air-pulse. The development of a strong FSI tool amenable to model the NCT, applied to different structural and anatomical configurations, provides the basis to find the biomechanical properties of the corneal tissue in vivo. This paper presents a high-fidelity finite-element model of a patient-specific 3D eye for in-silico NCT. A fluid-structure interaction (FSI) simulation is developed to virtually apply a defined air-pulse to a patient-specific eye model comprising cornea, limbus, sclera, and humors. Three different methodologies are tested to model the humors and the best approach is chosen. Then, a Montecarlo simulation is performed varying both the parameters describing the mechanical behaviour of the corneal tissue and the IOP. The analysis reveals that the mechanical properties of the corneal tissue and the IOP are perfectly coupled. A stiffer material with a low IOP can give the same deformation result on the cornea as a softer material with an higher IOP.
Fluid-Structure Interaction Simulations of Mechanical Heart Valves with LS-DYNA ICFD