研究目的
To identify the resulting stress profile on the cornea surface during a non-contact tonometry examination, which may be used to numerically solve an inverse problem to obtain the material properties.
研究成果
The study presents a mathematical description of the stress distribution on the cornea during a non-contact tonometry examination, depending on different deformation states. The nozzle inlet pressure at the Corvis ST is approximately equal to the pressure measured with the internal pressure sensor of the device. The distribution of pressure and shear stress clearly depends on the deformation state of the cornea, with shear stresses being one hundred times smaller than the pressure. The influence of the human face on the distribution of pressure and shear forces was also investigated, showing that the assumption of a rotationally symmetric load is not appropriate.
研究不足
The study assumes that the air puff generated by the Corvis ST is identical for each device in every investigation. The stress distributions provided apply to the eye and face geometry used in the paper. Future researches should examine the influence of variations in the cornea or eyelid geometry.
1:Experimental Design and Method Selection:
The study combines experimental characterization of the air puff created by the Corvis ST with computational fluid dynamic (CFD) simulations, adjusted to the experimental data.
2:Sample Selection and Data Sources:
A rigid glass eye model was used in the experiment to eliminate effects due to changes in shape. The deformed states of the cornea are identified for 140 time frames from a Corvis ST measurement of a healthy person.
3:List of Experimental Equipment and Materials:
Corvis ST, glass eye, laser triangulation sensor (optoNCDT 2300-20, Micro-Epsilon), ANSYS
4:1 CFX software. Experimental Procedures and Operational Workflow:
The air puff was applied to a rigid eye model hung up through a yarn and positioned in front of the nozzle exit. The movement of the eye model was measured to calculate the force applied by the air puff. CFD simulations were then used to identify the time-dependent pressure at the nozzle inlet.
5:Data Analysis Methods:
The movement-time data was numerically derived to acceleration-time-curve and processed by a Savitzky-Golay filter and a parabolic low-pass filter to remove noise and high frequent oscillations.
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