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EN
This paper presents a new form of a mathematical estimation of stochastic bio-hydrodynamic lubrication parameters for real human joint surfaces with phospholipid bilayers. In this work, the authors present the analytical and stochastic considerations, which are based on the measurements of human joint surfaces. The gap is restricted between two cooperating biological surfaces. After numerous experimental measurements, it directly follows that the random symmetrical as well as unsymmetrical increments and decrements of the gap height in human joints influence the hydrodynamic pressure, load-carrying capacity, friction forces, and wear of the cooperating cartilage surfaces in human joints. The main focus of the paper was to demonstrate the influence of variations in the expected values and standard deviation of human joint gap height on the hydrodynamic lubrication parameters occurring in the human joint. It is very important to notice that the new form of apparent dynamic viscosity of synovial fluid formulated by the authors depends on ultra-thin gap height variations. Moreover, evident connection was observed between the apparent dynamic viscosity and the properties of cartilage surface coated by phospholipid cells. The above observations indicate an indirect impact of stochastic changes in the height of the gap and the indirect impact of random changes in the properties of the joint surface coated with the phospholipid layers, on the value of hydrodynamic pressure, load carrying capacity and friction forces. In this paper the authors present a synthetic, comprehensive estimation of stochastic bio-hydrodynamic lubrication parameters for the cooperating, rotational cartilage bio-surfaces with phospholipid bilayers occurring in human joints. The new results presented in this paper were obtained taking into account 3D variations in the dynamic viscosity of synovial fluid, particularly random variations crosswise the film thickness for non-Newtonian synovial fluid properties. According to the authors’ knowledge, the obtained results are widely applicable in spatiotemporal models in biology and health science.
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