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Influence of refrigerated storage on tensile mechanical properties of porcine liver and spleen
(2015)
Influence of a freeze–thaw cycle on the stress–stretch curves of tissues of porcine abdominal organs
(2012)
The paper investigates both fresh porcine spleen and liver and the possible decomposition of these organs under a freeze–thaw cycle. The effect of tissue preservation condition is an important factor which should be taken into account for protracted biomechanical tests. In this work, tension tests were conducted for a large number of tissue specimens from twenty pigs divided into two groups of 10. Concretely, the first group was tested in fresh state; the other one was tested after a freeze-thaw cycle which simulates the conservation conditions before biomechanical experiments. A modified Fung model for isotropic behavior was adopted for the curve fitting of each kind of tissues. Experimental results show strong effects of the realistic freeze–thaw cycle on the capsule of elastin-rich spleen but negligible effects on the liver which virtually contains no elastin. This different behavior could be explained by the autolysis of elastin by elastolytic enzymes during the warmer period after thawing. Realistic biomechanical properties of elastin-rich organs can only be expected if really fresh tissue is tested. The observations are supported by tests of intestines.
The impact of surgical staplers on tissues has been studied mostly in an empirical manner. In this paper, finite element method was used to clarify the mechanics of tissue stapling and associated phenomena. Various stapling modalities and several designs of circular staplers were investigated to evaluate the impact of the device on tissues and mechanical performance of the end-to-end colorectal anastomosis. Numerical simulations demonstrated that a single row of staples is not adequate to resist leakage due to non-linear buckling and opening of the tissue layers between two adjacent staples. Compared to the single staple row configuration, significant increase in stress experienced by the tissue at the inner staple rows was observed in two and three rows designs. On the other hand, adding second and/or third staple row had no effect on strain in the tissue inside the staples. Variable height design with higher staples in outer rows significantly reduced the stresses and strains in outer rows when compared to the same configuration with flat cartridge.
This paper develops a new finite element method (FEM)-based upper bound algorithm for limit and shakedown analysis of hardening structures by a direct plasticity method. The hardening model is a simple two-surface model of plasticity with a fixed bounding surface. The initial yield surface can translate inside the bounding surface, and it is bounded by one of the two equivalent conditions: (1) it always stays inside the bounding surface or (2) its centre cannot move outside the back-stress surface. The algorithm gives an effective tool to analyze the problems with a very high number of degree of freedom. Our numerical results are very close to the analytical solutions and numerical solutions in literature.
FEM shakedown analysis of structures under random strength with chance constrained programming
(2022)
Direct methods, comprising limit and shakedown analysis, are a branch of computational mechanics. They play a significant role in mechanical and civil engineering design. The concept of direct methods aims to determine the ultimate load carrying capacity of structures beyond the elastic range. In practical problems, the direct methods lead to nonlinear convex optimization problems with a large number of variables and constraints. If strength and loading are random quantities, the shakedown analysis can be formulated as stochastic programming problem. In this paper, a method called chance constrained programming is presented, which is an effective method of stochastic programming to solve shakedown analysis problems under random conditions of strength. In this study, the loading is deterministic, and the strength is a normally or lognormally distributed variable.