Comparing the performance of finite element models of biological structures


Meeting Abstract

88.2  Wednesday, Jan. 7  Comparing the performance of finite element models of biological structures DUMONT, E.R*; GROSSE, I.R.; SLATER, G.J; Univ. of Massachusetts, Amherst; Univ. of Massachusetts, Amherst; Univ. of California, Los Angeles bdumont@bio.umass.edu

There has been a rapid increase in the use of finite element analysis to investigate mechanical function in living and extinct organisms. This brings to the fore two critical questions about how such comparative analyses can and should be conducted: 1) what metrics are appropriate for assessing the performance of biological structures using finite element analysis? and, 2) how can performance be compared such that the effects of size and shape are disentangled? With respect to performance, we argue that for force-transmitting structures, minimization of elastically stored strain energy is a reasonable optimality criterion. We show that volumetric average strain energy density (a measure of work expended by the organism in deforming the structure) is a robust metric for comparing mechanical efficiency. Results of finite element analyses can be interpreted with confidence when model input parameters (muscle forces, detailed material properties) and/or output parameters (reaction forces, strains) are well-documented by studies of living animals. However, many interesting questions require comparisons of species for which these input and validation data are difficult or impossible to acquire. In these cases, the performance of structures that differ in shape can be compared if variation in size is controlled. We offer a theoretical framework and empirical data demonstrating that scaling finite element models to equal total force to total surface area ratios removes the effects of model size and permits comparisons based solely on shape. Thus, while finite element analyses of biological structures should be validated experimentally whenever possible, the relative performance of un-validated models can be compared if they have been scaled properly.

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