Genetic algorithm for the determination of linear viscoelastic relaxation spectrum from experimental data

Abstract

Conventional procedures employed in the modeling of viscoelastic properties of polymer rely on the determination of the polymer's discrete relaxation spectrum from experimentally obtained data. In the past decades, several analytical regression techniques have been proposed to determine an explicit equation which describes the measured spectra. With a diverse approach, the procedure herein introduced constitutes a simulation-based computational optimization technique based on non-deterministic search method arisen from the field of evolutionary computation. Instead of comparing numerical results, this purpose of this paper is to highlight some subtle differences between both strategies and focus on what properties of the exploited technique emerge as new possibilities for the field. In oder to illustrate this, essayed cases show how the employed technique can outperform conventional approaches in terms of fitting quality. Moreover, in some instances, it produces equivalent results with much fewer fitting parameters, which is convenient for computational simulation applications. The problem formulation and the rationale of the highlighted method are herein discussed and constitute the main intended contribution.

Regularization-free multicriteria optimization of polymer viscoelasticity model

“…To handle the nonlinearity and ill-posedness of the regression problem, the aforementioned and other analytical and computational deterministic methods described in the literature are, to a broad extent, reliant on some type of assumptions that are extrinsic to the experimental data, such as heuristically tuned regularization parameters, constraints on the number of parameters and their distribution. Meanwhile, in a distinctively different vein, a non-deterministic optimization method has been proposed [17] which does not rely either on arbitrary or exogenous regularizing parameters nor technique-biased parameter distribution hypotheses. The introduced method, grounded on evolutionary computation theory, was successfully applied to the determination of the discrete linear viscoelastic relaxation spectrum from SOAS data, and experimental evidences indicate that it was able to fit the storage modulus G ′ and the loss modulus G ′′ of Eqs.…”

Two-Photon Polymerized Poly(2-Ethyl-2-Oxazoline) Hydrogel 3D Microstructures with Tunable Mechanical Properties for Tissue Engineering

“…These techniques can iterate several solutions simultaneously and combine the most promising ones to generate a new, improved set of solutions. EAs have been successfully applied to solve problems with nonlinear or even discontinuous objective functions, nonconvex objective function spaces, as well as to poorly conditioned problems [48][49][50]. The computational techniques take advantage of iterative applications of random variations and subsequent customized selection over a population of prospective solution instances.…”

Identification of constitutive parameters – optimization strategies and applications

“…Application of genetic algorithms to parameter identification is a relatively new endeavour, especially when addressing mechanical constitutive modelling. Identification of composite, organic tissue, viscoelastic, viscoplastic, elastic‐plastic and damage parameters illustrate application of genetic algorithms to this class of problems .…”