Identification of the viscoelastic parameters of a polymer model by the aid of a MCMC method |
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| Affiliation: | 1. Department of Mathematics and Physics, Lappeenranta University of Technology, FI-53851 Lappeenranta, Finland;2. Department of Applied Mechanics, School of Engineering, Aalto University, FI-00076 Aalto, Finland;3. Valmet Technologies Inc., FI-04400 Järvenpää, Finland;1. Business School, Zhengzhou University, School of Economics, Zhejiang University, China;2. School of Economics, Zhejiang University, China;3. School of Economics, Academy of Financial Research, Zhejiang University, China;4. University of Sydney Business School, University of Sydney, Australia;1. Science and Technology on Advanced Composites in Special Environments Laboratory, Harbin Institute of Technology, Harbin 150080, PR China;2. Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin 150080, PR China;1. Department of Physics, Korea Advanced Institute of Science and Technology, 373-1 Guseongdong, Daejeon 305–701, South Korea;3. Department of Physics, Hannam University, 133 Ojungdong, Daejon 306-791, South Korea |
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| Abstract: | ![]()
A procedure to identify the viscoelastic material parameters of a solid amorphous polymer and to estimate their values is presented. Stress–strain material data is obtained for the polymer by a compression experiment. The material behavior of the polymer is modeled according to the generalized Maxwell model, which is fitted to the experimental data by the method of least squares to obtain a first approximation for the model parameters. The identification of the model parameters is completed by a Markov chain Monte Carlo (MCMC) method, which generates the probability distributions of the relevant parameters of the material. The utilized MCMC method enables us to determine a suitable complexity (i.e., the number of Maxwell elements) for the generalized Maxwell model, so that the model best fits the data and, simultaneously, leads to an identifiable set of parameters. The numerical results imply that the uniqueness of the solution is lost when the number of model parameters becomes redundant. |
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| Keywords: | Polymer Generalized Maxwell model Compression experiment Parameter identification MCMC |
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