Linear viscoelastic relaxation in the α and α+ regions of linear polymers, crosslinked polymers and small molecules

Ni, Yelin; Medvedev, Grigori A.; Curliss, David B.; Caruthers, James M.

doi:10.1016/j.polymer.2020.122745

Cited by 1 publication

(2 citation statements)

References 43 publications

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“…The temperature-independent parameters of the H-N model, namely, the four angles (ϕ, θ, Φ, and Θ), can be initially determined based on Equations ( 10)- (15). In Figure 3, the Cole-Cole diagram is illustrated.…”

Section: Parameter Optimization Of the H-n Model 231 Theory Of The H-...mentioning

confidence: 99%

“…Common discretization methods for viscoelastic constitutive model parameters include Prony series models and Schapery constitutive models [11,12]. Since the parameters of viscoelastic constitutive models are difficult to measure directly, they need to be determined by comparing optimization methods with simulation results [13][14][15][16]. Since Prony series and Schapery constitutive models have a large research base and open source programs for solving the time-varying behavior of polymer materials and structures in complex states in combination with finite element methods, they are widely used for solving the time-dependent behavior of complex states in polymer materials and structures at present [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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Parameter Determination of the 2S2P1D Model and Havriliak–Negami Model Based on the Genetic Algorithm and Levenberg–Marquardt Optimization Algorithm

Qiu

Cao

et al. 2023

Polymers

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This study utilizes the genetic algorithm (GA) and Levenberg–Marquardt (L–M) algorithm to optimize the parameter acquisition process for two commonly used viscoelastic models: 2S2P1D and Havriliak–Negami (H–N). The effects of the various combinations of the optimization algorithms on the accuracy of the parameter acquisition in these two constitutive equations are investigated. Furthermore, the applicability of the GA among different viscoelastic constitutive models is analyzed and summarized. The results indicate that the GA can ensure a correlation coefficient of 0.99 between the fitting result and the experimental data of the 2S2P1D model parameters, and it is further proved that the fitting accuracy can be achieved through the secondary optimization via the L–M algorithm. Since the H–N model involves fractional power functions, high-precision fitting by directly fitting the parameters to experimental data is challenging. This study proposes an improved semi-analytical method that first fits the Cole–Cole curve of the H–N model, followed by optimizing the parameters of the H–N model using the GA. The correlation coefficient of the fitting result can be improved to over 0.98. This study also reveals a close relationship between the optimization of the H–N model and the discreteness and overlap of experimental data, which may be attributed to the inclusion of fractional power functions in the H–N model.

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Section: Parameter Optimization Of the H-n Model 231 Theory Of The H-...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter Determination of the 2S2P1D Model and Havriliak–Negami Model Based on the Genetic Algorithm and Levenberg–Marquardt Optimization Algorithm

Qiu

Cao

et al. 2023

Polymers

View full text Add to dashboard Cite

show abstract

Linear viscoelastic relaxation in the α and α+ regions of linear polymers, crosslinked polymers and small molecules

Cited by 1 publication

References 43 publications

Parameter Determination of the 2S2P1D Model and Havriliak–Negami Model Based on the Genetic Algorithm and Levenberg–Marquardt Optimization Algorithm

Parameter Determination of the 2S2P1D Model and Havriliak–Negami Model Based on the Genetic Algorithm and Levenberg–Marquardt Optimization Algorithm

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