Bubble growth model and its influencing factors in a polymer melt under nonisothermal conditions

ABSTRACT Traditionally, in order to simplify the bubble growth process in a polymer melt, an isothermal model is typically used. In fact, the temperature of the polymer melt is changing during the foaming process. In order to accurately study the growth mechanism of bubbles in polymer melts, we buil...

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Bibliographic Details
Published in:Journal of applied polymer science 2019-03, Vol.136 (12), p.n/a
Main Authors: Zhang, Yun, Xin, Chunling, Li, Xiaohu, Waqas, Mughal, He, Yadong
Format: Article
Language:English
Subjects:
Bubbles
Cooling rate
Design of experiments
Diffusion coefficient
Dimensional changes
Foaming
foams
functionalization of polymers
Materials science
Mathematical models
Melts
plasticizer
Polymer melts
Polymers
Process parameters
Relaxation time
Runge-Kutta method
Shear viscosity
Surface tension
Transfer functions
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Summary:ABSTRACT Traditionally, in order to simplify the bubble growth process in a polymer melt, an isothermal model is typically used. In fact, the temperature of the polymer melt is changing during the foaming process. In order to accurately study the growth mechanism of bubbles in polymer melts, we build a physical and mathematical model of bubble growth in a polymer melt under nonisothermal conditions. The parameters of pressure, zero‐shear viscosity, relaxation time, Henry's constant, diffusion coefficient, and surface tension were determined. The fourth‐order Runge–Kutta method was used to solve the nonisothermal bubble model in the polymer melt. A computational program is developed to find the dimensional change during the bubble growth process, and the correctness of the model is verified. The nonisothermal growth mechanism of and factors influencing bubbles in the polymer melt are analyzed. Combined with the design of experiment (DOE) analysis method, the transfer function of the bubble radius and the maximum growth rate of bubbles with the process parameters were obtained, such as cooling rate, system pressure, and gas concentration. The results show that system pressure has the most significant effect on bubble growth. At the same time, a bubble growth prediction model is built, which can be used to predict the growth of bubbles. Through optimization analysis, it can be used to control the growth of bubbles. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 136, 47210.
ISSN:0021-8995
1097-4628
DOI:10.1002/app.47210