Modeling and Simulation in Polymer Reaction Engineering

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Materials Processes".

Deadline for manuscript submissions: closed (20 October 2024) | Viewed by 4218

Special Issue Editor


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Guest Editor
Laboratoire Réactions et Génie des Procédés, Université de Lorraine, CNRS, LRGP, F-54000 Nancy, France
Interests: mathematical modeling; polymer reaction engineering; monte carlo methods; machine learning techniques; product design approach; design of experiments
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Special Issue Information

Dear Colleagues,

Polymers have been around for several decades and have greatly contributed to the improvement of our everyday lives in countless ways. However, they have never before been under as much scrutiny on the global stage as currently, for both good and bad reasons. Polymer science is faced with a tremendous challenge where it needs to respond to an increasing demand for tailored-property products while, at the same time, reducing their need for resources (both material and energy-wise), expanding their useful life and/or improving their circularity.

Mathematical modeling is one of the main tools that has been traditionally employed in polymer reaction engineering (PRE) in an attempt to better understand, describe, predict and ultimately optimize polymerization systems. To this end, several mathematical tools and approaches have been proposed, varying from traditional lumped or distributed deterministic models to detailed topological stochastic algorithms and, more recently (i.e., at least in its current widespread and more powerful form), to powerful data-driven machine learning methods.

This Special Issue welcomes original contributions in the broader area of PRE, including (but not limited to) the following methods and applications:

  • Modeling and simulation of commodity polymer production processes, in view of optimizing polymer quality or productivity indicators;
  • Modeling of processes involving the synthesis or the modification of bio-sourced polymers;
  • Modeling of polymer recycling techniques, including solvent-based, mechanical and chemical;
  • Modeling of enzymatic or bioorganism-assisted degradation of polymeric materials;
  • Exact microstructural modeling of non-linear, multi-monomer or multi-functionnal polymers;
  • Modeling of highly dimensional, complex polymerization systems.

Dr. Dimitrios Meimaroglou
Guest Editor

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Keywords

  • polymer reaction engineering
  • methods of moments
  • discretization methods
  • stochastic Monte Carlo simulations
  • artificial neural networks
  • support vector machines
  • polymer recycling processes

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Published Papers (2 papers)

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Research

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20 pages, 9094 KiB  
Article
Finite Element Simulation of Injection Mold Design Integrating Different Structures of Conformal Cooling Channels
by Meiyun Zhao and Zhengcheng Tang
Processes 2025, 13(1), 234; https://doi.org/10.3390/pr13010234 - 15 Jan 2025
Viewed by 1027
Abstract
Injection molding (IM) is a process in which completely melted plastic material is injected into the mold cavity under high pressure at a specific temperature, and the molded product is obtained after pressure holding, cooling, and demolding. During the mold cooling process, the [...] Read more.
Injection molding (IM) is a process in which completely melted plastic material is injected into the mold cavity under high pressure at a specific temperature, and the molded product is obtained after pressure holding, cooling, and demolding. During the mold cooling process, the conformal cooling channel system can improve the uniformity of mold temperature, reduce warping deformation, and significantly improve product accuracy. However, the cost consumption of conformal cooling channels for the cavity and core of injection molds is significant, which is a distinct disadvantage. This paper proposes an innovative conformal cooling channel. Compared with conventional cooling channels, the warpage of plastic parts has been reduced by 0.3401 mm. Moreover, the cooling time difference between C2 and C4 is relatively small, about 7.9 s. Among them, C4 takes the shortest time, C1 takes the longest, and C4 is 4.371 s shorter than C1. Compared with C1, the cooling efficiency of C4 has increased by 35.48%. In addition, from a commercial value perspective, many mold manufacturing companies’ real production applications are better suited for using conformal cooling channels alone on the injection mold core. This paper establishes injection molding models under different working conditions, simulates the cooling of dynamic mold temperature molds, and analyzes the effects of fluid media and various fluid rates on mold temperature changes. The results indicate that the cooling effect of cooling water is significantly better than that of cooling oil at the same fluid rate. When the fluid rate increases from 0.75 L/min to 6 L/min, the effect of cooling oil on the temperature change in the mold is significantly higher than that of cooling water. The influence of mold temperature on the cooling medium’s fluid rate tends to stabilize once the cooling medium’s flow rate reaches a specific value. Full article
(This article belongs to the Special Issue Modeling and Simulation in Polymer Reaction Engineering)
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Review

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19 pages, 15096 KiB  
Review
Self-Assembly in Curved Space: Ordering, Defect and Entropy
by Yuming Wang, Haixiao Wan, Lijuan Gao, Yibo Wu and Li-Tang Yan
Processes 2024, 12(1), 119; https://doi.org/10.3390/pr12010119 - 2 Jan 2024
Cited by 1 | Viewed by 2673
Abstract
Self-assembly of nanoscale objects is of essential importance in materials science, condensed matter physics, and biophysics. Curvature modifies the principles and sequence of self-assembly in Euclidean space, resulting in unique and more complex structures. Understanding self-assembly behavior in curved space is not only [...] Read more.
Self-assembly of nanoscale objects is of essential importance in materials science, condensed matter physics, and biophysics. Curvature modifies the principles and sequence of self-assembly in Euclidean space, resulting in unique and more complex structures. Understanding self-assembly behavior in curved space is not only instrumental for designing structural building blocks and assembly processes from a bottom-up perspective but is also critically important for delineating various biological systems. In this review, we summarize efforts made to unveil the physical nature of self-assembly in curved space through experiments and simulations. First, we outline the differences in the physical nature of self-assembly between curved space and Euclidean space by presenting relevant results of experiments and simulations. Second, we explore the principles of self-assembly in curved space at multiple scales and interactions, elucidating important factors that govern the self-assembly process from the perspectives of confinement and structural building blocks. Finally, we enumerate practical applications and control strategies for self-assembly in curved space and outline the challenges and prospects in this field. We hope that this review will encourage further efforts toward fundamental research and broaden the potential applications of designed assemblies in curved space. Full article
(This article belongs to the Special Issue Modeling and Simulation in Polymer Reaction Engineering)
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