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Go to Editorial ManagerPolyurethane (PU) products enjoy remarkable versatility due to their tunable chemistry, segmented structure, and a wide range of mechanical properties, making them useful in flexible foam products, structural systems, and biomedical applications. However, the complex multiphase morphology and the strong interaction between reaction and processing processes make experimental characterization incomprehensible on its own. In turn, computational studies have become essential to study and design PU systems at a range of spatial and temporal scales. The current review provides an overview of simulation methodologies that are relevant to polyurethane, including atomistic molecular dynamics (MD), coarse-grained (CG), and mesoscale simulations, including dissipative particle dynamics (DPD), finite element method (FEM) modeling, and computational fluid dynamics (CFD) simulations. Atomistic models provide data on molecular interactions, hydrogen bonding, and thermomechanical behavior, and CG and mesoscale methods on phase separation and morphological evolution. At the bigger length scale, nonlinear mechanical response can be predicted using FEM, whereas foaming and mold-filling processes can be predicted using CFD that is coupled with reaction kinetics and population balance equations. Its focus is on multiscale modeling strategies, which combine these apparently different approaches, hence allowing the explanation of structure-property-process links. New trends and modern issues, including the integration of machine learning and tool models of digital twins, are also mentioned, highlighting new opportunities in predictive design, based on simulations, of polyurethane materials.
The objective of the current study is to determine the accuracy of a computational model that has been developed to simulate polyurethane foaming reactions by comparing its results with experimental findings on the system using both physical and chemical blowing agents. There was high concordance between the model outputs and the laboratory results in regard to the temporal development of reaction temperature as well as the resulting foam density, both of which were highly faithful recreations. The discussion provided further information about the optimization of the performance of cyclohexane, particularly when used in synergy with chemically active blowing agents, which speed up foaming. Besides, the polymerization dynamics were contained in the simulation, thus providing rich information on the structural changes that occur during the foaming process. Taken together, the results present a strong basis for the process performance optimization, as well as the predictive modeling of the blowing agent behavior. In the future, it will involve expanding the simulation model to include a wider range of agents, reaction mechanisms, and kinetics.