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Applied Mathematics and Computation
Article . 2023 . Peer-reviewed
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Reducing model complexity by means of the optimal scaling: Population balance model for latex particles morphology formation

Authors: Simone Rusconi; Christina Schenk; Arghir Zarnescu; Elena Akhmatskaya;

Reducing model complexity by means of the optimal scaling: Population balance model for latex particles morphology formation

Abstract

Rational computer-aided design of multiphase polymer materials is vital for rapid progress in many important applications, such as: diagnostic tests, drug delivery, coatings, additives for constructing materials, cosmetics, etc. Several property predictive models, including the prospective Population Balance Model for Latex Particles Morphology Formation (LPMF PBM), have already been developed for such materials. However, they lack computational efficiency, and the accurate prediction of materials' properties still remains a great challenge. To enhance performance of the LPMF PBM, we explore the feasibility of reducing its complexity through disregard of the aggregation terms of the model. The introduced nondimensionalization approach, which we call Optimal Scaling with Constraints, suggests a quantitative criterion for locating regions of slow and fast aggregation and helps to derive a family of dimensionless LPMF PBM of reduced complexity. The mathematical analysis of this new family is also provided. When compared with the original LPMF PBM, the resulting models demonstrate several orders of magnitude better computational efficiency.

Country
Spain
Keywords

Latex particles morphology formation, Optimal scaling with constraints, Condensed Matter - Soft Condensed Matter, Polymerization, Population balance equation model, Physics - Chemical Physics, Polymerization; Latex particles morphology formation; Population balance equation model; Nondimensionalization; Reduction of model complexity; Optimal scaling with constraints, Reduction of model complexity, Mathematics - Numerical Analysis, Nondimensionalization, Computer Science - Computational Engineering, Finance, and Science

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
hybrid