Reducing model complexity by means of the Optimal Scaling: Population Balance Model for latex particles morphology formation
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909100358500352 |
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| author | Rusconi, Simone Schenk, Christina Zarnescu, Arghir Akhmatskaya, Elena |
| author_facet | Rusconi, Simone Schenk, Christina Zarnescu, Arghir Akhmatskaya, Elena |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06522 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reducing model complexity by means of the Optimal Scaling: Population Balance Model for latex particles morphology formation Rusconi, Simone Schenk, Christina Zarnescu, Arghir Akhmatskaya, Elena Soft Condensed Matter Computational Engineering, Finance, and Science Numerical Analysis Chemical Physics 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. |
| title | Reducing model complexity by means of the Optimal Scaling: Population Balance Model for latex particles morphology formation |
| topic | Soft Condensed Matter Computational Engineering, Finance, and Science Numerical Analysis Chemical Physics |
| url | https://arxiv.org/abs/2402.06522 |