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Extended melt-conveying models for single-screw extruders: Integrating domain knowledge into symbolic regression

  • Christian Marschik*
  • , Wolfgang Roland
  • , Michael Kommenda
  • *Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

8 Zitate (Scopus)

Abstract

The literature provides several analytical approximation methods for predicting the flow of non-Newtonian fluids in single-screw extruders. While these are based on various flow conditions, they were developed mostly for extruder screws with standard geometries. We present novel analytical melt-conveying models for predicting the flow and dissipation rates of fully developed flows of power-law fluids within three-dimensional screw channels. To accommodate a broad range of industrial screw designs, including both standard and high-performance screws, the main intention of this work was to significantly extend the scope of existing theories. The flow equations were first rewritten in a dimensionless form to reduce the mathematical problem to its dimensionless influencing parameters. These were varied within wide ranges to create a set of physically independent modeling setups, the flow and dissipation rates of which were evaluated by means of a finite-volume solver. The numerical results were then approximated analytically using symbolic regression based on genetic programming. To support the regression analysis in finding accurate solutions, we integrated domain-specific process knowledge in the preprocessing of the dataset. We obtained three regression models for predicting the flow and dissipation rates in melt-conveying zones and tested their accuracy successfully against an independent set of numerical solutions. Highlights: Flow of power-law fluids in three-dimensional screw channels Identification of independent influencing parameters by dimensional analysis Numerical parametric design study for a broad range of industrial applications Integration of domain knowledge in symbolic regression Surrogate models derived from numerical simulation results.

OriginalspracheEnglisch
Seiten (von - bis)3639-3656
Seitenumfang18
FachzeitschriftPolymer Engineering and Science
Jahrgang63
Ausgabenummer11
DOIs
PublikationsstatusVeröffentlicht - Nov. 2023

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