Description:
<jats:title>Abstract</jats:title><jats:sec><jats:label /><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Highlights</jats:title><jats:p><jats:list list-type="bullet">
<jats:list-item><jats:p>Flow of power‐law fluids in three‐dimensional screw channels</jats:p></jats:list-item>
<jats:list-item><jats:p>Identification of independent influencing parameters by dimensional analysis</jats:p></jats:list-item>
<jats:list-item><jats:p>Numerical parametric design study for a broad range of industrial applications</jats:p></jats:list-item>
<jats:list-item><jats:p>Integration of domain knowledge in symbolic regression</jats:p></jats:list-item>
<jats:list-item><jats:p>Surrogate models derived from numerical simulation results</jats:p></jats:list-item>
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