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Localised prediction accuracies of knowledge-guided symbolic regression models for melt conveying in single-screw extruders

Datensatz

Beschreibung

Excel spreadsheet with numeric values and textual description.

Contains the mean absolute errors (MAE) of symbolic regression models for predicting the dimensionless melt flow rate in single-screw extruders, averaged across all repetitive runs and distinct sub-regions for the dimensionless down-channel pressure gradient Pi_p and the power-law index n of the polymer melt.

The models were created from numerical simulation data with four different cases of integrated domain knowledge:

- Case 1: theory of similarity only,
- Case 2: additional derived input features for pure pressure flow,
- Case 3: logarithmic scaling of the dimensionless flow rate with channel aspect ratio as derived input feature,
- Case 4: theoretical approximation equation for superimposed drag and pressure flow in dimensionless space.
Datum zugänglich gemacht19 März 2025
VerlagZenodo

UN SDGs

Dieser Datensatz trägt zu den folgenden UN-Nachhaltigkeitszielen (SDGs) bei

  1. SDG 9 – Industrie, Innovation und Infrastruktur
    SDG 9 – Industrie, Innovation und Infrastruktur
  2. SDG 12 – Verantwortungsvoller Konsum und Produktion
    SDG 12 – Verantwortungsvoller Konsum und Produktion

Wissenschaftszweige

  • 205012 Kunststoffverarbeitung
  • 203037 Computational Engineering
  • 102019 Machine Learning

JKU-Schwerpunkte

  • Digital Transformation
  • Sustainable Development: Responsible Technologies and Management

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