CFD-DEM and machine learning for modelling of plugging in pipes with cohesive particles

DOI

This dataset contains model data for the CFD-DEM simulation of plugging in multiphase flows with cohesive particles. The main results available from the simulations are presented in the dataset together with the overall description of the model. The second model is the random forest-based machine learning (ML) routine trained using experiments and CFD-DEM simulations. The source of the ML model is provided together with the training dataset.

STAR-CCM+, 2210 r.17.06.007

Python, 3.10.12

The simulations reproduce experiments on plugging of ice-in-decane slurry conducted in the multiphase flow loop of Høgskulen på Vestlandet (FLOWCHART project)

Identifier
DOI https://doi.org/10.18710/ARTNTB
Related Identifier IsCitedBy https://doi.org/10.1038/s41598-023-44202-7
Related Identifier IsCitedBy https://doi.org/10.3390/computation12040067
Metadata Access https://dataverse.no/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18710/ARTNTB
Provenance
Creator Balakin, Boris ORCID logo; Saparbayeva, Nazerke
Publisher DataverseNO
Contributor Balakin, Boris; Western Norway University of Applied Sciences
Publication Year 2024
Funding Reference Research Council of Norway 300286
Rights CC0 1.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/publicdomain/zero/1.0
OpenAccess true
Contact Balakin, Boris (Western Norway University of Applied Sciences)
Representation
Resource Type simulation data; Dataset
Format text/plain; text/x-python; application/pdf
Size 6223; 19353; 7492; 2311123; 12546; 7132; 3030
Version 1.0
Discipline Chemistry; Construction Engineering and Architecture; Engineering; Engineering Sciences; Natural Sciences; Physics