Machine Learning Approaches to Close the Filtered Two-Fluid Model for Gas–Solid Flows: Models for Subgrid Drag Force and Solid Phase Stress-Reference-Cited by-同舟云学术

Machine Learning Approaches to Close the Filtered Two-Fluid Model for Gas–Solid Flows: Models for Subgrid Drag Force and Solid Phase Stress

Author:

Hardy Baptiste¹^ORCID,Rauchenzauner Stefanie²,Fede Pascal³,Schneiderbauer Simon⁴^ORCID,Simonin Olivier³,Sundaresan Sankaran⁵^ORCID,Ozel Ali⁶^ORCID

Affiliation:

1. Process and Energy Department, Faculty of Mechanical Engineering, TU Delft, Delft 2628 CB, Netherlands

2. Process Systems Engineering, TUM School of Life Sciences, Technical University of Munich, Freising 85354, Germany

3. Institut de Mécanique des Fluides de Toulouse, Université de Toulouse, CNRS, Toulouse 31400, France

4. Department of Particulate Flow Modelling, Johannes Kepler University, Linz 4040, Austria

5. Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, United States

6. School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, U.K.

Funder

Wallonie-Bruxelles International

Institut Carnot Isifor

Publisher

American Chemical Society (ACS)

Link

Reference66 articles.

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