Machine learning approaches to close the filtered two-fluid model for gas-solid flows: Models for subgrid drag force and solid phase stress

Baptiste Hardy; Stefanie Rauchenzauner; Pascal Fede; Simon Schneiderbauer; Olivier Simonin; Sankaran Sundaresan; Ali Ozel

doi:10.1021/acs.iecr.3c04652

Machine learning approaches to close the filtered two-fluid model for gas-solid flows: Models for subgrid drag force and solid phase stress

Baptiste Hardy
, Stefanie Rauchenzauner
, Pascal Fede
, Simon Schneiderbauer
, Olivier Simonin
, Sankaran Sundaresan
, Ali Ozel

Department of Particulate Flow Modelling

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	8383 - 8400
Number of pages	18
Journal	Industrial & Engineering Chemistry Research
Volume	63
Issue number	18
DOIs	https://doi.org/10.1021/acs.iecr.3c04652
Publication status	Published - 08 May 2024

Fields of science

203 Mechanical Engineering
203024 Thermodynamics
204007 Thermal process engineering
103032 Fluid mechanics
203016 Measurement engineering
204006 Mechanical process engineering
211104 Metallurgy
103043 Computational physics

JKU Focus areas

Digital Transformation
Sustainable Development: Responsible Technologies and Management

Access to Document

10.1021/acs.iecr.3c04652

Cite this

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title = "Machine learning approaches to close the filtered two-fluid model for gas-solid flows: Models for subgrid drag force and solid phase stress",

author = "Baptiste Hardy and Stefanie Rauchenzauner and Pascal Fede and Simon Schneiderbauer and Olivier Simonin and Sankaran Sundaresan and Ali Ozel",

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doi = "10.1021/acs.iecr.3c04652",

language = "English",

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T1 - Machine learning approaches to close the filtered two-fluid model for gas-solid flows: Models for subgrid drag force and solid phase stress

AU - Hardy, Baptiste

AU - Rauchenzauner, Stefanie

AU - Fede, Pascal

AU - Schneiderbauer, Simon

AU - Simonin, Olivier

AU - Sundaresan, Sankaran

AU - Ozel, Ali

PY - 2024/5/8

Y1 - 2024/5/8

UR - https://www.scopus.com/pages/publications/85191889512

U2 - 10.1021/acs.iecr.3c04652

DO - 10.1021/acs.iecr.3c04652

M3 - Article

SN - 0888-5885

VL - 63

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EP - 8400

JO - Industrial & Engineering Chemistry Research

JF - Industrial & Engineering Chemistry Research

IS - 18

ER -

Machine learning approaches to close the filtered two-fluid model for gas-solid flows: Models for subgrid drag force and solid phase stress

Fields of science

JKU Focus areas

Access to Document

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Cite this