TY - JOUR
T1 - Physics informed deep learning for flow and force predictions in dense ellipsoidal particle suspensions
AU - Ashwin, Neil Raj
AU - Tafti, Danesh
AU - Muralidhar, Nikhil
AU - Cao, Ze
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/4/15
Y1 - 2024/4/15
N2 - Solid-fluid multiphase systems are ubiquitous in many chemical, pharmaceutical, and energy based applications. These flows are challenging to study experimentally, thus several numerical simulation techniques have been developed. In this paper we use Particle Resolved Simulations (PRS) combined with Deep Learning (DL) to predict drag forces on each particle in suspensions of ellipsoidal particles for use in lower fidelity Euler-Lagrange and Euler-Euler simulations. Suspensions with solid fraction of 0.1, 0.2 and 0.3 are investigated at Reynolds numbers of 10, 50, 100 and 200. A UNet architecture is used to predict the velocity and pressure fields and a CNN is used to predict the drag forces. It is shown that drag force predictions using the predicted velocity and pressure fields give more accurate results than predictions that only use geometric information.
AB - Solid-fluid multiphase systems are ubiquitous in many chemical, pharmaceutical, and energy based applications. These flows are challenging to study experimentally, thus several numerical simulation techniques have been developed. In this paper we use Particle Resolved Simulations (PRS) combined with Deep Learning (DL) to predict drag forces on each particle in suspensions of ellipsoidal particles for use in lower fidelity Euler-Lagrange and Euler-Euler simulations. Suspensions with solid fraction of 0.1, 0.2 and 0.3 are investigated at Reynolds numbers of 10, 50, 100 and 200. A UNet architecture is used to predict the velocity and pressure fields and a CNN is used to predict the drag forces. It is shown that drag force predictions using the predicted velocity and pressure fields give more accurate results than predictions that only use geometric information.
KW - Convolution neural network (CNN)
KW - Deep learning
KW - Drag force
KW - Ellipsoidal particle suspension
KW - Flow field prediction
KW - UNet
UR - http://www.scopus.com/inward/record.url?scp=85189668490&partnerID=8YFLogxK
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U2 - 10.1016/j.powtec.2024.119684
DO - 10.1016/j.powtec.2024.119684
M3 - Article
AN - SCOPUS:85189668490
SN - 0032-5910
VL - 439
JO - Powder Technology
JF - Powder Technology
M1 - 119684
ER -