BibTex format
@article{Basha:2024:10.1016/j.ijmultiphaseflow.2024.104936,
author = {Basha, N and Arcucci, R and Angeli, P and Anastasiou, C and Abadie, T and Casas, CQ and Chen, J and Cheng, S and Chagot, L and Galvanin, F and Heaney, CE and Hossein, F and Hu, J and Kovalchuk, N and Kalli, M and Kahouadji, L and Kerhouant, M and Lavino, A and Liang, F and Nathanael, K and Magri, L and Lettieri, P and Materazzi, M and Erigo, M and Pico, P and Pain, CC and Shams, M and Simmons, M and Traverso, T and Valdes, JP and Wolffs, Z and Zhu, K and Zhuang, Y and Matar, OK},
doi = {10.1016/j.ijmultiphaseflow.2024.104936},
journal = {International Journal of Multiphase Flow},
title = {Machine learning and physics-driven modelling and simulation of multiphase systems},
url = {http://dx.doi.org/10.1016/j.ijmultiphaseflow.2024.104936},
volume = {179},
year = {2024}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - We highlight the work of a multi-university collaborative programme, PREMIERE (PREdictive Modelling with QuantIfication of UncERtainty for MultiphasE Systems), which is at the intersection of multi-physics and machine learning, aiming to enhance predictive capabilities in complex multiphase flow systems across diverse length and time scales. Our contributions encompass a variety of approaches, including the Design of Experiments for nanoparticle synthesis optimisation, Generalised Latent Assimilation models for drop coalescence prediction, Bayesian regularised artificial neural networks, eXtreme Gradient Boosting for microdroplet formation prediction, and a sub-sampling based adversarial neural network for predicting slug flow behaviour in two-phase pipe flows. Additionally, we introduce a generalised latent assimilation technique, Long Short-Term Memory networks for sequence forecasting mixing performance in stirred and static mixers, active learning via Bayesian optimisation to recover coalescence model parameters for high current density electrolysers, Gaussian process regression for drop size distribution predictions for sprays, and acoustic emission signal inversion using gradient boosting machines to characterise particle size distribution in fluidised beds. We also offer perspectives on the development of a shape optimisation framework that leverages the use of a multi-fidelity multiphase emulator. The results presented have applications in chemical synthesis, microfluidics, product manufacturing, and green hydrogen generation.
AU - Basha,N
AU - Arcucci,R
AU - Angeli,P
AU - Anastasiou,C
AU - Abadie,T
AU - Casas,CQ
AU - Chen,J
AU - Cheng,S
AU - Chagot,L
AU - Galvanin,F
AU - Heaney,CE
AU - Hossein,F
AU - Hu,J
AU - Kovalchuk,N
AU - Kalli,M
AU - Kahouadji,L
AU - Kerhouant,M
AU - Lavino,A
AU - Liang,F
AU - Nathanael,K
AU - Magri,L
AU - Lettieri,P
AU - Materazzi,M
AU - Erigo,M
AU - Pico,P
AU - Pain,CC
AU - Shams,M
AU - Simmons,M
AU - Traverso,T
AU - Valdes,JP
AU - Wolffs,Z
AU - Zhu,K
AU - Zhuang,Y
AU - Matar,OK
DO - 10.1016/j.ijmultiphaseflow.2024.104936
PY - 2024///
SN - 0301-9322
TI - Machine learning and physics-driven modelling and simulation of multiphase systems
T2 - International Journal of Multiphase Flow
UR - http://dx.doi.org/10.1016/j.ijmultiphaseflow.2024.104936
VL - 179
ER -