eprintid: 10195423 rev_number: 7 eprint_status: archive userid: 699 dir: disk0/10/19/54/23 datestamp: 2024-08-06 10:01:55 lastmod: 2024-08-06 10:01:55 status_changed: 2024-08-06 10:01:55 type: article metadata_visibility: show sword_depositor: 699 creators_name: Basha, Nausheen creators_name: Arcucci, Rossella creators_name: Angeli, Panagiota creators_name: Anastasiou, Charitos creators_name: Abadie, Thomas creators_name: Casas, César Quilodrán creators_name: Chen, Jianhua creators_name: Cheng, Sibo creators_name: Chagot, Loïc creators_name: Galvanin, Federico creators_name: Heaney, Claire E creators_name: Hossein, Fria creators_name: Hu, Jinwei creators_name: Kovalchuk, Nina creators_name: Kalli, Maria creators_name: Kahouadji, Lyes creators_name: Kerhouant, Morgan creators_name: Lavino, Alessio creators_name: Liang, Fuyue creators_name: Nathanael, Konstantia creators_name: Magri, Luca creators_name: Lettieri, Paola creators_name: Materazzi, Massimiliano creators_name: Erigo, Matteo creators_name: Pico, Paula creators_name: Pain, Christopher C creators_name: Shams, Mosayeb creators_name: Simmons, Mark creators_name: Traverso, Tullio creators_name: Valdes, Juan Pablo creators_name: Wolffs, Zef creators_name: Zhu, Kewei creators_name: Zhuang, Yilin creators_name: Matar, Omar K title: Machine learning and physics-driven modelling and simulation of multiphase systems ispublished: pub divisions: UCL divisions: B04 divisions: F43 keywords: Machine Learning, Numerical simulations, Multiphase, Multi-fidelity, Microfluidics, Hybrid methods note: Crown Copyright © 2024 Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). abstract: 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. date: 2024-09 date_type: published publisher: Elsevier BV official_url: https://doi.org/10.1016/j.ijmultiphaseflow.2024.104936 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2302869 doi: 10.1016/j.ijmultiphaseflow.2024.104936 lyricists_name: Zhu, Kewei lyricists_name: Angeli, Panagiota lyricists_name: Hossein, Fria lyricists_id: KZHUA89 lyricists_id: PANGE44 lyricists_id: FAHOS94 actors_name: Zhu, Kewei actors_id: KZHUA89 actors_role: owner full_text_status: public publication: International Journal of Multiphase Flow volume: 179 article_number: 104936 issn: 0301-9322 citation: Basha, Nausheen; Arcucci, Rossella; Angeli, Panagiota; Anastasiou, Charitos; Abadie, Thomas; Casas, César Quilodrán; Chen, Jianhua; ... Matar, Omar K; + view all <#> Basha, Nausheen; Arcucci, Rossella; Angeli, Panagiota; Anastasiou, Charitos; Abadie, Thomas; Casas, César Quilodrán; Chen, Jianhua; Cheng, Sibo; Chagot, Loïc; Galvanin, Federico; Heaney, Claire E; Hossein, Fria; Hu, Jinwei; Kovalchuk, Nina; Kalli, Maria; Kahouadji, Lyes; Kerhouant, Morgan; Lavino, Alessio; Liang, Fuyue; Nathanael, Konstantia; Magri, Luca; Lettieri, Paola; Materazzi, Massimiliano; Erigo, Matteo; Pico, Paula; Pain, Christopher C; Shams, Mosayeb; Simmons, Mark; Traverso, Tullio; Valdes, Juan Pablo; Wolffs, Zef; Zhu, Kewei; Zhuang, Yilin; Matar, Omar K; - view fewer <#> (2024) Machine learning and physics-driven modelling and simulation of multiphase systems. International Journal of Multiphase Flow , 179 , Article 104936. 10.1016/j.ijmultiphaseflow.2024.104936 <https://doi.org/10.1016/j.ijmultiphaseflow.2024.104936>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10195423/1/1-s2.0-S0301932224002131-main.pdf