Review of machine learning for hydrodynamics, transport, and reactions in multiphase flows and reactors

LT Zhu, XZ Chen, B Ouyang, WC Yan… - Industrial & …, 2022 - ACS Publications
Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …

Investigation of the characteristics and mechanisms of the layer inversion in binary liquid–solid fluidized beds with coarse particles

WL Ren, Y Zhang, XH Zhang, XB Lu - Physics of Fluids, 2022 - pubs.aip.org
This paper adopts an optimized Euler–Lagrange method proposed in our previous work to
study the characteristics and formation mechanisms of layer inversion in binary liquid–solid …

[HTML][HTML] Using machine learning to forecast and assess the uncertainty in the response of a typical PWR undergoing a steam generator tube rupture accident

TCH Nguyen, A Diab - Nuclear Engineering and Technology, 2023 - Elsevier
In this work, a multivariate time-series machine learning meta-model is developed to predict
the transient response of a typical nuclear power plant (NPP) undergoing a steam generator …

Clustering sparse sensor placement identification and deep learning based forecasting for wind turbine wakes

N Ali, M Calaf, RB Cal - Journal of Renewable and Sustainable Energy, 2021 - pubs.aip.org
A data-driven approach is an alternative to extract general models for wind energy
applications. A spatial sensitivity analysis is achieved using a probabilistic model to …

Virtual flow predictor using deep neural networks

R Mercante, TA Netto - Journal of Petroleum Science and Engineering, 2022 - Elsevier
Multiphase flowmeters are important to monitor oil wells, as they allow operators to obtain a
real-time estimate of the production. However, due to its high installation cost, uncertainty …

Multiphase flowrate measurement with time series sensing data and sequential model

H Wang, D Hu, M Zhang, Y Yang - International Journal of Multiphase Flow, 2022 - Elsevier
Accurate multiphase flowrate measurement is challenging but vital in the energy industry to
monitor the production process. Machine learning has recently emerged as a promising …

The development of an AI-based model to predict the location and amount of wax in oil pipelines

J Kim, S Han, Y Seo, B Moon, Y Lee - Journal of Petroleum Science and …, 2022 - Elsevier
The petroleum that flows within pipelines can contain impurities to form a solid wax which,
when aggregated in sufficient qualities within the pipeline, can impair liquid flow and …

Experimental investigation of sudden expansion's influence on the hydrodynamic behavior of different sub-regimes of intermittent flow

A Arabi, Y Salhi, Y Zenati, EK Si-Ahmed… - Journal of Petroleum …, 2021 - Elsevier
Sub-regimes of intermittent two-phase flows through a sudden expansion have been
investigated experimentally. Two pipes of 30 and 40 mm ID connected by a sudden …

The gas-liquid flow rate measurement based on multisensors and machine learning

Z Zhao, N Zhao, X Li, Y Zhu, L Fang, X Li… - IEEE Sensors …, 2022 - ieeexplore.ieee.org
Gas-liquid two-phase flow non-separation measurement plays a crucial role in industrial
production and two-phase flow theory research. In this paper, an intelligent multi-sensing …

Flow pattern identification of gas-liquid two-phase flow based on integrating mechanism analysis and data mining

X Zhang, L Hou, Z Zhu, J Liu, X Sun, Z Hu - Geoenergy Science and …, 2023 - Elsevier
The flow pattern identification of gas-liquid two-phase flow is very important to gas field
gathering pipeline. Due to the complexity of gas-liquid two-phase flow, it is difficult to …