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 …

Thermal performance of annulus with its applications; A review

HE Ahmed, MI Ahmed - Renewable and Sustainable Energy Reviews, 2017 - Elsevier
The topic of heat transfer enhancement has attractive attentions to develop the compact heat
exchanger design in order to obtain a high efficiency, low cost, light weight and size as small …

Identification and maximum impact force modeling investigation for critical slugging in underwater compressed gas energy storage systems

C Liang, W Xiong, H Wang, R Carriveau… - Journal of Energy …, 2023 - Elsevier
Underwater compressed gas (air, natural gas, hydrogen, etc.) energy storage (UWCGES) is
an emerging technology that is suitable for ocean energy storage. Liquid accumulation in …

[HTML][HTML] Numerical modeling of laminar and turbulent annular flows of power-law fluids in partially blocked geometries

H Miao, V Dokhani, Y Ma, D Zhang - Results in Engineering, 2023 - Elsevier
The presence of cuttings bed in deviated wellbores has an undesirable effect on wellbore
hydraulics by reducing the open flow area. In addition, pipe eccentricity affects the annular …

A machine learning approach to filtrate loss determination and test automation for drilling and completion fluids

S Gul, E van Oort - Journal of Petroleum Science and Engineering, 2020 - Elsevier
Drilling fluid property characterization currently involves several manually executed
analytical tests, conducted in accordance with American Petroleum Institute (API) …

Machine learning based models for pressure drop estimation of two-phase adiabatic air-water flow in micro-finned tubes: Determination of the most promising …

B Najafi, K Ardam, A Hanušovský, F Rinaldi… - … Research and Design, 2021 - Elsevier
The present study is focused on determining the most promising set of dimensionless
features and the optimal machine learning algorithm that can be employed for data-driven …

Machine learning based pressure drop estimation of evaporating R134a flow in micro-fin tubes: Investigation of the optimal dimensionless feature set

K Ardam, B Najafi, A Lucchini, F Rinaldi… - International Journal of …, 2021 - Elsevier
The present study is focused on proposing, implementing, and optimizing machine learning
based pipelines for estimating the pressure drop in evaporating R134a flow passing through …

Eulerian–Eulerian modeling of multiphase flow in horizontal annuli: Current limitations and challenges

A Shynybayeva, LR Rojas-Solórzano - Processes, 2020 - mdpi.com
Multiphase flows are present in many natural phenomena, processing technologies, and
industries. In the petroleum industry, the multiphase flow is highly relevant, and special …

Comparison of computational intelligence models for cuttings transport in horizontal and deviated wells

E Ulker, M Sorgun - Journal of Petroleum Science and Engineering, 2016 - Elsevier
Improper cleaning of the horizontal and deviated wellbores is one of the major problems
encountered during drilling process. Cuttings bed thickness is a key parameter to determine …

Computational intelligence models for PIV based particle (cuttings) direction and velocity estimation in multi-phase flows

H Tombul, AM Ozbayoglu, ME Ozbayoglu - Journal of Petroleum Science …, 2019 - Elsevier
In multi-phase flow, the gas phase, the liquid phase and the particles (cuttings) within the
liquid have different flow behaviors. Particle velocity and particle direction are two of the …