[HTML][HTML] Deep learning for safety assessment of nuclear power reactors: Reliability, explainability, and research opportunities

A Ayodeji, MA Amidu, SA Olatubosun, Y Addad… - Progress in Nuclear …, 2022‏ - Elsevier
Deep learning algorithms provide plausible benefits for efficient prediction and analysis of
nuclear reactor safety phenomena. However, research works that discuss the critical …

A CNN-based transfer learning method for leakage detection of pipeline under multiple working conditions with AE signals

P Liu, C Xu, J **e, M Fu, Y Chen, Z Liu… - Process Safety and …, 2023‏ - Elsevier
Pipeline leakage detection is a crucial part of pipeline integrity management. Acoustic
emission (AE) based leakage detection is widely used in this field. The latest detection …

Machine learning for soft and liquid molecular materials

T Orlova, A Piven, D Darmoroz, T Aliev, TMTA Razik… - Digital …, 2023‏ - pubs.rsc.org
This review discusses three types of soft matter and liquid molecular materials, namely
hydrogels, liquid crystals and gas bubbles in liquids, which are explored with an emergent …

Experimental research on in-pipe leaks detection of acoustic signature in gas pipelines based on the artificial neural network

W Wang, X Mao, H Liang, D Yang, J Zhang, S Liu - Measurement, 2021‏ - Elsevier
Gas pipe leakage is a common and significant problem around the word. To detect the
leakages, an in-pipe detector mounted on an acoustic inspection module is a direct and …

[HTML][HTML] An artificial neural network model for the prediction of entrained droplet fraction in annular gas-liquid two-phase flow in vertical pipes

AM Aliyu, R Choudhury, B Sohani, J Atanbori… - International Journal of …, 2023‏ - Elsevier
The entrained droplet fraction (e) is an important quantity in annuar gas-liquid two-phase
flows as it allows more precise calculation of the gas core density. This results in more …

Joint recurrence network-based dynamic parameter measurement of gas-liquid two-phase flow

R Wang, L **a, R Min, M Ding, M Li… - IEEE Transactions on …, 2024‏ - ieeexplore.ieee.org
Gas–liquid two-phase flow is vital in industrial production. The transient and unstable nature
of two-phase flow makes it a challenging task to accurately measure and analyze the flow …

[HTML][HTML] A deep reinforcement learning (DRL) based approach for well-testing interpretation to evaluate reservoir parameters

P Dong, ZM Chen, XW Liao, W Yu - Petroleum Science, 2022‏ - Elsevier
Parameter inversions in oil/gas reservoirs based on well test interpretations are of great
significance in oil/gas industry. Automatic well test interpretations based on artificial …

[HTML][HTML] Evaluation of frictional pressure drop correlations for air-water and air-oil two-phase flow in pipeline-riser system

NL Li, B Chen - Petroleum Science, 2024‏ - Elsevier
Accurate prediction of the frictional pressure drop is important for the design and operation
of subsea oil and gas transporting system considering the length of the pipeline. The …

Characterizing gas–liquid two-phase flow behavior using complex network and deep learning

MY Li, RQ Wang, JB Zhang, ZK Gao - Chaos: An Interdisciplinary …, 2023‏ - pubs.aip.org
Gas–liquid two-phase flow is polymorphic and unstable, and characterizing its flow behavior
is a major challenge in the study of multiphase flow. We first conduct dynamic experiments …

Foam stability: The key to inhibiting slug generation in gas–liquid flow

P Zhang, D Guo, X Cao, X Li, W **a, W Peng… - Journal of Petroleum …, 2022‏ - Elsevier
Surfactant-based foam drainage technology is a favored method of liquid accumulation
removal, and it has been demonstrated as an efficient way to improve gas production …