[HTML][HTML] Artificial intelligence for electricity supply chain automation
Abstract The Electricity Supply Chain is a system of enabling procedures to optimize
processes ranging from production to transportation and consumption of electricity. The …
processes ranging from production to transportation and consumption of electricity. The …
Applications of artificial neural networks for thermal analysis of heat exchangers–a review
M Mohanraj, S Jayaraj, C Muraleedharan - International Journal of Thermal …, 2015 - Elsevier
Artificial neural networks (ANN) have been widely used for thermal analysis of heat
exchangers during the last two decades. In this paper, the applications of ANN for thermal …
exchangers during the last two decades. In this paper, the applications of ANN for thermal …
Combination of X-ray tube and GMDH neural network as a nondestructive and potential technique for measuring characteristics of gas-oil–water three phase flows
In this investigation, a fan-beam photon attenuation based system, including one X-ray tube
and two sodium iodide crystal detectors, combined with group method of data handling …
and two sodium iodide crystal detectors, combined with group method of data handling …
Two-phase flow regime identification based on the liquid-phase velocity information and machine learning
Y Zhang, AN Azman, KW Xu, C Kang, HB Kim - Experiments in Fluids, 2020 - Springer
Two-phase flow regime identification in a horizontal pipe was realized based on the liquid
phase velocity information and the machine learning method. Ultrasound Doppler …
phase velocity information and the machine learning method. Ultrasound Doppler …
RETRACTED: Artificial neural networks applications in wind energy systems: A review
R Ata - 2015 - Elsevier
One of the conditions of submission of a paper for publication is that authors declare
explicitly that their work is original and has not been submitted to nor appeared in another …
explicitly that their work is original and has not been submitted to nor appeared in another …
On the prediction of critical heat flux using a physics-informed machine learning-aided framework
The critical heat flux (CHF) corresponding to the departure from nucleate boiling (DNB) crisis
is essential to the design and safety of a two-phase flow boiling system. Despite the …
is essential to the design and safety of a two-phase flow boiling system. Despite the …
Data-driven modeling for boiling heat transfer: using deep neural networks and high-fidelity simulation results
Boiling heat transfer occurs in many situations and can be used for thermal management in
various engineered systems with high energy density, from power electronics to heat …
various engineered systems with high energy density, from power electronics to heat …
[HTML][HTML] Precise void fraction measurement in two-phase flows independent of the flow regime using gamma-ray attenuation
Void fraction is an important parameter in the oil industry. This quantity is necessary for
volume rate measurement in multiphase flows. In this study, the void fraction percentage …
volume rate measurement in multiphase flows. In this study, the void fraction percentage …
[HTML][HTML] Deep learning for safety assessment of nuclear power reactors: Reliability, explainability, and research opportunities
Deep learning algorithms provide plausible benefits for efficient prediction and analysis of
nuclear reactor safety phenomena. However, research works that discuss the critical …
nuclear reactor safety phenomena. However, research works that discuss the critical …
Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation
Flow regime information can be used to improve measurement accuracy on gas volume
fractions and as complementary information for other types of flow instrumentation in order to …
fractions and as complementary information for other types of flow instrumentation in order to …