scholarly journals Optimization of motive nozzle position in a modified two-phase ejector expansion household refrigeration cycle using an artificial neural network

2022 ◽  
Vol 8 ◽  
pp. 1114-1123
Author(s):  
Yongseok Jeon ◽  
Dongchan Lee ◽  
Honghyun Cho
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Refrigeration Cycle ◽  
Two Phase ◽  
Nozzle Position ◽  
Artificial Neural

Gas Void Fraction Prediction for Air-Water and Air-Oil Two-Phase Flows via Artificial Neural Network

2019 ◽  
Author(s):  
Tiago Ferreira Souza ◽  
Caio Araujo ◽  
Maurício Figueiredo ◽  
FLAVIO SILVA ◽  
Ana Maria Frattini Fileti
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Void Fraction ◽  
Two Phase Flows ◽  
Two Phase ◽  
Artificial Neural ◽  
Gas Void Fraction

scholarly journals Thermodynamic analysis and simulation of a new combined power and refrigeration cycle using artificial neural network

2011 ◽  
Vol 15 (1) ◽  
pp. 29-41 ◽  
Author(s):  
Abdolreza Fazeli ◽  
Hossein Rezvantalab ◽  
Farshad Kowsary
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heat Source ◽  
Exergy Efficiency ◽  
Combined Cycle ◽  
Cycle Performance ◽  
Working Fluid ◽  
Refrigeration Cycle ◽  
Water Mixture ◽  
Artificial Neural

In this study, a new combined power and refrigeration cycle is proposed, which combines the Rankine and absorption refrigeration cycles. Using a binary ammonia-water mixture as the working fluid, this combined cycle produces both power and refrigeration output simultaneously by employing only one external heat source. In order to achieve the highest possible exergy efficiency, a secondary turbine is inserted to expand the hot weak solution leaving the boiler. Moreover, an artificial neural network (ANN) is used to simulate the thermodynamic properties and the relationship between the input thermodynamic variables on the cycle performance. It is shown that turbine inlet pressure, as well as heat source and refrigeration temperatures have significant effects on the net power output, refrigeration output and exergy efficiency of the combined cycle. In addition, the results of ANN are in excellent agreement with the mathematical simulation and cover a wider range for evaluation of cycle performance.

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