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• W2912632293 abstract "Artificial neural networks (ANN) provide a computationally efficient pathway for solving complex non-linear problems. Cascaded ANN are used to compute thermodynamic properties of the ammonia–water mixture working-pair commonly used in vapor absorption heat pumps. Thermodynamic property routines can affect the accuracy and pose a significant computational bottleneck for steady-state and transient cycle simulations of ammonia–water. The properties computed using the proposed method agree within 0.5% of equation of state data over a wide range of operating parameters. It is observed that the ANN based property routines, developed as explicit functions, offer ∼60% decrease in computational time over currently used property routines. As a case study, the property calculation modules developed using ANN are employed in simulating the dynamic response of a representative ammonia-water condenser for a vapor absorption cycle. The models predict the transient behavior accurately with an ∼80% computational speedup compared to conventional property routines." @default.
• W2912632293 created "2019-02-21" @default.
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• W2912632293 date "2019-04-01" @default.
• W2912632293 modified "2023-10-16" @default.
• W2912632293 title "Computing thermodynamic properties of ammonia–water mixtures using artificial neural networks" @default.
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• W2912632293 doi "https://doi.org/10.1016/j.ijrefrig.2019.02.011" @default.
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