A conformable artificial neural network model to improve the void fraction prediction in helical heat exchangers

被引：3
|
作者：
Hernandez, J. A. [1 ]
Solis-Perez, J. E. [2 ]
Parrales, A. [3 ]
Mata, A. [4 ]
Colorado, D. [5 ]
Huicoche, A. [1 ]
Gomez-Aguilar, J. F. [6 ]
机构：
[1] Univ Autonoma Estado Morelos, Ctr Invest Ingn & Ciencias Aplicadas, Ave Univ 1001 Col Chamilpa, Cuernavaca 62209, Morelos, Mexico
[2] Univ Nacl Autonoma Mexico, Escuela Nacl Estudios Super Unidad Juriquilla, Blvd Juriquilla 3001, Juriquilla 76230, Queretaro, Mexico
[3] Univ Autonoma Estado Morelos, CONAHCyT Ctr Invest Ingn & Ciencias Aplicadas, Ave Univ 1001 Col Chamilpa, Cuernavaca 62209, Morelos, Mexico
[4] Univ Autonoma Estado Morelos, Ctr Invest Ingn & Ciencias Aplicadas, POSGRADO, Ave Univ 1001 Col Chamilpa, Cuernavaca 62209, Morelos, Mexico
[5] Univ Veracruzana, Ctr Invest Recursos Energet & Sustentables, Ave Univ Km 7-5 Col St Isabel, Coatzacoalcos 96535, Veracruz, Mexico
[6] CONAHCyT Tecnol Nacl Mexico, CENIDET, Interior Internado Palmira Col S-N, Cuernavaca 62490, Morelos, Mexico
来源：
INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER | 2023年 / 148卷
关键词：
Conformable artificial neural network; Conformable activation function; Conformable calculus; Fractional activation functions; Void fraction prediction; Helical heat exchanger; FLOW;
D O I：
10.1016/j.icheatmasstransfer.2023.107035
中图分类号：
O414.1 [热力学];
学科分类号：
摘要：
This study proposes a conformable artificial neural network model to improve the void fraction prediction in helical heat exchangers. The obtained model had only one neuron in the hidden layer, achieving an algebraic structure simpler than the classic training. Furthermore, this model satisfies the interval condition of [0-1] and is a function of vapor fraction, density ratio, and viscosity ratio. The new conformable ANN void fraction satisfactorily described the two-phase flow in the systems mentioned above because the outlet temperatures were predicted with 2.96% of RMSE, lower than those obtained with other void fraction model analyses. This paper describes the conformable Logistic Sigmoid Transfer Function (CLOGSIG) and its application advantages in the ANN training process. Using CLOGSIG as a transfer function can get different sinusoidal behaviors that can modify the data distribution around the function's sinusoidal area, allowing better data adaptability and improving network performance.
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页数：12
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