Parrales Arianna, Hernández-Pérez José Alfredo, Flores Oliver, Hernandez Horacio, Gómez-Aguilar José Francisco, Escobar-Jiménez Ricardo, Huicochea Armando
CONACyT-Centro de Investigación en Ingeniería y Ciencias Aplicadas (CIICAp), Universidad Autónoma del Estado de Morelos, Cuernavaca C.P. 62209, Mexico.
Centro de Investigación en Ingeniería y Ciencias Aplicadas (CIICAp), Universidad Autónoma del Estado de Morelos, Cuernavaca C.P. 62209, Mexico.
Entropy (Basel). 2019 Jul 14;21(7):689. doi: 10.3390/e21070689.
In this study, two empirical correlations of the Nusselt number, based on two artificial neural networks (ANN), were developed to determine the heat transfer coefficients for each section of a vertical helical double-pipe evaporator with water as the working fluid. Each ANN was obtained using an experimental database of 1109 values obtained from an evaporator coupled to an absorption heat transformer with energy recycling. The Nusselt number in the annular section was estimated based on the modified Wilson plot method solved by an ANN. This model included the Reynolds and Prandtl numbers as input variables and three neurons in their hidden layer. The Nusselt number in the inner section was estimated based on the Rohsenow equation, solved by an ANN. This ANN model included the numbers of the Prandtl and Jackob liquids as input variables and one neuron in their hidden layer. The coefficients of determination were R 2 > 0.99 for both models. Both ANN models satisfied the dimensionless condition of the Nusselt number. The Levenberg-Marquardt algorithm was chosen to determine the optimum values of the weights and biases. The transfer functions used for the learning process were the hyperbolic tangent sigmoid in the hidden layer and the linear function in the output layer. The Nusselt numbers, determined by the ANNs, proved adequate to predict the values of the heat transfer coefficients of a vertical helical double-pipe evaporator that considered biphasic flow with an accuracy of ±0.2 for the annular Nusselt and ±4 for the inner Nusselt.
在本研究中，基于两个人工神经网络（ANN）开发了努塞尔数的两个经验关联式，以确定以水为工作流体的立式螺旋双管蒸发器各部分的传热系数。每个ANN均使用从与带有能量回收的吸收式热变换器相连的蒸发器获得的1109个值的实验数据库得到。环形部分的努塞尔数基于由ANN求解的修正威尔逊图解法估算。该模型将雷诺数和普朗特数作为输入变量，其隐藏层中有三个神经元。内部部分的努塞尔数基于由ANN求解的罗森诺方程估算。该ANN模型将普朗特数和雅各布液体数作为输入变量，其隐藏层中有一个神经元。两个模型的决定系数均为R²>0.99。两个ANN模型均满足努塞尔数的无量纲条件。选择列文伯格-马夸尔特算法来确定权重和偏差的最佳值。学习过程中使用的传递函数在隐藏层中为双曲正切S型函数，在输出层中为线性函数。由ANN确定的努塞尔数证明足以预测考虑双相流的立式螺旋双管蒸发器的传热系数值，环形努塞尔数的精度为±0.2，内部努塞尔数的精度为±4。
