Allakany Alaa, Alsedias Noura, Aly Abdelraheem M
Computer Science Department, Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh, 33516, Egypt.
Department of Mathematical Sciences, College of Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Sci Rep. 2025 Jan 31;15(1):3889. doi: 10.1038/s41598-024-64821-y.
This work aims to simulate the impacts of exothermic reaction and Soret-Dufour numbers on the double diffusion of Nano Enhanced Phase Change Materials (NEPCM) inside a porous annulus. The complex rectangular annulus contains two ellipses and two triangles on the walls' vertical sides. The complex proposals of closed domains during heat/mass transfer of NEPCM can be used in energy savings, cooling electronic devices, and heat exchangers. The fractional-time derivative of the governing systems is solved numerically based on the ISPH method. The artificial neural network (ANN) is combined with the ISPH results to predict the average Nusselt number and Sherwood number . The main objective of establishing the ANN model in this investigation is to create a reliable predictive instrument capable of estimating the values of and . The results described the impacts of dimensionless Frank-Kamenetskii number (Fk = 0-1), Darcy number (Da = 10-10), Dufour number (Du = 0-0.1), buoyancy ratio (N = - 2 to 5), Rayleigh number (Ra = 10-10), Lewis number (Le = 1-20), Soret number (Sr = 0-0.2), fusion temperature (θ = 0.05-0.9), and fractional order parameter (α = 0.9-1) on thermosolutal convection of a suspension. The overall heat/mass transition as well as the velocity field are dramatically enhanced when and were boosted. The fractional time derivative helps reach a steady state in less time instants. The phase change material (PCM) is always changed when temperature distribution changes and is controlled by a fusion temperature. The porous struggled with nanofluid flow at a lower Darcy number. Frank-Kamenetskii number is a promising factor in enhancing the temperature distributions in an annulus. As a result, this work may be applied in various engineering and industrial fields because it contains significant terms in improving heat/mass transmission as well as a phase change material. The ANN model introduced a precise agreement of the prediction values with the actual values of and . Then, the present ANN model can accurately estimate the and values.
这项工作旨在模拟放热反应和索雷特 - 杜福尔数对纳米增强相变材料（NEPCM）在多孔环形空间内双扩散的影响。复杂的矩形环形空间在壁的垂直边上包含两个椭圆和两个三角形。NEPCM在热/质传递过程中封闭域的复杂方案可用于节能、冷却电子设备和热交换器。基于光滑粒子流体动力学（ISPH）方法对控制方程组的分数阶时间导数进行数值求解。将人工神经网络（ANN）与ISPH结果相结合，以预测平均努塞尔数和舍伍德数。在本研究中建立ANN模型的主要目的是创建一种可靠的预测工具，能够估计努塞尔数和舍伍德数的值。结果描述了无量纲弗兰克 - 卡梅涅茨基数（Fk = 0 - 1）、达西数（Da = 10⁻¹⁰）、杜福尔数（Du = 0 - 0.1）、浮力比（N = - 2至5）、瑞利数（Ra = 10⁻¹⁰）、刘易斯数（Le = 1 - 20）、索雷特数（Sr = 0 - 0.2）、熔化温度（θ = 0.05 - 0.9）和分数阶参数（α = 0.9 - 1）对悬浮液热溶质对流的影响。当努塞尔数和舍伍德数增加时，整体热/质传递以及速度场会显著增强。分数阶时间导数有助于在更短的时间内达到稳态。相变材料（PCM）总是随着温度分布的变化而变化，并由熔化温度控制。在较低的达西数下，多孔介质与纳米流体流动存在阻力。弗兰克 - 卡梅涅茨基数是增强环形空间内温度分布的一个有前景的因素。因此，这项工作可应用于各种工程和工业领域，因为它在改善热/质传递以及相变材料方面包含重要因素。ANN模型引入了预测值与努塞尔数和舍伍德数实际值的精确一致性。然后，当前的ANN模型可以准确估计努塞尔数和舍伍德数的值。
