Nu Dataset for GNP Flow in Sinusoidal Microchannels

This dataset contains the Nusselt number (Nu) values obtained from a comprehensive 3D CFD study of graphene-water nanofluid flow through sinusoidal microchannels with square cross-sections. The study investigates the effects of six key parameters on convective heat transfer: Reynolds number (Re), nanoparticle concentration (wt.%), amplitude (A) and frequency (ω) of the sinusoidal path, microchannel thickness, and the number of sinusoidal waves (serpentines). A total of 30 CFD simulations were performed in three parametric groups to obtain the corresponding Nu values. The configurations vary systematically to isolate the influence of each parameter pair. The average Nusselt numbers for each case are reported and have been used further in a two-step machine learning framework for predictive modeling and optimization. This dataset is intended to support reproducibility, comparative analysis, and future research in microchannel heat sink design and nanofluid applications.本数据集包含通过全面三维计算流体动力学（Computational Fluid Dynamics，CFD）研究得到的努塞尔数（Nusselt number，Nu）值，该研究针对方形截面正弦形微通道内石墨烯水基纳米流体的流动展开。本研究探究了六个关键参数对对流换热的影响：雷诺数（Reynolds number，Re）、纳米颗粒质量分数（wt.%）、正弦流道的振幅与频率（ω）、微通道壁厚，以及正弦波（蛇形波）的数量。 本研究共开展30组计算流体动力学仿真，分为三个参数组以获取对应的努塞尔数值。各配置按系统规则变化，以分离每一组参数对的影响。本研究报告了所有工况下的平均努塞尔数，该数据已被应用于两步机器学习框架，用于开展预测建模与优化研究。 本数据集旨在为微通道散热器设计及纳米流体应用领域的可复现性研究、对比分析与未来科研工作提供支撑。