Numerical simulation and artificial neural network modeling of turbulent flow inside a pipe equipped with two spring turbulator samples with two different scales and a segmental cross-section have been investigated. Increased heat transfer rate (HTR) due to the use of a spring turbulator is predicted for the TiO₂Cu-Water hybrid nanofluid based on the single-phase model, feed-forward artificial neural network (ANN) and fitting method. The role of Reynolds number (Re), scale and volume fraction (ϕ) on Nusselt number (Nu), pressure drop (ΔP), performance evaluation coefficient (PEC), solar collector efficiency (η), and the field synergy principle (FSP), compared to simple pipe, is considered using the finite volume method. The results show that increasing the spring turbulator scale increased the contact surface of the working fluid and the spring turbulator. As a result, the flow turbulence is increased, which leads to better mixing of the nanofluid as the operating fluid of the solar collector absorber pipe. Finally, ANN outputs and fitting results are compared, and it has been observed that the obtained ANN could predict the targets accurately.

已经研究了配备两个不同尺度和分段横截面的两个弹簧湍流器样品的管道内湍流的数值模拟和人工神经网络建模。基于单相模型、前馈人工神经网络 (ANN) 和拟合方法，预测 TiO ₂ Cu-水混合纳米流体由于使用弹簧湍流器而增加的传热率 (HTR) 。雷诺数的作用（Re)、努塞尔数 (Nu) 上的尺度和体积分数 (φ)、压降 (ΔP)、性能评估系数 (PEC)、太阳能集热器效率 (η) 和场协同原理 (FSP)，与简单管道相比，考虑使用有限体积法。结果表明，增加弹簧扰流器规模，增加了工作流体与弹簧扰流器的接触面。结果，流动湍流增加，这导致作为太阳能收集器吸收管的工作流体的纳米流体更好地混合。最后，比较了人工神经网络的输出和拟合结果，观察到得到的人工神经网络可以准确地预测目标。