In this study, an artificial neural network (ANN) model was utilized to predict the friction factor for wire-wrapped rod assemblies. The 80 experimental data sets from UCTD correlations (Chen et al., 2018) were used to train and validate the ANN models. Three ANN models were introduced for laminar, transition, and turbulence flow conditions, respectively. The flow regime was determined based on the Reynolds number and the pitch to diameter ratio. To estimate the friction factor, Reynolds number, and multiple design parameters such as the number of rods, the rod's diameter, the diameter of the wire, the lattice pitch, the edge pitch, and the wire's helical pitch were used from the 80 bundles dataset. Three-quarters of the total data was used for training, while the other quarter was used for validation. The Levenberg-Marquardt method with Gauss-Newton approximation for Hessian of the training cost function was applied for training. For validation of the models, the cross-validation method was adopted. The ANN models were composed of seven inputs, R₁ neurons in the hidden layer, and a single-output (7-R₁-1). The R₁ was determined based on the minimum validation error principle. Training and validation were repeated 20 times using randomly shuffled data sets to estimate the ANN model's uncertainty and errors. The prediction of the trained models showed meaningfully low errors (0.00 % of Mean error, 6.59 % of standard deviation of error, 10.83 % of 90 % of confidence interval, and 6.58 % of Mean RMS of 80 bundles data) when they are compared with correlations suggested by previous studies (CTD, UCTD, CTS, UCTS, and REH).

在这项研究中，人工神经网络 (ANN) 模型被用来预测绕线杆组件的摩擦系数。来自 UCTD 相关性的 80 个实验数据集（Chen 等，2018）用于训练和验证 ANN 模型。分别为层流、过渡和湍流条件引入了三个 ANN 模型。基于雷诺数和节径比确定流态。为了估计摩擦系数、雷诺数和多个设计参数，例如杆数、杆直径、线径、晶格间距、边缘间距和线的螺旋间距，这些参数来自 80 束数据集. 总数据的四分之三用于训练，而另一季度用于验证。将训练成本函数的 Hessian 的 Gauss-Newton 近似的 Levenberg-Marquardt 方法应用于训练。对于模型的验证，采用了交叉验证方法。ANN 模型由七个输入组成，R隐藏层中有_{1 个}神经元，以及一个单输出 (7-R ₁ -1)。R ₁是根据最小验证误差原则确定的。使用随机打乱的数据集重复训练和验证 20 次，以估计 ANN 模型的不确定性和误差。训练模型的预测显示出有意义的低误差（0.00% 的平均误差、6.59% 的标准误差、10.83% 的 90% 置信区间和 6.58% 的 80 个捆绑数据的平均 RMS）与先前研究（CTD、UCTD、CTS、UCTS 和 REH）建议的相关性。