ABSTRACT

Two hybrid back-propagation neural network (BPNN) models optimized by two heuristic search algorithms, namely genetic algorithm (GA-BP) and particle swarm optimization (PSO-BP), are proposed in this paper to predict radial maximum wall shear stress instead of traditional computational fluid dynamics (CFD) methods. The two proposed models are trained and validated using a database of 150 radial maximum wall-shear-stress values obtained via CFD simulations. The best fit model is identified from various BPNN models based on the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), balancing the trade-off between goodness-of-fit and model complexity. The model performance is evaluated by MAE, RMSE, regression coefficient (R), and Nash-Sutcliffe efficiency (NSE). The best fit model is a three-layered BPNN model consisting of a 4:4:1 topology. In almost all evaluation indicators, the two hybrid BPNN methods outperform three existing algorithms, namely classical BPNN, random forest (RF), and gradient boosting decision tree (GBDT). Both PSO-BP and GA-BP can provide a more precise assessment of radial maximum wall shear stress, with maximum errors being 5.81% and 8.24% respectively. The proposed PSO-BP prediction model is promising and has great feasibility in predicting the radial maximum wall shear stress of UHP water-jet nozzle in engineering applications.

摘要

本文提出了两种通过遗传算法（GA-BP）和粒子群优化（PSO-BP）两种启发式搜索算法优化的混合反向传播神经网络（BPNN）模型来预测径向最大壁切应力，而不是传统的计算流体动力学（CFD）方法。使用通过 CFD 模拟获得的 150 个径向最大壁剪应力值的数据库对两个提议的模型进行训练和验证。基于 Akaike 信息准则 (AIC) 和贝叶斯信息准则 (BIC) 从各种 BPNN 模型中确定最佳拟合模型，平衡拟合优度和模型复杂度之间的权衡。模型性能通过MAE、RMSE、回归系数（R) 和纳什-萨特克利夫效率 ( NSE )。最佳拟合模型是由 4:4:1 拓扑组成的三层 BPNN 模型。在几乎所有的评估指标中，这两种混合 BPNN 方法都优于现有的三种算法，即经典 BPNN、随机森林（RF）和梯度提升决策树（GBDT）。PSO-BP 和 GA-BP 都可以更精确地评​​估径向最大壁面剪应力，最大误差分别为 5.81% 和 8.24%。所提出的 PSO-BP 预测模型在工程应用中预测超高压水射流喷嘴径向最大壁面剪应力具有很大的前景和可行性。