论文标题
使用神经网络预测冰心症的性能
Using neural networks to predict icephobic performance
论文作者
论文摘要
受超疏水表面启发的冰表面为结冰问题提供了一种被动的解决方案。然而，对冰镇的建模是具有挑战性的，因为某些有助于超疏水性的材料特征会对冰镇性能产生不利影响。这项研究提出了一种基于人工神经网络的新方法，以模拟冰心症。开发了人工神经网络模型来预测混凝土的冰分性能。对模型进行了实验数据的训练，以预测表面冰粘附强度和在冰点条件下从表面弹跳的水滴恢复的系数（COR）。将材料和涂料组成以及环境条件用作模型的输入变量。对多层感知器进行了训练，可以预测均方根误差为0.08，而90％的置信区间为[0.042，0.151]。该模型在部署后的确定系数为0.92。由于冰粘附强度在样品的广泛值中变化，因此开发了混合密度网络模型以学习多模式数据中的基本关系。模型的确定系数为0.96。输入变量在冰镇性能中的相对重要性是使用置换重要性计算的。开发的模型将有益于优化混凝土的冰分性。
Icephobic surfaces inspired by superhydrophobic surfaces offer a passive solution to the problem of icing. However, modeling icephobicity is challenging because some material features that aid superhydrophobicity can adversely affect the icephobic performance. This study presents a new approach based on artificial neural networks to model icephobicity. Artificial neural network models were developed to predict the icephobic performance of concrete. The models were trained on experimental data to predict the surface ice adhesion strength and the coefficient of restitution (COR) of water droplet bouncing off the surface under freezing conditions. The material and coating compositions, and environmental condition were used as the models' input variables. A multilayer perceptron was trained to predict COR with a root mean squared error of 0.08, and a 90% confidence interval of [0.042, 0.151]. The model had a coefficient of determination of 0.92 after deployment. Since ice adhesion strength varied over a wide range of values for the samples, a mixture density network was model was developed to learn the underlying relationship in the multimodal data. Coefficient of determination for the model was 0.96. The relative importance of the input variables in icephobic performance were calculated using permutation importance. The developed models will be beneficial to optimize icephobicity of concrete.
