Using neural networks to predict icephobic performance
This work addresses the problem of optimizing icephobic surfaces for concrete applications, representing an incremental advance in material science modeling.
The study tackled the challenge of modeling icephobic performance by developing artificial neural networks to predict ice adhesion strength and water droplet bouncing on concrete surfaces, achieving coefficients of determination of 0.96 and 0.92, respectively.
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.