A review of icing prediction techniques for four typical surfaces in low-temperature natural environments

As a common phenomenon in nature and industrial fields, icing always adversely affects life, and plays negative effects. Although series of anti-/de-icing techniques are widely investigated, icing prediction techniques on surfaces under low-temperature natural environment are more important due to effectively reduce or even prevent icing caused harms. To understand the current research progress of icing prediction techniques, four typical static and moving surfaces that are prone to ice formation were selected as the research objects in this study, including road, transmission line, wind turbine blade and aircraft surfaces. As summarized, prediction methods mainly include physical models, statistical models, and machine learning. For road surface, statistical analysis, theoretical analysis and data mining methods are widely used, with the average prediction accuracy reaching 80%. For wind turbine blade and transmission line surfaces, both model-driven and data-driven methods were used, resulting in average prediction accuracies of 80% and 90%, respectively. For aircraft surface, the method of numerical analysis combined with machine learning is the mainstay, with a deviation of less than 20%. Summary and outlook are finally given, which are beneficial for the optimization of ice prediction technology in different industrial fields.