Research on the Change of Airfoil Geometric Parameters of Horizontal Axis Wind Turbine Blades Caused by Atmospheric Icing

Icing can significantly change the geometric parameters of wind turbine blades,which in turn,can reduce the aerodynamic characteristics of the airfoil.In-depth research is conducted in this study to identify the reasons for the decline of wind power equipment performance through the icing process.An accurate experimental test method is proposed in a natural environment that examines the growth and distribution of ice formation over the airfoil profile.The mathematical models of the airfoil chord length,camber,and thickness are established in order to investigate the variation of geometric airfoil parameters under different icing states.The results show that ice accumulation varies considerably along the blade span.By environmental temperature drop,the minimum and maximum extents of ice accumulation are observed near the blade root(0.2 R)and the blade tip(0.95 R),respectively(R represents the blade length).The icing process steadily increases the chord length and decreases the airfoil curvature,reaching the largest value at the blade tip region.Furthermore,the maximum curvature is reduced to 41.50%of the original curvature.The maximum camber position of the airfoil moves towards the trailing edge,and the most prominent position occurs at the middle blade region(0.6 R),where it moves back by 19.43%.Ice accumulation steadily increases airfoil thickness.It leads to the maximum thickness growth of 53.40%that occurs at the blade tip region and moves forward to the leading edge by 10%.The research results can provide the required theoretical support for further monitoring the blades operating conditions to ensure reliable wind turbines’operation.

Three-step learning strategy for designing 15Cr ferritic steels with enhanced strength and plasticity at elevated temperature

15Cr ferrite steels are urgently required in advanced Ultra-supercritical power plants but meet design challenges in balancing excellent strength and plasticity at high temperatures.We developed a three-step learning strategy based on mutually driven machine learning and purposeful experiments to complete this multi-objective task.Compared with traditional adaptive learning and local-interpolation learning,this step-by-step modular manner provides good transparency and interpretability of the information flow,which is ensured by identifying essential factors from an exquisitely prepared composition-microstructure dataset,and learning valuable knowledge about the composition-property relationship.The requirement of only two groups of experiments indicates the low cost and high efficiency of the strategy.Performing the strategy,we found that Ti is another key element affecting the Laves phase besides Mo and W,and their effects on ultimate tensile strength(UTS)and elongation were also uncovered.Importantly,several low-cost steels free of Co were successfully designed,and the best steel exhibited 156%,31%,and 62%higher UTS and elongation at 650°C than the typical 9Cr,15Cr,and 20Cr steels,respectively.Based on the advantages and success of the strategy in terms of alloy improvement,we believe the strategy suits other multi-objective design tasks in more materials systems.