A novel interpolation-MLP coupled reconstruction method for turbulent natural convection thermal environment reconstruction of MW-class offshore wind turbine nacelles

Abstract

Localized overheating in the nacelle due to heat loss in high-power offshore wind turbines of 10 MW or more, where localized overheating affects the operation of other components, it is particularly important to visualize the thermal environment in the nacelle accurately and in real time in order to control the start-up and shutdown of cooling methods. However, the complexity of the high turbulence intensity (Rayleigh number (Ra) > 109) thermal environment in the nacelle of high-power units leads to significant errors in existing visualization techniques. Therefore, this paper presents a novel coupled interpolation-MLP reconstruction technique based on a data-driven deep neural network. Based on the interpolation method as primary reconstruction, a secondary reconstruction neural network model is obtained by training the thermal environment parameters (static pressure P, tangential velocity U, normal velocity V and temperature T) of specific computing nodes in the nacelle. The model is used to predict the values of thermal environment parameters at each time point for computing nodes with large gradients. An improved PSO algorithm is used to arrange the positions of a defined number of sensors to save sensor resources during the measurement process and to and ensure computing stability. The above improvements to the traditional interpolation reconstruction algorithms have led to an increase in the accuracy of the thermal environment reconstruction.