RECEPTIVITY COEFFICIENT MODEL OF HIGH-SPEED CONE BOUNDARY LAYERS TO FREESTREAM SLOW ACOUSTIC WAVES BASED ON SURROGATE MODEL

The transition process from laminar to turbulent flow can lead to a dramatic increase in the heat flux and frictional drag on aircraft surfaces, thereby posing severe challenges to aerodynamic performance. Consequently, transition prediction has become a critical factor to consider in engineering design. The eN method, based on linear stability theory, is the most widely adopted methodology for transition prediction. However, this method fails to account for the boundary layer receptivity to external disturbances, leading to theoretical uncertainties in its prediction results. Thus, developing an eN method capable of incorporating boundary layer receptivity has become an essential requirement. The receptivity coefficient, which quantifies the level of receptivity, can reflect the results of receptivity, and combining it with external disturbances as the initial value of eN integration is a commonly used approach. However, there are many factors influencing receptivity (such as bluntness, wall temperature, unit Reynolds number, and disturbance frequency) and the influence patterns are complex, making it difficult to establish a relationship for the receptivity coefficient that accounts for numerous factors. Surrogate model, a data-driven analytical technique leveraging sample datasets, which is the method for rapid analysis based on sample data, are beneficial for establishing a mathematical model of the receptivity coefficient. In this study, a surrogate model is employed to construct a receptivity coefficient model based on previously calculated samples of cone boundary-layer receptivity coefficients for varying key parameters such as bluntness, wall temperature, unit Reynolds number, and frequency. The reliability of the proposed model has been validated, providing a foundation for enhancing the eN method in future studies. Additionally, a preliminary analysis of the influence of receptivity on transition prediction reveals that accounting for receptivity enhances the role of the boundary-layer first mode in driving transition. Neglecting receptivity could lead to a significant underestimation of the first mode’s contribution to the transition process.