The sound-source-oriented coarse-grained Bayesian optimisation for active control of trailing-edge noise

This study implements blowing/suction control for aerofoil trailing-edge noise and systematically optimises blowing/suction angles and control locations within a Bayesian framework. Two distinct rounds were conducted for direct and sound-source-oriented coarse-grained Bayesian optimisations. In the direct optimisation, the mean overall sound pressure level of far-field noise is selected as the objective function. Optimal control parameters were obtained after 15 iterations, requiring 80 three-dimensional implicit large eddy simulations, and achieved a noise reduction of up to 3.7 dB. To reduce the substantial computational cost, a Gaussian process surrogate model was constructed using the sound source defined by multi-process acoustic theory. This enabled a second round of optimisation, termed sound-source-oriented coarse-grained Bayesian optimisation, which yielded comparable noise reduction. This refined approach exhibited low signal delay and rapid statistical convergence, which can significantly reduce both the computational cost per sampling and the iteration number. Consequently, the total computational cost was reduced to approximately one-sixth of the initial direct optimisation. Moreover, physical insights into noise reduction mechanisms were elucidated through dynamic mode decomposition (DMD), anisotropic invariant mapping and the analysis of source terms within the TNO model across several typical cases. The results indicate that the blowing-control case induces large-scale vortex shedding and enhances DMD mode energy and low-frequency noise emission. Furthermore, the suction control tends to disrupt coherent structures, reduce DMD mode energy and suppress radiated noise. Crucially, the suction control significantly decreases mean velocity gradients within the logarithmic layer and suppresses wall-normal Reynolds stresses, thereby considerably reducing TNO source intensity in this critical region. The optimal case exhibits superior performance across all metrics above, thus laying the foundation for the optimal control strategy. Additionally, the suction control facilitates attenuating the footprint of turbulent motions in wall-pressure fluctuations through pressure-velocity coherence analysis, hence promoting noise reduction. This work introduces a novel framework that integrates Bayesian optimisation with advanced noise diagnostic theory, and provides actionable insights for effective trailing-edge noise mitigation.