Abstract

Due to the large size and physical complexity of modern industrial aeroacoustics problems, the use of uniform meshes fine enough to achieve the desired accuracy is unfeasible, even with the computation power available nowadays. To reduce such a computational cost to affordable levels, engineers must be creative and locally refine the meshes using physical considerations, sometimes unknown a priori. In practical terms, since numerical predictions for aeroacoustics heavily rely on accurate source estimations from the flow information, this translate in back-and-forth iterations of: (i) Computational Fluid Dynamics (CFD) solutions used to estimate noise sources; then (ii) to be propagated with acoustic solvers to predict noise levels in the far field. If the noise level computed is not accurate enough, the mesh is refined, and this process is repeated until convergence is reached. Added to this complexity, for resolving the flow, a Large Eddy Simulation (LES) is frequently used requiring further mesh refinement applied across large parts of the computational domain. This intensifies even more the cost associated with the overall process because in such complex geometry an adequate mesh must be determined to perform a valid and numerically stable LES. This iterative process is not only tedious and computational expensive but also it consumes engineering time. A solution to this problem is adaptive mesh refinement techniques based on physical and relevant indicators, such techniques can save both computational and engineering time whilst it guarantees accurate numerical solutions. For this reason, a novel methodology for adaptive mesh refinement is proposed based on the Taylor micro-scale, where most of the noise-generating eddies are properly resolved. Although such a micro-scale reaches local minima, i.e. especially near the walls, some techniques are proposed to avoid excessive mesh refinement via the use of limiters. The methodology is evaluated in the context of a hybrid aeroacoustic simulation, where the flow solution is obtained using a CFD solution to then compute equivalent sources to be imposed in a Finite Element (FE) acoustic propagation solver. The results obtained are further compared with results from a manually refined mesh, both in terms of cost as well as accuracy.