Visualization of Multi-Shock Features for Unstructured-Grid Flows Based on Two-Level Sampling

Shock feature visualization plays an important role in flow visualization. To perform shock extraction, existing methods usually carry out shock detection with the normal Mach first and then filter the noise. When computing the normal Mach, they do not distinguish between the pressure gradient and the density gradient. Moreover, their noise filter has poor adaptability and accuracy with the availability depending on the test data. It usually makes the weak shock filtered along with the noise especially when there are multi-shock features with different strengths in flows. Besides this, the shock surface may be unconnected or split even for the single-shock flows. It is necessary to perform shock detection with the pressure gradient. On the basis of the shock attributes, a novel visualization method is proposed for multi-shock features using two-level sampling on the framework of recasting. The work is performed on GPU for the 3D unstructured-grid data with the complicated topology. The experimental results show that our method can automatically filter the noise and its adaptability and accuracy are much better than those of the existing methods even for the multi-shock flows. Meanwhile, a real-time performance is achieved for the large unstructured-grid data.