Remote Visualization and Optimization in Fluid Dynamics via Mixed Reality

The study presents an innovative pipeline for processing, compressing, and remotely visualizing large-scale numerical simulations of fluid dynamics in a virtual wind tunnel (VWT), leveraging Virtual and Augmented Reality (VR/AR) for enhanced analysis and high-end visualization. The workflow addresses the challenges of handling massive databases obtained via Direct Numerical Simulation (DNS) while maintaining visual fidelity, promoting full immersion, and ensuring efficient rendering for user interaction. We are performing fully immersive visualization of high-fidelity numerical results of supersonic spatially-developing turbulent boundary layers (SDTBL) under strong concave/convex curvatures at a freestream Mach number of 2.86 (i.e., supersonic flow). The selected numerical tool is Direct Numerical Simulation (DNS) with high spatial/temporal resolution. The comprehensive DNS information sheds important light on the transport phenomena inside turbulent boundary layers subject to strong deceleration or Adverse Pressure Gradient (APG) caused by concave walls as well as to strong acceleration or Favorable Pressure Gradient (FPG) caused by convex walls at different wall thermal conditions (i.e., Cold, Adiabatic and Hot walls). The process begins with .vts file input from DNS, which is visualized using the ParaView software. Multiple iso-contours for parameters such as velocity and temperature are generated, applying custom formulas to create visualizations at various floating-point precisions (16-bit, 32-bit, 64-bit). These visualizations, representing different fluid behaviors based on DNS with high spatial/temporal resolution and millions of “numerical sensors”, are treated as individual time frames and exported in GLTF format. Our approach demonstrates significant improvements in rendering speed and user experience, particularly when dealing with datasets comprising hundreds of high-resolution frames from Computational Fluid Dynamics (CFD) simulations. By utilizing server-side compression and cloud rendering, we overcome the limitations of on-device processing, enabling smooth and responsive interactions even with large, complex fluid dynamics datasets. This pipeline represents a substantial advancement in scientific visualization of fluid dynamics, offering researchers and engineers a powerful tool for exploring and analyzing large-scale CFD simulations in an immersive, intuitive environment. Additionally, we leverage Unity’s Profile Analyzer and Memory Profiling tools with the purpose of identifying major bottlenecks and resource-consuming events during contour running, with a keen focus on enhancing GPU and CPU efficiency. In conclusion, the materials and methods employed in this project were instrumental in systematically collecting, analyzing, and interpreting performance data from DNS databases. Future work will focus on optimizing compression algorithms for fluid-specific data and expanding the range of supported simulation parameters to enhance the pipeline’s versatility across various fluid dynamics applications.