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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. 

The present study provides fundamental knowledge on an issue in fluid dynamics that is not well understood: flow separation and its association with heat and contaminant transport. In the separated region, a swirling motion increases the fluid drag force on the object. Very often, this is undesirable because it can seriously reduce the performance of engineered devices such as aircraft and turbines. Furthermore, Computational Fluid Dynamics (CFD) has gained ground due to its relatively low cost, high accuracy, and versatility. The principal aim of this study is to numerically elucidate the details behind momentum and passive scalar transport phenomena during turbulent boundary layer separation resulting from a wall-curvature-driven pressure gradient. With Open- FOAM CFD software, the numerical discretization of Reynolds-Averaged Navier-Stokes and passive scalar transport equations will be described in two-dimensional domains via the assessment of two popular turbulence models (i.e., the Spalart-Allmaras and the K-w SST model). The computational domain reproduces a wind tunnel geometry from previously performed experiments by Baskaran et al. (JFM, vol. 182 and 232 “A turbulent flow over a curved hill.” Part 1 and Part 2). Only the velocity and pressure distribution were measured there, which will be used for validation purposes in the present study. A second aim in the present work is the scientific visualization of turbulent events and coherent structures via the ParaView toolkit and Unity game engine. Thus, fully immersive visualization approaches will be used via virtual reality (VR) and augmented reality (AR) technologies. A Virtual Wind Tunnel (VWT), developed for the VR approach, emulates the presence in a wind tunnel laboratory and has already employed fluid flow visualization from an existing numerical database with high temporal/spatial resolution, i.e., Direct Numeric Simulation (DNS). In terms of AR, a FlowVisXR app for smartphones and HoloLens has been developed for portability. It allows the user to see virtual 3D objects (i.e., turbulent coherent structures) invoked into the physical world using the device as the lens. 

In this video, we show high-fidelity numerical results of supersonic spatially-developing turbulent boundary layers (SDTBL) under strong concave and concave curvatures and Mach = 2.86. The selected numerical tool is Direct Numerical Simulation (DNS) with high spatial/temporal resolution. The prescribed concave geometry is based on the experimental study by Donovan et al. (J. Fluid Mech., 259, 1-24, 1994). Turbulent inflow conditions are based on extracted data from a previous DNS over a flat plate (i.e., turbulence precursors). 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). In this opportunity, the selected scientific visualization tool is Virtual Reality (VR) by extracting vortex core iso-surfaces via the Q-criterion to convert them to a file format readable by the HTC Vive VR toolkit. The reader is invited to visit our Virtual Wind Tunnel (VWT) under a fully immersive environment for further details. The video is available at: https://gfm.aps.org/meetings/dfd-2022/6313a60c199e4c2da9a946bc 

An incoming canonical spatially developing turbulent boundary layer (SDTBL) over a 2-D curved hill is numerically investigated via the Reynolds-averaged Navier–Stokes (RANS) equations plus two eddy-viscosity models: the K−ω SST (henceforth SST) and the Spalart–Allmaras (henceforth SA) turbulence models. A spatially evolving thermal boundary layer has also been included, assuming temperature as a passive scalar (Pr = 0.71) and a turbulent Prandtl number, Prt, of 0.90 for wall-normal turbulent heat flux modeling. The complex flow with a combined strong adverse/favorable streamline curvature-driven pressure gradient caused by concave/convex surface curvatures has been replicated from wind-tunnel experiments from the literature, and the measured velocity and pressure fields have been used for validation purposes (the thermal field was not experimentally measured). Furthermore, direct numerical simulation (DNS) databases from the literature were also employed for the incoming turbulent flow assessment. Concerning first-order statistics, the SA model demonstrated a better agreement with experiments where the turbulent boundary layer remained attached, for instance, in Cp, Cf, and Us predictions. Conversely, the SST model has shown a slightly better match with experiments over the flow separation zone (in terms of Cp and Cf) and in Us profiles just upstream of the bubble. The Reynolds analogy, based on the St/(Cf/2) ratio, holds in zero-pressure gradient (ZPG) zones; however, it is significantly deteriorated by the presence of streamline curvature-driven pressure gradient, particularly due to concave wall curvature or adverse-pressure gradient (APG). In terms of second-order statistics, the SST model has better captured the positively correlated characteristics of u′ and v′ or positive Reynolds shear stresses ( > 0) inside the recirculating zone. Very strong APG induced outer secondary peaks in and turbulence production as well as an evident negative slope on the constant shear layer.