High-Resolution Visualizations of Richtmyer–Meshkov Instability with Multi-Scale Perturbations

Research by Abhishek Misar and Dhruv Ram

University of Utah Kahlert School of Computing, Final Project for CS 6635: Visualization for Scientific Data

The Richtmyer–Meshkov instability (RMI) is a fundamental fluid dynamics phenomenon that occurs when a shock wave interacts with a perturbed interface between fluids of different densities. This interaction amplifies initial perturbations, leading to the formation of complex structures such as spikes and bubbles, and ultimately drives turbulent mixing. RMI plays an important role in a wide range of scientific and engineering applications, including astrophysical flows, inertial confinement fusion, and high-speed compressible mixing processes. This work was conducted as part of the final project for CS 6635: Visualization for Scientific Data at the University of Utah.

Our project focuses on the high-resolution visualization of a three-dimensional entropy field dataset representing a fully developed stage of Richtmyer–Meshkov instability. The dataset, consisting of approximately 8 billion voxels (~8 GB), captures intricate multi-scale flow structures at timestep 160, where the mixing layer has evolved into a highly complex turbulent state. Using ParaView, we applied a combination of advanced visualization techniques—including GPU-accelerated volume rendering, isosurface extraction, slice-based animations, and gradient-based glyph visualizations—to explore both large-scale coherent structures and fine-scale turbulent features. In particular, advanced rendering techniques such as global illumination and volumetric scattering were employed to enhance depth perception and reveal subtle internal structures within the volume, making variations in entropy and mixing behavior more visually distinguishable. These approaches enabled us to identify key flow characteristics such as mixing interfaces, regions of strong shear, and the coexistence of multiple spatial scales within the flow.

A major component of this work involved overcoming the challenges associated with previewing extremely large datasets. Traditional local computing environments were insufficient due to memory and rendering limitations. To address this, we developed a high-performance computing workflow using CHPC resources, leveraging interactive desktop sessions, containerized ParaView with EGL support, and headless GPU acceleration on NVIDIA A40 GPUs. By offloading computation and rendering to CHPC systems and connecting via SSH tunneling, we were able to achieve efficient, interactive exploration of the dataset.

This work highlights the essential role of high-performance computing and advanced visualization techniques in understanding complex, multi-scale physical phenomena. The ability to interactively explore extremely large, high-resolution datasets provided deeper insight into turbulent mixing behavior that would not be possible on conventional systems, underscoring the importance of HPC infrastructure in enabling modern scientific discovery.

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