RHEA-X: Distributed GPU-resident high-fidelity flow solver for multiphysics turbulence

Published: 11 June 2026| Version 1 | DOI: 10.17632/dwv8gx3z9z.1
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Description

RHEA-X is an open-source solver for the direct numerical simulation of complex multiphysics turbulent flows on modern accelerated architectures. It reconciles physical accuracy with computational efficiency, enabling simulations ranging from ideal gases and particle-laden turbulence to high-pressure transcritical fluids and external flow aerodynamics. At its core lies a modular C++ architecture built on MPI and OpenACC that combines low-dissipation spatial discretizations, strong-stability-preserving time integration, advanced thermodynamic formulations up to real-fluid equations of state and complex correlation-based transport-property models. In addition, RHEA-X incorporates immersed boundary methods and Lagrangian point particles to solve external and particle-laden flows, respectively. Through persistent GPU data residency, GPU-aware MPI communication, and portable design, RHEA-X achieves reproducible and scalable performance across diverse systems and flow configurations. Validation against canonical and multiphysics benchmarks demonstrates its ability to capture near-wall turbulence, vortex-dominated separation, and pseudo-boiling thermodynamics with accuracy consistent with reference data and published results. Performance analyses on multiple supercomputing platforms demonstrate that 97% of ideal strong-scaling behavior is maintained up to 64 GPUs, while weak-scaling efficiencies remain above 90%, and time-to-solution reductions exceeding one order of magnitude compared to CPU execution (up to approximately 70 × ). Both CPU and GPU runs exhibit consistent relative scalability, supporting deployment from academic clusters towards exascale systems. RHEA-X yields an open, extensible environment for high-fidelity computational fluid dynamics, where physical rigor, computational performance, and scientific reproducibility meet within a single, coherent framework.

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Computational Physics, Turbulent Flow

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