[論文レビュー] An AMReX-based Compressible Reacting Flow Solver for High-speed Reacting Flows relevant to Hypersonic Propulsion

Shivank Sharma, Ral Bielawski+9 more|arXiv (Cornell University)|Dec 1, 2024

Gas Dynamics and Kinetic Theory被引用数 15

ひとこと要約

AMReXをベースにした圧縮性反応性流れソルバを高速度推進用に提供し、埋め込み境界処理、AMR、GPU加速反応速度を組み込んだ検証を行う。

ABSTRACT

This work presents a comprehensive framework for the efficient implementation of finite-volume-based reacting flow solvers, specifically tailored for high speed propulsion applications. Using the exascale computing project (ECP) based AMReX framework, a compressible flow solver for handling high-speed reacting flows is developed. This work is complementary to the existing PeleC solver, emphasizing specific applications that include confined shock-containing flows, stationary and moving shocks and detonations. The framework begins with a detailed exposition of the numerical methods employed, emphasizing their application to complex geometries and their effectiveness in ensuring accurate and stable numerical simulations. Subsequently, an in-depth analysis evaluates the solver's performance across canonical and practical geometries, with particular focus on computational cost and efficiency. The solver's scalability and robustness are demonstrated through practical test cases, including flow path simulations of scramjet engines and detailed analysis of various detonation phenomena.

研究の動機と目的

• Motivate and enable efficient, scalable simulation of high-speed, reacting flows for hypersonic propulsion.
• Develop a finite-volume, AMR-based solver using AMReX with embedded boundary geometry handling for complex devices.
• Incorporate stable numerical methods (Riemann solvers, limiters) and operator splitting for stiff chemistry.
• Demonstrate solver capability on canonical tests and practical scramjet/detonation scenarios.
• Assess performance, accuracy, and memory efficiency on high-fidelity geometries.

提案手法

• Finite-volume formulation of compressible Navier–Stokes equations with species transport and chemical source terms.
• AMR with patch-based refinement using AMReX, including embedded boundary treatment for complex geometries.
• Two approximate Riemann solvers (HLLC and HLLC-LM) plus a Piro-Central flux option for interface fluxes.
• Second-order TVD interpolation with flux limiters for primitive variable reconstruction.
• Operator splitting for chemistry with Cantera integration; GPU-accelerated chemistry via ChemGPU.
• Embedded boundary state redistribution to handle small-cut cells and maintain CFL stability.
• Geometry representation via implicit functions or STL-based CAD data with EBGeometry acceleration and mesh pruning to reduce memory usage

実験結果

リサーチクエスチョン

• RQ1 How can an AMR-based, AMReX-enabled compressible reacting-flow solver accurately and efficiently handle high-speed, shock-containing reacting flows relevant to scramjet propulsion?
• RQ2 What is the impact of embedded boundary treatment and small-cut cell handling (state redistribution) on stability, accuracy, and memory efficiency in complex geometries?
• RQ3 How do different flux formulations (HLLC, HLLC-LM, Piro-Central) affect accuracy and stability in shock-dominated reacting flows?
• RQ4 What are the solver’s accuracy and convergence characteristics on canonical tests and realistic propulsion geometries, including detonation phenomena and scramjet flow paths?

主な発見

Max grid level	Convergence rate
L=0	1.854
L=1	1.7726
L=2	1.865

• The AMReX-based solver achieves scalable, efficient handling of high-speed reacting flows with complex geometries using AMR and EB methods.
• Embedded boundary pruning reduces memory by up to ~40% at higher AMR levels for large geometries.
• HLLC-LM flux mitigates carbuncle-type instabilities in strong shocks while preserving essential dissipation control in low-Mach regimes.
• Chemical kinetics are handled via operator splitting with Cantera chemistry and GPU-accelerated ChemGPU to manage stiffness.
• Canonical tests (Sod, HIT) illustrate convergence behavior and limiter effects; higher AMR levels reduce density error, with convergence trends discussed.
• Full-flow-path simulations of scramjet-like configurations demonstrate the solver’s capability to resolve multi-scale features in propulsion-relevant flows