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DOE OSTI · 3410680

Second-order invariant-domain preserving approximation to the multi-species Euler equations

Dzanic, Tarik [Lawrence Livermore National Laboratory]·Tovar, Eric [Los Alamos National Laboratory]·Clayton, Bennett [Los Alamos National Laboratory]
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Dzanic, Tarik [Lawrence Livermore National Laboratory], Tovar, Eric [Los Alamos National Laboratory], Clayton, Bennett [Los Alamos National Laboratory]. 2026-06-03. Second-order invariant-domain preserving approximation to the multi-species Euler equations. https://doi.org/10.1016/j.compfluid.2026.107159

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High-order meshes are crucial for achieving optimal convergence rates in curvilinear domains, preserving symmetry, and aligning with key flow features in moving mesh simulations [1], but their quality is challenging to control. In prior work, we have developed techniques based on Target-Matrix Optimization Paradigm (TMOP) to adapt a given high-order mesh to the geometry and solution of the partial differential equation (PDE) [2, 3]. Here, we extend this framework to address two key gaps in the literature for highorder mesh 𝑟-adaptivity. First, we introduce tangential relaxation on curved surfaces using solely the discrete mesh representation, eliminating the need for access to underlying geometry (e.g., CAD model). Second, we ensure a continuously positive Jacobian determinant throughout the domain. This determinant positivity is essential for using the high-order mesh resulting from 𝑟-adaptivity with arbitrary quadrature schemes in simulations. The proposed approach is demonstrated to be robust using a variety of numerical experiments.

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