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DOE OSTI · 1972908

Scientific Computing Algorithms to Learn Enhanced Scalable Surrogates for Mesh Physics

Bartoldson, B·Hu, Y·Saini, A·Cadena, J·Fu, Y·Bao, J·Xu, Z·Ng, B·Nguyen, P
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Bartoldson, B, Hu, Y, Saini, A, Cadena, J, Fu, Y, Bao, J, Xu, Z, Ng, B, Nguyen, P. 2023-04-01. Scientific Computing Algorithms to Learn Enhanced Scalable Surrogates for Mesh Physics. https://www.osti.gov/biblio/1972908

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DOE OSTI2026

High-Order Mesh r-Adaptivity with Tangential Relaxation and Guaranteed Mesh Validity

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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