A Trace--Logarithmic Variational Functional for Equidistribution and Alignment in Moving Mesh Adaptation

Abstract

Existing variational mesh functionals often require an empirical equidistribution--alignment weight or use a strongly nonlinear density. We propose a calibrated trace--logarithmic functional in the inverse pullback tensor $A=J^{-1}{M}^{-1}{J}^{-T}$,with the logarithmic coefficient fixed by the local target $A=\theta I$. We prove strict convexity in the symmetric positive-definite (SPD) tensor variable, inverse-Jacobian polyconvexity and coercivity, and weak minimizer existence under explicit admissibility assumptions. The density also satisfies the standard coercivity condition for semi-discrete physical-coordinate mesh nonsingularity; the geometric discretization yields a compact computational-coordinate residual for the reported direct-secant implementation. Numerical tests for metric-induced mesh adaptation, a Burgers benchmark, a Rayleigh--Taylor instability simulation, and a matched high-anisotropy stress test show robust mesh redistribution; in the stress test, the trace--logarithmic runs complete over a broader fixed-protocol range of the compression parameter than the scanned Huang benchmark.

24 pages,30 figures