An internal penalty discontinuous Galerkin method for simulating conjugate heat transfer in a closed cavity. (5th February 2018) - Cai, Z.
Summary: Using the discontinuous Galerkin (DG) method for conjugate heat transfer problems can provide improved accuracy close to the fluid‐solid interface, localizing the data exchange process, which may further enhance the convergence and stability of the entire computation. This paper presents a framework for the simulation of conjugate heat transfer problems using DG methods on unstructured grids. Based on an existing DG solver for the incompressible Navier‐Stokes equation, the fluid advection‐diffusion equation, Boussinesq term, and solid heat equation are introduced using an explicit DG formulation. A Dirichlet‐Neumann partitioning strategy has been implemented to achieve the data exchange process via the numerical flux of interface quadrature points in the fluid‐solid interface. Formal h and p convergence studies employing the method of manufactured solutions demonstrate that the expected order of accuracy is achieved. The algorithm is then further validated against 3 existing benchmark cases, including a thermally driven cavity, conjugate thermally driven cavity, and a thermally driven cavity with conducting solid, at Rayleigh numbers from 1000 to 100 000. The computational effort is documented in detail demonstrating clearly that, for all cases, the highest‐order accurate algorithm has several magnitudes lower error than first‐ or second‐order schemes for a given computational effort. Abstract : The main purpose of this paper is to present an accurate, high‐order viscous discontinuous Galerkin solver for the computation of conjugate heat transfer of the steady‐state Boussinesq flows. Formal h and p convergence studies employing the method of manufactured solutions demonstrate that the expected order of accuracy is achieved. Through the comparison with 3 existing benchmark cases, these higher‐order accurate schemes can achieve an equivalent error with the lower‐order schemes with orders of magnitude lower computational effort.