Paper

Temperature boundary conditions in thermal fluids have conventionally been approached as Robin-type boundary conditions. However, with the emergence of supercomputing capabilities, there is the opportunity to explore the solution of heat transfer in the surrounding domains and establish a strong coupling with the temperature equation in the fluid, giving rise to what is known as Conjugate Heat Transfer problems. This paper introduces two strategies based on volume and surface algebraic couplings, solved using either a block Gauss-Seidel method or a block Jacobi method. The volume coupling implies solving the heat transfer problem in the fluid and solid monolithically and coupling it to the Navier-Stokes equations solved in the fluid. On the other hand, in the case of surface coupling, the Boussinesq system is solved within the fluid and then coupled to the solid through their shared interface. A comparative analysis of these approaches is presented, considering both algorithmic and computational performances within the framework of a multi-code coupling strategy. In the parallel execution of such problems, a decision involves determining how to distribute the cores among the various coupled codes. We propose a method that involves overloading computational nodes, allowing different codes to utilize the entire available resources. To enhance efficiency, the overload approach is implemented with a barrier, utilizing the DLB library, to mitigate the busy wait induced by MPI subroutines during data exchange. The solution to a practical example demonstrates a nearly twofold speedup achieved by the proposed method compared to a classical approach when employing volume coupling.