SUMMARY

High-powered and high-energy density electronics are becoming more common with advances in computing, electric vehicles, and modern defense systems. Applications like these require efficient, compact, and economical heat exchanger designs capable of extremely large heat fluxes. Phase-change cooling methods allow for these characteristics; however, the design and optimization of these devices is extremely challenging. Numerical simulations can assist in this effort by providing details of the flow that are inaccessible to experimental measurements. One such system of interest to this work is acoustically enhanced nucleate boiling, which is capable of dramatic increases in the Critical Heat Flux (CHF). The focus of the present work is the development of a numerical simulation capable of predicting the behavior of acoustically enhanced nucleate boiling up to the CHF. A wavelet-adaptive Direct Numerical Simulation (DNS) that runs entirely on the Graphics Processing Unit (GPU) architecture has been developed in this work to allow accurate, error-controlled simulation of the acoustically enhanced nucleate boiling process. Nucleate boiling in the presence of acoustic fields suffers from a large disparity of important time scales, namely the acoustic time scale and the convective time scale near the incompressible limit. To address this issue, the compressible Navier-Stokes equations are solved using a preconditioned dual-time stepping method to allow for accurate simulation of the flow for all Mach numbers, everywhere in the domain. The governing equations are solved on a wavelet-adaptive grid that provides a direct measure of local error and is adapted at every time step to follow the evolution of the flow for a significant reduction in computational resources and expense. The use of the wavelet-adaptive grid and the dual-time stepping method together allows for rigorous error control in both space and time. All components of this simulation have been redesigned and optimized for efficient implementation on the GPU architecture to offset the overhead of grid adaptation and further reduce time-to-solution. The development of the high-performance, error-controlled framework and its verification and validation will be presented.