Determination of Morphology Effects on Bubble Nucleation and Growth in Micro/Nano Structured Surface Layers for High Performance Boiling Processes

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Thermal management is important for electronics, two phase pumped loop cooling systems for aerospace applications, vaporization processes in building energy systems, and Rankine cycle power plants. Development of micro/nano structured enhanced boiling heat transfer surfaces has typically been motivated by a desire to create vaporization process technologies that can handle high heat fluxes at low to moderate driving temperature difference to boost the energy efficiency and performance. Recent research on enhanced boiling surfaces has provided only a limited understanding of how the morphology of a micro/nano structured surface affects conditions of embryo bubble growth under superheated conditions, and has provided limited modeling capability that can be used to predict the dependence of heterogeneous nucleation on morphology and guide development of enhanced boiling surfaces. The research in this project develops two innovative ways to predict how morphology affects nucleation performance in micro/nano porous enhanced boiling surfaces. A machine-learning approach will be used to predict nucleation performance from learned trends in surface-feature and nucleation-performance data. The second will be a multiphase thermophysics model developed specifically to accurately predict onset of nanobubble nucleation and growth in nanoporous structures. These complementary models will both better illuminate the physics of the boiling process and can be used to guide development of next generation high-efficiency boiling surfaces for a variety of applications.The major goals of this research are to develop and evaluate two novel ways to predict the effects of surface morphology on the onset of nucleation and bubble growth in micro/nano porous layers of the type used for enhanced boiling surfaces. In one of two primary research threads, surface micrograph image analysis data will be combined with nucleation performance data for the corresponding surface, and the composite data will be used to train a neural network model that can predict the number density of active nucleation sites given the surface superheat temperature and the surface number density of features with the right combinations of low complexity (low surface image entropy) and above average feature size in the micro/nano structured layer. A second thread of this research will develop and evaluate a specialized 3D Lattice-Boltzmann multiphase simulation framework that will more accurately model micro/nano embryo bubble stability and growth in close proximity to micro/nano surface structures. The intellectual merit of the proposed research is that combining and comparing these two innovative strategies will provide a better understanding of the physics of nucleation in these circumstances, and will provide two complementary methods to guide the choice of micro/nano surface features for the design of optimized next-generation micro/nano-structured enhanced boiling surfaces for important applications. Both of these approaches can also be extended to ways of quantifying surface feature characteristics and predicting morphology effects in other phase change processes such as dropwise condensation, sublimation deposition, and thin liquid film melting and vaporization in laser processing of materials.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.