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

热管理对于电子产品、航空航天应用的两相泵循环冷却系统、建筑能源系统的汽化过程和朗肯循环发电厂都很重要。微/纳米结构增强型沸腾传热表面的发展通常是出于创造汽化工艺技术的愿望，这种技术可以在低到中等驱动温差下处理高热流，以提高能源效率和性能。最近对增强沸腾表面的研究仅对微/纳米结构表面的形貌如何影响过热条件下胚泡生长的条件提供了有限的理解，并且提供了有限的建模能力，用于预测非均相成核对形貌的依赖并指导增强沸腾表面的发展。本项目的研究开发了两种创新的方法来预测微/纳米多孔增强沸腾表面的形貌如何影响成核性能。机器学习方法将用于从表面特征和成核性能数据中学习趋势预测成核性能。第二个将是多相热物理模型，专门用于准确预测纳米气泡成核和纳米孔结构中的生长。这些互补模型将更好地阐明沸腾过程的物理性质，并可用于指导下一代高效沸腾表面的开发，用于各种应用。本研究的主要目标是开发和评估两种新的方法来预测表面形貌对用于增强沸腾表面的微/纳米多孔层的成核和气泡生长的影响。在两个主要研究方向之一中，表面显微图像分析数据将与相应表面的成核性能数据相结合，复合数据将用于训练神经网络模型，该模型可以在给定表面过热温度和低复杂性（低表面图像熵）和高于平均特征尺寸的正确组合下预测微/纳米结构层中活性成核位点的数量密度。本研究的第二部分将开发和评估一个专门的3D晶格-玻尔兹曼多相模拟框架，该框架将更准确地模拟微/纳米胚胎气泡的稳定性和生长，接近微/纳米表面结构。该研究的智力价值在于，结合和比较这两种创新策略将更好地理解这些情况下的成核物理，并将提供两种互补的方法来指导微/纳米表面特征的选择，以设计优化的下一代微/纳米结构增强沸腾表面，用于重要应用。这两种方法也可以扩展到定量表面特征特征和预测其他相变过程中的形态效应的方法，如激光加工材料中的水滴冷凝、升华沉积和薄液膜熔化和汽化。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.