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

热管理对于电子设备、用于航空航天应用的两相泵送回路冷却系统、建筑能源系统中的蒸发过程以及朗肯循环发电厂是重要的。微/纳米结构的增强沸腾传热表面的开发通常是由于期望产生可以在低至中等驱动温差下处理高热通量以提高能量效率和性能的汽化工艺技术。 最近的研究增强沸腾表面提供了一个有限的理解，如何微/纳米结构表面的形态影响条件下的过热条件下的胚胎气泡生长，并提供了有限的建模能力，可用于预测形态的异质成核的依赖性和指导发展的增强沸腾表面。 该项目的研究开发了两种创新方法来预测形态如何影响微/纳米多孔强化沸腾表面的成核性能。 机器学习方法将用于从表面特征和成核性能数据中的学习趋势预测成核性能。 第二个将是专门开发的多相热物理模型，以准确预测纳米多孔结构中纳米气泡成核和生长的发生。 这些互补的模型将更好地阐明沸腾过程的物理学，并可用于指导下一代高效沸腾表面的各种应用的发展，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态的影响，用于增强沸腾表面的类型的微/纳米多孔层的成核和气泡生长的开始。 在两个主要研究线索之一中，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，并且复合数据将被用于训练神经网络模型，该神经网络模型可以在给定表面温度和具有低复杂性的正确组合的特征的表面数密度的情况下预测活性成核位点的数密度（低表面图像熵）和高于微/纳米结构化层中的平均特征尺寸。这项研究的第二个线程将开发和评估一个专门的3D格子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接近微/纳米表面结构的生长。 所提出的研究的智力价值是，结合和比较这两种创新策略将提供更好地理解在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，用于设计优化的下一代微/纳米结构的增强沸腾表面的重要应用。 这两种方法也可以扩展到量化表面特征特性和预测其他相变过程中的形态效应的方法，例如滴状冷凝，升华沉积，该奖项反映了NSF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进行评估，被认为值得支持的搜索.

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.