Predictive AI platform on thin film evaporation in hierarchical structures

• Published in: International journal of heat and mass transfer 2021-06, Vol.171, p.121116, Article 121116
• Main Authors: Jafari, Parham, Sarmadi, Saeed, Tasoujian, Shahin, Ghasemi, Hadi
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Avionics; Computational fluid dynamics; Dimensionless numbers; Electric vehicles; Evaporation; Form factors; Heat flux; Heat transfer; Hierarchical structures; Independent variables; Integrated circuits; Miniaturization; Performance prediction; Photonics; Structural hierarchy; Thermal energy; Thermal management; Thin films; Working fluids
• ISSN: 0017-9310; 1879-2189
• DOI: 10.1016/j.ijheatmasstransfer.2021.121116

•A complete data set on thin film evaporation in hierarchical structures was collected from various independent research groups.•We cast a methodology and a predictive AI platform for thin film evaporation in hierarchical structures.•The length scale of the smaller length scale of the hierarchical structure (i.e. nanoscale) is the most ruling dimension for design of hierarchical structures for maximum heat dissipation.•The develop general AI model could be implemented for various form of working fluids.•This work provides a foundation and rational methodology to use data science to guide future development of hierarchical structures for thermal management of a wide spectrum of system including photonics/electronics. The trend in miniaturization and enhanced functional performance of integrated circuits and power electronics and photonics has amplified the generated thermal energy in these devices making thermal management a bottleneck for further advancement in these fields. A range of geometries of hierarchical structures are developed and examined to address this challenge. However, the numerous form factors and dimension of hierarchical structures in addition to cost and time-consuming synthesis and test procedures make it unfeasible to explore bountiful variations of hierarchical geometries through experimental methods. Here, we introduce a general Artificial Intelligence (AI) platform to address this challenge and guide discovery of hierarchical structures for extreme thermal management of high-performance photonics/electronics. The AI platform is based on Random Forest (RF) algorithm, a robust AI method, and was trained using a large collected experimental data set corresponding to thin film evaporation in various forms of hierarchical structures. Four geometrical dimensions of the hierarchical structures and two dimensionless numbers governing heat transfer and fluid dynamics in these structures were used as independent variables to predict heat flux in these structures. The trained model's performance was analyzed using statistical metrics and showed an excellent prediction of heat flux for all the structures with various working fluids. The performance of predictive AI platform was further validated by two independent studies of different research groups. This predictive platform provides a foundation for rational discovery of hierarchical structures and working fluids to address the ongoing challenge of thermal management in broad spectrum of tec