OPTIMISATION OF COMPACT BOILING HEAT EXCHANGERS USING ARTIFICIAL NEURAL NETWORK

紧凑型热交换器在许多工业应用中被越来越多地考虑用于涉及沸腾和冷凝的相变过程。在许多情况下，这种应用是全新的，没有以前的经验或信息可用于紧凑型热交换器的建设性设计。该项目旨在开发一种商业上可行的设计方法，该方法将是创新和新颖的，考虑到通过人工神经网络(ANN)模型对紧凑型沸腾换热器的关键设计参数进行稳健和快速的计算，并保持计算流体动力学(CFD)求解沸腾流动和传热过程的固有精度，并使用实验数据进行验证。它将为行业合作伙伴在设计和制造紧凑型沸腾换热器方面提供领先优势，在本地和全球市场上成功竞争。

Compact heat exchangers are increasingly considered for duties in many industrial applications, involving phase change processes of boiling and condensation. In many cases such applications are completely new and no previous experience or information is available for constructive design of compact heat exchangers. This project aims to develop a commercially-viable design methodology that will be innovative and novel with in mind of robust and speedy computations of key design parameters of compact boiling heat exchangers through the artificial neural networks (ANN) model and maintaining the inherent accuracy associated with the solutions of the boiling flow and heat transfer processes from computational fluid dynamics (CFD) and validated using experimental data. It will provide a cutting edge to the Industry Partner to compete successfully on the local and global market fronts in designing and manufacturing compact boiling heat exchangers.