OPTIMISATION OF COMPACT BOILING HEAT EXCHANGERS USING ARTIFICIAL NEURAL NETWORK

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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.