Machine Learning Based Heat Transfer Optimization of Nano-fluid flow in a Helically Coiled Pipe

Machine Learning algorithms are widely used in various fields such as energy sectors, manufacturing sectors and aerospace sectors. These algorithms are used mainly in predictive and optimization purpose. The present study deals with the application of two machine learning algorithms i.e. Random Forest algorithm and Support Vector Machine Algorithm to predict the heat transfer efficiency of a flowing nano-fluid in a helically coiled pipe. Keywords: Machine Learning; Optimization; Nano-fluid; Heat Transfer

Heat Transfer Optimization of NEXA Ballard Low-Temperature PEMFC

Hydrogen is one of the modern energy carriers, but its storage and practical use of the newest hydrogen technologies in real operation conditions still is a task of future investigations. This work describes the experimental hydrogen hybrid energy system (HHS). HHS is part of a laboratory off-grid system that stores electricity gained from photovoltaic panels (PVs). This system includes hydrogen production and storage units and NEXA Ballard low-temperature proton-exchange membrane fuel cell (PEMFC). Fuel cell (FC) loses a significant part of heat during converting chemical energy into electricity. The main purpose of the study was to explore the heat distribution phenomena across the FC NEXA Ballard stack during load with the next heat transfer optimization. The operation of the FC with insufficient cooling can lead to its overheating or even cell destruction. The cause of this undesirable state is studied with the help of infrared thermography and computational fluid dynamics (CFD) modeling with heat transfer simulation across the stack. The distribution of heat in the stack under various loads was studied, and local points of overheating were determined. Based on the obtained data of the cooling air streamlines and velocity profiles, few ways of the heat distribution optimization along the stack were proposed. This optimization was achieved by changing the original shape of the FC cooling duct. The stable condition of the FC stack at constant load was determined.