A study of TiO2-enhanced nanofluids in internal combustion engines using neural networks

Abstract

Abstract In this study, the effects of nanoparticle addition to internal combustion engines were investigated. Firstly, engine coolant was prepared by mixing nanoparticles with water in different ratios (0%, 0.15%, 0.3%, 0.5% and 0.6%). Nanoparticles were investigated by SEM and XRD techniques. Then, the prepared coolants with different ratios of nanoparticles were tested on the engine at different loads (2.5 kW, 3.8 kW, 6 kW, 9 kW and 10 kW), and their heat transfer performances were investigated. Then, an ANN model was trained using the results, and the optimal TiO2 nanoparticle doped mixing ratio (0.26%) was determined. At the last stage, the techno-economic analysis of the TiO2 added coolant determined with the help of ANN was carried out, and the payback period and cumulative net present value were determined. Unlike other studies, ANN and economic analyses were performed and a contribution to the literature for the use of nanoparticle doped liquids was presented. The results show that the highest improvement in heat transfer performance is in the case of 0.6% nanoparticle addition with 40.8%. According to the ANN study, the highest performance increase is with the addition of 0.26% nanoparticles. The economic analysis made according to the result of the ANN study shows that the payback period will be less than 4 years.

Keywords

• Cooling system
• Energy efficiency
• Neural network
• Techno-economic analysis
• Nanofluids
• Engineering economics