Multi-Response Optimization of Nanofluid-Based I. C. Engine Cooling System Using Fuzzy PIV Method

Effective cooling of the internal combustion (I. C.) engines is of utmost importance for their improved performance. Automotive heat exchangers used as radiator with low efficiency in the industry may pose a serious threat to the engines. Thus, thermal scientists and engineers are always looking for modern methods to boost the heat extraction from the engine. A novel idea of using nanofluids for engine cooling has been in the news for some time now, as they have huge potential because of better thermal properties, strength, compactness, etc. Nanofluids are expected to replace the conventional fluids such as ethylene glycol, propylene glycol, water etc. due to performance and environmental concerns. Overall performance of the engine cooling system depends on several input parameters and therefore they need to be optimised to achieve an optimum performance. This study is focussed on developing a nanofluid engine cooling system (NFECS) where Al2O3 nanoparticles mixed with ethylene glycol (EG) and water is used as nanofluid. Furthermore, it also explores the effect of four important input parameters of the NFECS i.e., nanofluid inlet temperature, engine load, nanofluid flow rate, and nanoparticle concentration on its five attributes (output responses) viz thermal conductivity of the nanofluid, heat transfer coefficient, viscosity of the nanofluid, engine pumping power required to pump the desired amount of the nanofluid, and stability of the nanofluid. Taguchi’s L18 orthogonal array is used as the design of experiment to collect experimental data. Weighting factors are determined for output responses using the Triangular fuzzy numbers (TFN) and optimal setting of the input parameters is obtained using a novel fuzzy proximity index value (FPIV) method.