The present work aimed to investigate the melting and solidification characteristics of nanoparticle enhanced phase change material (NEPCM). The NEPCM were prepared using paraffin as the phase change material and multiwall carbon nanotube (MWCNT) as the nanomaterial without using any dispersant. Thermal conductivity of the NEPCM was measured with respect to temperature and the measured data showed higher enhancement than the phase change material both in liquid and solid state, due to inherent high conductive and the continuous networking of the MWCNT. A reduction in solidification and melting time of 42% and 29% was achieved in the case of NEPCM with 0.9% and 0.3%, respectively. It is concluded that enhanced heat transfer characteristics of NEPCM is highly beneficial towards design and development of efficient thermal energy storage system for various applications.
Artificial Neural Network (ANN) approach was used for predicting and analyzing the mechanical properties of A413 aluminum alloy produced by squeeze casting route. The experiments are carried out with different controlled input variables such as squeeze pressure, die preheating temperature, and melt temperature as per Full Factorial Design (FFD). The accounted absolute process variables produce a casting with pore-free and ideal fine grain dendritic structure resulting in good mechanical properties such as hardness, ultimate tensile strength, and yield strength. As a primary objective, a feed forward back propagation ANN model has been developed with different architectures for ensuring the definiteness of the values. The developed model along with its predicted data was in good agreement with the experimental data, inferring the valuable performance of the optimal model. From the work it was ascertained that, for castings produced by squeeze casting route, the ANN is an alternative method for predicting the mechanical properties and appropriate results can be estimated rather than measured, thereby reducing the testing time and cost. As a secondary objective, quantitative and statistical analysis was performed in order to evaluate the effect of process parameters on the mechanical properties of the castings.
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