Mihir Kumar Das

Numerical Study of Cyclic Melting and Solidification of Nano Enhanced Phase Change Material Based Heat Sink in Thermal Management of Electronic Components

The Present investigation has been carried out to study the performance of nano enhanced phase change material (NEPCM) based heat sink for thermal management of electronic components. Enthalpy based finite volume method is used for the analysis of phase change process in NEPCM. To enhance the thermal conductivity of phase change material (PCM), copper oxide nano particles of volume fractions 1%, 2.5% and 5% are added to PCM. A heat flux of 2500 W/m2 is taken as input to the heat sink. The thermal performance of the heat sink with PCM is compared with NEPCM for each volume fraction of nano particle for both finned and unfinned configurations. It is observed that the nano particle volume concentration plays a major role in removing the heat from the chip in case of unfinned heat sink configuration. However, for finned heat sink configuration, the volume concentration effect is not appreciable. In addition, the performance of NEPCM based finned heat sink is studied under cyclic loading in both natural and forced convection boundary conditions. It is observed that under forced convection the solidification time is reduced.Copyright

Prediction of Heat Transfer Coefficient in Flow Boiling over Tube Bundles Using ANFIS

The applicability of the artificial intelligence technique called ANFIS (for adaptive neuro fuzzy inference system) to model the flow boiling heat transfer over a tube bundle is studied in this paper. The ANFIS model is trained and validated with the experimental data from literature. The heat flux, mass flux, and row height are taken as input and the flow boiling heat transfer coefficient as output. The developed model performance is evaluated in terms of performance parameters such as root mean square error, mean square error, correlation coefficient, variance accounted for, and computational time. The preceding parameters of the model are then determined for different combinations of type and number of membership functions. The model is found to predict experimental heat transfer coefficient within an error of ±5%. The developed model is also compared with the artificial neural network model and is found to be better in predicting the flow boiling heat transfer coefficient. The developed model is further used to observe the variation of heat transfer coefficient of the individual rows and bundle for intermediate value of parameters such as heat flux and mass flux that are not included in the analysis of experimental data. The analysis is able to provide complete information about variation of heat transfer coefficient of individual rows and the bundle with respect to heat flux and mass flux.

Artificial intelligence approach for the prediction of heat transfer coefficient in boiling over tube bundles

The determination of heat transfer coefficient plays an important role in optimal designing of heat transfer equipments as it directly affects the heat surface area and thereby the weight and cost of the equipment. Thus, prediction of heat transfer coefficient with minimum error reduces the exhaustive experimental work. Therefore, the prime objective of the present work is the application of computational intelligence methods for improving the prediction accuracy of heat transfer coefficient in flow boiling over tube bundles. The adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) are applied to predict the flow boiling heat transfer coefficient as output, taking pressure, pitch of the bundle, heat flux, mass flux and vapour quality as input. The performance of different derivatives of neural network such as multilayer perceptron, general regression neural network and radial basis network (RBF) is studied with varying the parameter. The ANFIS is tested with different types and number of membership functions for the prediction of flow boiling heat transfer coefficient. These methods are found to be better for predicting the flow boiling heat transfer coefficient than conventional correlations.