The objective of the current study is to investigate the importance of entropy generation and thermal radiation on the patterns of velocity,isentropic lines,and temperature contours within a thermal energy storage dev...The objective of the current study is to investigate the importance of entropy generation and thermal radiation on the patterns of velocity,isentropic lines,and temperature contours within a thermal energy storage device filled with magnetic nanoencapsulated phase change materials(NEPCMs).The versatile finite element method(FEM)is implemented to numerically solve the governing equations.The effects of various parameters,including the viscosity parameter,ranging from 1 to 3,the thermal conductivity parameter,ranging from 1 to 3,the Rayleigh parameter,ranging from 102 to 3×10^(2),the radiation number,ranging from 0.1 to 0.5,the fusion temperature,ranging from 1.0 to 1.2,the volume fraction of NEPCMs,ranging from 2%to 6%,the Stefan number,ranging from 1 to 5,the magnetic number,ranging from 0.1 to 0.5,and the irreversibility parameter,ranging from 0.1 to 0.5,are examined in detail on the temperature contours,isentropic lines,heat capacity ratio,and velocity fields.Furthermore,the heat transfer rates at both the cold and hot walls are analyzed,and the findings are presented graphically.The results indicate that the time taken by the NEPCMs to transition from solid to liquid is prolonged inside the chamber region as the fusion temperatureθf increases.Additionally,the contours of the heat capacity ratio Cr decrease with the increase in the Stefan number Ste.展开更多
Thermal characteristics of phase change material(PCM)are important in design and utilization of thermal energy storage or other applications.PCMs have great latent heat but suffer from low thermal conductivity.Then,in...Thermal characteristics of phase change material(PCM)are important in design and utilization of thermal energy storage or other applications.PCMs have great latent heat but suffer from low thermal conductivity.Then,in recent years,nano particles have been added to PCM to improve their thermophysical properties such as thermal conductivity.Effect of this nano particles on thermophysical properties of PCM has been a question and many experimental and numerical studies have been done to investigate them.Artificial intelligence-based approach can be a good candidate to predict thermophysical properties of nano enhance PCM(NEPCM).Then,in this study an artificial neural network(ANN)has been developed to predict the latent heat of the NEPCM.A comprehensive literature search was conducted to acquire thermal characteristics data from various NEPCM to train and test this artificial neural network model.Twenty different types of Nano particle and paraffin based PCMs were used in ANN development.The most important properties which are used as the input for the developed ANN model are NP size,density of NP,latent heat of PCM,density of PCM,concentration and latent heat of NEPCM in the range of 1-60 nm,100-8960 kg/m^(3),89.69-311 kJ/kg,760 to 1520 kg/m^(3),0.02-20 wt%and 60.72-338.6 kJ/kg,respectively.The output variable was latent heat of NEPCM.The result indicates that the ANN model can be applied to predict the latent heat of nano enhanced PCM satisfactory.The correlation coefficient of the created model was 0.97.This result shows ability of ANN to predict the latent heat of NEPCM.展开更多
文摘The objective of the current study is to investigate the importance of entropy generation and thermal radiation on the patterns of velocity,isentropic lines,and temperature contours within a thermal energy storage device filled with magnetic nanoencapsulated phase change materials(NEPCMs).The versatile finite element method(FEM)is implemented to numerically solve the governing equations.The effects of various parameters,including the viscosity parameter,ranging from 1 to 3,the thermal conductivity parameter,ranging from 1 to 3,the Rayleigh parameter,ranging from 102 to 3×10^(2),the radiation number,ranging from 0.1 to 0.5,the fusion temperature,ranging from 1.0 to 1.2,the volume fraction of NEPCMs,ranging from 2%to 6%,the Stefan number,ranging from 1 to 5,the magnetic number,ranging from 0.1 to 0.5,and the irreversibility parameter,ranging from 0.1 to 0.5,are examined in detail on the temperature contours,isentropic lines,heat capacity ratio,and velocity fields.Furthermore,the heat transfer rates at both the cold and hot walls are analyzed,and the findings are presented graphically.The results indicate that the time taken by the NEPCMs to transition from solid to liquid is prolonged inside the chamber region as the fusion temperatureθf increases.Additionally,the contours of the heat capacity ratio Cr decrease with the increase in the Stefan number Ste.
基金The authors would like to be obliged to Universiti Malaysia Pahang for providing laboratory facilities and financial assistance under the Grant No.RDU200347.
文摘Thermal characteristics of phase change material(PCM)are important in design and utilization of thermal energy storage or other applications.PCMs have great latent heat but suffer from low thermal conductivity.Then,in recent years,nano particles have been added to PCM to improve their thermophysical properties such as thermal conductivity.Effect of this nano particles on thermophysical properties of PCM has been a question and many experimental and numerical studies have been done to investigate them.Artificial intelligence-based approach can be a good candidate to predict thermophysical properties of nano enhance PCM(NEPCM).Then,in this study an artificial neural network(ANN)has been developed to predict the latent heat of the NEPCM.A comprehensive literature search was conducted to acquire thermal characteristics data from various NEPCM to train and test this artificial neural network model.Twenty different types of Nano particle and paraffin based PCMs were used in ANN development.The most important properties which are used as the input for the developed ANN model are NP size,density of NP,latent heat of PCM,density of PCM,concentration and latent heat of NEPCM in the range of 1-60 nm,100-8960 kg/m^(3),89.69-311 kJ/kg,760 to 1520 kg/m^(3),0.02-20 wt%and 60.72-338.6 kJ/kg,respectively.The output variable was latent heat of NEPCM.The result indicates that the ANN model can be applied to predict the latent heat of nano enhanced PCM satisfactory.The correlation coefficient of the created model was 0.97.This result shows ability of ANN to predict the latent heat of NEPCM.