The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a n...The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.展开更多
Electrical and electronic devices face significant challenges in heatmanagement due to their compact size and high heat flux,which negatively impact performance and reliability.Conventional coolingmethods,such as forc...Electrical and electronic devices face significant challenges in heatmanagement due to their compact size and high heat flux,which negatively impact performance and reliability.Conventional coolingmethods,such as forced air cooling,often struggle to transfer heat efficiently.In contrast,thermoelectric coolers(TECs)provide an innovative active cooling solution to meet growing thermal management demands.In this research,a refrigerant based on mono ethylene glycol and distilled water was used instead of using gases,in addition to using thermoelectric cooling units instead of using a compressor in traditional refrigeration systems.This study evaluates the performance of a Peltierbased thermalmanagement systemby analyzing the effects of using two,three,and four Peltiermodules on cooling rates,power consumption,temperature reduction,and system efficiency.Experimental results indicate that increasing the number of Peltier modules significantly enhances cooling performance.The four-module system achieved an optimal balance between cooling speed and energy efficiency,reducing the temperature of a liquidmixture(30% mono ethylene glycol+70% distilled water plus laser dyes)to 8℃ in just 17 min.It demonstrated a cooling rate of 0.794℃/min and a high coefficient of performance(COP)of 1.2 while consuming less energy than the two-and three-module systems.Furthermore,the study revealed that increasing the number of modules led to faster air cooling and improved temperature reduction.These findings highlight the importance of selecting the optimal number of Peltier modules to enhance efficiency and cooling speed whileminimizing energy consumption.This makes TEC technology a sustainable and effective solution for applications requiring rapid and reliable thermal management.展开更多
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1A6A1A10044950).
文摘The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.
文摘Electrical and electronic devices face significant challenges in heatmanagement due to their compact size and high heat flux,which negatively impact performance and reliability.Conventional coolingmethods,such as forced air cooling,often struggle to transfer heat efficiently.In contrast,thermoelectric coolers(TECs)provide an innovative active cooling solution to meet growing thermal management demands.In this research,a refrigerant based on mono ethylene glycol and distilled water was used instead of using gases,in addition to using thermoelectric cooling units instead of using a compressor in traditional refrigeration systems.This study evaluates the performance of a Peltierbased thermalmanagement systemby analyzing the effects of using two,three,and four Peltiermodules on cooling rates,power consumption,temperature reduction,and system efficiency.Experimental results indicate that increasing the number of Peltier modules significantly enhances cooling performance.The four-module system achieved an optimal balance between cooling speed and energy efficiency,reducing the temperature of a liquidmixture(30% mono ethylene glycol+70% distilled water plus laser dyes)to 8℃ in just 17 min.It demonstrated a cooling rate of 0.794℃/min and a high coefficient of performance(COP)of 1.2 while consuming less energy than the two-and three-module systems.Furthermore,the study revealed that increasing the number of modules led to faster air cooling and improved temperature reduction.These findings highlight the importance of selecting the optimal number of Peltier modules to enhance efficiency and cooling speed whileminimizing energy consumption.This makes TEC technology a sustainable and effective solution for applications requiring rapid and reliable thermal management.
