Thermal analysis of a fluid inside a cubicle surrounded by Peltier modules

Abstract

Peltier modules are thermoelectric devices that convert electric energy to thermal energy. The cost of cooling process by the module depends on the size of the module. The number of modules play a vital role in deciding the performance of the system. A Peltier module has a high value of COP for the applied DC voltage. The module finds lot of applications because of its compact size, eco-friendly nature, high durability, noise and vibration-free operation, and less maintenance. Despite these advantages the Peltier module faces constraints for large scale applications. This work presents a computational analysis of temperature distribution of a fluid volume surrounded by four Peltier modules. Nine different cuboids having multiple number of Peltier modules is analyzed. The temperature distribution as a function of time is presented. A Machine learning algorithm is developed to predict the temperature of the fluid for varying cuboid sizes surrounded by Peltier modules. The developed machine learning model can predict the average temperature of the fluid domain at any given Peltier size, fluid volume, applied current, and time.

Keywords

• Thermoelectric effect
• Peltier module
• Machine Learning
• XGBoost

Project Number

The project is not externally funded

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