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

Öz

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.

Anahtar Kelimeler

• Thermoelectric effect
• Peltier module
• Machine Learning
• XGBoost

Proje Numarası

The project is not externally funded

Kaynakça

1. AB, C., (2008). COMSOL Multiphysics Modeling Guide.
2. Antonova, E. E. & Looman, D. C., (2005). Finite Elements for Thermoelectric Device Analysis in ANSYS. Canonsburg, USA. https://doi.org/10.1109/ICT.2005.1519922
3. Aparicio, J. L. P., Palma, R. & Taylor, R. L., 2012. Finite element analysis and material sensitivity of Peltier thermoelectric cells coolers. International Journal of Heat and Mass Transfer, pp. 1363-1374. https://doi.org/10.1016/j.ijheatmasstransfer.2011.08.031
4. Bassi, A., Anika S., Arjun S., Hanna S., Connor G., and Jonathan H. Chan. (2021). Building Energy Consumption Forecasting: A Comparison of Gradient Boosting Models. Bangkok, Thailand, 12th International Conference on Advances in Information Technology. https://doi.org/10.1145/3468784.3470656
5. Carvalho, T. P., Soares, F. A., Vita, R., Francisco, R. D. P., Basto, J. P., & Alcalá, S. G. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024. https://doi.org/10.1016/j.cie.2019.106024
6. Chen, T., Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794). https://doi.org/10.1145/2939672.293978
7. Cotorogea, B. P. F., Marino, G., & Vogl, S. (2022). Data driven health monitoring of Peltier modules using machine-learning-methods. SLAS technology, 27(5), 319-326. https://doi.org/10.1016/j.slast.2022.07.002
8. Dang, W., Guo, J., Liu, M., Liu, S., Yang, B., Yin, L., & Zheng, W. (2022). A semi-supervised extreme learning machine algorithm based on the new weighted kernel for machine smell. Applied Sciences, 12(18), 9213. https://doi.org/10.3390/app12189213

9. Gao, X., Zhang, K., Zhang, Z., Wang, M., Zan, T., & Gao, P. (2024). XGBoost-based thermal error prediction and compensation of ball screws. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 238(1-2), 151-163. https://doi.org/10.1177/095440542311571
10. Jaegle, M., 2008. Multiphysics Simulation of Thermoelectric Systems - Modeling of PeltierCooling and Thermoelectric Generation. Hannover, s.n.
11. Lundberg, S. M. & Lee, S. I., 2017. A unified approach to interpreting model predictions. Advances in neural information processing systems, p. 30. https://doi.org/10.48550/arXiv.1705.07874
12. Lyu Y, Siddique A.R.M, Majid S.H., Biglarbegian M, Gadsden S.A., Mahmud S., (2019). Electric vehicle battery thermal management system with thermoelectric cooling. Energy Reports, 5, 822-827. https://doi.org/10.1016/j.egyr.2019.06.016
13. Nasri W, Djebali R, Dhaoui S, Abboudi S, Kharroubi H, (2017). Finite Elements Multiphysics Investigation of Thermoelectric Systems for Electricity and Cooling Generation. International Journal of Modern Studies in Mechanical Engineering, 3(4), pp. 1-13. http://dx.doi.org/10.20431/2454-9711.0304001
14. Park, H. I., Cho, T. J., Choi, I. G., Rhee, M. S., & Cha, Y. (2023). Object classification system using temperature variation of smart finger device via machine learning. Sensors and Actuators A: Physical, 356, 114338. https://doi.org/10.1016/j.sna.2023.114338
15. Qian N , Wang X, Fu Y, Zhao Z, Xu J, Chen J, (2019). Predicting heat transfer of oscillating heat pipes for machining processes based on extreme gradient boosting algorithm. Applied Thermal Engineering, 164, 114521. https://doi.org/10.1016/j.applthermaleng.2019.114521
16. Shapley, L. (1953) A Value for n-Person Games. In: Kuhn, H. and Tucker, A., Eds., Contributions to the Theory of Games II, Princeton University Press, Princeton, 307-317. https://doi.org/10.1515/9781400881970-018
17. Song J, Tian W, Xu X, Wang Y, Li Z, (2019). Thermal performance of a novel ultrasonic evaporator based on machine learning algorithms. Applied Thermal Engineering, 148, pp. 438-446. https://doi.org/10.1016/j.applthermaleng.2018.11.083
18. Teffah, K., Zhang, Y. & Mou, X., (2018). Modeling and Experimentation of New Thermoelectric Cooler–Thermoelectric Generator Module, Energies, 11(3), 576. https://doi.org/10.3390/en11030576
19. Venkatesan, K. & Venkataramanan, M., (2020). Experimental and Simulation Studies on Thermoelectric Cooler: A Performance Study Approach. International Journal of Thermophysics, 41, Article number 38. https://doi.org/10.1007/s10765-020-2613-2
20. Villasevil, F., López, A. & Fisac, M., (2013). Modeling and Simulation of a Thermoelectric Structure with Pellets of Non-Standard Geometry and Materials. International Journal of Refrigeration, 36 (5), 1570-1575. https://doi.org/10.1016/j.ijrefrig.2013.02.014
21. Vipin, K. E., & Padhan, P. (2024). Machine-Learning Guided Prediction of Thermoelectric Properties of Topological Insulator Bi2Te3-xSex. Journal of Materials Chemistry C, 12, 7415-7425. https://doi.org/10.1039/D4TC01058B
22. Zaferani, S., Sams, M., Ghomashchi, R. & Chen, Z.-G., (2021). Thermoelectric Coolers as Thermal Management Systems for Medical Applications: Design, Optimization, and Advancement. Nano Energy, Volume 90, Part A, 106572 https://doi.org/10.1016/j.nanoen.2021.106572
23. Zhan, T., Fang, L. & Xu, Y., (2017). Prediction of thermal boundary resistance by the machine learning method. Scientific Reports, 7(7109). https://doi.org/10.1038/s41598-017-07150-7
24. Yilmazoglu, M. Zeki, (2016). Experimental and numerical investigation of a prototype thermoelectric heating and cooling unit, Energy and Buildings, 113, 51–60. https://doi.org/10.1016/j.enbuild.2015.12.046
25. Yilmazoglu, M. Zeki, Karaaslan S, Calisir, T., Turgut O. Y., and Senol B., (2013). Experimental Study on Thermoelectric Generator Performance Applied to a Combi Boiler, 13th UK Heat Transfer Conference, UKHTC2013/143, 2 - 3 September 2013, Imperial College London, 143-1-8.