پیش بینی تغییرات حرارتی مخزن ذخیره کننده سیال یک خشک‌کن خورشیدی با استفاده از شبکه عصبی مصنوعی و روش دینامیک سیالات محاسباتی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه کردستان، سنندج، ایران

2 دانشجوی کارشناسی ارشد، گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه کردستان، سنندج، ایران

چکیده

در این تحقیق برای افزایش عملکرد جمع کننده خورشیدی سهموی از مواد تغییرفاز دهنده PCM درون مخزن ذخیره کننده سیال استفاده شد. تاثیر میزان جریان سیال در سه سطح 1، 5/2 و 5 لیتر بر دقیقه و جرم PCM در دو سطح 5/1 و 2/3 کیلوگرم بر دمای خروجی، بازده جمع کننده و بازده مخزن با استفاده روش آزمایشگاهی و CFD و ANN  ارزیابی و با هم مقایسه شد. میزان بازده خشک کردن از 11/21 تا 20/25 درصد و جمع کننده از  9/62 تا 03/64 تغییر نمود. میزان خطای به دست آمده از بازده جمع کننده از روش CFD و ANN به ترتیب از 31/5 تا 4/7 درصد و 22/1 تا 84/3 درصد متغیر بود. با توجه به داده­های آماری و مدت زمان صرف شده مشخص شد که روش ANN نسبت به روش CFD با دقت بیشتر و زمان صرف شده کمتر می­تواند برای پیش­بینی رفتار حرارتی سامانه استفاده شود.

کلیدواژه‌ها


عنوان مقاله [English]

Predicting the Thermal Changes of a Fluid Storage Tank of a Solar Dryer Using Artificial Neural Network and Computational Fluid Dynamics Method

نویسندگان [English]

  • Hadi Samimi-Akhijahani 1
  • Zakaria Alimohammadi 2
1 Assistant Professor, Department of Biosystem Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran
2 M.Sc. Student, Department of Biosystem Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran
چکیده [English]

In this research, to increase the performance of parabolic solar collector, PCM phase change materials were used inside the fluid storage tank. The effect of fluid flow rate at three levels of 1, 2.5 and 5 l/min and PCM mass at two levels of 1.5 and 3.2 kg on output temperature and thermal efficiency of the collector and tank efficiency using experimental methods and CFD and ANN were evaluated and compared. Drying efficiency changed from 21.11 to 25.20% and collector from 62.9 to 64.03. The collector efficiency error of CFD and ANN methods varied from 5.31 to 7.4% and 1.22 to 3.84%, respectively. According to the statistical data and the time spent, it was found that the ANN method can be used to predict the thermal behavior of the system more accurately and less time than the CFD method.

کلیدواژه‌ها [English]

  • CFD Method
  • Phase change materials
  • Solar dryer
  • Solar radiation
  • Thermal efficiency
Al-Waeli A. H. A., Sopiana, K., Kazemb, H. K., Yousif, J. H., Chaichanc, M. T., Ibrahima, A., Mat, S.  & Ruslana, M. (2018). Comparison of prediction methods of PV/T nanofluid and nano-PCM system using a measured dataset and artificial neural network. Solar Energy, 162, 378-396.
Baek, S. M., Nam, J. H., Hong, H. & Kim, C. (2011). Effect of brine flow rate on the performance of a spiral-jacketed thermal storage tank used for SDHW systems: A computational fluid dynamics study. Applied Thermal Engineering, 31, 2716-2725
Bellos, E., Tzivanidis, C., Antonopoulos, K. A. & Gkinis, G. (2016). Thermal enhancement of solar parabolic trough collectors by using nanofluids and converging-diverging absorber tube. Renewable Energy, 94, 213–22.
Cetiner, C., Halici, F., Cacur, H. & Taymaz, I. (2005). Generating hot water by solar energy and application of neural network. Applied Thermal Engeering, 25 (8-9), 1337–48.
Farkas, I. & Geczy-Vıg, P. (2003). Neural network modelling of flat-plate solar collectors. Computer and Electronic in Agriculture, 40 (1-3), 87–102.
Fischer, S., Frey, P. & Drück, H. (2012). Comparison between state-of-the-art and neural network modelling of solar collectors. Solar Energy, 86 (1), 3268–3277.
Forristall, R. (2003). Heat transfer analysis and modeling of a parabolic trough solar receiver implemented in engineering equation solver. Colorado: National Renewable Energy Laboratory (NREL).
Ghritlahre, H. K. & Prasad, R. K. (2018). Application of ANN technique to predict the performance of solar collector systems - A review. Renewable and Sustainable Energy Reviews, 84, 75-88.
Iranmanesh, M., Akhijahani, H. S., & Jahromi, M. S. B. (2020). CFD modeling and evaluation the performance of a solar cabinet dryer equipped with evacuated tube solar collector and thermal storage system. Renewable Energy, 145, 1192-1213.
Kalogirou, S. A. (2006). Prediction of flat-plate collector performance parameters using arti-ficial neural network. Solar Energy, 80 (3), 248–59.
Kalogirou, S. A, Panteliou, S. & Dentsoras, A. (1999). Modeling of solar domestic water heating systems using artificial neural networks. Solar Energy, 65(6), 335–342.
Kumaresan, G., Sridhar, R. & Velraj, R. (2012). Performance studies of a solar parabolic trough collector with a thermal energy storage system. Energy, 47 (1), 395-402.
Lecoeuche, S. & Lalot, S. (2005). Prediction of the daily performance of solar collectors. International Communication of Heat and Mass Transfer, 32 (5), 603–11.
Motahayyer, M., Arabhosseini, A., Samimi-Akhijahani, H. & Khashechi, M. (2018). Application of computational fluid dynamics in optimization design of absorber plate of solar dryer. Iranian Journal of Biosystem Engineering, 49 (2), 285-294. (In Farsi)
Schmidhuber, J. (2015). Deep Learning in Neural Networks: An Overview. Neural Networks, 61, 85-117.
Scapino, L., Zondag, H. A., Diriken, J., Rindt, C. C. M., Van Bael, J. & Sciacovelli, A. (2019). Modeling the performance of a sorption thermal energy storage reactor using artificial neural networks. Applied Energy, 253, 1-15.
Tay, N. H. S., Bruno, F. & Belusko, M. (2012). Experimental validation of a CFD model for tubes in a phase change thermal energy storage system. International Journal of heat and Mass Transfer, 55 (4), 574-585.
Varol, Y., Koca, A., Oztop, H. F. & Avci, E. (2010). Forecasting of thermal energy storage performance of phase change material in a solar collector using soft computing techniques. Expert System Applied, 37 (4), 2724–2732.
Xiaohong, G., Bin, L., Yongxian, G. & Xiugan, Y. (2011). Two-dimensional transient thermal analysis of PCM canister of a heat pipe receiver under microgravity. Applied Thermal Engineering, 31 (5): 735–41.
Xie, H., Liu, L., Ma, F. & Fan, H. (2009). Performance prediction of solar collectors using artificial neural networks. Proceeding of the international conference on artificial intelligence and computational intelligence, 573–576.