COMPARISON STUDY BETWEEN THEORETICAL ANALYSIS AND ARTIFICIAL NEURAL NETWORK OF THE CAPILLARY TUBE
Year 2021,
, 690 - 699, 01.03.2021
Basim Freegah
Ammar A. Hussain
Ahmed Ramadhan Al-obaidi
Abstract
The main purpose of expansion devices is reduced the higher pressure of the working fluid from the
condenser pressure to the evaporator pressure. There are several kinds of expansion devices, one of these types is
capillary tube which is common utilized in small size refrigeration systems. In this work, the effect of the diameter of
capillary tube and mass flow rate of the refrigerant on the physical properties of the refrigerant within the capillary
tube have been conducted. Moreover, an artificial neural network (ANN) technique has been utilized in order to
clarify the possibility of applying this theory to the effect of such parameters on the results of the capillary tube. The
study has been shown that there is a very good agreement between experimental and numerical results. The diameter
and mass flow rate have impact on the length of the capillary tube, increase diameter leads to increase the capillary
tube length while increase mass flow rate leads to decrease the length. Furthermore, the results shown that ANN
technique can be employed to study the effect of such as parameters that considered in this on length of capillary
tube. So, it can be using latter technique with accuracy 95%.
Thanks
The authors would like to thank Mustansiriyah University (www.uomustansiriyah.edu.iq) in Baghdad – Iraq
for its support in the present work.
References
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Part I: Mathematical formulation and numerical model." International Journal of Refrigeration 30, no. 4
(2007): 642-653. https://doi.org/10.1016/j.ijrefrig.2006.08.015.
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Network for isobutane flow in non-adiabatic capillary tubes." International journal of refrigeration 38 (2014):
281-289. doi.org/10.1016/j.ijrefrig.2013.08.018.
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tube." Applied thermal engineering 29, no. 14-15 (2009): 2816-2823.
https://doi.org/10.1016/j.applthermaleng.2009.02.001.
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tube suction line heat exchanger: an artificial neural network approach." Energy conversion and
management 46, no. 2 (2005): 223-232. https://doi.org/10.1016/j.enconman.2004.02.015.
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York (1982).
Year 2021,
, 690 - 699, 01.03.2021
Basim Freegah
Ammar A. Hussain
Ahmed Ramadhan Al-obaidi
References
- [1] Sainath, Kasuba, T. Kishen Kumar Reddy, and Suresh Akella. "Optimization of capillary tube dimensions
using different Refrigerants for 1.5 ton mobile air conditioner." Case Studies in Thermal Engineering 16
(2019): 100528. https://doi.org/10.1016/j.csite.2019.100528.
- [2] Ajayi, Oluseyi O., Dorothy E. Ibia, Mercy Ogbonnaya, Ameh Attabo, and Agarana Michael. "CFD analysis of
nanorefrigerant through adiabatic capillary tube of vapour compression refrigeration system." Procedia
Manufacturing 7 (2017): 688-695. https://doi.org/10.1016/j.promfg.2016.12.102.
- [3] Mittal, M. K., Ravi Kumar, and Akhilesh Gupta. "Numerical analysis of adiabatic flow of refrigerant through
a spiral capillary tube." International Journal of Thermal Sciences 48, no. 7 (2009): 1348-1354.
https://doi.org/10.1016/j.ijthermalsci.2009.01.003.
- [4] Wang, Jing, Feng Cao, Zhizhong Wang, Yuanyang Zhao, and Liangsheng Li. "Numerical simulation of coiled
adiabatic capillary tubes in CO2 transcritical systems with separated flow model including metastable
flow." International journal of refrigeration 35, no. 8 (2012): 2188-2198.
https://doi.org/10.1016/j.ijrefrig.2012.07.012.
- [5] Garcıa-Valladares, O., C. D. Perez-Segarra, and A. Oliva. "Numerical simulation of capillary-tube expansion
devices behaviour with pure and mixed refrigerants considering metastable region. Part II: experimental
validation and parametric studies." Applied thermal engineering 22, no. 4 (2002): 379-391.
https://doi.org/10.1016/S1359-4311(01)00097-7.
- [6] Wongwises, Somchai, and Mathurose Suchatawut. "A simulation for predicting the refrigerant flow
characteristics including metastable region in adiabatic capillary tubes." International journal of energy
research 27, no. 2 (2003): 93-109. https://doi.org/10.1002/er.860.
- [7] García-Valladares, O. "Numerical simulation of non-adiabatic capillary tubes considering metastable region.
Part I: Mathematical formulation and numerical model." International Journal of Refrigeration 30, no. 4
(2007): 642-653. https://doi.org/10.1016/j.ijrefrig.2006.08.015.
- [8] Heimel, Martin, Wolfgang Lang, and Raimund Almbauer. "Performance predictions using Artificial Neural
Network for isobutane flow in non-adiabatic capillary tubes." International journal of refrigeration 38 (2014):
281-289. doi.org/10.1016/j.ijrefrig.2013.08.018.
- [9] Vinš, Václav, and Václav Vacek. "Mass flow rate correlation for two-phase flow of R218 through a capillary
tube." Applied thermal engineering 29, no. 14-15 (2009): 2816-2823.
https://doi.org/10.1016/j.applthermaleng.2009.02.001.
- [10] Islamoglu, Yasar, Akif Kurt, and Cem Parmaksizoglu. "Performance prediction for non-adiabatic capillary
tube suction line heat exchanger: an artificial neural network approach." Energy conversion and
management 46, no. 2 (2005): 223-232. https://doi.org/10.1016/j.enconman.2004.02.015.
- [11] Duch, Włodzisław. "Coloring black boxes: visualization of neural network decisions." In Proceedings of the
International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1735-1740. IEEE, 2003.
https://doi.org/10.1109/IJCNN.2003.1223669.
- [12] Stoecker, W. F., and W. N. Jones. "Refrigeration and Air Conditioning. The McGaw-Hill." Inc. New
York (1982).