Machine Learning-Based Approach for Modeling the Nanofluid Flow in a Solar Thermal Panel in the Presence of Phase Change Materials
Abstract
:1. Introduction
2. Problem Statement
3. Governing Equations
3.1. NFD
3.2. PCM
4. Numerical Method, Grid Study, and Validation
5. Results and Discussion
6. Conclusions
- (1)
- The maximum and average panel temperatures dramatically increased from t = 0 to t = 10 min and then increased at a large or small rate, depending on the diameter.
- (2)
- An increase in the diameter, particularly from 4 to 6 mm, reduced the maximum and average panel temperatures. However, the increased diameter raised the melted PCM volume fraction.
- (3)
- An increase in D_Pipe reduced the output flow temperature and the amount of HTR between the pipes and PCM.
- (4)
- The output temperature and the amount of HTR between the pipe and PCM were found to be dependent on time and increased over time depending on D_Pipe.
- (5)
- The panel had significantly lower temperatures in areas in contact with the pipes than in the other areas.
- This study can be performed for a different number of fins and for various fin shapes at different flow rates.
- The effect of using different PCMs and different NFDs on the outputs should be examined.
- The influence of using a PV-thermal panel during the night should also be evaluated in the absence of radiation.
- The impact of radiation changes on the outputs should be investigated at different hours of the day.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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EG | 2430.8 | 0.2532 | 0.0141 | 1088 |
235 | 429 | - | 10,500 | |
Water | 4179 | 0.613 | 0.001 | 997.1 |
Element Number | 911,000 | 1,027,000 | 1,145,000 | 1,283,000 |
T-out | 300.41 | 300.68 | 300.80 | 300.81 |
1% | 2% | 3% | |
---|---|---|---|
Re = 200 | |||
Bizhaem and Abbassi (65) | 22.86 | 29.17 | 33.48 |
Present study | 22.19 | 28.78 | 32.89 |
Re = 500 | |||
Bizhaem and Abbassi (65) | 7.93 | 13.92 | 18.53 |
Present study | 7.59 | 13.12 | 18.09 |
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Alqaed, S.; Mustafa, J.; Almehmadi, F.A.; Alharthi, M.A.; Sharifpur, M.; Cheraghian, G. Machine Learning-Based Approach for Modeling the Nanofluid Flow in a Solar Thermal Panel in the Presence of Phase Change Materials. Processes 2022, 10, 2291. https://doi.org/10.3390/pr10112291
Alqaed S, Mustafa J, Almehmadi FA, Alharthi MA, Sharifpur M, Cheraghian G. Machine Learning-Based Approach for Modeling the Nanofluid Flow in a Solar Thermal Panel in the Presence of Phase Change Materials. Processes. 2022; 10(11):2291. https://doi.org/10.3390/pr10112291
Chicago/Turabian StyleAlqaed, Saeed, Jawed Mustafa, Fahad Awjah Almehmadi, Mathkar A. Alharthi, Mohsen Sharifpur, and Goshtasp Cheraghian. 2022. "Machine Learning-Based Approach for Modeling the Nanofluid Flow in a Solar Thermal Panel in the Presence of Phase Change Materials" Processes 10, no. 11: 2291. https://doi.org/10.3390/pr10112291
APA StyleAlqaed, S., Mustafa, J., Almehmadi, F. A., Alharthi, M. A., Sharifpur, M., & Cheraghian, G. (2022). Machine Learning-Based Approach for Modeling the Nanofluid Flow in a Solar Thermal Panel in the Presence of Phase Change Materials. Processes, 10(11), 2291. https://doi.org/10.3390/pr10112291