Estimation of the Effect of Oblique Positioned Obstacle Placement on Thermal Performance of a Horizontal Mantle Hot Water Tank with Machine Learning
Due to the growing popularity of vacuum tube solar collectors and their more esthetically pleasing look, horizontal hot water tanks are increasingly being used in solar hot water systems. In order to improve the thermal performance of a horizontal mantled hot water tank, this work numerically examin...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/1/48 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841549423803367424 |
|---|---|
| author | Aslı Durmuşoğlu Buket Turgut Yusuf Tekin Burak Turgut |
| author_facet | Aslı Durmuşoğlu Buket Turgut Yusuf Tekin Burak Turgut |
| author_sort | Aslı Durmuşoğlu |
| collection | DOAJ |
| description | Due to the growing popularity of vacuum tube solar collectors and their more esthetically pleasing look, horizontal hot water tanks are increasingly being used in solar hot water systems. In order to improve the thermal performance of a horizontal mantled hot water tank, this work numerically examines the impact of positioning inclination barriers parallel or coincident to one another at varying angles. The main input provided the velocity V = 0.036, 0.073, 0.11, and 0.147 m/s, and analysis were performed for each speed. The study concluded that V = 0.073 m/s was the ideal mains input velocity for each scenario and that raising the speed typically resulted in a lower mains outlet temperature. According to the study’s findings, the tank design with the first obstacle 150 mm away and the two obstacles 100 mm apart achieves the best efficiency. The residential water temperature in this model is 312 K, while the storage water temperature is 309.5 K. In this study, a feed-forward artificial neural network (ANN) model based predictor was designed to estimate the mantle outlet and main outlet temperatures and the temperature of the stored water. Analyses were performed for different network inlet velocities and obstacle combinations, and ANN showed superior performance in estimating temperature parameters. |
| format | Article |
| id | doaj-art-0d3ca732c05f47d9a4b9974745419b70 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-0d3ca732c05f47d9a4b9974745419b702025-01-10T13:14:16ZengMDPI AGApplied Sciences2076-34172024-12-011514810.3390/app15010048Estimation of the Effect of Oblique Positioned Obstacle Placement on Thermal Performance of a Horizontal Mantle Hot Water Tank with Machine LearningAslı Durmuşoğlu0Buket Turgut1Yusuf Tekin2Burak Turgut3Department of Mechanical Engineering, Faculty of Engineering, Hakkari University, Hakkari 30000, TurkeyDepartment of Mechanical Engineering, Faculty of Engineering and Naturel Sciences, Tokat Gaziosmanpaşa University, Tokat 60250, TurkeyDepartment of Mechanical Engineering, Faculty of Engineering, Erciyes University, Kayseri 38039, TurkeyDepartment of Mechanical Engineering, Faculty of Engineering, Erciyes University, Kayseri 38039, TurkeyDue to the growing popularity of vacuum tube solar collectors and their more esthetically pleasing look, horizontal hot water tanks are increasingly being used in solar hot water systems. In order to improve the thermal performance of a horizontal mantled hot water tank, this work numerically examines the impact of positioning inclination barriers parallel or coincident to one another at varying angles. The main input provided the velocity V = 0.036, 0.073, 0.11, and 0.147 m/s, and analysis were performed for each speed. The study concluded that V = 0.073 m/s was the ideal mains input velocity for each scenario and that raising the speed typically resulted in a lower mains outlet temperature. According to the study’s findings, the tank design with the first obstacle 150 mm away and the two obstacles 100 mm apart achieves the best efficiency. The residential water temperature in this model is 312 K, while the storage water temperature is 309.5 K. In this study, a feed-forward artificial neural network (ANN) model based predictor was designed to estimate the mantle outlet and main outlet temperatures and the temperature of the stored water. Analyses were performed for different network inlet velocities and obstacle combinations, and ANN showed superior performance in estimating temperature parameters.https://www.mdpi.com/2076-3417/15/1/48horizontal mantled hot water tanksensible thermal energy storagesolar domestic hot water systemartificial neural networks |
| spellingShingle | Aslı Durmuşoğlu Buket Turgut Yusuf Tekin Burak Turgut Estimation of the Effect of Oblique Positioned Obstacle Placement on Thermal Performance of a Horizontal Mantle Hot Water Tank with Machine Learning Applied Sciences horizontal mantled hot water tank sensible thermal energy storage solar domestic hot water system artificial neural networks |
| title | Estimation of the Effect of Oblique Positioned Obstacle Placement on Thermal Performance of a Horizontal Mantle Hot Water Tank with Machine Learning |
| title_full | Estimation of the Effect of Oblique Positioned Obstacle Placement on Thermal Performance of a Horizontal Mantle Hot Water Tank with Machine Learning |
| title_fullStr | Estimation of the Effect of Oblique Positioned Obstacle Placement on Thermal Performance of a Horizontal Mantle Hot Water Tank with Machine Learning |
| title_full_unstemmed | Estimation of the Effect of Oblique Positioned Obstacle Placement on Thermal Performance of a Horizontal Mantle Hot Water Tank with Machine Learning |
| title_short | Estimation of the Effect of Oblique Positioned Obstacle Placement on Thermal Performance of a Horizontal Mantle Hot Water Tank with Machine Learning |
| title_sort | estimation of the effect of oblique positioned obstacle placement on thermal performance of a horizontal mantle hot water tank with machine learning |
| topic | horizontal mantled hot water tank sensible thermal energy storage solar domestic hot water system artificial neural networks |
| url | https://www.mdpi.com/2076-3417/15/1/48 |
| work_keys_str_mv | AT aslıdurmusoglu estimationoftheeffectofobliquepositionedobstacleplacementonthermalperformanceofahorizontalmantlehotwatertankwithmachinelearning AT buketturgut estimationoftheeffectofobliquepositionedobstacleplacementonthermalperformanceofahorizontalmantlehotwatertankwithmachinelearning AT yusuftekin estimationoftheeffectofobliquepositionedobstacleplacementonthermalperformanceofahorizontalmantlehotwatertankwithmachinelearning AT burakturgut estimationoftheeffectofobliquepositionedobstacleplacementonthermalperformanceofahorizontalmantlehotwatertankwithmachinelearning |