Big data analysis on flow characteristics according to welded penetration locations for fire sprinkler piping system design
This study presents a big data-driven computational fluid dynamics (CFD) analysis of Tee‐type fire sprinkler pipelines, focusing on how different weld penetration depths affect flow behavior and overall system performance. Over 2000 simulation cases were generated by varying key parameters, includin...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Engineering Science and Technology, an International Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098625001892 |
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| author | Joo Hyun Moon Jae Heon Gu Dong Kyu Kim In Woo Jang |
| author_facet | Joo Hyun Moon Jae Heon Gu Dong Kyu Kim In Woo Jang |
| author_sort | Joo Hyun Moon |
| collection | DOAJ |
| description | This study presents a big data-driven computational fluid dynamics (CFD) analysis of Tee‐type fire sprinkler pipelines, focusing on how different weld penetration depths affect flow behavior and overall system performance. Over 2000 simulation cases were generated by varying key parameters, including inlet velocity, base and branch diameters, and penetration depths. The results show that deeper penetration can create pronounced recirculation zones and significant local pressure drops, especially in smaller‐diameter main pipes. These undesirable flow disturbances may undermine sprinkler efficiency by causing uneven velocity distribution or increasing cavitation risks. A regression model based on both a power‐law empirical approach and a Deep Neural Network was developed to predict average velocities at multiple monitoring points. The DNN model, supplemented by reinforcement learning, achieved a high accuracy within ±10% error, surpassing simpler regression techniques. This integrated approach highlights how big data simulations and machine learning can guide penetration depth selection, thus improving fire suppression reliability and reducing long‐term corrosion risks in sprinkler systems. |
| format | Article |
| id | doaj-art-d6e5645e19a24a3587dfe7b11b0529ad |
| institution | Kabale University |
| issn | 2215-0986 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering Science and Technology, an International Journal |
| spelling | doaj-art-d6e5645e19a24a3587dfe7b11b0529ad2025-08-20T03:56:41ZengElsevierEngineering Science and Technology, an International Journal2215-09862025-09-016910213410.1016/j.jestch.2025.102134Big data analysis on flow characteristics according to welded penetration locations for fire sprinkler piping system designJoo Hyun Moon0Jae Heon Gu1Dong Kyu Kim2In Woo Jang3Department of Building Science Engineering, Hanbat National University, Republic of Korea; Corresponding author.Department of Building Science Engineering, Hanbat National University, Republic of KoreaDuksung Tech, Republic of KoreaDuksung Tech, Republic of KoreaThis study presents a big data-driven computational fluid dynamics (CFD) analysis of Tee‐type fire sprinkler pipelines, focusing on how different weld penetration depths affect flow behavior and overall system performance. Over 2000 simulation cases were generated by varying key parameters, including inlet velocity, base and branch diameters, and penetration depths. The results show that deeper penetration can create pronounced recirculation zones and significant local pressure drops, especially in smaller‐diameter main pipes. These undesirable flow disturbances may undermine sprinkler efficiency by causing uneven velocity distribution or increasing cavitation risks. A regression model based on both a power‐law empirical approach and a Deep Neural Network was developed to predict average velocities at multiple monitoring points. The DNN model, supplemented by reinforcement learning, achieved a high accuracy within ±10% error, surpassing simpler regression techniques. This integrated approach highlights how big data simulations and machine learning can guide penetration depth selection, thus improving fire suppression reliability and reducing long‐term corrosion risks in sprinkler systems.http://www.sciencedirect.com/science/article/pii/S2215098625001892PipeSprinklerWeldingComputational fluid dynamicsBig-data analysisPenetration |
| spellingShingle | Joo Hyun Moon Jae Heon Gu Dong Kyu Kim In Woo Jang Big data analysis on flow characteristics according to welded penetration locations for fire sprinkler piping system design Engineering Science and Technology, an International Journal Pipe Sprinkler Welding Computational fluid dynamics Big-data analysis Penetration |
| title | Big data analysis on flow characteristics according to welded penetration locations for fire sprinkler piping system design |
| title_full | Big data analysis on flow characteristics according to welded penetration locations for fire sprinkler piping system design |
| title_fullStr | Big data analysis on flow characteristics according to welded penetration locations for fire sprinkler piping system design |
| title_full_unstemmed | Big data analysis on flow characteristics according to welded penetration locations for fire sprinkler piping system design |
| title_short | Big data analysis on flow characteristics according to welded penetration locations for fire sprinkler piping system design |
| title_sort | big data analysis on flow characteristics according to welded penetration locations for fire sprinkler piping system design |
| topic | Pipe Sprinkler Welding Computational fluid dynamics Big-data analysis Penetration |
| url | http://www.sciencedirect.com/science/article/pii/S2215098625001892 |
| work_keys_str_mv | AT joohyunmoon bigdataanalysisonflowcharacteristicsaccordingtoweldedpenetrationlocationsforfiresprinklerpipingsystemdesign AT jaeheongu bigdataanalysisonflowcharacteristicsaccordingtoweldedpenetrationlocationsforfiresprinklerpipingsystemdesign AT dongkyukim bigdataanalysisonflowcharacteristicsaccordingtoweldedpenetrationlocationsforfiresprinklerpipingsystemdesign AT inwoojang bigdataanalysisonflowcharacteristicsaccordingtoweldedpenetrationlocationsforfiresprinklerpipingsystemdesign |