Simulation of urban flooding using 3D computational fluid dynamics with turbulence model Article Swipe
Muhammad Waqar Saleem
,
Imran Rashid
,
Sajjad Haider
,
Mohiq Khalid
,
Amro Elfeki
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.rineng.2024.103609
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.rineng.2024.103609
An alarming increase in the frequency of extreme rainfall events necessitates advanced flood risk reduction methods. This study uses ANSYS Fluent to simulate the 'Model urban city' flow experiment, comparing aligned and staggered urban layouts. The simulation's goodness of fit, evaluated by flow depth at probe locations, achieved a Nash-Sutcliffe Efficiency (NSE) of 0.83, indicating a 'very good' performance. Sensitivity Analysis (SA) indicated that increasing grid coarseness from 1.7 million to 0.1 million cells raised the Root Mean Square Error (RMSE) by 15 %. This Research also assessed different mesh treatments for building incorporation in the computational mesh-building hole (BH) and Building Block (BB). The BH method was computationally more efficient, taking about 1 Day compared to 2–3 days for BB. The Building Resistance (BR) method yielded poor results. Employing a sophisticated 3D CFD model, this study provides detailed inputs for high-precision flood risk assessments in urban areas prone to rapid flooding, enhancing the precision and applicability of urban flood models.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.rineng.2024.103609
- OA Status
- gold
- Cited By
- 15
- References
- 57
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4405012143Canonical identifier for this work in OpenAlex
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https://doi.org/10.1016/j.rineng.2024.103609Digital Object Identifier
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Simulation of urban flooding using 3D computational fluid dynamics with turbulence modelWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-12-04Full publication date if available
- Authors
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Muhammad Waqar Saleem, Imran Rashid, Sajjad Haider, Mohiq Khalid, Amro ElfekiList of authors in order
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https://doi.org/10.1016/j.rineng.2024.103609Publisher landing page
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.rineng.2024.103609Direct OA link when available
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Turbulence, Computational fluid dynamics, K-epsilon turbulence model, K-omega turbulence model, Flooding (psychology), Computer science, Statistical physics, Mechanics, Physics, Psychology, PsychotherapistTop concepts (fields/topics) attached by OpenAlex
- Cited by
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15Total citation count in OpenAlex
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2025: 15Per-year citation counts (last 5 years)
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| abstract_inverted_index.raised | 74 |
| abstract_inverted_index.taking | 111 |
| abstract_inverted_index.aligned | 30 |
| abstract_inverted_index.extreme | 7 |
| abstract_inverted_index.million | 69, 72 |
| abstract_inverted_index.models. | 160 |
| abstract_inverted_index.yielded | 126 |
| abstract_inverted_index.Analysis | 60 |
| abstract_inverted_index.Building | 101, 122 |
| abstract_inverted_index.Research | 85 |
| abstract_inverted_index.achieved | 47 |
| abstract_inverted_index.advanced | 11 |
| abstract_inverted_index.alarming | 1 |
| abstract_inverted_index.assessed | 87 |
| abstract_inverted_index.building | 92 |
| abstract_inverted_index.compared | 115 |
| abstract_inverted_index.detailed | 138 |
| abstract_inverted_index.goodness | 37 |
| abstract_inverted_index.increase | 2 |
| abstract_inverted_index.layouts. | 34 |
| abstract_inverted_index.methods. | 15 |
| abstract_inverted_index.provides | 137 |
| abstract_inverted_index.rainfall | 8 |
| abstract_inverted_index.results. | 128 |
| abstract_inverted_index.simulate | 22 |
| abstract_inverted_index.Employing | 129 |
| abstract_inverted_index.comparing | 29 |
| abstract_inverted_index.different | 88 |
| abstract_inverted_index.enhancing | 152 |
| abstract_inverted_index.evaluated | 40 |
| abstract_inverted_index.flooding, | 151 |
| abstract_inverted_index.frequency | 5 |
| abstract_inverted_index.indicated | 62 |
| abstract_inverted_index.precision | 154 |
| abstract_inverted_index.reduction | 14 |
| abstract_inverted_index.staggered | 32 |
| abstract_inverted_index.Efficiency | 50 |
| abstract_inverted_index.Resistance | 123 |
| abstract_inverted_index.coarseness | 66 |
| abstract_inverted_index.efficient, | 110 |
| abstract_inverted_index.increasing | 64 |
| abstract_inverted_index.indicating | 54 |
| abstract_inverted_index.locations, | 46 |
| abstract_inverted_index.treatments | 90 |
| abstract_inverted_index.Sensitivity | 59 |
| abstract_inverted_index.assessments | 144 |
| abstract_inverted_index.experiment, | 28 |
| abstract_inverted_index.necessitates | 10 |
| abstract_inverted_index.performance. | 58 |
| abstract_inverted_index.simulation's | 36 |
| abstract_inverted_index.applicability | 156 |
| abstract_inverted_index.computational | 96 |
| abstract_inverted_index.incorporation | 93 |
| abstract_inverted_index.mesh-building | 97 |
| abstract_inverted_index.sophisticated | 131 |
| abstract_inverted_index.Nash-Sutcliffe | 49 |
| abstract_inverted_index.high-precision | 141 |
| abstract_inverted_index.computationally | 108 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.6499999761581421 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.96778923 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |