Application of AI-Based Techniques on Moody’s Diagram for Predicting Friction Factor in Pipe Flow Article Swipe
Ritusnata Mishra
,
C. S. P. Ojha
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/j6040036
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/j6040036
The friction factor is a widely used parameter in characterizing flow resistance in pipes and open channels. Recently, the application of machine learning and artificial intelligence (AI) has found several applications in water resource engineering. With this in view, the application of artificial intelligence techniques on Moody’s diagram for predicting the friction factor in pipe flow for both transition and turbulent flow regions has been considered in the present study. Various AI methods, like Random Forest (RF), Random Tree (RT), Support Vector Machine (SVM), M5 tree (M5), M5Rules, and REPTree models, are applied to predict the friction factor. While performing the statistical analysis (root-mean-square error (RMSE), mean absolute error (MAE), squared correlation coefficient (R2), and Nash–Sutcliffe efficiency (NSE)), it was revealed that the predictions made by the Random Forest model were the most reliable when compared to other AI tools. The main objective of this study was to highlight the limitations of artificial intelligence (AI) techniques when attempting to effectively capture the characteristics and patterns of the friction curve in certain regions of turbulent flow. To further substantiate this behavior, the conventional algebraic equation was used as a benchmark to test how well the current AI tools work. The friction factor estimates using the algebraic equation were found to be even more accurate than the Random Forest model, within a relative error of ≤±1%, in those regions where the AI models failed to capture the nature and variation in the friction factor.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/j6040036
- https://www.mdpi.com/2571-8800/6/4/36/pdf?version=1696683945
- OA Status
- gold
- Cited By
- 2
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387461789
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387461789Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/j6040036Digital Object Identifier
- Title
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Application of AI-Based Techniques on Moody’s Diagram for Predicting Friction Factor in Pipe FlowWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-10-07Full publication date if available
- Authors
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Ritusnata Mishra, C. S. P. OjhaList of authors in order
- Landing page
-
https://doi.org/10.3390/j6040036Publisher landing page
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https://www.mdpi.com/2571-8800/6/4/36/pdf?version=1696683945Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2571-8800/6/4/36/pdf?version=1696683945Direct OA link when available
- Concepts
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Mean squared error, Turbulence, Flow (mathematics), Artificial intelligence, Machine learning, Support vector machine, Root mean square, Mathematics, Statistics, Computer science, Algorithm, Engineering, Mechanics, Physics, Geometry, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
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17Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.even | 210 |
| abstract_inverted_index.flow | 10, 55, 61 |
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| abstract_inverted_index.most | 132 |
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| abstract_inverted_index.that | 121 |
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| abstract_inverted_index.well | 192 |
| abstract_inverted_index.were | 130, 206 |
| abstract_inverted_index.when | 134, 156 |
| abstract_inverted_index.(M5), | 86 |
| abstract_inverted_index.(R2), | 113 |
| abstract_inverted_index.(RF), | 76 |
| abstract_inverted_index.(RT), | 79 |
| abstract_inverted_index.While | 98 |
| abstract_inverted_index.curve | 168 |
| abstract_inverted_index.error | 104, 108, 221 |
| abstract_inverted_index.flow. | 174 |
| abstract_inverted_index.found | 28, 207 |
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| abstract_inverted_index.using | 202 |
| abstract_inverted_index.view, | 38 |
| abstract_inverted_index.water | 32 |
| abstract_inverted_index.where | 227 |
| abstract_inverted_index.work. | 197 |
| abstract_inverted_index.(MAE), | 109 |
| abstract_inverted_index.(SVM), | 83 |
| abstract_inverted_index.Forest | 75, 128, 216 |
| abstract_inverted_index.Random | 74, 77, 127, 215 |
| abstract_inverted_index.Vector | 81 |
| abstract_inverted_index.factor | 2, 52, 200 |
| abstract_inverted_index.failed | 231 |
| abstract_inverted_index.model, | 217 |
| abstract_inverted_index.models | 230 |
| abstract_inverted_index.nature | 235 |
| abstract_inverted_index.study. | 69 |
| abstract_inverted_index.tools. | 139 |
| abstract_inverted_index.widely | 5 |
| abstract_inverted_index.within | 218 |
| abstract_inverted_index.(NSE)), | 117 |
| abstract_inverted_index.(RMSE), | 105 |
| abstract_inverted_index.Machine | 82 |
| abstract_inverted_index.REPTree | 89 |
| abstract_inverted_index.Support | 80 |
| abstract_inverted_index.Various | 70 |
| abstract_inverted_index.applied | 92 |
| abstract_inverted_index.capture | 160, 233 |
| abstract_inverted_index.certain | 170 |
| abstract_inverted_index.current | 194 |
| abstract_inverted_index.diagram | 47 |
| abstract_inverted_index.factor. | 97, 241 |
| abstract_inverted_index.further | 176 |
| abstract_inverted_index.machine | 21 |
| abstract_inverted_index.models, | 90 |
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| abstract_inverted_index.present | 68 |
| abstract_inverted_index.regions | 62, 171, 226 |
| abstract_inverted_index.several | 29 |
| abstract_inverted_index.squared | 110 |
| abstract_inverted_index.M5Rules, | 87 |
| abstract_inverted_index.absolute | 107 |
| abstract_inverted_index.accurate | 212 |
| abstract_inverted_index.analysis | 102 |
| abstract_inverted_index.compared | 135 |
| abstract_inverted_index.equation | 183, 205 |
| abstract_inverted_index.friction | 1, 51, 96, 167, 199, 240 |
| abstract_inverted_index.learning | 22 |
| abstract_inverted_index.methods, | 72 |
| abstract_inverted_index.patterns | 164 |
| abstract_inverted_index.relative | 220 |
| abstract_inverted_index.reliable | 133 |
| abstract_inverted_index.resource | 33 |
| abstract_inverted_index.revealed | 120 |
| abstract_inverted_index.≤±1%, | 223 |
| abstract_inverted_index.Moody’s | 46 |
| abstract_inverted_index.Recently, | 17 |
| abstract_inverted_index.algebraic | 182, 204 |
| abstract_inverted_index.behavior, | 179 |
| abstract_inverted_index.benchmark | 188 |
| abstract_inverted_index.channels. | 16 |
| abstract_inverted_index.estimates | 201 |
| abstract_inverted_index.highlight | 148 |
| abstract_inverted_index.objective | 142 |
| abstract_inverted_index.parameter | 7 |
| abstract_inverted_index.turbulent | 60, 173 |
| abstract_inverted_index.variation | 237 |
| abstract_inverted_index.artificial | 24, 42, 152 |
| abstract_inverted_index.attempting | 157 |
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| abstract_inverted_index.performing | 99 |
| abstract_inverted_index.predicting | 49 |
| abstract_inverted_index.resistance | 11 |
| abstract_inverted_index.techniques | 44, 155 |
| abstract_inverted_index.transition | 58 |
| abstract_inverted_index.application | 19, 40 |
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| abstract_inverted_index.correlation | 111 |
| abstract_inverted_index.effectively | 159 |
| abstract_inverted_index.limitations | 150 |
| abstract_inverted_index.predictions | 123 |
| abstract_inverted_index.statistical | 101 |
| abstract_inverted_index.applications | 30 |
| abstract_inverted_index.conventional | 181 |
| abstract_inverted_index.engineering. | 34 |
| abstract_inverted_index.intelligence | 25, 43, 153 |
| abstract_inverted_index.substantiate | 177 |
| abstract_inverted_index.characterizing | 9 |
| abstract_inverted_index.characteristics | 162 |
| abstract_inverted_index.Nash–Sutcliffe | 115 |
| abstract_inverted_index.(root-mean-square | 103 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5113019213 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I154851008 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.8899999856948853 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.57856473 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |