Prediction of side thermal buoyant discharge in the cross flow using multi-objective evolutionary polynomial regression (EPR-MOGA)
| Autor: | Milad Khosravi, Mitra Javan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Atmospheric Science
020209 energy 0207 environmental engineering 02 engineering and technology Geotechnical Engineering and Engineering Geology Evolutionary polynomial regression law.invention Flow (mathematics) law Thermal 0202 electrical engineering electronic engineering information engineering Applied mathematics 020701 environmental engineering Electron paramagnetic resonance Geology Civil and Structural Engineering Water Science and Technology |
| Zdroj: | Journal of Hydroinformatics. 21:980-998 |
| ISSN: | 1465-1734 1464-7141 |
| DOI: | 10.2166/hydro.2019.010 |
| Popis: | The capability to predict the distribution of pollutants in water bodies is one of the most important issues in the design of jet outfalls. Three-dimensional computational fluid dynamics (CFD) model and multi-objective evolutionary polynomial regression (EPR-MOGA) are used and compared in modeling the temperature field in the side thermal buoyant discharge in the cross flow. The input variables used for training the EPR-MOGA models are spatial coordinates (x, y, z), jet to cross flow velocity ratio (R), depth of the channel (d), and the temperature excess (T0). A previous experimental study is used to verify and compare the performance of the EPR-MOGA and CFD models. The results show that the EPR-MOGA model predicts the thermal cross section of the flow and the spread of pollutants at the surface with a better accuracy than the CFD model. However, the CFD method performs significantly better than EPR-MOGA in predicting temperature profiles. The uncertainty analysis indicated that the EPR-MOGA model had lower mean prediction error and smaller uncertainty band than the CFD model. The relationships achieved by the EPR-MOGA model are very useful to predict temperature profiles, temperature half-thickness, and temperature spread on surface in practice. |
| Databáze: | OpenAIRE |
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