
Case Studies in Thermal Engineering, Год журнала: 2024, Номер unknown, С. 105690 - 105690
Опубликована: Дек. 1, 2024
Язык: Английский
Case Studies in Thermal Engineering, Год журнала: 2024, Номер unknown, С. 105690 - 105690
Опубликована: Дек. 1, 2024
Язык: Английский
Results in Engineering, Год журнала: 2025, Номер 25, С. 104287 - 104287
Опубликована: Фев. 6, 2025
Язык: Английский
Процитировано
1AIP Advances, Год журнала: 2025, Номер 15(1)
Опубликована: Янв. 1, 2025
Fountains injected into homogeneous fluids, characterized by combined temperature and concentration effects, are common in both natural environmental settings. In this study, the capacities of several machine learning models, including support vector regression, multi-layer perceptron, random forests, XGBoost, CatBoost, AdaBoost, LightGBM, were investigated to clarify transient flow behavior fountains. The results indicated that perceptron was superior other models as it provided improved coefficient determination, root mean squared error, absolute error. This study confirmed techniques have great potential
Язык: Английский
Процитировано
0Case Studies in Thermal Engineering, Год журнала: 2024, Номер unknown, С. 105690 - 105690
Опубликована: Дек. 1, 2024
Язык: Английский
Процитировано
0