Random Forest Regression-Based Machine Learning Model for Accurate Estimation of Fluid Flow in Curved Pipes
In industrial piping systems, turbomachinery, heat exchangers etc., pipe bends are essential components. Computational fluid dynamics (CFD), which is frequently used to analyse the flow behaviour in such systems, provides extremely precise estimates but is computationally expensive. As a result, a c...
Guardado en:
Autores principales: | Ganesh N., Paras Jain, Amitava Choudhury, Prasun Dutta, Kanak Kalita, Paolo Barsocchi |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/3a634bd9bb5446c5aa0f428699179932 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Modification in gate valve using flexible membrane pipe for flow measurement
por: S. L. Bhilare, et al.
Publicado: (2021) -
Performance of an Environmentally Friendly Alternative Fluid in a Loop Heat Pipe-Based Battery Thermal Management System
por: Marco Bernagozzi, et al.
Publicado: (2021) -
Industrial airflows numerical simulation in ducts and devices using all-speed algorithm in structured meshes
por: Leal da Silva,Robson, et al.
Publicado: (2018) -
The Effect of Magnetic Field with Nanofluid on Heat Transfer in a Horizontal Pipe
por: Abdulhassan A. Karamallah, et al.
Publicado: (2017) -
The Effects of Vortex Generator Types on Heat Transfer and Flow Structure in a Rectangular Duct Flows
por: Laith J.H, et al.
Publicado: (2008)