Determination of the Inside Diameter of Pressure Pipes for Drinking Water Systems Using Artificial Neural Networks
The fifth-degree polynomial equation determines the diameter in pressurized drinking water systems. The input variables are Q: flow (m3/s), H: pressure drop (m); L: pipe length (m); ε: roughness (m), ϑ: kinematic viscosity (m2/s), and Ʃk: sum of minor loss coefficients (dimensionless). After applyin...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Pedagógica y Tecnológica de Colombia
- Repositorio:
- RiUPTC: Repositorio Institucional UPTC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uptc.edu.co:001/14335
- Acceso en línea:
- https://revistas.uptc.edu.co/index.php/ingenieria/article/view/14037
https://repositorio.uptc.edu.co/handle/001/14335
- Palabra clave:
- Artificial Neural Network
cold chain.
Darcy-Weisbach
Levenberg-Marquardt
pipeline hydraulics
Colebrook-White
Darcy-Weisbach
hidráulica de tuberías
Levenberg-Marquardt
red neuronal artificial
- Rights
- License
- http://creativecommons.org/licenses/by/4.0