Development of a machine learning model based on results obtained from CFD solvers for the ease of the iterative processes in the early stages of design
In the following paper, three different approaches to study steady state fluid flow using machine learning are presented. Through the use of deep convolutional neural networks, the velocity and pressure fields were obtained by using computational fluid dynamics (CFD) simulations as training data. Th...
- Autores:
-
Giraldo Grueso, Felipe
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2020
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/51595
- Acceso en línea:
- http://hdl.handle.net/1992/51595
- Palabra clave:
- Dinámica de fluidos
Aprendizaje automático (Inteligencia artificial)
Redes neuronales convolucionales
Ingeniería
- Rights
- openAccess
- License
- https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
| Summary: | In the following paper, three different approaches to study steady state fluid flow using machine learning are presented. Through the use of deep convolutional neural networks, the velocity and pressure fields were obtained by using computational fluid dynamics (CFD) simulations as training data. The first approach, regarding the use of a convolutional neural network, proved to be successful by approximating the velocity and pressure fields with a normalized root mean squared error of (4.96¿1.83)% for the velocity field in the x direction, (17.95¿2.04)% for the velocity field in the y direction and (15.77¿1.66)% for the pressure field. As well as this, the use of this convolutional neural network proved to be (23410¿5405) times faster than common CFD solvers. The second approach, concerning the inclusion of a custom physics informed loss function in the latter convolutional neural network... |
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