Feasibility of Deep Neural Network Surrogate Models in Fluid Dynamics

Feasibility of Deep Neural Network Surrogate Models in Fluid Dynamics

This paper studies reduced-order-models for the fluid flow problem of a digital valve, and whether it may efficiently be formulated by a deep Artificial Neural Network (ANN) to model e.g. the valve flow, flow-induced force, stiction phenomena and steep local pressure gradients that arise before plun...

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Bibliographic Details
Main Authors: Niels C. Bender, Torben Ole Andersen, Henrik C. Pedersen
Format: Article
Language:English
Published: Norwegian Society of Automatic Control 2019-04-01
Series:Modeling, Identification and Control
Subjects:
Artificial Neural Networks
CFD
Digital Valves
Flow-induced Forces
Reduced Order Models
Lumped Parameter Models
Online Access:http://www.mic-journal.no/PDF/2019/MIC-2019-2-1.pdf

Internet

http://www.mic-journal.no/PDF/2019/MIC-2019-2-1.pdf

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