Affiliations: Osaka Electro-Communication University, 18-8
Hatsu-cho, Neyagawa, Osaka 572-8530, Japan. E-mail:
[email protected] | Osaka University, Yamadaoka, Suita, Osaka 565-0871,
Japan | Kyoto Institute of Technology, Matsugasaki, Sakyo-ku,
Kyoto 606-8585, Japan
Abstract: This paper proposes a novel algorithm using an artificial neural
network for modeling simultaneously both a 3-D flow velocity vector and a
concentration field. The neural network is trained so that four outputted
values of the network, three components of a 3-D velocity vector and a
concentration of substances such as air pollutants or bacilli, agree with
measured ones and additionally the continuity and diffusion equations are
satisfied in the flow field. An approximate model for the velocity and
concentration field can be constructed in the neural network from sparsely
measured data. When any 3-D position, (x, y, z), is inputted to the neural
network model, it outputs a 3-D velocity vector and a concentration at the
position. The entire 3-D velocity vector and concentration field, therefore,
can be easily estimated using the model. To validate the algorithm, the smoke
concentration distribution estimated from a very limited set of measured data
is compared with the measured one in which most of the data is unused for the
modeling. Even from sparsely measured velocity vectors and smoke
concentrations, the novel algorithm gives the entire concentration distribution
whose flow characteristics are almost similar to the experimental result.
