Publicou 4 edições por ano
ISSN Imprimir: 1065-3090
ISSN On-line: 1940-4336
Indexed in
DEVELOPING PARTICLE IMAGE VELOCIMETRY SOFTWARE BASED ON A DEEP NEURAL NETWORK
RESUMO
As an experimental technique for fluid mechanics, particle image velocimetry (PIV) can extract global and quantitative velocity field from images. With the development of artificial intelligence, designing PIV method based on deep learning is quite promising and worth exploring. First, in this paper, the authors introduce the optical flow neural network based on one proposed in the computer vision community. Second, a data set including particle images and the ground truth fluid motion is generated to train the parameters of the networks. This leads to a deep neural network for PIV which can provide estimation of dense motion (down to maximum one vector for one pixel) with the high degree of efficiency. The featuring of particle image extracted by the neural network is also investigated in this paper. It is found that feature matching improves the accuracy of estimation. The proposed network model is firstly evaluated by a synthetic image sequence of turbulent flow. An experiment measuring the flow over an aerofoil is used to validate the practicability. The experimental results indicate that compared with the traditional cross correlation method, the proposed deep neural network has advantages in accuracy, spatial resolution, and efficiency.
-
Adrian, L., Ronald, J.A., and Westerweel, J., Particle Image Velocimetry, no. 30, Cambridge, UK: Cambridge University Press.
-
Cai, S., Liang, J., Zhou, S., Gao, Q., Xu, C., Wei, R., Wereley, S., and Kwon, J.S., Deep-PIV: A New Framework of PIV using Deep Learning Techniques, Paper presented at 13th Int. Symp. on Particle Image Velocimetry, Munich, Germany, 2019a.
-
Cai, S., Zhou, S., and Xu, C., Dense Motion Estimation of Particle Images via a Convolutional Neural Network, Exp. Fluids, vol. 60, no. 4, pp. 60-73, 2019b.
-
Carlier, J., Second Set of Fluid Mechanics Image Sequences, European Project Fluid Image Analysis and Description (FLUID)-http://www. fluid.irisa.fr, 2005.
-
Chen, X., Zille, P., Shao, L., and Corpetti, T., Optical Flow for Incompressible Turbulence Motion Estimation, Exp. Fluids, vol. 56, no. 1, pp. 1-14, 2015.
-
Corpetti, T., Heitz, D., Cansino, A.G., Memin, E., and Santa Cruz, A., Fluid Experimental Flow Estimation Based on an Optical-Flow Scheme, Exp. Fluids, vol. 40, no. 1, pp. 80-97, 2006. Heitz, D., Memin, E., and Schnorr, C., Variational Fluid Flow Measurements from Image Sequences: Synopsis and Perspectives, Exp. Fluids, vol. 48, no. 3, pp. 369-393, 2010.
-
Horn, B.K.P. and Schunck, B.G., Determining Optical Flow, Artificial Intelligence, vol 17, nos. 1-3, pp. 185-203, 1981.
-
Hui, T.W., Tang, X., and Chen, C.L., Liteflownet: A Lightweight Convolutional Neural Network for Optical Flow Estimation, Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, 2018.
-
Krizhevsky, A., Sutskever, I., and Hinton, G.E., Imagenet Classification with Deep Convolutional Neural Networks, Adv. Neural Information Process. Syst., vol. 25, no. 2, pp. 1097-1105, 2012.
-
LeCun, Y., Bengio, Y., and Hinton, G., Deep Learning, Nature, vol. 521, pp. 436-444, 2015.
-
Lee, Y., Yang, H., and Yin, Z., PIV-DCNN: Cascaded Deep Convolutional Neural Networks for Particle Image Velocimetry, Exp. Fluids, vol. 58, no. 12, p. 171, 2017.
-
Li, Y., Perlman, E., Wan, M., Yang, Y., Meneveau, C., Burns, R.C., Chen, S., Szalay, A.S., and Eyink, G., A Public Turbulence Database Cluster and Applications to Study Lagrangian Evolution of Velocity Increments in Turbulence, J. Turbulence, vol. 9, no. 31, pp. 1-29, 2008.
-
Liu, T. and Shen, L., Fluid Flow and Optical Flow, J. Fluid Mech., vol. 614, pp. 253-291, 2008.
-
Marusic, I., Large Spatial Range Measurements in High Reynolds Number Wall-Bounded Flows, Paper presented at 13th Int. Symp. on Particle Image Velocimetry, Munich, Germany, 2019.
-
Rabault, J., Kolaas, J., and Jensen, A., Performing Particle Image Velocimetry using Artificial Neural Networks: A Proof-of-Concept, Meas. Sci. Technol., vol. 28, no. 12, p. 125301, 2017.
-
Raffel, M., Willert, C.E., Wereley, S., and Kompenhans, J., Particle Image Velocimetry: A Practical Guide, Springer, 2018.
-
Resseguier, V., Memin, E., and Chapron, B., Geophysical Flows under Location Uncertainty, Part II: Quasi-Geostrophy and Efficient Ensemble Spreading, Geophys. Astrophys. Fluid Dyn., vol. 111, no. 3, pp. 177-208, 2017.
-
Ruhnau, P., Kohlberger T., Schnorr, C., and Nobach, H., Variational Optical Flow Estimation for Particle Image Velocimetry, Exp. Fluids, vol. 38, no. 1, pp. 21-32, 2005.
-
Scarano, F., Iterative Image Deformation Methods in PIV, Meas. Sci. Technol., vol. 13, no. 1, pp. 1-19, 2002.
-
Wolański Wojciech, Ples Marek, Sobkowiak-Pilorz Marta, Gruszka Grzegorz, Burkacki Michał, Suchoń Sławomir, Gzik Marek, Experimental Study the Blood Flows in a Transparent Models of a Blood Vessels with Bifurcation—Preliminary Report, in Innovations in Biomedical Engineering, 409, 2023. Crossref
-
Piskur Paweł, Side Fins Performance in Biomimetic Unmanned Underwater Vehicle, Energies, 15, 16, 2022. Crossref