Newsletter  2022.11  Index

Theme : "Mechanical Engineering Congress, 2022 Japan (MECJ-22)” Preface Hideo MORI, Tetsuya KANAGAWA Wall Modelling for Engineering Turbulence CFD Kazuhiko SUGA (Osaka Metropolitan University) Prediction of the flow of granules Toshitsugu TANAKA (Osaka University) Design Optimization of Turbomachinery by Artificial Neural Networks as a Meta-model Daisaku SAKAGUCHI (Nagasaki University) Deep learning for viscoelastic fluids and turbulent diffusion Takahiro TSUKAHARA (Tokyo University of Science) Advanced fluid measurement using mode decomposition Taku NONOMURA (Tohoku University)

 

Advanced fluid measurement using mode decomposition

Taku NONOMURA Tohoku University

Abstract

Recently, fluid analysis using data-driven science and machine learning has attracted much attention. Modal decompositions such as proper orthogonal decompositions are being used for comprehension and modeling of phenomena, and for reduction of noise in experimental data. In particular, the application of noise reduction to fluid measurements is a good example of how data-driven science and machine learning can be very effectively used. However, few examples, which apply these techniques to advance fluid measurement by reconsidering the design of fluid measurement, have been reported. The author's group has been actively incorporating modal decomposition based on data-driven science into the measurement and has realized its advancement. In this presentation, the author's group introduces two recently published techniques: the sparse processing particle image velocimetry (SPPIV) and the spatiotemporal superresolution technique.

Key words

Modal decomposition, Data-driven science and engineering, sparse processing particle image velocimetry, spatiotemporal superresolution

Figures

Fig 1.  Sparse processing particle image velocimetry.

Fig 2.  Sparse processing particle image velocimetry.