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SCEJ 57th Autumn Meeting
Sep. 14 (Mon) - 16 (Wed), 2026
Higashi-hiroshima Campus, Hiroshima University
Japanese page
Understanding the transport phenomena of heat, mass, and momentum occurring in chemical equipment is essential for the design and operation of chemical processes. In recent years, advances in sophisticated measurement techniques and numerical simulation methods have enabled new approaches to complex transport phenomena. This symposium focuses on a wide range of topics, including multiphase flows, turbulence, reactive flows, and non-Newtonian fluids. It will feature presentations on the latest developments in measurement and computational techniques, as well as studies that contribute to a deeper understanding of complex transport phenomena.
\n\nThe "Encouragement Award" and "Presentation Award" of Symposium Award of Division of Fluid Particle Processing will be given to the outstanding presentations among those entries. For details, please check the application guidelines below.
\n\nhttps://scej-fpp.org/partluid/symposium/
\n\nPlease note that "Presentation Award" for students is also an entry-based competition. You can enter the competition via the link below.
\n\nMost recent update: 2026-06-07 07:33:01
 The keywords that frequently used in this topics code. | Keywords | Number | |
|---|---|---|---|
| Air Filter | 1 |  | |
| Coalescer | 1 | | |
| Viscosity | 1 | | |
| Phase-field method | 1 | | |
| CFD-DPM Simulation | 1 | | |
| Viscoelasticity | 1 | | |
| Counterion Ratio | 1 | | |
| Neural network | 1 | | |
| 3D Convolutional Neural Network (3D CNN) | 1 | | |
| ACKN No. | Title/Author(s) | Keywords | Style |
|---|---|---|---|
| 24 | CFD-DPM-Based 3D CNN Surrogate Model for Predicting Air Filter Performance from Microstructures | Air Filter CFD-DPM Simulation 3D Convolutional Neural Network (3D CNN) | O |
| 77 | Drag Reduction by Cationic Surfactants and Rheological Properties in Highly Concentrated Regimes | Viscoelasticity Viscosity Counterion Ratio | O |
| 101 | Development of Inverse Design Framework for Coalescer Microstructures Integrating Numerical Simulation and Machine Learning | Coalescer Neural network Phase-field method | O |
Organizing Committee of SCEJ 57th Annual Meeting (2026)
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