ISUAAAT-17: International Symposium on Unsteady Aerodynamics, Aeroacoustics and Aeroelasticity of Turbomachines University of Melbourne, Australia Melbourne, Australia, November 16-21, 2025 |
| Conference website | https://isuaaat.duke.edu/ |
| Submission link | https://easychair.org/conferences/?conf=isuaaat17 |
| Abstract registration deadline | April 11, 2025 |
| Submission deadline | April 11, 2025 |
On behalf of the ISUAAAT 16 Organising Committee, we would like to invite you to the 17th iteration of this conference on 16th-21st November, 2025 at Melbourne, Australia. The primary goal of the conference will be to bring together researchers in industry, academia, and governments to hear the latest developments in turbomachinery technologies. The conference focus is on innovations in the field of turbomachinery inspired by bridging academia and industry.Based on our past events we look forward to our diverse and dynamic group of speakers providing an in-depth insight into the current research and development within our industry. There will also be exciting opportunities for international networking and collaboration between all participants, exploring opportunities, and building trust relationships nationally and internationally.
Topics of interest include, but are not limited to:
- Unsteady blade row aerodynamics,including rotor/stator interaction
- Blade-passing and low-order forced response
- Compressor and turbine flutter
- Compressor stall and surge
- Vibration of bladed disks including mistuning
- Multi-stage/multi-component interactions and distortion effects
- Compressor, turbine, seal and combustor instabilities
- Aeroacoustics and acoustic resonance
- Control of unsteady flow, flutter/forced response and noise in turbomachines
- Computational (including design/optimization) methods and applications for unsteady aerodynamics, aeroelasticity and aeroacoustics of turbomachines (UAAAT)
- Experimental and instrumentation methods and applications for UAAAT
- Validation benchmark test cases and data for UAAAT
- Machine Learning/ Artificial Intelligence, Physics informed Neural networks, data driven computational methods
The extended abstracts (up to 2 A4 pages), should include the objectives, a brief description of the solution procedure and/or experimental methodology and the illustrative main results obtained, Abstract Submission
We look forward to your contributions to a successful and enjoyable ISUAAAT meeting