Related Experiment Video
Updated: Sep 10, 2025
13:02
Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
Published on: February 27, 2016
12.4K
High-Reynolds-Number Turbulence Database: AeroFlowData.
Weiwei Zhang1,2,3, Xianglin Shan4,5,6, Yilang Liu4,5,6
1School of Aeronautics, Northwestern Polytechnical University, Xi'an, 710072, China. aeroelastic@nwpu.edu.cn.
Scientific Data
|August 27, 2025
Summary
A new high-Reynolds number turbulence database, AeroFlowData, has been established to aid scientific research. It provides extensive data for complex engineering applications, overcoming limitations of existing turbulence databases.
Area of Science:
- Fluid Dynamics
- Computational Science
Background:
- Turbulence simulation is challenged by multi-scale structures.
- Existing turbulence databases lack high-Reynolds number data for engineering applications.
Purpose of the Study:
- To establish a globally shared, high-Reynolds number turbulence database.
- To support research in turbulence machine learning and complex engineering flows.
Main Methods:
- Numerical simulations
- Experimental measurements
- Data assimilation
Main Results:
- Established AeroFlowData, a high-Reynolds number turbulence database.
- Database includes nearly 40 models across various applications.
- Contains over 500 flow conditions and ~100TB of data.
Conclusions:
- AeroFlowData addresses the need for high-Reynolds number turbulence data.
- The database facilitates advancements in turbulence research and machine learning.
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