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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.

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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.

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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.