High-Reynolds-Number Turbulence Database: AeroFlowData

Abstract

Abstract Turbulence widely appears in natural and industrial environments, with its multi-scale structures posing significant challenges for accurate simulation and prediction. Nowadays, turbulence databases play a crucial role in advancing scientific research. However, existing turbulence databases primarily focus on fundamental turbulence problems and are predominantly limited to turbulent flows at low-to-moderate Reynolds number, making them insufficient to address high-Reynolds-number turbulence challenges in complex engineering applications. Under the support of National Natural Science Foundation of China, the research project “Integration research on construction of high Reynolds number turbulence databases and turbulence machine learning” has been conducted, leading to the establishment of the globally shared high-Reynolds-number turbulence database, AeroFlowData. This database is developed under the leadership of Northwestern Polytechnical University, in collaboration with eight research institutions in China. The project team employs numerical simulations, experimental measurements, and data assimilation methods to acquire turbulence data. AeroflowData currently includes nearly 40 models, covering hypersonic vehicles, civil aircrafts, and turbomachinery blades, with over 500 computational and experimental flow conditions and a total data of nearly 100TB.