This Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) provides $350,000 in funding to Harvard University to develop reliable and predictive computational models for simulating complex turbulent flows. The objective is to create generalized Reynolds Averaged Navier-Stokes (RANS) turbulence models that can accurately predict turbulent flows, which are essential for advancing key technologies in sectors such as aerospace, automotive, and power generation.
The approach involves formulating turbulence models that capture a richer representation of the statistical state of turbulence using structure tensors to characterize anisotropy and inhomogeneity. These models will be developed using scientific multi-agent reinforcement learning techniques applied to turbulence data from direct numerical simulations and experiments. The resulting turbulence models will be tested on a variety of complex flows. This award is expected to run from March 2024 through February 2027. No sub-awards are planned under this grant.
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