65 research outputs found
Unsteady Cylinder Wakes from Arbitrary Bodies with Differentiable Physics-Assisted Neural Network
This work delineates a hybrid predictive framework configured as a
coarse-grained surrogate for reconstructing unsteady fluid flows around
multiple cylinders of diverse configurations. The presence of cylinders of
arbitrary nature causes abrupt changes in the local flow profile while globally
exhibiting a wide spectrum of dynamical wakes fluctuating in either a periodic
or chaotic manner. Consequently, the focal point of the present study is to
establish predictive frameworks that accurately reconstruct the overall fluid
velocity flowfield such that the local boundary layer profile, as well as the
wake dynamics, are both preserved for long time horizons. The hybrid framework
is realized using a base differentiable flow solver combined with a neural
network, yielding a differentiable physics-assisted neural network (DPNN). The
framework is trained using bodies with arbitrary shapes, and then it is tested
and further assessed on out-of-distribution samples. Our results indicate that
the neural network acts as a forcing function to correct the local boundary
layer profile while also remarkably improving the dissipative nature of the
flowfields. It is found that the DPNN framework clearly outperforms the
supervised learning approach while respecting the reduced feature space
dynamics. The model predictions for arbitrary bodies indicate that the Strouhal
number distribution with respect to spacing ratio exhibits similar patterns
with existing literature. In addition, our model predictions also enable us to
discover similar wake categories for flow past arbitrary bodies. For the
chaotic wakes, the present approach predicts the chaotic switch in gap flows up
to the mid-time range.Comment: codes to follow shortly:
https://github.com/tum-pbs/DiffPhys-CylinderWakeFlow
Physics-Preserving AI-Accelerated Simulations of Plasma Turbulence
Turbulence in fluids, gases, and plasmas remains an open problem of both
practical and fundamental importance. Its irreducible complexity usually cannot
be tackled computationally in a brute-force style. Here, we combine Large Eddy
Simulation (LES) techniques with Machine Learning (ML) to retain only the
largest dynamics explicitly, while small-scale dynamics are described by an
ML-based sub-grid-scale model. Applying this novel approach to self-driven
plasma turbulence allows us to remove large parts of the inertial range,
reducing the computational effort by about three orders of magnitude, while
retaining the statistical physical properties of the turbulent system
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