Learning the structure of wind: A data-driven nonlocal turbulence model for the atmospheric boundary layer

07/23/2021
by   Brendan Keith, et al.
19

We develop a novel data-driven approach to modeling the atmospheric boundary layer. This approach leads to a nonlocal, anisotropic synthetic turbulence model which we refer to as the deep rapid distortion (DRD) model. Our approach relies on an operator regression problem which characterizes the best fitting candidate in a general family of nonlocal covariance kernels parameterized in part by a neural network. This family of covariance kernels is expressed in Fourier space and is obtained from approximate solutions to the Navier–Stokes equations at very high Reynolds numbers. Each member of the family incorporates important physical properties such as mass conservation and a realistic energy cascade. The DRD model can be calibrated with noisy data from field experiments. After calibration, the model can be used to generate synthetic turbulent velocity fields. To this end, we provide a new numerical method based on domain decomposition which delivers scalable, memory-efficient turbulence generation with the DRD model as well as others. We demonstrate the robustness of our approach with both filtered and noisy data coming from the 1968 Air Force Cambridge Research Laboratory Kansas experiments. Using this data, we witness exceptional accuracy with the DRD model, especially when compared to the International Electrotechnical Commission standard.

READ FULL TEXT
10/20/2021

A domain decomposition solution of the Stokes-Darcy system in 3D based on boundary integrals

A framework is developed for a robust and highly accurate numerical solu...
07/21/2020

Experiment data-driven modeling of tokamak discharge in EAST

A model for tokamak discharge through deep learning has been done on EAS...
04/13/2021

Finite Volume Neural Network: Modeling Subsurface Contaminant Transport

Data-driven modeling of spatiotemporal physical processes with general d...
05/01/2021

Data-driven discovery of physical laws with human-understandable deep learning

There is an opportunity for deep learning to revolutionize science and t...
02/20/2020

Are Gabor Kernels Optimal for Iris Recognition?

Gabor kernels are widely accepted as dominant filters for iris recogniti...
10/29/2019

Cell Association via Boundary Detection: A Scalable Approach Based on Data-Driven Random Features

The problem of cell association is considered for cellular users present...