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

TL;DR

This paper introduces a data-driven, nonlocal turbulence model called the DRD model for the atmospheric boundary layer, which accurately generates synthetic turbulent fields using operator regression and neural networks.

Contribution

It presents a novel nonlocal turbulence modeling approach that incorporates physical properties and can be calibrated with noisy data, advancing synthetic turbulence generation techniques.

Findings

DRD model achieves high accuracy with experimental data.

The model effectively incorporates physical constraints like mass conservation.

Scalable numerical methods enable efficient turbulence generation.

Abstract

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.