Conditional diffusion denoising probabilistic model for super-resolution of atmospheric boundary layer large eddy simulation

TL;DR

This paper introduces a conditional diffusion probabilistic model to efficiently generate high-resolution turbulent flow fields from coarse data, aiding wind energy simulations with reduced computational costs.

Contribution

It presents a novel physics-informed generative AI approach for super-resolution of atmospheric boundary layer simulations, improving accuracy and efficiency.

Findings

Accurately recovers fine-scale turbulent structures in interpolation scenarios.

Maintains physical consistency within the training domain.

Extrapolation to higher wind speeds increases noise and overpredicts stresses.

Abstract

Climate change necessitates rapid expansion of renewable energy, with wind energy offering a scalable and low-impact solution. However, accurate prediction of wind loads and power generation remains challenging due to uncertainties in wind shear and turbulence stresses under atmospheric boundary layer (ABL) conditions. High-fidelity Large Eddy Simulations (LES) are typically used to reduce these uncertainties but are computationally expensive and impractical for large-scale or real-time applications. This work addresses this limitation using generative AI, specifically Conditional Denoising Diffusion Probabilistic Models, to reconstruct high-resolution turbulent flow fields from coarse inputs. A high-fidelity dataset is generated using a parallel high-order finite-difference solver across varying geostrophic wind speeds, surface roughness conditions aligned with IEC wind classes, and multiple grid resolutions. The diffusion model is trained for super-resolution across different scale factors and evaluated under interpolation and extrapolation scenarios. Results show accurate recovery of fine-scale turbulent structures, Reynolds stresses, and statistical properties in interpolation cases, indicating strong physical consistency within the training domain. However, extrapolation to higher wind speeds leads to increased noise and overprediction of turbulent stresses, highlighting limitations in generalization. Overall, the study demonstrates that physics-informed generative models can significantly reduce computational cost while maintaining acceptable accuracy, enabling faster and more reliable turbulent inflow characterization for wind energy applications.