Work

AI Super-Resolution: Application to Turbulence and Combustion

Year: 2023

Type: book-chapter

Abstract: This article summarizes and discusses recent developments with respect to artificial intelligence (AI) super-resolution as a subfilter model for large-eddy simulations. The focus is on the application of physics-informed enhanced super-resolution generative adversarial networks (PIESRGANs) for subfilter closure in turbulence and combustion applications. A priori and a posteriori results are presented for various applications, ranging from decaying turbulence to finite-rate chemistry flows. The high accuracy of AI super-resolution-based subfilter models is emphasized, and advantages and shortcoming are described.

Source: Lecture notes in energy

DOI: 10.1007/978-3-031-16248-0_10

Author: M. Bode

Institutions: Forschungszentrum Jülich, Jülich Supercomputing Centre, RWTH Aachen University

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Funders: Forschungszentrum Jülich, Gauss Centre for Supercomputing

OpenAlex ID: W4313445807