Data-Driven Reduced-Complexity Modeling of Fluid Flows: A Community Challenge

TL;DR

A community challenge designed to facilitate direct comparisons between data-driven methods for compression, forecasting, and sensing of complex aerospace flows and invites broad participation to build a comprehensive and balanced picture of what works and where current methods fall short.

Abstract

We introduce a community challenge designed to facilitate direct comparisons between data-driven methods for compression, forecasting, and sensing of complex aerospace flows. The challenge is organized into three tracks that target these complementary capabilities: compression (compact representations for large datasets), forecasting (predicting future flow states from a finite history), and sensing (inferring unmeasured flow states from limited measurements). Across these tracks, multiple challenges span diverse flow datasets and use cases, each emphasizing different model requirements. The challenge is open to anyone, and we invite broad participation to build a comprehensive and balanced picture of what works and where current methods fall short. To support fair comparisons, we provide standardized success metrics, evaluation tools, and baseline implementations, with one classical and one machine-learning baseline per challenge. Final assessments use blind tests on withheld data. We explicitly encourage negative results and careful analyses of limitations. Outcomes will be disseminated through an AIAA Journal Virtual Collection and invited presentations at AIAA conferences.

Figures (19)

• Figure 1: Overview of the challenges, which span the core capabilities of compression, forecasting, and sensing. Each challenge type conceptually includes input data (plum boxes) and target output data (teal boxes) and seeks methods to perform the desired task (gray boxes).
• Figure 2: Challenge 1.1: Application of compression metrics of success in 2-D planes of the streamwise velocity in a turbulent boundary layer using 2D-CNN-AE and POD. The compression and reconstruction times are normalized using the POD method with 512 modes.
• Figure 3: Challenge 1.1: Application of compression to 2-D fields. Example snapshot of the streamwise velocity field (a), its reconstruction using a 2D-CNN-AE with a latent dimension of 128 (b), and reconstruction using 4096 POD modes (c). The streamwise ($x$) and wall-normal ($y$) coordinates are nondimensionalized by the boundary layer thickness at the inlet of the simulation ($\delta_\text{inlet}$).
• Figure 4: Challenge 1.2: Application of compression: metrics of success in 3-D fields of the streamwise velocity fluctuations in a turbulent boundary layer using 3D-CNN-AE and POD. The compression and reconstruction times are normalized using the POD method with 100 modes.
• Figure 5: Challenge 1.2: Application of compression to 3-D fields: Example of a 2-D cut extracted from the 3-D field of the streamwise velocity fluctuations (a), its reconstruction using a 3D-CNN-AE with a latent dimension of 12800 (b) and reconstruction using 400 POD modes (c). The streamwise ($x$) and spanwise ($z$) coordinates are nondimensionalized by the boundary layer thickness at the inlet of the simulation ($\delta_\text{inlet}$).