COMPACT REPRESENTATION OF MULTI-MODAL TRANSITION FROM HIGH-FIDELITY DATASETS OF UNSTEADY FLOWS

Laminar-to-turbulent transition, which critically affects efficiency-focused applications such as wind turbine blades and aircraft wings, remains poorly understood. These flows involve complex turbulent interactions (high turbulence levels, unsteady wakes, separated shear layers), which standard modeling techniques struggle to accurately capture.This project addresses these challenges by leveraging high-fidelity experiments and numerical simulations to study transition flow physics. While these techniques offer unparalleled detail, the resulting large datasets are difficult to interpret and integrate due to the diverse flow conditions and geometries involved. As a result, the datasets are not immediately usable for developing low-order models and flow control strategies.The project's goal is to create a compact representation of the complex transition scenario using a data-driven and theoretical framework. This methodology will reveal the driving effects behind the amplification of unstable coherent flow structures during transition. It will integrate three high-fidelity heterogeneous experimental and numerical datasets of turbomachinery flows at engine-relevant conditions. The COMPOSE project leverages these datasets to provide insights applicable across various fields where transition impacts performance.The outcomes will not only deepen our understanding of laminar-to-turbulent transition but also enable the development of accurate reduced-order models. These models will be essential for creating flow control strategies to reduce the adverse effects of transition in aerodynamic components, such as turbomachinery blading, wind turbine blades, and aircraft wings. The project's impact could lead to significant efficiency improvements in these components, with broad implications for the aerospace and energy sectors.