About

Laminar-to-turbulent transition is a dominant source of loss across efficiency-focused sectors such as turbomachinery blading, aircraft and rotorcraft wings, and wind-turbine blades, among others. In these applications, operating conditions (e.g., Mach and Reynolds numbers), high levels of freestream turbulence, and external unsteady forcing (such as upstream wakes) drive multi-modal transition pathways that modern models employed in industrial design still struggle to predict.

Turbomachinery components, such as turbine and compressor blading, provide a comprehensive testbed for capturing the physics governing transition. In both compressor and turbine blading, boundary layers are subjected to combined adverse and favourable pressure gradients, strong curvature, surface roughness, high freestream turbulence, and periodic wake interactions.

COMPOSE tackles multi-modal transition by combining high-fidelity experiments and simulations under engine-relevant turbomachinery conditions and assembling them into a large, heterogeneous dataset spanning both canonical and realistic cases. We will curate and normalize these data to isolate the most energetic, influential flow structures, then develop a data-driven framework that delivers a compact representation of multi-modal transition. Leveraging large-scale modal decompositions (e.g., POD/SPOD) coupled with operator-based methods (e.g., resolvent analysis), we will identify the mechanisms that amplify unstable coherent structures. This framework will support the development of reduced-order models and practical flow-control strategies to address transition-related loss and boost efficiency across aerospace and energy systems.