HIGH-FIDELITY LES/DNS DATA FOR INNOVATIVE TURBULENCE MODELS

The HIFI-TURB project, funded by the EU, aimed to develop new models to enhance the prediction of the transition from laminar to turbulent flow, which is crucial for better fluid flow control in various industries such as aerospace, automotive, maritime, and energy. The project used high-fidelity large-eddy simulations (LES) and direct numerical simulations (DNS) to predict complex fluid flows. Researchers applied novel artificial intelligence (AI) and machine learning (ML) algorithms to identify significant correlations between turbulent quantities, which were previously difficult to capture.

A significant challenge in fluid dynamics was the lack of understanding of turbulence-dependent features and the transition from laminar to turbulent flow. This gap in knowledge affected the accuracy of design and analysis in industrial equipment under challenging flow conditions. The project aimed to address this by improving models for complex fluid flows, potentially reducing energy consumption, emissions, and noise from combustion-based engines, thereby positively impacting economic and environmental factors.

The project was highly ambitious and innovative, focusing on several key pillars of excellence. It leveraged high-fidelity LES/DNS data for reference flows containing critical flow features, applied advanced AI and ML algorithms to identify key turbulent correlations, and was guided by four world-renowned experts in turbulence. The consortium included major industrial aeronautical companies, a software editor, an SME as the coordinator, well-known research centers, and academic groups, including ERCOFTAC, which provided turbulence expertise and served as a repository for the generated data, making it openly available.

By enhancing the capabilities of turbulence models, the HIFI-TURB project aimed to improve the efficiency and environmental acceptability of fluid-based energy transfer systems, ultimately contributing to industrial leadership in the competitive global market.