Joachim Dominique

von Karman Institute for Fluid Dynamics · Environmental and Applied Fluid Dynamics

This paper presents a new data-driven approach for the establishment of empirical models describing turbulent boundary layer wall-pressure spectra. Unlike other models presented in literature, the new models are not derived by extending previously existing ones, but are directly built from a given dataset through symbolic regression using a machine...

We analyse and compare various empirical models of wall pressure spectra beneath turbulent boundary layers and propose an alternative machine learning approach using Artificial Neural Networks (ANN). The analysis and the training of the ANN are performed on data from experiments and high-fidelity simulations by various authors, covering a wide rang...

Big data and machine learning are driving comprehensive economic and social transformations and are rapidly re-shaping the toolbox and the methodologies of applied scientists. Machine learning tools are designed to learn functions from data with little to no need of prior knowledge. As continuous developments in experimental and numerical methods i...

Presentation given as keynote talk at the NURETH conference (see https://app.azavista.com/w/event/606cc03e34677f00116965dd/?page_id=606cc112ba269400118f93b7 )

Big data and machine learning are driving comprehensive economic and social transformations and are rapidly reshaping the toolbox and the methodologies of applied scientists. Machine learning tools are designed to learn functions from data with little to no need of prior knowledge. As continuous developments in experimental and numerical methods im...

We analyze and compare various empirical models of wall pressure spectra beneath turbulent boundary layers and propose an alternative machine learning approach using Artificial Neural Networks (ANNs). The analysis and the training of the ANN are performed on data from experiments and high-fidelity simulations by various authors, covering a wide ran...

This course gives an overview and practical hands-on experience on how to integrate machine learning in fluid dynamics. The course originated as a compressed version of the course Data-Driven Fluid Mechanics and Machine Learning, given at the Research Master program at the von Karman Institute. After a brief review of the machine learning landscape...

View Video Presentation: https://doi.org/10.2514/6.2021-2155.vid Small ducted fans in HVAC applications are often affected by their integration within the final product as the flow and operating conditions induced by the fan working environment have a significant impact on its noise emission. This paper presents an experimental investigation of suc...

View Video Presentation: https://doi.org/10.2514/6.2021-2301.vid Aero-acoustic installation effects are investigated both experimentally and numerically in the framework of the H2020 EC project ARTEM, for two Distributed Electric Propulsion configurations. In the tractor configuration, three propellers are installed on pylons protruding upstream of...

In HVAC or domestic appliance applications, industrial fan noise is usually characterized under uniform inflow profile. However, those ideal conditions are rarely met when the fan is installed in its final product. The fan is often in the wake of an obstacle or after a bend, which causes non-uniform ingested flow. Under those distorted inflow condi...