Research Teams

Project Summary

Data-driven modelling of urban canopy flows. This research focuses on developing methods to build estimators and reduced-order models from simulation and experimental data, with particular emphasis on urban canopy flow dynamics.

Project Summary

Improving heat transfers in solar receivers through active control methods. The research focuses on enhancing thermal performance of concentrated solar power systems through innovative flow control techniques, particularly using spanwise wall oscillations.

Research Focus

Deep learning and machine learning for computational fluid dynamics. Developing data-driven reduced-order models using variational autoencoders and neural networks to predict turbulent flow behavior. His work focuses on extracting essential characteristics of turbulent flows and projecting them into reduced spaces, with applications to flow control and vortex-induced vibrations.

Project Summary

Modelling of near-wall turbulence at high Reynolds number and development of control strategies. This research focuses on understanding how outer flow structures affect wall shear and heat transfer, and developing predictive models using advanced data-driven approaches.

Project Summary

Modelling and Control of Near-Wall Turbulence: From Physical Understanding to Machine Learning Approaches. This research leverages machine learning algorithms to develop predictive models for near-wall turbulence in both incompressible and compressible flows, with dual objectives of reducing drag in transport systems and maximizing heat transfer in thermal applications. The project aims to nail the boundary-layer theory down to create models capable of predicting relevant physical characteristics and response to forcing near-wall turbulence.

The main ambition is to generate a low-dimensional dynamical model pertaining to near-wall turbulence using autoencoders to identify compact coordinate systems. By capitalizing on a solid foundation in turbulence physics augmented by novel machine learning strategies, this work seeks to contribute towards universal modelling of turbulence and develop effective control systems compatible with industrial applications, addressing current energy and environmental challenges.

Project Summary

Optimization of Heat Transfer by Active Turbulence Control: Development of Intelligent Strategies for Turbojet Engines. This CIFRE PhD project, conducted in collaboration between Institut Pprime and Safran Aircraft Engines, aims to develop an innovative approach for optimizing heat transfer in turbojet engines through active turbulence control. The main objective is to design and numerically validate intelligent control strategies that exploit natural flow instabilities, particularly Görtler instability, to significantly improve thermal performance while minimizing energy consumption.

The doctoral candidate will develop high-fidelity simulations, reduced-order models using machine learning techniques, and optimized control strategies. The approach combines plasma actuators (DBD) with advanced computational methods to manipulate turbulent structures and enhance heat transfer. This work integrates expertise in turbulence physics with cutting-edge machine learning algorithms to create self-adaptive systems capable of real-time thermal performance optimization.

The project specifically focuses on developing advanced numerical tools for understanding and controlling thermofluid instabilities, with a machine learning and reduced-order modeling approach. Experimental validation data will be provided through the ANR-BENEFIT project, conducted at IUSTI (Aix-Marseille Université).