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Michaël Bauerheim

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WELCOME

Current position : Associate Professor at ISAE-Supaero (DAEP)
E-mail : michael.bauerheim isae-supaero.fr
Phone : +33 5 61 33 80 85
Location : Building 38, office 223

Previous positions :
2015-2016 - Post-doctoral position at LMFA (France) and ETHZ (Switzerland) :

  • Aeroacoustic broad-band and tonal noise or rotor-stator interactions. LES and LNSE on the non-linear saturation of aeroacoustic sources.

2014-2015 - Post-doctoral position at IMFT (France) :

  • DNS and experiment of flame stabilization on a rotating cylinder.

2014 - Visiting scholar at Stanford University (USA) :

  • Uncertainty Quantification applied to symmetry breaking in thermoacoustics.

2012-2014 - PhD in thermo-acoustics at CERFACS and SNECMA (France) :

  • Theory and LES of thermoacoustic instabilities in annular combustion chambers.

2011 - Visiting scholar at Georgia Tech (USA) :

  • LES for combustion instabilities in methane-oxygen rocket engines.

Awards :
2014 - 3AF Clean Sky conference, nomination among the 10 best papers

2014 - 35th Int. Sytmposium on Combustion travelling grant 2014, and GFC travelling award with 2 papers accepted

2015 - Léopold Escande award for the best INP thesis 2014, delivered by the University of Toulouse

2015 - Award of Excellence for the contribution to "Ignition capability studies" in the FP7 European project LEMCOTECT

2015 - Paul Caseau award for the best PhD thesis on "Simulation and modelling", delivered by EDF

2015 - Paul Laffitte award for the best PhD thesis on "Combustion", delivered by the French Combustion Institute

RESEARCH ACTIVITIES

High Performance Learning for Aeronautical Flow Physics
Developing innovative Artificial Intelligence techniques to tackle fluid mechanics and aerodynamics problems

ACCELERATING CFD SOLVERS

I currently lead research at DAEP to develop AI-based CFD solvers. Some part of the CFD solvers are replaced by a deep neural network. It contribute to the effort of improving the performance of CFD codes, especially on GPUs. This activity is realized in collaboration with AI experts (Jolibrain) and Cerfacs (Helios group : http://cerfacs.fr/helios/)

Current PhDs :
- Antonio Alguacil : Developing new solvers for acoustic propagation.
- Ekhi Ajuria : Developing AI-based incompressible solvers.

AI FOR FLOW RECONSTRUCTION

Part of the AI activity is dedicated to flow reconstruction. In particular, denoising problems and missing data reconstruction are under investigation. First results on denoising and pressure reconstruction from PIV measurements show promising possibilities for deep learning to complement CFD and experiment in fluid mechanics.

AI FOR OPTIMIZATION

Prospective works are carried out to apply AI techniques to develop new optimization procedures. This current work is sustained by 2 research master projects and a postdoc fellow funded by DGA (ETAP projet).

Aeroacoustics
Using high-fidelity simulations to investigate aeroacoustics sources and propagation in complex flows

This activity is lead by Marc Jacob accross the whole DAEP groups. My research focuses on using high-fidelity simulations (Navier-Stokes CharlesX and LBM Palabos) to address aeroacoustics problems. Current investigations include :

  • Characterizing high-fidelity codes with acoustic benchmarks
  • Investigation of a jet-pump for propagation of acoustic waves in complex media, in collaboration with Stéphane Moreau from Sherbrooke Univ.
  • Hydrodynamic-Acoustic coupling using forced LES simulations, with applications to cavity flows and vortex-sound.

OPPORTUNITIES 2019-2020

PostDoc : Innovative methods for acoustic propagation and source detection

Artificial Intelligence (AI) recently emerges in many engineering fields as a new approach to handle complex systems and elaborate physical models. Based on the training of large neural networks, Deep Learning is one of those methods which has shown outstanding results. In fluid mechanics, breakthrough in numerical methods can be expected by using such a technique to develop complex physical models, or accelerating current numerical solvers. Yet, the small amount of studies dedicated to AI for fluid mechanics suggests that progress is still required to make these methods mature and reliable. The department of Aerodynamics, Energetic and Propulsion (DAEP) at ISAE- Supaero is currently applying deep learning techniques to several problems encountered in fluid mechanics, involving data from experiments or numerical simulations. This postdoc position will complement the current team to apply AI to tackle acoustic problems, in . particular :
- the propagation of acoustic waves in complex media, complementing the PhD. of Antonio Alguacil
- the experimental detection of acoustic sources
This postdoc will involve both simulations, experiments in the Anechoic chamber at Supaéro, and deep learning techniques.The position is for maximum 3 years. Application can be sent to michael.bauerheim isae-supaero.fr

PUBLICATIONS & HIGHLIGHTS

Invited talk at the PASC19 Conference in Zurich on Machine Learning and High Performance Computing.

https://scholar.google.fr/citations?user=tWRCvmEAAAAJ&hl=fr

COLLABORATIONS & PARTNERS

Helios group with Cerfacs : High Performance Learning for Computational Phyiscs (http://cerfacs.fr/helios/)

Jolibrain, a startup dedicated to Artificial Intelligence located in Toulouse

TIdDLe : the Toulouse Interdisciplinary Deep Learning Groupe (https://tiddle-group.github.io/)

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