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Stage "Machine learning for bubble reconstruction in two-phase flows"
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General information
Organisation
The French Alternative Energies and Atomic Energy Commission (CEA) is a key player in research, development and innovation in four main areas :
• defence and security,
• nuclear energy (fission and fusion),
• technological research for industry,
• fundamental research in the physical sciences and life sciences.
Drawing on its widely acknowledged expertise, and thanks to its 16000 technicians, engineers, researchers and staff, the CEA actively participates in collaborative projects with a large number of academic and industrial partners.
The CEA is established in ten centers spread throughout France
Reference
Category
Thermohydraulics and fluid mechanics
Contract
Internship
Job title
Stage "Machine learning for bubble reconstruction in two-phase flows"
Subject
Machine learning for bubble reconstruction in two-phase flows
Contract duration (months)
6
Job description
Two-phase flows can be found in many industrial applications, including nuclear power plants, cooling of electronics and heat transfer devices in spacecraft. In order to provide better design guidelines aiming at higher efficient and safer operation, the fundamental understanding of two-phase flows is required. At STMF, we investigate the physics of two-phase flows by performing experiments with and without phase change (bubble growth in nucleate boiling, Taylor bubble motion in a capillary, bubbly flow) employing high-resolution and high-speed optical diagnostics such as shadowgraphy, particle image velocimetry, interferometry, laser-induced fluorescence and infrared thermography. The shape of these bubbles can be complex due to coalescence phenomenon and/or even partially obstructed due to an overlapping of bubbles on the image, for instance, which makes the classical post-processing techniques quite time-consuming and limited. This internship aims at using the artificial intelligence as a tool to aid the post-processing analysis in two-phase flows. Within this framework, the student will perform the following tasks:
· Perform a preliminary literature review on artificial intelligence methodologies applied in two-phase flows.
· Establish a case testing scenario.
· Test different methodologies using synthetic images.
· Interpret the results.
· Present and discuss the work during technical meetings and write a final report.
Applicant Profile
The candidate is a master student who has a good knowledge of applied mathematics with some experience on neural networks. Knowledge on fluid-dynamics is a plus. Knowledge on Python and or Matlab is desired. A good knowledge of English is required. French would be a plus.
Site
Saclay
Job location
France, Ile-de-France, Essonne (91)
Location
Gif-sur-Yvette
Languages
English (Fluent)
Prepared diploma
Bac+5 - Diplôme École d'ingénieurs
Position start date
04/03/2024
