Title: Turbulent convection: numerical modelling and physics-enhanced machine learning
Date: 2022-03-29 15:37
Slug: job_9d15dd406dbe38fe3074bb6170510b13
Category: job
Authors: Anne Sergent | Didier Lucor
Email: anne.sergent@lisn.upsaclay.fr
Job_Type: Thèse
Tags: these
Template: job_offer
Job_Location: LISN - Campus Univ. Paris Saclay (Orsay)
Job_Duration: 3 years
Job_Website: https://perso.limsi.fr/sergent/
Job_Employer: Sorbonne Université
Expiration_Date: 2022-04-30
Attachment: job_9d15dd406dbe38fe3074bb6170510b13_attachment.pdf
**Research project description **
The Rayleigh-Benard convection is established in a cavity under the effect of a temperature difference imposed on the horizontal walls, the bottom wall being heated. The resulting flow in the turbulent regime is a multi-structured and multi-scale phenomenon characterized by the superposition of small-scale plumes (heat vectors), a large-scale mean flow filling the cavity, boundary layers and turbulent fluctuations.
For many years, we have been simulating this physical phenomenon by direct numerical simulation (DNS). The transition to massively parallel simulations now allows us to consider calculations at parameter levels close to experiments. However, these calculations are very heavy and even if the spatio-temporal description of the flow can be very fine, it is difficult to approach statistically all the scales of the flow, to store all the computed fields, or to easily replay the sequences.
This project aims at comparing similar experimental (from an ongoing collaboration) and numerical data over a wide range of Rayleigh numbers by performing massively parallel direct simulations and by developping machine learning techniques. At very high Rayleigh numbers, a very intense heat transfer regime appears for which the triggering mechanisms are still poorly understood by the scientific community. A single numerical or experimental model can hardly capture them all.
We therefore seek to take advantage of the capabilities of machine learning techniques to reduce the complexity of the data. These techniques will be deployed at the interface between numerical models and solvers, and experimentally acquired data, not only to facilitate comparison, but also to access unmeasured/unquantifiable information in terms of variables or resolution finesse, and to guide physical exploration. For this, we use deep or convolution neural networks, in which physical ingredients enrich the output data for an accelerated convergence. Several encouraging internships have already been carried out on the topic [Lucor et al. JCP 2022]. A DNS database already exists [Belkadi et al. JFM 2021], but it will be expanded as needed using the resources of GENCI’s national supercomputers.
The project will focus on the link between plumes and heat transfer enhancement, in particular in terms of scalar dynamics and dissipation scaling laws. In addition, one of the challenges of this project concerns the very high Rayleigh numbers, not easily accessible to measurements and simulations, for which the project hopes to obtain information about temperature fluctuations and intense transfer events.
**Profile required **
• Master of Science or equivalent in applied mathematics, physics, or mechanical enineering, with competences in fluid dynamics, statistics, or scientific computing
• Good experience in programming (Python) and in data post-processing and analysis
• Good writing skills **Expected profile **
• Master of Science or equivalent in applied mathematics, physics, or mechanical enineering, with competences in fluid dynamics, statistics, or scientific computing
• Good experience in programming (Python) and in data post-processing and analysis
• Good writing skills Contact and application procedure
For further information, please contact:
Anne Sergent
anne.sergent@lisn.fr
Didier Lucor
didier.lucor@lisn.fr
Please send by April 30, 2022, a detailed CV, a motivation letter, letters of recommendation if any and a transcript of higher education records (at Master level)