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Portail > Offres > Offre UPR3346-NADMAA-102 - CDD Chercheur-Post-Doctorat : Association de contrôle des petites échelles et de la modélisation des grandes échelles pour l'optimisation des transferts de chaleur (H/F)

Postdoctoral researcher - Integrated small-scale actuation and large-scale modeling for thermal transport control (M/F)

This offer is available in the following languages:
- Français-- Anglais

Date Limite Candidature : mardi 12 mars 2024

Assurez-vous que votre profil candidat soit correctement renseigné avant de postuler

Informations générales

Intitulé de l'offre : Postdoctoral researcher - Integrated small-scale actuation and large-scale modeling for thermal transport control (M/F) (H/F)
Référence : UPR3346-NADMAA-102
Nombre de Postes : 1
Date de publication : mardi 30 janvier 2024
Type de contrat : CDD Scientifique
Durée du contrat : 18 mois
Date d'embauche prévue : 1 octobre 2024
Quotité de travail : Temps complet
Rémunération : Between 2905 € et 4081 € gross monthly according to experience
Niveau d'études souhaité : Niveau 8 - (Doctorat)
Expérience souhaitée : Indifférent
Section(s) CN : Fluid and reactive environments: transport, transfer, transformation processes


At the CNRS, the Pprime Institute, based at the Futuroscope site, is recruiting a post-doctoral researcher as part of an INFERENCE research project funded by the ANR (National Research Agency).

The candidate will work on Integrated small-scale actuation and large-scale modeling for thermal transport control.


Turbulent flows dictate the performance characteristics of numerous industrial equipment and environmental applications. One important consequence of turbulence is to increase the mixing momentum leading to high friction drag on surfaces, the increase relative to laminar conditions easily reaching factors of 10‐100, depending on the Reynolds number of the flow. In many applications, the friction drag is extremely influential to the operational effectiveness of the device or process. This applies especially to transport, involving either self‐propelling bodies moving in a fluid or fluids being transported in ducts and pipes. There is significant pressure to reduce transport-related emissions, of which friction drag is a major constituent. On the other hand, enhancing the turbulent fluxes within the wall-bounded region, is generally beneficial for the heat transfer. Thus, in the case of heat exchangers, a balance need to be found between drag-induced losses and the heat transfer. For a wide variety of engineering applications, whether for a cooling or heating process, improving heat-exchanger capacity is a crucial technological challenge towards efficiency and addressing industrial and societal requirements for cost-effective energy transfer.

Controlling near-wall turbulence to reduce drag has been widely studied, and effective control strategies have been designed at low Reynolds number, when the flow is mainly populated by small-scale structures. However, as the Reynolds increases, these control strategies become rapidly inefficient. This degradation can be explained by the fact that the nature of the inner structures changes in response to external structures emerging and strengthening as the Reynolds number increases. Thus, this provides strong motivation for modelling the effects of external structures on the near-wall turbulence.

*Objectives and Scientific Challenges:
The research programme aims to advance fundamental understanding of heat transfer and turbulence physics in wall-bounded flows through numerical simulations, data-driven modelling, and machine learning techniques. Key goals include optimising convective heat transfer using wall oscillations, relating small-scale turbulence to heat transport, modelling large-scale outer flow effects, and developing low-order heat transfer models. Partnerships with industry will facilitate adoption of enhanced heat transfer methods into renewable energy and propulsion technologies. The insights and computational tools developed intend to significantly advance thermal engineering capabilities whilst supporting renewable energy and aerospace priorities. However, the research does not specifically aim to facilitate the construction of improved receiver design. Rather, it entails a series of fundamentally-oriented studies on generic receivers subjected to control and idealised heating scenarios, the aim being to derive answers to basic questions on the response of the flow to the proposed control methods in respect of heat transfer and drag

*Key Duties and Responsibilities:
The postdoc will apply data-driven techniques like autoencoders to extract coherent structures from DNS data. Symbolic regression will be leveraged to improve existing modulation models describing how large scales alter heat transfer. Optimal oscillations will be designed using reinforcement learning. Extending inner-outer interaction models to thermal boundary layers requires collaborating with the PhD student. Additional tasks include developing low-order outer flow models and disseminating research through publications.
The project will draw on combined expertise in simulations, optimisation, machine learning and turbulence modelling


The candidate must hold a doctorate in one of the following fields :
- fluid mechanics,
- applied mathematics,
- machine learning.

Contexte de travail

The Pprime laboratory is a CNRS Research Unit. Its scientific activity covers a wide spectrum from materials physics to mechanical engineering, including fluid mechanics, thermics and combustion. The PhD student will be attached to the team Curiosity

Le poste se situe dans un secteur relevant de la protection du potentiel scientifique et technique (PPST), et nécessite donc, conformément à la réglementation, que votre arrivée soit autorisée par l'autorité compétente du MESR.

Informations complémentaires

A PhD student will undertake direct numerical simulations (DNS) using in-house codes to analyse heat transfer enhancement under spanwise wall oscillations. Parametric studies relating oscillation parameters to heat transfer metrics will be conducted. A key challenge is developing predictive models for estimating the Nusselt number as a function of the oscillation waveform.