Missions

At the CNRS on the Futuroscope site, the Institut Pprime is recruiting a Researcher as part of the INFERENCE project funded by the Agence Nationale de la Reche (ANR) to work on the Modeling and control of turbulent thermal boundary layers.

Activités

1-Context
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.
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2-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.

Compétences

*The candidate will be entrusted with key 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, who 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.

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 modeling.

The researcher must hold a Phd in fluid mechanics / Applied mathematic / Machine Learning.

Contexte de travail

*Keywords :
flow control, heat transfer, wall-bounded flow, thermal boundary layer, numerical simulations, reduced order model, machine learning, data-driven algorithms, deep reinforcement learning

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.

Contraintes et risques

None