Researcher Contract - Modeling and Control of Turbulent Thermal Boundary Layers (M/F)

CNRS; France; Sur place; EUR 35 000 - 45 000; Plein temps

Résumé du poste

A leading research organization in France is seeking a Researcher to work on advanced modeling and control of turbulent thermal boundary layers. The position requires a PhD in fluid mechanics, applied mathematics, or machine learning. Responsibilities include applying data-driven techniques and conducting numerical simulations. This role offers an exciting opportunity to contribute to innovative research in thermal engineering and is based on-site in France.

Responsabilités

• Apply data-driven techniques to extract coherent structures from DNS data.
• Use symbolic regression to improve existing modulation models.
• Design optimal oscillations using reinforcement learning.
• Collaborate on extending models to thermal boundary layers.
• Conduct parametric studies on oscillation parameters.
• Develop predictive models for estimating heat transfer metrics.

Connaissances

data-driven techniques

symbolic regression

reinforcement learning

numerical simulations

machine learning

Formation

PhD in fluid mechanics / Applied mathematics / Machine Learning

Description du poste

Organisation/Company CNRS Department Institut P': Physique et Ingénierie en Matériaux, Mécanique et Énergétique Research Field Engineering Chemistry Physics Researcher Profile First Stage Researcher (R1) Country France Application Deadline 16 Dec 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Mar 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

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 Recherche (ANR) and Nouvelle Aquitaine Region to work on the Modeling and control of turbulent thermal boundary layers.

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 needs 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.

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.

Key Responsibilities

• Apply data‑driven techniques like autoencoders to extract coherent structures from DNS data.
• Use symbolic regression to improve existing modulation models describing how large scales alter heat transfer.
• Design optimal oscillations using reinforcement learning.
• Collaborate with the PhD student to extend inner‑outer interaction models to thermal boundary layers, undertaking direct numerical simulations (DNS) using in‑house codes to analyse heat transfer enhancement under spanwise wall oscillations.
• Conduct parametric studies relating oscillation parameters to heat transfer metrics.
• Develop predictive models for estimating the Nusselt number as a function of the oscillation waveform.
• models and disseminate research through publications.

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