Intitulé de l'offre : PhD contract (M/F) Physics-Based Modeling and Control of Near-Wall Turbulence for Enhanced Heat Transfer (H/F)
Référence : UPR3346-NADMAA-100
Nombre de Postes : 1
Lieu de travail : CHASSENEUIL DU POITOU
Date de publication : mardi 30 janvier 2024
Type de contrat : CDD Doctorant/Contrat doctoral
Durée du contrat : 36 mois
Date de début de la thèse : 1 octobre 2024
Quotité de travail : Temps complet
Rémunération : 2 135,00 € gross monthly
Section(s) CN : Fluid and reactive environments: transport, transfer, transformation processes
Description du sujet de thèse
At the CNRS, the Pprime Institute, based at the Futuroscope site, is recruiting a PhD student as part of an INFERENCE research project funded by the ANR (Agence Nationale de la Recherche).
The PhD student will work on the Physics-Based Modeling and Control of Near-Wall Turbulence for Enhanced Heat Transfer.
Contexte de travail
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:
Several challenges need to be addressed to complete this project. The position requires collaboration in a multidisciplinary research environment consisting of mathematicians, computer scientists and engineers.
*Specific responsibilities include to:
- perform direct numerical simulations (DNS) to analyse heat transfer enhancement under spanwise wall oscillations using the code Xcompact3D,
- conduct parametric studies to relate oscillation parameters to heat transfer metrics like Nusselt number,
- develop predictive models to estimate heat transfer as a function of oscillation waveform parameters,
- run many short DNS cases to provide training data for reinforcement learning optimisation of oscillation protocols,
- analyse DNS data to elucidate physics linking oscillation-modified near-wall turbulence structures to enhanced thermal transport,
- quantify complex interactions between oscillations, coherent structures, and convective heat transfer.
The candidate must have a Master's degree in one of the following fields :
- fluid mechanics,
- applied mathematics,
- machine learning.
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.
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.