Job offer

European Commission; France; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

Une institution de recherche de premier plan recherche un doctorant pour un projet innovant sur la turbulence et l'apprentissage automatique. Vous serez impliqué dans des études quantitatives visant à transformer des données en connaissances utiles pour le contrôle des flux turbulents, ce qui a un impact significatif sur l'efficacité industrielle. Ce poste offre l'opportunité de travailler avec des experts et d'acquérir des compétences clés dans un domaine en pleine évolution.

Prestations

Encadrement par des experts en mécanique des fluides et ML

Accès à des ressources computationnelles avancées

Stipendie conforme à la législation française

Qualifications

• Étudiant en dernière année d'ingénierie ou étudiant en Master.
• Forte connaissance de la mécanique des fluides.
• Intérêt pour la turbulence et le transfert de chaleur.

Responsabilités

• Conduire des simulations numériques en utilisant le code Xcompact3D.
• Analyser la relation entre les oscillations murales et la performance du transfert de chaleur.
• Étudier les structures cohérentes dans les flux turbulents.

Connaissances

Fluid mechanics

Machine learning

Data analysis

Programming

Analytical skills

Formation

Master Degree or equivalent

Outils

Python

TensorFlow

PyTorch

Fortran

Description du poste

Organisation/Company INSTITUT PPRIME Department Ressources Humaines Research Field Engineering » Aerospace engineering Researcher Profile First Stage Researcher (R1) Positions Master Positions Country France Application Deadline 1 Jul 2025 - 00:00 (Africa/Abidjan) Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Mar 2025 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

Internship + PhD Position: Modeling and Control of Near-Wall Turbulence Using Machine Learning Approaches

Overview

In today's rapidly advancing scientific landscape, both experimental and numerical simulations generate an unprecedented volume of highly detailed data. This surge necessitates sophisticated analytical tools for effective statistical analysis and data mining. Machine Learning (ML) algorithms present a promising avenue for tackling high-dimensional, nonlinear challenges inherent in near-wall turbulence studies. By integrating ML methodologies with a deep understanding of turbulence, this research aims to achieve significant breakthroughs in wall-bounded flows and their control.

Research Objectives

The primary objectives of this research program are:

• Refinement of Boundary-Layer Theory:
  • Utilize a broad spectrum of statistical analyses combined with diverse ML approaches to enhance boundary-layer theories.
  • Develop predictive models that accurately capture relevant physical characteristics and responses to forcing in near-wall turbulence for both incompressible and compressible flows, where density variations arise from pressure or temperature fluctuations.
• Drag Reduction and Heat Transfer Optimization:
  • Formulate solutions to reduce frictional drag.
  • In thermal boundary layers, maximize heat transfer efficiency while minimizing associated energy losses.

Societal Impact

Turbulent flows significantly influence the performance of various industrial equipment and environmental applications. High friction drag resulting from turbulence can drastically reduce the operational effectiveness of transport systems, ranging from self-propelling bodies in fluids to fluids transported through ducts and pipes. For instance, the International Civil Aviation Organization (ICAO) projects that aviation emissions could triple by 2050, with aircraft potentially accounting for 25% of the global carbon budget. Addressing friction drag is therefore critical in mitigating transport-related emissions.

Moreover, optimizing turbulent fluxes within wall-bounded regions enhances heat transfer processes, which is particularly beneficial for applications like heat exchangers in renewable energy technologies, including solar receivers. Balancing drag-induced losses with heat transfer efficiency is essential for advancing sustainable and efficient industrial processes.

Scientific Challenges

Fluid mechanics stands out with the fundamental Navier-Stokes (NS) equations governing flow behaviors, offering a theoretical foundation for predicting flow motions. However, the inherent nonlinearity and complexity of these equations make them difficult to solve, especially in turbulent regimes. Near-wall turbulence, characterized by chaotic turbulent structures across a wide range of length and time scales, remains only partially understood despite over two centuries of study.

Key challenges include:

• Complex Dynamics at High Reynolds Numbers: As Reynolds numbers increase, turbulent flows become more intricate with the emergence of diverse coherent structures, complicating the dynamics and making comprehensive understanding elusive.
• Data Abundance and Postprocessing: Advances in metrology and computational power have led to substantial growth in fluid mechanics databases, posing new challenges for effective data postprocessing and analysis.
• Integration of ML with Traditional Models: Developing ML methods that complement rather than replace traditional physics-based models is crucial. Imposing symmetry properties and known evolutionary models within ML frameworks can enhance the reliability and applicability of predictive models.

Addressing these challenges through the integration of ML techniques promises to advance the universal modeling of turbulence and the development of effective control systems aligned with industrial requirements.

Why Join Us?

This PhD position offers a unique opportunity to be at the forefront of fluid mechanics and machine learning research. You will work alongside leading experts in turbulence and ML, utilizing state-of-the-art computational resources and extensive datasets. Your research will contribute to critical advancements in both scientific understanding and practical applications, addressing pressing societal needs for sustainable and efficient industrial processes.

Qualifications

• Academic Background: Strong foundation in fluid mechanics, applied mathematics, physics, or a related field.
• Technical Skills: Proficiency in programming and experience with machine learning frameworks (e.g., Python, TensorFlow, PyTorch).
• Research Aptitude: Demonstrated ability to conduct independent research and work collaboratively in a multidisciplinary environment.
• Analytical Skills: Ability to perform complex data analyses and develop predictive models.

Application Process

Interested candidates are invited to submit the following:

• Cover Letter: Detailing your research interests and motivation for applying.

Join us in pioneering advancements in turbulent flow control and making a significant impact on society's sustainability efforts. We look forward to your application!

Where to apply

E-mail [e-mail]

Requirements

Research Field Engineering » Aerospace engineering Education Level Master Degree or equivalent

Skills/Qualifications

Research Objectives:

• Conduct numerical simualtions using Xcompact3D code
• Analyse the relationship between wall oscillations and heat transfer performance
• Study coherent structures in turbulent flows and their role in thermal transport
• Contribute to the development of predictive models for heat transfer estimation

Required Profile:

• Final year engineering student or Master's student
• Strong background in fluid mechanics
• Programming skills will be appreciated (Python, Fortran)
• Interest in turbulence and heat transfer
• Good analytical and problem-solving abilities

Skills to be Developed:

• Scientific computing and data analysis
• Physical modelling of complex flows
• Statiscal analysis and Machine learning algorithms
• Academic research methodology

Specific Requirements

Practical Information:

• Monthly stipend: According to French legislation
• The laboratory is classified as a ZRR (Restricted Access Zone)

with subject “internship_Turbu_application_#yourname” :

• Resume
• Transcripts from Master 1 & 2
• Contact information for two possible references

Note: Due to security regulations (ZRR), recruitment requires prior authorisation from Defence Security Officer.