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Portail > Offres > Offre UPR3346-NADMAA-036 - CHERCHEUR/POST-DOC (H/F) – Investigations expérimentales de stratégies de contrôle en boucle fermée pour la réduction d'impact du sillage d'un corps d'Ahmed

POST-DOC (M/F) - Control by Machine Learning of bluff body wakes

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

Date Limite Candidature : vendredi 12 mars 2021

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General information

Reference : UPR3346-NADMAA-036
Workplace : FUTUROSCOPE
Date of publication : Friday, February 19, 2021
Type of Contract : FTC Scientist
Contract Period : 12 months
Expected date of employment : 1 September 2021
Proportion of work : Full time
Remuneration : Between 2648 € and 3768 € gross monthly depending on experience
Desired level of education : PhD
Experience required : 1 to 4 years

Missions

At the CNRS-Laboratory PPRIME, based at the Futuroscope, this post-doctorate position is part of the French ANR COWAVE program between the laboratories PRISME in Orleans, Pprime in Poitiers, LHEEA in Nantes and the PSA automotive industry. This Post-Doc position concerns the Pprime contribution to the COWAVE project which aims the experimental exploration of closed-loop wake control strategies with mobile flaps in a water tunnel facility.

Three-dimensional bluff-body wakes generate pressure drag and side forces and thus contribute significantly to the fuel consumption and pollutant emission of road vehicles. Despite this crucial impact and the numerous attempts to reduce harmful environmental effect of bluff body wakes by flow control it is still unclear what is the most efficient control strategy!
In this context, the ANR project COWAVE addresses two fundamental aspects of wake control:

- First, what kind of actuators are most efficient? While most closed-loop control strategies use viscous entrainment effects to actuate the shear layers in the wake, the exploitation of pressure forces produced by mobile deflectors could be an interesting alternative to be tested.

- Second, for the implementation of closed-loop control, we want to test if control strategies obtained by machine learning techniques allow to obtain better efficiency and robustness than the more classical model-based approaches? The proposed Post-Doc position is part of the French ANR COWAVE program between the laboratories PRISME in Orleans, Pprime in Poitiers, LHEEA in Nantes and the PSA automotive industry. This Post-Doc position concerns the Pprime contribution to the COWAVE project which aims the experimental exploration of closed-loop wake control strategies with mobile flaps in a water tunnel facility.

Activities

The experiments will be carried out in a water tunnel facility.

- In a first test series, the flow around a bluff body model with fixed deflectors will be analyzed. Two complementary techniques will be used: the hydrogen bubble technique to analyze the origin of vortical structures and global in-time measurements of the drag force with a piezoelectric balance. In the same cases, PIV measurements will be also conducted to complete the investigation. Major questions concern the efficiency of deflectors in reducing harmful characteristics of the vortical wake and from a more global point of view the possibility to reduce the overall drag force.

- In a second test series, machine learning techniques in combination with mobile deflectors will be applied to study the gain and robustness with different closed-loop control strategies. From an experimental point of view, the challenge will be to find a robust way of sensing the flow. The development of real-time integral force measurements appears to be a promising way despite the required high sensitivity of 10-2 N. The final goal is to establish the cost balance between necessary expense and gain in view of future research of control strategies and their transfer to road vehicles.

Skills

The candidate must hold a doctorate in fluid mechanics or aerodynamics.
A strong interest in experimental work in aerodynamics and interest in machine learning applications are important.
Experience in data acquisition and processing, preferably in a python or Labview environment, are highly appreciable.
She/he should be able to work in a dynamic context with experimental and theoretical researchers.
Other interesting qualities are great autonomy and good communication skills

Work Context

The candidate will be integrated into the team CURIOSITY of the Pprime Institute. She/he will benefit from the strong dynamics of this group in the context of flow control and aerodynamics. Considerable resources of this research group are devoted to the development of new techniques in the domain of machine learning and their implementation in experiments.

Constraints and risks

Short-term trips, in France and abroad, are to be expected.

Additional Information

=> Coronavirus information:
However, depending on the pandemic situation and the candidate's availability, the exact start date can be negotiated.

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