Ricardo EL KHOURY

Biopolymer-induced drag reduction: a promising technique to minimize water and electricity wastes in water pumping systems.

The purpose of this project is to reduce both water and electricity wastes in water pumping systems by benefiting from a non-Newtonian fluid mechanics phenomenon called biopolymer drag reduction: a dramatic pressure drop decrease in turbulent flows induced by the addition of a tiny amount of biopolymers. The aloe vera produces a stable and non-toxic natural mucilage that can interact with turbulent flows, partially suppressing their chaotic nature and, consequently, the wall shear stress in pipelines. As a result, a tremendous reduction of pumping costs and water leaks is observed, which ultimately can be converted into productivity gain in farms and/or industrial complexes. The objective is to highlight the physical mechanism driving the biopolymer/flow interactions, narrowing the gap between the fundamental understanding of the turbulent drag reduction phenomenon and quantitative predictions for agricultural and industrial applications. This project is made feasible through a mixed fluid mechanics approach combining experiments, direct numerical simulations, and artificial intelligence.


Vincent LANNELONGUE

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Tianchi LI

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Ugo PELISSIER

Haemodynamics and thrombus formation for arterial wall remodeling prediction in intracranial aneurysms

Intracranial aneurysms are pathological dilations of blood vessels taking the shape of bulges or balloons mostly on wall sides or at arterial bifurcations. These anomalies bear the risk of rupture and subsequent subarachnoidal hemorrhage, which is associated with high mortality and morbidity rates. Fluid mechanical simulations can yet provide insights into the flow patterns and stress distributions, thus helping the evaluation of aneurysm rupture risks. This work aims at developing a framework for predicting blood clot formation inside intracranial aneurysms and the associated arterial wall remodeling. It should serve as basis to evaluate the effectiveness of a flow diverter stent, and enable its optimization through a Deep Reinforcement Learning workflow. Since the time scales involved far exceeds the ones of current blood flow simulation inside intracranial aneurysms, some techniques, based on Artificial Intelligence for instance, might be used to speed up the predictions.


Pungponhavoan TEP

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Eliane YOUNES

AI and digital twins in metallurgy- Front trackin modeling of evolving interface networks.

This thesis focuses on the application of artificial intelligence and digital twins in metallurgy. The objective is to develop computational strategies for designing metallic materials with low environmental impact. The microstructure of materials is crucial for their properties and durability. Understanding and predicting these evolutions is essential for the industry. Multiscale modeling is employed, with a emphasis on mesoscopic scale simulations. A 2D front tracking method called ToRealMotion is explored. Deep neural networks will be used to enhance the efficiency of computational operations and automatically interpret microstructural features.