High-fidelity Multiphase Flow Simulation of Surfactant Delivery in Human Airway Models // High-fidelity Multiphase Flow Simulation of Surfactant Delivery in Human Airway Models

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This work is conducted as part of INHALE, a selected ANR-funded project to launch in
September 2024, that seeks to leverage advanced modeling approaches combining
computational fluid dynamics (CFD) and artificial intelligence (AI) to develop data-informed
decision-making tools for ARDS management. As the first milestone in this process, this PhD aims to establish the general CFD framework for high-fidelity simulations of surfactant-laden liquid plug propagation in realistic conducting airway trees. Robust, flexible, and resourceefficient methods will be pursued to easily accommodate a large diversity of airways size and structure and surfactant physical properties.

The PhD is structured around the following main tasks:
• extend the existing CFD multiphase framework to Navier-Stokes flow solutions coupled
to surfactant diffusion, advection, adsorption and desorption kinetics,
• verify the implementation against analytical manufactured solutions
• check applicability and accuracy on challenging numerical cases from literature involving
Marangoni forces caused by surfactant concentration gradient,
• integrate all numerical developments into an automated workflow for pulmonary
simulations of surfactant delivery in large-scale, air-filled airway tree models,
• assess performance in a high-performance computing context.

Selected cases emphasizing the sensitivity of delivery efficiency (the fraction of surfactant that reaches the terminal airways) and homogeneity (the extent to which it is evenly distributed between all terminal airways) to geometric architecture, gravitational orientation and truncation level of the airway tree model will also be examined and validated by experimental observations, for which dedicated pilot studies will be conducted.
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Début de la thèse : 01/10/2025
WEB : https://www.cemef.minesparis.psl.eu/wp-content/uploads/2024/04/phd_2024_inhale.pdf

Other public funding

ANR Financement d'Agences de financement de la recherche

Mines Paris-PSL

364 SFA - Sciences Fondamentales et Appliquées

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Applicants will have (or be in the process of obtaining) a master's degree in fluid mechanics, applied mathematics, or a related field, with an outstanding academic record (at or near the top of their class) at master's level. Preferred candidates will possess demonstrable experience in the numerical modelling and simulation of flow transport phenomena, a working knowledge of finite element analysis, numerical programming ability using C++ and python, professional command of English, good presentation skills, and the ability and willingness to work collaboratively within a multi-disciplinary team. Prior experience in collaborative projects using git would be appreciated.