Phd Position F/m
Il y a 2 mois
Le descriptif de l’offre ci-dessous est en Anglais_
**Type de contrat **:CDD
**Niveau de diplôme exigé **:Bac + 5 ou équivalent
**Fonction **:Doctorant
**A propos du centre ou de la direction fonctionnelle**:
The Inria centre at Université Côte d'Azur includes 37 research teams and 8 support services. The centre's staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d'Azur, CNRS, INRAE, INSERM...), but also with the regiona economic players.
With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d'Azur is a major player in terms of scientific excellence through its results and collaborations at both European and international levels.
**Contexte et atouts du poste**:
Stochastic processes and stochastic differential equations (SDEs) are privilegieaded model tools involved in the descriptions of turbulent flow, and of the particles (passive or active) transported in it.
**Mission confiée**:
The doctoral student will be welcomed within** the CALISTO team:
By adopting an interdisciplinary approach mixing stochastic analysis and physics, the CaliSto team develops original and coherent stochastic models in this field. They are based on two complementary points of view, combining statistical descriptions of turbulence (so-called mean fields), where only limited information is computed, and detailed approach (where the fine description of the phenomena is obtained from direct numerical simulations, allowing fundamental analysis on the instantaneous structures of the flow.
**Principales activités**:
**Detailed workplan**
This Ph.D. project identifies three aspects related to the mathematical approach and modeling of turbulent transport and deposition for complex particles and proposes to investigate them. These three key sub-goals are detailed below
**1) Fluid particle model is CFD**
It is commonly accepted in today's Computational Fluid Dynamics (CFD) literature that modeling the motion of a Lagrangian tracer (an immaterial particle being transported) within the framework of CFD (where fluid motion is described statistically) is effectively captured by a class of Stochastic Differential Equations (SDEs) driven by Brownian motion. However, enhancements to these models to account for boundary conditions such as wall laws are typically proposed only at an algorithmic level [4] [5], often stemming from heuristic approaches. Yet, the expected behavior near the wall, such as the logarithmic law, should be clearly identified mathematically as a model before being discretized for simulation purposes. Only through model identification can a simulation strategy be analyzed and compared with others.
As a starting point, the construction of the corresponding class of reflected SDEs could be inspired by [6]. The analysis will also focus on the modulation of the particle's inertia and the level and type of surface model description.
**2) Near wall dynamics of non spherical particles**
Current large-scale models have predominantly been developed and validated for particles located away from boundaries, where the flow exhibits isotropic characteristics ([2] [3]).
The aim here will be to analyze proposals for boundary conditions for this type of models. These proposals can be applied to the case of small swimmers in turbulence oriented through collective aggregation behavior.
**3) Surface growth**
Models should reproduce how particles interact with solid surfaces and accumulate on them. One of the key challenges is to develop models that remain tractable in large-scale situations. For that purpose, researchers and engineers are increasingly relying on reduced statistical descriptions that capture the complexity of these phenomena [7].
New models are needed to have statistical descriptions of surface growth that are compatible with the models used for fluid turbulence as well as particle turbulent transport.
In this case as well, the aim will be to propose an SDE model that should consolidate the existing PDF approaches within a framework still closely aligned with experiments.
**References**
[1] Brandt, L., & Coletti, F. (2022). Particle-laden turbulence: progress and perspectives. _Annual Review of Fluid Mechanics_, _54_, 159-189.
[2] Campana, L., Bossy, M., & Henry, C. (2023). Lagrangian stochastic model for the orientation of inertialess spheroidal particles in turbulent flows: An efficient numerical method for CFD approach. _Computers & Fluids_, _257_, 105870.
[3] Voth, G. A., & Soldati, A. (2017). Anisotropic particles in turbulence. _Annual Review of Fluid Mech
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