Ph.D. Thesis offer 2022 | Funded by a Franco-American project

Microscopic images converted into dynamic mesoscopic models: application to mechanical food deconstruction

Summary

Microscopic 4D (x,y,z,t) or 5D (dynamic spectral images) techniques and mesoscopic modeling are becoming versatile techniques. This Ph.D. thesis proposes to combine the two approaches to parameterize mesoscopic models of food constituents that would allow the direct simulation of the mechanical deconstruction of food. The central idea is to convert the deformation or velocity field observed in the field of a confocal microscope into parameters of hybrid models mixing updated and total Lagrangian SPH (smoothed hydrodynamic particles) methods. The work aims at optimizing the mechanical solicitation protocol and the microfluidic cell to maximize the identifiability of soft matter mechanical behaviors.

Context

Food engineering contributes to feeding humankind, its well-being, and health. The following engineering trend looks at the connection of scales to predict and control the behavior of food in the digestive tract. Food physics obeys complex soft matter principles: entangled polymers, self-organized colloids, and surfactants, multiphase. The growing number of mesoscopic modeling methodologies and the advances in imaging techniques enable us to track the fate of food as it is broken down into its constituents during digestion.

Problem statement

We are designing several simulation frameworks capable of describing flows, mechanical ruptures, and evolutions under entropy-driven phenomena (dissolution, diffusion) at mesoscopic scales. Smoothed Particle Hydrodynamic and Dissipative Particle Dynamics techniques are the preferred methodologies at the upper and lower scale, respectively. The former acts as an effective flow, while the latter explicitly incorporates more physical details such as temperature effects. These principles made designing DPD- or SPH- fluids and solids possible by assembling and possibly connecting beads/particles.

In parallel, the observations from mesoscopic systems (primarily based on microfluidic setups) will feed data to the model and validate the parameter selection. Depending on the results, an optimized methodology will be used to accelerate the experimentation-parameterization loop. This first work should preclude the construction of a future digital twin scheme that automatically changes the perturbation strategy based on the difficulty of optimizing simulation parameters.

Project description

The Ph.D. will design microfluidic experiments to provoke interactions between objects (stabilized droplets, gel particles) with controlled characteristics and surfaces. The interactions will include shearing, crowding, compression, and impact in the presence of a continuous phase (water, saliva simulant). Observations will be carried out under a visible light microscope operating in different acquisition modes: confocal, phase contrast, epifluorescence, and fast Raman imaging. Interactions with walls, including mechanical stresses, will be reconstructed from the experimental deformation of the structures.

For an extended description of the work:

https://fitness.agroparistech.fr/fitness/jobs/PhDoffer2022.html

Application and recruitment

Application details

Prerequisites and procedure

• The applicant should have or be working towards a Master’s degree in microfluidics, physics, physical chemistry, soft matter physics, material sciences, fluid mechanics, chemical engineering, or similar.

• Strong English (and some French) or a clear willingness to improve your level will be essential.

• A prior experience in image analysis and programming will be appreciated.

• Send your application to Dr. Olivier Vitrac: olivier.vitrac@agroparistech.fr (contact E-mail)

• Cc. murielle.hayert@agroparistech.fr denis.flick@agroparistech.fr william.jenkinson@agroparistech.fr

• Please include your CV, cover letter, two recommendation letters, and undergraduate and Masters’ transcripts.