Martí i Franquès MSCA-COFUND-DP. Call for 1 PhD position in "Particulate models for dynamics of...

Updated: 3 months ago
Job Type: FullTime
Deadline: 06 Sep 2021

The Martí i Franquès COFUND Doctoral Programme (MFP) is a redesign of the existing MF programme, offering 100 doctoral contracts (in four editions: 2017, 2018 and 2020, 2021) at the Universitat Rovira i Virgili (URV). The programme is uniquely shaped to offer the best training stemming from the "triple i" principles of the Marie Sklodowska-Curie Actions: international, interdisciplinary and intersectoral.

In order to achieve these goals, we combine leading research groups at URV with scientific partners from world-class institutions, such that the candidates are be exposed to interdisciplinary training as well as mentoring from the industrial sector.

The positions are based on individual projects encompassing all areas of research at URV: Sciences, Health Sciences, Arts and Humanities, Engineering, Social and Legal Sciences. Prospective candidates can find here the full list of projects . The application process is entirely electronic.

Through MFP, URV is in a unique position to offer the best conditions for doctoral training, based on the principles of the European Charter for Researchers  and the Code of Conduct for the Recruitment of Researchers  (guaranteed by the HR award that URV has received in 2014), as well as the EU Principles for Innovative Doctorate Training .

Description of the research project (ref. 2021MFP-COFUND-2 )

The understanding of mesoscopic flow dynamics is crucial for the design of novel nanofluidic processes as well as to test new theories of fluctuations at the mesoscale, which are relevant for biological systems. One important aspect of the microscale is the importance of the thermal fluctuations, which are responsible for the diffusion processes, but they also play a fundamental role in e.g., the dynamics of biological micromotors. To understand and simulate such type of systems, the description of the fluctuating fluid is a key element. Lagrangian particulate methods, as the ones used to simulate macroscopic flows (e.g. SPH), are suitable for systems with free surfaces as well as containing embedded colloids. These methods can be scaled down to the microscale, by the inclusion of the appropriate stochastic (random) terms that mimic the thermal agitation, responsible for Brownian motion.

Recently, we have developed the necessary framework to construc this type of models at the mesoscale, which we have generically named as GenDPDE. However, to simulate small viscous systems (like blood), the main problem lies in the fact that in real systems the momentum is transferred fast as compared to the time the particles take to diffuse. The ratio of these two effects is commonly referred to as the Schmidt number, which is rather large in fluids like water (Sc≈400). Thus, the direct application of SPH, or the fluctuating counterpart SDPD, are computationally very expensive, as the resolution of the momentum relaxation limits the size of the time-steps that can be applied.

In this project, we aim at constructing a fluctuating particle-based method to describe viscous fluids at the microscale and mesoscale, with low computational cost, and yet able to reproduce the dominating hydrodynamic interactions for small systems with large Sc numbers. The targeted applications are colloidal suspensions of different types, but the intended simulation scheme has wide application to biological fluids, being blood its paramount representative.

The host team has a long experience in building and applying simulation codes based on Lagrangian methods, both for macroscopic and mesoscopic fluids (see references). The successful candidate will have support in all phases of the production of the simulation code, which ultimately will be designed to run in high performance computing (HPC), likely under the Lammps umbrella.

Highly desirable attributes of the ideal candidate:

  • Demonstrated previous experience in one or more of the following topics: programming in scientific languages is a necessary condition (see below). A strong background is required in the Physics of Fluids and Statistical Mechanics.
  • Hold a Master degree, or equivalent, in: Physics, Applied Mathematics, Mechanical, Chemical or Aeronautical Engineering, or Physical Chemistry.
  • Language skills: The successful candidate must be fluent in English.
  • Specific Software skills: the preferred scientific languages are Matlab and Fortran, followed by C, C++ or Python.
  • Personality traits: the successful candidate must be capable of teamwork, show initiative and creativity, and comply with the ethical guidelines of the university.

The URV team and its collaborators have ample experience in the modelling and implementation of mesoscopic fluid simulation methods (see the references), which will help the successful candidate to complete the PhD work.

References:

  • Generalised Energy-Conserving Dissipative Particle Dynamics Revisited: Insight from
  • the Thermodynamics of the Mesoparticle Leading to an Alternative Heat Flow Model (J.B. Avalos et al. Phys. Rev. E –in press)
  • Acoustic wave propagation and its application to fluid structure interaction using the Cumulant Lattice Boltzmann Method (DOI: 10.1016/j.camwa.2021.02.011)
  • Shear-viscosity-independent bulk-viscosity term in smoothed particle hydrodynamics (DOI:10.1103/PhysRevE.101.013302)
  • Generalised dissipative particle dynamics with energy conservation: density- and temperature-dependent potentials (DOI: 10.1039/c9cp04404c)
  • Logarithmic Exchange Kinetics in Monodisperse Copolymeric Micelles (DOI:10.1103/PhysRevLett.118.248001)
  • Molecular dynamics algorithm enforcing energy conservation for microcanonical simulations (DOI: 10.1103/PhysRevE.89.053314)
  • Dissipative particle dynamics at isothermal, isobaric, isoenergetic, and isoenthalpic conditions using Shardlow-like splitting algorithms (DOI: 10.1063/1.3660209)

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