Martí-Franquès COFUND Fellowship Programme


Details

Login to the application
Reference:

2021MFP-COFUND-8

Area:

Engineering and Architecture

Supervisor name and surname:

Josep Bonet Avalos

Supervisor email:

josep.bonet@urv.cat

Supervisor short biography

Co-supervisor name and surname:

Allan D. Mackie

Co-supervisor email:

allan.mackie@urv.cat

Co-supervisor institution:

URV

Co-supervisor short biography

PhD programme:

Nanoscience, Materials and Chemical Engineering

Title of the research project:

Particulate models for fluid mesoscopic simulations

Description of the research project:

Mesoscopic heat transport is of vital importance in miniaturised systems, such as molecular motors, microelectronic devices, but also in reactive fronts and interfaces. Large temperature gradients may induce strong couplings between non-equilibrium processes which are of paramount importance in the understanding and modelling of energy transfer at the nano-scale.

Simulation methods, such as Dissipative Particle Dynamics with energy conservation (DPDE), are suitable for the analysis of heat flow at the microscale, as they can describe the effect of thermal fluctuations in the field dynamics. DPDE was introduced by the URV team, and has been recently extensively applied. At present, a generalised DPDE model (GenDPDE) can describe microscopically complex systems, in which not only heat but changes in local compositions may occur, in parallel with chemical reactions. The main objective of this thesis is to contribute to the ongoing development of the GenDPDE, notably through the study of the transport properties of the model (viscosity, thermal conductivity, diffusion coefficients, etc.) as functions of the model parameters of GenDPDE. The outcomes of this research will allow us to map the model parameters to simulate real complex systems under non-equilibrium situations.

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)
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)
Dissipative particle dynamics at isothermal, isobaric, isoenergetic, and isoenthalpic conditions using Shardlow-like splitting algorithms (DOI: 10.1063/1.3660209)
Dissipative particle dynamics with energy conservation (DOI: 10.1209/epl/i1997-00436-6)
Dissipative particle dynamics with energy conservation: Modelling of heat flow. (DOI: 10.1039/a809502g)

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Ethics: This project involves ethical aspects

Workplace Location: Campus Sescelades, Tarragona

Gross anual salary:

27103.20 €

Dedication:

Full time

Working hours:

37.5 hours a week

Earliest expected start date:

14 February 2022

European union This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 945413