A PhD position on multiphysics-multiscale models and digital twins for redox 
flow batteries is available at the Center for Sustainable Energy of Fondazione 
Bruno Kessler and Politecnico di Torino (DISAT, group Multiscale modelling for 
materials science and process engineering).

Redox flow batteries (RFBs) are a promising technology for large scale energy 
storage. In RFBs power and energy are decoupled: the former depends mainly on 
the size of the stack while the latter on the size of the tanks containing the 
redox active species. This feature makes RFBs ideal for economical, large-scale 
energy storage. However, cost reductions are mandatory to allow a widespread 
diffusion of this technology. The required cost reductions involve two main 
components of the system: the electrolytes and the stack. Both need to be 
optimized for enabling a large-scale diffusion of RFBs. 

In this PhD project we propose to develop a multiphysics-multiscale platform 
aimed at supporting redox flow cell and stack design and upscaling. This tool 
will also enable design optimization supported by different algorithms. The 
selected candidate will be in charge of developing the models, extending 
opensource modelling platforms, such as OpenFOAM or FEniCSx and integrating 
optimization tools such as Dakota. 

The platform will be composed of different main components tightly connected 
with each other: 
1) Multiphysics cell-scale  model. At this scale the electrode will be described 
as a continuum with transport regulated via permeability and dispersion 
coefficients obtained from the literature. 
2) Stack-scale model. At this scale several cells in parallel will be simulated 
at once by considering each cell with a simplified description, to highlight 
possible maldistributions or other issues.
3) System-level redox flow battery model. This model considers the full battery 
system including the stack, tanks, pumps, piping, power electronics, etc. It is 
based on transient 1D-0D descriptions (by using OpenModelica or python) that 
integrates the stack-scale model or a further simplified version.
4) Optimization tool (by using Dakota or python).

The models and the battery digital twins will be validated with experimental 
data from known chemistries and representative prototypes and will be then 
employed to explore new chemistries. The candidate will be responsible for 
developing and implementing the physical models, validating the models based on 
experimental data, integrating different models for building a multiscale tool 
and integrating the optimization algorithms in the workflow to enable design 
optimization. To enable a strong cross-contamination of ideas and expertise, the 
candidate might also support the experimental activities related to the 
validation of redox flow cells with known and new chemistries.

Here you can find a more detailed description of the topic: 
https://www.polito.it/sites/default/files/2023-
08/borsa_scudo_15781_FBK_Development%20and%20validation%20of.pdf

Application deadline: 20th November 2023 (at 12 PM – CET time)

For applications please follow the instructions at this webpage:
https://www.polito.it/en/education/phd-programmes-and-postgraduate-
school/admissions-to-phd-programmes/admissions/call-for-applications

Interested candidates are encouraged to contact:
Dr. Edoardo Gino Macchi (emacchi@fbk.eu)
Prof. Daniele Marchisio (daniele.marchisio@polito.it) 

Qualifications:
-Strong interest on physics, modelling complex multiphysics transport
phenomena and programming
-Solid know-how on computational methods (e.g., FEM, FVM), experience
with opensource tools (e.g., OpenFOAM, FEniCSx) is highly valued
-Skills in programming (C++, python) and some experience in the
development of applications or libraries for modelling physical systems
-Know-how on electrochemistry and electrochemical devices (batteries,
electrolyzers, fuel cells)
- Good knowledge of written and spoken English