Developing a fast machine-learning framework to replace rigorous equilibrium calculations in reactive systems and integrating it into CFD codes

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Developing a fast machine-learning framework to replace rigorous equilibrium calculations in reactive systems and integrating it into CFD codes

The performance of lithium-ion batteries depends highly on the properties of their cathode active material, which in turn are determined by the properties of the precursor of cathode active material (pCAM). The pCAM is usually produced by the reactive precipitation process in mixed stirred reactors, which should be optimized to obtain the pCAM with desired materials. For this purpose, Computational Fluid Dynamics (CFD) simulations can help us to find the optimum reactor design and operating conditions according to the desired pCAM properties. Therefore, it is important to develop predictive CFD tools for simulating the precipitation process of pCAM.

Description

The CFD simulation of reactive systems often requires the calculation of equilibrium thermodynamic properties, which in turn, can be computationally demanding. An example of such reactive systems is the precipitation process for pCAM production, in which the nucleation and growth of particles usually depend on the distance between the actual state of the system and its equilibrium state. In this regard, accelerating the evaluation of equilibrium thermodynamic properties can significantly help to reduce the time required for conducting simulations of this process.

The main goal of this internship is to develop a fast machine-learning framework that can be used in the CFD simulation of the pCAM synthesis process. For this task, the training data is obtained from the rigorous equilibrium models already developed in IFPEN.

The work will be divided into the following tasks:

• Conduct a literature review to select a suitable framework for accelerating equilibrium calculations
• Prepare the training and verification data set by solving the rigorous equilibrium model at different operating conditions and compositions
• Train the machine-learning tool and validate it against the predictions by the rigorous model
• Integrate the developed tool into OpenFOAM and/or Ansys Fluent CFD solvers

Required profile

Experience in python scripting, programming skills, interested in developing machine-learning tools, familiar with equilibrium calculations of chemical systems, familiar with CFD

Additional information

Duration of the internship : 6 months
Workplace : IFPEN Lyon, Rond-point de l'échangeur de Solaize, 69360 Solaize
Transport : public transportation / personal vehicle
Paid internship (1080€/month)

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