researcher in scientific computing

University of technology of Compiègne (UTC) is recruiting a contract researcher in scientific computing for the Applied Mathematics Laboratory LMAC – Computer Science Department, as part of the ANR NumOpTES project.

The NumOpTES project – Numerical modeling and optimization for energy storage – aims to optimize thermal energy storage systems (TES) using phase change materials (PCMs), by improving their thermal conductivity without degrading their storage capacity.

As part of this project, the contract researcher will contribute to the design of innovative TESS using low-cost PCMs. Although these materials have a high heat capacity, they exhibit low thermal conductivity, which we aim to improve through the encapsulation of highly thermally conductive metal catalysts.

Mission

Thermal energy storage systems (TES) with phase change materials are highly capacitive (high amount of stored heat). Cheap materials generally suffer from a low thermal conduction. This major drawback has to be addressed and corrected. Encapsulating highly conductive balls in the TES support is an opportunity to design competitive composite systems that significantly improve the filling/storage/extraction short cycle. The key question is: how to disseminate these enhancing capsules to improve the effective conductivity without altering the high thermal storage capacity of PCMs?

Activities

• Development and implementation of numerical methods in the FreeFEM++ open source code (finite elements, shape optimisation).

• Integration of techniques combining neural networks and physical modelling (PINNS).

• Validation for relevant geometries.

• Writing scientific publications and presenting results at project meetings and conferences.

Context

The thermal energy storage (TES) systems in question use phase-change materials that are highly capacitive (very high quantity of heat stored). Cheap materials generally suffer from low thermal conductivity. This is a major drawback when it comes to extracting thermal energy when needed. This problem needs to be resolved or, failing that, its effects reduced. Encapsulating a highly conductive material in the SET substrate is an opportunity to ‘design’ a competitive composite system that considerably improves the fill/store/heat extraction cycle. The tricky question is: how can these capsules be disseminated to improve the effective conductivity without altering the high thermal storage capacity of PCMs?

ADDITIONAL INFORMATIONS

Type of contract and expected dates of recruitment

Fixed-term contract - expected duration 12 months - to be filled as soon as possible until 08/01/2027 at the latest

Gross monthly salary

According to experience and funding

Hours worked

1607 hours per annum

Working environment and context

The successful applicant will work in the LMAC/GI Applied Mathematics Laboratory and will strengthen the team in its research and development activities in a stimulating and interdisciplinary scientific environment. The research team, which is involved in several national projects, offers a framework conducive to scientific development.

The person hired will report to the UTC project manager and will maintain a regular dialogue with the latter and all the parties involved.

This project is part of an energy context in which the contribution of thermal storage is set to grow. This is a key issue for the future, enabling us to better adapt energy production and consumption in time and space.

This recruitment is funded by the French National Research Agency (ANR).

Diploma, training and accreditation :

Degree: doctorate, PhD

Field of training: numerical Analysis or Scientific Computing

Candidates from thermics field will be examined.

Operational knowledge and skills

• Numerical methods, scientific computing, programming and validation

• Knowledge of thermal modelling appreciated

• Good level of practice in numerical calculation software (ideally FreeFem++)

• Desirable experience in optimisation and/or neural networks applied to physics

• Interest in interdisciplinary research involving physics, applied mathematics and data science