Open PhD position: HPC and digital twins in metallurgy – 3D front-tracking modeling of evolving interface networks
PHD THESIS CEMEF 2021: HPC and digital twins in metallurgy - 3D front-tracking modeling of evolving interface networks.
Context and goals
The in-use properties and durability of metallic materials are strongly related to their microstructures, which are themselves inherited from the thermomechanical treatments. Hence, under-standing and predicting microstructure evolutions are nowadays a key to the competitiveness of industrial companies, with di-rect economic and societal benefits in all major economic sectors (aerospace, nuclear, renewable energy, and automotive industry).
Multiscale materials modeling, and more precisely simulations at the mesoscopic scale, constitute the most promising numeri-cal framework for the next decades of industrial simulations as it compromises between the versatility and robustness of physically-based models, computation times, and accuracy. The digimu con-sortium is dedicated to this topic at the service of major industrial companies.
In this context, the eﬀicient and robust modeling of evolving in-terfaces like grain boundary networks is an active research topic, and numerous numerical frameworks exist . In the context of hot metal forming and when large deformation of the calculation domain and the subsequent migration of grain boundary inter-faces are involved, a new promising, in terms of computational cost, 2D front tracking method called ToRealMotion algorithms [2,3] was recently developed.
This PhD will be firstly dedicated to developing a 3D ToReal-Motion algorithm. If the extension of the data structure will be quite natural, the 3D meshing/remeshing procedures/operators enabling to preserve valid data structure, a good quality of the finite element mesh while remaining frugal in terms of numerical cost remain to be invented.
Moreover, kinetics equations behind the interface networks migra-tion will be enriched to increase the number of modeled physical mechanisms. Finally, a supervised neural network-based remesh-ing strategy will also be developed to improve repetitive and non-optimal operations in the existing remeshing procedures.
The developments will be validated thanks to pre-existing ex-perimental and numerical data concerning the evolution of grain boundary interfaces during recrystallization and related phenom-ena for different materials. They will also be integrated in the DIGIMU® software.
A. Rollett, G. S. Rohrer, and J. Humphreys, Recrystallization and Related Annealing Phenomena. 3rd Edition, 2017.
S. Florez, K. Alvarado, and M. Bernacki. A new front-tracking lagrangian model for the modeling of dynamic and post-dynamic recrystallization. Modelling and Simulation in Materials Science and Engineering, In press, 2021.
S. Florez, K. Alvarado, D. Pino Muñoz and M. Bernacki. A novel highly e icient lagrangian model for massively multidomain simulation applied to microstructural evolutions. Computer Meth-ods in Applied Mechanics and Engineering, 367:113107, 2020.
Candidate profile and skills
Degree: MSc or MTech in Applied Mathematics, with excellent academic record.
Skills: Numerical Modeling, programming, proficiency in English, ability to work within a multi-disciplinary team.
- Doctoral speciality: Computational Mathematics, High Performance Computing and Data
- Location: MINES ParisTech - CEMEF, located in Sophia-Antipolis, on the French Riviera, France.
- Keywords: Digital twins, HPC, Computational Metallurgy, Interface networks, Front tracking, ToRealMotion algorithms, Mesh based algorithms, Deep learning strategy.
- Partners: MINES ParisTech, ArcelorMittal, Cea, Constellium, Transvalor, Safran, Albert-Duval, Framatome
- Duration: 3 years
- Gross annual salary: about 26k€
- Deadline to apply: 2021-08-30
- Team: Metallurgy, µStructure, Rheology – MSR
- Supervisors: Prof. M. Bernacki and Dr. S. Florez
- papers requiered to apply:
- your most recent CV
- Detailed, official proof of your grades during your most recent studies(maximum 3)
- One or more references from professors or heads of training programmes