Theme: Research

PhD defense of Ghaniyya Medghoul

13 December 2022

Ghaniyya Medghoul will defend her PhD in Computational Mathematics, High Performance Computing and Data on Jan. 16, 23

A posteriori error estimation and adaptive control for a finite element solver framework with dynamic remeshing: application to quenching process

Ghaniyya Medghoul conducted her PhD work in the CFL team under the supervision of Elie Hachem and Aurélien Larcher. She will defend her PhD in Computational Mathematics, High Performance Computing and Data, on January 16th, 23 in front of the following jury:

– Nissrine Akkari, SafranTech, reviewer
 
– Joan Baiges, Polytechnical University of Catalogna, Spain, reviewer
 
– Alvaro Coutinho, Federal University of Rio de Janeiro, Brazil, reviewer
 
– Aurélien Larcher, CEMEF Mines Paris – PSL
 
– Elie Hachem, CEMEF Mines Paris – PSL

 

Abstract:

Quenching is a heat treatment process used to modify the mechanical properties of the forged, moulded or welded metal part. It consists of heating a workpiece to change its microstructure and its properties like hardness, resistance and toughness. The workpiece is then cooled in a medium (oil, water, polymer solution or air). This process is commonly used to harden and reinforce metal parts for the automotive and aerospace sectors such as rings and gears and other transmission parts. It is also used in construction sector to avoid bards' and rods' distortions and in the energy domain (for example, seamless rolled crowns).
Nowadays, with the improvement of computing power, the numerical simulation of this process become an essential tool to predict physical phenomena charactering this process such as temperature and cooling velocity, these last two are essential factors allowing to determine final characteristics of the material. Numerical simulation is an excellent tool to understand those results and to optimize them.
However, the simulation of such phenomena posed scientific difficulties because their resolution implies long computational times despite the use of important computational resources. 
In this thesis, we are interested in the resolution of complex long time and large scale problems heat transfer and fluid flow problems. The goal is to offer a general adaptive stopping criteria for each iterative solver used in the aim of reducing the number of iterations and computational time. Those criteria are based on a posteriori error estimators computed on mesh's edges and based on recovery procedures. Those estimators are initially used to lead the anisotropic adaptive process to refine the mesh locally in the regions of interest. They allow to measure the quality of the approximated numerical solution by providing entirely computable upper bounds on the error between the exact solution and the approximated one. 
Our numerical tests highlight the accuracy of the estimators used and the reduction in terms of iterations' number and computational cost, this reflects the efficiency of our adaptive method. The numerical framework has been validated by confrontations with experimental results provided by our industrial partners.
 

Keywords: a posteriori error estimation, stopping criterion, CFD modelling, anisotropic mesh adaptation, Non-smooth interpolation operators, stabilized finite elements

 

PhD Defence of Ilusca Soares Janeiro

30 June 2023

Ilusca Soares Janeiro defends her PhD in Computational Mechanics and Materials on June 30, 23.

Evolutions of γ' phase precipitates during forging operations of René 65 alloy

Ilusca Soares Janeiro conducted her PhD work under the supervision of Nathalie Bozzolo (MSR team) and Jonathan Cormier (Institut Pprime). She defends her PhD in "Computational Mechanics and Materials" on June 30th, 23 in front of the following jury:

– Florence PETTINARI STURMEL, Université de Toulouse – CEMES
– Denis DELAGNES, Ecole des Mines d’Albi
– Franck TANCRET, Université de Nantes – IMN
– Madeleine BIGNON, Mines Paris – CEMEF
– Nathalie BOZZOLO, Mines Paris – CEMEF
– Jonathan CORMIER, ISAE ENSMA – Institut Pprime
 

Abstract:

Ni-based superalloys are high-performance metallic materials possessing excellent mechanical properties at high temperatures. They are widely used in the aerospace industry, both in wrought and cast states, in the hottest and most highly stressed parts of jet engines. In order to increase the operating temperature of engines and thus reduce fuel consumption and greenhouse gas emissions, the polycrystalline Ni-based superalloy René 65 has been chosen for the manufacturing of certain turbine disks in new generation jet engines.  
 
A non-negligible effect of the heating rate on the γ' phase evolutions was observed for temperatures closer to the γ' solvus temperature (T > 1010 °C) for the Rene 65 alloy. A phenomenological model describing the evolution of the of primary γ' precipitates fraction as a function of time and temperature was established for René 65 and AD730™ alloys. This relationship provides a better estimation of the grain size and γ' precipitates evolutions during γ' sub-solvus isothermal treatments. The dynamic precipitation phenomenon was observed for γ-γ' superalloys. Two types of dynamic γ' precipitates (i.e. developed during hot deformation) have been characterized. Dynamic precipitation seems to be favored at high temperatures, low strain rates and high strain levels up to a certain limit, defined as 1.0 for the René 65 alloy under the investigated conditions. In the post-dynamic regime, the evolution of the two types of dynamic γ' precipitates occurs distinctly from each other. A bimodal distribution of γ' precipitates was observed immediately after subsolvus solution treatment. A scenario was proposed to explain the origin of the γ' precipitation state, considering the thermomechanical history of the René 65 alloy.

 

Keywords: nickel based superalloy, γ-γ' microstructure, precipitation, hot forging, recrystallization

 

 

 

PhD defence of Tianqi Huang

28 June 2023

Tianqi Huang defends his PhD in Computational Mechanics and Materials on June 28, 23.

Characterization and modeling of the mechanical behavior of PE-vitrimers.
 

Tianqi Huang conducted his PhD work under the supervision of Jean-Luc Bouvard (MPI team) and Yannick Tillier (CSM team). He defends his PhD in "Computational Mechanics and Materials" on June 28th, 2023 in front of the following jury:

M. Xavier COLIN, Laboratoire Procédé et Ingénierie en Mécanique et Matériaux, Rapporteur
M. Stéphane ANDRE, Laboratoire Énergies & Mécanique Théorique et Appliquée, Université de Lorraine, Rapporteur
M. Renaud NICOLAÿ, Laboratoire Matière Molle et Chimie, CNRS, ESPCI Paris
Mme Sandrine HOPPE, Laboratoire Réactions et Génie des Procédés, Université de Lorraine
Mme Julie ALVES, Aliaxis Research & Technology
M. Yannick TILLIER, Centre de mise en formes des matériaux, Mines Paris PSL université
M. Jean-Luc BOUVARD, Centre de mise en formes des matériaux, Mines Paris PSL université
 
 
Abstract:
 
This Ph.D. thesis focuses on the characterization of the behavior of several polyethylene-based polymers. In particular, to evaluate their recycling properties, the behavior of several vitrimers (with different cross-linking degrees) is compared, before and after aging, with that of a thermoplastic (HDPE) and a thermoset (PEXb). To better understand the relationship between the microstructure and the properties of these materials, a physical model was also proposed to model these behaviors. The parameters of this model were identified thanks to numerous experimental observations performed at different scales. The crystalline structure (at the microstructural scale) was characterized using DSC and X-ray. The dynamic properties (at the mesoscopic scale) were characterized thanks to DMTA tests and rheological analysis. The mechanical behavior (at the macroscopic scale) was characterized using tensile and creep tests. The test conditions used to characterize the mechanical behavior at large deformation were chosen according to a methodology based on the "equivalent strain rate at a reference temperature" (at the α-transition temperature). Therefore, the double effect of temperature and strain rate is taken into account. For the initial unaged state, the degree of crystallinity changes little for the different types of polymers. However, the thickness of the crystal lamellae and the viscoelastic properties show a strong dependence on the type polymer studied. The application of the time-temperature equivalence below the melting temperature, validated here at small strain and also at large deformation, results in a unique master curve for the different polymers used in this study. However, this is not the case above this temperature where only vitrimers and PEXb show a rubbery plateau. For HDPE and vitrimers, the aging protocol causes chain scission, resulting in a decrease in molecular weight (Mw). This has a direct effect on the properties observed using DMTA and on the mechanical behavior at large deformation. For aged vitrimers and PEXb, the same true strain levels as HDPE are observed during a creep test at a longer time scale. Unlike PEXb, the effect of aging on vitrimers can be erased by heating above the α'-transition temperature. Finally, the model used in this study allows to reproduce the mechanical behavior observed experimentally for each type of PE. This model also shows its ability to take into account the specificities of the different chain networks that characterize these materials. To conclude, the vitrimers show thermomechanical properties that are globally close to those of HDPE and PEXb, but with a higher recycling potential compared to the latter.
 
Keywords: Vitrimer, Microstructure, Time-temperature equivalence, Mechanical behavior, Aging, Modeling
 
 

PhD defence of Théophile Camus

23 May 2023

Théophile Camus defends his PhD in Computational Mechanics and Materials on May 23rd, 2023.

Modeling of microstructures generated in additive manufacturing by LPBF process of a nickel based alloy
 

Théophile Camus conducted his PhD work under the supervision of Charles-André Gandin, Gildas Guillemot and Oriane Senninger (2MS team). He defends his PhD in "Computational Mechanics and Materials" on May, 23, 23 in front of the following jury:

– M. Cyril BORDREUIL, Université de Montpellier
– M. Joel ALEXIS, Ecole Nationale d’Ingénieurs de Tarbes
– Mme Salima BOUVIER, Université Technologique de Compiègne
– M. Daniel MAISONNETTE, CETIM
– M. Christophe COLIN, CMAT, Mines Paris – PSL
– M. Charles-André GANDIN, CEMEF, Mines Paris – PSL
– M. Gildas GUILLEMOT, CEMEF, Mines Paris – PSL
– Mme Oriane SENNINGER, CEMEF, Mines Paris – PSL

 

Abstract:

The laser powder bed fusion (LPBF) process makes it possible to produce metal parts with complex geometries and high added value. Its principle is based on the selective melting, using a laser, of successively stacked powder beds. The main applications of this additive manufacturing process concern the aeronautics and aerospace domains, for which Inconel 718 nickel-based superalloy parts are frequently produced. Mastering the mechanical properties of parts produced with LPBF process is therefore essential. These strongly depend on the microstructures generated during the successive solidifications occurring at the different layers. The microstructure is linked to the thermal conditions during solidification, directly influenced by the process parameters such as the laser power, its velocity, or its trajectories on the powder bed. In order to control the mechanical properties, it is necessary to control the development of the microstructures of the manufactured parts by working on the manufacturing parameters. As part of this research work, a finite element thermal-hydraulic model of the lasing of a powder bed is used to describe the thermal behavior as a function of process parameters, and a Cellular Automaton model is used for the prediction of grain structures. The thermal model requires high computation times, a new hybrid methodology is therefore developed to benefit from the stationary temperature field obtained by multiphysics simulation, on multi-pass multi-layer fabrications. The advantage is to reach a large size of the microstructure simulation domain while benefiting from a complete numerical solution of the process at the scale of the melt pool. Applied to different process parameters, it is possible to measure the influence of each parameter on the generated microstructures in representative elementary volumes. Thus, the understanding of the formation of microstructures in LPBF is improved thanks to these models.

Keywords: Microstructures, Modeling, Additive manufacturing, Laser powder bed fusion, Solidification, Inconel 718

 

Grain structure obtained by Cellular Automaton modelling of the LPBF process in a representative elementary volume

 

 

Matheus Brozovic Gariglio winner of the PhD Titanium Prize 2023

28 April 2023

It is a source of great pride for us to see our students rewarded for their research work. Our congratulations go to Matheus Brozovic Gariglio, winner of the Titanium 2023 thesis prize. This prize is awarded annually by the French Titanium Association and recognises excellence in thesis research. It concerns all work related to the metallurgy and properties of titanium and its alloys.

Matheus Brozovic Gariglio completed his PhD work in the MSR and CSM teams, under the supervision of Nathalie Bozzolo and Daniel Pino Munoz. His doctoral project was on a multiscale study of microstructural evolutions in hot-deformed two-phase titanium alloys. He defended his PhD on February 2, 2023 at Mines Paris, Pierre Laffitte campus in Sophia Antipolis.
 
Matheus was also the recipient of the Pierre Laffitte Medal and 1st Prize for his PhD research in 2021.
 
Congratulations to him!
 
 
 
 
 
 
 
 

PhD defence of Aakash Patil

1 February 2023

Aakash Patil will defend his PhD in Computational Mathematics, High Performance Computing and Data on Jan. 16, 23

Deep Learning-Assisted Modelling of Turbulence in Fluids
 
 
Aakash Patil conducted his PhD work in the CFL team under the supervision of Elie Hachem and Jonathan Viquerat. He will defend his PhD in Computational Mathematics, High Performance Computing and Data, on February 1st, 23 in front of the following jury:
 
Gianluigi Rozza, SISSA, Trieste, Italy, reviewer
Paola Cinnela, Sorbonne University
Ricardo Vinuesa, KTH Stockholm, Sweden
Elie Hachem, Mines Paris – PSL
Jonathan Viquerat, Mines Paris – PSL
 
 
Abstract:
 
Despite several advancements in experimental and computational resources, and despite progress in theoretical and mathematical procedures to address the closure of Navier-Stokes equations, turbulence remains an unsolved problem even after 200 years of continuous research. On the other hand, artificial machine intelligence and related technologies are making rapid advancements in several domains of science and engineering, helping us humans to efficiently solve modeling problems and discover new physics. Present work tries to combine these two branches and explore if computational machines can be used to efficiently study turbulence in fluids, and perhaps someday help us in the discovery of the missing universal laws.  Deep learning is employed to learn turbulence modeling and a patch-based method is proposed for robust learning. Learning of subgrid-scale turbulence from the resolved large scales is demonstrated along with investigation of effect of the coarse-graining methods and successive refinements.  Spatio-temporal learning of turbulent flows is proposed to learn the temporal snapshots and a posteriori simulations are performed.
 
Keywords: Turbulence, Modeling, Deep Learning