Subject
Suspension Plasma Spraying (SPS) is an emerging industrial process, particularly for the creation of ceramic coatings resistant to thermomechanical stresses, used as long-life thermal barriers for aircraft engine. For the aeronautics industry, it is classified as a special process whose output elements can only be verified by monitoring or post-measurement, and whose deficiencies therefore only become apparent once the product is in use. In this process, the liquid suspension containing the submicron particles of the material to be deposited is injected into a thermal plasma jet to be fragmented and evaporated, releasing individual or agglomerated submicron particles that are then accelerated and melted and will impact and spread over the part to be coated to form a coating. The structure of the coating is a function of the operating conditions, from the plasma torch to the droplet impact conditions (shape, velocity, temperature, and substrate roughness). A dense or columnar structure may occur, which influences the final thermomechanical properties of the material. A full CFD simulation of the entire process is beyond reach due to limitations in the number of particles that can be simulated. Therefore, we propose a three-step approach, consisting of CFD simulations at the droplet scale combined with a stochastic approach [1] enriched by AI at the coating scale:
- The stochastic approach aims to represent realistic spray conditions (spatial, temporal, radius, velocity, and temperature distributions of the particles).
- Simulations of droplet impacts using the CFD code Notus [2] aim to populate a database representing the topology of various instantaneous representative sprayed surfaces.
- A neural network-based on CFD results aims to surpass CFD simulations capacity by representing large impact surfaces and amounts of particles. The AI tool's results can be verified and refined through additional CFD simulations.
The PhD thesis focuses on the AI/CFD component of the project. Artificial intelligence has been used for several years to detect, segment and reconstruct the contour of the interface between two fluids in experimental images [3, 4]. Rather than segmentation or object detection in an image, the objective of the AI tool to be developed is the prediction of a final state from an initial state and relevant parameters. Given an initial condition consisting of a field of volume fractions representative of the surface state and a characterisation of the particle and its point of impact before impact, the tool must predict the new volume fraction state representative of the droplet's spreading and subsequent solidification. The work will start with a bibliography of available AI analysis tools based on image and volume fraction processing. It will also involve creating a database of droplets impacting substrates with an increasing level of topological complexity, Python programming, comparing the selected methods and 2D/3D verification of the proposed approaches. Particular attention will be paid to quantifying the reliability of predictions [5].
Research context
3-years PhD at I2M-Bordeaux; starting date: January 2026; French National Agency ANR funding; industrial collaboration with Safran TechExpected skills
Python, Fortran, AI, Computational Fluid Mechanics.Contact
Please send a detailed CV, undergraduate and Master's transcripts, Master's reports and reference contacts. Please send these documents to all the following contacts:
Emmanuelle Abisset-Chavanne I2M-Bordeaux : emmanuelle.abisset-chavanne@ensam.eu
Stéphane Glockner I2M-Bordeaux: glockner@bordeaux-inp.fr
Vincent Rat IRCER-Limoges: vincent.rat@unilim.frReferences
[1] M. Xue et al 2008, A stochastic coating model to predit the microstructure of plasma sparyed zirconia coatings, Modelling Simul. Mater. Sci. Eng., 16 065006.
[2] Notus CFD code : https://notus-cfd.org
[3] Jingzu Yee, Daichi Igarashi, Shun Miyatake and Yoshiyuki Tagawa, Prediction of the morphological evolution of a splashing drop using an encoder–decoder, Machine Learning Science and Technology, 2023.
[4] M. Giselle Fernández‐Godino, Donald D. Lucas & Qingkai Kong Predicting wind‐driven spatial deposition through simulated color images using deep autoencoders. Scientific Reports, 2023
[5] Morgane Suhas, Modèles de comportement et loi de défaillance de systèmes enrichis par les données, thèse de l’École doctorale Sciences des métiers de l'ingénieur de l’ENSAM, 2025.
