Machine learning approaches are applied by material and mechanical scientists to quickly vet element combinations for use in next-generation environmental barrier coating. These are required to safeguard vehicles used in extreme conditions of aerospace and space environments.
Image Credit: Rensselaer Polytechnic Institute.
The study was led by researchers at Rensselaer Polytechnic Institute and was supported by the National Science Foundation.
Environmental barrier coatings (EBCs) are applied to seal parts in engines and structural components of rockets, hypersonic jets and other space-bound vehicles. The coating serves as a protector from intense operating environments like high temperatures, intense stress, supersonic speeds and extreme oxidation and corrosion.
Rare earth silicates are the current option for EBCs employed to coat the silicon carbide-based ceramic matrix materials in state-of-the-art jet engines. However, these materials pose some challenges and are prone to performance degradation. Producing EBCs from multicomponent rare-earth phosphates rather than silicates is another option at hand.
New concepts and innovations are required in order to design next-generation EBCs with transformative performance. The proposed multicomponent rare-earth phosphates offer unlimited possibilities in designing future EBCs and extending their performance.
Jie Lian, Professor and Principal Investigator, Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute
The NSF grant worth $1.8 million aims to revolutionize and enable process development by synergizing high-throughput computation, experimentation and machine learning for data-driven materials development and discovery.
Advanced computer algorithms will be employed by the researchers to develop combinations of elements in several configurations. This helps to determine the most favorable high-performance EBCs, which are required for future aerospace and space transportation.
The empirical trial-error approach is too expensive and soon becomes impractical for material discovery over a large design space. We aim at a novel approach that couples physics-based modeling with machine learning to predict the optimal composition and microstructure of the next generation EBCs.
Liping Huang, Co-Principal Investigator, and Professor, Department of Materials Science and Engineering, Rensselaer Polytechnic Institute
Lian, an expert in experimentation and material behavior under extreme environments, and Huang, an expert in high-throughput atomic simulation, have collaborated in this four-year research done by Suvranu De, an expert in finite element analysis and director of the Center for Modeling, Simulation and Imaging in Medicine (CeMSIM) at Rensselaer, and Lucy Zhang, an expert in machine learning and a professor in the Department of Mechanical, Aerospace, and Nuclear Engineering.
Machine learning models trained on data generated from high-throughput multiscale simulations can speed up the design and optimization of the structure and performance of multicomponent rare earth phosphates as EBCs.
Lucy Zhang, Machine Learning Expert, and Professor, Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute
The research team from Rensselaer will further collaborate with researchers at the General Electric Global Research, a pioneering industry of EBCs.