TMS2024 will feature two special symposia as part of the Frontiers of Materials Award, a competitive award given to top-performing early career professionals. As part of the award, the honoree organizes a symposium on a hot or emergent technical topic and delivers a keynote lecture during the symposium. Meet this year’s awardees and make time to hear these invited presentations during TMS2024.
Novel Ceramics Processes for Nuclear Applications
Symposium Organizer and Keynote Speaker: Takaaki Koyanagi, Oak Ridge National Laboratory
Keynote Presentation Title: “Development of Next-Generation Silicon Carbide Composites for Nuclear Energy”
Location: Hyatt Regency Orlando
Sponsors: TMS Structural Materials Division; TMS Nuclear Materials Committee
Symposium Date: Tuesday, March 5
Keynote Presentation Time: 9:00 a.m.
About the Symposium
The nuclear ceramics community continues to discover novel processing routes. Drivers of new technology development in nuclear power include reducing cost, improving safety, increasing social acceptability, and creating new opportunities. Research needs include developing improved fuels for current-fleet reactors, advanced reactor concepts, fusion energy, and space propulsion. Ceramic materials play key roles in all of these mission spaces. Novel ceramic processing and innovative materials, components, and designs enabled by advanced processing technologies will contribute to the evolution of nuclear technology.
This special session will bring together scientists and engineers to discuss opportunities and needs for key enabling materials processing for application in nuclear energy systems. This will include the most up-to-date processing science and technology, which realize unique ceramic microstructure, properties, and component geometries that cannot be achieved by traditional processing methods. A variety of materials and applications are of interest: nuclear fuel and fuel assemblies; structural materials for fission and fusion reactors; materials and containment for neutron moderators, reflectors, and shielding; and emerging ceramics-based materials. The processing methods presented are intended for fabricating nuclear-grade materials with the potential to meet the performance requirements of neutronic property and resistance to the nuclear reactor environment.
About the Keynote Presentation
Silicon carbide (SiC) ceramic-based composites have been attractive material options for fusion in-vessel components and fission reactor core structures because of their exceptional high-temperature capability and favorable neutronic properties. As performance data accumulated, the limitations of the current generation of nuclear-grade SiC composites in high-dose radiation environments were identified. The material degradations mainly involved irradiation-induced degradation of SiC fiber strength and microstructural instability of pyrolytic carbon interphase. This paper reports recent efforts to enhance the radiation resistance by applying newer generation SiC fibers and developing alternative interphase layers by considering the performance and processing requirements. The new Tyranno SA4 SiC fiber showed improved mechanical properties compared with the current nuclear grade Tyranno SA3 and Hi-Nicalon Type S SiC fibers. Chemical vapor deposition and infiltration of oxide-based interphases were explored. Additionally, advancements in joining and integration technologies for SiC composites will be presented.
Physics-Informed Machine Learning for Modeling and Design of Materials and Manufacturing Processes
Symposium Organizer and Keynote Speaker: Pinar Acar, Virginia Tech
Keynote Presentation Title: “Inverse Design for Crystal Plasticity Model Identification via Physics-Informed Neural Networks”
Location: Hyatt Regency Orlando
Sponsors: TMS Materials Processing and Manufacturing Division; TMS Integrated Computational Materials Engineering Committee
Symposium Date: Monday, March 4
Keynote Presentation Time: 2:00 p.m.
About the Symposium
This Frontiers of Materials event will provide a forum on “physics-informed machine learning (ML)” with applications to the (multi-scale) modeling and design of material systems and manufacturing processes. While data-driven ML methods have found a wide range of applications in materials modeling, design, and manufacturing, the predictions of purely data-driven models may still be physically inconsistent due to observational biases. Recent research in materials informatics has addressed this challenge by enforcing underlying physical constitutive rules, constraints, and initial/boundary conditions in data-driven model development, thereby creating physics-informed ML models. These “domain-aware” physics-informed ML models are shown to achieve better prediction accuracy and interpretability even when trained with small datasets, in addition to faster training and improved generalization performance compared to purely data-driven approaches.
This event will feature presentations on modeling, multi-scale modeling, and the design of materials and manufacturing processes across different length scales (ranging from the atomistic scale to the macro-scale) using physics-informed ML techniques. Studies on the integration of physics-informed ML models into experimental datasets of materials and manufacturing processes will also be of interest.
About the Keynote Presentation
This study develops a physics-informed machine learning (ML) model to identify the crystal plasticity (CP) parameters of Ti-7Al alloy, a candidate aerospace alloy for jet engine components. An inverse design problem is solved to obtain the optimum slip and twin parameters of the alloy by minimizing the difference between the experimental data and ML model predictions on the deformed texture. We find that the physics-informed ML model performs more efficiently than the data-driven (physics-uninformed) ML model by improving accuracy, computational efficiency, and explainability. Our approach builds a Physics-Informed Neural Network (PINN) incorporating the underlying problem physics through the loss function definition. In this application problem, the PINN is tested in a small-data problem driven by a CP model that needs to satisfy the physics-based constraints of the microstructural orientation space while obtaining the slip and twin parameters of Ti-7Al alloy.