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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium AI/Data informatics: Tools for Accelerated Design of High-temperature Alloys
Presentation Title Uncertainty Quantification for Thermo-mechanical Behavior of Aircraft Engine Materials in Elevated Temperatures
Author(s) Arulmurugan Senthilnathan, Pinar Acar
On-Site Speaker (Planned) Arulmurugan Senthilnathan
Abstract Scope The Turbine inlet temperature (TIT) is a crucial parameter, which affects the overall efficiency of gas turbine engines. However, its indirect measurement leads to uncontrolled uncertainty in thermal and mechanical properties. An important example of turbine materials is the Titanium-Aluminum alloys owing to their resistance to high thermal and mechanical stresses. Consequently, the goal of the present study is to understand the changes in microstructural and thermo-mechanical behavior of Titanium-Aluminum alloys at TIT, by considering the effects of the uncertainty. The mechanical response of the alloy is computed using LAMMPS for a range of elevated temperature values to incorporate the uncertainty in TIT. The propagation of the TIT uncertainty on the mechanical properties is identified with the computed range for the stress-strain response. The future work will utilize these physics-based simulations to develop a machine learning model that can predict the stress-strain behavior of the alloy at a given temperature.
Proceedings Inclusion? Planned:

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Advanced Data SCiENce Toolkit for Non-data Scientists (ASCENDS) - A Case Study of the Oxidation Kinetics of NiCr-based Alloys
Coupling of Data Mining, Thermodynamics and Multi-objective Genetic Algorithms for the Design of High-temperature Alloys
Determining Solute Site Preference and Correlations to Antiphase Boundary Energy in Ni-based Superalloys
Domain and Uncertainty Quantification in Machine Learning Models of Alloy Properties
Domain Knowledge-informed, Process-mapping AI Graph for Designing Fe-based Alloys
Elastic Properties Machine-learning-based Descriptor for a Refractory High Entropy Alloy
Expanding Materials Selection via Transfer Learning for High-temperature Oxide Selection
Exploring the Compositional Space of High Entropy Alloys via Sequential Learning
Knowledge-driven Platform for Federated Multimodal Big Data Storage & Analytics
Machine Learning Augmented Predictive & Generative Models for Rupture Life in High Temperature Alloys
Optimal Design of High-temperature, Oxidation-resistant Complex Concentrated Alloys
Predicting Vibrational Entropy of FCC Solids Uniquely from Bond Chemistry Using Machine Learning
Predicting Yield Stress of High Temperature Alloys via Computer Vision and Machine Learning
Revealing Nanoscale Features Controlling Diffusion Within Multi-component Alloys through Machine Learning
Toward High Throughput Design and Development of Multi-principal Element Alloys for Corrosion and Oxidation Resistance (MPEAs)
Uncertainty Quantification for Thermo-mechanical Behavior of Aircraft Engine Materials in Elevated Temperatures
Uncertainty Reduction for Calculated Phase Equilibria

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