Finding materials for gas turbine engines using efficient predictive frameworks
Gas turbines are used widely for aircraft propulsion and power generation. According to thermodynamic laws, the higher a engine’s temperature, the more efficient it is. These laws have led to an increasing interest in increasing the temperature of turbines.
Researchers from Texas A&M University’s Department of Materials Science and Engineering, along with Ames National Laboratory researchers, developed an artificial intelligence framework that predicts high entropy (HEA) alloys capable of enduring extremely high temperatures and oxidizing environments. This method can reduce time and costs by reducing the number of experiments required to find alloys.
Recent research published in Material Horizons.