D. D. Whitis, GE Aviation, Evendale, OH; R. Schafrik, GE Aircraft Engines, Cincinnati, OH
The “jet age” is about 60 years old, and arguably materials development has played a major role in making jet engines practical and pervasive. The demands of these aeronautical applications spurred great advancements in titanium and superalloy materials and processing, and the drive for lighter weight solutions served as a compelling forcing function for a variety of composites technology. The path to success, however, was often tortuous, long, and expensive. Large national efforts and huge investments in industry were required to achieve success. Today, the situation has changed. The expectation is that new M&P will be developed faster with no unpleasant surprises. This challenge can only be met by aiding our materials development decisions with computational tools. These tools encapsulated our knowledge of materials science and engineering and leverage the impressive computing infrastructure. The best models are derived from basic knowledge, but data-driven models can also provide useful assistance. This presentation will summarize a few examples of applying models to provide critical insight into a variety of typical issues that arise when advancing the state of art in materials technology.
Summary: The “jet age” is about 60 years old, and arguably materials development has played a major role in making jet engines practical and pervasive. The demands of these aeronautical applications spurred great advancements in titanium and superalloy materials and processing, and the drive for lighter weight solutions served as a compelling forcing function for a variety of composites technology. The path to success, however, was often tortuous, long, and expensive. Large national efforts and huge investments in industry were required to achieve success. Today, the situation has changed. The expectation is that new M&P will be developed faster with no unpleasant surprises. This challenge can only be met by aiding our materials development decisions with computational tools. These tools encapsulated our knowledge of materials science and engineering and leverage the impressive computing infrastructure. The best models are derived from basic knowledge, but data-driven models can also provide useful assistance. This presentation will summarize a few examples of applying models to provide critical insight into a variety of typical issues that arise when advancing the state of art in materials technology.
