he development of aircraft for civil aviation is driven largely by the economics of the materials constituting the airframe. Improvements in strength and durability can reduce aircraft weight and allow regulators to increase the inspection intervals.
There is a continuous demand for better materials and a greater understanding of how these materials perform in aircraft components. However, introducing a new structural material for an airframe is costly and takes several years, so there is a significant need for better certification processes.
One of the ways of reducing the cost and lead-time of the material qualification process is to improve the predictive capability of material models. Improved models lead to a reduction of the amount of testing required to achieve an reduction in the overall weight of the airframe.
The approach taken in this experiment was to use KE-chain, together with Colosso’s data analysis and storage framework to calibrate a new algorithm to model materials based on data from fatigue tests. An HPC environment provided by Gompute was used to provide on-demand computing resources. This resulted in an improved ability to predict crack propagation in airframe components and a reduction in the required amount of fatigue testing.
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