DAYTON T. BROWN, INC. SBIR Phase I Award, August 2021

The physical testing of airframe designs is a vital component of ensuring their safety, reducing their long term costs and providing a robust platform that has long term fleet readiness. In order to be of the most value, an airframe fatigue test must not only accurately apply the proper loads to the structure, but must also do this in a time frame where the findings can be of value. Improvements to the design need to be introduced into production and retrofitted to the fleet early in the fleet’s life in order to provide the most benefit. The current established fatigue methodology can only apply loads at rates far slower than the load cycles accumulate on rotorcraft and therefore rely on spectrum truncation and load adjustments to theoretically match the fatigue damage. However there is evidence that this methodology results in unconservative crack growth rates. The test load cycle rate needs to be greatly accelerated in order to apply realistic loads, in a reasonable amount of test time. In order to accelerate the fatigue test, and to obtain more value from it, all aspects of the test system must be reexamined and improved. This includes the spectrum development, the load control system, load application methods, data acquisition and test monitoring. The key innovation proposed for the topic is to combine the knowledge from the DTB developed adaptive load control algorithm with a proven MIL (Model in the Loop) algorithm, to enable the load control system to respond in a predictable manner to large load / deflection loadings while adapting the load application parameters to adjust to nonlinear effects. DTB will bring to the table as team leader our years of structural testing experience. DTB is familiar with all aspects of airframe structural testing and will use this knowledge to integrate a solution based on inputs from all team members. We have formed an expert team including load control system developers and control system modeling experts to determine a framework for a complete implementation of a scalable, real-time, predictive, and adaptive model-based test frame control system that increases load cycling frequency while maintaining load accuracy.