Predicting Impact Damage for Carbon Composite Propeller Certification

Using ASTM D7136 and quasi-static testing to predict component impact damage.

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During this research, a new approach to predict impact damage was evaluated using the PAL-V Liberty’s propeller blade as the test subject. The purpose of this research was to provide a simple method for impact damage prediction which will support the certification activities of the PAL-V propeller.
This method involves conducting a threat assessment, a Probability of Detection analysis, executing ASTM D7136 impact and quasi-static tests on coupon samples and conducting quasi-static tests on the propeller blade itself. The results from these tests were used to create a two-mass model which can predict the contact force during a propeller impact event. This model was validated using impact test on the propeller blade.
The study conducted a series of ASTM D7136 impact tests and quasi-static tests on coupons, revealing that the coupons exhibited greater load-bearing capacity under impact loading compared to quasi-static loading. These tests also showed that the coupons have an increased stiffness when subjected to impact loading, requiring a multiplication factor of 1.47 to align their stiffness with quasi-static conditions.
The research also involved determining boundary conditions for propeller tests, which led to the development of a custom clamping mechanism. During the quasi-static tests, the differences in stiffness between coupon and propeller tests were identified. These coefficients served as the basis for a predictive
model involving a two-mass, spring and damper system, with parameters derived from coupon tests and the propeller quasi-static tests. After performing the validation impact tests on the propeller blade, it was determined that this model successfully predicted contact forces within 13% of actual test values.
The research demonstrated the potential to predict impact energy requirements for barely visible impact damage, without the use of FEA. The model’s accuracy, while it can be improved, is sufficient to predict impact events effectively, offering a simpler alternative to complex FEA models.