Aircraft Structural Safety : Effects of Explicit and Implicit Safety and Measures Uncertainty Reduction Mechanisms

Abstract

Aircraft structural safety is achieved by using different safety measures such as safety and knockdown factors, tests and redundancy. Safety factors or knockdown factors can be either explicit (e.g., load safety factor of 1.5) or implicit (e.g., conservative design decisions). Safety measures protect against uncertainties in loading, material and geometry properties along with uncertainties in structural modeling and analysis. The two main objectives of this dissertation are: (i) Analyzing and comparing the effectiveness of structural safety measures and their interaction. (ii) Allocating the resources for reducing uncertainties, instead of living with the uncertainties and allocating the resources for heavier structures for the given uncertainties.Certification tests are found to be most effective when error is large and variability is small. Certification testing is more effective for improving safety than increased safety factors, but it cannot compete with even a small reduction in errors. Variability reduction is even more effective than error reduction for our examples.

The effects of structural element tests on reducing uncertainty and the optimal choice of additional knockdown factors are explored. We find that instead of using implicit knockdown factors based on worst-case scenarios (current practice), using testdependent explicit knockdown factors may lead weight savings. Surprisingly, we find that a more conservative knockdown factor should be used if the failure stresses measured in tests exceeds predicted failure stresses in order to reduce the variability in knockdown factors generated by variability in material properties. Finally, we perform probabilistic optimization of a wing and tail system under limited statistical data for the stress distribution and show that the ratio of the probabilities of failure of the probabilistic design and deterministic design is not sensitive to errors in statistical data. We find that the deviation of the probabilistic design and deterministic design is a small perturbation, which can be achieved by a small redistribution of knockdown factors.