Uncertainty quantification of the ONERA 7A rotor performance and spanwise structural loads using a surrogate-based approach

The study demonstrates the role of uncertainty quantification for rotorcraft aeromechanics using the ONERA 7A rotor. The uncertainties in blade properties including torsion, flap, and lag stiffness on rotorcraft performance and spanwise structural loads are quantified. To support this effort, a stochastic-based computational framework is developed using a surrogate of the rotorcraft comprehensive analysis code with artificial neural networks. Statistical analysis is undertaken to quantify system uncertainties with probabilities and a qualitative approach with parallel coordinate plots is used for a visual interpretation of the trends between uncertain inputs and response outputs. It is shown that the stochastic-based approach will aid intelligent data driven decisions to follow as a design window with quantifiable probabilities of failure due to high loads and/or excessive power requirements are identifiable.