  The rise in computational capability has increased reliance on simulations to
inform aircraft design. However aircraft airworthiness testing for flight
certification remains rooted in real-world experiments performed after
manufacturing an aircraft prototype. Leveraging multi-fidelity modeling and
uncertainty quantification, we present a framework creating a stochastic
representation of the aircraft, uses it to simulate flight certification
maneuvers, and determines the likelihood of successfully meeting the
certification requirement. We focus on uncertainties associated with
Computational Fluid Dynamics simulations solving the Reynolds-Averaged
Navier-Stokes equations. The simulation predictions and associated
uncertainties are combined with data from other analysis tools to create
stochastic aerodynamics and controls databases. The databases describe the
aircraft's behavior across its flight envelope and provide probability
distributions for its predictions. Databases are generated for two aircraft
configurations, the National Aeronautics and Space Administration (NASA) Common
Research Model and the Generic T-tail Transport aircraft. Samples from the
databases, representing different aircraft behavior, are created. Each sample
is run through a flight simulation representing a real-world airworthiness test
performed by the Federal Aviation Administration (FAA). These tests are
agglomerated to create distributions of the performance metrics, quantifying
the probability that the aircraft succeeds in performing the certification
maneuver. Simulating flight certification testing before building a full-size
aircraft prototype mitigates the enormous costs of expensive redesigns late in
the aircraft design process. The calculation of the failure rates provides
design suggestions to ensure the aircraft can meet the certification
requirement with a prescribed success rate.
