In preliminary design of aircraft wings, a common practice is to set target loads and target weights as governing parameters for more detailed design phases. These values must be reliable in the presence of uncertainty because overly conservative target estimates could result in over-design of the wing while non-conservative estimates could lead to costly late-phase redesign to satisfy constraints. The process of confidently identifying these targets is typically very computationally expensive because a large multivariate space of flight scenarios and vehicle configurations must be investigated. Therefore, a method has been implemented to reduce the computational expense of reliability-based aero-structural preliminary design. To efficiently identify critical loading conditions for structural sizing, this method adaptively samples the discretized multivariate space with a kriging-based sequential routine. Each new sample point is identified by an expected improvement function based on the probabilistic distributions of internal structural loads. These distributions are determined by propagating uncertainty using Monte Carlo simulation through an aero-structural analysis code. This process is applied to the wing of a reference aircraft derived from the NASA Common Research Model, and the effect of reliability indices is shown on preliminary sizing and weight estimation.
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