Probabilistic Structural Optimization Under Reliability, Manufacturability, and Cost Constraints

Abstract

A methodology is presented for probabilistic design optimization of aircraft structures subject to a set of multidisciplinary constraints including reliability, manufacturability, and manufacturing cost. Using the advanced firstorder second-moment method, reliability of each structural component is evaluated by considering its primary static failure mode. Metrics-based analytical models are used in manufacturability analysis, and semi-empirical models are used for manufacturing cost estimation. The described design methodology is developed into a software tool and applied to probabilistic sizing optimization of a moderately loaded built-up wing spar. Using two alternative web design concepts, one limited by buckling and the other by diagonal semitension field action, the spar model is optimized for weight considering uncertainties in the material properties, structural sizing, and loading parameters. The optimization problem is solved using the method of sequential quadratic programming for different values of target reliability, with or without any limit on manufacturing cost. The optimization results are used to highlight the interactions between reliability, weight, and cost, whereas the probabilistic sensitivities are used to examine the influence of each random variable on structural reliability.

The Optimization of Civil Aircraft Product Option Selection Considering the Economy Response with an Improved Non-Dominated Sorting Genetic Algorithm

“…From the perspective of research and manufacturers, they are more willing to focus on the process of improving. For example, an innovative hybrid decision algorithm solved by the combination of finite element simulation and Tabu search [3], sequential quadratic programming [4], evolutionary algorithm [5], genetic algorithm [6] or exact Markov [7] solves a reliability optimization problem under cost and configuration constraints. Part of the research focuses on building an accurate and dynamic reliability analysis model; paper [8] improves the support vector machine with nonlinear conjugate mapping, and paper [9] improves the line-sampling-method-based slime mold algorithm for simulating the performance response and improving the accuracy and computational efficiency of complex structure reliability modeling.…”