An optimization study of an electric vertical takeoff and landing personal air vehicle (eVTOL PAV) was performed during the conceptual design stage using the design of experiments method. In defining the initial problem, a design target parameter was set. The PAV subsystem was based on a configuration tradeoff study matrix, which was used to effectively conduct configuration selection. Initial sizing was performed using the PAV sizing program developed by this research team using Microsoft Excel and Visual Basic for Application (VBA). A screening test was performed to find parameters with high sensitivity among independent design parameters. The response surface method was used to model design target parameters, and a regression equation was estimated using the experimental design method. A Monte Carlo simulation was performed to confirm the feasibility of the generated model. To optimize the design independent parameter, a satisfaction function was selected, and the appropriateness of the data was determined using a Pareto plot and p-value.
The anisotropic nature of fiber reinforced composite materials causes great challenges in predicting the inter-ply shear stress during forming. The complexity of understanding the functional dependency of inter-ply shear stress on multiple forming parameters such as blank temperature, pressure load, inter-ply slippage, and the relative fiber orientation angle of adjacent plies further limits the effort to produce a defect-free composite structure. Performing real experiments for various combinations of the mentioned parameters is both time consuming and economically costly. To overcome these difficulties, a surrogate-based analysis of inter-ply shear stress is proposed in this study. Based on the ranges of the forming parameters, computer experiments were performed. Using these experimental data, a radial basis function (RBF) based surrogate model that mimics inter-ply shear stress during composite press forming was developed. The fidelity of this model was checked with test data and found to be over 98% efficient.
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