Equivalent skin analysis gf wing structures using neural networks

An efficient method of modeling trapezoidal built-up wing structures is developed by coupling, in an indirect way, an equivalent plate analysis (EPA) with neural networks (NN). In the EPA the wing is assumed to behave like a Mindlin plate and is solved using the Ritz method with the Legendre polynomials as the trial functions. The EPA can be made more efficient by avoiding most of the computational effort spent on calculating contributions to the stiffness and mass matrices from every spar and rib. This is accomplished by replacing the wing inner-structure with an "equivalent" material that is combined to the skin and whose properties are simulated by neural networks. The constitutive matrix, which relates the stress vector to the strain vector, and the density distribution of the equivalent material are obtained by enforcing mass and stiffness matrix: equivalence with regard to the EPA in a least-squares sense. Neural networks for the material properties are trained in terms of the design variables of the wing structure. Examples show that the present method, which can be called an equivalent skin analysis (ESA) of the wing structure, is more efficient than the EPA, and still fairly good results fan be obtained. The present ESA is very promising to be used at the early stages of ming structure design.