A CAD-free methodology for volume and mass properties computation of 3-D lifting surfaces and wing-box structures

摘要

The geometry, volume, and mass properties (GVM) of lifting surfaces and wing box structures play an important role in aircraft design and optimization. Commercial Computer-Aided-Design packages can be employed to determine these features; however, they are difficult to embed in multidisciplinary frameworks because of their limited scripting capabilities and their black-box structure. In this context, the present work introduces an open-source, fully scriptable, high fidelity, and low computational demanding methodology to compute the volume and mass properties of lifting surfaces and wingbox structures using their three-dimensional geometric definition. NURBS modeling is employed to generate the 3-D geometry and derive the vertex representation of the lifting surface. The volume computation is based on the Divergence Theorem and employs a structured triangulation approach, tailored for lifting surfaces and their cross sections. The mass properties (i.e. center of mass and moments of inertia) are calculated as a system of mass particles. For this, a mass distribution model, based on the thickness and chord distribution throughout the lifting surface, has been elaborated. The methodology for volume computation and the mass distribution model has been validated analytically using a simple polytope that resembles to a lifting surface. The convergence and the robustness of the methodology has been evaluated for a straight tapered lifting surface. The results indicate a maximum error, compared with a commercial CAD software, of 0.3% and 0.5% for the volume and mass properties computation, respectively. In addition, to assess its suitability for complex lifting surfaces, the NASA Common Research Model aircraft (NASA-CRM) has been used as case of study. To summarize, the main contribution of this work lies on the development of an open-source and fully scriptable methodology, which can be easily implemented in any computational environment for aircraft design and optimization. (C) 2020 The Authors. Published by Elsevier Masson SAS.