Aerospace Technic and Technology

Modern aircraft design, including both manned aircraft and unmanned aerial vehicles (UAVs), faces computational challenges balancing aerodynamic efficiency, structural integrity, and weight optimization within practical timeframes. Conventional high-fidelity methods create bottlenecks that limit the design space exploration essential for UAV development. This paper presents a computational framework that integrates mesh-free structural analysis with generative knowledge-based engineering (KBE) and surrogate modelling for the optimization of rapid automated UAV wing design. The methodology combines the formalization of classical aerodynamic and structural mechanics knowledge with programmable CAD integration using the open-source Python package CadQuery. The developed framework automatically generates parametric wing geometries, extracts geometric properties, including cross-sectional moments of inertia and volumes, and performs structural analysis without mesh generation or finite element preprocessing. Aerodynamic loads are estimated using reusable meta-models from CFD studies stored as B-spline approximations in SplineCloud, enabling decoupled workflows and rapid evaluation.The mesh-free algorithm implements the numerical integration of beam bending equations, incorporating distributed aerodynamic and gravitational loads with variable cross-sectional properties. This eliminates the computational overhead of mesh generation while maintaining sufficient accuracy for preliminary design. The workflow is embedded in a KBE wing model, automating geometry generation and structural evaluation for swept wings with variable materials and geometries. The validation studies used three NACA airfoil families (2410, 2412, 2415) across aspect ratios (6-9), sweep angles (12°-18°), and spans (500-2500 mm). Individual evaluations completed in ~20 seconds versus hours/days for FEM simulations, achieving 2-3 orders of magnitude efficiency improvement. Generated 2nd-order meta-models enable sub-millisecond response evaluations suitable for iterative optimization requiring thousands of evaluations. This research advances automated design methodologies, providing computationally efficient alternatives to high-fidelity approaches while maintaining engineering accuracy for preliminary optimization. Open-source implementation ensures accessibility for the UAV design community. Future work will focus on FEM validation, aeroelastic coupling, and extensions to complex configurations.

Gray, J. S., Hwang, J. T., Martins, J. R. R. A., Moore, K. T., Naylor, B. A., Brelje, B. J., & et al. OpenMDAO: an open-source framework for multidisciplinary design, analysis, and optimization. Structural and Multidisciplinary Optimization, 2019, vol. 59, no. 4, pp. 1075–1104.

Hwang, J. T., & Martins, J. R. R. A. A computational architecture for coupling heterogeneous numerical models and computing coupled derivatives. ACM Transactions on Mathematical Software (TOMS), 2018, vol. 44, no. 4, pp. 1–39.

Sobieszczanski-Sobieski, J., & Haftka, R. T. Multidisciplinary aerospace design optimization: Survey of recent developments. Structural Optimization, 1997, vol. 14, no. 1, pp. 1–23.

La Rocca, G. Knowledge-based engineering: Between AI and CAD. Review of a language-based technology to support engineering design. Advanced Engineering Informatics, 2012, vol. 26, no. 2, pp. 159–179.

Chen, W., & Ramamurthy, A. Deep generative model for efficient 3D airfoil parameterization and generation. AIAA Scitech 2021 Forum, 2021, pp. 1–12.

Høghøj, L., Conlan-Smith, C., Sigmund, O., & Andreasen, C. S. Simultaneous shape and topology optimization of wings. Structural and Multidisciplinary Optimization, 2023, vol. 66, pp. 1–22.

Martins, J. R. R. A., & Lambe, A. B. Multidisciplinary design optimization: A survey of architectures. AIAA Journal, 2013, vol. 51, no. 9, pp. 2049–2075.

Amadori, K., Tarkian, M., Ölvander, J., & Krus, P. Flexible and robust CAD models for design automation. Advanced Engineering Informatics, 2012, vol. 26, no. 2, pp. 180–195.

Verhagen, W. J. C., Bermell-Garcia, P., van Dijk, R. E. C., & Curran, R. A critical review of knowledge-based engineering: An identification of research challenges. Advanced Engineering Informatics, 2012, vol. 26, no. 1, pp. 5–15.

La Rocca, G., & van Tooren, M. J. L. Knowledge-based engineering to support aircraft multidisciplinary design and optimization. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2007, vol. 221, no. 6, pp. 977–991.

Pedersen, A. Y., Clausen, A., & Mortensen, N. H. The evolution of knowledge-based engineering from a design research perspective: Literature review 2012–2021. Advanced Engineering Informatics, 2023, vol. 55. 500 p.

Chapman, C. B., & Pinfold, M. The application of a knowledge-based engineering approach to the rapid design and analysis of an automotive structure. Advances in Engineering Software, 2001, vol. 32, no. 12, pp. 903–912.

Wittenborg, T., Baimuratov, I., Knöös Franzén, L., Staack, I., Römer, U., & Auer, S. Knowledge-Based Aerospace Engineering – A Systematic Literature Review. arXiv preprint, arXiv:2505.10142, 2025, pp. 1–36.

McCormick, B. W. Aerodynamics, Aeronautics, and Flight Mechanics. 2nd ed. Chichester, John Wiley & Sons, 1995. 652 p.

Swept wing generated results (online “SplineCloud”). Available at: https://splinecloud.com/repository/nomad-vagabond/Swept_wing_generated_results/ (accessed 15 June 2025).