COMPARATIVE ASSESSMENT OF ADVANCED STRUCTURAL OPTIMIZATION PARADIGMS FOR AEROSPACE LOAD-BEARING COMPONENTS
DOI:
https://doi.org/10.55766/sujst11637Keywords:
Additive Manufacturing, Aerospace Engineering, Comparative Benchmarking, Computational Mechanics, Structural Design, Topology OptimizationAbstract
This study presents a systematic comparative evaluation of four structural optimization methods - Solid Isotropic Material with Penalization (SIMP), Level Set, Mixable Density, and Shape optimization applied to aerospace components under standardized conditions. A helicopter pitch arm subjected to 1077 N loading derived from rigid body dynamics analysis served as the benchmark case. The investigation encompassed 36 optimization scenarios with weight reduction targets of 30-50%, stress constraints of 150-200 MPa, and a 3 mm minimum feature size for additive manufacturing. Level Set methods achieved 388.5 N·mm compliance compared to 412.7 N·mm for SIMP at 40% weight reduction, representing 6% superior stiffness at 3.75× computational cost. SIMP generated 8-12% intermediate densities requiring post-processing, while Level Set produced binary material distributions suitable for direct manufacturing. Stress constraints below 175 MPa activated in >25% of the design domain, forcing material redistribution that increased compliance by 18-25%. Manufacturing constraints significantly influenced performance: 3 mm features incurred 12% compliance penalty while 5 mm conventional machining constraints resulted in 38% penalty. The results establish that 35-45% weight reduction with 175-200 MPa stress limits represents the optimal parameter range for aerospace applications, providing quantitative guidance for method selection based on specific performance-cost requirements.
References
Aage, N., Andreassen, E., Lazarov, B. S., & Sigmund, O. (2017). Giga-voxel computational morphogenesis for structural design. Nature, 550(7674), 84-86. https://doi.org/10.1038/nature23911
Airbus Engineering. (2022). Standardized topology optimization process for A350 XWB (Internal Report AIR-ENG-2022-TO).
Airbus. (2023). Pioneering bionic 3D printing: The Airbus A320 partition (Innovation Report AIR-2023-BIO).
Amir, O. (2015). Revisiting approximate reanalysis in topology optimization: On the advantages of recycled preconditioning in a minimum weight procedure. Structural and Multidisciplinary Optimization, 51(1), 41-57. https://doi.org/10.1007/s00158-014-1098-7
Amir, O., Aage, N., & Lazarov, B. S. (2014). On multigrid-CG for efficient topology optimization. Structural and Multidisciplinary Optimization, 49(5), 815-829. https://doi.org/10.1007/s00158-013-1015-5
Challis, V. J., Roberts, A. P., & Wilkins, A. H. (2008). Fracture resistance via topology optimization. Structural and Multidisciplinary Optimization, 36(3), 263-271. https://doi.org/10.1007/s00158-007-0160-0
Chi, H., Zhang, Y., Tang, T. L. E., Mirabella, L., Dalloro, L., Song, L., & Paulino, G. H. (2021). Universal machine learning for topology optimization. Computer Methods in Applied Mechanics and Engineering, 375, 112739. https://doi.org/10.1016/j.cma.2019.112739
da Silva, G. A., Beck, A. T., & Sigmund, O. (2019). Stress-constrained topology optimization considering uniform manufacturing uncertainties. Computer Methods in Applied Mechanics and Engineering, 344, 512-537. https://doi.org/10.1016/j.cma.2018.10.020
Deaton, J. D., & Grandhi, R. V. (2014). A survey of structural and multidisciplinary continuum topology optimization: Post 2000. Structural and Multidisciplinary Optimization, 49(1), 1-38. https://doi.org/10.1007/s00158-013-0956-z
EADS Innovation Works. (2014). World premiere: Additive layer manufacturing optimized structure takes off on Airbus A320 [Press release].
Gaynor, A. T., & Guest, J. K. (2016). Topology optimization considering overhang constraints: Eliminating sacrificial support material in additive manufacturing through design. Structural and Multidisciplinary Optimization, 54(5), 1157-1172. https://doi.org/10.1007/s00158-016-1551-x
Guo, X., Zhang, W., & Zhong, W. (2014). Explicit feature control in structural topology optimization via level set method. Computer Methods in Applied Mechanics and Engineering, 272, 354-378. https://doi.org/10.1016/j.cma.2014.01.010
International Air Transport Association. (2023). Airline cost management group (ACMG) enhanced report.
International Civil Aviation Organization. (2022). Carbon offsetting and reduction scheme for international aviation (CORSIA) implementation framework (Doc. 9501).
Krog, L., Tucker, A., & Rollema, G. (2002). Application of topology, sizing and shape optimization methods to optimal design of aircraft components. In Proceedings of the 3rd Altair UK HyperWorks Users Conference.
Langelaar, M. (2017). An additive manufacturing filter for topology optimization of print-ready designs. Structural and Multidisciplinary Optimization, 55(3), 871-883. https://doi.org/10.1007/s00158-016-1522-2
Lazarov, B. S., Schevenels, M., & Sigmund, O. (2012). Topology optimization considering material and geometric uncertainties using stochastic collocation methods. Structural and Multidisciplinary Optimization, 46(4), 597-612. https://doi.org/10.1007/s00158-012-0791-7
Le, C., Norato, J., Bruns, T., Ha, C., & Tortorelli, D. (2010). Stress-based topology optimization for continua. Structural and Multidisciplinary Optimization, 41(4), 605-620. https://doi.org/10.1007/s00158-009-0440-y
Liu, J., Gaynor, A. T., Chen, S., Kang, Z., Suresh, K., Takezawa, A., Li, L., Kato, J., Tang, J., Wang, C. C. L., Cheng, L., Liang, X., & To, A. C. (2018). Current and future trends in topology optimization for additive manufacturing. Structural and Multidisciplinary Optimization, 57(6), 2457-2483. https://doi.org/10.1007/s00158-018-1994-3
Mueller, T. (2023). Full flow staged combustion design optimization at SpaceX. In AIAA Propulsion and Energy Forum (AIAA Paper No. 2023-4981).
NASA. (2023). Topology optimization for CubeSat primary structure (NASA/TM-2023-220012). NASA Ames Research Center.
Rozvany, G. I. N. (2009). A critical review of established methods of structural topology optimization. Structural and Multidisciplinary Optimization, 37(3), 217-237. https://doi.org/10.1007/s00158-007-0217-0
Sanders, E. D., Pereira, A., Aguilo, M. A., & Paulino, G. H. (2018). PolyMat: An efficient MATLAB code for multi-material topology optimization. Structural and Multidisciplinary Optimization, 58(6), 2727-2759. https://doi.org/10.1007/s00158-018-2094-0
Sigmund, O., & Maute, K. (2013). Topology optimization approaches: A comparative review. Structural and Multidisciplinary Optimization, 48(6), 1031-1055. https://doi.org/10.1007/s00158-013-0978-6
Space Foundation. (2024). The space report 2024.
SpaceX. (2023). Advancing rocket engine design through topology optimization (Technical Report SPX-2023-001).
Tomlin, M., & Meyer, J. (2011). Topology optimization of an additive layer manufactured (ALM) aerospace part. In Proceedings of the 7th Altair CAE Technology Conference (pp. 1-9).
van Dijk, N. P., Langelaar, M., & van Keulen, F. (2012). Explicit level-set-based topology optimization using an exact Heaviside function and consistent sensitivity analysis. International Journal for Numerical Methods in Engineering, 91(1), 67-97. https://doi.org/10.1002/nme.4258
Woldseth, R. V., Aage, N., Bærentzen, J. A., & Sigmund, O. (2022). On the use of artificial neural networks in topology optimization. Structural and Multidisciplinary Optimization, 65(10), 294. https://doi.org/10.1007/s00158-022-03347-1
Wu, J., Clausen, A., & Sigmund, O. (2017). Minimum compliance topology optimization of shell-infill composites for additive manufacturing. Computer Methods in Applied Mechanics and Engineering, 326, 358-375. https://doi.org/10.1016/j.cma.2017.08.018
Zegard, T., & Paulino, G. H. (2016). Bridging topology optimization and additive manufacturing. Structural and Multidisciplinary Optimization, 53(1), 175-192. https://doi.org/10.1007/s00158-015-1274-4
Zhang, W., Chen, J., Zhu, X., Zhou, J., Xue, D., Lei, X., & Guo, X. (2017). Explicit three-dimensional topology optimization via moving morphable void (MMV) approach. Computer Methods in Applied Mechanics and Engineering, 322, 590-614. https://doi.org/10.1016/j.cma.2017.05.002
Zhou, M., Lazarov, B. S., Wang, F., & Sigmund, O. (2015). Minimum length scale in topology optimization by geometric constraints. Computer Methods in Applied Mechanics and Engineering, 293, 266-282. https://doi.org/10.1016/j.cma.2015.05.003
Zhou, M., Shyy, Y. K., & Thomas, H. L. (2001). Checkerboard and minimum member size control in topology optimization. Structural and Multidisciplinary Optimization, 21(2), 152-158. https://doi.org/10.1007/s001580050179








