Gradient-based optimisation of rectangular honeycomb core sandwich panels

Journal article


Luis Santos, Bassam A. Izzuddin, Lorenzo Macorini and Pinho Santos, L. (2022). Gradient-based optimisation of rectangular honeycomb core sandwich panels. Structural and Multidisciplinary Optimization. 65 (242). https://doi.org/10.1007/s00158-022-03341-7
AuthorsLuis Santos, Bassam A. Izzuddin, Lorenzo Macorini and Pinho Santos, L.
Abstract

When subjected to bending loads, sandwich panels are highly efficient structural components with the potential to achieve substantial weight reduction. A successful design methodology for sandwich panels should aim at maximising this potential for weight reduction while considering the various possible failure mode in a simple yet accurate manner. This paper investigates the application of steel sandwich panels as two-way deck systems. Near-optimal designs for all-steel Rectangular Honeycomb Core Sandwich Panels (RHCSPs) under general out-of-plane loading are achieved using a gradient-based optimisation method. The method relies on continuously optimising the design limit state constraints while the response constraints are considered a priori in the analysis stage using simplified analytical assessment. Plate bending solutions and sandwich bending solutions are used as alternatives to estimate the internal stresses on each layer of the sandwich panel under out-of-plane loads, where comparisons are made between these two analysis methods in terms of computational efficiency and accuracy. The internal stresses are then used to formulate design limit state equations for each relevant failure mode, including material yielding, plate buckling and deformation control. The Method of Moving Asymptotes is used for the optimisation of RHCSPs, considering the limit states as the constraints of the optimisation problem and weight as the objective function to be minimised. The proposed methodology for simplified assessment is verified against detailed nonlinear finite element models for optimal design solutions. The implications of the results of the proposed optimisation strategy on the development of a systematic design methodology for RHCSPs are also highlighted, making specific reference to critical failure modes.

Year2022
JournalStructural and Multidisciplinary Optimization
Journal citation65 (242)
PublisherSpringer
ISSN1615-1488
Digital Object Identifier (DOI)https://doi.org/10.1007/s00158-022-03341-7
Web address (URL)https://doi.org/10.1007/s00158-022-03341-7
Publication dates
Online17 Aug 2022
Sep 2022
Publication process dates
Accepted18 Jul 2022
Deposited22 Aug 2022
Publisher's version
License
File Access Level
Open
Permalink -

https://openresearch.lsbu.ac.uk/item/91v2x

Download files


Publisher's version
s00158-022-03341-7.pdf
License: CC BY 4.0
File access level: Open

  • 63
    total views
  • 46
    total downloads
  • 0
    views this month
  • 2
    downloads this month

Export as

Related outputs

Machine learning for optimal design of circular hollow section stainless steel stub columns: A comparative analysis with Eurocode 3 predictions
Abarkan, I, Rabi, M., Ferreira, F., Shamass, R., Limbachiya, V., Jweihan, Y. and Pinho Santos, L. (2024). Machine learning for optimal design of circular hollow section stainless steel stub columns: A comparative analysis with Eurocode 3 predictions. Engineering Applications of Artificial Intelligence. 132 (107952). https://doi.org/10.1016/j.engappai.2024.107952
Machine learning-driven web-post buckling resistance prediction for high-strength steel beams with elliptically-based web openings
Rabi, M, Jweihan, Y, Abarkan, I, Ferreira, F, Shamass, R, Limbachiya, V., Tsavaridis, K and Pinho Santos, L. (2024). Machine learning-driven web-post buckling resistance prediction for high-strength steel beams with elliptically-based web openings. Results in Engineering. 21, p. 101749. https://doi.org/10.1016/j.rineng.2024.101749
Web-Post Buckling Prediction Resistance of Steel Beams with Elliptically-Based Web Openings using Artificial Neural Networks (ANN)
Shamass, R., Ferreira, F., Limbachiya, V., Pinho Santos, L. and Tsavdaridis, K.D. (2022). Web-Post Buckling Prediction Resistance of Steel Beams with Elliptically-Based Web Openings using Artificial Neural Networks (ANN). Thin-Walled Structures. 180 (109959). https://doi.org/10.1016/j.tws.2022.109959
EC3 design of web-post buckling resistance for perforated steel beams with elliptically-based web openings
Ferreira, F., Shamass, R., Pinho Santos, L., Limbachiya, V. and Tsavdaridis, K. (2022). EC3 design of web-post buckling resistance for perforated steel beams with elliptically-based web openings. Thin-Walled Structures. 175, p. 109196. https://doi.org/10.1016/j.tws.2022.109196
High-fidelity non-linear analysis of metal sandwich panels
Nordas, A.N., Pinho Santos, L., Izzuddin, B. and Macorini, L. (2018). High-fidelity non-linear analysis of metal sandwich panels. Proceedings of the Institution of Civil Engineers - Engineering and Computational Mechanics. 171 (2), pp. 79-96. https://doi.org/10.1680/jencm.18.00022
Mechanical models for local buckling of metal sandwich panels
Pinho Santos, L., Nordas, A.N., Izzuddin, B. and Macorini, L. (2018). Mechanical models for local buckling of metal sandwich panels. Proceedings of the Institution of Civil Engineers - Engineering and Computational Mechanics. 171 (2), pp. 65-78. https://doi.org/10.1680/jencm.18.00021
Improved Seismic Design of Concentrically X-Braced Steel Frames to Eurocode 8
Silva, A., Pinho Santos, L., Pinto Ribeiro, T. and Castro, J.M. (2018). Improved Seismic Design of Concentrically X-Braced Steel Frames to Eurocode 8. Journal of Earthquake Engineering. https://doi.org/10.1080/13632469.2018.1528912