DOI: 10.5281/zenodo.15411556
title: Dataset for Incorporating bird strike crashworthiness requirements within the design of wing structures

description: This dataset is the official implementation of the following paper published in the Structural and Multidisciplinary Optimization journal:



Raluca-Ioana Ciobotia, Ton van der Laan, Wydo van de Waerdt, Daniël Peeters, Saullo G. P. Castro. "Incorporating bird strike crashworthiness requirements within the design of wing structures". Structural and Multidisciplinary Optimization, 2025. 10.1007/s00158-025-04158-w


 

 

Abstract:

The present study, which was carried out in collaboration with GKN Fokker, focuses on incorporating bird strike crashworthiness requirements within a multidisciplinary optimization (MDO) framework. During the preceding three-month internship in the same company, a pivotal contribution to this project was the development of an Abaqus interface for the Multidisciplinary Modeller, MDM, created within the Center of Competence in Design department. MDM is a Python/ParaPy-based automated generator of wings, moveables and flaps, starting from a set of user-specified parameters. The generation of ready-to-run input files thus lays the foundation for the subsequent optimization process, as any changes in materials or geometry can be easily accommodated.

The core objective of the research is to minimize the weight of an aircraft wing while taking into account additional requirements related to the extent of damage caused by bird strikes. Unfortunately, such events occur more frequently than one would be comfortable with, and stringent requirements are set in place to guarantee the safety of the passengers. Among these requirements, the aircraft must be capable of landing safely after such an event, being subject to loads associated with get-home conditions.

As a consequence, two critical constraints are formulated within the optimization framework, addressing the residual strength of the damaged front spar following a bird strike, coupled with a requirement based on a maximum penetration depth. The last constraint has also been included due to the rising popularity of the electric vertical take-off and landing aircraft, which not only fly at low altitudes, thus increasing the risk of bird strike, but may also contain battery packs in the leading edge, for instance, which can pose a significant risk if damaged. To tackle the complexity of this highly-dimensional optimization challenge, a methodology based on Bayesian optimization is proposed, employing surrogate models coupled with a preliminary variable ranking procedure.

The Kriging metamodel is identified as a suitable candidate, thanks to its error prediction capabilities, which are paramount in Bayesian optimization. A variance-based dimensionality reduction method is proposed, which makes use of an initial surrogate to estimate the main and interaction effects of the variables. The quantification of the significance of a variable is expressed as its percentage contribution to the total variance, thus allowing for an intuitive selection of the most important parameters. After the screening procedure is complete, the optimization procedure is carried out in the reduced design space, which uses the constrained expected improvement as an acquisition function. The proposed methodology is then applied on a case study problem, involving a five-bay metallic wing segment subject to the constraints aforementioned, involving 19 design variables representing the thicknesses of various components.

Remarkable weight savings have been achieved, the final result being 40\% lighter than the lightest feasible design among the initial data points. A significant dimensional reduction has also been attained for the maximum depth constraint, which is expected due to the local nature of the impact. Not only did the number of variables greatly decrease from 19 to just 3, but a considerable increase in the accuracy of the corresponding metamodel has also been registered, thanks to an increase in sampling density in the reduced space. However, the variable screening procedure revealed intricate interaction effects with respect to the residual strength of the front spar, emphasizing the nuanced complexity inherent in crashworthiness considerations. Nevertheless, a moderate dimensional reduction has been achieved for this constraint as well, reducing the number of variables to 8, thus proving the efficacy of the proposed variable screening procedure.

In conclusion, the utilization of Kriging models, variable ranking procedures, and Bayesian optimization collectively contributed to the success of achieving remarkable weight savings, proving the efficiency of the proposed methodology. Moreover, it has been shown that the integration of a residual strength requirement is necessary, as many cases were uncovered where no significant penetration occurred, although the application of the considered load case, which is not from critical to an undamaged wing, resulted in high stresses to the front spar of the damaged structure.

 

Description:

The present project includes all of the code needed to build surrogate models, perform variable screening and conduct Bayesian optimization for a bird strike structural optimization problem.

The whole dataset is also provided as an example: Abaqus input, replay, status and .dat files are included, post-impact images from the ODB of each analysis, energy CSV files, as well as the surrogate models at each optimization iteration are included.

 

Authors:

Raluca-Ioana Ciobotia  (ORCID: https://orcid.org/0009-0004-7949-744X)  - Affiliation: CrashProofLab, Department of Aerospace Structures and Materials, Delft University of Technology, Delft, 2629HS, the Netherlands    - Role: Corresponding author    - Email: R.I.Ciobotia@tudelft.nl 

Saullo G.P. Castro  (ORCID: https://orcid.org/0000-0001-9711-0991)   - Affiliation: CrashProofLab, Department of Aerospace Structures and Materials, Delft University of Technology, Delft, 2629HS, the Netherlands    - Role: Corresponding author    - Email: S.G.P.Castro@tudelft.nl  Daniël Peeters  (ORCID: https://orcid.org/0000-0001-7388-8424)  - Affiliation: Department of Aerospace Structures and Materials, Delft University of Technology, Delft, 2629HS, the Netherlands    - Role: Contributing author    - Email: D.M.J.Peeters@tudelft.nl  Ton van der Laan  (ORCID: https://orcid.org/0009-0004-1080-7227)  - Affiliation: Fokker Aerostructures B.V., Anthony Fokkerweg 4, Papendrecht, 3351NL, the Netherlands    - Role: Contributing author    - Email: Ton.vanderLaan@fokker.com

Wydo van de Waerdt  (ORCID: https://orcid.org/0000-0002-1450-9866)  - Affiliation: Fokker Aerostructures B.V., Edisonstraat 1, Hoogeveen, 7903AN, the Netherlands    - Role: Contributing author  

 

Project structure:

Bird_strike_MDO├───Bird_strike_MDO└───Dataset    ├───additional_points    ├───csv_files    ├───initial_points    └───optimization_points        ├───batch_approach        └───sequential_approach

The Bird_strike_MDO folder contains the code repository. More information on how to use the code can be found in the corresponding README.md file located in the code folder.

The Dataset folder contains the following files for each category of data points:1. Initial data       Such data was obtained by generating a Latin Hypercube Sample in the 19-variable dimensional design space. In Dataset/initial_points, there are 76 subfolders named initial_point$i, where $i is the index of each initial data point. Each subfolder contains the modal, dynamic and static analysis files, as well as energy CSV files and post-impact images taken from the ODB.2. Additional data       Such data was obtained after an initial surrogate model fitting by maximizing the variance of the surrogate. The same files are available as for the initial data.3. Optimization data       Such data was obtained by performing Bayesian optimization in the dimensionally reduced design space after the ANOVA procedure. Two approaches were tested: the batch approach using the Kriging believer method, where more points are tested at each iteration, and the sequential one, where only one point is tested at each iteration. Both approaches aim to maximize the constrained improvement acquisition function. Each subfolder contains the following: the modal, dynamic and static analysis input files along their status and .dat Abaqus files, Abaqus replay files for results extraction, energy CSV files, post-impact images taken from the ODB, and the Trieste submodels trained up until that specific iteration. 4. CSV files       These files contain summaries of all results.

License:

The contents of this repository are licensed under a 3-Clause BSD license (see LICENSE file).

© 2025 R. I. Ciobotia, S. G. P. Castro, D. M. J. Peeters, T. van der Laan, W. van de Waerdt