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Aerospace
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Aircraft Structural Design Materials, Modeling, and Optimization
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Aircraft Structural Design Materials, Modeling, and Optimization

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Aeronautics".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 758

Special Issue Editor

Dr. Mostafa El Sayed

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada
Interests: solid and structural mechanics; lattice materials; multifunctional metamaterials; fluid–structure interaction; multiscale mechanics; structural optimization in multi-physics applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in aircraft structural engineering are increasingly driven by the convergence of novel materials, high-fidelity and computationally efficient modeling techniques, and powerful optimization frameworks. Modern aircraft structures must simultaneously satisfy stringent requirements for strength, stiffness, durability, weight efficiency, manufacturability, cost, and environmental sustainability. Consequently, the structural design landscape is rapidly evolving toward multidisciplinary and multiscale methodologies that integrate advanced computational modeling, experimental validation, and emerging technologies such as artificial intelligence and machine learning.

This Special Issue, Aircraft Structural Design Materials, Modeling, and Optimization, aims to highlight recent advances and emerging directions in the analysis, design, and optimization of aircraft structural systems. We welcome contributions spanning fundamental research, applied studies, and industrial applications related to structural analysis, lightweight design, structural optimization, and advanced materials for aerospace applications. Topics of interest include, but are not limited to, innovative metallic and composite structures; architected materials and lattice systems; structural modeling and simulation; model order reduction techniques; multiscale and multiphysics design methodologies; topology, shape, and size optimization; aeroelasticity and structural dynamics; digital twins; structural health monitoring; additive manufacturing; and AI-enabled structural design frameworks.

By bringing together cutting-edge contributions from academia, industry, and government laboratories, this Special Issue seeks to provide a comprehensive perspective on the enabling technologies and design philosophies shaping the next generation of aircraft structures. Through this collection, we aim to foster interdisciplinary collaboration, stimulate innovative research directions, and support the development of efficient, robust, and sustainable aircraft structural systems.

We look forward to receiving your contributions and to advancing the state of the art in aircraft structural design through this Special Issue.

Dr. Mostafa El Sayed
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • aircraft structures
  • structural design and optimization
  • multidisciplinary design optimization (MDO)
  • composite and advanced materials
  • additive manufacturing
  • multiscale and multiphysics modeling
  • aeroelasticity and structural dynamics
  • topology, shape, and size optimization
  • digital twin and structural health monitoring
  • AI-enabled structural design

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Published Papers (2 papers)

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Research

25 pages, 1514 KB  
Article
Reliability Allocation Method for Aircraft Mechanical Systems Involving Motion Performance and Failure Correlation
by Linjie Shen, Lu Wang, Feng Xiao and Jiawei Du
Aerospace 2026, 13(4), 376; https://doi.org/10.3390/aerospace13040376 - 16 Apr 2026
Abstract
One of the most important design requirements for aircraft mechanical systems is to ensure that their motion functions can be executed smoothly. In this paper, an unconstrained reliability allocation method is proposed, taking into account the characteristics of aircraft mechanical systems. A decomposition [...] Read more.
One of the most important design requirements for aircraft mechanical systems is to ensure that their motion functions can be executed smoothly. In this paper, an unconstrained reliability allocation method is proposed, taking into account the characteristics of aircraft mechanical systems. A decomposition principle for assessing the motion performance of aircraft mechanical systems has been proposed, and the contribution of each subsystem is analyzed. Weighting factors for system allocation are proposed and refined, and a failure correlation index is proposed to account for the influence of the interaction between subsystems on the potential failure rate. Furthermore, non-destructive failure events that could have a significant impact on motion performance have been taken into account in the potential improvement of subsystems. Subsequently, reliability prediction models of the systems are established using the Copula function, and a calculation method is introduced to distinguish and quantify the correlation between different subsystems. Finally, the applicability and validity of the proposed method are demonstrated through an engineering case. The results indicate that when failure correlation is considered, the reliability allocated to subsystems is significantly lower than that obtained using traditional methods, providing theoretical guidance for the reliability design of aircraft mechanical systems. Full article
(This article belongs to the Special Issue Aircraft Structural Design Materials, Modeling, and Optimization)
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29 pages, 3640 KB  
Article
Analysis of Wing Structures via Machine Learning-Based Surrogate Models
by Hasan Kiyik, Metin Orhan Kaya and Peyman Mahouti
Aerospace 2026, 13(4), 338; https://doi.org/10.3390/aerospace13040338 - 3 Apr 2026
Viewed by 342
Abstract
Accurate structural analysis is essential for the design and optimization of aircraft wings; however, repeated high-fidelity finite element analysis (FEA) becomes computationally expensive when embedded in iterative design loops. This study presents a machine learning-based surrogate modeling framework for the efficient analysis and [...] Read more.
Accurate structural analysis is essential for the design and optimization of aircraft wings; however, repeated high-fidelity finite element analysis (FEA) becomes computationally expensive when embedded in iterative design loops. This study presents a machine learning-based surrogate modeling framework for the efficient analysis and optimization of metallic commercial wing structures. A detailed Airbus A320-like wing model was developed and analyzed in ANSYS 2023 R1 under modal, static, and eigenvalue buckling conditions. The general dimensions of the Airbus A320 wing were used only as a reference; the resulting model is a conceptual benchmark rather than a one-to-one geometric replica or a validated digital twin of a specific aircraft wing. Using Latin Hypercube Sampling, 340 high-fidelity samples were generated, with 300 samples used for training and validation and 40 retained as an independent holdout set. The proposed Pyramidal Deep Regression Network (PDRN), a deep learning-based surrogate model whose architecture is automatically tuned using Bayesian Optimization, was benchmarked against Artificial Neural Networks (ANNs), Ensemble Learning, Support Vector Regression (SVR), and Gaussian Process Regression (GPR). On the unseen test set, the PDRN achieved the best overall predictive performance, with RMS errors of 0.8% for mass, 3.1% for the first natural frequency, 11.5% for load factor, and 11.4% for safety factor. To evaluate its practical utility, the trained PDRN was embedded into a PSO-based optimization framework for mass minimization under minimum safety factor, load factor, and first-frequency constraints. The surrogate-guided optimum was verified in ANSYS and remained feasible, yielding a mass of 10,485 kg, a first natural frequency of 1.4142 Hz, a load factor of 1.307, and a safety factor of 1.158. Compared with direct ANSYS in-the-loop optimization, the proposed workflow reached a comparable feasible design with substantially fewer high-fidelity evaluations. These results demonstrate that the PDRN provides an accurate and computationally efficient surrogate for rapid wing analysis and constraint-driven structural optimization. Full article
(This article belongs to the Special Issue Aircraft Structural Design Materials, Modeling, and Optimization)
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