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Applied Sciences
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Multidisciplinary Design Optimization for Aerospace Applications
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Multidisciplinary Design Optimization for Aerospace Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Aerospace Science and Engineering".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 2507

Special Issue Editors

Dr. Mayank Bendarkar

E-Mail Website
Guest Editor
Department of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Interests: aircraft design; optimization; sustainability; safety; certification
Special Issues, Collections and Topics in MDPI journals
Dr. Sarojini Darshan

E-Mail Website
Guest Editor
Department of Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, VA 24061, USA
Interests: aircraft design; multidisciplinary analysis and design optimization; high-performance computing; probabilistic and robust design

Special Issue Information

Dear Colleagues,

Aerospace systems designers must deal with complex system engineering challenges that include constraints imposed from safety regulations and environment/sustainability/performance considerations while ensuring efficiency and cost competitiveness. The design of complex aerospace systems is a multidisciplinary exercise that couples multiple domains like aerodynamics, structures, propulsion, and control. Establishing system performance models that can integrate these domains with sufficient fidelity requires distinct groups of highly skilled experts and system integrators.

Multidisciplinary Design Optimization (MDO) is a field that builds a layer of mathematical optimization algorithms atop such integrated system models to design and optimize them. MDO can be conducted at different stages of system design and development. Incorporating MDO processes with suitable fidelity in different development phases greatly mitigates risks in program costs and delivery timelines.

In this Special Issue, we invite contributions that showcase the latest approaches related to multidisciplinary design optimization technology in aerospace applications. We call for papers for this Special Issue on topics and themes that include (but are not limited to) the following:

  • Advances in aerospace MDO techniques related to:
    • MDO architectures;
    • Artificial intelligence and machine learning-based approaches;
    • Multi-fidelity optimization;
    • Reliability-based design optimization (RBDO);
    • Optimization under uncertainty (OUU);
    • Surrogate-based approaches, including reduced order models or decomposition-based methods.
  • Demonstrations of new aerospace MDO applications that couple at least two or more disciplines, such as:
    • Structures, aerodynamics, propulsion, flight mechanics, or controls;
    • Cost and manufacturing;
    • Novel electric architectures;
    • Certification and safety;
  • Integration between model-based systems engineering (MBSE) and MDO.

Dr. Mayank Bendarkar
Dr. Sarojini Darshan
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • multidisciplinary design optimization
  • aircraft design
  • robust design
  • reliability-based design optimization
  • model-based systems engineering and optimization
  • multi-fidelity optimization
  • optimization under uncertainty

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Published Papers (2 papers)

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Research

28 pages, 8667 KiB  
Article
Design and Optimization of a Compliant Morphing Trailing Edge for High-Lift Generation
by Salvatore Ameduri, Bernardino Galasso, Maria Chiara Noviello, Ignazio Dimino, Antonio Concilio, Pietro Catalano, Francesco Antonio D’Aniello, Giovanni Marco Carossa, Laurent Pinazo, John Derry, Britney Biju and Shruthi Shreedharan
Appl. Sci. 2025, 15(5), 2529; https://doi.org/10.3390/app15052529 - 26 Feb 2025
Cited by 1 | Viewed by 628
Abstract
This work focuses on the design and optimization of a morphing-compliant system developed within the project HERWINGT (Clean Aviation) and aimed at generating high lift during take-off and landing. The device was conceived to replace a conventional flap of a regional aircraft and [...] Read more.
This work focuses on the design and optimization of a morphing-compliant system developed within the project HERWINGT (Clean Aviation) and aimed at generating high lift during take-off and landing. The device was conceived to replace a conventional flap of a regional aircraft and work in synergy with a droop nose and a flow control system. The architecture is based on a compliant layout, specifically selected to obtain a final morphed shape of the trailing edge of the wing efficient for high-lift purposes and adequately smooth even in cruise clean configuration. At first, the requirements at aircraft level were critically examined and then elaborated to produce the specifications of the morphing device. A layout was then sketched, considering on its potential in approaching the target morphed shape and on its intrinsic criticalities. Starting from this scheme, a simplified FE model was introduced. The scope was to have an efficient predictive tool suited for optimization processes. After having identified the most relevant design parameters (skin thickness distribution, topology of the structure, and actuator interface parameters), the cost function, and the constraints of the problem (structural integrity and stability), a genetic optimization was implemented. Repeating the genetic process starting from different initial populations, some optimized configurations were identified. A trade-off was thus organized on different criteria, such as the lightness of the structure, the load-bearing capability, the force, and the stroke needed by the actuator. The best compromise was finally taken as baseline for the realization of an advanced FE model used to validate the numerical outcomes obtained during the optimization process and as starting point for the next steps planned in the project. The achieved design is characterized by an enhanced aerodynamic performance with the absence of steps and gaps and external track fairings, reduced weight of both the structure and the actuator, reduced maintenance costs due to a simple layout, and smaller take-off and landing distances owing to the high-lift capability and the intrinsic lightness. Full article
(This article belongs to the Special Issue Multidisciplinary Design Optimization for Aerospace Applications)
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26 pages, 2083 KiB  
Article
ALMO: Active Learning-Based Multi-Objective Optimization for Accelerating Constrained Evolutionary Algorithms
by Karanpreet Singh and Rakesh K. Kapania
Appl. Sci. 2024, 14(21), 9975; https://doi.org/10.3390/app14219975 - 31 Oct 2024
Viewed by 1403
Abstract
In multi-objective optimization, standard evolutionary algorithms, such as NSGA-II, are computationally expensive, particularly when handling complex constraints. Constraint evaluations, often the bottleneck, require substantial resources. Pre-trained surrogate models have been used to improve computational efficiency, but they often rely heavily on the model’s [...] Read more.
In multi-objective optimization, standard evolutionary algorithms, such as NSGA-II, are computationally expensive, particularly when handling complex constraints. Constraint evaluations, often the bottleneck, require substantial resources. Pre-trained surrogate models have been used to improve computational efficiency, but they often rely heavily on the model’s accuracy and require large datasets. In this study, we use active learning to accelerate multi-objective optimization. Active learning is a machine learning approach that selects the most informative data points to reduce the computational cost of labeling data. It is employed in this study to reduce the number of constraint evaluations during optimization by dynamically querying new data points only when the model is uncertain. Incorporating machine learning into this framework allows the optimization process to focus on critical areas of the search space adaptively, leveraging predictive models to guide the algorithm. This reduces computational overhead and marks a significant advancement in using machine learning to enhance the efficiency and scalability of multi-objective optimization tasks. This method is applied to six challenging benchmark problems and demonstrates more than a 50% reduction in constraint evaluations, with varying savings across different problems. This adaptive approach significantly enhances the computational efficiency of multi-objective optimization without requiring pre-trained models. Full article
(This article belongs to the Special Issue Multidisciplinary Design Optimization for Aerospace Applications)
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