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Aerospace
Special Issues
Multidisciplinary Intelligent Design and Optimization of Flight Vehicle Systems
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Multidisciplinary Intelligent Design and Optimization of Flight Vehicle Systems

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 759

Special Issue Editor

Dr. Renhe Shi

E-Mail Website
Guest Editor
School of Aerospace Engineering, Beijing Institute of Technology, Beijing, China
Interests: flight vehicle system design; multidisciplinary design optimization; metamodel based design and optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Flight vehicle design is a sophisticated systems engineering process consisting of many intercoupled disciplines. To improve system performance, multidisciplinary design optimization (MDO) has been widely employed in the design of flight vehicle systems. Due to the evaluation of expensive black-box simulations and the exploration of complex design space, huge computational expenses are associated with solving complex flight vehicle MDO problems, posing a challenge in engineering practice. To address this challenge, state-of-the-art intelligent techniques (e.g., artificial intelligence, data mining, digital twin) are expected to be integrated with MDO technology to further improve optimization efficiency and design quality. This Special Issue aims to provide an overview of recent advances in multidisciplinary intelligent design and optimization of flight vehicle systems. Authors are invited to submit full research articles and review manuscripts addressing topics including (but not limited to) the following:

  • Novel design theories and applications of flight vehicle systems;
  • Intelligent design optimization methods and applications;
  • Data-driven multidisciplinary design optimization;
  • Artificial intelligence-enhanced design optimization.

Dr. Renhe Shi
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

  • flight vehicle design
  • multidisciplinary design optimization
  • data-driven design
  • artificial intelligence-based design
  • metamodel-based design and optimization

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

22 pages, 10000 KB  
Article
Neural Network-Enhanced Performance Rapid Prediction and Matching Optimization Framework for Solid Rocket Motor
by Nianhui Ye, Sheng Luo, Dengwei Gao and Renhe Shi
Aerospace 2026, 13(5), 393; https://doi.org/10.3390/aerospace13050393 - 22 Apr 2026
Abstract
During the preliminary design of flight vehicles, i.e., missiles or guided rockets, propulsion system performance serves as a critical determinant of both maximum range and terminal velocity. However, complex grain configurations in solid rocket motors (SRMs) typically require geometric modeling software to obtain [...] Read more.
During the preliminary design of flight vehicles, i.e., missiles or guided rockets, propulsion system performance serves as a critical determinant of both maximum range and terminal velocity. However, complex grain configurations in solid rocket motors (SRMs) typically require geometric modeling software to obtain burning surface area, which severely constrains efficiency. To address this challenge, this study presents a neural network-enhanced rapid performance prediction and matching optimization framework for solid rocket motors (NN-SRM). In NN-SRM, neural networks are employed to simulate the evolution of key parameters during grain combustion, including burning surface area, grain volume, and moment of inertia. The zero-dimensional internal ballistics equations coupled with one-dimensional steady isentropic flow relations are incorporated into the framework to rapidly obtain thrust curves. A discrete–continuous mixed differential evolution algorithm is further employed to identify the optimal grain configuration that satisfies specific thrust requirements. Results demonstrate that, as for cylindrical, star, and finocyl grains, the neural network achieves R2 exceeding 0.95. Finally, thrust matching optimization is conducted on three grains and achieves promising thrust solutions for the conditions of large thrust with short time and small thrust with long time, which demonstrates the effectiveness and practicality of the constructed NN-SRM. Full article
(This article belongs to the Special Issue Multidisciplinary Intelligent Design and Optimization of Flight Vehicle Systems)
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18 pages, 6123 KB  
Article
Efficient Prediction of Unsteady Aerodynamic Characteristics Based on Kriging Model for Flexible Variable-Sweep Wings
by Xiaochen Hang, Jincheng Liu, Rui Zhu and Yanxin Huang
Aerospace 2026, 13(4), 305; https://doi.org/10.3390/aerospace13040305 - 25 Mar 2026
Viewed by 308
Abstract
Numerical simulations employing the dynamic mesh method were performed to investigate the unsteady aerodynamics of variable-sweep wings during morphing. Quasi-steady and unsteady aerodynamic characteristics were compared, and the effects of key operating conditions (freestream velocity, angle of attack, morphing period, wingspan, chord length) [...] Read more.
Numerical simulations employing the dynamic mesh method were performed to investigate the unsteady aerodynamics of variable-sweep wings during morphing. Quasi-steady and unsteady aerodynamic characteristics were compared, and the effects of key operating conditions (freestream velocity, angle of attack, morphing period, wingspan, chord length) on unsteady aerodynamics were analyzed. To enable the rapid prediction of unsteady aerodynamics, a Kriging surrogate model was established and validated against high-fidelity CFD results. The results indicate that unsteady effects manifest as hysteresis loops in aerodynamic coefficients within the morphing cycle. The wing morphing period, angle of attack, freestream velocity, and wingspan have a pronounced impact on the unsteady aerodynamic characteristics, whereas the effect of chord length is negligible. Reduced morphing periods, increased angles of attack, and increased wingspans amplify the hysteresis loop size and enhance the unsteady effects. An increase in the freestream velocity intensifies unsteady effects in the subsonic flow, while it attenuates unsteady effects in the supersonic flow. Compared to direct CFD simulations, the Kriging model for unsteady aerodynamic characteristics prediction achieves a 97% improvement in overall computational efficiency, while its predicted hysteresis loops are in good agreement with CFD results in both trend and magnitude, with an average prediction error below 4% and a maximum error of less than 6%. The Kriging surrogate model developed in this study offers substantial practical value for engineering applications by meeting the demand for rapid aerodynamic computation in the concept design phase for morphing aircraft. Full article
(This article belongs to the Special Issue Multidisciplinary Intelligent Design and Optimization of Flight Vehicle Systems)
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