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Aerospace
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Advances in Reduced-Order Modeling for Aerodynamic Analysis, Optimization, and Aeroelasticity
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Advances in Reduced-Order Modeling for Aerodynamic Analysis, Optimization, and Aeroelasticity

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1349

Special Issue Editor

Dr. Weigang Yao

E-Mail Website
Guest Editor
Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK
Interests: CFD; aeroelasticity; reduced order modelling; multidisciplinary design optimization (MDO)

Special Issue Information

Dear Colleagues,

Reduced-order modeling (ROM) has become a critical simulation technology for aerodynamic analysis, aeroelasticity, and aerodynamic shape optimization. By reducing the complexity of high-fidelity simulations such as computational fluid dynamics (CFDs), while preserving essential physics, ROM enables real-time analysis, fast design optimization, and uncertainty quantification. Its applications extend across the aerospace engineering field, including aircraft design, turbomachinery, and wind energy systems.

Research efforts are increasingly concentrated on data-driven ROM and the integration of artificial intelligence (AI) to enhance predictive capabilities and bridge the gap between reduced-order and high-fidelity simulations. Despite its advantages, key challenges persist, including improving accuracy, capturing nonlinear flow physics, and integrating physics-informed machine learning. Addressing these challenges is essential for facilitating the integration of ROM into mainstream engineering workflows.

This Special Issue invites original research articles and comprehensive reviews that advance the state of the art of ROM and its applications in aerodynamics analysis, design optimization, and aeroelasticity. 

Dr. Weigang Yao
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 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. 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

  • reduced-order modeling (ROM)
  • aeroelasticity
  • aerodynamic shape optimization
  • machine learning in computational fluid dynamics (CFDs)
  • data-driven modeling

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Published Papers (3 papers)

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Research

24 pages, 2797 KiB  
Article
Multi-Objective Optimization of Transonic Variable Camber Airfoil with Leading- and Trailing-Edge Deflections Using Kriging Surrogate Model
by Wei Wang, He Feng, Shenao Cui and Zhandong Li
Aerospace 2025, 12(8), 659; https://doi.org/10.3390/aerospace12080659 - 24 Jul 2025
Viewed by 162
Abstract
To investigate the aerodynamic characteristics and multi-objective optimization of the variable camber airfoils, the influence of leading- and trailing-edge deflections on aerodynamic performance is conducted. A novel prediction model is presented using the Kriging surrogate model, with leading and trailing edge deflection angles [...] Read more.
To investigate the aerodynamic characteristics and multi-objective optimization of the variable camber airfoils, the influence of leading- and trailing-edge deflections on aerodynamic performance is conducted. A novel prediction model is presented using the Kriging surrogate model, with leading and trailing edge deflection angles as inputs and lift coefficients and drag coefficients as outputs. The Non-dominated Sorting Genetic Algorithm II (NSGA II) multi-objective optimization technique is applied to ascertain the ideal deflection parameters. The results show that upward deflection of the leading edge raises the lift, whereas downward deflection increases the value of the critical angle of attack. The deflection of the trailing edge increases the value of the critical angle of attack, while the downward deflection can enhance the lift coefficient. Appropriate upward deflections of both leading and trailing edges can delay the critical Mach number, while downward deflections of both the leading and trailing edges can enhance the value of the critical Mach number. The discrepancies between the Kriging model prediction and the CFD simulation are less than 2%. Compared to the basic airfoil, the aerodynamic performance of the optimized airfoil has been improved, with the lift coefficient increasing by 7.55% and 7.37% and the lift-to-drag ratio rising by 6.97% and 10.27% at two Mach numbers, respectively. The efficiency and reliability of this method have been verified. Full article
(This article belongs to the Special Issue Advances in Reduced-Order Modeling for Aerodynamic Analysis, Optimization, and Aeroelasticity)
17 pages, 2961 KiB  
Article
Geometric Optimization of Coanda Jet Chamber Fins via Response Surface Methodology
by Hui Zhang, Kai Yue and Yiming Zhang
Aerospace 2025, 12(7), 571; https://doi.org/10.3390/aerospace12070571 - 23 Jun 2025
Viewed by 217
Abstract
A highly loaded axial flow compressor often leads to significant flow separation, resulting in increased pressure loss and deterioration of the pressure increase ability. Improving flow separation within a compressor is crucial for enhancing aeroengine performance. This study proposes adding a fin structure [...] Read more.
A highly loaded axial flow compressor often leads to significant flow separation, resulting in increased pressure loss and deterioration of the pressure increase ability. Improving flow separation within a compressor is crucial for enhancing aeroengine performance. This study proposes adding a fin structure to the jet cavity of the Coanda jet cascade to improve flow separation at the trailing edge and corner area. The fin structure is optimized using response surface technique and a multi-objective genetic algorithm based on numerical simulation, enabling more effective control of the simultaneous separation of the boundary corner and trailing edge of the layer. The response surface model developed in this study is accurately validated. The numerical results demonstrate a 2.13% reduction in the optimized blade total pressure loss coefficient and a 12.74% reduction in the endwall loss coefficient compared to those of the original unfinned construction under the same air injection conditions. The optimization procedure markedly improves flow separation in the compressor, leading to a considerable decrease in the volume of low-energy fluid on the blade’s suction surface, particularly in the corner area. The aerodynamic performance of the high-load cascade is enhanced. Full article
(This article belongs to the Special Issue Advances in Reduced-Order Modeling for Aerodynamic Analysis, Optimization, and Aeroelasticity)
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22 pages, 59021 KiB  
Article
Manifold Learning for Aerodynamic Shape Design Optimization
by Boda Zheng, Abhijith Moni, Weigang Yao and Min Xu
Aerospace 2025, 12(3), 258; https://doi.org/10.3390/aerospace12030258 - 19 Mar 2025
Cited by 3 | Viewed by 705
Abstract
The significant computational cost incurred due to the iterative nature of Computational Fluid Dynamics (CFD) in traditional aerodynamic shape design frameworks poses a major challenge, especially in the context of modern integrated design requirements and increasingly complex design conditions. To address the demands [...] Read more.
The significant computational cost incurred due to the iterative nature of Computational Fluid Dynamics (CFD) in traditional aerodynamic shape design frameworks poses a major challenge, especially in the context of modern integrated design requirements and increasingly complex design conditions. To address the demands of modern design, we developed an efficient aerodynamic shape design framework based on our previous work involving the locally linear embedding plus constrained optimization genetic algorithm (LLE+COGA) high-fidelity reduced-order model (ROM). An active manifold (AM) auto-en/decoder was employed to address the dimensionality curse arising from an excessively large design space. The fast mesh deformation method was utilized for high-precision, rapid mesh deformation, significantly reducing the computational cost associated with transferring geometric deformations to CFD fine mesh. This work addressed the transonic optimization problem of the undeflected Common Research Model (uCRM) three-dimensional wing (with an aspect ratio of 9), involving 241 design variables. The results demonstrate that the optimized design achieved a significant reduction in the drag coefficient by 38.9% and 54.5% compared to the baseline in Case 1 and Case 2, respectively. Additionally, the total optimization time was shortened by 62.6% and 57.7% in the two cases. Moreover, the optimization outcomes aligned well with those obtained from the FOM-based framework, further validating the effectiveness and practical applicability of the proposed approach. Full article
(This article belongs to the Special Issue Advances in Reduced-Order Modeling for Aerodynamic Analysis, Optimization, and Aeroelasticity)
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