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Aerospace
Special Issues
Machine Learning for Aerodynamic Analysis and Optimization
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Machine Learning for Aerodynamic Analysis and Optimization

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

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1628

Editors

Dr. Runze Li

E-Mail Website
Guest Editor
Department of Aeronautics, Faculty of Engineering, Imperial College London, London, UK
Interests: machine learning technologies in aerodynamic studies and optimizations; adaptive sampling methods; surrogate model assisted optimization; reinforcement learning
Prof. Dr. Andrea Da-Ronch

E-Mail Website
Guest Editor
Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO171BJ, UK
Interests: aerodynamics; structures; aeroelasticity; model reduction; control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue brings together cutting-edge research at the intersection of aerospace engineering and machine learning, with a focus on advancing aerodynamic analysis and optimization. While data-driven models have shown remarkable promise in reducing computational cost and accelerating design, their adoption in real-world aerospace applications depends on more than just accuracy. Physics-driven approaches are key to enhancing interpretability, generality, and reliability, ensuring that machine learning methods remain consistent with the fundamental laws of aerodynamics.

The contributions in this Special Issue highlight novel frameworks that integrate physics-driven approaches, enable robust predictions across diverse flow regimes, and extend learning-based methods from simplified cases to realistic three-dimensional aircraft configurations. Beyond prediction, we also emphasize the role of machine learning in guiding aerodynamic design and optimization, opening new pathways for innovation in aircraft performance, efficiency, and sustainability.

This Special Issue serves both as a resource for researchers developing new data-driven and physics-driven techniques and as a guide for practitioners seeking reliable, interpretable, and scalable tools for aerospace applications.

Dr. Runze Li
Dr. Andrea Da-Ronch
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 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-anonymized 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.

Dr. Runze Li
Dr. Andrea Da-Ronch
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 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-anonymized 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

  • aerodynamics
  • machine learning
  • physics-driven
  • generality
  • interpretability
  • reliability

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

Published Papers (2 papers)

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21 pages, 10359 KB  
Article
Explainable AI in Rotorcraft Aerodynamics: Autonomous Discovery and Dynamic Tracking of Vortex Ring State Mechanisms via Vision Transformers
by Xiang Zhou, Jiawei Sun, Jiannan Zhao and Feng Shuang
Aerospace 2026, 13(7), 590; https://doi.org/10.3390/aerospace13070590 - 30 Jun 2026
Viewed by 342
Abstract
The Vortex Ring State (VRS) is a critical aerodynamic hazard for rotorcraft, characterized by highly unsteady fluid–structure interactions and severe low-frequency vibrations. While data-driven deep learning models have shown promise in aviation state monitoring, their inherent “black-box” nature fundamentally contradicts the stringent interpretability [...] Read more.
The Vortex Ring State (VRS) is a critical aerodynamic hazard for rotorcraft, characterized by highly unsteady fluid–structure interactions and severe low-frequency vibrations. While data-driven deep learning models have shown promise in aviation state monitoring, their inherent “black-box” nature fundamentally contradicts the stringent interpretability requirements of airworthiness certification. To address this, we propose an “AI for Science” paradigm, investigating whether advanced Vision Transformers (ViT) can autonomously discover underlying aerodynamic mechanisms without human physical priors. First, to ensure absolute data fidelity, flight test datasets of a coaxial unmanned aerial vehicle were rigorously labeled using cross-validation from high-fidelity Computational Fluid Dynamics (CFD) simulations and wind tunnel tests. One-dimensional vibration signals were then transformed into two-dimensional Continuous Wavelet Transform (CWT) spectrograms. By employing Target-Layer Gradient Adaptation (Grad-CAM) techniques, we conducted a systematic comparison between traditional Convolutional Neural Networks (ResNet50) and ViT. The results demonstrate that while CNNs suffer from diffuse attention caused by high-frequency noise, the frozen-backbone ViT model achieves a physically interpretable accuracy of 93.24%, while autonomously locking its global attention onto a perfectly horizontal feature band centered at 41.7 Hz. Crucially, this autonomously discovered feature precisely aligns with the theoretically derived once-per-revolution (1P) fundamental frequency of the rotor’s flap-lag coupling response under VRS aerodynamic turbulence. This research provides direct visual evidence bridging black-box AI decisions with classical fluid mechanics, proposing a “Mechanism-Guided Verification” framework that offers a trustworthy pathway for the future certification of AI in safety-critical aerospace systems. Full article
(This article belongs to the Special Issue Machine Learning for Aerodynamic Analysis and Optimization)
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19 pages, 5020 KB  
Article
Mesh-Agnostic Model for the Prediction of Transonic Flow Field of Supercritical Airfoils
by Runze Li, Yue Fu, Yufei Zhang and Haixin Chen
Aerospace 2026, 13(2), 117; https://doi.org/10.3390/aerospace13020117 - 24 Jan 2026
Viewed by 728
Abstract
Mesh-agnostic models have advantages in processing flow field data with various topologies and densities, and they can easily incorporate partial differential equations. Beyond physics-informed neural networks, mesh-agnostic models have been studied for data-driven predictions of simple flows. In this study, a data-driven mesh-agnostic [...] Read more.
Mesh-agnostic models have advantages in processing flow field data with various topologies and densities, and they can easily incorporate partial differential equations. Beyond physics-informed neural networks, mesh-agnostic models have been studied for data-driven predictions of simple flows. In this study, a data-driven mesh-agnostic model is proposed to predict the transonic flow field of various supercritical airfoils. The model consists of two subnetworks, i.e., ShapeNet and HyperNet. ShapeNet is an implicit neural representation used to predict spatial bases of the flow field. HyperNet is a simple neural network that determines the weights of these bases. The input of ShapeNet is extended to ensure accurate prediction for different airfoil geometries. To reduce overfitting while capturing shock waves and boundary layers, a multi-resolution ShapeNet combining two activation functions is proposed. Additionally, a physics-guided loss function is proposed to enhance accuracy. The proposed model is trained and tested on various supercritical airfoils under different free-stream conditions. Results show that the model can effectively utilize airfoil samples with different grid sizes and distributions, and it can accurately predict the shock wave and boundary layer velocity profile. The proposed mesh-agnostic model can be used as a decoder in any conventional models, contributing to their application in complex and three-dimensional geometries. Full article
(This article belongs to the Special Issue Machine Learning for Aerodynamic Analysis and Optimization)
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