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Aerospace
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Artificial Intelligence in Aeroacoustics for Aerospace Applications
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Artificial Intelligence in Aeroacoustics for Aerospace Applications

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

Deadline for manuscript submissions: 20 April 2025 | Viewed by 1214

Special Issue Editors

Dr. Alessandro Di Marco

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Guest Editor
Department of Civil, Computer Science, and Aeronautical Technologies Engineering, Università Degli Studi Roma Tre, 00146 Rome, Italy
Interests: aerodynamics; aeronautics; aeroacoustics; fluid mechanics; aerospace; turbulence; rotor noise
Special Issues, Collections and Topics in MDPI journals
Dr. Elisa De Paola

E-Mail Website
Guest Editor
Department of Civil, Computer Science and Aeronautical Technologies Engineering, Università Degli Studi Roma Tre, 00146 Rome, Italy
Interests: aerodynamics; aeronautics; aeroacoustics; fluid mechanics; aerospace; turbulence; rotor noise
Special Issues, Collections and Topics in MDPI journals
Dr. Luana Georgiana Stoica

E-Mail
Guest Editor
Department of Civil, Computer Science, and Aeronautical Technologies Engineering, Università Degli Studi Roma Tre, 00146 Rome, Italy
Interests: aerodynamics; aeronautics; aeroacoustics; fluid mechanics; aerospace; turbulence; rotor noise
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The recent progress in data-driven science and engineering is making an impact in the multidisciplinary field of aerospace. Artificial intelligence and machine learning have been introduced in the aerospace field for applications connected to reducing environmental impacts, including testing and evaluation, data analysis and interpretation, and aircraft modelling. These techniques are also employed to generate databases at a reduced cost, which are needed for the solution of optimization problems. With particular regard to aeroacoustics, artificial intelligence can help to address the issues of noise prediction, buffeting and fluid–structure interaction, aeroacoustic optimization, and noise localization.

This Special Issue welcomes papers on analytical, computational, or experimental studies contributing to the state of the art on the use of artificial intelligence in aeroacoustics, including topics such as:

  • New aircraft configurations aimed at reducing noise and pollution emissions;
  • Novel active or passive flow control techniques for noise reduction;
  • Numerical methods for aeroacoustic modeling;
  • Buffeting and wall pressure fluctuations;
  • Physical-informed neural networks applied to aeroacoustics.

Finally, we would like to thank Dr. Giorgia Capobianchi for her valuable work in assisting us with this Special Issue.

Dr. Alessandro Di Marco
Dr. Elisa De Paola
Dr. Luana Georgiana Stoica
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. 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

  • aeroacoustics
  • machine learning
  • artificial intelligence
  • noise reduction
  • noise prediction
  • noise localization
  • buffeting
  • wall pressure fluctuations

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Published Papers (1 paper)

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Research

39 pages, 18913 KiB  
Article
Application of Deep Learning Models to Predict Panel Flutter in Aerospace Structures
by Yi-Ren Wang and Yu-Han Ma
Aerospace 2024, 11(8), 677; https://doi.org/10.3390/aerospace11080677 - 16 Aug 2024
Viewed by 839
Abstract
This study investigates the application of deep learning models—specifically Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Long Short-Term Memory Neural Networks (LSTM-NN)—to predict panel flutter in aerospace structures. The goal is to improve the accuracy and efficiency of predicting aeroelastic behaviors [...] Read more.
This study investigates the application of deep learning models—specifically Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Long Short-Term Memory Neural Networks (LSTM-NN)—to predict panel flutter in aerospace structures. The goal is to improve the accuracy and efficiency of predicting aeroelastic behaviors under various flight conditions. Utilizing a supersonic flat plate as the main structure, the research integrates various flight conditions into the aeroelastic equation. The resulting structural vibration data create a large-scale database for training the models. The dataset, divided into training, validation, and test sets, includes input features such as panel aspect ratio, Mach number, air density, and decay rate. The study highlights the importance of selecting appropriate hidden layers, epochs, and neurons to avoid overfitting. While DNN, LSTM, and LSTM-NN all showed improved training with more neurons and layers, excessive numbers beyond a certain point led to diminished accuracy and overfitting. Performance-wise, the LSTM-NN model achieved the highest accuracy in classification tasks, effectively capturing sequential features and enhancing classification precision. Conversely, LSTM excelled in regression tasks, adeptly handling long-term dependencies and complex non-linear relationships, making it ideal for predicting flutter Mach numbers. Despite LSTM’s higher accuracy, it required longer training times due to increased computational complexity, necessitating a balance between accuracy and training duration. The findings demonstrate that deep learning, particularly LSTM-NN, is highly effective in predicting panel flutter, showcasing its potential for broader aerospace engineering applications. By optimizing model architecture and training processes, deep learning models can achieve high accuracy in predicting critical aeroelastic phenomena, contributing to safer and more efficient aerospace designs. Full article
(This article belongs to the Special Issue Artificial Intelligence in Aeroacoustics for Aerospace Applications)
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