Advanced Model Optimization and Data Fusion Methods in Aircraft

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 595

Special Issue Editors


E-Mail Website
Guest Editor
Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China
Interests: aircraft design; aero-engine; optimization; flow control; computational fluid dynamics; prognostic and health management; artificial intelligence; data-driven model; digital twin

E-Mail Website
Guest Editor Assistant
Department of Aeronautics & Astronautics, Fudan University, Shanghai 200433, China
Interests: optimization design; flow control; biomimetic surface structure; data-driven model

Special Issue Information

Dear Colleagues,

Advanced model optimization and data fusion methods play an increasingly important role in aircraft applications. Recent work has demonstrated the effectiveness of advanced models in the analysis, prediction, and optimization design of aircraft. The present Special Issue, titled “Advanced Model Optimization and Data Fusion Methods in Aircraft”, focuses on topics related to the application of machine learning, deep learning, data fusion methods, and other emerging data-driven techniques to support and improve the development of aircraft applications. Authors are invited to submit full research articles or review manuscripts addressing (but not limited to) the following topics:

  • Application of advanced model in aircraft shape optimization;
  • Application of advanced model in flow mechanism analysis;
  • Big data, machine learning, and data mining in aircraft applications;
  • Application of data-driven model in numerical simulation and performance prediction;
  • Digital twin method in aircraft applications;
  • Intelligent flow control;
  • Application of data fusion methods in aerodynamic analysis;
  • Application of advanced model in prognostic and health management.

The focal topics listed above are not meant to exclude articles from additional related areas. We are looking forward to receiving your submissions and invite you to contact the Guest Editor should you have further questions.

Prof. Dr. Gang Sun
Guest Editor

Dr. Liyue Wang
Guest Editor Assistant

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. Mathematics 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 2600 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

  • advanced model
  • optimization
  • aircraft
  • aircraft design
  • data-driven method
  • data fusion
  • digital twin

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Review

21 pages, 2731 KiB  
Review
A Review of Intelligent Airfoil Aerodynamic Optimization Methods Based on Data-Driven Advanced Models
by Liyue Wang, Haochen Zhang, Cong Wang, Jun Tao, Xinyue Lan, Gang Sun and Jinzhang Feng
Mathematics 2024, 12(10), 1417; https://doi.org/10.3390/math12101417 - 7 May 2024
Viewed by 325
Abstract
With the rapid development of artificial intelligence technology, data-driven advanced models have provided new ideas and means for airfoil aerodynamic optimization. As the advanced models update and iterate, many useful explorations and attempts have been made by researchers on the integrated application of [...] Read more.
With the rapid development of artificial intelligence technology, data-driven advanced models have provided new ideas and means for airfoil aerodynamic optimization. As the advanced models update and iterate, many useful explorations and attempts have been made by researchers on the integrated application of artificial intelligence and airfoil aerodynamic optimization. In this paper, many critical aerodynamic optimization steps where data-driven advanced models are employed are reviewed. These steps include geometric parameterization, aerodynamic solving and performance evaluation, and model optimization. In this way, the improvements in the airfoil aerodynamic optimization area led by data-driven advanced models are introduced. These improvements involve more accurate global description of airfoil, faster prediction of aerodynamic performance, and more intelligent optimization modeling. Finally, the challenges and prospect of applying data-driven advanced models to aerodynamic optimization are discussed. Full article
(This article belongs to the Special Issue Advanced Model Optimization and Data Fusion Methods in Aircraft)
Show Figures

Figure 1

Back to TopTop