Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.4
2.1
Journals
Aerospace
Special Issues
Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace...
share announcement

Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering (Volume II)

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 10728

Special Issue Editor

Dr. Jules Simo

E-Mail Website
Guest Editor
School of Engineering, University of Central Lancashire, Preston PR1 2HE, UK
Interests: aerospace engineering; multidisciplinary optimization; machine learning; data science; artificial intelligence; space robotics; UAVs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multidisciplinary Optimization (MDO) and Artificial Intelligence (AI)/Machine Learning (ML) play an increasingly important role in aerospace applications. All this is particularly true in space and modern aerospace engineering, where a variety of technological opportunities have arisen, each requiring novel approaches and algorithms to address the corresponding technological challenges. The aerospace industry presents several unique opportunities and challenges for the integration of data intensive analysis techniques. These methods hold the promise of bringing new possibilities to the development of new robotic platform designs, multisensory navigation and space exploration approaches to previously unsolvable problems. With the advancement in AI, robotics and unmanned aerial vehicle (UAV) technology, ML has emerged as a viable technology for solving constrained multi-objective optimization problems (CMOPs). Aided by advances in hardware and algorithms, modern ML is poised to enable this optimization, allowing a much broader and integrated perspective.

The goal of this Special Issue is to illustrate applications of Multidisciplinary Optimization, Machine Learning and Artificial Intelligence methods to problems in Aerospace Engineering. Novel ML/AI algorithms and/or application of existing approaches to problems involving space exploration, UAV operations, space robotics and other fields of aerospace engineering will be examined.

Topics of interest include but are not limited to:

  • Machine Learning and Artificial Intelligence for Space and Aerospace Applications;
  • Multi-objective Optimization for Space and Aerospace Applications;
  • Autonomous Agents and Multiagent Systems in Aerospace Applications;
  • UAV Trajectory Optimization Using Machine Learning;
  • Energy-efficient UAV Communications;
  • Robotics, Perception and Vision in Aerospace Applications;
  • Intelligent Control for Space and Aerospace Systems;
  • Big Data, Machine Learning and Data Mining in Aerospace Applications;
  • Planning and Scheduling for Autonomous Systems;
  • Data Processing and Satellite Applications;
  • Bio-inspired Solutions for System Design and Control;
  • AI for Air and Space Traffic Management and Operations.

Dr. Jules Simo
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

  • aerospace engineering
  • multidisciplinary optimization
  • artificial intelligence
  • machine learning
  • data science
  • UAVs

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Related Special Issue

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

31 pages, 5634 KiB  
Article
Advanced UAV Design Optimization Through Deep Learning-Based Surrogate Models
by Hasan Karali, Gokhan Inalhan and Antonios Tsourdos
Aerospace 2024, 11(8), 669; https://doi.org/10.3390/aerospace11080669 - 14 Aug 2024
Viewed by 2657
Abstract
The conceptual design of unmanned aerial vehicles (UAVs) presents significant multidisciplinary challenges requiring the optimization of aerodynamic and structural performance, stealth, and propulsion efficiency. This work addresses these challenges by integrating deep neural networks with a multiobjective genetic algorithm to optimize UAV configurations. [...] Read more.
The conceptual design of unmanned aerial vehicles (UAVs) presents significant multidisciplinary challenges requiring the optimization of aerodynamic and structural performance, stealth, and propulsion efficiency. This work addresses these challenges by integrating deep neural networks with a multiobjective genetic algorithm to optimize UAV configurations. The proposed framework enables a comprehensive evaluation of design alternatives by estimating key performance metrics required for different operational requirements. The design process resulted in a significant improvement in computational time over traditional methods by more than three orders of magnitude. The findings illustrate the framework’s capability to optimize UAV designs for a variety of mission scenarios, including specialized tasks such as intelligence, surveillance, and reconnaissance (ISR), combat air patrol (CAP), and Suppression of Enemy Air Defenses (SEAD). This flexibility and adaptability was demonstrated through a case study, showcasing the method’s effectiveness in tailoring UAV configurations to meet specific operational requirements while balancing trade-offs between aerodynamic efficiency, stealth, and structural weight. Additionally, these results underscore the transformative impact of integrating AI into the early stages of the design process, facilitating rapid prototyping and innovation in aerospace engineering. Consequently, the current work demonstrates the potential of AI-driven optimization to revolutionize UAV design by providing a robust and effective tool for solving complex engineering problems. Full article
(This article belongs to the Special Issue Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering (Volume II))
►▼ Show Figures

Figure 1

18 pages, 4812 KiB  
Article
On the Exploration of Temporal Fusion Transformers for Anomaly Detection with Multivariate Aviation Time-Series Data
by Bulent Ayhan, Erik P. Vargo and Huang Tang
Aerospace 2024, 11(8), 646; https://doi.org/10.3390/aerospace11080646 - 9 Aug 2024
Viewed by 1447
Abstract
In this work, we explored the feasibility of using a transformer-based time-series forecasting architecture, known as the Temporal Fusion Transformer (TFT), for anomaly detection using threaded track data from the MITRE Corporation’s Transportation Data Platform (TDP) and digital flight data. The TFT architecture [...] Read more.
In this work, we explored the feasibility of using a transformer-based time-series forecasting architecture, known as the Temporal Fusion Transformer (TFT), for anomaly detection using threaded track data from the MITRE Corporation’s Transportation Data Platform (TDP) and digital flight data. The TFT architecture has the flexibility to include both time-varying multivariate data and categorical data from multimodal data sources and conduct single-output or multi-output predictions. For anomaly detection, rather than training a TFT model to predict the outcomes of specific aviation safety events, we train a TFT model to learn nominal behavior. Any significant deviation of the TFT model’s future horizon forecast for the output flight parameters of interest from the observed time-series data is considered an anomaly when conducting evaluations. For proof-of-concept demonstrations, we used an unstable approach (UA) as the anomaly event. This type of anomaly detection approach with nominal behavior learning can be used to develop flight analytics to identify emerging safety hazards in historical flight data and has the potential to be used as an on-board early warning system to assist pilots during flight. Full article
(This article belongs to the Special Issue Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering (Volume II))
►▼ Show Figures

Figure 1

13 pages, 2651 KiB  
Article
Speech Recognition for Air Traffic Control Utilizing a Multi-Head State-Space Model and Transfer Learning
by Haijun Liang, Hanwen Chang and Jianguo Kong
Aerospace 2024, 11(5), 390; https://doi.org/10.3390/aerospace11050390 - 14 May 2024
Viewed by 1132
Abstract
In the present study, a novel end-to-end automatic speech recognition (ASR) framework, namely, ResNeXt-Mssm-CTC, has been developed for air traffic control (ATC) systems. This framework is built upon the Multi-Head State-Space Model (Mssm) and incorporates transfer learning techniques. Residual Networks with Cardinality (ResNeXt) [...] Read more.
In the present study, a novel end-to-end automatic speech recognition (ASR) framework, namely, ResNeXt-Mssm-CTC, has been developed for air traffic control (ATC) systems. This framework is built upon the Multi-Head State-Space Model (Mssm) and incorporates transfer learning techniques. Residual Networks with Cardinality (ResNeXt) employ multi-layered convolutions with residual connections to augment the extraction of intricate feature representations from speech signals. The Mssm is endowed with specialized gating mechanisms, which incorporate parallel heads that acquire knowledge of both local and global temporal dynamics in sequence data. Connectionist temporal classification (CTC) is utilized in the context of sequence labeling, eliminating the requirement for forced alignment and accommodating labels of varying lengths. Moreover, the utilization of transfer learning has been shown to improve performance on the target task by leveraging knowledge acquired from a source task. The experimental results indicate that the model proposed in this study exhibits superior performance compared to other baseline models. Specifically, when pretrained on the Aishell corpus, the model achieves a minimum character error rate (CER) of 7.2% and 8.3%. Furthermore, when applied to the ATC corpus, the CER is reduced to 5.5% and 6.7%. Full article
(This article belongs to the Special Issue Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering (Volume II))
►▼ Show Figures

Figure 1

19 pages, 14886 KiB  
Article
Bayesian Optimization for Fine-Tuning EKF Parameters in UAV Attitude and Heading Reference System Estimation
by Assefinew Wondosen, Yisak Debele, Seung-Ki Kim, Ha-Young Shi, Bedada Endale and Beom-Soo Kang
Aerospace 2023, 10(12), 1023; https://doi.org/10.3390/aerospace10121023 - 9 Dec 2023
Cited by 2 | Viewed by 2401
Abstract
In various applications, the extended Kalman filter (EKF) has been vital in estimating a vehicle’s translational and angular motion in 3-dimensional (3D) space. It is also essential for the fusion of data from multiple sensors. However, for the EKF to perform effectively, the [...] Read more.
In various applications, the extended Kalman filter (EKF) has been vital in estimating a vehicle’s translational and angular motion in 3-dimensional (3D) space. It is also essential for the fusion of data from multiple sensors. However, for the EKF to perform effectively, the optimal process noise covariance matrix (Q) and measurement noise covariance matrix (R) must be chosen correctly. The use of EKF has been challenging due to the need for an easy mechanism to select Q and R values. As a result, this research focused on developing an algorithm that can be easily applied to determine Q and R, allowing us to harness the full potential of EKF. Accordingly, an EKF innovation consistency statistics-driven Bayesian optimization algorithm was employed to achieve this goal. Q and R values were tuned until the expected result met the performance requirement for minimum error through improved measurement innovation consistency. The comprehensive results demonstrate that when the optimum Q and R, as tuned by the suggested technique, were used, the performance of the EKF significantly improved. Full article
(This article belongs to the Special Issue Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering (Volume II))
►▼ Show Figures

Figure 1

19 pages, 4287 KiB  
Article
A Study on the Optimal Design Method for Star-Shaped Solid Propellants through a Combination of Genetic Algorithm and Machine Learning
by Seok-Hwan Oh, Tae-Seong Roh and Hyoung Jin Lee
Aerospace 2023, 10(12), 979; https://doi.org/10.3390/aerospace10120979 - 22 Nov 2023
Viewed by 1841
Abstract
This study was focused on the configuration design of a star grain by using machine learning in the optimal design process. The key to optimizing the grain design is aimed at obtaining a set of configuration variables that satisfy the requirements. The optimization [...] Read more.
This study was focused on the configuration design of a star grain by using machine learning in the optimal design process. The key to optimizing the grain design is aimed at obtaining a set of configuration variables that satisfy the requirements. The optimization problem consists of an objective area profile subject to certain constraints and an objective function that quantitatively calculates the design level. Designers must formulate suitable optimization problems to achieve an optimal design. However, because a method to alleviate the influence of the sliver section is not yet available, the optimization problem is typically solved based on experience, which is time- and effort-intensive. Consequently, a more practical and objective grain design method must be developed. In this study, an optimal design method using machine learning was developed to increase the convenience and success rate. A support vector machine was used to train a classification model that predicts a class. The classification model was used to alleviate the influence of the sliver zone and correct the search problem to ensure that an optimal solution existed in the region satisfying the requirements. The proposed method was validated through star grain optimal design using the genetic algorithm. The optimization was performed considering the area profiles, and the effectiveness of the proposed method was demonstrated by the enhanced accuracy. Full article
(This article belongs to the Special Issue Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering (Volume II))
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop