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Aerospace
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Recent Research on UAM/AAM Aircraft and Systems: Modeling, Advanced Control,...
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Recent Research on UAM/AAM Aircraft and Systems: Modeling, Advanced Control, and Emerging Technologies

A special issue of Aerospace (ISSN 2226-4310).

Deadline for manuscript submissions: 31 October 2024 | Viewed by 4909

Special Issue Editors

Prof. Dr. Shu-Guang Zhang

E-Mail Website
Guest Editor
School of Transportation Science and Engineering, Beihang University, Beijing 102206, China
Interests: green transportation; electric aircraft; flight dynamics and control; safety
Special Issues, Collections and Topics in MDPI journals
Dr. Mingkai Wang

E-Mail Website
Guest Editor
School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: electric aircraft; optimization; optimal Control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the emergence of electric propulsive technologies in the past decade, electric vertical takeoff and landing (eVTOL) aircraft have gained an increased interest, which paves the path for sustainable urban air mobility (UAM) and advanced air mobility (AAM). eVTOL aircraft are characterized by distributed electric propulsion, which enables them to have high safety redundancies and zero-emission, thus providing access to convenient UAM/AAM. Nevertheless, challenges also arise from the implementation of UAM/AAM aircraft, which include, but are not limited to:

(1) Efficient operational strategy of sustainable UAM/AAM. UAM/AAM missions with complex airspace and strict safety requirements necessitate the use of eVTOL aircraft. It is of particular interest to investigate operational strategies that guarantee the safety level while reducing noise and saving energy.

(2) Multi-physical nature of eVTOL aircraft. The electric aircraft is governed by electric propulsive constraints that stem from power conservation among energy supply units (lithium-ion batteries, fuel cells, hydrogen cells, and solar cells), motors, and propellers/fans. The complex effects need to be reflected in the simulation model with suitable fidelity, which, in turn, serves as the cornerstone for improving operational efficiency.

(3) Practical energy management strategy. The endurance of eVTOL aircraft is significantly limited by state-of-the-art energy supply technology. Hence, there is an urgent need to provide an efficient energy management strategy to enlarge the coverage of vehicles, which calls for the integration of advanced algorithms into UAM/AAM systems.

(4) Energy-optimal scheduling of the UAM/AAM fleet. Unlike the current operational mode of airspace, which presets allowable routines for agents, the flexible use of airspace is applied. Specifically, FUA assumes airspace as a “continuum” where all the user requirements are satisfied to the greatest possible extent. Thus, it is of great importance to schedule the whole UAM/AAM fleet in one airspace with energy-optimal and safe operational routines.

(5)  Advanced and intelligent control laws are facilitating future applications. The envisioned widespread application of eVTOL aircraft necessitates more advanced and intelligent control laws that help reduce training costs and ease the burden on the pilot. The artificial intelligence (AI)-based control technique brings great potential for convenient aircraft operation. The key technology provides suggestions for flight management, making human pilots the center of the complex decision-making process.

Original research articles and reviews are welcome in this Special Issue. Research areas may include (but are not limited to) the following:

  • Development of operational strategies for sustainable air transportation;
  • Development of simulation models for UAM/AAM aircraft and systems;
  • Development of energy management strategy and energy-optimal control methods for eVTOL agents driven by various types of energy supply units;
  • Advanced and intelligent control law design facilitating future applications;
  • Air traffic management strategy that maximizes the UAM/AAM system efficiency;
  • Other innovative technologies and progress applied to UAM/AAM.

We look forward to receiving your contributions.

You may choose our Joint Special Issue in Sustainability.

Prof. Dr. Shu-Guang Zhang
Dr. Mingkai Wang
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

  • green air mobility
  • sustainable air transportation
  • urban air traffic
  • advanced air mobility
  • electric propulsion
  • vertical takeoff and landing
  • sustainable energy

Published Papers (5 papers)

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Research

20 pages, 26621 KiB  
Article
Numerical Simulation on Aerodynamic Characteristics of Transition Section of Tilt-Wing Aircraft
by Qingjin Huang, Guoyi He, Jike Jia, Zhile Hong and Feng Yu
Aerospace 2024, 11(4), 283; https://doi.org/10.3390/aerospace11040283 - 06 Apr 2024
Viewed by 507
Abstract
The tilt-wing aircraft has attracted widespread attention due to its excellent performance. However, its aerodynamic characteristics during the tilt transition section are characterized by unsteadiness, nonlinearity, and strong coupling, making it difficult to control. Using computational fluid dynamics (CFD) methods and moving overset [...] Read more.
The tilt-wing aircraft has attracted widespread attention due to its excellent performance. However, its aerodynamic characteristics during the tilt transition section are characterized by unsteadiness, nonlinearity, and strong coupling, making it difficult to control. Using computational fluid dynamics (CFD) methods and moving overset grids to control the tilt-wing motion, the momentum source method is employed to replace actual propellers. The influence of the propeller on the aerodynamic characteristics of the tiltrotor at different tilt angles is investigated under incoming flow velocities of 8 m/s and 45 m/s in steady conditions. Additionally, the differences between steady and unsteady calculations of the tilt transition section are investigated at incoming flow velocities of 8 m/s, 15 m/s, 30 m/s, and 45 m/s in unsteady conditions. The research results indicate the following information: 1. the slipstream from the propellers significantly enhances the lift, drag, and stall angle of attack of the tilt-wing aircraft but reduces the lift-to-drag ratio; 2. there are noticeable differences in the forces acting on the tilt-wing aircraft between steady calculations with fixed tilt angles and unsteady calculations with continuous tilting. Full article
(This article belongs to the Special Issue Recent Research on UAM/AAM Aircraft and Systems: Modeling, Advanced Control, and Emerging Technologies)
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31 pages, 7140 KiB  
Article
YOLOTransfer-DT: An Operational Digital Twin Framework with Deep and Transfer Learning for Collision Detection and Situation Awareness in Urban Aerial Mobility
by Nan Lao Ywet, Aye Aye Maw, Tuan Anh Nguyen and Jae-Woo Lee
Aerospace 2024, 11(3), 179; https://doi.org/10.3390/aerospace11030179 - 23 Feb 2024
Cited by 1 | Viewed by 935
Abstract
Urban Air Mobility (UAM) emerges as a transformative approach to address urban congestion and pollution, offering efficient and sustainable transportation for people and goods. Central to UAM is the Operational Digital Twin (ODT), which plays a crucial role in real-time management of air [...] Read more.
Urban Air Mobility (UAM) emerges as a transformative approach to address urban congestion and pollution, offering efficient and sustainable transportation for people and goods. Central to UAM is the Operational Digital Twin (ODT), which plays a crucial role in real-time management of air traffic, enhancing safety and efficiency. This study introduces a YOLOTransfer-DT framework specifically designed for Artificial Intelligence (AI) training in simulated environments, focusing on its utility for experiential learning in realistic scenarios. The framework’s objective is to augment AI training, particularly in developing an object detection system that employs visual tasks for proactive conflict identification and mission support, leveraging deep and transfer learning techniques. The proposed methodology combines real-time detection, transfer learning, and a novel mix-up process for environmental data extraction, tested rigorously in realistic simulations. Findings validate the use of existing deep learning models for real-time object recognition in similar conditions. This research underscores the value of the ODT framework in bridging the gap between virtual and actual environments, highlighting the safety and cost-effectiveness of virtual testing. This adaptable framework facilitates extensive experimentation and training, demonstrating its potential as a foundation for advanced detection techniques in UAM. Full article
(This article belongs to the Special Issue Recent Research on UAM/AAM Aircraft and Systems: Modeling, Advanced Control, and Emerging Technologies)
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16 pages, 7604 KiB  
Article
Objective Detection of Trust in Automated Urban Air Mobility: A Deep Learning-Based ERP Analysis
by Yuhan Li, Shuguang Zhang, Ruichen He and Florian Holzapfel
Aerospace 2024, 11(3), 174; https://doi.org/10.3390/aerospace11030174 - 21 Feb 2024
Viewed by 952
Abstract
Urban Air Mobility (UAM) has emerged in response to increasing traffic demands. As UAM involves commercial flights in complex urban areas, well-established automation technologies are critical to ensure a safe, accessible, and reliable flight. However, the current level of acceptance of automation is [...] Read more.
Urban Air Mobility (UAM) has emerged in response to increasing traffic demands. As UAM involves commercial flights in complex urban areas, well-established automation technologies are critical to ensure a safe, accessible, and reliable flight. However, the current level of acceptance of automation is insufficient. Therefore, this study sought to objectively detect the degree of human trust toward UAM automation. Electroencephalography (EEG) signals, specifically Event-Related Potentials (ERP), were employed to analyze and detect operators’ trust towards automated UAM, providing insights into cognitive processes related to trust. A two-dimensional convolutional neural network integrated with an attention mechanism (2D-ACNN) was also established to enable the end-to-end detection of trust through EEG signals. The results revealed that our proposed 2D-ACNN outperformed other state-of-the-art methods. This work contributes to enhancing the trustworthiness and popularity of UAM automation, which is essential for the widespread adoption and advances in the UAM domain. Full article
(This article belongs to the Special Issue Recent Research on UAM/AAM Aircraft and Systems: Modeling, Advanced Control, and Emerging Technologies)
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20 pages, 5943 KiB  
Article
Conceptual Design of Layered Distributed Propulsion System to Improve Power-Saving Benefit of Boundary-Layer Ingestion
by Zhiping Li, Yujiang Lu and Tianyu Pan
Aerospace 2024, 11(2), 141; https://doi.org/10.3390/aerospace11020141 - 08 Feb 2024
Viewed by 870
Abstract
DPS (distributed propulsion system) utilizing BLI (boundary-layer ingestion) has shown great potential for reducing the power consumption of sustainable AAM (advanced air mobility), such as BWB (blended-wing body) aircraft. However, the ingesting boundary layer makes it easier for flow separation to occur within [...] Read more.
DPS (distributed propulsion system) utilizing BLI (boundary-layer ingestion) has shown great potential for reducing the power consumption of sustainable AAM (advanced air mobility), such as BWB (blended-wing body) aircraft. However, the ingesting boundary layer makes it easier for flow separation to occur within the S-shaped duct, and the consequent distortion due to flow separation can dramatically reduce the aerodynamic performance of the fan, which offsets the power-saving benefit of BLI. By analyzing the source of power saving and power loss of BLI, this paper presents the LDPS (layered distributed propulsion system) concept, in which the freestream flow and boundary-layer flow are ingested separately to improve the power-saving benefit of BLI. In order to preliminarily verify the feasibility of LDPS, an existing DPS is modified. The design parameters and the system performances of LDPS are studied using a 1D engine model. The results show that there is an optimal ratio of the FPR (fan pressure ratio) for the FSE (freestream engine) to the BLE (boundary-layer engine) that maximizes the PSC (power-saving coefficient) of LDPS. This optimal ratio of FPR for the two fans can be obtained when the exit velocities of FSE and BLE are the same. Under the optimal ratio of FPR for the two fans, the PSC of LDPS is improved by 5.83% compared to conventional DPS. Full article
(This article belongs to the Special Issue Recent Research on UAM/AAM Aircraft and Systems: Modeling, Advanced Control, and Emerging Technologies)
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20 pages, 4943 KiB  
Article
Multi-Phase Vertical Take-Off and Landing Trajectory Optimization with Feasible Initial Guesses
by Zhidong Lu, Haichao Hong and Florian Holzapfel
Aerospace 2024, 11(1), 39; https://doi.org/10.3390/aerospace11010039 - 29 Dec 2023
Viewed by 1188
Abstract
The advancement of electric vertical take-off and landing (eVTOL) aircraft has expanded the horizon of urban air mobility. However, the challenge of generating precise vertical take-off and landing (VTOL) trajectories that comply with airworthiness requirements remains. This paper presents an approach for optimizing [...] Read more.
The advancement of electric vertical take-off and landing (eVTOL) aircraft has expanded the horizon of urban air mobility. However, the challenge of generating precise vertical take-off and landing (VTOL) trajectories that comply with airworthiness requirements remains. This paper presents an approach for optimizing VTOL trajectories considering six degrees of freedom (6DOF) dynamics and operational constraints. Multi-phase optimal control problems are formulated to address specific constraints in various flight stages. The incremental nonlinear dynamic inversion (INDI) controller is employed to execute the flight mission in each phase. Controlled flight simulations yield dynamically feasible trajectories that serve as initial guesses for generating sub-optimal trajectories within individual phases. A feasible and sub-optimal initial guess for the holistic multi-phase problem is established by concatenating these single-phase trajectories. Focusing on a tilt-wing eVTOL aircraft, this paper computes VTOL trajectories leveraging the proposed initial guess generation procedure. These trajectories account for complex flight dynamics, align with various operation constraints, and minimize electric energy consumption. Full article
(This article belongs to the Special Issue Recent Research on UAM/AAM Aircraft and Systems: Modeling, Advanced Control, and Emerging Technologies)
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