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Sustainable Development and Application of Aerospace Engineering
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Sustainable Development and Application of Aerospace Engineering

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 7565

Special Issue Editors

Dr. Feng Lu

E-Mail Website
Guest Editor
College of Energy and Power Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China
Interests: aeroengine modelling; control; prediction and fault diagnosis
Dr. Xin Zhou

E-Mail Website
Guest Editor
College of Energy and Power Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China
Interests: aero-engine; intelligent control; fault-tolerant control; direct performance control; fault diagnosis
Dr. Jing Cai

E-Mail Website
Guest Editor
College of Aviation, Nanjing University of Aeronautics & Astronautics, 29 Yudao Street, Nanjing 210016, China
Interests: aerospace engineering; condition maintainance; fault-tolerant control; direct performance control; fault diagnosis

Special Issue Information

Dear Colleagues,

Sustainable Development and Application of Aerospace Engineering is a Special Issue of Sustainability, which publishes original papers and review articles related to all fields of aerospace research, fundamental and applied, the potential applications of which are clearly related to: intelligent modeling, fault-tolerant control, condition detection, health monitoring, fault diagnosis and performance prediction.

It provides an advanced forum for studies related to sustainability and the sustainable development of aerospace engineering. Authors from a wide range of countries are invited to submit papers on the scientific and technological achievements and frontiers in aeronautic engineering and astronautic engineering, in both theory and practice.

The Special Issue lays a theoretical and technical foundation for the sustainable development of the aeronautics and astronautics field by revealing the new methods, new principles, and new concepts, furthering breaking through the bottle neck of the sustainable development aerospace science and expanding the Sustainability in aerospace engineering.

Dr. Feng Lu
Dr. Xin Zhou
Dr. Jing Cai
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. Sustainability 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 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

  • sustainability
  • condition detection
  • health monitoring
  • intelligent modeling
  • fault-tolerant control
  • performance prediction
  • direct performance control
  • fault diagnosis

Published Papers (6 papers)

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Research

18 pages, 4459 KiB  
Article
Robust Gas-Path Fault Diagnosis with Sliding Mode Applied in Aero-Engine Distributed Control System
by Xiaodong Chang and Xiaojie Qiu
Sustainability 2023, 15(13), 10278; https://0-doi-org.brum.beds.ac.uk/10.3390/su151310278 - 29 Jun 2023
Viewed by 742
Abstract
The technology of aero-engine gas-path fault diagnosis is an important way to improve flight safety and reliability and reduce maintenance costs. With the maturity of the new-generation engine distributed control system (DCS), uncertainties such as bus packet loss, time delay, and node function [...] Read more.
The technology of aero-engine gas-path fault diagnosis is an important way to improve flight safety and reliability and reduce maintenance costs. With the maturity of the new-generation engine distributed control system (DCS), uncertainties such as bus packet loss, time delay, and node function degradation have increasingly highlighted new challenges to engine fault diagnosis. At present, linear Kalman filter (LKF) is widely researched and used in engine fault detection and isolation (FDI), but its robustness has proved to be not strong. However, the sliding mode observer (SMO) is not only capable of fault reconstruction but also robust to system uncertainties and disturbances due to its unique discontinuous switching term, which tends to be an effective way to achieve robust fault diagnosis for aero engines and DCS with many uncertainties. This paper initially develops a distributed bus packaging model that supports time-delay and packet-loss simulating and timing planning based on SimEvents, providing a basis for the model-based design and verification. Then the SMO is adopted to design a robust gas-path diagnosis method for engine DCS, and the robust observing accuracy is improved by combining high-order sliding mode theory, LMI optimized observation matrix, and variable gain. The simulation results show the effectiveness and advantages in engine DCS application scenarios. Full article
(This article belongs to the Special Issue Sustainable Development and Application of Aerospace Engineering)
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25 pages, 7762 KiB  
Article
A Novel Adaptive Generation Method for Initial Guess Values of Component-Level Aero-Engine Start-Up Models
by Wenxiang Zhou, Sangwei Lu, Wenjie Kai, Jichang Wu, Chenyang Zhang and Feng Lu
Sustainability 2023, 15(4), 3468; https://0-doi-org.brum.beds.ac.uk/10.3390/su15043468 - 14 Feb 2023
Viewed by 1057
Abstract
To solve the difficult problem of selecting initial guess values for component-level aero-engine start-up models, a novel method based on the flow-based back-calculation algorithm (FBBCA) is investigated. By exploiting the monotonic feature of low-speed aero-engine component characteristics and the principle of flow balance [...] Read more.
To solve the difficult problem of selecting initial guess values for component-level aero-engine start-up models, a novel method based on the flow-based back-calculation algorithm (FBBCA) is investigated. By exploiting the monotonic feature of low-speed aero-engine component characteristics and the principle of flow balance abided by components in the start-up process, this method traverses all the flows in each component characteristic at a given engine rotor speed. This method also limits the pressure ratios and flow rates of each component, along with the surplus power of the high-pressure rotor. Finally, a set of “fake initial values” for iterative calculation of the aero-engine start-up model can be generated and approximate true initial guess values that meet the accuracy requirement according to the Newton–Raphson iteration method. Extensive simulation verifies the low computational cost and high computational accuracy of this method as a solver for the initial guess values of the aero-engine start-up model. Full article
(This article belongs to the Special Issue Sustainable Development and Application of Aerospace Engineering)
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18 pages, 4306 KiB  
Article
A Hybrid Degradation Evaluation Model for Aero-Engines
by Likun Ren, Haiqin Qin, Na Cai, Bianjiang Li and Zhenbo Xie
Sustainability 2023, 15(1), 29; https://0-doi-org.brum.beds.ac.uk/10.3390/su15010029 - 20 Dec 2022
Cited by 2 | Viewed by 1054
Abstract
The non-convergence and low efficiency of the thermodynamic model make them difficult to be used in the aero-engines degradation evaluation, while the negligence of the thermodynamics process of data-driven degradation evaluation methods makes them inaccurate and hard to analyze the actual degradation of [...] Read more.
The non-convergence and low efficiency of the thermodynamic model make them difficult to be used in the aero-engines degradation evaluation, while the negligence of the thermodynamics process of data-driven degradation evaluation methods makes them inaccurate and hard to analyze the actual degradation of air path components. So, we propose a thermodynamic-based and data-driven hybrid model for aero-engine degradation evaluation. Different from thermodynamic-based methods, the iteration calculation is converted to the forward flow in the proposed neural network, thus improving convergence. Moreover, a multi-objective loss function considering the components co-operation process and fusion training process fully taking advantage of simulation and degradation trajectory datasets are proposed to improve the degradation evaluation accuracy. The test case is carried out on NASA’s benchmark for aero-engine degradation evaluation. The result shows that the proposed method can improve the accuracy significantly, which suggests its effectiveness. Full article
(This article belongs to the Special Issue Sustainable Development and Application of Aerospace Engineering)
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12 pages, 4952 KiB  
Article
Performance Degradation Evaluation of Low Bypass Ratio Turbofan Engine Based on Flight Data
by Haiqin Qin, Jie Zhao, Likun Ren, Bianjiang Li and Zhengguang Li
Sustainability 2022, 14(13), 8052; https://0-doi-org.brum.beds.ac.uk/10.3390/su14138052 - 01 Jul 2022
Cited by 1 | Viewed by 1269
Abstract
A low bypass ratio turbofan engine operates in a hostile environment, resulting in performance degradation. This seriously affects the security and reliability of the engine. Therefore, a performance degradation evaluation method for engines based on flight data is proposed. The method expands the [...] Read more.
A low bypass ratio turbofan engine operates in a hostile environment, resulting in performance degradation. This seriously affects the security and reliability of the engine. Therefore, a performance degradation evaluation method for engines based on flight data is proposed. The method expands the equation system to solve the underdetermined problem caused by the lack of engine sensors based on multiple operating point analysis. The improved evolution algorithm is employed to solve the equation system, which relieves the problem of insufficient precision. The engine performance degradation dataset is established based on the engine performance calculation model to verify the reliability of the degradation evaluation method. The results show that the method is applicable to the dataset. Finally, the method is applied to the actual flight data to study the law of the performance degradation of the researched engine, which indicates that the engine’s fan efficiency and high-pressure compressor flow capacity have an apparent downward trend over time. Full article
(This article belongs to the Special Issue Sustainable Development and Application of Aerospace Engineering)
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15 pages, 1569 KiB  
Article
A Thermodynamics-Oriented and Neural Network-Based Hybrid Model for Military Turbofan Engines
by Likun Ren, Haiqin Qin, Zhenbo Xie, Jing Xie and Bianjiang Li
Sustainability 2022, 14(10), 6373; https://0-doi-org.brum.beds.ac.uk/10.3390/su14106373 - 23 May 2022
Cited by 1 | Viewed by 1307
Abstract
Traditional thermodynamic models for military turbofans suffer from non-convergence and inaccuracy due to inaccuracy of the component maps and the instability of the iterative process. To address these problems, a thermodynamically oriented and neural network-based hybrid model for military turbofans is proposed. Different [...] Read more.
Traditional thermodynamic models for military turbofans suffer from non-convergence and inaccuracy due to inaccuracy of the component maps and the instability of the iterative process. To address these problems, a thermodynamically oriented and neural network-based hybrid model for military turbofans is proposed. Different from iteration-based thermodynamic models, the proposed hybrid model transforms the iteration process into a multi-objective optimization and training process for a component-level neural network in order to improve convergence and modeling accuracy. The experiment shows that the accuracy of the proposed hybrid model can reach about 7%, 5% better than the map-fitting-based thermodynamic model and 8% better than the purely data-driven method, with a similar number of network neutrons, verifying its effectiveness. The contributions of this work mainly lie in the following aspects: a new component-level neural network structure is proposed to improve convergence and computational efficiency; a multi-objective loss function based on component co-working is proposed to direct the model to converge toward the physical thermodynamic process; a fusion training method of multiple data sources is established to train the model with good convergence and high computational accuracy. Full article
(This article belongs to the Special Issue Sustainable Development and Application of Aerospace Engineering)
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11 pages, 3593 KiB  
Article
Research on the Analytical Redundancy Method for the Control System of Variable Cycle Engine
by Xiaojie Qiu, Xiaodong Chang, Jie Chen and Baiqing Fan
Sustainability 2022, 14(10), 5905; https://0-doi-org.brum.beds.ac.uk/10.3390/su14105905 - 13 May 2022
Cited by 5 | Viewed by 1498
Abstract
The safety and reliability of the measuring elements of an aero-engine are important preconditions of the stable operation of the engine control system. The number of control parameters of a variable cycle engine increases by 20%–40% compared to traditional engines. Therefore, it is [...] Read more.
The safety and reliability of the measuring elements of an aero-engine are important preconditions of the stable operation of the engine control system. The number of control parameters of a variable cycle engine increases by 20%–40% compared to traditional engines. Therefore, it is important to conduct study on the analytical redundancy, design fault diagnosis and isolation of the sensors, as well as the signal reconstruction system, so as to increase the ratability and fault-tolerant capability of the variable cycle engine control system. The analytical redundancy method relies on the accuracy of the mathematical model of the engine. During the service cycle of the engine, it is inevitable that the engine performance will deteriorate, resulting in a mismatch with the model. In this paper, the adaptive model of the variable cycle engine is built with a Kalman filter. Based on this, the strategy of analytical redundancy logic is built and the dynamic adaptive calculation of the threshold is introduced. Simulation results reflect that this method can effectively increase the reliability of sensor fault diagnosis and the accuracy of the analytical redundancy when there is performance degradation of the variable cycle engine. Full article
(This article belongs to the Special Issue Sustainable Development and Application of Aerospace Engineering)
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