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Aerospace
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Computing Methods for Aerospace Reliability Engineering
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Computing Methods for Aerospace Reliability Engineering

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 11802

Special Issue Editors

Dr. Lukai Song

E-Mail Website
Guest Editor
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon 999077, Hong Kong
Interests: reliability analysis; modeling and simulation; surrogate modeling; artificial neural networks; probabilistic analysis; aircraft engine; reliability-based design optimization; deep learning
Dr. Yat Sze Choy

E-Mail Website
Guest Editor
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon 999077, Hong Kong
Interests: sound-induced vibration; noise control; building acoustics; environmental noise measurement and control; sound source identification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid progress in aerospace science and technology, structural complexity, functional integration, and environmental diversification have increasingly become the development trends in advanced aerospace equipment such as aircraft engines, steam turbines, satellites, drones, rockets, spacecraft, etc. Under harsh operating environments and multi-source uncertain factors, this advanced aerospace equipment may be subject to severe safety and reliability problems. Therefore, effective reliability analysis and design techniques for aerospace engineering systems are becoming extremely important. With the help of advanced mathematical approaches/tools, increasing interest is currently being paid to new computing methods to reveal accurate reliability modeling of aerospace engineering, from materials to components, and components to systems. In this case, novel computing methods and applications based on these advanced computational technologies are desired to provide more accurate and efficient reliability design for aerospace engineering systems.

This Special Issue would aim to establish a common understanding about the state of the field and draw a road map on where the research is heading, highlight the issues and discuss the possible solutions, and provide the data, models, and tools necessary to perform high-efficacy modeling and reliability design for aerospace engineering systems. Potential topics include, but are not limited to:

  • Reliability evaluation
  • Reliability-based optimization design
  • Multidisciplinary design optimization
  • Uncertainty modeling
  • Uncertainty quantification
  • Structural integrity
  • Surrogate models
  • Complex structural systems
  • Artificial intelligence
  • Evolutionary algorithms
  • Fuzzy logic
  • Interval modeling
  • Mixed uncertainties
  • Bayesian modeling
  • Machine learning
  • Deep learning
  • Signal processing
  • Data-driven/model-driven modeling
  • Physics-informed modeling
  • Neural network computing for aerospace

Dr. Lukai Song
Dr. Yat Sze Choy
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

  • machine learning
  • reliability analysis
  • optimization design
  • aerospace reliability
  • computational methods

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Published Papers (6 papers)

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Research

28 pages, 12686 KiB  
Article
Reliability-Based Topology Optimization with a Proportional Topology for Reliability
by Noppawit Kumkam and Suwin Sleesongsom
Aerospace 2024, 11(6), 435; https://doi.org/10.3390/aerospace11060435 - 28 May 2024
Cited by 1 | Viewed by 1362
Abstract
This research proposes an efficient technique for reliability-based topology optimization (RBTO), which deals with uncertainty and employs proportional topology optimization (PTO) to achieve the optimal reliability structure. The recent technique, called proportional topology optimization for reliability (PTOr), uses Latin hypercube sampling (LHS) for [...] Read more.
This research proposes an efficient technique for reliability-based topology optimization (RBTO), which deals with uncertainty and employs proportional topology optimization (PTO) to achieve the optimal reliability structure. The recent technique, called proportional topology optimization for reliability (PTOr), uses Latin hypercube sampling (LHS) for uncertainty quantification. The difficulty of the double-loop nested problem in uncertainty quantification (UQ) with LHS can be alleviated by the power of PTO, enabling RBTO to be performed easily. The rigorous advantage of PTOr is its ability to accomplish topology optimization (TO) without gradient information, making it faster than TO with evolutionary algorithms. Particularly, for reliability-based topology design, evolutionary techniques often fail to achieve satisfactory results compared to gradient-based techniques. Unlike recent PTOr advancement, which enhances the RBTO performance, this achievement was previously unattainable. Test problems, including an aircraft pylon, reveal its performances. Furthermore, the proposed efficient framework facilitates easy integration with other uncertainty quantification techniques, increasing its performance in uncertainty quantification. Lastly, this research provides computer programs for the newcomer studying cutting-edge knowledge in engineering design, including UQ, TO, and RBTO, in a simple manner. Full article
(This article belongs to the Special Issue Computing Methods for Aerospace Reliability Engineering)
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20 pages, 4540 KiB  
Article
An Anomaly Detection Method for UAV Based on Wavelet Decomposition and Stacked Denoising Autoencoder
by Shenghan Zhou, Zhao He, Xu Chen and Wenbing Chang
Aerospace 2024, 11(5), 393; https://doi.org/10.3390/aerospace11050393 - 14 May 2024
Viewed by 1329
Abstract
The paper proposes an anomaly detection method for UAVs based on wavelet decomposition and stacked denoising autoencoder. This method takes the negative impact of noisy data and the feature extraction capabilities of deep learning models into account. It aims to improve the accuracy [...] Read more.
The paper proposes an anomaly detection method for UAVs based on wavelet decomposition and stacked denoising autoencoder. This method takes the negative impact of noisy data and the feature extraction capabilities of deep learning models into account. It aims to improve the accuracy of the proposed anomaly detection method with wavelet decomposition and stacked denoising autoencoder methods. Anomaly detection based on UAV flight data is an important method of UAV condition monitoring and potential abnormal state mining, which is an important means to reduce the risk of UAV flight accidents. However, the diversity of UAV mission scenarios leads to a complex and harsh environment, so the acquired data are affected by noise, which brings challenges to accurate anomaly detection based on UAV data. Firstly, we use wavelet decomposition to denoise the original data; then, we used the stacked denoising autoencoder to achieve feature extraction. Finally, the softmax classifier is used to realize the anomaly detection of UAV. The experimental results demonstrate that the proposed method still has good performance in the case of noisy data. Specifically, the Accuracy reaches 97.53%, the Precision is 97.50%, the Recall is 91.81%, and the F1-score is 94.57%. Furthermore, the proposed method outperforms the four comparison models with more outstanding performance. Therefore, it has significant potential in reducing UAV flight accidents and enhancing operational safety. Full article
(This article belongs to the Special Issue Computing Methods for Aerospace Reliability Engineering)
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20 pages, 10526 KiB  
Article
Research on a Method for Online Damage Evaluation of Turbine Blades in a Gas Turbine Based on Operating Conditions
by Hongxin Zhu, Yimin Zhu, Xiaoyi Zhang, Jian Chen, Mingyu Luo and Weiguang Huang
Aerospace 2023, 10(11), 966; https://doi.org/10.3390/aerospace10110966 - 16 Nov 2023
Viewed by 1699
Abstract
Performing online damage evaluation of blades subjected to complex cyclic loads based on the operating state of a gas turbine enables real-time reflection of a blade’s damage condition. This, in turn, facilitates the achievement of predictive maintenance objectives, enhancing the economic and operational [...] Read more.
Performing online damage evaluation of blades subjected to complex cyclic loads based on the operating state of a gas turbine enables real-time reflection of a blade’s damage condition. This, in turn, facilitates the achievement of predictive maintenance objectives, enhancing the economic and operational stability of gas turbine operations. This study establishes a hybrid model for online damage evaluation of gas turbine blades based on their operational state. The model comprises a gas turbine performance model based on thermodynamic simulation, a component load calculation model based on a surrogate model, an updated cycle counting method based on four-point rainflow, and an improved damage mechanism evaluation model. In the new model, the use of a surrogate model for the estimation of blade loading information based on gas turbine operating parameters replaces the conventional physical modeling methods. This substitution enhances the accuracy of blade loading calculations while ensuring real-time performance. Additionally, the new model introduces an updated cycle counting method based on four-point rainflow and an improved damage mechanism evaluation model. In the temperature counting part, a characteristic stress that represents the stress information during the cyclic process is proposed. This inclusion allows for the consideration of the impact of stress fluctuations on creep damage, thereby enhancing the accuracy of the fatigue damage assessment. In the stress counting part, the model incorporates time information associated with each cycle. This concept is subsequently applied in determining the identified cyclic strain information, thereby improving the accuracy of the fatigue damage evaluation. Finally, this study applies the new model to an online damage evaluation of a turbine stationary blade using actual operating data from a micro gas turbine. The results obtained from the new model are compared with the EOH recommended by the OEM, validating the accuracy and applicability of the new model. Full article
(This article belongs to the Special Issue Computing Methods for Aerospace Reliability Engineering)
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21 pages, 7447 KiB  
Article
Very High Cycle Fatigue Life Prediction of SLM AlSi10Mg Based on CDM and SVR Models
by Yibing Yu, Linlin Sun, Zhi Bian, Xiaojia Wang, Zhe Zhang, Chao Song, Weiping Hu and Xiao Chen
Aerospace 2023, 10(9), 823; https://doi.org/10.3390/aerospace10090823 - 21 Sep 2023
Cited by 2 | Viewed by 1616
Abstract
A novel fatigue evolution model considering the effect of defect size and additive manufacturing building direction based on the theories of continuum damage mechanics and its numerical implementation in ABAQUS is proposed in this paper. First, the constitutive model, fatigue damage evolution model [...] Read more.
A novel fatigue evolution model considering the effect of defect size and additive manufacturing building direction based on the theories of continuum damage mechanics and its numerical implementation in ABAQUS is proposed in this paper. First, the constitutive model, fatigue damage evolution model and their parameter calibration methods are presented. Second, using the ABAQUS platform, the proposed model is implemented with user-defined subroutines. After that, based on the proposed model and its numerical implementation, the fatigue life of additively manufactured AlSi10Mg is predicted and its applicability is verified through experimental results. Finally, a support vector regression model is established to predict the fatigue life, and its results are compared to those of the numerical finite element method. The results show that the support vector regression model makes better predictions than the finite element method. Full article
(This article belongs to the Special Issue Computing Methods for Aerospace Reliability Engineering)
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25 pages, 15607 KiB  
Article
Fatigue Reliability Analysis of Composite Material Considering the Growth of Effective Stress and Critical Stiffness
by Jian-Xiong Gao, Fei Heng, Yi-Ping Yuan and Yuan-Yuan Liu
Aerospace 2023, 10(9), 785; https://doi.org/10.3390/aerospace10090785 - 6 Sep 2023
Cited by 25 | Viewed by 2383
Abstract
Fatigue damage accumulation will not only cause the degradation of material performance but also lead to the growth of effective stress and critical stiffness. However, the existing fatigue reliability models usually ignore the effective stress growth and its influence on the critical stiffness [...] Read more.
Fatigue damage accumulation will not only cause the degradation of material performance but also lead to the growth of effective stress and critical stiffness. However, the existing fatigue reliability models usually ignore the effective stress growth and its influence on the critical stiffness of a composite material. This study considers the combined effects of performance degradation and effective stress growth, and a pair of fatigue reliability models for a composite material are presented. Firstly, the fatigue damage in a composite material is quantified by its performance degradation, and the fitting accuracy of several typical fatigue damage models is compared. Subsequently, the uncertainties of initial strength and initial stiffness are considered, and a pair of probabilistic models of residual strength and residual stiffness are proposed. The performance degradation data of Gr/PEEK [0/45/90/−45]2S laminates are utilized to verify the proposed probabilistic models. Finally, the effective stress growth mechanism and its influence on the failure threshold are elaborated, and a pair of fatigue reliability models for composite materials are developed. Moreover, the differences between the strength-based and stiffness-based reliability analysis results of composite materials are compared and discussed. Full article
(This article belongs to the Special Issue Computing Methods for Aerospace Reliability Engineering)
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18 pages, 4388 KiB  
Article
The Influence of Coordinate Systems on the Stability Analysis of Lateral–Torsional Coupled Vibration
by Xin Qian, Yu Fan, Yaguang Wu, Wenjun Wang and Lin Li
Aerospace 2023, 10(8), 699; https://doi.org/10.3390/aerospace10080699 - 8 Aug 2023
Viewed by 1361
Abstract
Stability analysis of lateral–torsional coupled vibration is obligatory for rotating machinery, such as aero-engines. However, the state-of-the-art method may lead to stability misjudgment under different coordinate systems. The cause of this misjudgment has not yet been well explored. The purpose of this paper [...] Read more.
Stability analysis of lateral–torsional coupled vibration is obligatory for rotating machinery, such as aero-engines. However, the state-of-the-art method may lead to stability misjudgment under different coordinate systems. The cause of this misjudgment has not yet been well explored. The purpose of this paper is to clarify the error source of the stability analysis in a more comprehensive manner. A vertical Jeffcott rotor model including torsion vibration is built, and the Lagrange approach is applied to establish the motion equations. The coordinate transformation matrix is used to transfer the motion equations into the rotating coordinate system, making the coefficients of the motion equation constants. The differences in the unstable speed regions in the two coordinate systems are captured. The limitations of the Floquet theory and Hill’s determinant analysis in the stability estimation of the lateral–torsional coupled vibration are explained. It is found that, for Hill’s method, increasing the number of the harmonic truncation cannot correct the misjudgment, and the matrix truncation is the fundamental error source. The above research provides more accurate theoretical support for the analysis of the lateral–torsional coupling instability of rotors. Full article
(This article belongs to the Special Issue Computing Methods for Aerospace Reliability Engineering)
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