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Aerospace
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Machine Learning for Aeronautics
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Machine Learning for Aeronautics

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

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 55903

Special Issue Editor

Dr. Olivia J. Pinon Fischer

E-Mail Website
Guest Editor
Aerospace Systems Design Laboratory, School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Interests: digital engineering; digital twin/thread; ML/AI in engineering design; aerospace and defense
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The present Special Issue entitled “Machine Learning for Aeronautics” focuses on topics related to the application of machine learning, deep learning, and other emerging data-driven techniques to support and improve the design, development, analysis, testing, production, operation, and maintenance/inspection of aircraft. Authors are invited to submit full research articles or review manuscripts addressing (but not limited to) the following topics:

  • Application of AI/ML to requirement engineering;
  • Generative design;
  • Application of AI/ML to problems with a small amount of data;
  • Application of AI/ML for problems of increasing efficiency with expensive physical testing;
  • Application of AI/ML in support of certification by analysis;
  • Application of AI/ML in support of factory automation;
  • Real-time fault detection and forecasting;
  • Optimization of flight profile/performance;
  • Application of AI/ML for pilot training.

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.

Dr. Olivia J. Pinon Fischer
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

  • generative design
  • machine learning
  • deep learning
  • natural language processing
  • certification by analysis
  • efficiency
  • optimization

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Related Special Issue

Published Papers (19 papers)

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Research

21 pages, 4521 KiB  
Article
Artificial Intelligence Approach in Aerospace for Error Mitigation
by Jorge Bautista-Hernández and María Ángeles Martín-Prats
Aerospace 2024, 11(4), 300; https://doi.org/10.3390/aerospace11040300 - 11 Apr 2024
Cited by 1 | Viewed by 1828
Abstract
Many of the reports created at assembly lines, where all components of an aircraft are installed, frequently indicate that errors threaten safety. The proposed methodology in this study evaluates error prediction and risk mitigation to prevent failures and their consequences. The results linked [...] Read more.
Many of the reports created at assembly lines, where all components of an aircraft are installed, frequently indicate that errors threaten safety. The proposed methodology in this study evaluates error prediction and risk mitigation to prevent failures and their consequences. The results linked to a typical electrical harness manufacture of a military aircraft estimated reductions of 93% in time and 90% in error during the creation of engineering manufacturing processes using AI techniques. However, traditional risk assessments methods struggle to identify and mitigate errors effectively. Thus, developing an advanced methodology to ensure systems safety is needed. This paper addresses how innovative AI technology solutions can overcome these challenges, mitigate error risks, and enhance safety in aerospace. Technologies, such as artificial intelligence, predictive algorithms, machine learning, and automation, can play a key role in enhancing safety. The aim of this study is to develop a model that considers the factors that can potentially contribute to error creation, through an artificial intelligence (AI) approach. The specific AI techniques used such as support vector machine, random forest, logistic regression, K-nearest neighbor, and XGBoost (Python 3.8.5) show good performance for use in error mitigation. We have compared the modeled values obtained in this study with the experimental ones. The results confirm that the best metrics are obtained by using support vector machine and logistic regression. The smallest deviation between the measured and modeled values for these AI methods do not exceed 5%. Furthermore, using advancements in machine learning methods can enhance error mitigation in aerospace. The use of AutoML can play a key role in automatically finding an appropriate model which provides the best performance metrics and therefore the most reliable forecast for data prediction and error mitigation. Full article
(This article belongs to the Special Issue Machine Learning for Aeronautics)
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21 pages, 3304 KiB  
Article
Exhaust Gas Temperature Prediction of Aero-Engine via Enhanced Scale-Aware Efficient Transformer
by Sijie Liu, Nan Zhou, Chenchen Song, Geng Chen and Yafeng Wu
Aerospace 2024, 11(2), 138; https://doi.org/10.3390/aerospace11020138 - 5 Feb 2024
Cited by 1 | Viewed by 1796
Abstract
This research introduces the Enhanced Scale-Aware efficient Transformer (ESAE-Transformer), a novel and advanced model dedicated to predicting Exhaust Gas Temperature (EGT). The ESAE-Transformer merges the Multi-Head ProbSparse Attention mechanism with the established Transformer architecture, significantly optimizing computational efficiency and effectively discerning key temporal [...] Read more.
This research introduces the Enhanced Scale-Aware efficient Transformer (ESAE-Transformer), a novel and advanced model dedicated to predicting Exhaust Gas Temperature (EGT). The ESAE-Transformer merges the Multi-Head ProbSparse Attention mechanism with the established Transformer architecture, significantly optimizing computational efficiency and effectively discerning key temporal patterns. The incorporation of the Multi-Scale Feature Aggregation Module (MSFAM) further refines 2 s input and output timeframe. A detailed investigation into the feature dimensionality was undertaken, leading to an optimized configuration of the model, thereby improving its overall performance. The efficacy of the ESAE-Transformer was rigorously evaluated through an exhaustive ablation study, focusing on the contribution of each constituent module. The findings showcase a mean absolute prediction error of 3.47∘R, demonstrating strong alignment with real-world environmental scenarios and confirming the model’s accuracy and relevance. The ESAE-Transformer not only excels in predictive accuracy but also sheds light on the underlying physical processes, thus enhancing its practical application in real-world settings. The model stands out as a robust tool for critical parameter prediction in aero-engine systems, paving the way for future advancements in engine prognostics and diagnostics. Full article
(This article belongs to the Special Issue Machine Learning for Aeronautics)
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28 pages, 2217 KiB  
Article
Mission-Driven Inverse Design of Blended Wing Body Aircraft with Machine Learning
by Rohan S. Sharma and Serhat Hosder
Aerospace 2024, 11(2), 137; https://doi.org/10.3390/aerospace11020137 - 5 Feb 2024
Cited by 1 | Viewed by 1924
Abstract
The intent of this work was to investigate the feasibility of developing machine learning models for calculating values of airplane configuration design variables when provided time-series, mission-informed performance data. Shallow artificial neural networks were developed, trained, and tested using data pertaining to the [...] Read more.
The intent of this work was to investigate the feasibility of developing machine learning models for calculating values of airplane configuration design variables when provided time-series, mission-informed performance data. Shallow artificial neural networks were developed, trained, and tested using data pertaining to the blended wing body (BWB) class of aerospace vehicles. Configuration design parameters were varied using a Latin-hypercube sampling scheme. These data were used by a parametric-based BWB configuration generator to create unique BWBs. Performance for each configuration was obtained via a performance estimation tool. Training and testing of neural networks was conducted using a K-fold cross-validation scheme. A random forest approach was used to determine the values of predicted configuration design variables when evaluating neural network accuracy across a blended wing body vehicle survey. The results demonstrated the viability of leveraging neural networks in mission-dependent, inverse design of blended wing bodies. In particular, feed-forward, shallow neural network architectures yielded significantly better predictive accuracy than cascade-forward architectures. Furthermore, for both architectures, increasing the number of neurons in the hidden layer increased the prediction accuracy of configuration design variables by at least 80%. Full article
(This article belongs to the Special Issue Machine Learning for Aeronautics)
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24 pages, 1763 KiB  
Article
Empirical Assessment of Non-Intrusive Polynomial Chaos Expansions for High-Dimensional Stochastic CFD Problems
by Nikhil Iyengar, Dushhyanth Rajaram and Dimitri Mavris
Aerospace 2023, 10(12), 1017; https://doi.org/10.3390/aerospace10121017 - 6 Dec 2023
Cited by 1 | Viewed by 1546
Abstract
Uncertainties in the atmosphere and flight conditions can drastically impact the performance of an aircraft and result in certification delays. However, uncertainty propagation in high-fidelity simulations, which have become integral to the design process, can pose intractably high computational costs. This study presents [...] Read more.
Uncertainties in the atmosphere and flight conditions can drastically impact the performance of an aircraft and result in certification delays. However, uncertainty propagation in high-fidelity simulations, which have become integral to the design process, can pose intractably high computational costs. This study presents a non-intrusive, parametric reduced order modeling (ROM) method to enable the prediction of uncertain fields with thousands of random variables and nonlinear features under limited sampling budgets. The methodology combines linear dimensionality reduction with sparse polynomial chaos expansions and is assessed in a variety of CFD-based test cases, including 3D supersonic flow over a passenger aircraft with uncertain flight conditions. Each problem has strong nonlinearities, such as shocks, to investigate the effectiveness of models in real-world aerodynamic simulations that may arise during conceptual or preliminary design. The performance is assessed by comparing the uncertain mean, variance, point predictions, and integrated quantities of interest obtained using the ROMs to Monte Carlo simulations. It is observed that if the flow is entirely supersonic or subsonic, then the method can predict the pressure field accurately and rapidly. Moreover, it is also seen that statistical moments can be efficiently obtained using closed-form analytical expressions and closely match Monte Carlo results. Full article
(This article belongs to the Special Issue Machine Learning for Aeronautics)
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20 pages, 5624 KiB  
Article
The Optimization of a Model for Predicting the Remaining Useful Life and Fault Diagnosis of Landing Gear
by Yuan-Jen Chang, He-Kai Hsu, Tzu-Hsuan Hsu, Tsung-Ti Chen and Po-Wen Hwang
Aerospace 2023, 10(11), 963; https://doi.org/10.3390/aerospace10110963 - 15 Nov 2023
Cited by 2 | Viewed by 1780
Abstract
With the development of next-generation airplanes, the complexity of equipment has increased rapidly, and traditional maintenance solutions have become cost-intensive and time-consuming. Therefore, the main objective of this study is to adopt predictive maintenance techniques in daily maintenance in order to reduce manpower, [...] Read more.
With the development of next-generation airplanes, the complexity of equipment has increased rapidly, and traditional maintenance solutions have become cost-intensive and time-consuming. Therefore, the main objective of this study is to adopt predictive maintenance techniques in daily maintenance in order to reduce manpower, time, and the cost of maintenance, as well as increase aircraft availability. The landing gear system is an important component of an aircraft. Wear and tear on the parts of the landing gear may result in oscillations during take-off and landing rolling and even affect the safety of the fuselage in severe cases. This study acquires vibration signals from the flight data recorder and uses prognostic and health management technology to evaluate the health indicators (HI) of the landing gear. The HI is used to monitor the health status and predict the remaining useful life (RUL). The RUL prediction model is optimized through hyperparameter optimization and using the random search algorithm. Using the RUL prediction model, the health status of the landing gear can be monitored, and adaptive maintenance can be carried out. After the optimization of the RUL prediction model, the root-mean-square errors of the three RUL prediction models, that is, the autoregressive model, Gaussian process regression, and the autoregressive integrated moving average, decreased by 45.69%, 55.18%, and 1.34%, respectively. In addition, the XGBoost algorithm is applied to simultaneously output multiple fault types. This model provides a more realistic representation of the actual conditions under which an aircraft might exhibit multiple faults. With an optimal fault diagnosis model, when an anomaly is detected in the landing gear, the faulty part can be quickly diagnosed, thus enabling faster and more adaptive maintenance. The optimized multi-fault diagnosis model proposed in this study achieves average accuracy, a precision rate, a recall rate, and an F1 score of more than 96.8% for twenty types of faults. Full article
(This article belongs to the Special Issue Machine Learning for Aeronautics)
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19 pages, 23635 KiB  
Article
Learning to Predict 3D Rotational Dynamics from Images of a Rigid Body with Unknown Mass Distribution
by Justice J. Mason, Christine Allen-Blanchette, Nicholas Zolman, Elizabeth Davison and Naomi Ehrich Leonard
Aerospace 2023, 10(11), 921; https://doi.org/10.3390/aerospace10110921 - 29 Oct 2023
Cited by 1 | Viewed by 2035
Abstract
In many real-world settings, image observations of freely rotating 3D rigid bodies may be available when low-dimensional measurements are not. However, the high-dimensionality of image data precludes the use of classical estimation techniques to learn the dynamics. The usefulness of standard deep learning [...] Read more.
In many real-world settings, image observations of freely rotating 3D rigid bodies may be available when low-dimensional measurements are not. However, the high-dimensionality of image data precludes the use of classical estimation techniques to learn the dynamics. The usefulness of standard deep learning methods is also limited, because an image of a rigid body reveals nothing about the distribution of mass inside the body, which, together with initial angular velocity, is what determines how the body will rotate. We present a physics-based neural network model to estimate and predict 3D rotational dynamics from image sequences. We achieve this using a multi-stage prediction pipeline that maps individual images to a latent representation homeomorphic to SO(3), computes angular velocities from latent pairs, and predicts future latent states using the Hamiltonian equations of motion. We demonstrate the efficacy of our approach on new rotating rigid-body datasets of sequences of synthetic images of rotating objects, including cubes, prisms and satellites, with unknown uniform and non-uniform mass distributions. Our model outperforms competing baselines on our datasets, producing better qualitative predictions and reducing the error observed for the state-of-the-art Hamiltonian Generative Network by a factor of 2. Full article
(This article belongs to the Special Issue Machine Learning for Aeronautics)
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26 pages, 1087 KiB  
Article
Examining the Potential of Generative Language Models for Aviation Safety Analysis: Case Study and Insights Using the Aviation Safety Reporting System (ASRS)
by Archana Tikayat Ray, Anirudh Prabhakara Bhat, Ryan T. White, Van Minh Nguyen, Olivia J. Pinon Fischer and Dimitri N. Mavris
Aerospace 2023, 10(9), 770; https://doi.org/10.3390/aerospace10090770 - 31 Aug 2023
Cited by 7 | Viewed by 4361
Abstract
This research investigates the potential application of generative language models, especially ChatGPT, in aviation safety analysis as a means to enhance the efficiency of safety analyses and accelerate the time it takes to process incident reports. In particular, ChatGPT was leveraged to generate [...] Read more.
This research investigates the potential application of generative language models, especially ChatGPT, in aviation safety analysis as a means to enhance the efficiency of safety analyses and accelerate the time it takes to process incident reports. In particular, ChatGPT was leveraged to generate incident synopses from narratives, which were subsequently compared with ground-truth synopses from the Aviation Safety Reporting System (ASRS) dataset. The comparison was facilitated by using embeddings from Large Language Models (LLMs), with aeroBERT demonstrating the highest similarity due to its aerospace-specific fine-tuning. A positive correlation was observed between the synopsis length and its cosine similarity. In a subsequent phase, human factors issues involved in incidents, as identified by ChatGPT, were compared to human factors issues identified by safety analysts. The precision was found to be 0.61, with ChatGPT demonstrating a cautious approach toward attributing human factors issues. Finally, the model was utilized to execute an evaluation of accountability. As no dedicated ground-truth column existed for this task, a manual evaluation was conducted to compare the quality of outputs provided by ChatGPT to the ground truths provided by safety analysts. This study discusses the advantages and pitfalls of generative language models in the context of aviation safety analysis and proposes a human-in-the-loop system to ensure responsible and effective utilization of such models, leading to continuous improvement and fostering a collaborative approach in the aviation safety domain. Full article
(This article belongs to the Special Issue Machine Learning for Aeronautics)
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34 pages, 13870 KiB  
Article
Machine Learning and IoT Trends for Intelligent Prediction of Aircraft Wing Anti-Icing System Temperature
by E. S. Abdelghany, Mohamed B. Farghaly, Mishari Metab Almalki, H. H. Sarhan and Mohamed El-Sayed M. Essa
Aerospace 2023, 10(8), 676; https://doi.org/10.3390/aerospace10080676 - 29 Jul 2023
Cited by 10 | Viewed by 3974
Abstract
Airplane manufacturers are frequently faced with formidable challenges to improving both aircraft performance and customer safety. Ice accumulation on the wings of aircraft is one of the challenges, which could result in major accidents and a reduction in aerodynamic performance. Anti-icing systems, which [...] Read more.
Airplane manufacturers are frequently faced with formidable challenges to improving both aircraft performance and customer safety. Ice accumulation on the wings of aircraft is one of the challenges, which could result in major accidents and a reduction in aerodynamic performance. Anti-icing systems, which use the hot bleed airflow from the engine compressor, are considered one of the most significant solutions utilized in aircraft applications to prevent ice accumulation. In the current study, a novel approach based on machine learning (ML) and the Internet of Things (IoT) is proposed to predict the thermal performance characteristics of a partial span wing anti-icing system constructed using the NACA 23014 airfoil section. To verify the proposed strategy, the obtained results are compared with those obtained using computational ANSYS 2019 software. An artificial neural network (ANN) is used to build a forecasting model of wing temperature based on experimental data and computational fluid dynamics (CFD) data. In addition, the ThingSpeak platform is applied in this article to realize the concept of the IoT, collect the measured data, and publish the data in a private channel. Different performance metrics, namely, mean square error (MSE), maximum relative error (MAE), and absolute variance (R2), are used to evaluate the prediction model. Based on the performance indices, the results prove the efficiency of the proposed approach based on ANN and the IoT in designing a forecasting model to predict the wing temperature compared to the numerical CFD method, which consumes a lot of time and requires high-speed simulation devices. Therefore, it is suggested that the ANN-IoT approach be applied in aviation. Full article
(This article belongs to the Special Issue Machine Learning for Aeronautics)
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21 pages, 10212 KiB  
Article
JT9D Engine Thrust Estimation and Model Sensitivity Analysis Using Gradient Boosting Regression Method
by Hung-Ta Wen, Hom-Yu Wu, Kuo-Chien Liao and Wei-Chuan Chen
Aerospace 2023, 10(7), 639; https://doi.org/10.3390/aerospace10070639 - 15 Jul 2023
Cited by 2 | Viewed by 2353
Abstract
In recent years, artificial intelligence (AI) technology has been applied in different research fields. In this study, the XGBoost regression model is proposed to estimate JT9D engine thrust. The model performance mean absolute error (MAE) is 0.004845, the mean-squared error (MSE) is 0.000161, [...] Read more.
In recent years, artificial intelligence (AI) technology has been applied in different research fields. In this study, the XGBoost regression model is proposed to estimate JT9D engine thrust. The model performance mean absolute error (MAE) is 0.004845, the mean-squared error (MSE) is 0.000161, and the coefficient of determination (R2) values of the training, validation, and testing subsets are 0.99, 0.99, and 0.98, respectively. Based on a model sensitivity analysis, the four parameters’ optimal values are as follows: the number of estimators is 900; the learning rate is 0.1; the maximum depth is 4, and the random state is 3. In addition, a comparison between the model performance in this study and that in a previous one was conducted. The MSE value is as low as 0.000021. Full article
(This article belongs to the Special Issue Machine Learning for Aeronautics)
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20 pages, 993 KiB  
Article
A Rapid Surrogate Model for Estimating Aviation Noise Impact across Various Departure Profiles and Operating Conditions
by Howard Peng, Jirat Bhanpato, Ameya Behere and Dimitri N. Mavris
Aerospace 2023, 10(7), 627; https://doi.org/10.3390/aerospace10070627 - 11 Jul 2023
Cited by 3 | Viewed by 1688
Abstract
Aviation noise remains a key barrier to the sustainable growth of commercial aviation. The advent of emerging technologies, such as urban air mobility, and the renewed interest in commercial supersonic transport aircraft, has only further raised concerns over the resultant community noise exposure. [...] Read more.
Aviation noise remains a key barrier to the sustainable growth of commercial aviation. The advent of emerging technologies, such as urban air mobility, and the renewed interest in commercial supersonic transport aircraft, has only further raised concerns over the resultant community noise exposure. The foundation of any noise mitigation effort is the ability to accurately model noise metrics over a wide range of scenarios. Aviation noise is influenced by a wide variety of factors, including aircraft type, payload weight, thrust settings, airport elevation, ambient weather, and flight trajectory. Traditional noise modeling paradigms rely on physics-based and empirical calculations, which are computationally expensive. Attempts at speeding up the computations with alternate models could deliver on speed or accuracy, but not both. Recent research has indicated that model order reduction techniques hold promise for transforming and greatly reducing the number of quantities that need to be modeled. Paired with surrogate modeling techniques, a rapid and accurate noise model can be generated. The research presented in this manuscript expands on the model order reduction method and develops a rapid noise surrogate model, which can account for the piloting actions, the ambient temperature, and airport elevation. The presented results indicate that the method works well with minimal error for most modeling scenarios. The results also outline avenues for improvement, such as using a different class of surrogate models or modeling additional training cases. The model developed in this research has numerous applications for multi-query applications, such as parametric trade-off analyses and optimization studies. With the inclusion of airport and aircraft parameters, the model enables the development of frameworks that optimize piloting actions for noise mitigation on the ground. Full article
(This article belongs to the Special Issue Machine Learning for Aeronautics)
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19 pages, 6843 KiB  
Article
Fuzzy Neural Network PID Control Used in Individual Blade Control
by Renguo Yang, Yadong Gao, Huaming Wang and Xianping Ni
Aerospace 2023, 10(7), 623; https://doi.org/10.3390/aerospace10070623 - 9 Jul 2023
Cited by 10 | Viewed by 1574
Abstract
In order to further reduce the vibration level of helicopters, the active vibration control technology of helicopters has been extensively studied. Among them, individual blade control (IBC) independently applies high-order harmonics to each blade with an actuator, which can improve the aerodynamic environment [...] Read more.
In order to further reduce the vibration level of helicopters, the active vibration control technology of helicopters has been extensively studied. Among them, individual blade control (IBC) independently applies high-order harmonics to each blade with an actuator, which can improve the aerodynamic environment of the blade and effectively reduce the vibration load of the hub. The rotor structural dynamics model based on the Hamilton energy variation principle and the medium deformation beam theory were established firstly, and the aerodynamic model based on the dynamic inflow model and the Leishman–Beddoes unsteady aerodynamic model were also established. The structural finite element method and the direct numerical integration method were used to calculate the vibration response of the rotor to determine the vibration load of the hub. After these, the steepest descent-golden section combinatorial optimization algorithm was used to find the optimization parameters of IBC. Based on this, the input parameters of fuzzy neural network PID control were determined, and the rotor hub vibration load control simulation was conducted. Under the effect of IBC, the vibration loads of the hub could be reduced by about 60%. The article gives the best control laws of individual harmonic pitch control and their combinations. These results can theoretically be applied to the design of control law to reduce helicopter vibration loads. Full article
(This article belongs to the Special Issue Machine Learning for Aeronautics)
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25 pages, 7955 KiB  
Article
Unstable Approach Detection and Analysis Based on Energy Management and a Deep Neural Network
by Tzu-Ying Chiu and Ying-Chih Lai
Aerospace 2023, 10(6), 565; https://doi.org/10.3390/aerospace10060565 - 16 Jun 2023
Cited by 3 | Viewed by 1647
Abstract
The study of managing risk in aviation is the key to improving flight safety. Compared to the other flight operation phases, the approach and landing phases are more critical and dangerous. This study aims to detect and analyze unstable approaches in Taiwan through [...] Read more.
The study of managing risk in aviation is the key to improving flight safety. Compared to the other flight operation phases, the approach and landing phases are more critical and dangerous. This study aims to detect and analyze unstable approaches in Taiwan through historical flight data. In addition to weather factors such as low visibility and crosswinds, human factors also account for a large part of the risk. From the accidents studied in the stochastic report of the Flight Safety Foundation, nearly 70% of the accidents occurred during the approach and landing phases, which were caused by improper control of aircraft energy. Since the information of the flight data recorder (FDR) is regarded as the airline’s confidential information, this study calculates the aircraft’s energy-related metrics and investigates the influence of non-weather-related factors on unstable approaches through a publicly available source, automatic dependent surveillance-broadcast (ADS-B) flight data. To evaluate the influence of weather- and non-weather-related factors, the outliers of each group classified by weather labels are detected and eliminated from the analysis by applying hierarchical density-based spatial clustering of applications with noise (HDBSCAN), which is utilized for detecting abnormal flights that are spatial anomalies. The deep learning method was adopted to detect and predict unstable arrival flights landing at Taipei Songshan Airport. The accuracy of the prediction for the normalized total energy and trajectory deviation of all flights is 85.15% and 82.11%, respectively. The results show that in different kinds of weather conditions, or not considering the weather, the models have similar good performance. The input features were analyzed after the model was obtained, and the flights detected as abnormal are discussed. Full article
(This article belongs to the Special Issue Machine Learning for Aeronautics)
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20 pages, 18259 KiB  
Article
Compressor Performance Prediction Based on the Interpolation Method and Support Vector Machine
by Lingfeng Zhong, Rui Liu, Xiaodong Miao, Yufeng Chen, Songhong Li and Haocheng Ji
Aerospace 2023, 10(6), 558; https://doi.org/10.3390/aerospace10060558 - 13 Jun 2023
Cited by 8 | Viewed by 3032
Abstract
Compressors are important components in various power systems in the field of energy and power. In practical applications, compressors often operate under non-design conditions. Therefore, accurate calculation on performance under various operating conditions is of great significance for the development and application of [...] Read more.
Compressors are important components in various power systems in the field of energy and power. In practical applications, compressors often operate under non-design conditions. Therefore, accurate calculation on performance under various operating conditions is of great significance for the development and application of certain power systems equipped with compressors. To calculate and predict the performance of a compressor under all operating conditions through limited data, the interpolation method was combined with a support vector machine (SVM). Based on the known data points of compressor design conditions, the interpolation method was adopted to obtain training samples of the SVM. In the calculation process, preliminary screening was conducted on the kernel functions of the SVM. Two interpolation methods, including linear interpolation and cubic spline interpolation, were used to obtain sample data. In the subsequent training process of the SVM, the genetic algorithm (GA) was used to optimize its parameters. After training, the available data were compared with the predicted data of the SVM. The results show that the SVM uses the Gaussian kernel function to achieve the highest prediction accuracy. The prediction accuracy of the SVM trained with the data obtained from linear interpolation was higher than that of cubic spline interpolation. Compared with the back propagation neural network optimized by the genetic algorithm (GA-BPNN), the genetic algorithm optimization of extreme learning machine neural network (GA-ELMNN), and the genetic algorithm optimization of generalized regression neural network (GA-GRNN), the support vector machine optimized by the genetic algorithm (GA-SVM) has a better generalization, and GA-SVM is more accurate in predicting boundary data than the GA-BPNN. In addition, reducing the number of original data points still enables the GA-SVM to maintain a high level of predictive accuracy. Full article
(This article belongs to the Special Issue Machine Learning for Aeronautics)
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19 pages, 5741 KiB  
Article
Attrition Risk and Aircraft Suitability Prediction in U.S. Navy Pilot Training Using Machine Learning
by Jubilee Prasad-Rao, Olivia J. Pinon Fischer, Neil C. Rowe, Jesse R. Williams, Tejas G. Puranik, Dimitri N. Mavris, Michael W. Natali, Mitchell J. Tindall and Beth W. Atkinson
Aerospace 2023, 10(4), 379; https://doi.org/10.3390/aerospace10040379 - 19 Apr 2023
Cited by 2 | Viewed by 4618
Abstract
The cost to train a basic qualified U.S. Navy fighter aircraft pilot is nearly USD 10 M. The training includes primary, intermediate, and advanced stages, with the advanced stage involving extensive flight training, and, thus, is very expensive as a result. Despite the [...] Read more.
The cost to train a basic qualified U.S. Navy fighter aircraft pilot is nearly USD 10 M. The training includes primary, intermediate, and advanced stages, with the advanced stage involving extensive flight training, and, thus, is very expensive as a result. Despite the screening tests in place and early-stage attrition, 4.5% of aviators undergo attrition in this most expensive stage. Key reasons for aviator attrition include poor flight performance, voluntary withdrawals, and medical reasons. The reduction in late-stage attrition offers several financial and operational benefits to the U.S. Navy. To that end, this research leverages feature extraction and machine learning techniques on the very sparse flight test grades of student aviators to identify those with a high risk of attrition early in training. Using about 10 years of historical U.S. Navy pilot training data, trained models accurately predicted 50% of attrition with a 4% false positive rate. Such models could help the U.S. Navy save nearly USD 20 M a year in attrition costs. In addition, machine learning models were trained to recommend a suitable training aircraft type for each student aviator. These capabilities could help better answer the need for pilots and reduce the time and cost to train them. Full article
(This article belongs to the Special Issue Machine Learning for Aeronautics)
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23 pages, 10454 KiB  
Article
Optimal Design and Analysis of a High-Load Supersonic Compressor Based on a Surrogate Model
by Shiji Zhou, Shengfeng Zhao, Chuangxin Zhou, Yunfeng Wu, Hang Yuan and Xingen Lu
Aerospace 2023, 10(4), 364; https://doi.org/10.3390/aerospace10040364 - 10 Apr 2023
Cited by 3 | Viewed by 2085
Abstract
To explore the internal flow mechanism and improve the performance of a supersonic compressor, an efficient global optimization design method was developed for an axial flow compressor and applied in the optimization design of a prototype supersonic compressor. Based on the multiple circular [...] Read more.
To explore the internal flow mechanism and improve the performance of a supersonic compressor, an efficient global optimization design method was developed for an axial flow compressor and applied in the optimization design of a prototype supersonic compressor. Based on the multiple circular arc (MCA) blade parameters, the method can be used to parameterize the elementary stage of the blade. The optimized solution is obtained by changing the elementary stage and stacking lines of the blade during the optimization process. It has the advantages of fewer optimization variables, strong physical intuition, and a smooth surface. The optimization results show that a change in the rotor blade shape parameters has a significant effect on the compressor efficiency under design conditions, while a change in the skewed-swept parameters of the stator is the main factor that improves the compressor’s performance under near-stall conditions. Further numerical results show that the optimized rotor changes the form of the shock, weakens the degree of shock boundary layer interference, inhibits the radial migration flow of the supersonic rotor, reduces the loss of the rotor blade top, and improves the performance of the compressor under design conditions. The stator’s optimization restrains the generation of a concentrated shedding vortex at the root of the blades and greatly improves the stall margin of the compressor. Finally, the total pressure ratio and flow rate are less than 1% of the values based on the prototype operating conditions, the design mass flow of the optimized high-load supersonic compressor is increased by 0.25%, the isentropic efficiency is increased by 1.05%, and the stall margin is enhanced by 3.5%, thus verifying the effectiveness of the optimization method. Full article
(This article belongs to the Special Issue Machine Learning for Aeronautics)
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23 pages, 1594 KiB  
Article
aeroBERT-Classifier: Classification of Aerospace Requirements Using BERT
by Archana Tikayat Ray, Bjorn F. Cole, Olivia J. Pinon Fischer, Ryan T. White and Dimitri N. Mavris
Aerospace 2023, 10(3), 279; https://doi.org/10.3390/aerospace10030279 - 11 Mar 2023
Cited by 24 | Viewed by 5338
Abstract
The system complexity that characterizes current systems warrants an integrated and comprehensive approach to system design and development. This need has brought about a paradigm shift towards Model-Based Systems Engineering (MBSE) approaches to system design and a departure from traditional document-centric methods. While [...] Read more.
The system complexity that characterizes current systems warrants an integrated and comprehensive approach to system design and development. This need has brought about a paradigm shift towards Model-Based Systems Engineering (MBSE) approaches to system design and a departure from traditional document-centric methods. While MBSE shows great promise, the ambiguities and inconsistencies present in Natural Language (NL) requirements hinder their conversion to models directly. The field of Natural Language Processing (NLP) has demonstrated great potential in facilitating the conversion of NL requirements into a semi-machine-readable format that enables their standardization and use in a model-based environment. A first step towards standardizing requirements consists of classifying them according to the type (design, functional, performance, etc.) they represent. To that end, a language model capable of classifying requirements needs to be fine-tuned on labeled aerospace requirements. This paper presents an open-source, annotated aerospace requirements corpus (the first of its kind) developed for the purpose of this effort that includes three types of requirements, namely design, functional, and performance requirements. This paper further describes the use of the aforementioned corpus to fine-tune BERT to obtain the aeroBERT-Classifier: a new language model for classifying aerospace requirements into design, functional, or performance requirements. Finally, this paper provides a comparison between aeroBERT-Classifier and other text classification models such as GPT-2, Bidirectional Long Short-Term Memory (Bi-LSTM), and bart-large-mnli. In particular, it shows the superior performance of aeroBERT-Classifier on classifying aerospace requirements over existing models, and this is despite the fact that the model was fine-tuned using a small labeled dataset. Full article
(This article belongs to the Special Issue Machine Learning for Aeronautics)
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17 pages, 4390 KiB  
Article
Aero-Engine Modeling and Control Method with Model-Based Deep Reinforcement Learning
by Wenbo Gao, Muxuan Pan, Wenxiang Zhou, Feng Lu and Jin-Quan Huang
Aerospace 2023, 10(3), 209; https://doi.org/10.3390/aerospace10030209 - 23 Feb 2023
Cited by 2 | Viewed by 2028
Abstract
Due to the strong representation ability and capability of learning from data measurements, deep reinforcement learning has emerged as a powerful control method, especially for nonlinear systems, such as the aero-engine control system. In this paper, a novel application of deep reinforcement learning [...] Read more.
Due to the strong representation ability and capability of learning from data measurements, deep reinforcement learning has emerged as a powerful control method, especially for nonlinear systems, such as the aero-engine control system. In this paper, a novel application of deep reinforcement learning (DRL) is presented for aero-engine control. In addition, transition dynamic characteristic information of the aero-engine is extracted from the replay buffer of deep reinforcement learning to train a neural-network dynamic prediction model for the aero-engine. In turn, the dynamic prediction model is used to improve the learning efficiency of reinforcement learning. The practical applicability of the proposed control system is demonstrated by the numerical simulations. Compared with the traditional control system, this novel aero-engine control system has faster response speed, stronger self-learning ability, and avoids the complicated manual parameter adjustment without sacrificing the control performance. Moreover, the dynamic prediction model has satisfactory prediction accuracy, and the model-based method can achieve higher learning efficiency than the model-free method. Full article
(This article belongs to the Special Issue Machine Learning for Aeronautics)
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15 pages, 3399 KiB  
Article
Remaining Useful Life Prediction for Aero-Engines Using a Time-Enhanced Multi-Head Self-Attention Model
by Xin Wang, Yi Li, Yaxi Xu, Xiaodong Liu, Tao Zheng and Bo Zheng
Aerospace 2023, 10(1), 80; https://doi.org/10.3390/aerospace10010080 - 13 Jan 2023
Cited by 10 | Viewed by 3383
Abstract
Data-driven Remaining Useful Life (RUL) prediction is one of the core technologies of Prognostics and Health Management (PHM). Committed to improving the accuracy of RUL prediction for aero-engines, this paper proposes a model that is entirely based on the attention mechanism. The attention [...] Read more.
Data-driven Remaining Useful Life (RUL) prediction is one of the core technologies of Prognostics and Health Management (PHM). Committed to improving the accuracy of RUL prediction for aero-engines, this paper proposes a model that is entirely based on the attention mechanism. The attention model is divided into the multi-head self-attention and timing feature enhancement attention models. The multi-head self-attention model employs scaled dot-product attention to extract dependencies between time series; the timing feature enhancement attention model is used to accelerate and enhance the feature selection process. This paper utilises Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) turbofan engine simulation data obtained from NASA Ames’ Prognostics Center of Excellence and compares the proposed algorithm to other models. The experiments conducted validate the superiority of our model’s approach. Full article
(This article belongs to the Special Issue Machine Learning for Aeronautics)
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19 pages, 5674 KiB  
Article
Data-Driven Exhaust Gas Temperature Baseline Predictions for Aeroengine Based on Machine Learning Algorithms
by Zepeng Wang and Yongjun Zhao
Aerospace 2023, 10(1), 17; https://doi.org/10.3390/aerospace10010017 - 25 Dec 2022
Cited by 12 | Viewed by 2670
Abstract
The exhaust gas temperature (EGT) baseline of an aeroengine is key to accurately analyzing engine health, formulating maintenance decisions and ensuring flight safety. However, due to the complex performance characteristics of aeroengine and the constraints of many external factors, it is [...] Read more.
The exhaust gas temperature (EGT) baseline of an aeroengine is key to accurately analyzing engine health, formulating maintenance decisions and ensuring flight safety. However, due to the complex performance characteristics of aeroengine and the constraints of many external factors, it is difficult to obtain accurate non-linear features between various operating factors and EGT. In order to diagnose and forecast aeroengine performance quickly and accurately, four data-driven baseline prediction frameworks for EGT are proposed. These baseline frameworks took engine operating conditions and operating state control parameters as input variables and EGT as predicted output variables. The original data were collected from CFM56-5B engine ACARS flight data. Four typical machine learning methods, including Generalized Regression Neural Network (GRNN), Radial Basis Neural Network (RBF), Support Vector Regression (SVR) and Random Forest (RF) are trained to develop the models. Four aeroengine EGT baseline models were validated by comparing the after-flight data of another engine. The results show that the developed GRNN models have the best accuracy and computational efficiency compared with other models, and their RE and CPU calculation time on the verification set are 1.132 × 10−3 and 3.512 × 10−3 s, respectively. The developed baseline prediction frameworks can meet the needs of practical engineering applications for airlines. The methodologies developed can be employed by airlines to predict the EGT baseline for the purpose of engine performance monitoring and health management. Full article
(This article belongs to the Special Issue Machine Learning for Aeronautics)
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