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Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 30885

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Dr. Jules Simo

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School of Engineering, University of Central Lancashire, Preston PR1 2HE, UK
Interests: aerospace engineering; multidisciplinary optimization; machine learning; data science; artificial intelligence; space robotics; UAVs
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Dear Colleagues,

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

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

Topics of interest include, but are not limited to:

Machine Learning and Artificial Intelligence for Space and Aerospace Applications
Multiobjective Optimization for Space and Aerospace Applications
Autonomous agents and multiagent systems in aerospace applications
UAV trajectory optimization using machine learning
Energy-efficient UAV communications
Robotics, perception, and vision in aerospace applications
Intelligent Control for Space and Aerospace Systems
Big Data, machine learning, and data mining in aerospace applications
Planning and Scheduling for Autonomous Systems
Data Processing and Satellite Applications
Bio-inspired Solutions for System Design and Control
AI for Air and Space Traffic Management and Operations

Dr. Jules Simo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Aerospace engineering
Multidisciplinary Optimization
Artificial Intelligence
Machine Learning
Data science
UAVs

Related Special Issue

Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering (Volume II) in Aerospace (2 articles)

Published Papers (11 papers)

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Research

29 pages, 7105 KiB

Open AccessArticle

An Efficient and Robust Sizing Method for eVTOL Aircraft Configurations in Conceptual Design

by Osita Ugwueze, Thomas Statheros, Nadjim Horri, Michael A. Bromfield and Jules Simo

Aerospace 2023, 10(3), 311; https://doi.org/10.3390/aerospace10030311 - 21 Mar 2023

Cited by 6 | Viewed by 6221

Abstract

This paper presents the development of a robust sizing method to efficiently estimate and compare key performance parameters in the conceptual design stage for the two main classes of fully electric vertical take-off and landing (eVTOL) aircraft, the powered lift and wingless aircraft types. The paper investigates hybrids of classical root-finding methods: the bisection, fixed-point and Newton-Rapson methods for use in eVTOL aircraft sizing. The improved convergence efficiency of the hybrid methods is at least 70% faster than the standard methods. This improved efficiency is significant for complex sizing problems. The developed sizing method is used to investigate the comparative performance of the wingless and powered lift eVTOL aircraft types for varying mission lengths. For a generic air taxi mission with a payload of 400 kg, the powered lift type demonstrates its mass efficiency when sized for missions above 10 km in range. However, the simpler architecture of the wingless eVTOL aircraft type makes it preferable for missions below 10 km in range when considering energy efficiency. The results of the sizing study were compared against a selection of eVTOL aircraft data. The results showed a good agreement between the estimated aircraft mass using the proposed sizing method and published eVTOL aircraft data. Full article

(This article belongs to the Special Issue Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering)

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18 pages, 7592 KiB

Open AccessArticle

Multiobjective Optimization Method of Solid Rocket Motor Finocyl Grain Based on Surrogate Model

by Qiuwen Miao, Zhibin Shen, Huihui Zhang and Haitao Sun

Aerospace 2022, 9(11), 679; https://doi.org/10.3390/aerospace9110679 - 02 Nov 2022

Cited by 2 | Viewed by 1880

Abstract

To improve the performance of a solid rocket motor (SRM), a multiobjective optimal design method that can consider the structural integrity, internal ballistic performance, and loading performance of the SRM was proposed based on parametric modeling and surrogate modeling technology. Firstly, the parametric modeling technology was introduced into the field of structural integrity analysis for a high-loading SRM, based on which the influences of load and geometric parameters on the maximum von Mises strain of the SRM grain were analyzed, which effectively improved the sampling speed and prediction accuracy of the surrogate model. Combining the calculation models of the combustion surface area and volume loading fraction of the SRM, the Pareto optimal solution set was obtained based on the NSGA-II algorithm. Under the constraints of the optimization model, the maximum von Mises strain can be reduced by up to 26.72% and the volume loading fraction can be increased by up to 1.83% compared with the original. In addition, the optimal design method proposed in this paper is significantly superior in efficiency, capable of reducing both the single sampling time by more than 95% and the number of numerical simulations from 20,000 to 400, and the average prediction deviation is only 1.87%. Full article

(This article belongs to the Special Issue Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering)

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18 pages, 2310 KiB

Open AccessArticle

Learning-Based Pose Estimation of Non-Cooperative Spacecrafts with Uncertainty Prediction

by Kecen Li, Haopeng Zhang and Chenyu Hu

Aerospace 2022, 9(10), 592; https://doi.org/10.3390/aerospace9100592 - 11 Oct 2022

Cited by 5 | Viewed by 1483

Abstract

Estimation of spacecraft pose is essential for many space missions, such as formation flying, rendezvous, docking, repair, and space debris removal. We propose a learning-based method with uncertainty prediction to estimate the pose of a spacecraft from a monocular image. We first used a spacecraft detection network (SDN) to crop out the rectangular area in the original image where only spacecraft exist. A keypoint detection network (KDN) was then used to detect 11 pre-selected keypoints with obvious features from the cropped image and predict uncertainty. We propose a keypoints selection strategy to automatically select keypoints with higher detection accuracy from all detected keypoints. These selective keypoints were used to estimate the 6D pose of the spacecraft with the EPnP algorithm. We evaluated our method on the SPEED dataset. The experiments showed that our method outperforms heatmap-based and regression-based methods, and our effective uncertainty prediction can increase the final precision of the pose estimation. Full article

(This article belongs to the Special Issue Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering)

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22 pages, 8727 KiB

Open AccessArticle

A Novel Data-Driven-Based Component Map Generation Method for Transient Aero-Engine Performance Adaptation

by Wenxiang Zhou, Sangwei Lu, Jinquan Huang, Muxuan Pan and Zhongguang Chen

Aerospace 2022, 9(8), 442; https://doi.org/10.3390/aerospace9080442 - 12 Aug 2022

Cited by 2 | Viewed by 1606

Abstract

Accurate component maps, which can significantly affect the efficiency, reliability and availability of aero-engines, play a critical role in aero-engine performance simulation. Unfortunately, the information of component maps is insufficient, leading to substantial limitations in practical application, wherein compressors are of particular interest. Here, a data-driven-based compressor map generation approach for transient aero-engine performance adaptation is investigated. A multi-layer perceptron neural network is utilized in simulating the compressor map instead of conventional interpolation schemes, and an adaptive variable learning rate backpropagation (ADVLBP) algorithm is employed to accelerate the convergence and improve the stability in the training process. Aside from that, two different adaptation strategies designed for steady state and transient conditions are implemented to adaptively retrain the compressor network according to measurement deviations until the accuracy requirements are satisfied. The proposed method is integrated into a turbofan component-level model, and simulations reveal that the ADVLBP algorithm has the capability of more rapid convergence compared with conventional training algorithms. In addition, the maximum absolute measurement deviation decreased from 6.35% to 0.44% after steady state adaptation, and excellent agreement between the predictions and benchmark data was obtained after transient adaptation. The results demonstrate the effectiveness and superiority of the proposed component map generation method. Full article

(This article belongs to the Special Issue Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering)

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15 pages, 6938 KiB

Open AccessArticle

A Real-Time Reentry Guidance Method for Hypersonic Vehicles Based on a Time2vec and Transformer Network

by Jia Song, Xindi Tong, Xiaowei Xu and Kai Zhao

Aerospace 2022, 9(8), 427; https://doi.org/10.3390/aerospace9080427 - 04 Aug 2022

Cited by 2 | Viewed by 1917

Abstract

In this paper, a real-time reentry guidance law for hypersonic vehicles is presented to accomplish rapid, high-precision, robust, and reliable reentry flights by leveraging the Time to Vector (Time2vec) and transformer networks. First, referring to the traditional predictor–corrector algorithm and quasi-equilibrium glide condition (QEGC), the reentry guidance issue is described as a univariate root-finding problem based on bank angle. Second, considering that reentry guidance is a sequential decision-making process, and its data has inherent characteristics in time series, so the Time2vec and transformer networks are trained to obtain the mapping relation between the flight states and bank angles, and the inputs and outputs are specially designed to guarantee that the constraints can be well satisfied. Based on the Time2vec and transformer-based bank angle predictor, an efficient and precise reentry guidance approach is proposed to realize on-line trajectory planning. Simulations and analysis are carried out through comparison with the traditional predictor-corrector algorithm, and the results manifest that the developed Time2vec and transformer-based reentry guidance algorithm has remarkable improvements in accuracy and efficiency under initial state errors and aerodynamic parameter perturbations. Full article

(This article belongs to the Special Issue Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering)

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27 pages, 1618 KiB

Open AccessArticle

Using Reinforcement Learning in a Layered Airspace to Improve Layer Change Decision

by Marta Ribeiro, Joost Ellerbroek and Jacco Hoekstra

Aerospace 2022, 9(8), 413; https://doi.org/10.3390/aerospace9080413 - 30 Jul 2022

Cited by 2 | Viewed by 1459

Abstract

Current predictions for future operations with drones estimate traffic densities orders of magnitude higher than any observed in manned aviation. Such densities call for further research and innovation, in particular, into conflict detection and resolution without the need for human intervention. The layered airspace concept, where aircraft are separated per vertical layer according to their heading, has been widely researched and proven to increase traffic capacity. However, aircraft traversing between layers do not benefit from this separation and alignment effect. As a result, interactions between climbing/descending and cruising aircraft can lead to a large increase in conflicts and intrusions. This paper looks into ways of reducing the impact of vertical transitions within the environment. We test two reinforcement learning methods: a decision-making module and a control execution module. The former issues a lane change command based on the planned route. The latter performs operational control to coordinate the longitude and vertical movement of the aircraft for a safe merging manoeuvre. The results show that reinforcement learning is capable of optimising an efficient driving policy for layer change manoeuvres, decreasing the number of conflicts and losses of minimum separation compared to manually defined navigation rules. Full article

(This article belongs to the Special Issue Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering)

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16 pages, 25741 KiB

Open AccessArticle

Speech GAU: A Single Head Attention for Mandarin Speech Recognition for Air Traffic Control

by Shiyu Zhang, Jianguo Kong, Chao Chen, Yabin Li and Haijun Liang

Aerospace 2022, 9(8), 395; https://doi.org/10.3390/aerospace9080395 - 22 Jul 2022

Cited by 9 | Viewed by 1699

Abstract

The rise of end-to-end (E2E) speech recognition technology in recent years has overturned the design pattern of cascading multiple subtasks in classical speech recognition and achieved direct mapping of speech input signals to text labels. In this study, a new E2E framework, ResNet–GAU–CTC, is proposed to implement Mandarin speech recognition for air traffic control (ATC). A deep residual network (ResNet) utilizes the translation invariance and local correlation of a convolutional neural network (CNN) to extract the time-frequency domain information of speech signals. A gated attention unit (GAU) utilizes a gated single-head attention mechanism to better capture the long-range dependencies of sequences, thus attaining a larger receptive field and contextual information, as well as a faster training convergence rate. The connectionist temporal classification (CTC) criterion eliminates the need for forced frame-level alignments. To address the problems of scarce data resources and unique pronunciation norms and contexts in the ATC field, transfer learning and data augmentation techniques were applied to enhance the robustness of the network and improve the generalization ability of the model. The character error rate (CER) of our model was 11.1% on the expanded Aishell corpus, and it decreased to 8.0% on the ATC corpus. Full article

(This article belongs to the Special Issue Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering)

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18 pages, 9079 KiB

Open AccessArticle

Adaptive Local Maximum-Entropy Surrogate Model and Its Application to Turbine Disk Reliability Analysis

by Jiang Fan, Qinghao Yuan, Fulei Jing, Hongbin Xu, Hao Wang and Qingze Meng

Aerospace 2022, 9(7), 353; https://doi.org/10.3390/aerospace9070353 - 30 Jun 2022

Cited by 2 | Viewed by 1463

Abstract

The emerging Local Maximum-Entropy (LME) approximation, which combines the advantages of global and local approximations, has an unsolved issue wherein it cannot adaptively change the morphology of the basis function according to the local characteristics of the sample, which greatly limits its highly nonlinear approximation ability. In this research, a novel Adaptive Local Maximum-Entropy Surrogate Model (ALMESM) is proposed by constructing an algorithm that adaptively changes the LME basis function and introduces Particle Swarm Optimization to ensure the optimality of the adaptively changed basis function. The performance of the ALMESM is systematically investigated by comparison with the LME approximation, a Radial basis function, and the Kriging model in two explicit highly nonlinear mathematical functions. The results show that the ALMESM has the highest accuracy and stability of all the compared models. The ALMESM is further validated by a highly nonlinear engineering case, consisting of a turbine disk reliability analysis under geometrical uncertainty, and achieves a desirable result. Compared with the direct Monte Carlo method, the relative error of the ALMESM is less than 1%, which indicates that the ALMESM has considerable potential for highly nonlinear problems and structural reliability analysis. Full article

(This article belongs to the Special Issue Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering)

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17 pages, 3033 KiB

Open AccessArticle

Semi-Physical Simulation of Fan Rotor Assembly Process Optimization for Unbalance Based on Reinforcement Learning

by Huibin Zhang, Mingwei Wang, Zhiang Li, Jingtao Zhou, Kexin Zhang, Xin Ma and Manxian Wang

Aerospace 2022, 9(7), 342; https://doi.org/10.3390/aerospace9070342 - 25 Jun 2022

Cited by 4 | Viewed by 1903

Abstract

An aero engine fan rotor is composed of a multi-stage disk and multi-stage blades. Excessive unbalance of the aero engine fan rotor after assembly is the main cause of aero engine vibration. In the rotor assembly process, blade sequencing optimization and multi-stage blade set assembly phase optimization are important for reducing the overall rotor unbalance. To address this problem, this paper proposes a semi-physical simulation method based on reinforcement learning to optimize the balance in the fan rotor assembly process. Firstly, based on the mass moments of individual blades, the diagonal mass moment difference is introduced as a constraint to build a single-stage blade sorting optimization model, and reinforcement learning is used to find the optimal sorting path so that the balance of the single-stage blade after sorting is optimal. Then, on the basis of the initial unbalance of the disk and the unbalance of the single-stage blade set, a multi-stage blade assembly phase optimization model is established, and reinforcement learning is used to find the optimal assembly phase so that the overall balance of the rotor is optimal. Finally, based on the collection of data during the assembly of the rotor, the least-squares method is used to fit and calculate the real-time assembly unbalance to achieve a semi-physical simulation of the optimization of balance during the assembly process. The feasibility and effectiveness of the proposed method are verified by experiments. Full article

(This article belongs to the Special Issue Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering)

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18 pages, 2326 KiB

Open AccessArticle

Application of Neural Networks and Transfer Learning to Turbomachinery Heat Transfer

by Markus Baumann, Christian Koch and Stephan Staudacher

Aerospace 2022, 9(2), 49; https://doi.org/10.3390/aerospace9020049 - 20 Jan 2022

Cited by 4 | Viewed by 2595

Abstract

Model-based predictive maintenance using high-frequency in-flight data requires digital twins that can model the dynamics of their physical twin with high precision. The models of the twins need to be fast and dynamically updatable. Machine learning offers the possibility to address these challenges in modeling the transient performance of aero engines. During transient operation, heat transferred between the engine’s structure and the annulus flow plays an important role. Diabatic performance modeling is demonstrated using non-dimensional transient heat transfer maps and transfer learning to extend turbomachinery transient modeling. The general form of such a map for a simple system similar to a pipe is reproduced by a Multilayer Perceptron neural network. It is trained using data from a finite element simulation. In a next step, the network is transferred using measurements to model the thermal transients of an aero engine. Only a limited number of parameters measured during selected transient maneuvers is needed to generate suitable non-dimensional transient heat transfer maps. With these additional steps, the extended performance model matches the engine thermal transients well. Full article

(This article belongs to the Special Issue Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering)

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22 pages, 10167 KiB

Open AccessArticle

Machine Learning Applications in Modelling and Analysis of Base Pressure in Suddenly Expanded Flows

by Jaimon Dennis Quadros, Sher Afghan Khan, Abdul Aabid, Mohammad Shohag Alam and Muneer Baig

Aerospace 2021, 8(11), 318; https://doi.org/10.3390/aerospace8110318 - 27 Oct 2021

Cited by 4 | Viewed by 1771

Abstract

Base pressure becomes a decisive factor in governing the base drag of aerodynamic vehicles. While several experimental and numerical methods have already been used for base pressure analysis in suddenly expanded flows, their implementation is quite time consuming. Therefore, we must develop a progressive approach to determine base pressure (β). Furthermore, a direct consideration of the influence of flow and geometric parameters cannot be studied by using these methods. This study develops a platform for data-driven analysis of base pressure (β) prediction in suddenly expanded flows, in which the influence of flow and geometric parameters including Mach number (M), nozzle pressure ratio (η), area ratio (α), and length to diameter ratio (φ) have been studied. Three different machine learning (ML) models, namely, artificial neural networks (ANN), support vector machine (SVM), and random forest (RF), have been trained using a large amount of data developed from response equations. The response equations for base pressure (β) were created using the response surface methodology (RSM) approach. The predicted results are compared with the experimental results to validate the proposed platform. The results obtained from this work can be applied in the right way to maximize base pressure in rockets and missiles to minimize base drag. Full article

(This article belongs to the Special Issue Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering)

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