Pub Date : 2024-03-29DOI: 10.3390/aerospace11040269
Kun Zhang, Jianyao Yao, Wenxiang Zhu, Zhifu Cao, Teng Li, Jianqiang Xin
The thermal protection system (TPS) represents one of the most critical subsystems for vehicle re-entry. However, due to uncertainties in thermal loads, material properties, and manufacturing deviations, the thermal response of the TPS exhibits significant randomness, posing considerable challenges in engineering design and reliability assessment. Given that uncertain aerodynamic heating loads manifest as a stochastic field over time, conventional surrogate models, typically accepting scalar random variables as inputs, face limitations in modeling them. Consequently, this paper introduces an effective characterization approach utilizing proper orthogonal decomposition (POD) to represent the uncertainties of aerodynamic heating. The augmented snapshots matrix is used to reduce the dimension of the random field by the decoupling method of independently spatial and temporal bases. The random variables describing material properties and geometric thickness are also employed as inputs for probabilistic analyses. An uncoupled POD Gaussian process regression (UPOD-GPR) model is then established to achieve highly accurate solutions for transient heat conduction. The model takes random heat flux fields as inputs and thermal response fields as outputs. Using a typical multi-layer TPS and thermal structure as two examples, probabilistic analyses are conducted. The mean square relative error of a typical multi-layer TPS is less than 4%. For the thermal structure, the averaged absolute error of the radiation and insulation layer is less than 25 ∘C and 6 ∘C when the maximum reaches 1200 ∘C and 150 ∘C, respectively. This approach can provide accurate and rapid predictions of thermal responses for TPS and thermal structures throughout their entire operating time when furnished with input heat flux fields and structural parameters.
{"title":"Parameterized Reduced-Order Models for Probabilistic Analysis of Thermal Protection System Based on Proper Orthogonal Decomposition","authors":"Kun Zhang, Jianyao Yao, Wenxiang Zhu, Zhifu Cao, Teng Li, Jianqiang Xin","doi":"10.3390/aerospace11040269","DOIUrl":"https://doi.org/10.3390/aerospace11040269","url":null,"abstract":"The thermal protection system (TPS) represents one of the most critical subsystems for vehicle re-entry. However, due to uncertainties in thermal loads, material properties, and manufacturing deviations, the thermal response of the TPS exhibits significant randomness, posing considerable challenges in engineering design and reliability assessment. Given that uncertain aerodynamic heating loads manifest as a stochastic field over time, conventional surrogate models, typically accepting scalar random variables as inputs, face limitations in modeling them. Consequently, this paper introduces an effective characterization approach utilizing proper orthogonal decomposition (POD) to represent the uncertainties of aerodynamic heating. The augmented snapshots matrix is used to reduce the dimension of the random field by the decoupling method of independently spatial and temporal bases. The random variables describing material properties and geometric thickness are also employed as inputs for probabilistic analyses. An uncoupled POD Gaussian process regression (UPOD-GPR) model is then established to achieve highly accurate solutions for transient heat conduction. The model takes random heat flux fields as inputs and thermal response fields as outputs. Using a typical multi-layer TPS and thermal structure as two examples, probabilistic analyses are conducted. The mean square relative error of a typical multi-layer TPS is less than 4%. For the thermal structure, the averaged absolute error of the radiation and insulation layer is less than 25 ∘C and 6 ∘C when the maximum reaches 1200 ∘C and 150 ∘C, respectively. This approach can provide accurate and rapid predictions of thermal responses for TPS and thermal structures throughout their entire operating time when furnished with input heat flux fields and structural parameters.","PeriodicalId":505273,"journal":{"name":"Aerospace","volume":"93 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140366337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-29DOI: 10.3390/aerospace11040266
Fernando Montano, I. Dimino, Alberto Milazzo
Morphing structures are a relatively new aircraft technology currently being investigated for a variety of applications, from civil to military. Despite the lack of literature maturity and its complexity, morphing wings offer significant aerodynamic benefits over a wide range of flight conditions, enabling reduced aircraft fuel consumption and airframe noise, longer range and higher efficiency. The aim of this study is to investigate the impact of morphing horizontal tail design on aircraft performance and flight mechanics. This study is conducted on a 1:5 scale model of a Preceptor N-3 Pup at its trim condition, of which the longitudinal dynamics is implemented in MATLAB. Starting from the original horizontal tail airfoil NACA 0012 with the elevator deflected at the trim value, this is modified by using the X-Foil tool to obtain a smooth morphing airfoil trailing edge shape with the same CLα. By comparing both configurations and their influence on the whole aircraft, the resulting improvements are evaluated in terms of stability in the short-period mode, reduction in the parasitic drag coefficient CD0, and increased endurance at various altitudes.
{"title":"A Preliminary Evaluation of Morphing Horizontal Tail Design for UAVs","authors":"Fernando Montano, I. Dimino, Alberto Milazzo","doi":"10.3390/aerospace11040266","DOIUrl":"https://doi.org/10.3390/aerospace11040266","url":null,"abstract":"Morphing structures are a relatively new aircraft technology currently being investigated for a variety of applications, from civil to military. Despite the lack of literature maturity and its complexity, morphing wings offer significant aerodynamic benefits over a wide range of flight conditions, enabling reduced aircraft fuel consumption and airframe noise, longer range and higher efficiency. The aim of this study is to investigate the impact of morphing horizontal tail design on aircraft performance and flight mechanics. This study is conducted on a 1:5 scale model of a Preceptor N-3 Pup at its trim condition, of which the longitudinal dynamics is implemented in MATLAB. Starting from the original horizontal tail airfoil NACA 0012 with the elevator deflected at the trim value, this is modified by using the X-Foil tool to obtain a smooth morphing airfoil trailing edge shape with the same CLα. By comparing both configurations and their influence on the whole aircraft, the resulting improvements are evaluated in terms of stability in the short-period mode, reduction in the parasitic drag coefficient CD0, and increased endurance at various altitudes.","PeriodicalId":505273,"journal":{"name":"Aerospace","volume":"30 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140365660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Given the randomness inherent in fluid dynamics problems and limitations in human cognition, Computational Fluid Dynamics (CFD) modeling and simulation are afflicted with non-negligible uncertainties, casting doubts on the credibility of CFD. Scientifically and rigorously quantifying the uncertainty of CFD is paramount for assessing its credibility and informing engineering decisions. In order to quantify the uncertainty of multidimensional flow field responses stemming from uncertain model parameters, this paper proposes a method based on Gappy Proper Orthogonal Decomposition (POD) for supplementing high-fidelity flow field data within a framework that leverages POD and surrogate models. This approach enables the generation of corresponding high-fidelity flow fields from low-fidelity ones, significantly reducing the cost of high-fidelity flow field computation in uncertainty propagation modeling. Through an analysis of the impact of uncertainty in the coefficients of the Spalart–Allmaras (SA) turbulence model on the distribution of wall friction coefficients for the NACA0012 airfoil and pressure coefficients for the M6 wing, the proposed multi-fidelity modeling approach is demonstrated to offer significant advancements in both accuracy and efficiency compared to single-fidelity methods, providing a robust and efficient prediction model for large-scale random sampling.
{"title":"A Multi-Fidelity Uncertainty Propagation Model for Multi-Dimensional Correlated Flow Field Responses","authors":"Jiangtao Chen, Jiao Zhao, Wei Xiao, Luogeng Lv, Wei Zhao, Xiaojun Wu","doi":"10.3390/aerospace11040263","DOIUrl":"https://doi.org/10.3390/aerospace11040263","url":null,"abstract":"Given the randomness inherent in fluid dynamics problems and limitations in human cognition, Computational Fluid Dynamics (CFD) modeling and simulation are afflicted with non-negligible uncertainties, casting doubts on the credibility of CFD. Scientifically and rigorously quantifying the uncertainty of CFD is paramount for assessing its credibility and informing engineering decisions. In order to quantify the uncertainty of multidimensional flow field responses stemming from uncertain model parameters, this paper proposes a method based on Gappy Proper Orthogonal Decomposition (POD) for supplementing high-fidelity flow field data within a framework that leverages POD and surrogate models. This approach enables the generation of corresponding high-fidelity flow fields from low-fidelity ones, significantly reducing the cost of high-fidelity flow field computation in uncertainty propagation modeling. Through an analysis of the impact of uncertainty in the coefficients of the Spalart–Allmaras (SA) turbulence model on the distribution of wall friction coefficients for the NACA0012 airfoil and pressure coefficients for the M6 wing, the proposed multi-fidelity modeling approach is demonstrated to offer significant advancements in both accuracy and efficiency compared to single-fidelity methods, providing a robust and efficient prediction model for large-scale random sampling.","PeriodicalId":505273,"journal":{"name":"Aerospace","volume":"29 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140371887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigated combustion characteristics of composite fuel grains designed based on a modular fuel unit strategy. The modular fuel unit comprised a periodical helical structure with nine acrylonitrile–butadiene–styrene helical blades. A paraffin-based fuel was embedded between adjacent blades. Two modifications of the helical structure framework were researched. One mirrored the helical blades, and the other periodically extended the helical blades by perforation. A laboratory-scale hybrid rocket engine was used to investigate combustion characteristics of the fuel grains at an oxygen mass flux of 2.1–6.0 g/(s·cm2). Compared with the composite fuel grain with periodically extended helical blades, the modified composite fuel grains exhibited higher regression rates and a faster rise of regression rates as the oxygen mass flux increased. At an oxygen mass flux of 6.0 g/(s·cm2), the regression rate of the composite fuel grains with perforation and mirrored helical blades increased by 8.0% and 14.1%, respectively. The oxygen-to-fuel distribution of the composite fuel grain with mirrored helical blades was more concentrated, and its combustion efficiency was stable. Flame structure characteristics in the combustion chamber were visualized using a radiation imaging technique. A rapid increase in flame thickness of the composite fuel grains based on the modular unit was observed, which was consistent with their high regression rates. A simplified numerical simulation was carried out to elucidate the mechanism of the modified modular units on performance enhancement of the composite hybrid rocket grains.
{"title":"Regression Rate and Combustion Efficiency of Composite Hybrid Rocket Grains Based on Modular Fuel Units","authors":"Junjie Pan, Xin Lin, Zezhong Wang, Ruoyan Wang, Kun Wu, Jinhu Liang, Xilong Yu","doi":"10.3390/aerospace11040262","DOIUrl":"https://doi.org/10.3390/aerospace11040262","url":null,"abstract":"This study investigated combustion characteristics of composite fuel grains designed based on a modular fuel unit strategy. The modular fuel unit comprised a periodical helical structure with nine acrylonitrile–butadiene–styrene helical blades. A paraffin-based fuel was embedded between adjacent blades. Two modifications of the helical structure framework were researched. One mirrored the helical blades, and the other periodically extended the helical blades by perforation. A laboratory-scale hybrid rocket engine was used to investigate combustion characteristics of the fuel grains at an oxygen mass flux of 2.1–6.0 g/(s·cm2). Compared with the composite fuel grain with periodically extended helical blades, the modified composite fuel grains exhibited higher regression rates and a faster rise of regression rates as the oxygen mass flux increased. At an oxygen mass flux of 6.0 g/(s·cm2), the regression rate of the composite fuel grains with perforation and mirrored helical blades increased by 8.0% and 14.1%, respectively. The oxygen-to-fuel distribution of the composite fuel grain with mirrored helical blades was more concentrated, and its combustion efficiency was stable. Flame structure characteristics in the combustion chamber were visualized using a radiation imaging technique. A rapid increase in flame thickness of the composite fuel grains based on the modular unit was observed, which was consistent with their high regression rates. A simplified numerical simulation was carried out to elucidate the mechanism of the modified modular units on performance enhancement of the composite hybrid rocket grains.","PeriodicalId":505273,"journal":{"name":"Aerospace","volume":"124 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140370355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-28DOI: 10.3390/aerospace11040264
Xuedong Li, Yuan Xie, Yumo Tian, Fengjiang An
Extravehicular activity (EVA) is a key point and a difficult point for manned spaceflight tasks, as well as an inevitable trend in the development of the manned spaceflight industry. Equipment maintenance, load installation, and extravehicular routing inspection via EVA on the track are necessary to guarantee the safety and reliability of the long-term in-orbit operation of the China Space Station. In this paper, a comprehensive analysis was conducted on the features of multiple tasks, diverse working modes, and strong systematic coupling during the EVA of the China Space Station (CSS). On this basis, the design, implementation technologies’ development, and in-orbit performance evaluation during EVA were expounded. In the space station system, an extravehicular reliability verification and evaluation system suitable for the requirement for EVA under the conditions of China’s multi-mission, multi-module combination, and repairable spacecraft was constructed. Finally, the in-orbit EVA implementation of the China Space Station since the launch of the core module to the present was summarized, and the subsequent application of the extravehicular technologies in manned lunar landing projects and optical modules was anticipated.
舱外活动（EVA）是载人航天任务的重点和难点，也是载人航天事业发展的必然趋势。通过轨道舱外活动进行设备维护、载荷安装和舱外路由检查，是保证中国空间站长期在轨运行安全可靠的必要手段。本文针对中国空间站舱外活动任务多、工作方式多样、系统耦合性强的特点进行了综合分析。在此基础上，阐述了 EVA 的设计、实施技术开发和在轨性能评估。在空间站系统中，构建了适合中国多任务、多模块组合、可修复航天器条件下舱外飞行可靠性验证与评估系统。最后，总结了中国空间站自核心舱发射至今的在轨 EVA 实施情况，并对后续舱外技术在载人登月工程和光学舱中的应用进行了展望。
{"title":"A Study on the Design and Implementation Technologies of EVA at the China Space Station","authors":"Xuedong Li, Yuan Xie, Yumo Tian, Fengjiang An","doi":"10.3390/aerospace11040264","DOIUrl":"https://doi.org/10.3390/aerospace11040264","url":null,"abstract":"Extravehicular activity (EVA) is a key point and a difficult point for manned spaceflight tasks, as well as an inevitable trend in the development of the manned spaceflight industry. Equipment maintenance, load installation, and extravehicular routing inspection via EVA on the track are necessary to guarantee the safety and reliability of the long-term in-orbit operation of the China Space Station. In this paper, a comprehensive analysis was conducted on the features of multiple tasks, diverse working modes, and strong systematic coupling during the EVA of the China Space Station (CSS). On this basis, the design, implementation technologies’ development, and in-orbit performance evaluation during EVA were expounded. In the space station system, an extravehicular reliability verification and evaluation system suitable for the requirement for EVA under the conditions of China’s multi-mission, multi-module combination, and repairable spacecraft was constructed. Finally, the in-orbit EVA implementation of the China Space Station since the launch of the core module to the present was summarized, and the subsequent application of the extravehicular technologies in manned lunar landing projects and optical modules was anticipated.","PeriodicalId":505273,"journal":{"name":"Aerospace","volume":"5 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140373248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-27DOI: 10.3390/aerospace11040260
Hugo Valayer, Nathalie Bartoli, Mauricio Castaño-Aguirre, R. Lafage, Thierry Lefebvre, A. F. López-Lopera, Sylvain Mouton
In aerodynamics, characterizing the aerodynamic behavior of aircraft typically requires a large number of observation data points. Real experiments can generate thousands of data points with suitable accuracy, but they are time-consuming and resource-intensive. Consequently, conducting real experiments at new input configurations might be impractical. To address this challenge, data-driven surrogate models have emerged as a cost-effective and time-efficient alternative. They provide simplified mathematical representations that approximate the output of interest. Models based on Gaussian Processes (GPs) have gained popularity in aerodynamics due to their ability to provide accurate predictions and quantify uncertainty while maintaining tractable execution times. To handle large datasets, sparse approximations of GPs have been further investigated to reduce the computational complexity of exact inference. In this paper, we revisit and adapt two classic sparse methods for GPs to address the specific requirements frequently encountered in aerodynamic applications. We compare different strategies for choosing the inducing inputs, which significantly impact the complexity reduction. We formally integrate our implementations into the open-source Python toolbox SMT, enabling the use of sparse methods across the GP regression pipeline. We demonstrate the performance of our Sparse GP (SGP) developments in a comprehensive 1D analytic example as well as in a real wind tunnel application with thousands of training data points.
在空气动力学中，描述飞机的空气动力学行为通常需要大量的观测数据点。实际实验可以生成数千个具有适当精度的数据点，但这些实验既耗时又耗费资源。因此，在新的输入配置下进行真实实验可能不切实际。为了应对这一挑战，数据驱动的代用模型应运而生，成为一种具有成本效益和时间效率的替代方法。这些模型提供了简化的数学表示方法，可近似于相关输出。基于高斯过程（GPs）的模型在空气动力学领域很受欢迎，因为它们能够提供准确的预测并量化不确定性，同时保持可控的执行时间。为了处理大型数据集，人们进一步研究了 GPs 的稀疏近似，以降低精确推理的计算复杂性。在本文中，我们重新审视并调整了 GPs 的两种经典稀疏方法，以满足空气动力学应用中经常遇到的特定要求。我们比较了选择诱导输入的不同策略，这些策略对降低复杂性有显著影响。我们将实现方法正式集成到开源 Python 工具箱 SMT 中，使稀疏方法的使用贯穿 GP 回归管道。我们在一个全面的一维分析示例中，以及在一个具有数千个训练数据点的真实风洞应用中，展示了我们的稀疏 GP (SGP) 开发成果的性能。
{"title":"A Python Toolbox for Data-Driven Aerodynamic Modeling Using Sparse Gaussian Processes","authors":"Hugo Valayer, Nathalie Bartoli, Mauricio Castaño-Aguirre, R. Lafage, Thierry Lefebvre, A. F. López-Lopera, Sylvain Mouton","doi":"10.3390/aerospace11040260","DOIUrl":"https://doi.org/10.3390/aerospace11040260","url":null,"abstract":"In aerodynamics, characterizing the aerodynamic behavior of aircraft typically requires a large number of observation data points. Real experiments can generate thousands of data points with suitable accuracy, but they are time-consuming and resource-intensive. Consequently, conducting real experiments at new input configurations might be impractical. To address this challenge, data-driven surrogate models have emerged as a cost-effective and time-efficient alternative. They provide simplified mathematical representations that approximate the output of interest. Models based on Gaussian Processes (GPs) have gained popularity in aerodynamics due to their ability to provide accurate predictions and quantify uncertainty while maintaining tractable execution times. To handle large datasets, sparse approximations of GPs have been further investigated to reduce the computational complexity of exact inference. In this paper, we revisit and adapt two classic sparse methods for GPs to address the specific requirements frequently encountered in aerodynamic applications. We compare different strategies for choosing the inducing inputs, which significantly impact the complexity reduction. We formally integrate our implementations into the open-source Python toolbox SMT, enabling the use of sparse methods across the GP regression pipeline. We demonstrate the performance of our Sparse GP (SGP) developments in a comprehensive 1D analytic example as well as in a real wind tunnel application with thousands of training data points.","PeriodicalId":505273,"journal":{"name":"Aerospace","volume":"34 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140375203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Scene taxiing time is an important indicator for assessing the operational efficiency of airports as well as green airports, and it is also a fundamental parameter in flight regularity statistics. The accurate prediction of taxiing time can help decision makers to further optimize flight pushback sequences and improve airport operational efficiency while increasing flight punctuality. In this paper, we propose a hybrid deep learning model for departure taxiing time prediction based on the new influence factors of taxiing time. Taking Pudong International Airport as the research object, after analyzing the scene operation mode, we construct the origin–destination pairs (ODPs) with stand groups and runways and then propose two structure-related factors, corridor departure flow and departure flow proportion of ODP, as the new features. Based on the new feature set, we construct a departure taxiing dataset for training the prediction model. Then, a departure taxiing time prediction model based on convolutional neural networks (CNNs) and gated recurrent units (GRUs) is proposed, which uses a CNN model to extract the high-dimensional features from the taxiing data and then inputs them to a GRU model for taxiing time prediction. Finally, we conduct a series of comparison experiments on the historical taxiing dataset of Pudong Airport. The prediction results show that the proposed hybrid prediction model has the best performances compared with other deep learning models, and the proposed structure-related features have high correlations with departure taxiing time. The prediction results of taxiing time for different ODPs also verify the generalizability of the proposed model.
{"title":"A CNN-GRU Hybrid Model for Predicting Airport Departure Taxiing Time","authors":"Ligang Yuan, Jing Liu, Haiyan Chen, Daoming Fang, Wenlu Chen","doi":"10.3390/aerospace11040261","DOIUrl":"https://doi.org/10.3390/aerospace11040261","url":null,"abstract":"Scene taxiing time is an important indicator for assessing the operational efficiency of airports as well as green airports, and it is also a fundamental parameter in flight regularity statistics. The accurate prediction of taxiing time can help decision makers to further optimize flight pushback sequences and improve airport operational efficiency while increasing flight punctuality. In this paper, we propose a hybrid deep learning model for departure taxiing time prediction based on the new influence factors of taxiing time. Taking Pudong International Airport as the research object, after analyzing the scene operation mode, we construct the origin–destination pairs (ODPs) with stand groups and runways and then propose two structure-related factors, corridor departure flow and departure flow proportion of ODP, as the new features. Based on the new feature set, we construct a departure taxiing dataset for training the prediction model. Then, a departure taxiing time prediction model based on convolutional neural networks (CNNs) and gated recurrent units (GRUs) is proposed, which uses a CNN model to extract the high-dimensional features from the taxiing data and then inputs them to a GRU model for taxiing time prediction. Finally, we conduct a series of comparison experiments on the historical taxiing dataset of Pudong Airport. The prediction results show that the proposed hybrid prediction model has the best performances compared with other deep learning models, and the proposed structure-related features have high correlations with departure taxiing time. The prediction results of taxiing time for different ODPs also verify the generalizability of the proposed model.","PeriodicalId":505273,"journal":{"name":"Aerospace","volume":"20 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140377213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-26DOI: 10.3390/aerospace11040258
Pablo Brusola, S. García-Nieto, JV Salcedo, Miguel Martinez, Robert H. Bishop
This paper presents a mathematical modeling approach utilizing a fuzzy modeling framework for fixed-wing aircraft systems with the goal of creating a highly desirable mathematical representation for model-based control design applications. The starting point is a mathematical model comprising fifteen non-linear ordinary differential equations representing the dynamic and kinematic behavior applicable to a wide range of fixed-wing aircraft systems. Here, the proposed mathematical modeling framework is applied to the AIRBUS A310 model developed by ONERA. The proposed fuzzy modeling framework takes advantage of sector non-linearity red techniques to recast all the non-linear terms from the original model to a set of combined fuzzy rules. The result of this fuzzification is a more suitable mathematical description from the control system design point of view. Therefore, the combination of this fuzzy model and the wide range of control techniques available in the literature for such kind of models, like parallel and non-parallel distributed compensation control laws using linear matrix inequality optimization, enables the development of control algorithms that guarantee stability conditions for a wide range of operations points, avoiding the classical gain scheduling schemes, where the stability issues can be extremely challenging.
本文介绍了一种利用固定翼飞机系统模糊建模框架的数学建模方法，目的是为基于模型的控制设计应用创建一种非常理想的数学表示方法。数学模型由 15 个非线性常微分方程组成，代表了适用于各种固定翼飞机系统的动态和运动行为。在这里，所提出的数学建模框架被应用于 ONERA 开发的 AIRBUS A310 模型。拟议的模糊建模框架利用部门非线性重构技术，将原始模型中的所有非线性项重构为一组组合模糊规则。从控制系统设计的角度来看，这种模糊化的结果是一种更合适的数学描述。因此，将这种模糊模型与文献中针对此类模型的多种控制技术（如使用线性矩阵不等式优化的并行和非并行分布式补偿控制法）相结合，就能开发出在多种操作点上都能保证稳定性的控制算法，从而避免了传统增益调度方案所面临的极具挑战性的稳定性问题。
{"title":"Fuzzy Modeling Framework Using Sector Non-Linearity Techniques for Fixed-Wing Aircrafts","authors":"Pablo Brusola, S. García-Nieto, JV Salcedo, Miguel Martinez, Robert H. Bishop","doi":"10.3390/aerospace11040258","DOIUrl":"https://doi.org/10.3390/aerospace11040258","url":null,"abstract":"This paper presents a mathematical modeling approach utilizing a fuzzy modeling framework for fixed-wing aircraft systems with the goal of creating a highly desirable mathematical representation for model-based control design applications. The starting point is a mathematical model comprising fifteen non-linear ordinary differential equations representing the dynamic and kinematic behavior applicable to a wide range of fixed-wing aircraft systems. Here, the proposed mathematical modeling framework is applied to the AIRBUS A310 model developed by ONERA. The proposed fuzzy modeling framework takes advantage of sector non-linearity red techniques to recast all the non-linear terms from the original model to a set of combined fuzzy rules. The result of this fuzzification is a more suitable mathematical description from the control system design point of view. Therefore, the combination of this fuzzy model and the wide range of control techniques available in the literature for such kind of models, like parallel and non-parallel distributed compensation control laws using linear matrix inequality optimization, enables the development of control algorithms that guarantee stability conditions for a wide range of operations points, avoiding the classical gain scheduling schemes, where the stability issues can be extremely challenging.","PeriodicalId":505273,"journal":{"name":"Aerospace","volume":"117 40","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140380119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-26DOI: 10.3390/aerospace11040259
Stephen Schade, Robert Jaron, Lukas Klähn, Antoine Moreau
The rotor–stator interaction noise is a major source of fan noise. Especially for low-speed fan stages, the tonal component is typically a dominant noise source. A challenge is to reduce this tonal noise, as it is typically perceived as unpleasant. Therefore, in this paper, we analytically, numerically and experimentally investigate an acoustic effect to lower the tonal noise excitation. Our study on an existing low-speed fan indicates a reduction in tonal interaction noise of more than 9 dB at the source if the excited acoustic modes propagate parallel to the stator leading edge angle. Moreover, a design-to-low-noise approach is demonstrated in order to apply this effect to two new fan stages with fewer stator than rotor blades. The acoustic design of both fans is determined by an appropriate choice of the rotor and stator blade numbers in order to align the modal propagation angle with the stator stagger angle. The blade geometries are obtained from aerodynamic optimization. Both fans provide similar aerodynamic but opposing acoustic radiation characteristics compared to the baseline fan and a significant tonal noise reduction resulting from the impact of the modal propagation angle on noise excitation. To ensure that this effect can also be applied to other low-speed fans, a design rule is derived and validated.
{"title":"Smart Blade Count Selection to Align Modal Propagation Angle with Stator Stagger Angle for Low-Noise Ducted Fan Designs","authors":"Stephen Schade, Robert Jaron, Lukas Klähn, Antoine Moreau","doi":"10.3390/aerospace11040259","DOIUrl":"https://doi.org/10.3390/aerospace11040259","url":null,"abstract":"The rotor–stator interaction noise is a major source of fan noise. Especially for low-speed fan stages, the tonal component is typically a dominant noise source. A challenge is to reduce this tonal noise, as it is typically perceived as unpleasant. Therefore, in this paper, we analytically, numerically and experimentally investigate an acoustic effect to lower the tonal noise excitation. Our study on an existing low-speed fan indicates a reduction in tonal interaction noise of more than 9 dB at the source if the excited acoustic modes propagate parallel to the stator leading edge angle. Moreover, a design-to-low-noise approach is demonstrated in order to apply this effect to two new fan stages with fewer stator than rotor blades. The acoustic design of both fans is determined by an appropriate choice of the rotor and stator blade numbers in order to align the modal propagation angle with the stator stagger angle. The blade geometries are obtained from aerodynamic optimization. Both fans provide similar aerodynamic but opposing acoustic radiation characteristics compared to the baseline fan and a significant tonal noise reduction resulting from the impact of the modal propagation angle on noise excitation. To ensure that this effect can also be applied to other low-speed fans, a design rule is derived and validated.","PeriodicalId":505273,"journal":{"name":"Aerospace","volume":"78 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140377794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The complex layout of the airport surface, coupled with interrelated vehicle behaviors and densely mixed traffic flows, frequently leads to operational conflict risks. To address this issue, research was conducted on the recognition of characteristics and risk assessment for airport surface operations in mixed traffic flows. Firstly, a surface topological network model was established based on the analysis of the physical structure features of the airport surface. Based on the Monte Carlo simulation method, the simulation framework for airport surface traffic operations was proposed, enabling the simulation of mixed traffic flows involving aircraft and vehicles. Secondly, from various perspectives, including topological structural characteristics, network vulnerabilities, and traffic complexity, a comprehensive system for feature indices and their measurement methods was developed to identify risk hotspots in mixed traffic flows on the airport surface, which facilitated the extraction of comprehensive risk elements for any node’s operation. Finally, a weighting rule for risk hotspot feature indices based on the CRITIC–entropy method was designed, and a risk assessment method for surface operations based on TOPSIS–gray relational analysis was proposed. This method accurately measured risk indices for airport surface operations hotspots. Simulations conducted at Shenzhen Bao’an International Airport demonstrate that the proposed methods achieve high simulation accuracy. The identified surface risk hotspots closely matched actual conflict areas, resulting in a 20% improvement in the accuracy of direct risk hotspot identification compared to simulation experiments. Additionally, 10.9% of nodes in the airport surface network were identified as risk hotspots, including 3 nodes with potential conflicts between aircraft and ground vehicles and 21 nodes with potential conflicts between aircraft. The proposed methods can effectively provide guidance for identifying potential “aircraft–vehicle” conflicts in complex airport surface layouts and scientifically support informed decisions in airport surface operation safety management.
{"title":"Identification of Key Risk Hotspots in Mega-Airport Surface Based on Monte Carlo Simulation","authors":"Wen Tian, Xuefang Zhou, Jianan Yin, Yuchen Li, Yining Zhang","doi":"10.3390/aerospace11040254","DOIUrl":"https://doi.org/10.3390/aerospace11040254","url":null,"abstract":"The complex layout of the airport surface, coupled with interrelated vehicle behaviors and densely mixed traffic flows, frequently leads to operational conflict risks. To address this issue, research was conducted on the recognition of characteristics and risk assessment for airport surface operations in mixed traffic flows. Firstly, a surface topological network model was established based on the analysis of the physical structure features of the airport surface. Based on the Monte Carlo simulation method, the simulation framework for airport surface traffic operations was proposed, enabling the simulation of mixed traffic flows involving aircraft and vehicles. Secondly, from various perspectives, including topological structural characteristics, network vulnerabilities, and traffic complexity, a comprehensive system for feature indices and their measurement methods was developed to identify risk hotspots in mixed traffic flows on the airport surface, which facilitated the extraction of comprehensive risk elements for any node’s operation. Finally, a weighting rule for risk hotspot feature indices based on the CRITIC–entropy method was designed, and a risk assessment method for surface operations based on TOPSIS–gray relational analysis was proposed. This method accurately measured risk indices for airport surface operations hotspots. Simulations conducted at Shenzhen Bao’an International Airport demonstrate that the proposed methods achieve high simulation accuracy. The identified surface risk hotspots closely matched actual conflict areas, resulting in a 20% improvement in the accuracy of direct risk hotspot identification compared to simulation experiments. Additionally, 10.9% of nodes in the airport surface network were identified as risk hotspots, including 3 nodes with potential conflicts between aircraft and ground vehicles and 21 nodes with potential conflicts between aircraft. The proposed methods can effectively provide guidance for identifying potential “aircraft–vehicle” conflicts in complex airport surface layouts and scientifically support informed decisions in airport surface operation safety management.","PeriodicalId":505273,"journal":{"name":"Aerospace","volume":" 76","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140384141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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