Global air traffic demand has shown rapid growth for the last three decades. This growth led to more delays and congestion within terminal manoeuvring areas (TMAs) around major airports. The efficient use of airport capacities through the careful planning of air traffic flows is imperative to overcome these problems. In this study, a mixed-integer nonlinear programming (MINLP) model with a multi-objective approach was developed to solve the aircraft sequencing and scheduling problem for mixed runway operations within the TMAs. The model contains fuel cost functions based on airspeed, altitude, bank angle, and the aerodynamic characteristics of the aircraft. The optimisation problem was solved by using the $varepsilon$ -constraint method where total delay and total fuel functions were simultaneously optimised. We tested the model with different scenarios generated based on the real traffic data of Istanbul Sabiha Gökçen Airport. The results revealed that the average total delay and average total fuel were reduced by 26.4% and 6.7%, respectively.
{"title":"A multi-objective nonlinear integer programming model for mixed runway operations within the TMAs","authors":"Z. Kaplan, C. Çetek, T. Saraç","doi":"10.1017/aer.2023.50","DOIUrl":"https://doi.org/10.1017/aer.2023.50","url":null,"abstract":"\u0000 Global air traffic demand has shown rapid growth for the last three decades. This growth led to more delays and congestion within terminal manoeuvring areas (TMAs) around major airports. The efficient use of airport capacities through the careful planning of air traffic flows is imperative to overcome these problems. In this study, a mixed-integer nonlinear programming (MINLP) model with a multi-objective approach was developed to solve the aircraft sequencing and scheduling problem for mixed runway operations within the TMAs. The model contains fuel cost functions based on airspeed, altitude, bank angle, and the aerodynamic characteristics of the aircraft. The optimisation problem was solved by using the \u0000 \u0000 \u0000 \u0000$varepsilon$\u0000\u0000 \u0000 -constraint method where total delay and total fuel functions were simultaneously optimised. We tested the model with different scenarios generated based on the real traffic data of Istanbul Sabiha Gökçen Airport. The results revealed that the average total delay and average total fuel were reduced by 26.4% and 6.7%, respectively.","PeriodicalId":22567,"journal":{"name":"The Aeronautical Journal (1968)","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90841338","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}
Evaluation of the power required in level flight is essential to any new or modified helicopter performance flight-testing effort. The conventional flight-test method is based on an overly simplification of the induced and profile power components required for a helicopter in level flight. This simplistic approach incorporates several drawbacks that not only make execution of flight sorties inefficient and time consuming, but also compromise the level of accuracy achieved. This paper proposes an alternative flight-test method for evaluating the level-flight performance of a conventional helicopter while addressing and rectifying all identified deficiencies of the conventional method. The proposed method, referred to as the corrected-variables screening using dimensionality reduction (CVSDR), uses an original list of 36 corrected variables derived from basic dimensional analysis principles. This list of 36 corrected variables is reduced using tools of dimensionality reduction to keep only the most effective level-flight predictors. The CVSDR method is demonstrated and tested in this paper using flight-test data from a MBB BO-105 helicopter. It is shown that the CVSDR method predicts the power required for level flight about 21% more accurately than the conventional method while reducing the required flight time by an estimate of at least 60%. Unlike the conventional method, the CVSDR is not bounded by the high-speed approximation associated with the induced power estimation, therefore it is also relevant to the low airspeed regime. This low-airspeed relevancy allows the CVSDR method to bridge between the level-flight regime and the hover. Although demonstrated in this paper for a specific type of helicopter, the CVSDR method is applicable for level-flight performance flight testing of any type of conventional helicopter.
{"title":"A dimensionality reduction approach in helicopter level flight performance testing","authors":"I. Arush, M. Pavel, M. Mulder","doi":"10.1017/aer.2023.57","DOIUrl":"https://doi.org/10.1017/aer.2023.57","url":null,"abstract":"\u0000 Evaluation of the power required in level flight is essential to any new or modified helicopter performance flight-testing effort. The conventional flight-test method is based on an overly simplification of the induced and profile power components required for a helicopter in level flight. This simplistic approach incorporates several drawbacks that not only make execution of flight sorties inefficient and time consuming, but also compromise the level of accuracy achieved. This paper proposes an alternative flight-test method for evaluating the level-flight performance of a conventional helicopter while addressing and rectifying all identified deficiencies of the conventional method. The proposed method, referred to as the corrected-variables screening using dimensionality reduction (CVSDR), uses an original list of 36 corrected variables derived from basic dimensional analysis principles. This list of 36 corrected variables is reduced using tools of dimensionality reduction to keep only the most effective level-flight predictors. The CVSDR method is demonstrated and tested in this paper using flight-test data from a MBB BO-105 helicopter. It is shown that the CVSDR method predicts the power required for level flight about 21% more accurately than the conventional method while reducing the required flight time by an estimate of at least 60%. Unlike the conventional method, the CVSDR is not bounded by the high-speed approximation associated with the induced power estimation, therefore it is also relevant to the low airspeed regime. This low-airspeed relevancy allows the CVSDR method to bridge between the level-flight regime and the hover. Although demonstrated in this paper for a specific type of helicopter, the CVSDR method is applicable for level-flight performance flight testing of any type of conventional helicopter.","PeriodicalId":22567,"journal":{"name":"The Aeronautical Journal (1968)","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84422338","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 deformable wing structure can change its aerodynamic shape according to the change of flight mission and flight environment, so as to obtain better lift-drag, stability and control characteristics, which is considered as one of the future research directions of aviation technology. Considering the current technology maturity and reliability, a gradient corrugated fin is designed to realise the bending deformation of the wing. The structure of the skin is optimised to keep the skin smooth during deformation. In addition, a progressive push and pull rod is proposed to drive the wing deformation, and the fluid-structure interaction simulation is carried out for the wing deformation. At the same time, the changes of wing aerodynamic characteristics under different angles of leading and trailing edges and different push rod action schemes are analysed. Finally, a dry wind tunnel simulation test of the designed progressive flexible variable bending wing is carried out. The results of fluid-structure interaction simulation and dry wind tunnel test show that the progressive flexible variable bending wing proposed in this paper has a simple and reliable structure and remarkable deformation effect. It has advantages in increasing lift and reducing drag, ensuring high lift-drag ratio and providing wing trim moment. The deformable wing dry wind tunnel test platform designed by this method is structurally reliable, easy to operate, and can accurately reflect the influence of wing deformation on its aerodynamic force, which provides a verification means for the development of the design method and the design of practical aircraft in the future.
{"title":"Design and dry wind tunnel test of progressive flexible variable bending wing","authors":"X. Xu, G. G. Chen, S. Li, T. Lv","doi":"10.1017/aer.2023.59","DOIUrl":"https://doi.org/10.1017/aer.2023.59","url":null,"abstract":"\u0000 The deformable wing structure can change its aerodynamic shape according to the change of flight mission and flight environment, so as to obtain better lift-drag, stability and control characteristics, which is considered as one of the future research directions of aviation technology. Considering the current technology maturity and reliability, a gradient corrugated fin is designed to realise the bending deformation of the wing. The structure of the skin is optimised to keep the skin smooth during deformation. In addition, a progressive push and pull rod is proposed to drive the wing deformation, and the fluid-structure interaction simulation is carried out for the wing deformation. At the same time, the changes of wing aerodynamic characteristics under different angles of leading and trailing edges and different push rod action schemes are analysed. Finally, a dry wind tunnel simulation test of the designed progressive flexible variable bending wing is carried out. The results of fluid-structure interaction simulation and dry wind tunnel test show that the progressive flexible variable bending wing proposed in this paper has a simple and reliable structure and remarkable deformation effect. It has advantages in increasing lift and reducing drag, ensuring high lift-drag ratio and providing wing trim moment. The deformable wing dry wind tunnel test platform designed by this method is structurally reliable, easy to operate, and can accurately reflect the influence of wing deformation on its aerodynamic force, which provides a verification means for the development of the design method and the design of practical aircraft in the future.","PeriodicalId":22567,"journal":{"name":"The Aeronautical Journal (1968)","volume":"1992 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89003280","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}
A synthetic flow angle sensor, able to estimate angle-of-attack and angle-of-sideslip, can exploit different methods to solve a set of equations modelling data fusion from other onboard systems. In operative scenarios, measurements used for data fusion are characterised by several uncertainties that would significantly affect the synthetic sensor performance. The off-line use of neural networks is not a novelty to model deterministic synthetic flow angle sensors and to mitigate issues arising from real flight applications. A common practice is to train the neural network with corrupted data that are representative of uncertainties of the current application. However, this approach requires accurate tuning on the target aircraft and extensive flight test campaigns, therefore, making the neural network tightly dependent on the specific aircraft. In order to overcome latter issues, this work proposes the use of neural networks to solve a model-free scheme, derived from classical flight mechanics, that is independent from the target aircraft, flight regime and avionics. It is crucial to make use of a training dataset that is not related to any specific aircraft or avionics to preserve the generality of the scheme. Under these circumstances, global and local neural networks are herein compared with an iterative method to assess the neural capabilities to generalise the proposed model-free solver. The final objective of the present work, in fact, is to select the neural technique that can enable a flow angle synthetic sensor to be used on board any flying body at any flight regime without any further training sessions.
{"title":"Assessment of global and local neural network’s performance for model-free estimation of flow angles","authors":"A. Lerro, L. de Pasquale","doi":"10.1017/aer.2023.55","DOIUrl":"https://doi.org/10.1017/aer.2023.55","url":null,"abstract":"\u0000 A synthetic flow angle sensor, able to estimate angle-of-attack and angle-of-sideslip, can exploit different methods to solve a set of equations modelling data fusion from other onboard systems. In operative scenarios, measurements used for data fusion are characterised by several uncertainties that would significantly affect the synthetic sensor performance. The off-line use of neural networks is not a novelty to model deterministic synthetic flow angle sensors and to mitigate issues arising from real flight applications. A common practice is to train the neural network with corrupted data that are representative of uncertainties of the current application. However, this approach requires accurate tuning on the target aircraft and extensive flight test campaigns, therefore, making the neural network tightly dependent on the specific aircraft. In order to overcome latter issues, this work proposes the use of neural networks to solve a model-free scheme, derived from classical flight mechanics, that is independent from the target aircraft, flight regime and avionics. It is crucial to make use of a training dataset that is not related to any specific aircraft or avionics to preserve the generality of the scheme. Under these circumstances, global and local neural networks are herein compared with an iterative method to assess the neural capabilities to generalise the proposed model-free solver. The final objective of the present work, in fact, is to select the neural technique that can enable a flow angle synthetic sensor to be used on board any flying body at any flight regime without any further training sessions.","PeriodicalId":22567,"journal":{"name":"The Aeronautical Journal (1968)","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79336216","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}
Fanrong Sun, Xueji Xu, Huimin Zhang, Di Shen, Yao Mu, Y. Chen
Air route networks can no longer meet operational efficiency requirements because of the rapid growth of complex traffic flows. Machine learning is employed to investigate the evolutionary mechanism of congestion in such networks in view of their high complexity and high density, and a reasonable network optimisation scheme is presented. First, deviations between nominal and actual routes are investigated with reference to radar track data, and a network reflecting actual route operations is constructed using adversarial neural networks. Second, flight time is used to characterise congestion in route networks. Actual network operations are considered, and congestion is defined from the perspective of road traffic engineering. The effects of the operational properties of traffic flows on flight times are analysed to establish various congestion indicators. A gradient boosting model is used to select indicator characteristics and analyse patterns in the variations of indicator values for each flight segment in distinct periods. The indicator–time relationship is leveraged to explore the evolutionary mechanism of congestion in the route network. Third, on the basis of this mechanism, a multiobjective optimisation model of congestion is formulated, and a particle swarm optimisation algorithm is executed to adjust the route passage structure, thereby solving the optimisation model. Finally, calculation validation is conducted using radar track data from the control sector of the Yunnan region. The average flight time in a route segment is 10% shorter in the optimised route network than in the nonoptimised route network, which confirms that the optimisation solution is practicable.
{"title":"Evolution mechanism and optimisation of traffic congestion","authors":"Fanrong Sun, Xueji Xu, Huimin Zhang, Di Shen, Yao Mu, Y. Chen","doi":"10.1017/aer.2023.56","DOIUrl":"https://doi.org/10.1017/aer.2023.56","url":null,"abstract":"\u0000 Air route networks can no longer meet operational efficiency requirements because of the rapid growth of complex traffic flows. Machine learning is employed to investigate the evolutionary mechanism of congestion in such networks in view of their high complexity and high density, and a reasonable network optimisation scheme is presented. First, deviations between nominal and actual routes are investigated with reference to radar track data, and a network reflecting actual route operations is constructed using adversarial neural networks. Second, flight time is used to characterise congestion in route networks. Actual network operations are considered, and congestion is defined from the perspective of road traffic engineering. The effects of the operational properties of traffic flows on flight times are analysed to establish various congestion indicators. A gradient boosting model is used to select indicator characteristics and analyse patterns in the variations of indicator values for each flight segment in distinct periods. The indicator–time relationship is leveraged to explore the evolutionary mechanism of congestion in the route network. Third, on the basis of this mechanism, a multiobjective optimisation model of congestion is formulated, and a particle swarm optimisation algorithm is executed to adjust the route passage structure, thereby solving the optimisation model. Finally, calculation validation is conducted using radar track data from the control sector of the Yunnan region. The average flight time in a route segment is 10% shorter in the optimised route network than in the nonoptimised route network, which confirms that the optimisation solution is practicable.","PeriodicalId":22567,"journal":{"name":"The Aeronautical Journal (1968)","volume":"144 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76790828","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}
In this paper, a super-twisting disturbance observer (STDO)-based adaptive reinforcement learning control scheme is proposed for the straight air compound missile system with aerodynamic uncertainties and unmodeled dynamics. Firstly, neural network (NN)-based adaptive reinforcement learning control scheme with actor-critic design is investigated to deal with the tracking problems for the straight gas compound system. The actor NN and the critic NN are utilised to cope with the unmodeled dynamics and approximate the cost function that are related to control input and tracking error, respectively. In other words, the actor NN is used to perform the tracking control behaviours, and the critic NN aims to evaluate the tracking performance and give feedback to actor NN. Moreover, with the aid of the STDO disturbance observer, the problem of the control signal fluctuation caused by the mismatched disturbance can be solved well. Based on the proposed adaptive law and the Lyapunov direct method, the eventually consistent boundedness of the straight gas compound system is proved. Finally, numerical simulations are carried out to demonstrate the feasibility and superiority of the proposed reinforcement learning-based STDO control algorithm.
{"title":"Adaptive reinforcement learning control for a class of missiles with aerodynamic uncertainties and unmodeled dynamics","authors":"X. Ning, S. Cao, B. Han, Z. Wang, Y. Yin","doi":"10.1017/aer.2023.36","DOIUrl":"https://doi.org/10.1017/aer.2023.36","url":null,"abstract":"\u0000 In this paper, a super-twisting disturbance observer (STDO)-based adaptive reinforcement learning control scheme is proposed for the straight air compound missile system with aerodynamic uncertainties and unmodeled dynamics. Firstly, neural network (NN)-based adaptive reinforcement learning control scheme with actor-critic design is investigated to deal with the tracking problems for the straight gas compound system. The actor NN and the critic NN are utilised to cope with the unmodeled dynamics and approximate the cost function that are related to control input and tracking error, respectively. In other words, the actor NN is used to perform the tracking control behaviours, and the critic NN aims to evaluate the tracking performance and give feedback to actor NN. Moreover, with the aid of the STDO disturbance observer, the problem of the control signal fluctuation caused by the mismatched disturbance can be solved well. Based on the proposed adaptive law and the Lyapunov direct method, the eventually consistent boundedness of the straight gas compound system is proved. Finally, numerical simulations are carried out to demonstrate the feasibility and superiority of the proposed reinforcement learning-based STDO control algorithm.","PeriodicalId":22567,"journal":{"name":"The Aeronautical Journal (1968)","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82754824","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 paper studied the back-stepping adaptive sliding mode control (SMC) attitude problem of quaternion aircraft model based on radial basis function (RBF) network approximation. Firstly, a sliding mode controller is designed based on the back-stepping method (BSM) for the nonlinear aircraft model. Secondly, a RBF network algorithm is designed to compensate for the unknown and uncertain parts of the aircraft system. RBF network has simple network structure and good generalisation ability, avoids lengthy and unnecessary calculations, realises adaptive approximation of unknown parts in the aircraft model, and through the adjustment of adaptive weights, the convergence and stability of the entire closed-loop system (CLS) are guaranteed. Finally, the anti-interference performance of the controller is verified by simulation of the actuator fault model. Our proposed method has all-right control performance indicated by the simulation results.
{"title":"Adaptive sliding mode attitude control of quaternion model for aircraft based on neural network minimum parameter learning method","authors":"H. Zhuang","doi":"10.1017/aer.2023.53","DOIUrl":"https://doi.org/10.1017/aer.2023.53","url":null,"abstract":"\u0000 This paper studied the back-stepping adaptive sliding mode control (SMC) attitude problem of quaternion aircraft model based on radial basis function (RBF) network approximation. Firstly, a sliding mode controller is designed based on the back-stepping method (BSM) for the nonlinear aircraft model. Secondly, a RBF network algorithm is designed to compensate for the unknown and uncertain parts of the aircraft system. RBF network has simple network structure and good generalisation ability, avoids lengthy and unnecessary calculations, realises adaptive approximation of unknown parts in the aircraft model, and through the adjustment of adaptive weights, the convergence and stability of the entire closed-loop system (CLS) are guaranteed. Finally, the anti-interference performance of the controller is verified by simulation of the actuator fault model. Our proposed method has all-right control performance indicated by the simulation results.","PeriodicalId":22567,"journal":{"name":"The Aeronautical Journal (1968)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81329601","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}
Machine vision has been extensively researched in the field of unmanned aerial vehicles (UAV) recently. However, the ability of Sense and Avoid (SAA) largely limited by environmental visibility, which brings hazards to flight safety in low illumination or nighttime conditions. In order to solve this critical problem, an approach of image enhancement is proposed in this paper to improve image qualities in low illumination conditions. Considering the complementarity of visible and infrared images, a visible and infrared image fusion method based on convolutional sparse representation (CSR) is a promising solution to improve the SAA ability of UAVs. Firstly, the source image is decomposed into a texture layer and structure layer since infrared images are good at characterising structural information, and visible images have richer texture information. Both the structure and the texture layers are transformed into the sparse convolutional domain through the CSR mechanism, and then CSR coefficient mapping are fused via activity level assessment. Finally, the image is synthesised through the reconstruction results of the fusion texture and structure layers. In the experimental simulation section, a series of visible and infrared registered images including aerial targets are adopted to evaluate the proposed algorithm. Experimental results demonstrates that the proposed method increases image qualities in low illumination conditions effectively and can enhance the object details, which has better performance than traditional methods.
{"title":"A CSR-based visible and infrared image fusion method in low illumination conditions for sense and avoid","authors":"N. Ma, Y. Cao, Z. Zhang, Y. Fan, M. Ding","doi":"10.1017/aer.2023.51","DOIUrl":"https://doi.org/10.1017/aer.2023.51","url":null,"abstract":"\u0000 Machine vision has been extensively researched in the field of unmanned aerial vehicles (UAV) recently. However, the ability of Sense and Avoid (SAA) largely limited by environmental visibility, which brings hazards to flight safety in low illumination or nighttime conditions. In order to solve this critical problem, an approach of image enhancement is proposed in this paper to improve image qualities in low illumination conditions. Considering the complementarity of visible and infrared images, a visible and infrared image fusion method based on convolutional sparse representation (CSR) is a promising solution to improve the SAA ability of UAVs. Firstly, the source image is decomposed into a texture layer and structure layer since infrared images are good at characterising structural information, and visible images have richer texture information. Both the structure and the texture layers are transformed into the sparse convolutional domain through the CSR mechanism, and then CSR coefficient mapping are fused via activity level assessment. Finally, the image is synthesised through the reconstruction results of the fusion texture and structure layers. In the experimental simulation section, a series of visible and infrared registered images including aerial targets are adopted to evaluate the proposed algorithm. Experimental results demonstrates that the proposed method increases image qualities in low illumination conditions effectively and can enhance the object details, which has better performance than traditional methods.","PeriodicalId":22567,"journal":{"name":"The Aeronautical Journal (1968)","volume":"100 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90665620","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}
Helicopter collisions with obstacles are one of the most frequent and most devastating causes of accidents. To avoid these collisions in low-speed operations a “haptic ticker” cue in form of repetitive impulses as a force feedback was designed for an active sidestick. Various design questions were examined in pilot campaigns using a full flight simulator and four test scenarios. As a result, the pilots always knew which distance-based hazard area (green, yellow, red) they were in. Furthermore, the ticker is disruptive and roughly reduces the handling qualities from Level 1 to Level 2. It is therefore primarily activated as a hazard warning and not as a main input to control the distance. As a warning cue the ticker was evaluated as non-disturbing. The force threshold to detect the direction of a tick was determined. With tick strengths above this threshold, the direction is still not recognised at all in around 2% of the ticks. For the remaining ticks, the accuracy with which the direction is recognised is about 15°. In the fourth scenario, obstacles were moved towards the hovering helicopter, potentially forcing a collision. However, with the ticker a collision occurred in less than 4% of the cases, instead of 84% without the ticker. The ticker was rated as very intuitive and worth recommending. When asked how many accidents of this kind could be prevented with this ticker, all five pilots independently estimated 75%.
{"title":"Design of a haptic obstacle avoidance for low-speed helicopter operations using active sidesticks","authors":"C. Walko, M. Müllhäuser","doi":"10.1017/aer.2023.48","DOIUrl":"https://doi.org/10.1017/aer.2023.48","url":null,"abstract":"\u0000 Helicopter collisions with obstacles are one of the most frequent and most devastating causes of accidents. To avoid these collisions in low-speed operations a “haptic ticker” cue in form of repetitive impulses as a force feedback was designed for an active sidestick. Various design questions were examined in pilot campaigns using a full flight simulator and four test scenarios. As a result, the pilots always knew which distance-based hazard area (green, yellow, red) they were in. Furthermore, the ticker is disruptive and roughly reduces the handling qualities from Level 1 to Level 2. It is therefore primarily activated as a hazard warning and not as a main input to control the distance. As a warning cue the ticker was evaluated as non-disturbing. The force threshold to detect the direction of a tick was determined. With tick strengths above this threshold, the direction is still not recognised at all in around 2% of the ticks. For the remaining ticks, the accuracy with which the direction is recognised is about 15°. In the fourth scenario, obstacles were moved towards the hovering helicopter, potentially forcing a collision. However, with the ticker a collision occurred in less than 4% of the cases, instead of 84% without the ticker. The ticker was rated as very intuitive and worth recommending. When asked how many accidents of this kind could be prevented with this ticker, all five pilots independently estimated 75%.","PeriodicalId":22567,"journal":{"name":"The Aeronautical Journal (1968)","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81536629","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}
Accurate and reliable computation of the aerodynamic characteristics of wind turbines is very important for the development of new efficient designs. The flow around a wind turbine is modeled by a permeable disc (PD), solved through the Unsteady Reynolds-Averaged Navier–Stokes equations (URANS), here named PD/URANS method. The finite volume method and a total variation diminishing (TVD) scheme solve numerically the flow governing equations. The turbulent flow in the wake of the wind turbine is simulated utilising a one-equation turbulence model. The Glauert correction calculation considers a uniform normal force distribution (CT) on the virtual permeable disc applied to the flow, while the axial induction factor is obtained directly from the numerical solution of the URANS equations. The numerical axial induction factor obtained agrees fairly well with Glauert correction, except if the flow behind the turbine is highly unsteady and Reynolds number dependent.
准确可靠地计算风力机的气动特性对于开发新的高效设计具有重要意义。采用可渗透圆盘(PD)来模拟风力机周围的流动，通过非定常reynolds - average Navier-Stokes方程(URANS)求解，本文将其命名为PD/URANS方法。采用有限体积法和全变分递减(TVD)格式对流动控制方程进行数值求解。利用单方程湍流模型对风力机尾迹湍流进行了模拟。在Glauert校正计算中，考虑了施加在虚拟渗透盘上的均匀法向力分布(CT)，而轴向诱导因子则直接从URANS方程的数值解中获得。得到的数值轴向感应系数与格劳艾特校正相当吻合，除非涡轮后的流动高度不稳定且依赖于雷诺数。
{"title":"Numerical improvement to Glauert correction for the flow around a wind turbine","authors":"J. Wanderley, C. Levi","doi":"10.1017/aer.2023.54","DOIUrl":"https://doi.org/10.1017/aer.2023.54","url":null,"abstract":"\u0000 Accurate and reliable computation of the aerodynamic characteristics of wind turbines is very important for the development of new efficient designs. The flow around a wind turbine is modeled by a permeable disc (PD), solved through the Unsteady Reynolds-Averaged Navier–Stokes equations (URANS), here named PD/URANS method. The finite volume method and a total variation diminishing (TVD) scheme solve numerically the flow governing equations. The turbulent flow in the wake of the wind turbine is simulated utilising a one-equation turbulence model. The Glauert correction calculation considers a uniform normal force distribution (CT) on the virtual permeable disc applied to the flow, while the axial induction factor is obtained directly from the numerical solution of the URANS equations. The numerical axial induction factor obtained agrees fairly well with Glauert correction, except if the flow behind the turbine is highly unsteady and Reynolds number dependent.","PeriodicalId":22567,"journal":{"name":"The Aeronautical Journal (1968)","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87068209","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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