Flight Management Research Articles

Improved modelling of low-pressure rotor speed in commercial turbofan engines: A comprehensive analysis of machine learning approaches

Accurate engine thrust modelling offers notable opportunities for reducing fuel consumption, mitigating emission effects, managing air traffic, and optimizing the preliminary design of aircraft. This study focuses on the development of machine learning models to predict the low-pressure rotor speed (N1) parameter, which is closely related to the thrust of turbofan engines, for commercial aircraft during the cruise phase. The analysis utilizes a Flight Data Recorder (FDR) dataset from 1086 actual flights involving twin-engine, narrow-body, short-to-medium haul commercial aircraft equipped with CFM56-7B turbofan engines. To achieve accurate N1 parameter predictions, the study employs machine learning models, including Deep Learning (DL), Random Forest (RF), Gradient Boosted Machines (GBM), and Artificial Neural Networks (ANN). These models are evaluated using statistically significant indicators, demonstrating that machine learning models yield highly accurate results. Among the models tested, the DL model provides the most precise estimates of the N1 parameter. This study not only advances the understanding of engine thrust modelling through machine learning but also provides practical insights for the aerospace industry. The findings underline the potential of machine learning techniques in delivering superior prediction accuracy, which can be integrated into real-time flight management systems, ultimately contributing to more sustainable and efficient aviation operations.

Flight Plan Optimisation of Unmanned Aerial Vehicles with Minimised Radar Observability Using Action Shaping Proximal Policy Optimisation

The increasing use of unmanned aerial vehicles (UAVs) is overwhelming air traffic controllers for the safe management of flights. There is a growing need for sophisticated path-planning techniques that can balance mission objectives with the imperative to minimise radar exposure and reduce the cognitive burden of air traffic controllers. This paper addresses this challenge by developing an innovative path-planning methodology based on an action-shaping Proximal Policy Optimisation (PPO) algorithm to enhance UAV navigation in radar-dense environments. The key idea is to equip UAVs, including future stealth variants, with the capability to navigate safely and effectively, ensuring their operational viability in congested radar environments. An action-shaping mechanism is proposed to optimise the path of the UAV and accelerate the convergence of the overall algorithm. Simulation studies are conducted in environments with different numbers of radars and detection capabilities. The results showcase the advantages of the proposed approach and key research directions in this field.

Pilots' Reactions to Different Types of Alerts When Using Head-Up Displays.

INTRODUCTION: The benefits of using head-up displays (HUDs) include reducing head-down time during critical flight phases, enhancing awareness of the external environment, and improving in-flight crew performance. However, the monochromatic nature of HUDs, increased head rotation, and longer gaze movement paths might affect pilots' reactions to different types of alerts.METHODS: Pilot workload and behavior differences were examined between HUD and head-down display (HDD) configurations in three alert scenarios. The study was carried out in an A320 flight simulator and 12 pilots participated.RESULTS: Except for one engine-on-fire scenario, pilot workload when using a HUD was significantly lower than using an HDD. In one engine-on-fire (3.98 s vs. 3.57 s) and one gear-disagree (5.42 s vs. 4.69 s) scenario, pilot response time to alerts using HUD was significantly longer than using an HDD. The angle deviations were significantly smaller when using HUDs in both go-around-under-crosswind (2.67° vs. 3.37°) and one engine-on-fire scenario (1.22° vs. 1.89°).DISCUSSION: The HUD is suitable for a lengthy process of manual flight control inputs, which not only reduces workload but also promotes control accuracy. For tasks that rely on automation, the benefits for workload become less obvious. In addition, head rotation and reorientation of attention adversely affected the response time to non-time-critical warnings and cautions. For instantaneous control with high precision requirements, HUDs did not demonstrate a significant advantage.Zheng Y, Lu Y, Jie Y, Fu S. Pilots' reactions to different types of alerts when using head-up displays. Aerosp Med Hum Perform. 2024; 95(9):688-694.

Adaptive Navigation Performance Evaluation Method for Civil Aircraft Navigation Systems with Unknown Time-Varying Sensor Noise.

During civil aviation flights, the aircraft needs to accurately monitor the real-time navigation capability and determine whether the onboard navigation system performance meets the required navigation performance (RNP). The airborne flight management system (FMS) uses actual navigation performance (ANP) to quantitatively calculate the uncertainty of aircraft position estimation, and its evaluation accuracy is highly dependent on the position estimation covariance matrix (PECM) provided by the airborne integrated navigation system. This paper proposed an adaptive PECM estimation method based on variational Bayes (VB) to solve the problem of ANP misevaluation, which is caused by the traditional simple ANP model failing to accurately estimate PECM under unknown time-varying noise. Combined with the 3D ANP model proposed in this paper, the accuracy of ANP evaluation can be significantly improved. This enhancement contributes to ensured navigation integrity and operational safety during civil flight.

Drones in the Airspace of the Republic of Poland - steps to Safe Flights of UAS Over Poland

The emergence of a new user of the airspace of the Republic of Poland, Unmanned Aerial Vehicles - drones, as part of regular flights, implies the need not only to provide an appropriate legal framework, but also to rebuild the entire air traffic management ecosystem for the integration of manned and unmanned aviation. The first attempts to include UAS in the integrated air traffic system in the Warsaw FIR were based on the electronic/digital planning, coordination and management of UAS flights called Pansa UTM (Polish Air Navigation Services Agency Unmanned Traffic Management). This system is an innovative solution used by the Polish Air Navigation Services Agency (PANSA) and potential drone users. The operation and development of the Pansa UTM system generates the need for air traffic management entities to take specific actions by planning and establishing regulations, rules, safety criteria and flight conditions for the new airspace user. This requires involvement in projects, pilotages and technology demonstrators aimed at creating adequate tools for the safe implementation of UAS flights in Poland. These activities, both already implemented and at the planning stage, aim to achieve a state in which it will be possible to talk about integrated and safe controlled air traffic over Poland. Due to the dynamic nature of the subject matter, this study is only an attempt to present a number of projects undertaken by a wide range of aviation-related entities that may allow achieving an acceptable level of air traffic management over the territory of the Republic of Poland.

Leadership and Next Generation Unmanned System Integration

The purpose of this study is to explore the challenges and successes Aerospace Industry leaders encounter regarding the safe integration of unmanned aerial vehicles (UAVs) that weigh over 55 lbs. into the National Airspace System (NAS). Specifically, how will leadership in the Aerospace Industry respond to a rogue UAV flying in the National Airspace when the communication link is lost due to hardware or software failure. A qualitative intrinsic case study was used for this study. Using snowball sampling, a total of 15 participants responded to 12 open-ended survey questions. The findings presented challenges the Aerospace Industry must overcome for larger UAVs to fly in the national airspace. The major themes identified were: (a) Issues with integration efforts between the FAA and the Aerospace Industry, (b) Safety in the national airspace for all aircraft, and (c) Progress and Challenges. The study recommends that the Aerospace Industry consider implementing Industry 4.0 technology into their business, examining the UAV flight management system, and a redesigning of the COA process.

Pilot Assistance Systems for Energy-Optimized Approaches: Is It Possible to Reduce Fuel Consumption and Noise at the Same Time?

Air traffic has appreciable environmental impacts, especially regarding gaseous emissions and noise. Recent studies have shown that the energy management during approach is a driving factor regarding environmental impact and is especially challenging for pilots. In a previous project, a newly developed pilot assistance system called LNAS (Low Noise Augmentation System) showed the potential of energy-optimized approaches to reduce fuel consumption and noise. Within the SESAR Exploratory Research project DYNCAT, novel functions based on LNAS have been integrated in the flight management system. In this contribution, results from real-time simulations with the enhanced FMS are presented, and mitigation of the environmental impact is analyzed. It was shown that with DYNCAT, the energy management could be improved, resulting in a later configuration and engines mostly in idle. With DYNCAT, procedures were also flown more uniformly and the variability in noise and fuel outcomes was reduced. However, the results revealed a trade-off for optimizing noise and fuel consumption simultaneously, whereby both parameters can be improved along specific optimum curves. A perfect strategy to minimize noise would be to first reduce speed and only secondly height, as high speeds lead to higher levels of airframe noise and sound exposure increases with decreasing distance. In contrast, saving fuel might be achieved by reducing the flight time, as the engines consume fuel even when being in idle.

Travelers' perception of smart airport facilities: An X (Twitter) sentiment analysis

This study investigates travelers' perceptions of smart airport facilities through sentiment analysis. It collected 39,616 comments from thirteen smart airports, and 25,572 tweets were analyzed. Most mentioned words about the smart airport included "technology" and "security". Comments also highlighted concerns about airport customer service, including "bag," "transit," and "disabled." The results also reveal that travelers are more concerned about the outcome of these facilities, such as the efficiency and time management of flights via smart facilities. The most mentioned words in the tweets related to smart airport operations are "flight," "waiting," and "time". Despite most smart facilities being equipped in the airports aiming to raise travelers' satisfaction, the results showed that 12 smart airports' tweets were generally neutral. Smart airport facilities might not add as much value to the airport impression as we expect. Brisbane International Airport was the only one with a positive perception of smart airport facilities. Most travelers mentioned that the airport had faster and better wifi and the airport is continuously improving. In contrast, Charles de Gaulle Airport and London Heathrow Airport had the highest percentage of negative sentiment, with 25.90% and 27.52% of tweets being negative, respectively. Travelers' complained that self-check-in kiosks were a mess, unhelpful staff and poor wifi. The results reveal travelers' concerns regarding smart airport facilities, and they let us know the importance of smart facility management. Smart airport facilities were initially designed to shorten travelers' time and enhance satisfaction, yet unhelpful staff and poor managed kiosks raise dissatisfaction. This study helps airport managers and operators to address the weakest part of smart airports as reflected in social media comments. It also fills the academic voids in examining travelers' satisfaction with smart facilities using social media and sentiment analysis via artificial intelligence.

Multicast Radar Data Transmission Using the Tunnel in Tunnel Method and IGMP (Internet Group Management Protocol) Filtering by Utilizing the Internet Network

This research proposes the implementation of multicast radar data transmission using the tunnel in tunnel method by utilizing the installed IP VPN network. The background of this study is the failure in radar data transmission resulting in a change of service from surveillance to procedural service, as well as the impact of the Covid-19 pandemic on aviation organizations. The purpose of this implementation is to enhance flight safety while achieving cost efficiency for the company. Objectives include changing transmission methods, budget efficiency, improving operational safety, compliance with international standards, and support for the concept of flight information management. Advantages of multicast radar data transmission include centralized IP base, ease of monitoring, and the use of FO converters for surge protection. Implementation is based on aviation regulations, international standards, and partnership contracts. Contingency plans cover network and device failure scenarios. Cost benefit analysis shows significant financial gains from this implementation. It is hoped that this research will make a positive contribution to improving efficiency and operational safety in aviation.

Formal modelling and verification of autonomous reasoning based flight simulation system

The emerging trends towards intelligent computing have drastically changed the human lifestyle in every facet of their lives. Due to the swift escalation of smart systems and emerging technologies, human life has become much more dependent and even more addicted to fulfilling their desire using smart and tiny resource-bounded gadgets. This revolution has evolved towards autonomous decision support systems. Autonomous decision support systems have the ability to acquire information autonomously, reason the information, and adapt behavior accordingly. As these systems are deployed in a highly decentralized environment and exhibit complex adaptive behavior, however, the inconsistent nature of information may raise different challenging issues. This paper presents a multi-agent environmentally-aware framework for modeling and reasoning flight management systems. This system has a sound reasoning mechanism to execute and monitor flight control activities while considering liveness and safety-critical constraints. We use the UPPAAL model checker to formally analyze the system’s behavior and verify correctness properties.

Synergy in Future Avionics: An Overview of Multiple Technologies for Small Air Traffic Segment in the COAST project

The paper describes research and development activities under Clean Sky 2 Cost optimized Avionic System (COAST) program. The main goal of this development was to deliver technology enablers at TRL 5 for affordable cockpit and avionics. The target segment for the technology enablers is aircraft with 1 to 19 passengers and small cargo aircraft belonging to CS-23 category. The main aim is to provide overall summer during the whole COAST program development per individual technology. Sections are divided per each technology with their results and overall contribution to the program. The Clean Sky 2 COAST program covered the development of following technologies: Cockpit Architecture SAT avionic system architecture, Flight Management Tactical Separation System (TSS), Advanced Weather Awareness System (AWAS), Flight Reconfiguration System (FRS), Navigation and Surveillance Dual Frequency Multi-Constellation GNSS Receiver (GNSS), Low-cost Integrated Navigation System (NAV), Affordable Integrated Surveillance System (SURV), Platform technologies Compact Computing Platform (CCP), High Integrity Electronics for health monitoring (HIE), Integrated Mission Management System Integrated Mission Management System (IMMS). These technologies were part of several flight test campaigns which took place in the Czech Republic with Evektor company using EV-55 aircraft.

Evaluating fatigue management regulations for flight crew in Australia using a new Fatigue Regulation Evaluation Framework (FREF)

Flight crew experience fatigue due to sleep loss, circadian desynchrony, early duty start times, long duty periods, high and low workloads, and unscheduled duties. As fatigue is considered a significant safety risk in the air transport industry, the primary approaches to managing fatigue are mandating prescriptive limits for work and rest periods or adopting performance-based fatigue risk management systems (FRMS). However, recent aviation incidents indicate that current regulations may not be adequate to manage the fatigue risk experienced by the flight crew. This study evaluates Australian flight crew fatigue management regulations using a new fatigue regulation evaluation framework (FREF) adapted from Jones et al. (2005). Results show that Australian regulations for flight crew include limits on flight duty period (FDP), off duty period, FDP start time, sleep requirements for flight crew before starting an FDP, high and low workloads, circadian rhythm disruptions, and fatigue awareness. However, there are regulation variations in addressing lengths of sectors and methods to report fatigue, which may need to be reviewed to manage fatigue better. Recommendations are made to consider incorporating these factors into fatigue regulations to ensure a safe air transport system.

A 3D urban scene reconstruction enhancement approach based on adaptive viewpoint selection of panoramic videos

AbstractThe widely used unmanned aerial vehicle oblique photogrammetry often suffers from information loss in complex urban environments, leading to geometric and textural defects in the resulting models. In this study, a close‐range panoramic optimal viewpoint selection assisted 3D urban scene reconstruction enhancement method is proposed for areas prone to defects. We first introduce the ground panoramic data acquisition equipment and strategy, which are different from those of the single‐lens supplementary photography method. Data acquisition is accomplished through a single and continuous surround‐style collection approach. The full coverage of the panoramic video in the space–time dimension enables the acquisition of texture details without considering camera station planning. Then, a panoramic multiview image generation approach is proposed. Adaptive viewpoint selection is achieved using unbiased sampling points from the rough scene model, and viewpoint optimisation is adopted to ensure sufficient image overlap and intersection effects, thus improving the scene reconstructability. Finally, the 3D model is generated by photogrammetric processing of the panoramic multiview images, resulting in an enhanced modelling effect. To validate the proposed method, we conducted experiments using real data from Qingdao, China. Both the qualitative and quantitative results demonstrate a significant improvement in the quality of geometric and textural reconstruction. The tie‐point reprojection errors are less than 1 pixel, and the registration accuracy with the model from oblique photogrammetry is comparable to that of optimised‐view photography. By eliminating the need for on‐site camera station planning or manual flight operations and effectively minimising the redundancy of panoramic videos, our approach significantly reduces the photography and computation costs associated with reconstruction enhancement. Thus, it presents a feasible technical solution for the generation of urban 3D fine models.

Path Planning via Swarm Intelligence Algorithms in Unmanned Aerial Vehicle Population

Unmanned Aerial Vehicle (UAV) is an autonomous aerial vehicle capable of operating autonomously or in swarm cooperation, performing various tasks in civilian and military domains that exceed human capabilities. These vehicles, which can be produced in different models with varying hardware and software features, include flight control systems, route tracking systems, sensors, and numerous additional components. UAVs have the ability to process data from themselves, the control center, and the external environment. Data processing enables functions such as flight management, swarm optimization, and target and route analysis. In this analysis process, optimization algorithms and especially swarm intelligence algorithms inspired by creatures that move in flocks in nature are used. In this study, the aim was to determine the optimal route and distance from 10 different coordinate points for collective task optimization within a UAV swarm. Artificial Bee Colony (ABC) Optimization and Particle Swarm Optimization (PSO) were used during the task optimization process. The application was coded in Python. As a result of the application, the optimal distance was calculated as 0.123 km for the ABC algorithm and 0.167 km for the PSO algorithm. In addition, both algorithms determined the best routes according to different start and end points in route planning task optimisation.

Improved A-Star Path Planning Algorithm in Obstacle Avoidance for the Fixed-Wing Aircraft

The flight management system is a basic component of avionics for modern airliners. However, the airborne flight management system needs to be improved and relies on imports; path planning is the key to the flight management system. Based on the classical A* algorithm, this paper proposes an improved A* path planning algorithm, which solves the problem of low planning efficiency and following a non-smooth path. In order to solve the problem of the large amount of data calculation and long planning time of the classical A* algorithm, a new data structure called a “value table” is designed to replace the open table and close table of the classical A* algorithm to improve the retrieval efficiency, and the Heap sort algorithm is used to optimize the efficiency of node sorting. Aiming at the problem that the flight trajectory is hard to follow, the trajectory smoothing optimization algorithm combined with turning angle limit is proposed. The gray value in the digital map is added to the A* algorithm, and the calculation methods of gray cost, cumulative cost, and estimated cost are improved, which can better meet the constraints of obstacle avoidance. Through the comparative simulation verification of the algorithm, the improved A* algorithm can significantly reduce the path planning time to 1% compared to the classical A* algorithm; it can be seen that the proposed algorithm improves the efficiency of path planning and the smoother planned path, which has obvious advantages compared to the classical A* algorithm.

Neural Network‐based Method to Compute Recommended Cruise Level and Optimal Climb Location

AbstractSystem engineering towards utilizing machine learning solutions in the aviation domain is a fast‐progressing discipline. This paper tries to bring out the system engineering and implementation aspects of ML solution as an improvement in a sub‐system of an existing aviation system. The flight management systems (FMSs) compute the recommended cruise level (RCL) considering the aircraft's gross weight, its speed schedule, and the forecast wind. The RCL increases with distance in the no wind conditions as the fuel get consumed, reducing the gross aircraft weight. The RCL profile may have frequent climbs and descents in the presence of winds, which is not desired for flight operations.The document proposes a neural network‐based method for computing an operationally acceptable cruise profile considering the wind and temperature weather forecast. The method recommends step climbs and step descents (optimal level and optimal step location) in such a way that they are separated by a large enough cruise phase. From system engineering point of view two guiding documents for such an implementation to look forward are “EASA Concept Paper: First usable guidance for Level 1 machine learning applications” [12] and “Process Standard for Development and Certification/Approval of Aeronautical Safety‐Related Products Implementing AI” [13].Two neural networks have been created to estimate wind compensated ECON cruise speeds and required time‐fuel to cover the next 500NM distance. The RCL profiles are generated for various test wind scenarios for Airbus A220 aircraft. It has been recorded the proposed neural network‐based method is better at recommending a cruise altitude and it is 9X faster than the existing method to calculate the RCL and optimal climb location. The neural network‐based design also helped in reducing the memory footprint by more than 100 times as there is no requirement of storing the performance database files required by the existing system. Only the parameters of the trained neural networks are required to be stored. Most importantly, the RCL computed by the proposed neural network‐based method yields a smaller operational cost than that determined by the existing method. The proposed method is designed to replace the existing traditional subsystem to give an overall improved performance while the functionalities of other subsystems remain unaffected.

Airport service quality perception and flight delays: Examining the influence of psychosituational latent traits of respondents in passenger satisfaction surveys

The service quality of a passenger transport operator can be measured through face-to-face surveys at the terminals or on board. However, the resulting responses may suffer from the influence of the intrinsic aspects of the respondent's personality and emotional context at the time of the interview. This study proposes a methodology to generate and select control variables for these latent psychosituational traits, thus mitigating the risk of omitted variable bias. We developed an econometric model of the determinants of passenger satisfaction in a survey conducted at the largest airport in Latin America, São Paulo GRU Airport. Our focus was on the role of flight delays in the perception of quality. The results of this study confirm the existence of a relationship between flight delays and the global satisfaction of passengers with airports. In addition, favorable evaluations regarding airports' food/beverage concessions and Wi-Fi services, but not their retail options, have a relevant moderating effect on that relationship. Furthermore, dissatisfaction arising from passengers' interaction with the airline can have negative spillover effects on their satisfaction with the airport. We also found evidence of blame-attribution behavior, in which only delays of internal origin, such as failures in flight management, are significant, indicating that passengers overlook weather-related flight delays. Finally, the results suggest that an empirical specification that does not consider the latent psychosituational traits of passengers produces a relevant overestimation of the absolute effect of flight delays on passenger satisfaction.

A coordinated optimization method of energy management and trajectory optimization for hybrid electric UAVs with PV/Fuel Cell/Battery

There are complex coupling relationships between flight motion and energy management for hybrid electric fixed wing UAVs with photovoltaic/fuel cell/battery. Aiming at realizing high-energy-efficiency long endurance autonomous flight, this paper proposes a coordinated optimization method of trajectory optimization and energy management for hybrid electric UAVs. Numerical simulation and experiment are carried out. Separated by surplus demand energy except that produced by PV, this method includes an optimal energy flight trajectory optimization layer and an energy management layer. For the former, fuzzy neural network sequential convex optimization (FNNSCP) is proposed to handle the flight trajectory optimization problem for maximizing utilization solar energy. For the latter, an online energy management strategy based on convex quadratic programming MPC (CQPMPC) is proposed to realize the power allocation. Numerical simulation results show that FNNSCP reduced 2% energy requirement compared with Radau pseudo-spectral method (RPM) from flight trajectory optimization level. From energy management level, CQPMPC saves hydrogen consumption by 3.1% and 16.3% compared with nonlinear model predictive control (NMPC) and fuzzy logic state machine (FLSM), respectively. In the experiment, less hydrogen fuel is consumed by proposed method by 5.2% and 15.6% compared with NMPC and FLSM, respectively. Experimental results show that the proposed method has better energy-saving performance and excellent real time performance. It indicates that optimizing flight trajectory and improving energy management efficiency can realize the high-energy-efficiency flight of hybrid electric UAVs. This method is conducive to promoting the application of hydrogen energy in the field of UAVs.

Functional Analysis and Validation of a UAM Integrated Flight Management (UIFM) System

Functional Analysis and Validation of a UAM Integrated Flight Management (UIFM) System

Wing strike reduction for small fixed wing uncrewed aerial vehicles

This work presents the development of a leading edge strike resilient system for small, fixed wing unmanned aerial vehicles. This method uses passive, mechanically-linked wing sweep motion with a spring and buckling strip in parallel. Additive manufacturing is leveraged to prototype a geared substructure where rotation is resisted by a buckling strip and wing position is restored after impact by a linear spring. Experimental validation of the mechanism is conducted via drop-tower testing. For a rigid configuration the yaw impulse associated with impact grows with impact speed, while yaw impulse is thresholded by the yaw alleviation system. For the tested cases, impact moment is reduced by approximately 50% compared to the rigid case. A wind tunnel model was created to study the impact loading and changes in aerodynamic loads as the wings sweep backward and return forward after impact. Flight experimentation was conducted for symmetric and asymmetric impacts with manual flight recovery after impact while flying at 31 m/s at 2.4 m altitude. In the laboratory and in flight, mechanical impact loading on the vehicle is significantly reduced at a variety of airspeed and impact speeds with aerodynamic loading shown to damp the detrimental moments associated with leading edge strike.