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Aircraft landing gear assemblies comprise of various subsystems working in unison to enable functionalities such as taxiing, take-off and landing. As development cycles and prototyping iterations begin to shorten, it is important to develop and improve practical methodologies to meet certain design metrics. This paper presents an efficient methodology that applies high-fidelity multi-disciplinary design optimization techniques to commercial landing gear assemblies, for weight, cost, and structural performance by considering both structural and dynamic behaviours. First, a simplified landing gear assembly model was created to complement with an accurate slave link subassembly, generated based of drawings supplied from the industrial partner, Safran Landing Systems. Second, a Multi-Body Dynamic (MBD) analysis was performed using realistic input motion signals to replicate the dynamic behaviour of the physical system. The third stage involved performing topology optimization with results from the MBD analysis; this can be achieved through the utilization of the Equivalent Static Load Method (ESLM). Lastly, topology results were generated and design interpretation was performed to generate two designs of different approaches. The first design involved trying to closely match the topology results and resulted in a design with an overall weight savings of 67%, peak stress increase of 74%, and no apparent cost savings due to complex features. The second design focused on manufacturability and achieved overall weight saving of 36%, peak stress increase of 6%, and an estimated 60% in cost savings.

The aircraft landing gear system is vital in ensuring the aircraft’s functional completeness and operational safety. The mechanical structures of the landing gear must withstand significant operational forces, including repeated high-intensity impact loads, throughout their service life. At the same time, they must resist environmental degradation, such as corrosion, temperature fluctuations, and humidity, to ensure structural integrity and long-term reliability. Under this premise, investigating material-related mechanical failures in the landing gear is of great significance for preventing landing gear failures and ensuring aviation safety. Compared to failure investigations, structural health monitoring (SHM) plays a more active role in failure prevention for aircraft landing gears. SHM technologies identify the precursors of potential failures and continuously monitor the operational or health conditions of landing gear structures, which facilitates condition-based maintenance. This paper reviews various landing gear material-related failure investigations. The review suggests a significant portion of these failures can be attributed to material fatigue, which is either induced by abnormal high-stress concentration or corrosion. This paper also reviews a series of load monitoring-based landing gear SHM studies. It is revealed that weight and balance measurement, hard landing detection, and structure load monitoring are the most typical monitoring activities in landing gears. An analytical discussion is also presented on the correlation between reviewed landing gear failures and SHM activities, a comparison of sensors, and the potential shift in load-based landing gear SHM in response to the transition of landing gear design philosophy from safe life to damage tolerance.

This manuscript presents a new approach to describe aircraft landing gear systems equipped with magnetorheological (MR) dampers, integrating a reinforcement learning-based neural network control strategy. The main target of the proposed system is to improve the shock absorber efficiency in the touchdown phase, in addition to reducing the vibration due to rough ground in the taxing phase. The dynamic models of the aircraft landing system in the taxing phase with standard landing ground roughness, one-point touchdown, two-point touchdown, and third-point touchdown are built as the first step. After that, Q-learning-based reinforcement learning is developed. In order to verify the effectiveness of the controller, the co-simulations based on RECURDYN V8R4-MATLAB R2019b of the proposed system and the classical skyhook controller are executed. Based on the simulation results, the proposed controller provides better performance compared to the skyhook controller. The proposed controller provided a maximum improvement of 16% in the touchdown phase and 10% in the taxing phase compared to the skyhook controller.

This paper focuses on the aircraft landing problem with time windows (ALP-TW), which consists of determining a landing schedule for each aircraft within a specified time window and determining the minimum required separation interval between successive operations. This NP-hard state-dependent scheduling problem plays a key role in the operational efficiency of busy airports. We propose a fast and efficient heuristic, called the CAS-TW (Closest Aircraft Sequence with Time Windows), to generate landing sequences that minimize total delay while respecting operational constraints. The method combines a greedy algorithm with a discretization strategy to explore feasible landing intervals. We validate the approach using real data from São Paulo/Guarulhos International Airport (GRU), comparing the CAS-TW to traditional scheduling strategies and optimal solutions obtained via a commercial solver. Computational experiments show reductions in makespan up to 21% in theoretical datasets and 5% in real-world datasets. The CAS-TW solved instances with 50 aircraft in less than 1 s of computation time. The results showed that our algorithm was quickly implemented, equitable, easy to use, and obtained good solutions. These results translated into an increase in airport capacity.

The aircraft landing problem (ALP) is a challenging scheduling and optimization problem in the industry and engineering, which has attracted attention in recent decades. Existing research has predominantly concentrated on optimizing aircraft delay and the financial implications of early or late landings. However, given the paramount significance of airport fuel costs at airports and the critical need for efficient fuel utilization, we aim to minimize airplane fuel consumption by streamlining operational time. In this paper, we present an innovative model with two main objectives: minimizing airplane fuel consumption by reducing dwell time and minimizing cost operation. To address these dual objectives concurrently, we propose a new method known as the multi populations of multiple objectives (MPMO) framework, which is modeled through a non-dominated sorting genetic algorithm-II (NSGA-II) called MPNSGA-II. First, MPNSGA-II employs two separate populations to optimize each objective. Second, to prevent populations from fixating solely on their respective single objectives, MPNSGA-II introduces an archive sharing strategy (ASS). This technique stores elite solutions gathered from two populations. Additionally, we introduce an archive update strategy (AUS) to enhance the quality of solutions stored in the archive. The proposed algorithm has been compared with other well-known algorithms, NSGA-II, multi-objective particle swarm optimization (MOPSO), and NSGA-III. The proposed algorithm shows a cost reduction in 18.01%, 16.75%, and 15.21%. Statistical precision, underscored through the application of the nonparametric Friedman test, corroborates the supremacy of the proposed method, clinching the highest ranking compared to state-of-the-art methods.

The aircraft landing problem (ALP) is an important issue of assigning an airport’s runways to the arrival aircrafts as well as to schedule the landing time of these aircrafts in practice. A large number of the extant studies have tried to address such a practical problem with using various algorithms for one or more runways. For a static single-runway of the ALP, this paper proposes a new approach to develop an alternative powerful algorithm. For a given sequence of planes, we develop a faster algorithm for solving the ALP with the running time O ( n log n ) , where n is the number of aircrafts in the schedule. Alternatively, we reduce the proposed problem of minimizing the total cost by determining the landing times for a given landing sequence into a min-cost flow problem. We conduct a set of experimental studies to compare the performance of our near-linear time algorithm to the quadratic time algorithm whose time complexity is O ( n 2 ) , for computing the optimal landing times. The computational results show that the proposed heuristic based on our algorithm could be much faster than both such quadratic time algorithm and the one using linear programming.

During takeoff and landing, aircraft operate in a variety of situations, posing significant challenges to landing gear systems. Passive hydraulic–pneumatic dampers are commonly used in conventional landing gear to absorb impact energy and reduce vibration. However, due to their fixed damping characteristics and inability to adjust to changing operating conditions, these passive systems have several limitations. Recent research has focused on creating intelligent landing gear systems with magnetic dampers (MR) to overcome these limitations. By changing the magnetic field acting on the MR fluid, MR dampers provide semi-active control of the landing gear dynamics and adjust the damping force in real time. This flexibility reduces structural load during landing, increases riding comfort, and improves energy absorption efficiency. This study examines the current state of MR damper application for aircraft landing gear. The review categorizes current control techniques and highlights the structural integration of MR dampers in landing gear assemblies. Purpose: The magnetorheological (MR) damper has become a promising semiactive system to replace the conventional passive damper in aircraft landing gear. However, the mechanical structure and control strategy of the MR damper must be designed to be suitable for aircraft landing gear applications. Methods: Researchers have explored the potential structure designed, the mathematical model of the MR landing gear system, and the control algorithm that was developed for aircraft landing gear applications. Results: According to the mathematical model of the MR damper, three types of models, which are pseudo-static models, parametric models, and unparameterized models, are detailed with their application. Based on these mathematical models, many control algorithms were studied, from classical control, such as PID and skyhook control, to modern control, such as intelligent control and SMC control.

Accurate fatigue life prediction of aircraft landing gear is crucial for ensuring flight safety and preventing catastrophic structural failures. However, traditional empirical methods face significant limitations in capturing complex multiaxial loading conditions, while machine learning approaches suffer from lack of interpretability in critical safety applications. To address the dual challenges of prediction accuracy and model interpretability, a multi-agent reinforced symbolic regression (MA-RSR) framework is proposed by integrating multi-agent reinforcement learning with symbolic regression (SR) techniques. Specifically, MA-RSR employs a collaborative mechanism that decomposes complex mathematical expressions into parallel components constructed by independent agents, effectively addressing the search space explosion problem in traditional SR. The system incorporates Transformer-based architecture to enhance symbolic selection capabilities, while an intelligent masking mechanism ensures mathematical rationality through multi-level constraints. To demonstrate effectiveness of the proposed method, validation is conducted using SAE4340 steel multiaxial fatigue data and landing gear finite element simulation. The MA-RSR framework successfully discovers two mathematical expressions achieving R2 of 0.96. Compared to traditional empirical formulas, MA-RSR achieves prediction accuracy improvements exceeding 50% while providing complete interpretability that machine learning methods lack. Furthermore, the multi-agent collaborative mechanism significantly enhances search efficiency through parallel expression construction compared to existing symbolic regression approaches.

In recent years, airport runways have become a more critical bottleneck in airports, and it is very unusual to use only one runway to solve the Aircraft Landing Problem (ALP). The ALP includes the aircraft's landing scheduling and assigning them to runways. In addition to certain limited time frames for aircraft while landing, to prevent accidents, the distance between aircraft should be restricted during the flight and in the landing phase. In this paper, to solve the problem in multi-runway mode, a solution is proposed that has considered all the limitations to create a trade-off between the runways to reduce the traffic in the runways. The present study considers the balance between bands and offers a new method of improving the Imperialist Competitive Algorithm (ICA) while reducing the cost due to the early and late landing of the aircraft. In other words, a novel approach for addressing the ALP with multiple criteria, employing a delay and early landing cost optimization technique and runway balance strategy, as well as using multi-runway, which will reflect the current realities of the aviation industry and provide a more accurate and relevant analysis, has been presented. Thirty-two benchmark instances were selected and compared with four famous algorithms: Particle Swarm Optimization (PSO), Immunoglobulin-Based Artificial Immune System (IAIS), Grey Wolf Optimizer (GWO), and flower pollination algorithm (FPA) as the results on a small-scale indicate, the ICA method performs superior outcomes, except for one case. Regarding the two objectives, the presented method, compared to other methods, managed to reduce the cost by 3.5% (on a small-scale). Furthermore, on a large scale (500 aircraft), improved ICA has been able to reduce the cost of the early or late arrival of the aircraft by 35%.

Camera localization approaches often degrade in challenging environments characterized by illumination variations and significant viewpoint changes, presenting critical limitations for fixed-wing aircraft landing applications. To address these challenges, we propose LandNet—a novel absolute camera pose estimation network specifically designed for airborne scenarios. Our framework processes images from forward-looking aircraft cameras to directly predict 6-DoF camera poses, subsequently enabling aircraft pose determination through rigid transformation. As a first step, we design two encoders from Transformer and CNNs to capture complementary spatial–temporal features. Furthermore, a novel Feature Interactive Block (FIB) is employed to fully utilize spatial clues from the CNN encoder and temporal clues from the Transformer encoder. We also introduce a novel Attentional Convtrans Fusion Block (ACFB) to fuse the feature maps from encoder and transformer encoder, which can enhance the image representations to promote the accuracy of the camera pose. Finally, two Multi-Layer Perceptron (MLP) heads are applied to estimate 6-DOF of camera position and orientation, respectively. Thus the estimated position and orientation of our LandNet can be further used to acquire the pose and orientation of the aircraft through the rigid connection between the airborne camera and the aircraft. The experimental results from simulation and real flight data demonstrate the effectiveness of our proposed method.