Aircraft Operations Research Articles

Smart Materials And Their Applications In Aerospace Engineering

The dual imperatives of improving performance and guaranteeing safety while maximizing efficiency are driving the aerospace industry's continuous innovation. Smart materials have become a ground-breaking option as the need for cutting-edge technology grows. Real-time adaptability to changing operating circumstances is made possible by these materials' exceptional capacity to react dynamically to external stimuli including temperature, stress, electric forces, and magnetic fields. This study explores the many kinds of smart materials and looks at how they work, including magnetorheological fluids, electrostrictive polymers, shape memory alloys (SMAs), and piezoelectric materials. The conversation also covers the wide range of uses for these materials in aerospace engineering, from vibration control and thermal management to adaptable structures and structural health monitoring. The aircraft industry can significantly increase performance, reduce weight, and improve safety by utilizing the multipurpose qualities of smart materials. This study also discusses the difficulties in incorporating smart materials into current systems, highlighting the necessity of continued research and development to realize their full potential. The study concludes that smart materials, which have the potential to completely transform aircraft design and operation, are not only a fad but rather an essential part of the aerospace engineering landscape of the future

Health monitoring of the composite honeycomb insulation panels using thermographic image processing

ABSTRACT Honeycomb-structured materials are extensively utilised in both commercial and military aircraft. The occurrence of manufacturing defects and operational damage has emerged as a significant safety concern, consequently elevating the necessity for non-destructive testing (NDT) to identify flaws and damage during aircraft operation and maintenance. In addition to merely detecting defects, it is crucial to accurately characterise or classify them. In this paper, the honeycomb sandwich samples were designed and manufactured from two CFRP face-sheets and Nomex core with thick resin containing nano clay 30B mixed with ultrasonic coverage. Defects were placed into carbon sheets, resin, and core materials. An NDT technique based on infrared thermography was applied, along with PCA image processing, which automatically categorises prevalent defects found in honeycomb materials. These defects encompass debonding, adhesive issues, and fractures within the honeycomb core. Thermography results showed that defects such as delamination, core fracture, lack of adhesion, and resin inhomogeneity can be identified, especially with high accuracy using PCA image processing.

Planning and Evaluation of Water-Dropping Strategy for Fixed-Wing Fire Extinguisher Based on Multi-Resolution Modeling

The deployment of fixed-wing aircraft in fire-extinguishing operations represents a significant advancement in the domain of aviation emergency rescue. Addressing the challenge of enhancing firefighting efficacy, this study delves into the water-dropping strategies of fixed-wing extinguishers and provides a methodological framework for the strategic planning and assessment of water-dropping tactics, employing multi-resolution modeling. The formulation of the planning algorithm and the structure of the effectiveness evaluation index system are explained accordingly. The corresponding prototype system was designed, comprising four subsystems that utilized distinct resolution models: fire environment simulation, water-dropping point scheme planning, approaching path planning, and mission evaluation simulation. Case studies validate the system’s capability to forecast fire and smoke propagation, plan a water-dropping trajectory based on the fire line, optimize flight paths based on the trajectory, and simulate as well as evaluate the whole firefighting mission process. The above research comprehensively constructs the model, finishes the iterative optimization, and evaluates the water-dropping strategy by simulation. The technical path and methodological framework of studying water-dropping strategies are established. The outcomes of this study provide invaluable support for the parameter inversion design of the fixed-wing extinguisher, offering decision-making assistance to commanders and supplying training scenarios for new aviation crews.

Advanced semantic segmentation of aircraft main components based on transfer learning and data-driven approach

Abstract The implementation of Smart Airport and Airport 4.0 visions relies on the integration of automation, artificial intelligence, data science, and aviation technology to enhance passenger experiences and operational efficiency. One essential factor in the integration is the semantic segmentation of the aircraft main components (AMC) perception, which is essential to maintenance, repair, and operations in aircraft and airport operations. However, AMC segmentation has challenges from low data availability, high-quality annotation scarcity, and categorical imbalance, which are common in practical applications, including aviation. This study proposes a novel AMC segmentation solution, employing a transfer learning framework based on a sophisticated DeepLabV3 architecture optimized with a custom-designed Focal Dice Loss function. The proposed solution remarkably suppresses the categorical imbalance challenge and increases the dataset variability with manually annotated images and dynamic augmentation strategies to train a robust AMC segmentation model. The model achieved a notable intersection over union of 84.002% and an accuracy of 91.466%, significantly advancing the AMC segmentation performance. These results demonstrate the effectiveness of the proposed AMC segmentation solution in aircraft and airport operation scenarios. This study provides a pioneering solution to the AMC semantic perception problem and contributes a valuable dataset to the community, which is fundamental to future research on aircraft and airport semantic perception. Graphical abstract

Estimation of the Partial Discharge Inception Voltage of Electrical Asset Components at Variable Environmental Pressure: A Modelling Approach

Since partial discharges, PD, are a major accelerated degradation mechanism of organic electrical insulation systems, measuring partial discharge inception voltage, PDIV, of electrical asset components in aviation and aerospace is a fundamental tool to cope with life, safety, and reliability requirements. Partial discharge phenomenology and inception voltage depend on pressure, specifically, PDIV decreases with pressure. To avoid PD inception during aircraft or aerospace vehicle operation, the value of PDIV must be known at any pressure that electrical asset components will experience. However, a lack of experimental facilities adequate to test PD on real-size asset components might prevent from having PD-related information at pressures lower (or higher) than standard atmospheric pressure, SAP. This paper presents a heuristic approach, based on physics-derived PD field inception models, that allows the estimation of PDIV at low pressure to be carried out based on measurements made at SAP, when the typology of the defect causing partial discharge is known (from SAP PD measurements), but the precise type, size, and location of the PD-generating defect is unknown. It is shown that PDIV estimates obtained by the proposed models for internal and surface discharges are in good agreement with measured values in a range from SAP to 0.05 bar, testing simple insulation geometries but also real asset components, such as motors.

Application of Causal Analysis based on Systems Theory (CAST) to Regulatory Decision-Making: A Case Study of the Sikorsky S92A

Ensuring aviation safety requires maintaining the integrity of product design and operations. The Federal Aviation Administration (FAA) regulates Transport Category rotorcraft design through 14 CFR Part 29, establishing Categories A and B of certification for multiengine rotorcraft, and requires aircraft to be operated according to the certified procedures in flight manuals. This paper presents a case study of the Sikorsky S92A, a Transport Category rotorcraft that is not certified for elevated helideck operations according to Part 29, but operates primarily in the offshore market through FAA-authorized exemptions from applicable regulation. To understand how this discrepancy between the aircraft's certification and operation came to be, a relatively new accident analysis methodology called Casual Analysis based on Systems Theory (CAST) is applied to a hypothetical accident involving an S92A, strongly based on a real incident described in a service difficulty report. The CAST results identify unsafe decisions on the part of flight crews, air operators, the aircraft manufacturer, and the FAA that contribute to the accident. We explain these contributions by identifying several systemic factors generalizable to the entire offshore rotorcraft industry that underlie unsafe decisions, and we propose a set of recommendations to address them.

Implications of Wet Leasing to Paper Airlines: Generating Capacity Outside an Air Service Agreement

Operating aircraft under leasing has increased greatly over the past twenty years. One type of aircraft leasing, wet leasing, is the practice of providing not only the aircraft to the lessee but also at least some of the crew, and potentially the maintenance, and insurance for the aircraft. In a wet lease agreement, because the lessor usually provides the majority of the aircraft’s servicing functions, the lessor is typically considered the operator of the aircraft but the aircraft is flown under the lessee’s designator code. In states where substantial ownership regulations are less restricted, an airline in one state can own a paper airline in another state. In such a scenario, the lessor may own the foreign airline that it wet leases to and use the lessee’s traffic rights.This paper focuses on the implications of a lessor wet leasing to a foreign paper airline in which the lessor has a financial stake. One such implication includes how the lessor can use the lessee’s traffic rights to generate a route between the lessor airline’s state of registry and a third state without an air service agreement between the states. The primary, and seemingly only, example of this innovation is the TACA airlines arrangement with LACSA that created a route between El Salvador and Canada from 2006 to 2010, when El Salvador and Canada did not have an air service agreement. Other implications of similarly innovative wet leasing arrangements to a paper airline or struggling airline are also analysed, such as an expansion of capacities beyond that provided in an air services agreement (ASA).

Drone Regulation and AI Law: Assessing the Intersection of the EU Legal Frameworks for Unmanned Aircraft and Artificial Intelligence

Drones are becoming a more familiar sight in the skies. This growth has potential implications for all aspects of society. To ensure safe, secure and sustainable drone activities, regulatory authorities have been revoking, amending or introducing laws. The primary focus has been on aviation safety rules for the design, manufacture and operation of these unmanned aircraft. These rules do not directly address other domains. This paper assesses how the EU’s drone-specific safety-based rules interact with the EU’s AI legal framework that is currently being developed. While drone rules do not address AI directly, they do indirectly as, for example, autonomous drone operations are permitted in the single European sky. The general AI legal framework also applies to drone activities, despite such not being mentioned. This paper brings together the sector-specific EU drone rules and the EU’s generalAI legal framework to understand what the term ‘AI’ means within the context of drone operations, and assesses whether there are any inconsistencies, contradictions, overlaps or lacunae. This will determine the applicability and suitability of applying the general AI legal framework to a specific technology (drones) and sector (aviation), and whether the drone rules can contribute to the regulation of AI more generally.

Daily Engine Performance Trending using Common Flight Regime Identification

Abstract Accurate flight regime identification is critical for enhancing aircraft efficiency and safety. Traditionally, predictive models for aircraft operation have relied on complex, black-box machine learning techniques that lack transparency. This study introduces a more interpretable approach by leveraging the New Comprehensive Modular Aero-propulsion System Simulation (N-CMAPSS) dataset and combining expanding window classification, voting schemes, and spectral clustering to detect distinct flight regimes. The method applies elastic registration to align time-shifted patterns and Functional Principal Component Analysis (FPCA) to reduce dimensionality, capturing core dynamics across flight regimes. These transformed features are fed into a genetic algorithm-assisted orthogonal matching pursuit for sparse feature selection. Through evolutionary selection, crossover, and mutation, the most informative features are identified, enabling accurate predictions while maintaining transparency. This method outperforms more complex models in certain test cases, offering a balance between accuracy and interpretability that is essential for predictive maintenance and safety applications.

Aircraft human‐machine interaction assistant design: A novel multimodal data processing and application framework

AbstractDuring aircraft operations, pilots rely on human‐machine interaction platforms to access essential information services. However, the development of a highly usable aerial assistant necessitates the incorporation of two interaction modes: active‐command and passive‐response modes, along with three input modes: voice inputs, situation inputs, and plan inputs. This research focuses on the design of an aircraft human‐machine interaction assistant (AHMIA), which serves as a multimodal data processing and application framework for human‐to‐machine interaction in a fully voice‐controlled manner. For the voice mode, a finetuned FunASR model is employed, leveraging private aeronautical datasets to enable specific aeronautical speech recognition. For the situation mode, a hierarchical situation events extraction model is proposed, facilitating the utilization of high‐level situational features. For the plan mode, a multi‐formations double‐code network plan diagram with a timeline is utilized to effectively represent plan information. Notably, to bridge the gap between human language and machine language, a hierarchical knowledge engine named process‐event‐condition‐order‐skill (PECOS) is introduced. PECOS provides three distinct products: the PECOS model, the PECOS state chart, and the PECOS knowledge description. Simulation results within the air confrontation scenario demonstrate that AHMIA enables active‐command and passive‐response interactions with pilots, thereby enhancing the overall interaction modality.

Enter the CORSIA First Phase

Abstract The Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) entered its First Phase on 1 January 2024. Aircraft operators are required to offset their emissions for the first time in this Phase to mitigate their climate impact. This note chronologises its major developmental trajectory leading up to this Phase. Furthermore, developments of climate initiatives at the regional level are related to CORSIA. It is noteworthy to see CORSIA from a European Union (EU) perspective because of the recent amendment to the EU Emissions Trading System (EU ETS). The European Commission has set a ‘deadline’ to review the effectiveness of CORSIA and made efforts to establish the EU ‘world premiere’ in the monitoring, reporting, and verification (MRV) of aviation non-CO2 emissions. This indicates a broader scope of the EU ETS than that of CORSIA. With the deadline and regional developments, CORSIA is approaching a juncture for the potential design changes from its sole focus on CO2 emissions to include non-CO2 emissions.

Uncrewed Aerial Vehicle Routing Problem for Integrated Crewed and Uncrewed Aircraft Operations

Last-mile delivery systems are one of the numerous industrial applications of uncrewed aircraft systems (UAS). This study analyzes UAS-based package delivery systems in the context of the integrated operations of crewed and uncrewed aircraft. It introduces a multi-trip, time-dependent vehicle routing problem aimed at optimizing UAS delivery routes within integrated operations. By conducting numerical simulations of helicopters (crewed aircraft) and UAS missions sharing the same airspace, the optimal UAS routes for resolving potential conflicts in the shared airspace can be effectively determined without any adverse impact on helicopter operations. Knowing the helicopter mission times in advance enables the efficient planning of UAS missions. The results of this study provide valuable insights regarding the implementation of integrated crewed and uncrewed aircraft operations.

Aircraft engine fault diagnosis based on fused convolutional Transformer

Aircraft engines will face corrosion and erosion problems when working in harsh gas path environments for a long time, and the fault parameter characteristics are not obvious. Therefore, accurate aircraft engine fault diagnosis methods are of great significance to ensure the safe operation of aircraft. In order to improve the prediction accuracy, an aircraft engine fault diagnosis method based on fused convolutional Transformer is proposed. The self-attention mechanism is used to extract useful features and suppress redundant information. The maximum pooling layer (MaxPool) is introduced into the Transformer model to further reduce the model memory consumption and parameter quantity, and alleviate the overfitting phenomenon. The simulation data set of turbofan engines based on GasTurb modeling is used for verification. The results show that compared with the Transformer network and other traditional deep learning models Back Propagation Neural Networks (BP network), Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN), the accuracy rates are improved by 6.552%、28.117%、13.189%and respectively 10.29%, which proves the effectiveness of the proposed method and can provide a certain reference for aircraft engine fault diagnosis.

Asphalt pavement structural competency during staged airfield runway construction strategy

ABSTRACT This paper reports the results from a full-scale evaluation of asphalt pavement structural competency during a multi-stage construction strategy. The staged pavement construction included three stages: (1) new conventional asphalt pavement, (2) unpaved full depth reclaimed (FDR) pavement, and (3) thin surface asphalt paved FDR layer. The full-scale pavement sections consisted of a standard and substandard pavement structural design. The accelerated pavement testing (APT) experiments were executed by simulating the loading conditions of an aircraft with a critical combination of relatively high wheel load and high tire inflation pressure. The structural capacity of the pavement test items was monitored using a falling weight deflectometer. From this study, the staged pavement construction concept was found to be a potential approach to extend the service life of asphalt pavements. Unpaved FDR layers are structurally competent to support a low volume of aircraft operations. These findings support the transition to the use of asphalt pavements containing an FDR layer from an experimental to routine rehabilitation practice for airfield asphalt pavements.

Impact of operator characteristics on landing gear ground manoeuvre occurrences

Abstract The variability in ground manoeuvre occurrences for aircraft landing gear is intrinsically linked to the airport geometries served by aircraft in-service and consequently, the cyclic loads that landing gear carry are driven by the route network and characteristics of aircraft operators. Currently, assumptions must be made when deriving fatigue load spectra for aircraft landing gear, which may fail to capture the operator characteristics, potentially leading to design conservatism. This paper presents the enhanced characterisation of ground turning manoeuvres within the Automatic Dependent Surveillance-Broadcast (ADS-B) trajectories for six narrow-body aircraft across a full-service carrier (FSC) and a low-cost carrier (LCC) fleet. The methodology presented within this paper employs ADS-B latitude and longitude information to overcome limitations of previous approaches, increasing the rate of correct manoeuvre identification within ADS-B trajectories to 77% of flights from the 50% rate achieved previously. When characterising the ground manoeuvres across 3,000 flights, significant differences in manoeuvre occurrences were observed between individual aircraft within the LCC fleet and between the FSC and LCC fleets. The occurrence of tight and pivot turns were shown to vary across the six aircraft with six and eight fatigue-critical turns being performed by the FSC and LCC fleet for every 10 flights performed. In addition, it was observed that the direction of fatigue critical turns is biased in specific directions, suggesting that individual main landing gear assemblies will accumulate fatigue damage at an increased rate, leading to greater justification for operator-specific spectra and structural health monitoring of aircraft landing gear.

Aircraft Surface Movement and Operation Monitoring Systems in General Aviation and Commercial Airports: A State-of-the-Art Review

Aircraft Surface Movement and Operation Monitoring Systems in General Aviation and Commercial Airports: A State-of-the-Art Review

Rapid Aircraft Wake Vortex Identification Model Based on Optimized Image Object Recognition Networks

Wake vortices generated by aircraft during near-ground operations have a significant impact on airport safety during takeoffs and landings. Identifying wake vortices in complex airspaces assists air traffic controllers in making informed decisions, ensuring the safety of aircraft operations at airports, and enhancing the intelligence level of air traffic control. Unlike traditional image recognition, identifying wake vortices using airborne LiDAR data demands a higher level of accuracy. This study proposes the IRSN-WAKE network by optimizing the Inception-ResNet-v2 network. To improve the model’s feature representation capability, we introduce the SE module into the Inception-ResNet-v2 network, which adaptively weights feature channels to enhance the network’s focus on key features. Additionally, we design and incorporate a noise suppression module to mitigate noise and enhance the robustness of feature extraction. Ablation experiments demonstrate that the introduction of the noise suppression module and the SE module significantly improves the performance of the IRSN-WAKE network in wake vortex identification tasks, achieving an accuracy rate of 98.60%. Comparative experimental results indicate that the IRSN-WAKE network has higher recognition accuracy and robustness compared to common recognition networks, achieving high-accuracy aircraft wake vortex identification and providing technical support for the safe operation of flights.

A Composite Approach for Evaluating Operational Cloud Seeding Effect in Stratus Clouds

Robust water management is in intense demand in many water scarcity areas, such as arid and semi-arid regions in the world. As part of the regional water management strategy, rain enhancement is vital to replenish groundwater reservoirs, and the key challenge is how to assess its effectiveness. Some recent weather modification experiments attained cloud seeding effect through advanced in situ measurement coupled with accurate numerical simulation. However, there is still a lack of an objective and scientific approach to quantitatively evaluate the rain enhancement effect, especially for many non-randomized operational cloud seeding activities in China. In this study, we proposed a composite evaluation approach by analyzing two operational aircraft cloud seeding cases in stratus clouds in Shaanxi, China. By calculating the aircraft cloud seeding agent plumes, the target areas (as well as the control areas) of cloud seeding were dynamically and roughly determined. Physical properties, such as radar reflectivity and precipitation, were individually quantified in these areas. The cloud seeding effect was then evaluated by calculating the difference in parameter variation between target and control areas. This approach can be applied to qualitative analysis in a single aircraft cloud seeding operation and can also provide quantitative statistical results from multiple cloud seeding cases. We found that the average precipitation enhancement percentage of 18 operational aircraft cloud seeding cases is ~4.84%. Note that the homogeneity hypothesis of the seeding cloud, the error in the calculation of the target area, and the selection of control areas are the major uncertainties likely in the evaluation of the cloud seeding effect by this approach.

Prediction of Low-Visibility Events by Integrating the Potential of Persistence and Machine Learning for Aviation Services

Fog typically results in reduced atmospheric visibility. Severely limited visibility has a significant impact on transportation, particularly the operations of aircraft. Precise forecasts of low visibility are essential for aviation services, primarily for the efficient planning of airport activities. Despite the utilization of sophisticated numerical weather prediction (NWP) models, the prediction of fog and limited visibility remains challenging. The intricacy of fog prediction is due to limitations in understanding the micro-scale factors that lead to fog genesis, intensification, persistence, and dissipation. This study investigates the occurrence of fog (surface visibility <1000 m) and dense fog (surface visibility < 200 m) throughout the climatological low-visibility months (November to February) to analyze the persistence of low-visibility events and predict them in the specific conditions of the frog prone Indo-Gangetic Plain (IGP) regions. A representative station, Jay Prakash Narayan International (JPNI) Airport in Patna, India, has been considered given the availability of instrumental quality datasets. The analysis investigates the long-term and short-term persistence and prediction of the series using a diverse variety of machine learning (ML) algorithms. To conduct a comprehensive analysis over an extended period, detrended fluctuation analysis (DFA) is employed to determine the similarities between the time series of large-scale fog and dense fog. A Markov chain model is used to look at the binary time series and figure out how long low-visibility events (like fog and dense fog) last in the short term ( 1-5 hours). Ultimately, we analyze a short-term forecast (Nowcast) with a lead time of one to five hours for instances of low visibility (fog or dense fog). This nowcasting is generated utilizing diverse methodologies, including Markov chain models, persistence analysis, and machine learning (ML) methods. Finally, establish that the most favorable and reliable results in this prediction problem are attained by employing a Mixture of Experts model that integrates persistence-based methods and ML algorithms.

Aircraft Structural Assessments in Data-Limited Environments: A Validated FE Method

Aircraft operators often modify aircraft configurations, install new equipment, and alter airframes to accommodate this equipment, leading to operations in flight envelopes different from original design profile. These modifications necessitate airframe structural assessments, which typically require comprehensive aircraft design data, often unavailable to operators. This study aims to develop and validate a practical method for finite element analysis (FEA) of aircraft structures in the absence of this detailed design data. Focusing on a case study involving structural analysis of an aircraft wing, this study presents assumptions and idealizations used to develop 2.5D finite element (FE) model of the wing. Fidelity of this model is established by comparing FE analysis results with experimental data. Key validation metrics include reaction forces, load distribution at wing-fuselage attachments, and deformation at reference points on the wing under design load. Comparison between FE analysis and experimental results is carried out to substantiates accuracy of these geometric simplifications and idealizations of load-carrying behaviour of structural members. Therefore, practicality of these idealizations in absence of design data is demonstrated. This study offers a novel approach for structural assessments of aircraft without relying on proprietary design data. The validated method enhances capability of aircraft operators to perform effective structural analyses, thereby extending service life of aircraft with continued airworthiness.