Aircraft Operations Research Articles

Numerical study on air-cooled battery thermal management system considering the sheer altitude effect

Due to energy shortage and environmental pollution, vehicles and aircraft powered by Li-ion batteries have now received widespread attention. Among various types of battery thermal management systems (BTMSs), the air-cooled BTMS is still the preferred choice due to its affordability, longevity, and simplicity. To ensure the reliable operation of electric vehicles and aircraft at different altitudes, it is extremely meaningful and significant to study the thermal behavior of batteries at different altitudes. Therefore, for the investigation of altitude impact, this paper firstly proposes an indirect decoupling method to address the limitations of using ambient temperature as a single variable. Based on this, several different configurations of air-cooled BTMS have been investigated through numerical simulation. Then, for the tapering-type BTMS with the best thermal performance, the battery behavior at different altitudes is investigated and the influence law of sheer altitude factor is summarized. Subsequently, to address the thermal performance issues at higher altitudes, this research proposes three optimization measures, which include increasing inlet velocity, decreasing inlet temperature, and incorporating phase change material (PCM) layers, respectively. Ultimately, the entropy weight-TOPSIS method is adopted to seek the optimal measure at different parameters. The results indicate that as altitude increases from the standard altitude to 4000 m, the maximum temperature rises significantly, exceeding the permissible temperature range of Li-ion batteries. Besides, in order to effectively confine the maximum temperature within the permissible temperature range at an altitude of 4000 m, the inlet velocity should be increased by at least 2 m/s or the inlet temperature should be reduced by at least 3 °C. Among all optimized solutions, the solution with the addition of 0.4 mm PCM layers is the best based on the comprehensive evaluation of multiple indicators.

A Study of TMA Aircraft Conflict-Free Routing and Operation: With Mixed Integer Linear Programming, Multi-Agent Path Finding, and Metaheuristic-Based Neighborhood Search

A Study of TMA Aircraft Conflict-Free Routing and Operation: With Mixed Integer Linear Programming, Multi-Agent Path Finding, and Metaheuristic-Based Neighborhood Search

Machine learning-based synthesis of diagnostic algorithms for electromechanical actuators to improve the aerospace flight safety

The relevance of research aimed at developing diagnostic technologies for electromechanical actuators is due to the need to improve flight safety in conditions of increasing intensity of highly electrified aircraft and spacecraft operations. The paper discusses one of the promising approaches to electromechanical actuator health management, which involves the use of machine learning methods to synthesize health monitoring algorithms. Machine learning methods make it possible to build classification models based on empirical data, which are used to generate recommendations for making operational decisions. Empirical data, which is a source of valuable experience and the basis of a training sample necessary for formalizing patterns in classification models, can be formed as a result of life tests, mathematical modeling, and actuator operation. In order to improve the safety of space flights, the article focuses on the integration of electromechanical actuator mathematical model methods, optimal space filling, and machine learning. Optimal space filling methods are used to reduce the computational costs associated with representative training sampling. Examples of developing classification models are given to determine failures associated with changes in gear (backlash, Coulomb friction and viscous friction) which is the most critical actuator link. As a result of computational studies, the main advantages of the proposed approach to the synthesis of electromechanical actuator health assessment algorithms are shown.

Risk assessment of continued airworthiness event of transport aircraft rudder pedal strut damage

Abstract To ensure the safe operation of aircraft after delivery, a set of continued airworthiness risk assessment processes and methods for transport aircraft is studied and proposed, including unsafe condition identification, consequence severity determination, occurrence possibility assessment, risk level determination, and reaction time calculation. The continued airworthiness risk matrix is established. Based on the established process and method, the risk assessment is carried out for the continued airworthiness event of transport aircraft rudder pedal strut damage. The relationship between the risk level of the fleet aircraft and the flight duration is determined, and the reaction time of the unsafe condition of the aircraft in each flight duration is calculated. The results show that the process and method are feasible.

Impacts of Backcalculation Software Selection on Airport Flexible Pavements Using the Aircraft Classification Rating/Pavement Classification Rating Method

From November 2024, the aircraft classification rating/pavement classification rating (ACR/PCR) method will be accepted by civil aviation authorities to notify the bearing capacity of airport pavements. Structural characteristics are necessary to calculate the parameters relating to this method, such as the modulus of elasticity which, in the case of existing pavements, can be obtained by backcalculation. However, the results of this procedure may vary depending on the type of method and algorithms used to minimize adjustment errors between the deflection basins adopted in the software. Given the above, this article aims to evaluate whether the choice of backcalculation software affects the resistance classification and admissibility of aircraft on airport pavements using the ACR/PCR method based on the backcalculated elastic moduli. The elastic modulus values of the materials from two runways were backcalculated using BAKFAA, BackCAP, BackMedina, and ELMOD software, and then used to calculate the PCR. The results of the backcalculation showed differences in the elastic moduli obtained by the software, mainly in the subgrade. It was found that the backcalculation software used can limit the type of aircraft and the runway resistance. Therefore, the choice of software for backcalculation of deflection basins can influence the results of the ACR/PCR method because of divergences in determining the resistance of the pavement’s bearing capacity and the admissibility of aircraft operations in the analyzed structures.

Digital Twin Framework for Aircraft Lifecycle Management Based on Data-Driven Models

This paper presents a comprehensive framework for implementing digital twins in aircraft lifecycle management, with a focus on using data-driven models to enhance decision-making and operational efficiency. The proposed framework integrates cutting-edge technologies such as IoT sensors, big data analytics, machine learning, 6G communication, and cloud computing to create a robust digital twin ecosystem. This paper explores the key components of the framework, including lifecycle phases, new technologies, and models for digital twins. It discusses the challenges of creating accurate digital twins during aircraft operation and maintenance and proposes solutions using emerging technologies. The framework incorporates physics-based, data-driven, and hybrid models to simulate and predict aircraft behavior. Supporting components like data management, federated learning, and analytics tools enable seamless integration and operation. This paper also examines decision-making models, a knowledge-driven approach, limitations of current implementations, and future research directions. This holistic framework aims to transform fragmented aircraft data into comprehensive, real-time digital representations that can enhance safety, efficiency, and sustainability throughout the aircraft lifecycle.

Radiotelephony Untuk Personel Pangkalan TNI AU Atang Sendjaja

In the aviation industry, radiotelephony is the primary communication tool between pilots and personnel involved in aircraft operations. According to the Director General of Civil Aviation Regulation Number KP 038 of 2017, personnel providing apron management services are required to have specific licenses, such as Air Traffic Controllers, Aeronautical Communication Officers (ACO), or Apron Movement Controllers (AMC), with additional skills in radiotelephony. However, the personnel at the Atang Sendjaja Air Force Base currently do not meet the radiotelephony competency requirements. To perform services according to national and international standards, the use of radiotelephony is essential. To address this need, a training program was conducted as part of a community service initiative, aimed at equipping personnel with radiotelephony skills in line with applicable regulations. This training includes both theoretical and practical sessions conducted on-site, utilizing table-top exercise simulation methods. The outcome of this training indicates that the personnel at the Atang Sendjaja Air Force Base have acquired adequate skills to implement radiotelephony in their daily operational activities.

DESIGN OF IMAGE PROCESSING APPLICATION FOR FOREIGN OBJECT INSPECTION USING DRONE ON AIRCRAFT RUNWAYS

Flight safety is a crucial aspect of the aviation industry, heavily reliant on optimal runway conditions for successful takeoffs and landings. Foreign Object Debris (FOD) such as stones and potholes on the runway pose significant threats, necessitating preventive measures to avoid operational failures and aircraft damage. This research focuses on designing an image processing application for drone usage to enhance runway inspections. The Research and Development (R&D) method is employed from application design to field testing. Image processing utilizes the You Only Look Once (YOLO) method for swift and accurate FOD detection, maximizing drone effectiveness in identifying risks. Findings indicate that object size and image capture distance significantly affect detection performance. This analysis provides a basis for model optimization through improved data augmentation techniques and parameter adjustments to address challenges in detecting objects of various sizes.

Civil Liability of The Air Carrier for Errors of The Aircraft Crew in Jordanian Law

The aircraft crew bears significant responsibility as they operate the aircraft in the sky, carrying numerous duties and exercising various authorities to perform their tasks effectively. The aircraft crew is obligated to fulfill their commitments towards the air carrier. Current study addresses the issues surrounding the extent of the air carrier's liability for errors committed by the aircraft crew. These errors may be personal, technical, or administrative during flight. We used a comparative, descriptive, and analytical approach to achieve the objectives of the thesis and answer its questions, which focus on defining the obligations and authorities of the aircraft crew and determining the nature of the air carrier's responsibility for errors of the aircraft crew. The key finding of study is that the Jordanian legislator has largely held the air carrier responsible for damages resulting from errors committed by the aircraft crew. Both the air carrier and the aircraft crew share responsibility for these damages towards third parties based on solidarity and mutual assistance. It is suggested to amend the text of Article (42/C) to include detailed provisions specifying the extent of responsibility for each member of the aircraft crew, the responsibility of the aircraft commander, operator, and air carrier in the face of actions leading to harm.

Early Design Stage Evaluation of All Electric Aircraft Power Systems Focusing on Long-Term Behavior

In the aircraft industry, there is a shift towards more and all-electric power systems resulting in great research efforts on single components like batteries. At the same time there is an increasing need to investigate and evaluate the long-term behavior of the whole electric power system to ensure safe and sustainable aircraft operation. Focusing on this challenge, the objective of this article is to propose a framework for electric power system assessment in the early design stages. In particular, the focus is on identifying and handling uncertainties regarding failure behavior and degradation, both on the component and system level. The evaluation of different power system topologies is based on the integration of Model-Based Systems Engineering and robust design methods. In this context, another central aspect is the definition of system and component requirements derived from the flight mission profile. SysML diagrams are used to define use cases and possible system topologies. Sensitivity of degradation effects are evaluated using robust design methods. The application of the framework and these methods is illustrated using a short-range aircraft with an all-electric power system. The results highlight the applicability of the framework to cope with the uncertainties that occur in the early design stages and point out fields of further research.

Optimalisasi Pagar Perimeter terhadap Standart Keamanan oleh Petugas Avsec di Daerah Keamanan Terbatas Bandar Udara Internasional Adi Soemarmo Boyolali

A limited security area is an area used for aircraft movement activities, employee or staff movement, and work equipment for flight activities, passenger and baggage movement that will board the aircraft, and vital installations/objects that are directly related to aircraft operations. The purpose of this study was to determine whether the perimeter fence of the limited security area of Adi Soemarmo International Airport was in accordance with applicable provisions and what efforts were made by Airport Security officers to meet the perimeter fence standards in the limited security area of Adi Soemarmo Boyolali International Airport. This study uses a qualitative approach with the types and sources of data, namely primary data and secondary data. This research was conducted at Adi Soemarmo Boyolali International Airport and the time of implementation of this research was carried out from August 1 to January 11 at the Airport Security (AVSEC) unit. The results of this study are that the condition of the perimeter fence of the limited security area of Adi Soemarmo International Airport is not in accordance with applicable regulations. This can disrupt flight activities and threaten flight safety. Efforts made by Airport Security officers for security are patrols in the perimeter fence area and approaches to the community to prevent unlawful acts.

Creation of a System Taxonomy for Advanced Air Mobility Operations

Advanced Air Mobility (AAM) is an emerging field that envisions highly automated, high-tempo, passenger-carrying aircraft operating in urban environments. Numerous stakeholders have emerged in this space, each proposing unique concepts of operation (CONOPS). While this technology will enable a revolutionary leap in modern transportation, many challenges remain unaddressed. Discrepancies exist between envisioned AAM operations, current technology, and outdated federal regulations. This has led to an unclear vision within the industry of the current maturity level of technology and the path of development needed to achieve proposed CONOPS. We seek to fill this gap by integrating multiple proposed conceptual systems into a single, unified path forward for the field. The literature on AAM covers a diverse array of interconnected themes, each containing a multitude of considerations that require extensive exploration and understanding. In the present work, a literature integration was conducted to collate the numerous proposed concepts and envisioned works that exist within the AAM literature. With this information, a comprehensive AAM taxonomy was created to represent this integration of concepts and rectify the inconsistencies within industry CONOPS. This taxonomy will assist future stakeholders in navigating this intricate field while contributing to the safe and efficient development and deployment of AAM.

COMPARISON OF THE IMPACT OF THE COVID-19 PANDEMIC AND TECHNOLOGICAL DEVELOPMENTS ON EMISSIONS OF CO2 IN THE AIR TRANSPORT

Air transport is a complex and intricate mode of transportation that has both benefits and consequences resulting from the operations of planes. Air travel offers the advantage of saving time during journeys or business visits, but it also has the disadvantage of contributing to carbon dioxide emissions caused by aircraft operations. These emissions present potential risks to both human health and the environment. In order to reduce the negative impacts, new technical advancements can be utilized to limit emissions and, consequently, the discharge of carbon dioxide. The COVID-19 epidemic caused an unprecedented situation where flights were temporarily suspended and aircraft were grounded, leading to a significant decrease in emissions from airline operations. This research utilizes graphical representations to clarify the main factors that lead to reduced carbon dioxide emissions. Moreover, it examines the effects of particular advancements in air transportation and assesses the impact of the pandemic on the overall decrease in emissions.

Method for Remaining Useful Life Prediction of Turbofan Engines Combining Adam Optimization-Based Self-Attention Mechanism with Temporal Convolutional Networks

Conducting the remaining useful life (RUL) prediction for an aircraft engines is of significant importance in enhancing aircraft operation safety and formulating reasonable maintenance plans. Addressing the issue of low prediction model accuracy due to traditional neural networks’ inability to fully extract key features, this paper proposes an engine RUL prediction model based on the adaptive moment estimation (Adam) optimized self-attention mechanism–temporal convolutional network (SAM-TCN) neural network. Firstly, the raw data monitored by sensors are normalized, and RUL labels are set. A sliding window is utilized for overlapping sampling of the data, capturing more temporal features while eliminating data dimensionality. Secondly, the SAM-TCN neural network prediction model is constructed. The temporal convolutional network (TCN) neural network is used to capture the temporal dependency between data, solving the mapping relationship of engine degradation characteristics. A self-attention mechanism (SAM) is employed to adaptively assign different weight contributions to different input features. In the experiments, the root mean square error (RMSE) values on four datasets are 11.50, 16.45, 11.62, and 15.47 respectively. These values indicate further reduction in errors compared to methods reported in other literature. Finally, the SAM-TCN prediction model is optimized using the Adam optimizer to improve the training effectiveness and convergence speed of the model. Experimental results demonstrate that the proposed method can effectively learn feature data, with prediction accuracy superior to other models.

Study of low-level wind shear at a Qinghai-Tibetan Plateau airport

The Qinghai-Tibetan Plateau (QTP) is recognized as the world's largest and highest plateau, characterized by intricate topography and underlying surfaces. Within this region, volatile meteorological conditions and severe synoptic systems, including thunderstorms, turbulence, sandstorms, and notably low-level wind shear (LLWS), present safety hazards to aviation operations. Therefore, a comprehensive examination of wind shear at typical QTP airports is essential. Xining Caojiapu International Airport (ZLXN) serves as a crucial transportation hub in the northeastern QTP. The frequent incidence of LLWS events due to the airport's unique geographical and meteorological features makes it an ideal location for investigating LLWS on the plateau. This study analyzed 80 pilot reports collected from 2016 to 2021 to elucidate the spatial and temporal characteristics of LLWS events at this plateau-valley airport. Subsequently, reanalysis data and observations from ground automated weather observing systems (AWOS), a geostationary satellite, a Doppler weather radar (DWR), and a Doppler wind lidar (DWL) were comprehensively employed to investigate the underlying meteorological factors and weather patterns associated with these LLWS events. The findings revealed that LLWS events at ZLXN are predominantly triggered by convective systems, followed by cold fronts and downward momentum transportation, with a smaller proportion being induced by orographic winds and turbulence. Analysis of four representative LLWS events utilizing high-resolution DWL measurements provided insights into the types of LLWS generated and their potential impacts on aircraft operations. Conceptual models, aimed at establishing a foundation for the forecasting and warning of local LLWS, were also proposed based on multiple data sources.

Recognition of carrier-based aircraft flight deck operations based on dynamic graph

Accurate recognition of flight deck operations for carrier-based aircraft, based on operation trajectories, is critical for optimizing carrier-based aircraft performance. This recognition involves understanding short-term and long-term spatial collaborative relationships among support agents and positions from long spatial–temporal trajectories. While the existing methods excel at recognizing collaborative behaviors from short trajectories, they often struggle with long spatial–temporal trajectories. To address this challenge, this paper introduces a dynamic graph method to enhance flight deck operation recognition. First, spatial–temporal collaborative relationships are modeled as a dynamic graph. Second, a discretized and compressed method is proposed to assign values to the states of this dynamic graph. To extract features that represent diverse collaborative relationships among agents and account for the duration of these relationships, a biased random walk is then conducted. Subsequently, the Swin Transformer is employed to comprehend spatial–temporal collaborative relationships, and a fully connected layer is applied to deck operation recognition. Finally, to address the scarcity of real datasets, a simulation pipeline is introduced to generate deck operations in virtual flight deck scenarios. Experimental results on the simulation dataset demonstrate the superior performance of the proposed method.

Study on the evaluation method of pilot workload in eVTOL aircraft operation

This paper investigates the pilot control strategy and workload associated with the manned electric Vertical Take-Off and Landing (eVTOL) aircraft. This research aims to identify control strategies that are both rational and effective in reducing the pilot's workload. Although the Cooper-Harper rating (HQR, Handling Quality Rating) remains the prevalent benchmark for assessing pilot workload, its subjective nature often complicates the precise quantitative identification of the key factors influencing workload. This study introduces an assessment method for pilot control workload using wavelet transform to evaluate pilot workload, focusing on the correlation between pilot control magnitude, frequency components, and complexity. We begin with a review of control action analysis methods in both time and frequency domains. Subsequently, by selecting an appropriate wavelet function and a sampling frequency, we develop the assessment method for pilot control strategy and workload based on wavelet analysis. Finally, we assess pilot workload using real pilot inputs with HQRs obtained from helicopter flight tests and tilt-rotor aircraft conversion simulations. The results indicate that this approach is capable of precisely pinpointing the frequency components and energy levels within pilot control actions throughout different time periods. Furthermore, a significant correlation is observed between pilot control characteristics, frequency components, and HQRs. Consequently, the developed approach provides a rational framework for quantifying and analyzing pilot workload in a range of eVTOL aircraft scenarios.

PELAYANAN SAFETY RISK MANAJEMEN PESAWAT UDARA TANPA AWAK (PUTA) OLEH PERUM LEMBAGA PENYELENGGARA PELAYANAN NAVIGASI PENERBANGAN INDONESIA (LPPNPI) AIRNAV INDONESIA CABANG PEMBANTU PADANG

Unmanned Aerial Vehicles (PUTA) or drones have increasingly dominated various sectors, from surveillance mapping to sending goods from one place to another. The sustainability and operational security of PUTA requires effective and efficient safety assessment services. This research aims to evaluate the safety assessment service for unmanned aircraft or drones at Perum LPPNPI AirNav Indonesia Padang Sub-Branch. The research methodology involves analysis of operational data, interviews with related parties and evaluation of compliance with safety standards. The research results show that safety risk management services for unmanned aerial vehicles or drones at the Padang Sub-Branch have experienced significant development, however there are still challenges in managing PUTA operational safety risks as a whole. In implementing the Safety Risk Management Service, several aspects were found that needed further attention, including the development of a monitoring system and increased involvement of related parties, as well as understanding the operational characteristics of PUTA in the Padang Sub-Branch work area. It is hoped that the results of this research can provide an important contribution in improving the safety of unmanned aircraft operations at the Padang sub-branch and provide a basis for developing better safety assessment services in the future.

A Deep Learning Framework for Real-Time Bird Detection and Its Implications for Reducing Bird Strike Incidents.

Bird strikes are a substantial aviation safety issue that can result in serious harm to aircraft components and even passenger deaths. In response to this increased tendency, the implementation of new and more efficient detection and prevention technologies becomes urgent. The paper presents a novel deep learning model which is developed to detect and alleviate bird strike issues in airport conditions boosting aircraft safety. Based on an extensive database of bird images having different species and flight patterns, the research adopts sophisticated image augmentation techniques which generate multiple scenarios of aircraft operation ensuring that the model is robust under different conditions. The methodology evolved around the building of a spatiotemporal convolutional neural network which employs spatial attention structures together with dynamic temporal processing to precisely recognize flying birds. One of the most important features of this research is the architecture of its dual-focus model which consists of two components, the attention-based temporal analysis network and the convolutional neural network with spatial awareness. The model's architecture can identify specific features nested in a crowded and shifting backdrop, thereby lowering false positives and improving detection accuracy. The mechanisms of attention of this model itself enhance the model's focus by identifying vital features of bird flight patterns that are crucial. The results are that the proposed model achieves better performance in terms of accuracy and real time responses than the existing bird detection systems. The ablation study demonstrates the indispensable roles of each component, confirming their synergistic effect on improving detection performance. The research substantiates the model's applicability as a part of airport bird strike surveillance system, providing an alternative to the prevention strategy. This work benefits from the unique deep learning feature application, which leads to a large-scale and reliable tool for dealing with the bird strike problem.

Impact characteristics of S2-glass fibre/FM94-epoxy composites under high and cryogenic temperatures: Experimental and numerical investigation

The aerospace industry uses glass fibre reinforced polymer (GFRP) composites to manufacture structural and non-structural parts of an aircraft as they possess superior strength to weight ratio and exceptional corrosion resistance. Commercial aircraft operate in a very wide temperature ranges from −54 to 55 °C. Potential GFRP laminates are susceptible to impact during aircraft operation, and the temperature at impact governs the nature of damage and failure mechanisms. As a result, the current study focuses on examining how aeronautical GFRP composites behave in various temperature environments that are encountered during high- and low-altitude operations. Using S2-glass fibre/FM94-epoxy unidirectional prepreg, GFRP plates were created. Drop weight impact tests were conducted at ambient (25 °C), high (50, 75, 100 °C), and low (−25, −55 °C) temperatures, as well as at various impact energies (75, 150, 225 J). The damages were assessed visually, along with their sizes. Each testing scenario's impact parameters, including the impact load, deflection, and energy absorption, were also examined. In Abaqus/Explicit, a coupled temperature-displacement numerical model was created to predict the onset of stress and damage. According to experimental findings, GFRP plates are stiffer and show less apparent damage at cryogenic temperatures (∼15−34 % lower displacement) than they do at other temperatures. Furthermore, it was observed that the matrix softens at high temperatures, showing larger damaged area at entry but with less obvious damage and increasing energy absorption, while semi-perforation occurred under cryogenic temperatures at entry with smaller damaged area. A strong correlation is shown between the experimental and FE data, confirming the capability of FE models to predict impact damage and deflections at different temperatures in the future.