Aviation System Research Articles

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.

Semi-Empirical Models for Stack and Balance of Plant in Closed-Cathode Fuel Cell Systems for Aviation

In recent years, there has been a growing interest in utilizing hydrogen as an energy carrier across various transportation sectors, including aerospace applications. This interest stems from its unique capability to yield energy without generating direct carbon dioxide emissions. The conversion process is particularly efficient when performed in a fuel cell system. In aerospace applications, two crucial factors come into play: power-to-weight ratio and the simplicity of the powerplant. In fact, the transient behavior and control of the fuel cell are complicated by the continuously changing values of load and altitude during the flight. To meet these criteria, air-cooled open-cathode Proton Exchange Membrane (PEM) fuel cells should be the preferred choice. However, they have limitations regarding the amount of thermal power they can dissipate. Moreover, the performances of fuel cell systems are significantly worsened at high altitude operating conditions because of the lower air density. Consequently, they find suitability primarily in applications such as Unmanned Aerial Vehicles (UAVs) and Urban Air Mobility (UAM). In the case of ultralight and light aviation, liquid-cooled solutions with a separate circuit for compressed air supply are adopted. The goal of this investigation is to identify the correct simulation approach to predict the behavior of such systems under dynamic conditions, typical of their application in aerial vehicles. To this aim, a detailed review of the scientific literature has been performed, with specific reference to semi-empirical and control-oriented models of the whole fuel cell systems including not only the stack but also the complete balance of plant.

Искусственный интеллект в адаптивных внутрикабинных интерфейсах

The principles of developing adaptive in-cabin control interfaces for advanced aviation systems based on the methodology of integrated modular avionics (IMA), which change their functionality depending on the situation and flight conditions, are considered. Existing and promising solutions and technologies for making in-cabin interfaces are described, such as gesture recognition systems, voice commands and other methods of interaction with the pilot, including the use of induced virtual reality and artificial intelligence technologies. The problems of piloting in conditions of increased automation of aircraft control processes are shown. Proposals are considered to create even more efficient and secure interfaces in the near future, which will improve the aviation system performance, the aircraft control quality and reduce the likelihood of pilots’ operational errors.

Bort dinamik sistemlərin identifikasiyası və nəzarətində neyron şəbəkələrindən istifadə İ.M. İsmayılov, T.V. Binnətəliyeva

The article presents the principles of creating a predictive control strategy based on a neural network model of aviation systems. The use of neural networks for identifying and controlling dynamic systems is substantiated, including the use of model predictive control for predicting the state (parameters) of an aircraft as a dynamic system, more suitable among neural network architectures. For model predictive control, a model of an object is used to predict the future behavior of the object, and an optimization algorithm is used to select a control input that optimizes the future operation of the object. To reflect the dynamics of the system in real time, the issues of training a neural network were considered, for which a standard model, the NARMA model, was chosen as the structure of the model. Keywords: flight safety, flight dynamics, aviation intelligent control systems, predictive control, neural networks, static methods, regression analysis.

ANALYSIS OF SOLID SOLUTION DECOMPOSITION IN HIGH-ALLOY AUSTENITIC STEEL

In the field of aviation and rocket engineering, austenitic chrome-nickel steels are widely employed due to their exceptional properties, including high strength, heat resistance, ductility, and phase stability over a broad range of operating temperatures. Nevertheless, the scarcity of chromium and nickel has necessitated the development and utilization of new high-alloy austenitic steels for these industries, which do not contain the aforementioned deficit alloying elements. Modern high-alloy austenitic steels used in aviation and rocket industries are based on Fe – Mn – Al-Calloys. These steels exhibit lower specific weight, approximately 13-15% less than conventional steels, and possess superior plasticity and the ability to strengthen during deformation. It is essential to note that the high strength of these steels is achieved through the precipitation of complex carbide (Fe, Mn)3 Al particles during thermal treatment, with a crystalline lattice period of d = 0.376 nm.Extensive research has been conducted to establish the temperature-time boundaries of the aging process, its stages, and the mechanisms of strengthening during aging for these steels. Geometric models illustrating the changes in mechanical properties during thermal treatment have been constructed utilizing the mathematical package of functional programming, Wolfram Mathematica. The obtained results have facilitated the formulation of practical recommendations for the development of effective aging regimes for these steels. Mechanisms and temperature-time boundaries for the three stages of the aging process of the steel have been identified, opening the possibility of devising more optimal and productive methods for processing these materials.Hence, research in the domain of high-alloy austenitic steels for aviation and rocket construction is a pertinent and significant direction, as it contributes to the enhancement of quality and reliability in structures employing these materials. The findings of these investigations can be invaluable for advancing contemporary technologies in these fields and ensuring the safety and efficiency of aviation and rocket systems.

BASIC PRINCIPLES OF BUILDING INTELLIGENT SYSTEMS FLIGHT CONTROL

The issues of creating intelligent control systems in a dynamically changing flight environment are considered and the principle of constructing an onboard intelligent flight control system is proposed. The implementation of the proposed system is based on the pilot's situational awareness of the flight progress in real time, which allows avoiding errors and maintaining enhanced synergy between human and avionics systems. Constant adaptation of the cockpit, as well as onboard. automation successfully maintains the pilot's workload within an optimal range, mitigating the occurrence of dangerous levels of fatigue. Methods for assessing and predicting the threat of an aviation accident based on direct control of the variation in the values of characteristics that affect flight safety, using methods and tools of artificial intelligence, are proposed. Keywords: flight safety, aircraft, intelligent aviation system, accident threat, prevention, control system, artificial intelligence, aircraft control systems

A Comparative Analysis of Performance-Based Resilience Metrics via a Quantitative-Qualitative Combined Approach: Are We Measuring the Same Thing?

Since the proposal of the pioneering “resilience triangle” paradigm, various time-series performance-based metrics have been devised for resilience quantification. The numerous choices diversify the toolbox for measuring this compound system concept; however, this multiplicity causes intractable questions for applications, including “Do these metrics measure the same resilience?” and “Which one to pick under what circumstance?” In this study, we attempted to address these two fundamental issues using a comprehensive comparative investigation. Through a quantitative-qualitative combined approach, 12 popular performance-based resilience metrics are compared using empirical data from China’s aviation system under the disturbance of COVID-19. Quantitative results indicate that only 12 of the 66 metric pairs are strongly positively correlated and with no significant differences in quantification outcomes; qualitative results indicate that the majority of the metrics are based on different definition interpretations, basic components, and expression forms, and thus essentially measure different resilience. The advantages and disadvantages of each metric are comparatively discussed, and a “how to choose” guideline for metric users is proposed. This study is an introspective investigation of resilience quantification studies, aiming to offer a new perspective to scrutinize those benchmarking metrics.

Prognostic and Health Management of Critical Aircraft Systems and Components: An Overview.

Prognostic and health management (PHM) plays a vital role in ensuring the safety and reliability of aircraft systems. The process entails the proactive surveillance and evaluation of the state and functional effectiveness of crucial subsystems. The principal aim of PHM is to predict the remaining useful life (RUL) of subsystems and proactively mitigate future breakdowns in order to minimize consequences. The achievement of this objective is helped by employing predictive modeling techniques and doing real-time data analysis. The incorporation of prognostic methodologies is of utmost importance in the execution of condition-based maintenance (CBM), a strategic approach that emphasizes the prioritization of repairing components that have experienced quantifiable damage. Multiple methodologies are employed to support the advancement of prognostics for aviation systems, encompassing physics-based modeling, data-driven techniques, and hybrid prognosis. These methodologies enable the prediction and mitigation of failures by identifying relevant health indicators. Despite the promising outcomes in the aviation sector pertaining to the implementation of PHM, there exists a deficiency in the research concerning the efficient integration of hybrid PHM applications. The primary aim of this paper is to provide a thorough analysis of the current state of research advancements in prognostics for aircraft systems, with a specific focus on prominent algorithms and their practical applications and challenges. The paper concludes by providing a detailed analysis of prospective directions for future research within the field.

Low pressure influence on a direct fuel cell battery hybrid system for aviation

Fuel-cell-based, hydrogen-driven aircraft can significantly reduce the climate impact of aviation but the low ambient pressures at high altitude negatively affect the performance of fuel cells. The influence of low pressures on the behavior of a direct hybrid-system that connects a fuel cell and a battery without a DC/DC converter, is examined and compared to a fuel-cell-only system. Low ambient pressure leads to a reduced fuel cell voltage and power output. The voltage change between maximum voltage and the voltage level at the operational power increases with a decreasing air pressure for both the hybrid-system and the fuel-cell-only system. However, in a direct hybrid, the battery stabilizes the system voltage level and the voltage decrease is significantly smaller for the hybrid-system than for the fuel-cell-only system. At an ambient pressure of 300 mbar, the voltage variation of a 4 kW system is 96% lower for a hybrid-system compared to a fuel-cell-only system. The lower pressure also reduces the power output of the fuel cell. In the direct hybrid the battery contributes a demand related share of power and the contribution from the fuel cell decreases with air pressure as the contribution of the battery rises.

Optimum design of a composite drone component using slime mold algorithm

Abstract Composite materials have a wide range of applications in many industries due to their manufacturability, high strength values, and light filling. The sector where composite materials are mostly used is the aviation industry. Today, as a result of the development of aviation systems, drones have started to be actively used, and many studies have started to be carried out to mitigate them. In this study, the subcarrier part, which is part of the drone, was designed using glass and carbon fiber–reinforced composite materials. Using the data obtained at the end of the analysis, the stacking angle with the optimal displacement and stress value was determined by using the genetic algorithm (GA), gray wolf algorithm (GWO), and slime mold optimization (SMO) techniques in order to develop a carrier with a minimum displacement and stress value of more than 60 MPa. As a result of the optimization, it was determined that artificial intelligence algorithms could be used effectively in determining the stacking angle of composite materials, and the optimum values were determined in the slime mold algorithm.

AI-driven assistants for education and research? A case study on ChatGPT for air transport management

Artificial Intelligence is in the process to transform various parts of the aviation industry, from the reduction of delays and increasing fuel efficiency to better demand prediction models. The latest kid on the block is ChatGPT, a large language model developed by OpenAI, which has made into the news for its mind-blowing ability to create textual content in any structured language. Doing so, ChatGPT has the potential to revolutionize the way we communicate with computers, and it could have a lasting impact on aviation education and research. In this study, we investigate the potential of this impact and, the extent to which it has already materialized, based on a set of graduate student surveys and experiments with ChatGPT. The results of our surveys indicate the interest of students in efficient learning, time saving, and improvement in programming/writing skills. Our experiments on terminology explanation, state-of-the-art identification of selected research tasks as well as programming design, highlight the tradeoffs between benefits and potential risks inherent to the usage of ChatGPT and AI-driven assistants in general. Overall, we believe that our study makes a first contribution to evaluating an exciting new technology which has the potential to revolutionize our aviation system.

Analysis on coupling dynamic effect of human errors in aviation safety

Human factors have increasingly been the leading cause of aircraft accidents. In most cases, human factors are not working alone, instead they are coupled with complex environment, mechanical factors, physiological and psychological factors of pilots, and organizational management, all of which form a complex aviation safety system. It is vital to investigate the coupling impact of human errors to avoid the occurrence of aviation accidents. In view that the Human Factors Analysis and Classification System (HFACS) provides a hierarchical classification principle of human errors in aviation accidents, and the System Dynamics (SD) approach is helpful to describe the risk evolution process, this paper establishes a hybrid HFACS-SD model by employing the HFACS and the SD approach to reveal the aviation human factors risk evolution mechanism, in which the HFACS is first used to capture the causal factors of human errors risk, and a coupling SD model is then built to describe the evolution of aviation human factors risk supported by historical data. The eigenvalue elasticity analysis is taken to identify critical loops and parameters that have a substantial impact on the system structural behavior, and the influence of parameters and loops is assessed. Simulation results show that the evolution trend of the accident rate can be replicated by the proposed HFACS-SD model, and the structural dominance analysis can efficiently identify critical loops and parameters. Simulation results further show that, with the recommended safety enhancement measures, the stability of the aviation system is increased, and thus lowering the overall accident rate.

Study of a New Software Reliability Growth Model under Uncertain Operating Environments and Dependent Failures

The coronavirus disease (COVID-19) outbreak has prompted various industries to embark on digital transformation efforts, with software playing a critical role. Ensuring the reliability of software is of the utmost importance given its widespread use across multiple industries. For example, software has extensive applications in areas such as transportation, aviation, and military systems, where reliability problems can result in personal injuries and significant financial losses. Numerous studies have focused on software reliability. In particular, the software reliability growth model has served as a prominent tool for measuring software reliability. Previous studies have often assumed that the testing environment is representative of the operating environment and that software failures occur independently. However, the testing and operating environments can differ, and software failures can sometimes occur dependently. In this study, we propose a new model that assumes uncertain operating environments and dependent failures. In other words, the model proposed in this study takes into account a wider range of environments. The numerical examples in this study demonstrate that the goodness of fit of the new model is significantly better than that of the existing SRGM. Additionally, we show the utilization of the sequential probability ratio test (SPRT) based on the new model to assess the reliability of the dataset.

Model Development and Optimization of a Solid Oxide Fuel Cell/Gas Turbine Hybrid Power System for Electric Aviation

With countless global industries pledging and pursuing decarbonization targets set for the 2030 – 2050 timeframe, the aerospace industry faces many key challenges including reduction of greenhouse gas emissions, contrail abatement, and development/adoption of technologies with higher Fuel-to-Electricity (FTE) conversion efficiencies. One such technology identified as a potential fit for commercial aircraft is the solid oxide fuel cell and gas turbine (SOFC-GT) hybrid power system. Progress in system-level SOFC-GT modeling is critical to produce higher fidelity feasibility studies used to influence SOFC design and performance targets of the future. In this study, the first ProMax 5.0-based SOFC-GT model was developed, validated, and optimized. The impact of process configuration, balance-of-plant, and operating conditions were investigated. An optimal process design comprising 8 SOFC stacks and 7 Energy Storage and Power Generation (ESPG) modules with a total power output of > 7.0-MW and FTE conversion efficiencies of 73 – 76% is reported. Additionally, preliminary airplane architecture with the 7 onboard ESPG modules is presented to show the feasibility of future, hybrid electric airplane design.

Improved singular filtering-Gaussian process regression-long short-term memory model for whole-life-cycle remaining capacity estimation of lithium-ion batteries adaptive to fast aging and multi-current variations

For the development of low-temperature power systems in aviation, the transport synergistic carrier optimization of lithium-ions and electrons is conducted to improve the low-temperature adaptability of lithium-ion batteries. In this paper, an improved robust multi-time scale singular filtering-Gaussian process regression-long short-term memory (SF-GPR-LSTM) modeling method is proposed for the remaining capacity estimation. The optimized multi-task training strategy is constructed for the rapid battery performance evaluation, realizing the refined mathematical dynamic characterization for the mapping relationship of the physical carrier transports to obtain the simultaneous improvement of multi-dimensional physical features and a spiral-up iterative optimization scheme. The adaptability of the model is verified using datasets from the whole-life-cycle test conducted on two batteries, and evaluated by root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R-squared metrics. Even when only using 55% of the datasets to estimate the remaining capacity, the estimation has good effects, with RMSE of 2.3484%, MAE of 0.82526%, MAPE of 0.90716%, and R-squared value of 92.457%. The proposed SF-GPR-LSTM model enables the carrier transport synergistic optimization effectively, laying a theoretical foundation for the whole-life-cycle battery remaining capacity estimation at extremely low temperatures.

Competition Analysis of Superior Product Heart Services a Medical Tourism Organizer in Medan

This becomes increasingly important when this number increases every year with the development of information technology and aviation systems that are easier and cheaper. Only Covid has succeeded in reducing the number of medical tourists from North Sumatra to Penang. Since the beginning of 2022, North Sumatra has declared to be one of the world's medical tourism destinations. This is important not only to bring in foreign tourists, but at the same time to hide out the outflow of the North Sumatra community itself. There are seven hospitals that are launched will become medical tourism destinations in North Sumatra, one of which is a hospital that is studied for providing heart services which is the most sought, after service by the people of North Sumatra. Based on this phenomenon it is felt to be very important to conduct competition analysis of integrated heart service products organized by this hospital, so that the product you want to launch receives an appropriate welcome. The method used in this study is to use a descriptive strategy method related to supply side, demand side, and product competition obtained with structured questionnaires. The results of the study show that the service, the administrative process of type A hospitals in Medan is quite good but still must be evaluated and improved, while cleanliness and health protocols and facilities at type A hospitals in Medan are still below the "pretty good" range that interpret it is still very need to be improved. With later efforts to increase, it is hoped that this type A hospital in Medan will be able to compensate for some of its competitors.

A Data-Light and Trajectory-Based Machine Learning Approach for the Online Prediction of Flight Time of Arrival

The ability to accurately predict flight time of arrival in real time during a flight is critical to the efficiency and reliability of aviation system operations. This paper proposes a data-light and trajectory-based machine learning approach for the online prediction of estimated time of arrival at terminal airspace boundary (ETA_TAB) and estimated landing time (ELDT), while a flight is airborne. Rather than requiring a large volume of data on aircraft aerodynamics, en-route weather, and traffic, this approach uses only flight trajectory information on latitude, longitude, and speed. The approach consists of four modules: (a) reconstructing the sequence of trajectory points from the raw trajectory that has been flown, and identifying its best-matched historical trajectory which bears the most similarity; (b) predicting the remaining trajectory, based on what has been flown and the best-matched historical trajectory; this is achieved by developing a long short-term memory (LSTM) network trajectory prediction model; (c) predicting the ground speed of the flight along its predicted trajectory, iteratively using the current position and previous speed information; to this end, a gradient boosting machine (GBM) speed prediction model is developed; and (d) predicting ETA_TAB using trajectory and speed prediction from (b) and (c), and using ETA_TAB to further predict ELDT. Since LSTM and GBM models can be trained offline, online computation efforts are kept at a minimum. We apply this approach to real-world flights in the US. Based on our findings, the proposed approach yields better prediction performance than multiple alternative methods. The proposed approach is easy to implement, fast to perform, and effective in prediction, thus presenting an appeal to potential users, especially those interested in flight ETA prediction in real time but having limited data access.

Analysis of airport design for introducing infrastructure for autonomous drones

PurposeConnecting autonomous drones to ground operations and services is a prerequisite for the adoption of scalable and sustainable drone services in the built environment. Despite the rapid advance in the field of autonomous drones, the development of ground infrastructure has received less attention. Contemporary airport design offers potential solutions for the infrastructure serving autonomous drone services. To that end, this paper aims to construct a framework for connecting air and ground operations for autonomous drone services. Furthermore, the paper defines the minimum facilities needed to support unmanned aerial vehicles for autonomous logistics and the collection of aerial data.Design/methodology/approachThe paper reviews the state-of-the-art in airport design literature as the basis for analysing the guidelines of manned aviation applicable to the development of ground infrastructure for autonomous drone services. Socio-technical system analysis was used for identifying the service needs of drones.FindingsThe key findings are functional modularity based on the principles of airport design applies to micro-airports and modular service functions can be connected efficiently with an autonomous ground handling system in a sustainable manner addressing the concerns on maintenance, reliability and lifecycle.Research limitations/implicationsAs the study was limited to the airport design literature findings, the evolution of solutions may provide features supporting deviating approaches. The role of autonomy and cloud-based service processes are quintessentially different from the conventional airport design and are likely to impact real-life solutions as the area of future research.Practical implicationsThe findings of this study provided a framework for establishing the connection between the airside and the landside for the operations of autonomous aerial services. The lack of such framework and ground infrastructure has hindered the large-scale adoption and easy-to-use solutions for sustainable logistics and aerial data collection for decision-making in the built environment.Social implicationsThe evolution of future autonomous aerial services should be accessible to all users, “democratising” the use of drones. The data collected by drones should comply with the privacy-preserving use of the data. The proposed ground infrastructure can contribute to offloading, storing and handling aerial data to support drone services’ acceptability.Originality/valueTo the best of the authors’ knowledge, the paper describes the first design framework for creating a design concept for a modular and autonomous micro-airport system for unmanned aviation based on the applied functions of full-size conventional airports.

Optimizing Emergency Plane Selection in Civil Aviation Using Extended Dombi Hybrid Operators

Airports located in densely populated areas often face challenges due to asymmetrical traffic patterns. Efficient management and careful planning are required to handle the disproportionate flow of passengers, aircraft, and ground services. The significance of symmetry and asymmetry in civil aviation extends to international regulations and agreements. By harmonizing standards and practices among different nations, it is possible to achieve symmetry in safety measures and operational procedures, thereby promoting a unified and secure global aviation system. Conversely, asymmetry in regulations, infrastructure development, or technological advancements among countries can create obstacles in establishing a cohesive and equitable international aviation framework. This article discusses the weaknesses of the existing score function in handling the MADM problem in an Interval-Valued Pythagorean Fuzzy (IVPF) environment. To tackle this issue, an enhanced score function is developed as a solution. The article proposes the IVPF Dombi hybrid arithmetic and IVPF Dombi hybrid geometric operators based on IVPF information. Furthermore, the article proves some fundamental properties of these operators. In the context of recently introduced techniques using IVPF settings, an effective method is developed for selecting the best airline. Additionally, a comparative investigation is carried out to demonstrate the legitimacy and viability of this unique strategy in comparison to earlier approaches.

Feasibility of new types of technological equipment in the manufacture of nonrigid flat aluminum parts

In this work, we assess the efficiency of the SCHUNK VERO-S Aviation clamping system in manufacturing nonrigid aluminum parts in comparison with the existing technology of their small-scale production at domestic aircraft plants. The research was conducted using the facilities of mechanical shops that manufacture nonrigid aircraft parts to estimate the time and economic expenditures involved in their production when changing the cutting mode and machining allowances. The proposed technological process was implemented by the HALTEC Russian engineering company at an aircraft manufacturing plant of the Russian Federation. The duration of the technological process amounted to a total of 14 hours, with the existing production technology lasting for 300 hours. The process duration was reduced by eliminating two thermal stabilization operations and shortening the machine-setting time by 50%. The machine-setting time was reduced by increasing the number of cutting mode elements during manufacturing of a non-rigid aluminum part using this tooling, as well as by using a modified machining strategy. The use of the SCHUNK VERO -S Aviation system together with a modified machining strategy for a thin-walled non-rigid workpiece allows for an almost complete compensation of deformations caused by residual stresses of the first kind. The new modern technology of SCHUNK VERO -S Aviation proves to be effective for the small-volume and series production of thin-walled nonrigid components of the required quality without warping, multiple straightening operations, thermal and temporal stabilization.