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Abstract A key problem of the construction of fully electric aircraft is the limited energy density of battery packs. It is generally accepted that this can only be overcome via new, denser battery chemistry together with a further increase in the efficiency of power utilization. One appealing approach for achieving the latter is using laser-assisted filler-based joining technologies, which offers unprecedented flexibility for achieving battery cell connections with the least possible electrical loss. This contribution presents our results on the effect of various experimental and process parameters on the electrical and mechanical properties of the laser-formed bond.

Abstract Improving the energy performance of batteries can increase the reliability of electric aircraft. To achieve this goal, battery management systems (BMS) are required to keep the temperature within the battery pack and cells below the safety limits and make the temperature distribution as even as possible. Batteries have a limited service life as a result of unwanted chemical reactions, physical changes that cause the loss of active materials in the structure, and internal resistance increase during the charging and discharging cycle of the battery. These changes usually affect the electrical performance of batteries. Battery life can be increased only by reducing or preventing unwanted chemical reactions. Lithium-ion (Li-ion) batteries are a suitable option due to their high specific energy and energy density advantages. In this study, the necessity of heat management is emphasized. The discharge tests of the Li-ion battery provided 94.6 Wh under 10C and 90.9 Wh under 1C.

Abstract The range of an aircraft is determined by the amount of energy that its batteries can store. Today, larger batteries are used to increase the range of electric vehicles, although energy efficiency decreases as the weight of the vehicles increases. Among the elements, lithium (Li) is the lightest and has the highest electrochemical potential. Therefore, the use of Li-ion batteries is recommended for hybrid aircraft. In addition, Li-ion batteries are the most common type of battery that is used in portable electronic devices such as smartphones, tablets, and laptops. However, Li-ion batteries may explode due to temperature. Therefore, the thermal analysis of Li-ion batteries was investigated both experimentally and numerically. Li-ion batteries were connected in series (the number is 9). Noboru’s theory of heat generation was discussed in the estimation of energy data.

Abstract The main focus of this article is the online estimation of the terminal airspace sector capacity from the Air Traffic Controller 0ATC) dynamical neural model using Neural Partial Differentiation (NPD) with permissible safe separation and affordable workload. For this purpose, a primarily neural model of a multi-input-single-output (MISO) ATC dynamical system is established, and the NPD method is used to estimate the model parameters from the experimental data. These estimated parameters have a less relative standard deviation, and hence the model validation results show that the predicted neural model response is well matched with the intervention of the ATC workload. Moreover, the proposed neural network-based approach works well with the experimental data online as it does not require the initial values of model parameters, which are unknown in practice.

Abstract The supersession of metallic alloys with lightweight, high-strength composites is popular in the aircraft industry. However, aviation electronic enclosures for large format batteries and high power conversion electronics are still primarily made of aluminum alloys. These aluminum enclosures have attractive properties regrading structural integrity for the heavy internal parts, electromagnetic interference (EMI) suppression, electrical bonding for the internal cells, and/or electronics and failure containment. This paper details a lightweight carbon fiber composite chassis developed at Meggitt Sensing Systems (MSS) Securaplane, with a copper metallic mesh co-cured onto the internal surfaces resulting in a 50% reduction in weight when compared to its aluminum counterpart. In addition to significant weight reduction, it provides equal or improved performance with respect to EMI, structural and flammability performance.

Abstract Aircraft subsystem development involves various combinations of testing and qualification activities to realize a flight-worthy system. The subsystem needs to be verified for a massive number of customer requirements. Power quality (PQ) testing is also an important testing activity carried out as part of the environmental qualification test. It is intended to verify the functionality of subsystems with various kinds of power disturbances and to determine the ability of a subsystem to withstand PQ disturbances. The subsystem being designed should be reliable enough to handle PQ anomalies. A PQ test results in an enormous amount of data for analysis with millions of data samples depending on the test and can be identified as big data. The engineer needs to analyze each set of test data as part of post-processing to ensure the power disturbances during testing are as per the standard requirements and that the functional performance of the subsystem is met.

Abstract Despite more and more rigorous defense mechanisms in place for cyber-physical systems, cybercriminals are increasingly attacking systems for benefits using a variety of means including malware, phishing, ransomware, and denial of service. Cyberattacks could not only cause significant economic loss but also disastrous consequences for individuals and organizations. Therefore, it is advantageous to detect and fix potential cyber vulnerabilities before the system is fielded. To this end, this article presents a language, VERDICT, and a novel framework, Cyber Vulnerability Analysis Framework (CyVAF) to (i) define cyber threats and mitigation defenses based on system properties, (ii) detect cyber vulnerabilities of system architecture automatically, and also (iii) suggest mitigation defenses. VERDICT is developed as an annex to the Architecture Analysis and Design Language (AADL) but can also be used independently.

Abstract The numerical analysis of the three-dimensional (3D) flow over a National Advisory Committee for Aeronautics (NACA) 6321 airfoil to evaluate the mass flow rate by using a novel method Improved Blowing and Suction System (IBSS) to control the boundary layer is presented in this study. Analysis is performed based on 3D Reynolds-Averaged Navier-Stokes (RANS) equation with a K-omega SST solver. The aerodynamic performance of the NACA 6321 is analyzed at a Mach number of 0.10 with three different mass flow rates, namely, 0.08 kg/s, 0.10 kg/s, and 0.12 kg/s. From the study, it is seen that when the mass flow rate decreased, the aerodynamics performance also reduced, and the aerodynamic performance improved with the increase in mass flow rate.

Abstract Letter from the Guest Editors

Abstract Possible oil contamination of aircraft bleed air is an ongoing operational issue for commercial aircraft. A sensitive and reliable method to detect contamination, especially at very low levels, has been elusive due, in part, to the lack of information about the physical nature of oil that results when entrained in the bleed air by an engine compressor. While it was expected that high shear rates in the compressors would result in very finely dispersed particles, detailed data on the size characteristics of these droplets were not available, making it difficult to develop reliable detection techniques. The goal of the reported research was to collect experimental data to provide this information. The concentration and size distribution of particles were measured for bleed air with different rates of controlled oil contamination under various engine operating conditions.

Abstract Water quality monitoring is needed for the effective management of water resources. Periodic sampling and regular inspection/analysis allow one to classify water and identify changes or trends in water quality over time. This article presents a novel concept of an Amphibious Hybrid Unmanned Aerial Vehicle (AHUAV) that can operate in air and water for rapid water sampling, real-time water quality analysis, and water body management. A methodology using the developed AHUAV system for water body management has also been proposed for an easier and effective way of monitoring water bodies using advanced drone technologies. Using drones for water body management can be a cost-effective and efficient way of carrying out regular inspections and continual monitoring.

Abstract Slag, generated from basic oxygen furnace (BOF) or Linz-Donawitz (LD) converter, is one of the recyclable wastes in an integrated steel plant. The present work aims at utilization of waste LD slag to develop surface coatings by plasma spraying technique. This study reveals that LD slag can be gainfully used as a cost-effective wear-resistant coating material. A prediction model based on an artificial neural network (ANN) is also proposed to predict the erosion performance of these coatings. The 2.27% error shows that ANN successfully predicts the erosion wear rate of the coatings both within and beyond the experimental domain. In addition to it, a novel optimization algorithm called imperialist competitive algorithm (ICA) is used to obtain minimum erosion wear rate of 12.12 mg/kg.

Abstract Threat identification and security analysis have become mandatory steps in the engineering design process of high-assurance systems, where successful cyberattacks can lead to hazardous property damage or loss of lives. This article describes a novel approach to perform security analysis on embedded systems modeled at the architectural level. The tool, called Security Threat Evaluation and Mitigation (STEM), associates threats from the Common Attack Pattern Enumeration and Classification (CAPEC) library with components and connections and suggests potential defense patterns from the National Institute of Standards and Technology (NIST) Special Publication (SP) 800-53 security standard. This article also provides an illustrative example based on a drone package delivery system modeled in AADL.

According to the International Civil Aviation Organization, the world aviation air traffic has grown by an average yearly rate of 5% over the last thirty years, until the devastating downturn brought on by the COVID crisis of 2020. Regardless of the current situation, there are still a number of issues and challenges that the industry is confronted with, not the least of which are related to sustainability, the conversion to electrical usage, the challenge of increasing propulsion efficiency in conventional propulsion, the digital transformation of the entire ecosystem, etc. In response, system developers and researchers in the field are working on a number of key technologies and methodologies to solve some of these issues. The Sustainable Aviation Research Society (SARES), a global organization that seeks to encourage research in this area and helps disseminate knowledge via conferences and symposia, has been organizing meetings to promote sustainable aviation over the five years.

Abstract The general English speech recognition is based on the techniques of n-grams where the words before and after are predicted and the utterance prediction is produced. At the same time, having a significantly lengthier n-gram has its own impact in training and the accuracy. Shorter n-grams require the utterances to be split and predicted than using the complete utterance. This article discusses specific techniques to address the specific problems in Air Traffic Speech, which is a medium length utterance domain. Moving from the adapted language models (LMs) to rescored LM, a combined technique of syntax analysis along with a deep learning model is proposed, which improves the overall accuracy. It is explained that this technique can help to adapt the proposed method for different contexts within the same domain and can be successful.

Abstract Fighter pilots must study models of aircraft dynamics before learning complex maneuvers and tactics. Similarly, autonomous fighter aircraft applications may benefit from a model-based learning approach. Instead of using a preexisting physics model of a given aircraft, a machine learning system can learn a predictive model of the aircraft physics from training data. Furthermore, it can model interactions between multiple friendly aircraft, enemy aircraft, and the environment. Such a system can also learn to represent state variables that are not directly observable, as well as dynamics that are not hard coded. Existing model-based methods use a deep neural network that takes observable state information and agent actions as input and provides predictions of future observations as output. The proposed method builds upon this approach by adding a residual feedforward skip connection from some of the inputs to all of the outputs of the deep neural network.

Abstract Identity-Anonymized CAN (IA-CAN) protocol is a secure CAN protocol, which provides the sender authentication by inserting a secret sequence of anonymous IDs (A-IDs) shared among the communication nodes. To prevent malicious attacks from the IA-CAN protocol, a secure and robust system error recovery mechanism is required. This article presents a central management method of IA-CAN, named the IA-CAN with a global A-ID, where a gateway plays a central role in the session initiation and system error recovery. Each ECU self-diagnoses the system errors, and (if an error happens) it automatically resynchronizes its A-ID generation by acquiring the recovery information from the gateway. We prototype both a hardware version of an IA-CAN controller and a system for the IA-CAN with a global A-ID using the controller to verify our concept.

Abstract Aircraft cybersecurity efforts have tended to focus at the strategic or tactical levels without a clear connection between the two. There are many excellent engineering tools already in widespread use, but many organizations have not yet integrated and linked them into an overarching “campaign plan” that connects those tactical actions such as process hazard analysis, threat modeling, and probabilistic methods to the desired strategic outcome of secure and resilient systems. This article presents the combined systems security engineering process (CSSEP) as a way to fill that gap. Systems theory provides the theoretical foundation on which CSSEP is built. CSSEP is structured as a control loop in which the engineering team is the controller of the design process. The engineering team needs to have an explicit process model on how systems should be secured, and a control algorithm that determines what control actions should be selected.

Abstract Statistical machine learning classification methods have been widely used in the fault detection analysis in several engineering domains. This motivates us to provide in this article an overview on the application of these methods in the fault diagnosis strategies and also their successful use in unmanned aerial vehicles (UAVs) systems. Different existing aspects including the implementation conditions, offline design, and online computation algorithms as well as computation complexity and detection time are discussed in detail. Evaluation and validation of these aspects have been ensured by a simple demonstration of the basic classification methods and neural network techniques in solving the fault detection and diagnosis problem of the propulsion system failure of a multirotor UAV. A testing platform of an Hexarotor UAV is completely realized.

Abstract This article reports a reduced-order modeling framework of bladed disks on a rotating shaft to simulate the vibration signature of faults in different components, aiming toward simulated data-driven machine learning. We have employed lumped and one-dimensional analytical models of the subcomponents for better insight into the complex dynamic response. The framework addresses some of the challenges encountered in analyzing and optimizing fault detection and identification schemes for health monitoring of aeroengines and other rotating machinery. We model the bladed disks and shafts by combining lumped elements and one-dimensional finite elements, leading to a coupled system. The simulation results are in good agreement with previously published data. We model and analyze the cracks in a blade with their effective reduced stiffness approximation.