Showing papers in "Aerospace in 2022"

Autonomous Trajectory Generation Algorithms for Spacecraft Slew Maneuvers

Abstract: Spacecraft need to be able to reliably slew quickly and rather than simply commanding a final angle, a trajectory calculated and known throughout a maneuver is preferred. A fully solved trajectory allows for control based off comparing current attitude to a time varying desired attitude, allowing for much better use of control effort and command over slew orientation. This manuscript introduces slew trajectories using sinusoidal functions compared to optimal trajectories using Pontryagin’s method. Use of Pontryagin’s method yields approximately 1.5% lower control effort compared to sinusoidal trajectories. Analysis of the simulated system response demonstrates that correct understanding of the effect of cross-coupling is necessary to avoid unwarranted control costs. Additionally, a combination of feedforward with proportional derivative control generates a system response with 3% reduction in control cost compared to a Feedforward with proportional integral derivative control architecture. Use of a calculated trajectory is shown to reduce control cost by five orders of magnitude and allows for raising of gains by an order of magnitude. When control gains are raised, an eight orders of magnitude lower error is achieved in the slew direction, and rather than an increase in control cost, a decrease by 11.7% is observed. This manuscript concludes that Pontryagin’s method for generating slew trajectories outperforms the use of sinusoidal trajectories and trajectory generation schemes are essential for efficient spacecraft maneuvering.

Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery

Abstract: Drones are becoming increasingly popular not only for recreational purposes but also in a variety of applications in engineering, disaster management, logistics, securing airports, and others. In addition to their useful applications, an alarming concern regarding physical infrastructure security, safety, and surveillance at airports has arisen due to the potential of their use in malicious activities. In recent years, there have been many reports of the unauthorized use of various types of drones at airports and the disruption of airline operations. To address this problem, this study proposes a novel deep learning-based method for the efficient detection and recognition of two types of drones and birds. Evaluation of the proposed approach with the prepared image dataset demonstrates better efficiency compared to existing detection systems in the literature. Furthermore, drones are often confused with birds because of their physical and behavioral similarity. The proposed method is not only able to detect the presence or absence of drones in an area but also to recognize and distinguish between two types of drones, as well as distinguish them from birds. The dataset used in this work to train the network consists of 10,000 visible images containing two types of drones as multirotors, helicopters, and also birds. The proposed deep learning method can directly detect and recognize two types of drones and distinguish them from birds with an accuracy of 83%, mAP of 84%, and IoU of 81%. The values of average recall, average accuracy, and average F1-score were also reported as 84%, 83%, and 83%, respectively, in three classes.

Rocket Lab Mission to Venus

Abstract: Regular, low-cost Decadal-class science missions to planetary destinations will be enabled by high-ΔV small spacecraft, such as the high-energy Photon, and small launch vehicles, such as Electron, to support expanding opportunities for scientists and to increase the rate of science return. The Rocket Lab mission to Venus is a small direct entry probe planned for baseline launch in May 2023 with accommodation for a single ~1 kg instrument. A backup launch window is available in January 2025. The probe mission will spend about 5 min in the Venus cloud layers at 48–60 km altitude above the surface and collect in situ measurements. We have chosen a low-mass, low-cost autofluorescing nephelometer to search for organic molecules in the cloud particles and constrain the particle composition.

Aircraft 4D Trajectory Prediction in Civil Aviation: A Review

Abstract: Aircraft four dimensional (4D, including longitude, latitude, altitude and time) trajectory prediction is a key technology for existing automation systems and the basis for future trajectory-based operations. This paper firstly summarizes the background and significance of the trajectory prediction problems and then introduces the definition and basic process of trajectory prediction, including four modules: preparation, prediction, update, and output. In addition, the trajectory prediction methods are summarized into three types: the state estimation model, the Kinetic model, and the machine learning model, and in-depth analysis of various models is carried out. Further, the relevant databases required for the study are introduced, including the aircraft performance database, aircraft monitoring database, and meteorological database. Finally, challenges and future development directions of the current trajectory prediction problem are summarized.

Venus Life Finder Missions Motivation and Summary

Abstract: Finding evidence of extraterrestrial life would be one of the most profound scientific discoveries ever made, advancing humanity into a new epoch of cosmic awareness. The Venus Life Finder (VLF) missions feature a series of three direct atmospheric probes designed to assess the habitability of the Venusian clouds and search for signs of life and life itself. The VLF missions are an astrobiology-focused set of missions, and the first two out of three can be launched quickly and at a relatively low cost. The mission concepts come out of an 18-month study by an MIT-led worldwide consortium.

Design and Experimental Comparison of PID, LQR and MPC Stabilizing Controllers for Parrot Mambo Mini-Drone

Abstract: Parrot Mambo mini-drone is a readily available commercial quadrotor platform to understand and analyze the behavior of a quadrotor both in indoor and outdoor applications. This study evaluates the performance of three alternative controllers on a Parrot Mambo mini-drone in an interior environment, including Proportional–Integral–Derivative (PID), Linear Quadratic Regulator (LQR), and Model Predictive Control (MPC). To investigate the controllers’ performance, initially, the MATLAB®/Simulink™ environment was considered as the simulation platform. The successful simulation results finally led to the implementation of the controllers in real-time in the Parrot Mambo mini-drone. Here, MPC surpasses PID and LQR in ensuring the system’s stability and robustness in simulation and real-time experiment results. Thus, this work makes a contribution by introducing the impact of MPC on this quadrotor platform, such as system stability and robustness, and showing its efficacy over PID and LQR. All three controllers demonstrate similar tracking performance in simulations and experiments. In steady state, the maximal pitch deviation for the PID controller is 0.075 rad, for the LQR, it is 0.025 rad, and for the MPC, it is 0.04 rad. The maximum pitch deviation for the PID-based controller is 0.3 rad after the take-off impulse, 0.06 rad for the LQR, and 0.17 rad for the MPC.

Constrained Motion Planning of 7-DOF Space Manipulator via Deep Reinforcement Learning Combined with Artificial Potential Field

Abstract: During the on-orbit operation task of the space manipulator, some specific scenarios require strict constraints on both the position and orientation of the end-effector, such as refueling and auxiliary docking. To this end, a novel motion planning approach for a space manipulator is proposed in this paper. Firstly, a kinematic model of the 7-DOF free-floating space manipulator is established by introducing the generalized Jacobian matrix. On this basis, a planning approach is proposed to realize the motion planning of the 7-DOF free-floating space manipulator. Considering that the on-orbit environment is dynamical, the robustness of the motion planning approach is required, thus the deep reinforcement learning algorithm is introduced to design the motion planning approach. Meanwhile, the deep reinforcement learning algorithm is combined with artificial potential field to improve the convergence. Besides, the self-collision avoidance constraint is considered during planning to ensure the operational security. Finally, comparative simulations are conducted to demonstrate the performance of the proposed planning method.

Fractional-Order Sliding Mode Guidance Law for Intercepting Hypersonic Vehicles

Abstract: This paper addresses the problem of a low-speed missile intercepting a hypersonic vehicle in the longitudinal plane. Firstly, based on the concept of the zero of the angular rate of the line-of-sight (LOS) angle, the guidance system is established by defining the LOS angular rate as the state variable. Secondly, in view of the difficulty of precisely measuring the external disturbance caused by the hypersonic vehicle’s maneuver in the guidance system, a non-homogeneous disturbance observer is designed to precisely estimate the disturbance information. Then, by introducing the fractional-order operator into the sliding surface, a fractional-order fast power reaching (FOFPR) guidance law is proposed based on the fast power reaching law. Simulation examples are carried out in two different maneuver modes of the hypersonic vehicle: the bang-bang maneuver mode and sinusoidal maneuver mode. Besides, comparative experiments are conducted with the proportional navigation (PN) and the integer-order fast power reaching (IOFPR) guidance laws. Finally, the simulation results demonstrate the superiority of the effectiveness of the proposed guidance law.

Mission Architecture to Characterize Habitability of Venus Cloud Layers via an Aerial Platform

Abstract: Venus is known for its extreme surface temperature and its sulfuric acid clouds. But the cloud layers on Venus have similar temperature and pressure conditions to those on the surface of Earth and are conjectured to be a possible habitat for microscopic life forms. We propose a mission concept to explore the clouds of Venus for up to 30 days to evaluate habitability and search for signs of life. The baseline mission targets a 2026 launch opportunity. A super-pressure variable float altitude balloon aerobot cycles between the altitudes of 48 and 60 km, i.e., primarily traversing the lower, middle, and part of the upper cloud layers. The instrument suite is carried by a gondola design derived from the Pioneer Venus Large Probe pressure vessel. The aerobot transmits data via an orbiter relay combined with a direct-to-Earth link. The orbiter is captured into a 6-h retrograde orbit with a low, roughly 170-degree, inclination. The total mass of the orbiter and entry probe is estimated to be 640 kg. An alternate concept for a constant float altitude balloon is also discussed as a lower complexity option compared to the variable float altitude version. The proposed mission would complement other planned missions and could help elucidate the limits of habitability and the role of unknown chemistry or possibly life itself in the Venus atmosphere.

Fault Tolerant Attitude and Orbit Determination System for Small Satellite Platforms

Abstract: Small satellite platforms are experiencing increasing interest from the space community, because of the reduced cost and the performance available with current technologies. In particular, the hardware composing the attitude and orbit control system (AOCS) has reached a strong maturity level, and the dimensions of the components allow redundant sets of sensors and actuators. Thus, the software shall be capable of managing these redundancies with a fault tolerant structure. This paper presents an attitude and orbit determination system (AODS) architecture, with embedded failure detection and isolation functions, and autonomous redundant component management and reconfiguration for basic failure recovery. The system design and implementation has been sized for small satellite platforms, characterized by limited computing capacities, and reduced autonomy level. The discussion describes the system architecture, with particular emphasis on the failure detection and isolation blocks at the component level. The set of functions managing failure detection at system level is also described in the paper. The proposed system is capable of reconfiguring and autonomously recalibrating after various failures had occurred. Attention is also dedicated to the achieved performance, satisfying stringent requirements for a small satellite platform. In these regards, the simulation results used to verify the performance of the proposed system at the model-in-the-loop (MIL) level are also reported.

Compressive Behaviour of 3D-Printed PETG Composites

Abstract: It is known that 3D-printed PETG composites reinforced with carbon or Kevlar fibres are materials that can be suitable for specific applications in the aeronautical and/or automotive sector. However, for this purpose, it is necessary to understand their mechanical behaviour, which is not yet fully understood in terms of compression. Therefore, this study intends to increase the knowledge in this domain, especially in terms of static behaviour, as well as with regard to creep and stress relaxation due to the inherent viscoelasticity of the matrix. In this context, static, stress relaxation and creep tests were carried out, in compressive mode, using neat PETG and PETG composites reinforced with carbon and Kevlar fibres. From the static tests, it was found that the yield compressive strength decreased in both composites compared to the neat polymer. Values around 9.9% and 68.7% lower were found, respectively, when carbon and Kevlar fibres were added to the PETG. Similar behaviour was observed for compressive displacement, where a reduction of 20.4% and 46.3% was found, respectively. On the other hand, the compressive modulus increased by 12.4% when carbon fibres were added to the PETG matrix and decreased by 39.6% for Kevlar fibres. Finally, the stress relaxation behaviour revealed a decrease in compressive stresses over time for neat PETG, while the creep response promoted greater compressive displacement. In both situations, the response was very dependent on the displacement/stress level used at the beginning of the test. However, when the fibres were added to the polymer, higher stress relaxations and compressive displacements were observed.

Global Optimization of UAV Area Coverage Path Planning Based on Good Point Set and Genetic Algorithm

Abstract: When performing area coverage tasks in some special scenarios, fixed-wing aircraft conventionally adopt the scan-type of path planning, where the distance between two adjacent tracks is usually less than the minimum turning radius of the aircraft. This results in increased energy consumption during turning between adjacent tracks, which means a reduced task execution efficiency. To address this problem, the current paper proposes an area coverage path planning method for a fixed-wing unmanned aerial vehicle (UAV) based on an improved genetic algorithm. The algorithm improves the primary population generation of the traditional genetic algorithm, with the help of better crossover operator and mutation operator for the genetic operation. More specifically, the good point set algorithm (GPSA) is first used to generate a primary population that has a more uniform distribution than that of the random algorithm. Then, the heuristic crossover operator and the random interval inverse mutation operator are employed to reduce the risk of local optimization. The proposed algorithm is verified in tasks with different numbers of paths. A comparison with the conventional genetic algorithm (GA) shows that our algorithm can converge to a better solution.

An End-to-End UAV Simulation Platform for Visual SLAM and Navigation

Abstract: Visual simultaneous localization and mapping (v-SLAM) and navigation of unmanned aerial vehicles (UAVs) are receiving increasing attention in both research and education. However, extensive physical testing can be expensive and time-consuming due to safety precautions, battery constraints, and the complexity of hardware setups. For the efficient development of navigation algorithms and autonomous systems, as well as for education purposes, the ROS-Gazebo-PX4 simulator was customized in-depth, integrated into our previous released research works, and provided as an end-to-end simulation (E2ES) solution for UAV, v-SLAM, and navigation applications. Unlike most other similar works, which can only stimulate certain parts of the navigation algorithms, the E2ES platform simulates all of the localization, mapping, and path-planning kits in one simulator. The navigation stack performs well in the E2ES test bench with the absolute pose errors of 0.3 m (translation) and 0.9 degree (rotation), respectively, for an 83 m length trajectory. Moreover, the E2ES provides an out-of-box, click-and-fly autonomy in UAV navigation. The project source code is opened for the benefit of the research community.

A Comprehensive Survey on Climate Optimal Aircraft Trajectory Planning

Abstract: The strong growth rate of the aviation industry in recent years has created significant challenges in terms of environmental impact. Air traffic contributes to climate change through the emission of carbon dioxide (CO2) and other non-CO2 effects, and the associated climate impact is expected to soar further. The mitigation of CO2 contributions to the net climate impact can be achieved using novel propulsion, jet fuels, and continuous improvements of aircraft efficiency, whose solutions lack in immediacy. On the other hand, the climate impact associated with non- CO2 emissions, being responsible for two-thirds of aviation radiative forcing, varies highly with geographic location, altitude, and time of the emission. Consequently, these effects can be reduced by planning proper climate-aware trajectories. To investigate these possibilities, this paper presents a survey on operational strategies proposed in the literature to mitigate aviation’s climate impact. These approaches are classified based on their methodology, climate metrics, reliability, and applicability. Drawing upon this analysis, future lines of research on this topic are delineated.

Comparison of Tethered Post-Capture System Models for Space Debris Removal

Abstract: The space debris problem poses a huge threat to operational satellites and has to be addressed. Multiple removal methods have been proposed to keep Earth’s orbit stable. Flexible connection capturing methods, such as the harpoon system, tether–gripper system and the net system, are potential candidate methods for space debris removal in the future. However, the tethered system is usually assumed as a dumbbell model where two end masses are connected by a rigid bar. This traditional model is not accurate enough to predict the motion of the target, neither the whole system. In this paper, three models, namely the modified dumbbell model, lumped-mass model and the ANCF model, to describe a tethered post-capture system for space debris removal are described and compared. Moreover, modal analysis of the tethered system is performed, and an analytical solution of the system’s natural frequency is derived. In addition, two configurations of the tethered system, namely the single tether configuration and the sub-tether configuration are simulated and compared based on three models, respectively. Finally, the influence on the chaser satellite by the initial angular velocity of the target is analyzed.

Implicit Extended Kalman Filter for Optical Terrain Relative Navigation Using Delayed Measurements

Abstract: The exploration of celestial bodies such as the Moon, Mars, or even smaller ones such as comets and asteroids, is the next frontier of space exploration. One of the most interesting and attractive purposes from the scientific point of view in this field, is the capability for a spacecraft to land on such bodies. Monocular cameras are widely adopted to perform this task due to their low cost and system complexity. Nevertheless, image-based algorithms for motion estimation range across different scales of complexities and computational loads. In this paper, a method to perform relative (or local) terrain navigation using frame-to-frame features correspondences and altimeter measurements is presented. The proposed image-based approach relies on the implementation of the implicit extended Kalman filter, which works using nonlinear dynamic models and corrections from measurements that are implicit functions of the state variables. In particular, here, the epipolar constraint, which is a geometric relationship between the feature point position vectors and the camera translation vector, is employed as the implicit measurement fused with altimeter updates. In realistic applications, the image processing routines require a certain amount of time to be executed. For this reason, the presented navigation system entails a fast cycle using altimeter measurements and a slow cycle with image-based updates. Moreover, the intrinsic delay of the feature matching execution is taken into account using a modified extrapolation method.

Maneuvering Spacecraft Orbit Determination Using Polynomial Representation

Abstract: This paper proposed a polynomial representation-based method for orbit determination (OD) of spacecraft with the unknown maneuver. Different from the conventional maneuvering OD approaches that rely on specific orbit dynamic equation, the proposed method needs no priori information of the unknown maneuvering model. The polynomials are used to represent the unknown maneuver. A transformation is made for the polynomials to improve the convergence and robustness. The Extended Kalman Filter (EKF) is used to process incoming observation data by compensating the unknown maneuver using the polynomials. The proposed method is successfully applicated into the OD problem of spacecraft with trigonometric maneuver. Numerical simulations show that the eighth-order polynomials are accurate enough to represent a trigonometric maneuver. Moreover, Monte Carlo simulations show that the position errors are smaller than 1 km, and the maneuver estimated errors are no more than 0.1 mm/s2 using the eighth-order polynomials. The proposed method is accurate and efficient, and has potential applications for tracking maneuvering space target.

An Optimal Fuzzy Logic-Based Energy Management Strategy for a Fuel Cell/Battery Hybrid Power Unmanned Aerial Vehicle

Abstract: With the development of high-altitude and long-endurance unmanned aerial vehicles (UAVs), optimization of the coordinated energy dispatch of UAVs’ energy management systems has become a key target in the research of electric UAVs. Several different energy management strategies are proposed herein for improving the overall efficiency and fuel economy of fuel cell/battery hybrid electric power systems (HEPS) of UAVs. A rule-based (RB) energy management strategy is designed as a baseline for comparison with other strategies. An energy management strategy (EMS) based on fuzzy logic (FL) for HEPS is presented. Compared with classical rule-based strategies, the fuzzy logic control has better robustness to power fluctuations in the UAV. However, the proposed FL strategy has an inherent defect: the optimization performances will be determined by the heuristic method and the past experiences of designers to a great extent rather than a specific cost function of the algorithm itself. Thus, the paper puts forward an improved fuzzy logic-based strategy that uses particle swarm optimization (PSO) to track the optimal thresholds of membership functions, and the equivalent hydrogen consumption minimization is considered as the objective function. Using a typical 30 min UAV mission profile, all the proposed EMS were verified by simulations and rapid controller prototype（RCP）experiments. Comprehensive comparisons and analysis are presented by evaluating hydrogen consumption, system efficiency and voltage bus stability. The results show that the PSO-FL algorithm can further improve fuel economy and achieve superior overall dynamic performance when controlling a UAV’s fuel-cell powertrain.

Deducing the Composition of Venus Cloud Particles with the Autofluorescence Nephelometer (AFN)

Abstract: The composition, sizes and shapes of particles in the clouds of Venus have previously been studied with a variety of in situ and remote sensor measurements. A number of major questions remain unresolved, however, motivating the development of an exploratory mission that will drop a small probe, instrumented with a single-particle autofluorescence nephelometer (AFN), into Venus’s atmosphere. The AFN is specifically designed to address uncertainties associated with the asphericity and complex refractive indices of cloud particles. The AFN projects a collimated, focused, linearly polarized, 440 nm wavelength laser beam through a window of the capsule into the airstream and measures the polarized components of some of the light that is scattered by individual particles that pass through the laser beam. The AFN also measures fluorescence from those particles that contain material that fluoresce when excited at a wavelength of 440 nm and emit at 470–520 nm. Fluorescence is expected from some organic molecules if present in the particles. AFN measurements during probe passage through the Venus clouds are intended to provide constraints on particle number concentration, size, shape, and composition. Hypothesized organics, if present in Venus aerosols, may be detected by the AFN as a precursor to precise identification via future missions. The AFN has been chosen as the primary science instrument for the upcoming Rocket Lab mission to Venus, to search for organic molecules in the cloud particles and constrain the particle composition.

Advanced Sizing Methodology for a Multi-Mode eVTOL UAV Powered by a Hydrogen Fuel Cell and Battery

Abstract: A critical drawback of battery-powered eVTOL UAVs is their limited range and endurance, and this drawback could be solved by using a combination of hydrogen fuel cells and batteries. The objective of this paper is to develop a sizing methodology for the lift+cruise-type eVTOL UAV powered by a hydrogen fuel cell and battery. This paper presents the constraints analysis method for forward flight/VTOL multi-mode UAV, the regression model for electric propulsion system sizing, a sizing method for an electric propulsion system and hydrogen fuel cell system, and a transition analysis method. The total mass of the UAV is iteratively calculated until convergence, and the optimization method is used to ensure that the sizing results satisfy the design requirements. The sizing results are the UAV’s geometry, mass, and power data. To verify the accuracy of the proposed sizing methodology, the sizing and the conceptual design phase results of a 25 kg hydrogen fuel-cell-powered UAV are compared. All parameters had an error within 10% and satisfied the design requirements.

Performance Assessment of an Integrated Environmental Control System of Civil Hypersonic Vehicles

Abstract: This paper discloses the architecture and related performance of an environment control system designed to be integrated within a complex multi-functional thermal and energy management system that manages the heat loads and generation of electric power in a hypersonic vehicle by benefitting from the presence of cryogenic liquid hydrogen onboard. A bleed-less architecture implementing an open-loop cycle with a boot-strap sub-freezing air cycle machine is suggested. Hydrogen boil-off reveals to be a viable cold source for the heat exchangers of the system as well as for the convective insulation layer designed around the cabin walls. Including a 2 mm boil-off convective layer into the cabin cross-section proves to be far more effective than a more traditional air convective layer of approximately 60 mm. The application to STRATOFLY MR3, a Mach 8 waverider cruiser using liquid hydrogen as propellant, confirmed that presence of cryogenic tanks provides up to a 70% reduction in heat fluxes entering the cabin generated outside of it but inside the vehicle, by the propulsive system and other onboard systems. The effectiveness of the architecture was confirmed for all Mach numbers (from 0.3 to 8) and all flight altitudes (from sea level to 35 km).

Investigation of Merge Assist Policies to Improve Safety of Drone Traffic in a Constrained Urban Airspace

Abstract: Package delivery via autonomous drones is often presumed to hold commercial and societal value when applied to urban environments. However, to realise the benefits, the challenge of safely managing high traffic densities of drones in heavily constrained urban spaces needs to be addressed. This paper applies the principles of traffic segmentation and alignment to a constrained airspace in efforts to mitigate the probability of conflict. The study proposes an en-route airspace concept in which drone flights are directly guided along a one-way street network. This one-way airspace concept uses heading-altitude rules to vertically segment cruising traffic as well as transitioning flights with respect to their travel direction. However, transition flights trigger a substantial number of merging conflicts, thus negating a large part of the benefits gained from airspace structuring. In this paper, we aim to reduce the occurrence of merging conflicts and intrusions by using a delay-based and speed-based merge-assist strategy, both well-established methods from road traffic research. We apply these merge assistance strategies to the one-way airspace design and perform simulations for three traffic densities for the experiment area of Manhattan, New York. The results indicate, at most, a 9–16% decrease in total number of intrusions with the use of merge assistance. By investigating mesoscopic features of the urban street network, the data suggest that the relatively low efficacy of the merge strategies is mainly caused by insufficient space for safe manoeuvrability and the inability for the strategies to fully respond and thus resolve conflicts on short-distance streets.

Assessment of the Impact of Material Selection on Aviation Sustainability, from a Circular Economy Perspective

Abstract: Climate change and global warming pose great sustainability challenges to the aviation industry. Alternatives to petroleum-based fuels (hydrogen, natural gas, etc.) have emerged as promising aviation fuels for future aircraft. The present study aimed to contribute to the understanding of the impact of material selection on aviation sustainability, accounting for the type of fuel implemented and circular economy aspects. In this context, a decision support tool was introduced to aid decision-makers and relevant stakeholders to identify and select the best-performing materials that meet their defined needs and preferences, expressed through a finite set of conflicting criteria associated with ecological, economic, and circularity aspects. The proposed tool integrates life-cycle-based metrics extending to both ecological and economical dimensions and a proposed circular economy indicator (CEI) focused on the material/component level and linked to its quality characteristics, which also accounts for the quality degradation of materials which have undergone one or more recycling loops. The tool is coupled with a multi-criteria decision analysis (MCDA) methodology in order to reduce subjectivity when determining the importance of each of the considered criteria.

Simulation and Analysis of Fluid–Solid–Thermal Unidirectional Coupling of Near-Space Airship

Abstract: Based on the biaxial experiment data of the membrane material under hot and cold conditions, the mechanical properties calculation model of envelope material was established with consideration of the effects of varying stress ratios, stress magnitudes and temperatures on the mechanical properties of near-space airship material. Using the heat source model, Computational Fluid Dynamics (CFD) simulation, User-Defined Function (UDF), structural finite element analysis software and the user subroutine of an airship to define the behaviour of fabric material, the fluid–structure–thermal coupling model of airship envelopes was established. In addition, a near-space airship was selected as the research subject to calculate the diurnal temperature differences during the summer solstice and analyse the diurnal temperature distribution of the envelope. Under controlled environmental conditions, the deformation law of the near-space airship under the influence of fluid–structure–thermal coupling was calculated and summarised. The present fluid–solid–thermal coupling model takes into account the anisotropy of materials, temperature, stress magnitude, stress ratio and other influencing factors, which can more accurately reflect and predict the stress–strain distribution and the deformation law of near-space airships.

Estimation of Liquid Hydrogen Fuels in Aviation

Abstract: As the demand for alternative fuels to solve environmental problems increases worldwide due to the greenhouse gas problem, this study predicted the demand for liquid hydrogen fuel in aviation to achieve ‘zero-emission flight’. The liquid hydrogen fuel models of an aircraft and all aviation sectors were produced based on the prediction of aviation fleet growth through the classification of currently operated aircraft. Using these models, the required amount of liquid hydrogen fuel and the total cost of liquid hydrogen were also calculated when various environmental regulations were satisfied. As a result, it was found to be necessary to convert approximately 66% to 100% of all aircraft from existing aircraft to liquid hydrogen aircraft in 2050, according to regulations. The annual liquid hydrogen cost was 4.7–5.2 times higher in the beginning due to the high production cost, but after 2030, it will be maintained at almost the same price, and it was found that the cost was rather low compared to jet fuel.

Thermal Vacuum Test Campaign of the EIRSAT-1 Engineering Qualification Model

Abstract: CubeSats facilitate rapid development and deployment of missions for educational, technology demonstration, and scientific purposes. However, they are subject to a high failure rate, with a leading cause being the lack of system-level verification. The Educational Irish Research Satellite (EIRSAT-1) is a CubeSat mission under development in the European Space Agency’s (ESA) Fly Your Satellite! Programme. EIRSAT-1 is a 2U CubeSat with three novel payloads and a bespoke antenna deployment module, which all contribute to the complexity of the project. To increase the likelihood of mission success, a prototype model philosophy is being employed, where both an engineering qualification model (EQM) and a flight model of EIRSAT-1 are being built. Following the assembly of the EQM, the spacecraft underwent a successful full functional test and month-long mission test. An environmental test campaign in ESA Education Office’s CubeSat Support Facility was then conducted with the EQM where both vibration and thermal verification test campaigns were performed. The focus of this paper is the thermal testing and verification of the EIRSAT-1 EQM. Over three weeks, the EQM was subjected to one non-operational cycle, three and a half operational cycles, and a thermal balance test in a thermal vacuum chamber. After dwelling at each temperature extreme, functional tests were performed to investigate the performance of the spacecraft in this space representative environment. The approach to planning and executing the thermal testing is described in detail including the documentation required, set up of the test equipment, and determination of the test levels. Overall, the campaign demonstrated that the mission can successfully operate in a space environment similar to that expected in orbit, despite encountering a number of issues. These issues included a payload displaying anomalous behaviour at cold temperatures and needing to redefine test levels due to an insufficient understanding of the internal dissipation in the spacecraft. A total of two major and three minor non-conformances were raised. Crucially, these issues could not have been found without thermal testing, despite the comprehensive ambient tests performed. The main results and lessons learned during this thermal test campaign are presented with the aim of guiding future missions on optimal approaches in organising and executing the thermal testing of their CubeSats.

The ORIGIN Space Instrument for Detecting Biosignatures and Habitability Indicators on a Venus Life Finder Mission

Abstract: Recent and past observations of chemical and physical peculiarities in the atmosphere of Venus have renewed speculations about the existence of life in its clouds. To find signs of Venusian life, a dedicated astrobiological space exploration mission is required, and for this reason the Venus Life Finder mission is currently being prepared. A Venus Life Finder mission will require dedicated and specialized instruments to hunt for biosignatures and habitability indicators. In this contribution, we present the ORIGIN space instrument, a laser desorption/laser ablation ionization mass spectrometer. This instrument is designed to detect large, non-volatile molecules, specifically biomolecules such as amino acids and lipids. At the same time, it can also be used in ablation mode for elemental composition analysis. Recent studies with this space prototype instrument of amino acids, polycyclic aromatic hydrocarbons, lipids, salts, metals, sulphur isotopes, and microbial elemental composition are discussed in the context of studies of biosignatures and habitability indicators in Venus’s atmosphere. The implementation of the ORIGIN instrument into a Venus Life Finder mission is discussed, emphasizing the low weight and low power consumption of the instrument. An instrument design and sample handling system are presented that make optimal use of the capabilities of this instrument. ORIGIN is a highly versatile instrument with proven capabilities to investigate and potentially resolve many of the outstanding questions about the atmosphere of Venus and the presence of life in its clouds.

Design of an ATC Tool for Conflict Detection Based on Machine Learning Techniques

Abstract: Given the ongoing interest in the application of Machine Learning (ML) techniques, the development of new Air Traffic Control (ATC) tools is paramount for the improvement of the management of the air transport system. This article develops an ATC tool based on ML techniques for conflict detection. The methodology develops a data-driven approach that predicts separation infringements between aircraft within airspace. The methodology exploits two different ML algorithms: classification and regression. Classification algorithms denote aircraft pairs as a Situation of Interest (SI), i.e., when two aircraft are predicted to cross with a separation lower than 10 Nautical Miles (NM) and 1000 feet. Regression algorithms predict the minimum separation expected between an aircraft pair. This data-driven approach extracts ADS-B trajectories from the OpenSky Network. In addition, the historical ADS-B trajectories work as 4D trajectory predictions to be used as inputs for the database. Conflict and SI are simulated by performing temporary modifications to ensure that the aircraft pierces into the airspace in the same time period. The methodology is applied to Switzerland’s airspace. The results show that the ML algorithms could perform conflict prediction with high-accuracy metrics: 99% for SI classification and 1.5 NM for RMSE.

The Study of Electrical Energy Power Supply System for UAVs Based on the Energy Storage Technology

Abstract: Unmanned aerial vehicles (UAVs) are increasingly attracting investment and development attention from many countries all over the world due to their great advantages. However, one of the biggest challenges for researchers is the problem of supplying energy to UAVs to ensure they can operate for a longer time. Especially in the case of rotary wings, they consume more energy than other UAV types as the motors need to spend a lot of energy to operate in order to overcome the gravity of the earth. The article aims to research power supply, energy consumption on UAVs, and a method of taking advantage of external energy sources to provide power for the operation of UAVs and discuss UAVs’ structure, categories, and control. Two experiments were conducted separately to evaluate the energy consumption of UAVs and the energy conversion from external energy sources to electrical energy. A test bench was designed to evaluate and determine the maximum efficiency using regenerative braking mode. The measuring device was manufactured to measure the necessary parameters to calculate the energy consumption and performance of the system. Experimental numerical results show that energy conversion from external sources is one of methods that can help increase the flight time of the UAV.

Flight Anomaly Detection via a Deep Hybrid Model

Abstract: In the civil aviation industry, security risk management has shifted from post-accident investigations and analyses to pre-accident warnings in an attempt to reduce flight risks by identifying currently untracked flight events and their trends and effectively preventing risks before they occur. The use of flight monitoring data for flight anomaly detection is effective in discovering unknown and potential flight incidents. In this paper, we propose a time-feature attention mechanism and construct a deep hybrid model for flight anomaly detection. The hybrid model combines a time-feature attention-based convolutional autoencoder with the HDBSCAN clustering algorithm, where the autoencoder is constructed and trained to extract flight features while the HDBSCAN works as an anomaly detector. Quick access record (QAR) flight data containing information of aircraft landing at Kunming Changshui International and Chengdu Shuangliu International airports are used as the experimental data, and the results show that (1) the time-feature-based convolutional autoencoder proposed in this paper can better extract the flight features and further discover the different landing patterns; (2) in the representation space of the flights, anomalous flight objects are better separated from normal objects to provide a quality database for subsequent anomaly detection; and (3) the discovered flight patterns are consistent with those at the airports, resulting in anomalies that could be interpreted with the corresponding pattern. Moreover, several examples of anomalous flights at each airport are presented to analyze the characteristics of anomalies.