WindSeer: real-time volumetric wind prediction over complex terrain aboard a small uncrewed aerial vehicle

Real-time high-resolution wind predictions are beneficial for various applications including safe crewed and uncrewed aviation. Current weather models require too much compute and lack the necessary predictive capabilities as they are valid only at the scale of multiple kilometers and hours - much lower spatial and temporal resolutions than these applications require. Our work demonstrates the ability to predict low-altitude time-averaged wind fields in real time on limited-compute devices, from only sparse measurement data. We train a deep neural network-based model, WindSeer, using only synthetic data from computational fluid dynamics simulations and show that it can successfully predict real wind fields over terrain with known topography from just a few noisy and spatially clustered wind measurements. WindSeer can generate accurate predictions at different resolutions and domain sizes on previously unseen topography without retraining. We demonstrate that the model successfully predicts historical wind data collected by weather stations and wind measured by drones during flight.

Integrated optimization of scheduling for unmanned follow-me cars on airport surface

To promote the application of automated vehicles in large airports, in this study, we present an integrated optimization method for scheduling Unmanned follow-me cars. The scheduling process is divided into three phases: Dispatch, Guidance, and Recycle. For the Dispatch phase, we establish a vehicle assignment model, to allocate the vehicle resource equitably. For the Guidance phase, we offer an quantitative way, to measure the spacing between Unmanned follow-me car and aircraft. To optimize the efficiency of airport operation in the three phases and ensure safety, the collaborative planning model, and the conflict prediction model are established. An improved grey wolf optimization algorithm is adopted to enhance the convergence speed and generalization performance. A case study at Ezhou Huahu Airport in China demonstrates the effectiveness of the methods. The results show that the model of collaborative planning can make the balance of path selection, Unmanned follow-me car's working time, and departure sequence. The convergence speed of the improved algorithm has been increased by 18.75%. The inequity index of vehicle assignment is only 0.015731, and the spatiotemporal distribution of conflicts is influenced by the airport's surface layout.

A framework for design optimization across multiple concepts

In engineering design, there often exist multiple conceptual solutions to a given problem. Concept design and selection is the first phase of the design process that is estimated to affect up to 70% of the life cycle cost of a product. Currently, optimization methods are rarely used in this phase, since standard optimization methods inherently assume a fixed (given) concept; and undertaking a full-fledged optimization for each possible concept is untenable. In this paper, we aim to address this gap by developing a framework that searches for optimum solutions efficiently across multiple concepts, where each concept may be defined using a different number, or type, of variables (continuous, binary, discrete, categorical etc.). The proposed approach makes progressive data-driven decisions regarding which concept(s) and corresponding solution(s) should be evaluated over the course of search, so as to minimize the computational budget spent on less promising concepts, as well as ensuring that the search does not prematurely converge to a non-optimal concept. This is achieved through the use of a tree-structured Parzen estimator (TPE) based sampler in addition to Gaussian process (GP), and random forest (RF) regressors. Aside from extending the use of GP and RF to search across multiple concepts, this study highlights the previously unexplored benefits of TPE for design optimization. The performance of the approach is demonstrated using diverse case studies, including design of a cantilever beam, coronary stents, and lattice structures using a limited computational budget. We believe this contribution fills an important gap and capitalizes on the developments in the machine learning domain to support designers involved in concept-based design.

A GAN-based genetic algorithm for solving the 3D bin packing problem

The 3D bin packing problem is a challenging combinatorial optimization problem with numerous real-world applications. In this paper, we present a novel approach for solving this problem by integrating a generative adversarial network (GAN) with a genetic algorithm (GA). Our proposed GAN-based GA utilizes the GAN to generate high-quality solutions and improve the exploration and exploitation capabilities of the GA. We evaluate the performance of the proposed algorithm on a set of benchmark instances and compare it with two existing algorithms. The simulation studies demonstrate that our proposed algorithm outperforms both existing algorithms in terms of the number of used bins while achieving comparable computation times. Our proposed algorithm also performs well in terms of solution quality and runtime on instances of different sizes and shapes. We conduct sensitivity analysis and parameter tuning simulations to determine the optimal values for the key parameters of the proposed algorithm. Our results indicate that the proposed algorithm is robust and effective in solving the 3D bin packing problem. The proposed GAN-based GA algorithm and its modifications can be applied to other optimization problems. Our research contributes to the development of efficient and effective algorithms for solving complex optimization problems, particularly in the context of logistics and manufacturing. In summary, the proposed algorithm represents a promising solution to the challenging 3D bin packing problem and has the potential to advance the state-of-the-art in combinatorial optimization.

Tangential velocity constraint for orbital maneuvers with Theory of Functional Connections

Maneuvering a spacecraft in the cislunar space is a complex problem, since it is highly perturbed by the gravitational influence of both the Earth and the Moon, and possibly also the Sun. Trajectories minimizing the needed fuel are generally preferred in order to decrease the mass of the payload. A classical method to constrain maneuvers is mathematically modeling them using the Two Point Boundary Value Problem (TPBVP), defining spacecraft positions at the start and end of the trajectory. Solutions to this problem can then be obtained with optimization techniques like the nonlinear least squares conjugated with the Theory of Functional Connections (TFC) to embed the constraints, which recently became an effective method for deducing orbit transfers. In this paper, we propose a tangential velocity (TV) type of constraints to design orbital maneuvers. We show that the technique presented in this paper can be used to transfer a spacecraft (e.g. from the Earth to the Moon) and perform gravity assist maneuvers (e.g. a swing-by with the Moon). In comparison with the TPBVP, solving the TV constraints via TFC offers several advantages, leading to a significant reduction in computational time. Hence, it proves to be an efficient technique to design these maneuvers.

Cryogenic propellant management in space: open challenges and perspectives

This paper presents open challenges and perspectives of propellant management for crewed deep space exploration. The most promising propellants are liquid hydrogen and liquid methane, together with liquid oxygen as an oxidizer. These fluids remain liquid only at cryogenic conditions, that is, at temperatures lower than 120 K. To extend the duration of space exploration missions, or even to enable them, the storage and refueling from a cryogenic on-orbit depot is necessary. We review reference missions, architectures, and technology demonstrators and explain the main operations that are considered as enablers for cryogenic storage and transfer. We summarize the state of the art for each of them, showing that many gaps in physical knowledge still need to be filled. This paper is based on recommendations originally proposed in a White Paper for ESA's SciSpacE strategy.

Levels of evidence for human system risk evaluation

NASA uses a continuous risk management process to seek out new knowledge of spaceflight-induced risk to human health and performance. The evidence base that informs the risk assessments in this domain is constantly changing as more information is gleaned from a continuous human presence in space and from ongoing research. However, the limitations of this evidence are difficult to characterize because fewer than 700 humans have ever flown in space, and information comes from a variety of sources that span disciplines, including engineering, medicine, food and nutrition, and many other life sciences. The Human System Risk Board (HSRB) at NASA is responsible for assessing risk to astronauts and communicating this risk to agency decision-makers. A critical part of that communication is conveying the uncertainty regarding the understanding of the changes that spaceflight induces in human processes and the complex interactions between humans and the spacecraft. Although the strength of evidence grades is common in the academic literature, these scores are often not useful for the problems of human spaceflight. The HSRB continues to update the processes used to report the levels of evidence. This paper describes recent updates to the methods used to assign the level of evidence scores to the official risk postures and to the causal diagrams used by the HSRB.

Causal diagramming for assessing human system risk in spaceflight

For over a decade, the National Aeronautics and Space Administration (NASA) has tracked and configuration-managed approximately 30 risks that affect astronaut health and performance before, during and after spaceflight. The Human System Risk Board (HSRB) at NASA Johnson Space Center is responsible for setting the official risk posture for each of the human system risks and determining-based on evaluation of the available evidence-when that risk posture changes. The ultimate purpose of tracking and researching these risks is to find ways to reduce spaceflight-induced risk to astronauts. The adverse effects of spaceflight begin at launch and continue throughout the duration of the mission, and in some cases, across the lifetime of the astronaut. Historically, research has been conducted in individual risk "silos" to characterize risk, however, astronauts are exposed to all risks simultaneously. In January of 2020, the HSRB at NASA began assessing the potential value of causal diagramming as a tool to facilitate understanding of the complex causes and effects that contribute to spaceflight-induced human system risk. Causal diagrams in the form of directed acyclic graphs (DAGs) are used to provide HSRB stakeholders with a shared mental model of the causal flow of risk. While primarily improving communication among those stakeholders, DAGs also allow a composite risk network to be created that can be tracked and configuration managed. This paper outlines the HSRB's pilot process for this effort, the lessons learned, and future goals for data-driven risk management approaches.

Electrothermally controlled origami fabricated by 4D printing of continuous fiber-reinforced composites

Active origami capable of precise deployment control, enabling on-demand modulation of its properties, is highly desirable in multi-scenario and multi-task applications. While 4D printing with shape memory composites holds great promise to realize such active origami, it still faces challenges such as low load-bearing capacity and limited transformable states. Here, we report a fabrication-design-actuation method of precisely controlled electrothermal origami with excellent mechanical performance and spatiotemporal controllability, utilizing 4D printing of continuous fiber-reinforced composites. The incorporation of continuous carbon fibers empowers electrothermal origami with a controllable actuation process via Joule heating, increased actuation force through improved heat conduction, and enhanced mechanical properties as a result of reinforcement. By modeling the multi-physical and highly nonlinear deploying process, we attain precise control over the active origami, allowing it to be reconfigured and locked into any desired configuration by manipulating activation parameters. Furthermore, we showcase the versatility of electrothermal origami by constructing reconfigurable robots, customizable architected materials, and programmable wings, which broadens the practical engineering applications of origami.

Enhancing multi-UAV air combat decision making via hierarchical reinforcement learning

In the realm of air combat, autonomous decision-making in regard to Unmanned Aerial Vehicle (UAV) has emerged as a critical force. However, prevailing autonomous decision-making algorithms in this domain predominantly rely on rule-based methods, proving challenging to design and implement optimal solutions in complex multi-UAV combat environments. This paper proposes a novel approach to multi-UAV air combat decision-making utilizing hierarchical reinforcement learning. First, a hierarchical decision-making network is designed based on tactical action types to streamline the complexity of the maneuver decision-making space. Second, the high-quality combat experience gained from training is decomposed, with the aim of augmenting the quantity of valuable experiences and alleviating the intricacies of strategy learning. Finally, the performance of the algorithm is validated using the advanced UAV simulation platform JSBSim. Through comparisons with various baseline algorithms, our experiments demonstrate the superior performance of the proposed method in both even and disadvantaged air combat environments.

Versatile spaceborne photonics with chalcogenide phase-change materials

Recent growth in space systems has seen increasing capabilities packed into smaller and lighter Earth observation and deep space mission spacecraft. Phase-change materials (PCMs) are nonvolatile, reconfigurable, fast-switching, and have recently shown a high degree of space radiation tolerance, thereby making them an attractive materials platform for spaceborne photonics applications. They promise robust, lightweight, and energy-efficient reconfigurable optical systems whose functions can be dynamically defined on-demand and on-orbit to deliver enhanced science or mission support in harsh environments on lean power budgets. This comment aims to discuss the recent advances in rapidly growing PCM research and its potential to transition from conventional terrestrial optoelectronics materials platforms to versatile spaceborne photonic materials platforms for current and next-generation space and science missions. Materials International Space Station Experiment-14 (MISSE-14) mission-flown PCMs outside of the International Space Station (ISS) and key results and NASA examples are highlighted to provide strong evidence of the applicability of spaceborne photonics.

A Ka-band one-dimensional beam scanning leaky-wave antenna based on liquid crystal

Fixed frequency beam-scanning leaky-wave antennas have been a focus of attention for many scholars in recent years, and numerous related results have been obtained. However, these antennas suffer from several issues such as small beam-scanning range, low gain, and unsatisfactory impedance matching. To address these problems, this paper proposes a microstrip line (ML) antenna unit based on liquid crystal (LC) materials etched Complementary Split Ring Resonator (CSRR). In a first-of-its-kind approach, the substrate integrated waveguide (SIW) structure and the ML transmission structure are combined to present the SIW-ML transmission structure. The antenna operates in the Ka-band with excellent resonance characteristics at 34.7 GHz, and the S11 parameters are below - 13 dB in the frequency range of 30-40 GHz, indicating outstanding impedance matching. By arranging 56 antenna units, a periodic leaky-wave antenna is created, enabling fixed-frequency beam-scanning at 34.7 GHz. Experimental results show that the antenna can achieve scanning of angles between - 53° and + 60° with a gain of up to 12.63 dB. Once single-beam scanning is achieved, a method combining LC and discrete amplitude weighting technique, as well as multi-beam theory, is proposed for multi-beam study. Experimental results reveal that the designed 56-unit beam-scanning antenna can effectively realize beam scanning in two directions.

Two-dimensional high resolution electron properties of femtosecond laser-induced plasma filament in atmospheric pressure argon

This work reports the measurement of two-dimensional electron properties over a nanosecond scale integration time across a femtosecond laser-induced plasma filament in atmospheric pressure argon. Radial electron properties across the [Formula: see text] [Formula: see text]m diameter filament are obtained at discrete axial locations at 2.5 mm steps by one-dimensional high-resolution laser Thomson scattering with a spatial resolution of 10 [Formula: see text]m. These measurements reveal plasma structural information in the filament. The Thomson spectral lineshapes exhibit clear spectral sidebands with an [Formula: see text] parameter [Formula: see text], enabling the measurement of both electron temperature and density profiles. These measurements yield electron densities on the order of [Formula: see text]/m[Formula: see text] and electron temperatures of [Formula: see text] eV. Heating from the probe laser due to inverse bremsstrahlung is taken into account to correct the Thomson scattering electron temperature measurements. Under these conditions, electron-neutral collision induced bremsstrahlung becomes the dominant laser-induced plasma heating process associated with the probe laser. The measurements reveal structural features of the filament, including an asymmetrically skewed density structure in the axial direction and reversed radial distributions of electron density and temperature.

Tracking an untracked space debris after an inelastic collision using physics informed neural network

With the sustained rise in satellite deployment in Low Earth Orbits, the collision risk from untracked space debris is also increasing. Often small-sized space debris (below 10 cm) are hard to track using the existing state-of-the-art methods. However, knowing such space debris' trajectory is crucial to avoid future collisions. We present a Physics Informed Neural Network (PINN)-based approach for estimation of the trajectory of space debris after a collision event between active satellite and space debris. In this work, we have simulated 8565 inelastic collision events between active satellites and space debris. To obtain the states of the active satellite, we use the TLE data of 1647 Starlink and 66 LEMUR satellites obtained from space-track.org. The velocity of space debris is initialized using our proposed velocity sampling method, and the coefficient of restitution is sampled from our proposed Gaussian mixture-based probability density function. Using the velocities of the colliding objects before the collision, we calculate the post-collision velocities and record the observations. The state (position and velocity), coefficient of restitution, and mass estimation of un-tracked space debris after an inelastic collision event along with the tracked active satellite can be posed as an optimization problem by observing the deviation of the active satellite from the trajectory. We have applied the classical optimization method, the Lagrange multiplier approach, for solving the above optimization problem and observed that its state estimation is not satisfactory as the system is under-determined. Subsequently, we have designed Deep Neural network-based methods and Physics Informed Neural Network (PINN) based methods for solving the above optimization problem. We have compared the performance of the models using root mean square error (RMSE) and interquartile range of the predictions. It has been observed that the PINN-based methods provide a better estimation performance for position, velocity, mass and coefficient of restitution of the space debris compared to other methods.

Direct numerical simulation of the turbulent flow around a Flettner rotor

The three-dimensional turbulent flow around a Flettner rotor, i.e. an engine-driven rotating cylinder in an atmospheric boundary layer, is studied via direct numerical simulations (DNS) for three different rotation speeds ([Formula: see text]). This technology offers a sustainable alternative mainly for marine propulsion, underscoring the critical importance of comprehending the characteristics of such flow. In this study, we evaluate the aerodynamic loads produced by the rotor of height h, with a specific focus on the changes in lift and drag force along the vertical axis of the cylinder. Correspondingly, we observe that vortex shedding is inhibited at the highest [Formula: see text] values investigated. However, in the case of intermediate [Formula: see text], vortices continue to be shed in the upper section of the cylinder ([Formula: see text]). As the cylinder begins to rotate, a large-scale motion becomes apparent on the high-pressure side, close to the bottom wall. We offer both a qualitative and quantitative description of this motion, outlining its impact on the wake deflection. This finding is significant as it influences the rotor wake to an extent of approximately one hundred diameters downstream. In practical applications, this phenomenon could influence the performance of subsequent boats and have an impact on the cylinder drag, affecting its fuel consumption. This fundamental study, which investigates a limited yet significant (for DNS) Reynolds number and explores various spinning ratios, provides valuable insights into the complex flow around a Flettner rotor. The simulations were performed using a modern GPU-based spectral element method, leveraging the power of modern supercomputers towards fundamental engineering problems.

Lightweight air-to-air unmanned aerial vehicle target detection model

The rapid expansion of the drone industry has resulted in a substantial increase in the number of low-altitude drones, giving rise to concerns regarding collision avoidance and countermeasure strategies among these unmanned aerial vehicles. These challenges underscore the urgent need for air-to-air drone target detection. An effective target detection model must exhibit high accuracy, real-time capabilities, and a lightweight network architecture to achieve a balance between precision and speed when deployed on embedded devices. In response to these requirements, we initially curated a dataset comprising over 10,000 images of low-altitude operating drones. This dataset encompasses diverse and intricate backgrounds, significantly enhancing the model's training capacity. Subsequently, a series of enhancements were applied to the YOLOv5 algorithm to realize lightweight object detection. A novel feature extraction network, CF2-MC, streamlined the feature extraction process, while an innovative module, MG, in the feature fusion section aimed to improve detection accuracy and reduce model complexity. Concurrently, the original CIoU loss function was replaced with the EIoU loss function to further augment the model's accuracy. Experimental results demonstrate an enhancement in the accuracy of drone target detection, achieving mAP values of 95.4% on the UAVfly dataset and 82.2% on the Det-Fly dataset. Finally, real-world testing conducted on the Jetson TX2 revealed that the YOLOv5s-ngn model achieved an average inference speed of 14.5 milliseconds per image. The code utilized in this paper can be accessed via https://github.com/lucien22588/yolov5-ngn.git .

A neuromorphic dataset for tabletop object segmentation in indoor cluttered environment

Event-based cameras are commonly leveraged to mitigate issues such as motion blur, low dynamic range, and limited time sampling, which plague conventional cameras. However, a lack of dedicated event-based datasets for benchmarking segmentation algorithms, especially those offering critical depth information for occluded scenes, has been observed. In response, this paper introduces a novel Event-based Segmentation Dataset (ESD), a high-quality event 3D spatial-temporal dataset designed for indoor object segmentation within cluttered environments. ESD encompasses 145 sequences featuring 14,166 manually annotated RGB frames, along with a substantial event count of 21.88 million and 20.80 million events from two stereo-configured event-based cameras. Notably, this densely annotated 3D spatial-temporal event-based segmentation benchmark for tabletop objects represents a pioneering initiative, providing event-wise depth, and annotated instance labels, in addition to corresponding RGBD frames. By releasing ESD, our aim is to offer the research community a challenging segmentation benchmark of exceptional quality.

Exploring the measurement of psychological resilience in Chinese civil aviation pilots based on generalizability theory and item response theory

Understanding and accurately measuring resilience among Chinese civil aviation pilots is imperative, especially concerning the psychological impact of distressing events on their well-being and aviation safety. Despite the necessity, a validated and tailored measurement tool specific to this demographic is absent. Addressing this gap, this study built on the widely used CD-RISC-25 to analyze and modify its applicability to Chinese civil aviation pilots. Utilizing CD-RISC-25 survey data from 231 Chinese pilots, correlational and differential analyses identified items 3 and 20 as incongruent with this population's resilience profile. Subsequently, factor analysis derived a distinct two-factor resilience psychological framework labeled "Decisiveness" and "Adaptability", which diverged from the structure found in American female pilots and the broader Chinese populace. Additionally, to further accurately identify the measurement characteristics of this 2-factor measurement model, this study introduced Generalized Theory and Item Response Theory, two modern measurement analysis theories, to comprehensively analyze the overall reliability of the measurement and issues with individual items. Results showed that the 2-factor model exhibited high reliability, with generalizability coefficient reaching 0.89503 and dependability coefficient reaching 0.88496, indicating the 2-factor measurement questionnaire can be effectively utilized for relative and absolute comparison of Chinese civil aviation pilot resilience. However, items in Factor 2 provided less information and have larger room for optimization than those in Factor 1, implying item option redesign may be beneficial. Consequently, this study culminates in the creation of a more accurate and reliable two-factor psychological resilience measurement tool tailored for Chinese civil aviation pilots, while exploring directions for optimization. By facilitating early identification of individuals with lower resilience and enabling the evaluation of intervention efficacy, this tool aims to positively impact pilot psychological health and aviation safety in the context of grief and trauma following distressing events.

Large dynamic range stellar radiation simulation optical system

For the current stellar spectral simulation can not realize the stellar color temperature information with large dynamic range simulation, this paper proposes a broad spectrum high-resolution subdivision and spatial beam zoning modulation combined with a large dynamic range of stellar radiation information simulation method, designed a kind of imaging and non-imaging stellar radiation information simulation optical system, using an optical system to achieve multi-color temperature spectrum and large dynamic range stellar simulation. The experimental results show that the designed system can simultaneously achieve the spectral simulation accuracy (single point evaluation) better than ± 7% in the range of spectral 450-1000 nm and color temperature 3000-11,000 K; on the premise of ensuring the spectral simulation accuracy, the magnitude simulation range reaches 0 to  + 12 Mv, and the magnitude simulation accuracy is better than ± 0.05 Mv; Accurate simulation of stellar spectral information and energy large dynamic range tuning is realized, and the system is extended. The system function has been extended to realize the switching of broadband and narrowband modes, The half-peak width of the narrowband output beam is better than 4.1 nm, which extends the application of the spectral simulation technology and provides the theoretical and technical basis for the ground calibration of the development of the high-precision stellar radiation information ground simulation system.

Space object identification and classification from hyperspectral material analysis

This paper presents a data processing pipeline designed to extract information from the hyperspectral signature of unknown space objects. The methodology proposed in this paper determines the material composition of space objects from single pixel images. Two techniques are used for material identification and classification: one based on machine learning and the other based on a least square match with a library of known spectra. From this information, a supervised machine learning algorithm is used to classify the object into one of several categories based on the detection of materials on the object. The behaviour of the material classification methods is investigated under non-ideal circumstances, to determine the effect of weathered materials, and the behaviour when the training library is missing a material that is present in the object being observed. Finally the paper will present some preliminary results on the identification and classification of space objects.

Integrated guidance and control design by active disturbance rejection method for high-velocity target interceptor with DCS thruster

The present paper proposes a novel integrated guidance and control (IGC) method for engaging with high-speed targets such as ballistic projectiles. considering an extreme short period of terminal engagement due to high relative velocity between target and interceptor, it is particularly important for IGC law to show desirable performance in the presence of various uncertainties (e.g. variation in aerodynamic coefficients) and disturbances (e.g. target maneuver and drag). This article extends the ICG law for mismatched and feedback form equations based on the Active Disturbance Rejection Control (ADRC) method using the back-stepping technique and the Reduced-order Extended State Observer (RESO). The primary consideration is the application of thrusters on the center of mass as the Divert Control System (DCS), along with the daisy-chain technique for control allocation between the fins and thruster commands. Contrary to previous research, the filter and angle measurement error are modeled for the seeker as a crucial parameter to highlight the significance of the thruster. The simulation results indicate the efficiency of the developed method for near-miss or hit-to-kill engagement with tactical ballistic targets. It is shown that the thruster plays a significant role in high-altitude engagements, specifically in the presence of non-ideal seeker. Finally, using the Monte Carlo simulation, it is proved that adding inner loops to the developed technique will not remove the IGC's advantage over the conventional approach and Non-singular Terminal Sliding Mode (NTSM) guidance law.

Sensorimotor adaptation of locomotor synergies to gravitational constraint

This study investigates the impact of gravity on lower limb muscle coordination during pedaling. It explores how pedaling behaviors, kinematics, and muscle activation patterns dynamically adapts to changes in gravity and resistance levels. The experiment was conducted in parabolic flights, simulating microgravity, hypergravity (1.8 g), and normogravity conditions. Participants pedaled on an ergometer with varying resistances. The goal was to identify potential changes in muscle synergies and activation strategies under different gravitational contexts. Results indicate that pedaling cadence adjusted naturally in response to both gravity and resistance changes. Cadence increased with higher gravity and decreased with higher resistance levels. Muscular activities were characterized by two synergies representing pull and push phases of pedaling. The timing of synergy activation was influenced by gravity, with a delay in activation observed in microgravity compared to other conditions. Despite these changes, the velocity profile of pedaling remained stable across gravity conditions. The findings strongly suggest that the CNS dynamically manages the shift in body weight by finely tuning muscular coordination, thereby ensuring the maintenance of a stable motor output. Furthermore, electromyography analysis suggest that neuromuscular discharge frequencies were not affected by gravity changes. This implies that the types of muscle fibers recruited during exercise in modified gravity are similar to those used in normogravity. This research has contributed to a better understanding of how the human locomotor system responds to varying gravitational conditions, shedding light on the potential mechanisms underlying astronauts' gait changes upon returning from space missions.

Named entity recognition in aerospace based on multi-feature fusion transformer

In recent years, along with the rapid development in the domain of artificial intelligence and aerospace, aerospace combined with artificial intelligence is the future trend. As an important basic tool for Natural Language Processing, Named Entity Recognition technology can help obtain key relevant knowledge from a large number of aerospace data. In this paper, we produced an aerospace domain entity recognition dataset containing 30 k sentences in Chinese and developed a named entity recognition model that is Multi-Feature Fusion Transformer (MFT), which combines features such as words and radicals to enhance the semantic information of the sentences. In our model, the double Feed-forward Neural Network is exploited as well to ensure MFT better performance. We use our aerospace dataset to train MFT. The experimental results show that MFT has great entity recognition performance, and the F1 score on aerospace dataset is 86.10%.

Modeling advanced air mobility aircraft in data-driven reduced order realistic urban winds

The concept of Advanced Air Mobility involves utilizing cutting-edge transportation platforms to transport passengers and cargo efficiently over short distances in urban and suburban areas. However, using simplified atmospheric models for aircraft simulations can prove insufficient for modeling large disturbances impacting low-altitude flight regimes. Due to the complexities of operating in urban environments, realistic wind modeling is necessary to ensure trajectory planning and control design can maintain high levels of safety. In this study, we simulate the dynamic response of a representative advanced air mobility platform operating in wing-borne flight through an urban wind field generated using Large Eddy Simulations (LES) and a wind field created using reduced-order models based on full-order computational solutions. Our findings show that the longitudinal response of the aircraft was not greatly affected by the fidelity of the LES models or if the spatial variation was considered while evaluating the full-order wind model. This is encouraging as it indicates that the full LES generation of the wind field may not be necessary, which decreases the complexity and time needed in this analysis. Differences are present when comparing the lateral response, owing to the differences in the asymmetric loading of the planform in the full and reduced order models. These differences seen in the lateral responses are expected to increase for planforms with smaller wing loadings, which could pose challenges. Additionally, the response of the aircraft to the mean wind field, the temporal average of the full order model, was misrepresentative in the longitudinal response and greatly under-predicted control surface activity, particularly in the lateral response.

Dual-band 5G MIMO antenna with enhanced coupling reduction using metamaterials

This article introduces a miniaturized dual-band multiple input multiple output (MIMO) antenna with wide bandwidth and high isolation. The design incorporates ground plane modifications and utilizes metamaterials to achieve dual-band operation in the millimeter wave spectrum for 5G applications, specifically operating at the 28/38 GHz frequency bands. The proposed antenna maintains its dual-band functionality despite its compact size of 3.8 [Formula: see text] 3.7 [Formula: see text] 0.787 [Formula: see text] (without the feed line). The antenna is fabricated on a Rogers RT5880 substrate with a thickness of 0.787 mm and with relative permittivity [Formula: see text] = 2.2. The MIMO system comprises two symmetric radiating elements positioned in close proximity, resulting in mutual coupling levels of [Formula: see text] 20 dB and [Formula: see text] 12 dB at 25 GHz and 37 GHz, respectively. Modifications are made to the ground length to enhance the isolation at the higher frequency band while embedding metamaterials effectively reduces the coupling at the lower frequency band. The incorporation of metamaterials leads to an enhanced bandwidth from 3.8 to 4.8 GHz in the desired lower band (24-28.8 GHz) and from 3.8 to 4.2 GHz in the higher band (36.6-40.8 GHz). The proposed system can operate across the 28/38 GHz bands using a compact design, thus offering reasonable isolation, an envelope correlation coefficient below 0.0001, and a significant diversity gain (> 9.99 dB). These attributes emphasize the system's suitability for 5G millimeter-wave cellular communications.

Formation optimization of airborne radar coordinated detection system using an improved Artificial Fish Swarm Algorithm

In modern air combat, collaborative detection and engagement among multiple aircraft have gradually become a predominant combat approach. In response to the challenges posed by modern stealth aircraft, although their external factors such as coatings significantly reduce the chances of enemy detection, once these stealth aircraft activate their radar systems, they become susceptible to detection. Therefore, an application model has been proposed to mitigate enemy detection of our stealth aircraft through a collaborative approach. The underlying principle involves employing the concept of multi-aircraft collaboration, where the aircraft are divided into transmitters and receivers. The transmitters emit radar waves while the receivers are responsible for receiving these waves. This approach effectively mitigates the increased probability of enemy detection resulting from the activation of our receivers' radar systems. The optimization problem we aim to address is determining the optimal formation configuration for cooperative flight, specifically a formation with a specific configuration that maximizes the detectable range. This optimization problem is known as the configuration optimization problem for Airborne Radar Network with Separate Transmitting and Receiving (ARN-STAR). Existing methods for this problem typically suffer from limitations in either effectiveness or efficiency. To overcome these limitations, we propose an optimized configuration method based on an improved Artificial Fish Swarm Algorithm (IFSA) for ARN-STAR. Firstly, leveraging the distribution characteristics of the target radar wave's spatial scattering and the concept of dual-radar spatial diversity, we establish a mathematical model and an optimization objective function for ARN-STAR. Secondly, to address efficiency concerns, we optimize the computational process using the IAFS, successfully improving the speed of computation. To address the issue of effectiveness, we introduce adaptive adjustments to the movement step size of the artificial fish and improve the implementation of the three behavioral modes, thereby avoiding local optima and enhancing the accuracy of finding the optimal configuration. Finally, using our self-developed multi-aircraft collaborative simulation platform, we apply the improved AFSA to obtain the optimal formation configuration scheme and compare it with other methods. Simulation results demonstrate that our proposed method effectively solves the problem of finding the optimal formation configuration in multi-aircraft collaborative detection scenarios with "one transmission and multiple receptions." It overcomes the low computational efficiency associated with traditional methods while maintaining good accuracy. This approach enables the enhancement of overall combat capabilities while ensuring the safety of our aircraft to the greatest extent possible. It should be noted that the scenarios discussed in this study are at the configurational configuration level between UAVs, rather than involving the design of the UAVs combat control system itself.

Maximum correentropy-based robust Square-root Cubature Kalman Filter for vehicular cooperative navigation

As the core method of cooperative navigation, relative positioning plays a key role in realizing intelligent vehicle driving and vehicle self-assembling network collaboration algorithms. However, when the contamination rate of measurement noise is high, the performance of filtering will be seriously affected. To better address the filtering performance degradation problem due to noise contamination, this paper proposes a vehicular cooperative localization method based on the Maximum Correentropy Robust Square-root Cubature Kalman Filter (MCSCKF). The algorithm not only retains the advantages of Square-root Cubature Kalman Filter (SCKF) but also has strong robustness to non-Gaussian noise. The experimental results of tightly integrated vehicular cooperative navigation show that compared with the Extended Kalman Filter (EKF) and Cubature Kalman Filter (CKF), the localization accuracy of MCSCKF is improved by 35.08% and 31.83%, respectively, which verified the effectiveness in improving the accuracy and robustness of the relative position estimation.

Experimental study and simulations of hydrogen cooling effectiveness for aviation PEM fuel cells

Proton exchange membrane fuel cells (PEMFCs) are seen as one possible future means of driving the change towards a zero-emission society. In a civil aircraft, fuel cell systems can have multiple potential benefits, such as reduced noise, lowered emissions and higher fuel economy compared to jet aircraft. For controlling the fuel cell temperature, thermal management systems are required which can be optimized for aircraft applications regarding their weight and reliability. In this work, a simplified and light-weight thermal management system relying on hydrogen cooling is presented and analysed. To investigate the feasibility, a test rig and a three-dimensional, singular channel model in ANSYS Fluent were designed. Fuel cell temperature could be maintained within the set threshold in the model and the test rig, thus showing that controlling the fuel cell temperature via the hydrogen reactant flow is a viable alternative thermal management system. Results from the model indicate that both the hydrogen mass flow and hydrogen inlet temperature should be used to control the fuel cell temperature. Furthermore, operating the fuel cell at medium to low current densities is favourable for hydrogen cooling. Future studies will explore alternate flow field designs to facilitate thermal management system relying on hydrogen.

Material aspects of sintering of EAC-1A lunar regolith simulant

Future lunar exploration will be based on in-situ resource utilization (ISRU) techniques. The most abundant raw material on the Moon is lunar regolith, which, however, is very scarce on Earth, making the study of simulants a necessity. The objective of this study is to characterize and investigate the sintering behavior of EAC-1A lunar regolith simulant. The characterization of the simulant included the determination of the phase assemblage, characteristic temperatures determination and water content analysis. The results are discussed in the context of sintering experiments of EAC-1A simulant, which showed that the material can be sintered to a relative density close to 90%, but only within a very narrow range of temperatures (20-30 °C). Sintering experiments were performed for sieved and unsieved, as well as for dried and non-dried specimens of EAC-1A. In addition, an analysis of the densification and mechanical properties of the sintered specimens was done. The sintering experiments at different temperatures showed that the finest fraction of sieved simulant can reach a higher maximum sintering temperature, and consequently a higher densification and biaxial strength. The non-dried powder exhibited higher densification and biaxial strength after sintering compared to the dried specimen. This difference was explained with a higher green density of the non-dried powder during pressing, rather than due to an actual influence on the sintering mechanism. Nevertheless, drying the powder prior to sintering is important to avoid the overestimation of the strength of specimens to be fabricated on the Moon.

A multi-stable deployable quadrifilar helix antenna with radiation reconfigurability for disaster-prone areas

In disaster-prone areas, damaged infrastructure requires impromptu communications leveraging lightweight and adaptive antennas. Accordingly, we introduce a bi-stable deployable quadrifilar helix antenna that passively reconfigures its radiation characteristics in terms of pattern and polarization. The proposed structure is composed of counter-rotating helical strips, connected by rotational joints to allow a simultaneous change in the helix height and radius. Each helical strip is composed of a fiber-reinforced composite material to achieve two stable deployed states that are self-locking. The reconfiguration between an almost omnidirectional pattern and a circularly polarized directive pattern enables the antenna to be suitable for both terrestrial and satellite communication within the L-band. More specifically, the presented design in infrastructure-less areas achieves satellite localization with directive circularly polarized waves and point-to-point terrestrial connectivity with an almost omnidirectional state. Hence, we present a portable, agile, and passively reconfigured antenna solution for low-infrastructure areas.

A broad-spectrum gas sensor based on correlated two-dimensional electron gas

Designing a broad-spectrum gas sensor capable of identifying gas components in complex environments, such as mixed atmospheres or extreme temperatures, is a significant concern for various technologies, including energy, geological science, and planetary exploration. The main challenge lies in finding materials that exhibit high chemical stability and wide working temperature range. Materials that amplify signals through non-chemical methods could open up new sensing avenues. Here, we present the discovery of a broad-spectrum gas sensor utilizing correlated two-dimensional electron gas at a delta-doped LaAlO3/SrTiO3 interface with LaFeO3. Our study reveals that a back-gating on this two-dimensional electron gas can induce a non-volatile metal to insulator transition, which consequently can activate the two-dimensional electron gas to sensitively and quantitatively probe very broad gas species, no matter whether they are polar, non-polar, or inert gases. Different gas species cause resistance change at their sublimation or boiling temperature and a well-defined phase transition angle can quantitatively determine their partial pressures. Such unique correlated two-dimensional electron gas sensor is not affected by gas mixtures and maintains a wide operating temperature range. Furthermore, its readout is a simple measurement of electric resistance change, thus providing a very low-cost and high-efficient broad-spectrum sensing technique.

Strain-based method for fatigue failure prediction of additively manufactured lattice structures

Lattice structures find application in numerous technological domains, including aerospace and automotive industries for structural components, biomedical sector implants, and heat exchangers. In many instances, especially those pertaining to structural applications, fatigue resistance stands as a critical and stringent requirement. The objective of this paper is to advance the analysis of fatigue failure in additively manufactured lattice structures by introducing a predictive fatigue failure model based on the finite element (FE) method and experimentally validating the results. The model utilizes linear homogenization to reduce computational effort in FE simulations. By employing a strain-based parameter, the most critical lattice cell is identified, enabling the prediction of fatigue crack nucleation locations. The Crossland multiaxial fatigue failure criterion is employed to assess the equivalent stress, furnishing the fatigue limit threshold essential for predicting component failure. Inconel 625 specimens are manufactured via the laser-based powder bed fusion of metals additive manufacturing process. In order to validate the model, cantilevers comprising octa-truss lattice cells in both uniform and graded configurations undergo experimental testing subjected to bending loads within the high cycle fatigue regime. The proposed methodology effectively forecasts the location of failure in seventeen out of eighteen samples, establishing itself as a valuable tool for lattice fatigue analysis. Failure consistently manifests in sections of uniform and graded lattice structures characterized by the maximum strain tensor norm. The estimated maximum force required to prevent fatigue failure in the samples is 20 N, based on the computed Crossland equivalent stress.

Non-myopic multipoint multifidelity Bayesian framework for multidisciplinary design

The adoption of high-fidelity models in multidisciplinary design optimization (MDO) permits to enhance the identification of superior design configurations, but would prohibitively rise the demand for computational resources and time. Multifidelity Bayesian Optimization (MFBO) efficiently combines information from multiple models at different levels of fidelity to accelerate the MDO procedure. State-of-the-art MFBO methods currently meet two major limitations: (i) the sequential adaptive sampling precludes parallel computations of high-fidelity models, and (ii) the search scheme measures the utility of new design evaluations only at the immediate next iteration. This paper proposes a Non-Myopic Multipoint Multifidelity Bayesian Optimization (NM3-BO) algorithm to sensitively accelerate MDO overcoming the limitations of standard methods. NM3-BO selects a batch of promising design configurations to be evaluated in parallel, and quantifies the expected long-term improvement of these designs at future steps of the optimization. Our learning scheme leverages an original acquisition function based on the combination of a two-step lookahead policy and a local penalization strategy to measure the future utility achieved evaluating multiple design configurations simultaneously. We observe that the proposed framework permits to sensitively accelerate the MDO of a space vehicle and outperforms popular algorithms.

Progressive failure analysis of perforated composite laminates considering nonlinear shear effect

Composites are widely used in high performance structures such as aerospace structures due to their excellent properties. The analysis of failure evolution of composite perforated structures by finite element simulation is of great significance for practical work as engineering composite structures often contain notches and voids. In this paper, the numerical simulation of failure evolution and failure modes of carbon fiber reinforced resin composite laminates with large openings was carried out. A UMAT subroutine was written based on the 3D Hashin-Ye failure criterion and progressive damage model theory. The characteristic length and viscosity coefficient were introduced into the model to reduce mesh dependency and improve computational convergence. The nonlinear shear constitutive relationship defined by the Ramberg-Osgood equation was introduced into the continuous damage degradation model. The effect of nonlinear shear on the failure evolution of laminates with different stacking sequence was studied.

Dynamic modeling and analysis of the rotor-stator coupling system of a coaxial contra-rotating gearbox

In this paper, a main gearbox using an encased differential gear train to achieve coaxial contra-rotating is considered, and a dynamic modeling method of the rotor-stator coupling system of the gearbox based on box model updating is introduced. The transverse torsional dynamic model of the gear transmission subsystem is established based on the lumped parameter method. The finite element model of the box is updated according to the modal test data, and the reduced dynamic parameters of the box are obtained. According to the displacement coordination condition, the dynamic model of the rotor-stator system of the gearbox is established. The vibration response of the transmission components with or without the coupling box is calculated by numerical integration, and the response of the box caused by the dynamic support reaction force is analyzed by the finite element method. The results show that the vibration peak and fluctuation range of the transmission parts with coupling box are smaller than those without coupling box. The box response at the support of the input bevel gear pair is large, while that at the support of the output shaft is small.

Exploiting nonlinearities through geometric engineering to enhance the auxetic behaviour in re-entrant honeycomb metamaterials

Classical approaches to enhance auxeticity quite often involve exploring or designing newer architectures. In this work, simple geometrical features at the member level are engineered to exploit non-classical nonlinearities and improve the auxetic behaviour. The structural elements of the auxetic unit cell are here represented by thin strip-like beams, or thin-walled tubular beams. The resulting nonlinear stiffness enhances the auxeticity of the lattices, especially under large deformations. To quantify the influence of the proposed structural features on the resulting Poisson's ratio, we use here variational asymptotic method (VAM) and geometrically exact beam theory. The numerical examples reveal that 2D re-entrant type micro-structures made of thin strips exhibit an improvement in terms of auxetic behaviour under compression. For the auxetic unit cell with thin circular tubes as members, Brazier's effect associated with cross-sectional ovalisation improves the auxetic behaviour under tension; the enhancement is even more significant for the 3D re-entrant geometry. Thin strip-based auxetic unit cells were additively manufactured and tested under compression to verify the numerical observations. The experimentally measured values of the negative Poisson's ratio are in close agreement with the numerical results, revealing a 66% increase due to the nonlinearity. Simulation results showcase these alternative approaches to improve the auxetic behaviour through simple geometric engineering of the lattice ribs.

Fault-tolerant attitude control of the satellite in the presence of simultaneous actuator and sensor faults

In this paper, a robust attitude control algorithm is developed based on backstepping sliding mode control for a satellite using reaction wheels and thrusters that can perform its mission despite faulty actuators. In this method, the actuator dynamics have been considered to design the controller and the asymptotic stability of the proposed algorithm has been proven based on the Lyapunov theory. The designed controller can converge the attitude of the system into the desired path in the presence of faulty actuators. Then a fault-tolerant attitude estimation system is designed based on federated unscented Kalman filters that can be effectively employed to detect and isolate sensor faults. Finally, the performance of the designed attitude estimation and controller is investigated by simulation in the presence of both actuator and sensor faults.

Resolve integer ambiguity based on the global deep grid-based algorithms

Grid theory is rather commonly-used through out the research of integer ambiguity. In order to promote the efficiency of computation, it is of great necessity to reduce the correlations of the grid basis through the reduction. The classical reduction algorithm is known as the LLL (Lenstra-Lenstra-Lovász) algorithm. So as to further enhance the reduction effect, the deep-insertion LLL algorithm can be utilized as an alternative to the basis vector exchange algorithm. In practice, the deep-insertion LLL algorithm can achieve a better reduction effect, but it requires more time for reduction. The PotLLL algorithm replaces the basis vector exchange condition of deep-insertion LLL with an improving in the basis quality, and it can run in polynomial time, but with certain limitations. Therefore, this article proposes a global deep-insertion PLLL algorithm (GS-PLLL) to address the issue of integer ambiguity. GS-PLLL adopts a global strategy for deep-insertion processing, and introduces a rotation sorting method for preconditioning the grid basis. Comparative evaluations were conducted using simulation experiments and real-world measurements on the LLL, DeepLLL, PotLLL, and GS-PLLL algorithms. The experimental results indicate that the GS-PLLL algorithm achieves a better reduction effect than the PotLLL algorithm while improving the efficiency of reduction.

Design and optimization of a transparent and flexible MIMO antenna for compact IoT and 5G applications

This work presents an optically transparent and flexible MIMO antenna that features two square patch elements placed in close proximity, aiming to meet the demands of compactness, flexibility, optical transparency, and visual appeal for IoT applications and future 5G wireless communication. The design includes a simple offset fed configuration to achieve the required isolation and impedance matching. It simplifies the process of creating closely spaced transparent MIMO antenna configurations. By optimizing and analyzing this structure, the antenna achieves better isolation and diversity gain performance, even when the patch elements are positioned very close to each other. To achieve optical transparency and flexibility, the antenna uses thin polyethylene terephthalate (PET) material as a substrate, which is a thermoplastic polymer resin from the polyester family. The wired metal mesh parameters for conducting parts of the MIMO antenna and offset position of the feed are carefully optimized to achieve required optical transparency, isolation, impedance matching and radiation performance without any complex decoupling or impedance matching network.

Aircraft trajectory prediction and aviation safety in ADS-B failure conditions based on neural network

With the rapid expansion of transportation demand, the number of global flights has rapidly increased, which also poses challenges to air traffic management (ATM). Considering that the radar system in ATM can no longer meet the requirements of flight safety, a very promising next-generation air traffic control technology-Automatic Dependent Surveillance Broadcast (ADS-B) technology has been introduced. However, in the event of on-board equipment failure and local area signal interference, the ADS-B's signal will disappear or be interrupted. This sudden situation can pose a danger to aviation safety. To solve this problem, this article proposes a bidirectional long short-term memory (Bi-LSTM) network prediction method combining historical ADS-B data to short-term predict the trajectory of aircraft, which can improve aviation safety in busy airspace. Firstly, the problem of frequent dynamic modeling of different types of aircraft was solved by utilizing historical ADS-B data as the data source. Secondly, the data cleansing method is proposed for ADS-B raw data. Furthermore, considering that the spatial trajectory of the aircraft is a complex time series with continuity and interactivity, a bidirectional LSTM based aircraft trajectory prediction framework is proposed to further improve prediction accuracy. Finally, a trajectory with frequent changes was selected for prediction, and compared with 7 prediction methods. The results showed that the proposed method had high prediction accuracy, thus also improving the aviation safety of the aircraft.

Simulative validation of a novel experiment carrier for the Einstein-Elevator

In order to develop hardware that can be used in space, tests under those space conditions are often important to ensure the functionality in advance. Facilities that are used to recreate gravity conditions of space include space stations, satellites, parabolic flights and earthbound facilities. Drop towers are earthbound facilities, that can replicate the gravitational conditions of free falling in space by dropping objects. Those objects would not experience any measurable force due to gravity according to Einstein's famous thought experiment. The Einstein-Elevator is one of the first active driven drop towers with an experiment carrier falling inside a gondola. A major indicator for the quality of the facility is the residual acceleration of the payload. With the Einstein-Elevators current setup vibrations of the experiment carrier cause measurable residual accelerations of higher than [Formula: see text]g. To achieve the targeted 0-g-quality with a residual acceleration of less than 1 [Formula: see text]g (microgravity) in the Einstein-Elevator, a new experiment carrier is required that minimizes the residual acceleration for a payload. This paper describes a design of the experiment carrier for the Einstein-Elevator that is able to reach microgravity and analyzes its functionality using FEM-simulations.

Engineering planar antenna using geometry arrangements for wireless communications and satellite applications

A triple-band microstrip patch antenna designed for the IEEE 802.16e WiMAX, IEEE 802.11a WLAN, C-band downlink communications, and Ku-band radar recent applications is suggested in this article. The planned antenna operates at 2.45, 6, and 14 GHz resonant frequencies. The antenna fulfilled triple-band physical characteristics covering industrial, scientific, and medical (ISM) bands between (2.1-2.8) GHz; (5.6-6.5) GHz for wireless local area network (WLAN) or ultra-wideband (UWB) services; and 12.7-16 GHz for future two-way 5G:6G either broadcasting or mobile satellite communications. To achieve better return loss performance, parametric studies are carried out using Microwave Studio (CST MWS). The proposed antenna is designed on the FR4 as a hosting medium of total size 46 × 38 × 1.6 mm3, combined with a planar transmission line (T.L.) feed and defected ground structure (DGS). The simulated antenna's input reflection coefficient (S11) results and the far-field measurements show good agreement. The fabricated prototype achieves peak gain values of 2.8, 3.8, and 4.7 dBi, respectively, and bidirectional radiation characteristics. A comparative study with other recent publications is implemented to validate the consistency of the design.

Acoustic transmission loss in Hilbert fractal metamaterials

Acoustic metamaterials are increasingly being considered as a viable technology for sound insulation. Fractal patterns constitute a potentially groundbreaking architecture for acoustic metamaterials. We describe in this work the behaviour of the transmission loss of Hilbert fractal metamaterials used for sound control purposes. The transmission loss of 3D printed metamaterials with Hilbert fractal patterns related to configurations from the zeroth to the fourth order is investigated here using impedance tube tests and Finite Element models. We evaluate, in particular, the impact of the equivalent porosity and the relative size of the cavity of the fractal pattern versus the overall dimensions of the metamaterial unit. We also provide an analytical formulation that relates the acoustic cavity resonances in the fractal patterns and the frequencies associated with the maxima of the transmission losses, providing opportunities to tune the sound insulation properties through control of the fractal architecture.

Application of multi-extruded fuel injectors for mixing enhancement of hydrogen gas at scramjet engine: computational study

Scramjet engines are considered a highly promising technology for improving high-speed flight. In this study, we investigate the effects of using multi-extruded nozzles on fuel mixing and distribution inside the combustion chamber at supersonic flow. Additionally, we explore the impact of an inner air jet on fuel mixing in annular nozzles. To model fuel penetration in the combustor, we employ a computational technique. Our study compares the roles of three different extruded injectors on fuel diffusion and distribution at supersonic cross-flow. Our findings reveal that the use of an inner air jet increases fuel mixing in the annular jet, while the use of extruded nozzles improves fuel distribution by enhancing the vortices between injectors. These results demonstrate the potential benefits of incorporating multi-extruded nozzles and inner air jets in the design of scramjet engines.

A multi-objective optimization design approach of large mining planetary gear reducer

A two-stage computational framework is proposed to optimize the radiated noise and weight of a large mining planetary gear reducer under the rated conditions, based on a combination of response surface methodology and multi-objective optimization. The well-established transient dynamic analysis model of a large mining planetary gear reducer, which is used to analyze the mechanical strength and acoustic characteristics of the gear reducer. A unified experimental design is developed to obtain the response surface of the gearbox radiated noise and the mass of the gearbox housing. After obtaining the multi-objective optimization function, the multi-objective optimization problem for a lightweight and low-noise gearbox is performed using non-dominated sorting from the Genetic Algorithm II (NSGA-II). The research results demonstrates the effectiveness of the proposed optimization method in reducing vibrating amplitude and weight of the gearbox. This is crucial for minimizing energy consumption and enhancing the overall performance of the system. Additionally, the optimized gearbox design not only saves energy but also contributes to the reduction of carbon emissions, making it environmentally friendly.

Fuel mixing enhancement of transverse coaxial air and fuel jet by upstream shock wave on in scramjet engines: numerical study

In this study, computational fluid dynamics (CFD) is used to disclose the impacts of upstream shock waves on fuel mixing of cross coaxial air and fuel jet at a scramjet engine. This study has tried to investigate the impact of three different lobe injectors (2-lobe, 3-lobe, and 4-lobe nozzle) on the fuel penetrations along the scramjet combustor. The supersonic air stream is M = 4 while cross hydrogen and air jet are released in sonic velocity. This study uses CFD simulations to analyze the effects of upstream shock waves on fuel mixing in the transverse coaxial jet and assess their potential for improving combustion efficiency. The results demonstrate that the usage of upstream shock waves significantly increases shock interactions and augments the vortex region downstream of the jet. Our results show that the impacts of shock waves on the penetration of fuel jet released from the coaxial lobe nozzle are substantial.

Estimating global identifiability using conditional mutual information in a Bayesian framework

A novel information-theoretic approach is proposed to assess the global practical identifiability of Bayesian statistical models. Based on the concept of conditional mutual information, an estimate of information gained for each model parameter is used to quantify the identifiability with practical considerations. No assumptions are made about the structure of the statistical model or the prior distribution while constructing the estimator. The estimator has the following notable advantages: first, no controlled experiment or data is required to conduct the practical identifiability analysis; second, unlike popular variance-based global sensitivity analysis methods, different forms of uncertainties, such as model-form, parameter, or measurement can be taken into account; third, the identifiability analysis is global, and therefore independent of a realization of the parameters. If an individual parameter has low identifiability, it can belong to an identifiable subset such that parameters within the subset have a functional relationship and thus have a combined effect on the statistical model. The practical identifiability framework is extended to highlight the dependencies between parameter pairs that emerge a posteriori to find identifiable parameter subsets. The applicability of the proposed approach is demonstrated using a linear Gaussian model and a non-linear methane-air reduced kinetics model. It is shown that by examining the information gained for each model parameter along with its dependencies with other parameters, a subset of parameters that can be estimated with high posterior certainty can be found.

Complex network analysis techniques for the early detection of the outbreak of pandemics transmitted through air traffic

Air transport has been identified as one of the primary means whereby COVID-19 spread throughout Europe during the early stages of the pandemic. In this paper we analyse two categories of methods - dynamic network markers (DNMs) and network analysis-based methods - as potential early warning signals for detecting and anticipating COVID-19 outbreaks in Europe on the basis of accuracy regarding the daily confirmed cases. The analysis was carried out from 15 February 2020, around two weeks before the first COVID-19 cases appeared in Europe, and 1 May 2020, approximately two weeks after all the air traffic in Europe had been shut down. Daily European COVID-19 information sourced from the World Health Organization was used, whereas air traffic data from Flightradar24 has been incorporated into the analyses by means of four alternative adjacency matrices. Some DNMs have been discarded since they output multiple time series, which makes it very difficult to interpret their results. The only DNM outputting a single time series does not emulate the COVID-19 trend: it does not detect all the main peaks, which means that peak heights do not match up with the increase in the number of infected people. However, many combinations of network analysis based methods and adjacency matrices output good results (with high accuracy and 20-day advance forecasts), with only minor differences from one to another. The number of edges and the network density methods are slightly better when dynamic flight frequency information is used.

Fusion localization for indoor airplane inspection using visual inertial odometry and ultrasonic RTLS

In this paper, the fusion localization system for the visual inertial odometry (VIO) and ultrasonic real-time localization system (RTLS) for indoor airplane inspection using drones is proposed. In a hangar environment, either trilateration-based RTLS or vision-based localization shows disadvantages and neither can be used alone. In this research, we design a configuration of VIO suitable for hangar environment and outlier filter on ultrasonic RTLS for non-line of sight situations, so that both can be fused using graph optimization. The proposed solution can provide more accurate localization than the visual odometry-only system as well as continue estimating positions in the absence of RTLS data. Localization and real-time performance of the proposed algorithm are evaluated through experimentation in a hangar and a flight test in an outdoor space.