Aerospace Systems最新文献

The paper examines computational schemes for calculating the gradient of fluid dynamic quantities using grids of various types. The Green–Gauss method and the least squares method (LSM) used to develop a hybrid gradient calculation scheme are considered. It is demonstrated that the accuracy of gradient calculations may vary depending on the geometry of the control volume: the Green–Gauss method exhibits lower errors for strongly elongated thin cells and cells with curved edges, while for cells with orthogonal edges, it is preferable to use LSM. In order to improve the accuracy of calculations on unstructured grids, a hybrid gradient calculation scheme is proposed. This scheme computes the gradient by summing values derived from both the Green–Gauss method and LSM, given the weight function that incorporates the geometry of the control volume. The paper presents a formula for the weight function, which determines the contribution of each method within the hybrid scheme. The developed scheme is applied to the problem of supersonic flow around a cylinder with a needle on two unstructured grids, namely truncated hexagons and tetrahedra. It is shown that the proposed hybrid scheme reduces the error in calculating the aerodynamic characteristics of a streamlined object.

Space exploration demands robust spacecraft(SC) subsystems to endure the harsh conditions of space and ensure mission success. Attitude determination and control subsystems (ADCS), as a significant subsystem within SC, are essential for providing the necessary pointing accuracy and stability, and failures in the ADCS can lead to mission failure. Therefore, robust design, thorough testing, and Fault Detection, Isolation and Identification(FDII) techniques are crucial for spacecraft operations. This paper focuses on developing advanced FDII techniques for reaction wheels(RW) within ADCS, evaluating the Prony-based FDII technique for RW, considering its accuracy, time complexity, and memory usage, and Additionally, it introduces new machine learning-based FDII techniques, including enhancements to the Prony-based FDII technique, to manage single faults more effectively. The new proposed techniques used to explore the novel area of multiple faults within the same subsystem. Results indicate that the proposed FDII techniques significantly improve fault detection accuracy, isolation time, and memory efficiency compared to traditional techniques. These advancements enhance the reliability and longevity of spacecraft missions, ensuring that critical subsystems like ADCS operate effectively in the challenging conditions of space. The contributions presented in the paper are introducing three different FDII machine learning-based techniques that support identifying five types of single faults in spacecraft ADCS RW, outperform the Prony-based FDII technique for spacecraft ADCS RW in terms of time and memory complexity, and Finally, improves the fault tolerance of the spacecraft system by detecting Multiple fault combinations that may occur at the same time in one system.

A comprehensive simulation model is established to design the altitude adjustment of the stratospheric airship with the application of the adjustable ballonets for pitch control. A series of mathematical models, including atmosphere, thermal, dynamics and kinematics, airship pressure and pitch control, are developed to achieve the altitude adjustment when the stratospheric airship flying at the stationary phase. The altitude adjustment strategy takes the thermodynamics, dynamics, and pressure control requirements together into consideration, to better fulfill the realistic flight conditions. Based on these models, the characteristics of stratospheric airship’s flight performance are simulated and discussed in detail. The results show that taking adjustable ballonets as the actuator can realize the pitch and pressure control simultaneously and satisfy the requirements of the flight missions. Furthermore, stratospheric airship can achieve altitude adjustment with the application of adjustable ballonets and propulsion system coordinately. Moreover, the simulation model can accurately present the interaction of thermodynamics, pressure, and dynamics, which better satisfies the realistic flight situation. The results and conclusions presented herein contribute to the design and operation of stratospheric airship.

Trajectory prediction (TP) of ballistic missile (BM) is a critical task in the field of military and defense security. However, influenced by various external factors, including target maneuverability, interference, and atmospheric conditions, BMs encounter complex forces during the boost flight phase, making their trajectories complex and variable. Furthermore, the conventional numerical integration and polynomial fitting TP methods are highly dependent on fixed motion models and precise initial observations, so they tend to engender error accumulation and inaccurate predictions. Thus, in terms of this issue, this paper proposed a TP method based on adaptive tracking and Gaussian Process Regression (GPR) to achieve stability in short-term TP for boost phase BM. Specifically, a database of trajectories for boost phase BM is created and used for training GPR predictive models, in which the unknown noise's covariance matrix is dynamically adjusted according to the statistical characteristics of observations to provide more precise trajectory data for model training. At the same time, incremental learning is adopted to add tracking results from real-time tests to improve further and update the predictive model. Additionally, the output uncertainty of GPR is also beneficial for decision-making systems usually making decisions in accordance with the uncertainty. Simulation results based on the GEO dual-satellite positioning system show that the absolute average TP RMSE of the boost phase BM of our proposed method can be less than 0.35 km, 0.51 km, and 0.62 km in future 20 s, 40 s, and 60 s, which outperforms those of the numerical integration method of 2.1 km, 3.7 km, and 6.9 km and the function approximation method of 0.89 km, 3.1 km, and 6.1 km. This paper provides a significant reference for the positioning, tracking, and prediction of BM in boost phase.

In this paper, adaptive optimizer-based deep neural network approaches are used to predict nonlinear aerodynamic model using flight test data of standard aircraft. Adaptive optimizers namely Adam and RMSprop algorithms are chosen to model the force and moment coefficients during steady stall phenomena. The effectiveness of these two methods are being investigated and validated. The estimated results from adaptive optimizer based methods are statistically analysed and compared with the conventionally used maximum likelihood method. Comparison results from the above methods are found to be relatively better than the maximum likelihood estimates in terms of RMSE and correlation. Moreover, the adaptive optimization methods are proven to be advantageous over conventionally used methods which strongly depend on solving equations of motion.

This paper introduces the current and new Satellite solutions for local and global tracking of ships for enhanced Ship Traffic Control (STC) and Ship Traffic Management (STM) at sea, in sea passages, approaching to the anchorages and inside of seaports. All transportation systems and especially for maritime applications require far more sophisticated technology solutions, networks and onboard equipment for modern Satellite ship tracking than current standalone the US Global Positioning System (GPS) or Russian Global Navigation Satellite System (GLONAS) networks. The forthcoming Global Ship Tracking (GST), Satellite Data Link (SDL), Maritime GNSS Augmentation SDL (GASDL) and Maritime Satellite Automatic Dependent Surveillance-Broadcast (SADS-B) networks with Space and Ground Segment infrastructures for all three systems are discussed including benefits of these new technologies and solution for improved STC.

Stratospheric airships are a type of large aircraft capable of operating for extended periods in the stratosphere. This paper focuses on real-time trajectory planning for stratospheric airships. It constructs an optimization path dataset based on the Gauss pseudospectral method and utilizes deep learning neural networks to solve the real-time path planning problem for stratospheric airships. The article first establishes a six-degree-of-freedom airship spatial motion model. It uses the Gauss pseudospectral method to transform the original optimization problem into a parameter optimization problem, which is then solved using sequential quadratic programming. During the ascent phase, based on the airship's speed, yaw angle, and pitch angle when transitioning from the troposphere to the stratosphere, a total of 26,901 optimized paths are generated using the Gauss pseudospectral method, and the influence of different initial states on the optimized paths is analyzed. During the level flight phase, 3960 optimized paths are generated based on different initial speeds and yaw angles, and an analysis of the impact of the initial yaw angle on the optimized paths is conducted. Finally, the dataset generated by the Gauss pseudospectral method is divided into training and testing sets. Long short-term memory (LSTM) networks and Transformer networks are employed to learn and generate optimized paths from the dataset. Comparison results show that the neural network model is highly consistent with the optimized paths obtained using the Gauss pseudospectral method. Furthermore, the path generation time is reduced from hundreds of seconds to seconds, leading to a significant improvement in generation time stability.

The Engine Bleed Air system is a critical component in aircraft operations, providing necessary air supply for various onboard systems. Failures in the Engine Bleed Air (EBA) System can lead to flight delays, extended downtime, and safety risks. The current practice of using fixed pressure thresholds for EBA monitoring has limitations in terms of maintenance efficiency and aircraft safety. This paper presents a data-driven approach to dynamic thresholding and health index construction for the Airbus A330 EBA. A substantial EBA flight dataset is constructed using Quick Access Recorder (QAR) data, incorporating normal and faulty states. To explore the extensive QAR data of the EBA system, a data-driven baseline mining model is proposed in this study. To efficiently process high-dimensional feature data and model the pressure baseline, the LightGBM tree-based algorithm is employed. Additionally, this study proposes a health index (HI) construction method based on the baseline model, along with the EBA diagnosis and prognosis experiments based on the HI index. The Diagnosis and Prognosis methods, utilizing the proposed HI, demonstrate superior diagnostic effectiveness compared to fixed threshold methods and uncover a clearer trend of EBA health degradation. These contributions highlight the potential of data-driven approaches in managing aircraft EBA systems, emphasizing the advantages of dynamic thresholds and health index models for improved diagnosis and prognosis.

The major problem with the Scramjet engine is the starting and unstarting conditions, which depend on intake performance. Although the engine starts efficiently at designed conditions without any problem, at off-design conditions, due to misalignment of shocks, not satisfying either shock on the lip or shock on the shoulder and this leads to spillage flow and loss of total pressure. The intake design is modified to satisfy shock on shoulder condition to improve the operating range of the scramjet engine. Using ANSYS Fluent, Inviscid flow simulations are carried on modified design, it satisfied the shock-on-shoulder requirement, but in viscous simulations, the flow leads to unstarting conditions because of shock wave interaction with the boundary layer. Thus, shock on shoulder can be neglected in the design of hypersonic intake for scramjet engines. This paper analyzes a two-ramp, two-dimensional scramjet intake design using ANSYS Fluent. An elaborative CFD analysis was performed to estimate the efficiency of the hypersonic intake isolator because of changes in the flight conditions concerning free-stream conditions such as Mach number, angle of attack, and real-flow atmospheric conditions concerning altitude. This analysis shows that performance parameters such as total pressure recovery decreases during off design conditions. However, the normalized pressure ratio increases from 19 at Mach 4 to 72 at Mach 8. Due to an increase in the angle of attack, there is a increase in the pressure ratio and decrease in total pressure recovery. The flow separation bubble size increases as the Mach number increases leading to unstarting condition and increases as the angle of attack increases. An injection technique is used to suppress the flow separation. Out of the various orifices analysed the research concludes diamond shape injectors at 45° angle with total Nine injectors for mass flow rate not greater than 4% of intake mass flow satisfying all the performance parameters has reduced the flow separation bubble size from 4 mm to 0.95 mm in hypersonic intakes of Scramjet Engine at Mach 5.

Given the complex flight mission and structural characteristics of special-shaped tanks in new-generation space vehicles, this study investigates the sloshing characteristics and suppression methods of liquid propellant. Initially, the numerical calculation and structural suppression approaches for liquid propellant periodic sloshing are introduced. Subsequently, a new equivalent dynamic analysis approach based on the Volume of Fluid (VOF) method is presented and validated to simulate liquid sloshing and determine dynamic characteristic parameters such as sloshing mass, frequency, and damping ratio. Furthermore, anti-sloshing baffles are designed for sloshing suppression, and the influence of baffle height on sloshing frequency and damping ratio is examined. These significant findings provide crucial references and foundations for enhancing the flight stability and reliability of the attitude control system in new-generation space vehicles.