Aerospace Systems最新文献

As the use of composite materials in aerospace is growing fast, more metal-composite hybrid structures come into being and thermal stress becomes increasingly a concern that may affect structural safety. In this paper, experimental and numerical studies are conducted on the mechanical strain induced by thermal stress in an AL/CFRP hybrid structure subjected to a heating–cooling–heating cycle. The studied hybrid structure consists of a metal plate and a composite laminate fastened by three bolts. The experimental results show that the mechanical strain in either metal or composite exhibits a hysteresis as the structure undergoes the temperature cycle, which implies the existence of structural nonlinearities. Finite element analysis, which incorporates details of the bolt joint, reproduces the hysteretic responses that reach a reasonable agreement with the experimental ones. Numerical studies disclose the effects of the structural parameters, i.e., friction coefficient, clamping force, fastener-hole clearance and bolt spacing, on the hysteresis and provide insights into the physical events during the thermal cycling. The reported work reveals that the movement of the bolts inside the surrounding holes is the key mechanism that drives the hysteretic thermal stress in the tested structure and sheds light on further investigations of structural safety of such hybrid structures under cyclic thermomechanical conditions.

This paper aims to create an effective flight controller that can reject severe disturbances, improving accuracy and efficiency. Current UAV control research fails to reduce large external disturbances. Integrating Linear Quadratic Regulator and Extremum Seeking Control helps overcome these negative influences. This paper describes a novel controller that stabilizes UAV output responses and handles external disturbances. Linear Quadratic Regulator is used to stabilize and control the UAV under optimal flight conditions, whereas Extremum Seeking Control is utilized to counteract external disturbances. The recommended flight controller is compared to the Linear Quadratic Gaussian Regulator, which uses the Kalman Filter to reduce disturbances. This comparison analysis demonstrates our method's superiority. In addition, the UAV's pitch and yaw angles experience aggressive maneuver motions to test the controller. The proposed strategy reduces noise and harsh disturbances including step, ramp, and sinusoidal variables during agile maneuvers. This study defines disturbances as follows: External noise in control systems is random signal variations generated by external disturbance; Step disturbances are fast, long-lasting system signal changes; ramp disturbances are sluggish; and sinusoidal disturbances are periodic oscillations. These disturbances make system stability and functionality difficult. Since our control strategy reduces disturbances, the recommended method can adapt system output to random fluctuations, rapid changes, gradual changes, and periodic oscillations. Linear Quadratic Gaussian Regulator is able to reduce noise from the system's output, but it fails to produce satisfactory results during major disturbances. The proposed method, however, is unique since it develops a controller with advanced disturbance rejection capabilities.

In this study, the commercially available CFD software ANSYS-Fluent is used to conduct a three-dimensional unsteady-state analysis of the aerodynamic coefficients of an ejection seat system. The aerodynamic coefficients are calculated by solving Reynolds-averaged Navier–Stokes equations. ANSYS meshing software is utilized to create an unstructured grid of tetrahedral cells for this analysis. The validation of the numerical methodology is performed initially on a sphere at subsonic Mach number (Ma) for different Reynolds numbers (Re) before the validation of the ejection seat system. The computed unsteady-state results are compared with the experimental and available numerical results for both the sphere and the ejection seat. Later the aerodynamic coefficients of the ejection seat are further investigated at Ma = 0.7 by changing the angle of attack (α) and yaw angle (β). The findings of this study show that the magnitude of the axial force coefficient (C_X) and values of the side force coefficient (C_Y), normal force coefficient (C_Z), pitching moment coefficient (C_m), yawing moment coefficient (C_n), and rolling moment coefficient (C_l) changes with the variation of the α and β.

With the development of artificial intelligence and information technology, drones working in tandem with manned aerial vehicle have become the new normal. Current paper focuses on the following theme: how to assess the effectiveness of manned/unmanned aerial vehicle systems under different formations. However, the analysis of the effectiveness of manned/unmanned aircraft cooperation faces the problem of unclear mechanisms and difficulty in tracing the key influencing factors. Therefore, in this paper, a closed frequent pattern mining method is used to analyze and design a new data structure based on cross-linked table improvement. In this paper, the units and capabilities in the manned/unmanned aerial vehicle system are mined and analyzed in a time-series manner to obtain the effectiveness of the patterns of manned/unmanned aircraft utilization in tandem under different formations. Finally, a typical maritime application scenario is used as an example to effectively compare the effectiveness of different manned/unmanned aircraft cooperative modes and to provide guidance for the subsequent development of manned/unmanned aircraft cooperative applications.

Electronic Warfare is a type of military operation that uses electromagnetic radiation to identify, exploit, limit, or prohibit the use of the electromagnetic spectrum. The main objective of this paper is to improve the range resolution of multiple targets in Electronic Warfare systems under noisy conditions. Multiple Signal Classification (MUSIC), a high-resolution algorithm is used with denoising techniques to enhance the ability of digital receivers to detect multiple targets. The Savitzky Golay filter is used in the first stage as a pre-processing filter, and the novel noise removal technique is used in the second stage to achieve better target detection and discrimination. This modification to the MUSIC algorithm aims to address its limitations in the presence of noise and when targets are in proximity, resulting in improved performance in Electronic Warfare scenarios. Using the proposed method, we are able to detect three (3) target frequencies up to SNR of − 12 dB and four (4) target frequencies up to SNR of − 19 dB, with the percentage of error in the estimation of frequency is 0.58%. The optimized computation complexity is highlighted as a strength, making the proposed method more efficient compared to alternative approaches.

Digital Image Correlation (DIC) is a vital optical measurement technique that finds diverse applications in the domain of mechanics of materials. In aerospace applications, DIC has excellent scope in structural health monitoring of aircraft components. Aircraft wings, one of the critical components are subjected to different loads during flight. Ground testing and In-flight testing of wings can benefit substantially by DIC monitoring. DIC can be utilized to analyze the time-based variation in the speckle pattern or grid, applied to the wing’s surface. High-resolution images processed through a suitable correlation software helps decipher the data into stress and strain contours. Thus, any potential material failure or component defects can be identified. DIC also finds a role in flutter analysis, enabling the scrutiny of wing vibrations and deformations. In this review, the applications of DIC in analysis of aircraft components has been taken up, as in-flight structural health monitoring is a critical activity for a safe flight.

Aerial Gliding Vehicles (AGVs) play a crucial role in military operations owing to their versatile and multipurpose capabilities. Achieving accurate modeling of AGVs is paramount for understanding their behavior and optimizing performance. While nonlinear models excel in capturing intricate phenomena, their complexity and computational demands make them less suitable for control system design. Hence, the utilization of linear models becomes imperative, offering a more comprehensible depiction of AGV dynamics and facilitating effective control system analysis and design. This study aims to develop a precise linear model for AGVs, providing a clear and interpretable framework for analysis and control system development. The constructed linear model serves as the foundation for devising various control strategies, significantly enhancing our comprehension of AGV behavior. Moreover, a comprehensive investigation into the AGV’s actuation system is conducted, employing advanced system identification techniques to establish an accurate actuation model. This phase is critical for ensuring the precise and efficient operation of the control system. The research encompasses the design and evaluation of two distinct AGV control strategies. Firstly, the Modified Proportional-Integral-Derivative (PI-D) controller, a conventional approach widely employed in control systems, serves as a stable benchmark for comparison. Secondly, the innovative Fuzzy-PI-D (F-PI-D) controller is introduced, harnessing fuzzy logic to augment control accuracy and responsiveness, particularly advantageous for complex systems like AGVs. To validate the performance of these control strategies, the study adopts the robust Processor in the Loop (PIL) methodology, integrating LabVIEW and an embedded device to conduct reliable testing and verification of control systems in a simulated environment. PIL offers the distinct advantage of evaluating control strategies under diverse conditions without the necessity of costly and hazardous real-world flight tests. Simulation outcomes furnish valuable insights into the efficacy of these control strategies. Significantly, the F-PI-D controller emerges as the preferred choice for enhancing AGV flight stability, precision, and responsiveness, thus contributing to the advancement of AGV control systems and their utility in military operations.

The stability criteria of any fin-stabilized flying object are a decisive metric in evaluating its overall performance and results in mission success. Flight stability depends on many parameters such as body configuration, the center of gravity location, atmospheric conditions, and flight manoeuvres. These manoeuvres are needed for better target interception especially for moving targets located at short ranges, resulting in high frequencies either in pitch or yaw directions. This study examines the impact of body pitch frequency on the stability of a supersonic fin-stabilized object. Time-dependent numerical simulations are implemented to model the unsteady flow field induced by a simple harmonic motion in the case study missile. The missile’s tail section dominates the lift force generated compared to the forebody, resulting in a downstream shift of the missile’s center of pressure and, consequently, an increase in the static stability margin as the pitching frequency increases. However, pitch-damp aerodynamic derivatives remain unchanged at various pitching frequencies, indicating frequency independence. The validity of the results is confirmed compared with wind tunnel data.

The objective of the paper is to explore the fields of propulsion for rockets and defence systems to meet the increasing demands for cost-effectiveness and faster and friendly manufacturing processes to increase the efficiency of the burn time/rate of solid rocket motors. This particular research includes the use of powerful machine learning algorithms applied on the burn rate dataset to predict the best burn rate. The main focus of this particular research is based on the burning rate study which has been carried out at ambient and different pressures of 2.068 MPa, 4.760 MPa and 6.895 MPa with the use of binder as Hydroxyl-Terminated Polybutadiene, oxidizer as Ammonium Perchlorate and a catalyst as Iron Oxide. Two types of models are designed and created to predict the best burn rate from the experiments conducted namely; Empirical Mathematical Model and Machine Learning Regression. Empirical modelling gave an accuracy of 47% while Machine Learning Regression gave a prediction accuracy of nearly 99%.

This article presents the synthesis of a dynamic output feedback controller for a satellite orbital system confronted with uncertainties. The investigated method transforms the closed-loop system, synthesized by the controller, into an (alpha )-strictly negative-imaginary system. It utilizes the DC-loop gain condition associated with negative-imaginary systems theory to demonstrate robust stability of the satellite orbital system in the presence of uncertainties. Furthermore, the synthesized negative-imaginary closed-loop system exhibits notable time-domain performance. The numerical simulation outcomes presented in this article validate the investigated synthesis method.