Aerospace Systems最新文献

The method presented in the article is based on a complex simulation model of gas-dynamic processes that take place in sectioned cabins during depressurization. This model allows the theoretical calculation of decompression parameters (decompression time, cabin pressure, gas leakage from the cabin) depending on flight parameters and design features of the aircraft pressurised cabin (height, cabin volume, defect area, etc.) and determine the interdependence of pressure control parameters in critical operating modes. In computational experiments simulating decompression during depressurisation, the rate of cabin pressure drop as a function of the defect area, residual overpressure, decompression time, values of drops between compartment sections and mass flow rate during pressure changes; safe descent height and other parameters were determined. On the basis of computational experiments, a methodology for assessing the portability of decompression was developed, taking into account different levels of impact tolerance, allowing for a rational choice of hermetic and gas dynamic parameters of the cabin, as well as flight performance characteristics, taking into account the possible decompression of the cabin in flight or, conversely, with the specified parameters of the cabin and flight data at the design stage of the aircraft to assess the degree of danger in case of depressurization and to provide in advance a set of security measures. The transition for decompression safety analysis along the Chadov V. I. curve has advantages since it is applicable for various types of aircraft from spacecraft to aircraft and for various atmospheres with different combinations of pressures and concentrations.

The paper considers control system design for linearized three-dimensional perturbations about a nominal laminar boundary layer over a flat plate (the Blasius profile). The objective is prevention of the laminar to turbulent transition using appropriate inputs, outputs, and feedback controllers. They are synthesized with a view to reducing transient energy growth, a known precursor to important transition scenarios. The linearized Navier–Stokes equations are reduced to the Orr–Sommerfeld and Squire equations with wall-normal velocity actuation entering through the boundary conditions on the wall. The sensor output is taken to be the wall-normal derivative of the wall-normal vorticity measured on the plate. Several multivariable output controllers are examined, including simple constant gain output feedback, loop transfer recovery, and (H_{infty }) loop shaping. Reduced order compensators are developed using balanced truncation and analyzed for robustness using the gap metric between reduced order models and full order models. It is demonstrated that the level of minimum transient energy growth that can be achieved is similar for these diverse controller methodologies but falls short of that which can be achieved using optimal state feedback.

The objective of this study is to design a Ku-band tracking, command, and ranging (TCR) antenna for small satellite communications applications. The proposed design combines the conical log spiral antenna (CLSA) with the discone antenna, which is an omnidirectional antenna suitable for installation in next-generation small communication satellites. The novelty of this TCR antenna lies in the selection of parameters to enhance its structure. Consequently, the radiating element and feeding system are optimized to reduce the antenna's size. As a result, the proposed antenna achieves a gain exceeding 6 dBi for uplink frequencies in the Ku-band and it reaches up to 7 dBi for the downlink range which is responsive to the system requirement's to achieve mission objectives.

This paper aims to figure out the robust zero-sum differential game problem using an off-policy reinforcement learning technique. The robust system model is first established based on the nominal one. The control strategy is proposed with the asymptotic stability and optimality being strictly proved. The off-policy reinforcement learning technique is built from the Bellman equation to generate the control policy. A potentially inaccurate system dynamic model’s influence is avoided because the outcome is attained from the system data set obtained. It is the first-time application of the off-policy RL algorithm on this robust two-player zero-sum differential game problem. Additionally, the final algorithm’s convergence is demonstrated, and a simulation example is run to confirm its efficacy.

Spacecraft pose estimation plays an important role in an increasing number of on-orbit services: rendezvous and docking, formation flights, debris removal, and so on. Current solutions achieve excellent performance at the cost of a huge number of model parameters and are not applicable in space environments where computational resources are limited. In this paper, we present the Squeeze-and-Excitation based Spacecraft Pose Network (SESPNet). Our primary objective is to make a trade-off between minimizing model parameters and preserving performance to be more applicable to edge computing in space environments. Our contributions are primarily manifested in three aspects: first, we adapt the lightweight PeleeNet as the backbone network; second, we incorporate the SE attention mechanism to bolster the network’s feature extraction capabilities; third, we adopt the Smooth L1 loss function for position regression, which significantly enhances the accuracy of position estimation.

The electrical power system (EPS) of a spacecraft (SC) plays a crucial role in the mission's success. This system provides electrical power to all loads of SC until its end of life (EOL). The primary power source onboard for the SC is the solar array (SA), while the storage battery serves as the secondary power source. We have developed a software program called the SC Charging Analysis Tool with Battery Management Algorithm (BMA) to address the challenges associated with the SC battery's charging and discharging processes. This tool fulfills two main objectives: first, to develop a BMA that effectively controls the battery charging and discharging processes through all modes of SC operation; second, to create a battery management environment with SC energy budget calculation capabilities to simulate the dynamic behavior of the SC battery using the developed BMA, thereby ensuring battery health. The battery management environment is specifically designed to verify the proper performance of the battery throughout the SC's lifetime and to execute operations during nominal conditions and throughout worst-case scenarios. Moreover, through the SC energy budget calculation, we can optimize the requirements of the chosen scenario to fit with the selected battery. However, during worst-case scenarios, the battery's vital parameters, including state of charge (SOC), thermal emission, voltage, and pressure, are calculated to verify its proper operation under the developed BMA. The dynamic behavior of the battery, in conjunction with the developed algorithms, will be validated using real telemetry data. The key result of this paper is the invention of a BMA, which contributes to the advanced management of SC battery systems and, consequently, better durability during challenging operational scenarios.

This work explores the thrust vector control in a 2D convergent divergent nozzle with wedge shaped jet tab. The Computational analysis has been carried out with different thickness and height of jet tabs. The flow topology analysis is carried out qualitatively and quantitatively. The wedge-shaped jet tabs with 10%, 20%, 30% and 40% of height and 10 mm, 12 mm, 14 mm, 16 mm thickness are considered for right isosceles, left isosceles and isosceles shapes. The deflection angle is calculated to identify the best effective thrust deflection configuration. Results indicate that, when thickness and height of jet tab increases the deflection angle reduces due to the formation of mixing layer and shock wave generation. Also, depending on the shape of jet tab the oblique shock formation and angle of shock varied. The thickness-based CFD optimization shows that a 4.66° deflection angle with 10 mm thickness and 40% height isosceles tab produces the best deflection. The right jet tab with 12 mm and 14 mm thickness with 30% and 20% height shows 4.75° and 4.14° deflection angle. In the case of 16 mm thickness tab, left isosceles jet tab with 10% height shows 4.14° of deflection angle. It is concluded form the study that, the highest rate of deflection was produced by the right isosceles tab with 12 mm thickness and 30% height. This can be used for thrust deflection in supersonic aircrafts and missiles.

The objective of this paper is to enhance the aerodynamic performance of trainer aircraft wing using active flow control method of co flow jet (CFJ). In this numerical study, free stream velocity of 20 m/s and k epsilon turbulence model is used to compare baseline and CFJ aerofoils with different angles of attack (AOA). This work introduced CFJ method into the trainer aircraft wing to improve the aerodynamic performance by controlling the boundary layer over it at different AOA and at the same time to reduce the take-off landing distance. The obtained results show that the stalling AOA improved by 40% and lift coefficient increased by 52%. The same method could be used to all trainer aircrafts.

The focus of this research is the creation of a deep reinforcement learning approach to tackle the challenging task of robotic gripping through tactile sensor data feedback. Leveraging deep reinforcement learning, we have sidestepped the necessity to design features manually, which simplifies the issue and allows the robot to acquire gripping strategies via trial-and-error learning. Our technique utilizes an off-policy reinforcement learning model, integrating deep deterministic policy gradient structure and twin delayed attributes to facilitate maximum precision in gripping floating items. We have formulated a comprehensive reward function to provide the agent with precise, insightful feedback to facilitate the learning of the gripping task. The training of our model was executed solely in a simulated environment using the PyBullet framework and did not require demonstrations or pre-existing knowledge of the task. We examined a gripping task with a 3-finger Robotiq gripper for a case study, where the gripper had to approach a floating object, pursue it, and eventually grip it. This training methodology in a simulated setting allowed us to experiment with various scenarios and conditions, thereby enabling the agent to develop a resilient and adaptable grip policy.

The position accuracy of GNSS is limited by several errors including multipath error. The multipath error is well known as one of the dominant error sources in most of the high-precision GNSS applications, as its fast-changing and site-dependent nature make it challenging to model and mitigate. The Non-Line-of-Sight (NLOS) signals in combination with the original Line-of-Sight (LOS) signal lead to multipath (MP), which results in erroneous range estimation. To mitigate the effect of multipath, detecting the presence of NLOS/multipath signals plays a vital role. In this paper, GPS and IRNSS signals are considered in simulated multipath environment and in open-sky conditions. A machine learning (ML) approach for classification of LOS/NLOS/multipath is presented in both the environments. In this paper, two classifiers are proposed. The proposed classifiers are trained with signal strength, elevation angle, Doppler shift, delta pseudorange, and pseudorange residuals as attributes. The accuracies of these models are computed and compared and it is found that, among all the algorithms, K-Nearest Neighbors, Decision Tree, and its ensemble functions have demonstrated superior performance. Experimental results are presented using GPS L1, IRNSS L5, and S1 data. A comparative analysis on both the classifiers is also presented. Further, to substantiate these results, another experiment is conducted in a complex real-time dynamic multipath environment and the obtained results are also presented.