The design of a two-dimensional supersonic inlet with large bleed window at low Mach number was developed. Numerical simulation and wind tunnel experiments were carried out to investigate the aerodynamic performance and variable geometric rules of the inlet. The result indicates that the single-degree-of-freedom variable geometry scheme adopted in this paper guarantees the steady work of the inlet over a wide speed range. The large bleed window caused by rotation of the compression ramp appears near the throat at low Mach number. Low-pressure airflow near the bleed window neutralises the original high-pressure airflow behind the shock train, which decreases the overall pressure of the downstream region of the internal contraction section. To match the lower pressure, the structure of the shock train changes from strong $\lambda$-type to weak $\lambda$-type, and finally to a normal shock wave as backpressure increases at Mach number 2.5. Herein, the total pressure recovery coefficient of the inlet near the critical condition improves by 8.5% as the backpressure ratio (Pe/P0) adds from 13 to 14.6 at Mach number 2.5. It proves that the scheme is effective on terminal shock wave control and inlet performance improvement. In addition, due to the background wave and the bleed window, two kinds of shock wave oscillation occur when the backpressure ratio is 13.1.

The aerodynamic performance of conventional aircraft configurations are mainly affected by the wing and horizontal tail. Drag reduction by shape optimisation of the wing, while taking into account the aircraft trimmed constraint, has more benefit than focusing solely on the wing. So in order to evaluate this approach, the following study presents results of a single and multipoint aerodynamic shape optimisation of the wing-body-tail configuration, defined by the Aerodynamic Design Discussion Group (ADODG). Most of the aerodynamic shape optimisation problems published in the last years are focused mainly on the wing as the main driver for performance improvement, with no trim constraint and/or excess drag obtained from the fuselage, fins or other parts. This work partially fills this gap by an investigation of RANS-based aerodynamic optimisation for transonic trimmed flight. Mesh warping and geometry parametrisation is accomplished by fitting the multi-block structured grid to a B-spline volumes and performing the mesh movement by using surface control points embedded within the free-form deformation (FFD) volumes. A gradient-based optimisation algorithm is used with an adjoint method in order to compute the derivatives of the objective and constraint functions with respect to the design variables. In this work the aerodynamic shape optimisation of the CRM wing-body-tail configuration is investigated, including a trim constraint that is satisfied by rotating the horizontal tail. The shape optimisation is driven by 432 design variables that envelope the wing surface, and 120 shape variables for the tail, as well as the angle of attack and tail rotation angles. The constraints are the lift coefficient, wing’s thickness controlled by 1,000 control points, and the wing’s volume. For the untrimmed configuration the drag coefficient is reduced by 5.76%. Optimising the wing with a trim condition by tail rotation results in shock-free design with a considerably improved drag, even better than the untrimmed-optimised case. The second optimisation problem studied is a single and multi-point lift constraint drag minimisation of a gliding configuration wing in transonic viscous flow. The shock is eliminated, reducing the drag of the untrimmed configuration by more than 60%, using 192 design variables. Further robustness is achieved through a multi-point optimisation with more than 45% drag reduction.

The satellite attitude control system (SACS) is a complicated system. In order to reflect the relationship among different components in SACS and analyse the impact of component faults on system performance, a complete simulation platform of the SACS based on Simulink is built in this paper. With the embedding of the specific reaction flywheel, gyroscope and earth sensor model, and the design of the controller based on the quaternion feedback, the simulation platform can not only simulate the real SACS at the component level, but it can also realise the injection of component faults for analysing the system performance. Simulations are conducted to verify the performance of the simulation platform. Simulation results show that this simulation platform has the ability to accurately reflect the control performance of the SACS, and the output accuracy of the component model is high. The research results reveal that this simulation platform can provide model support for verifying the algorithm of fault diagnosis, prediction and tolerant control of the SACS. This simulation platform is easy to use and can be expanded and improved.

This paper introduces the Low Acoustic Noise and Turbulence (LANT) wind tunnel of the Sao Carlos School of Engineering, University of Sao Paulo (USP-EESC), Brazil. The closed-loop wind tunnel features several devices to improve flow uniformity, reduce swirl, and lower the background acoustic noise and turbulence, enabling stability and aeroacoustic experiments. The design criteria was based on the best practices reported, in particular for low turbulence wind tunnels. Yet, these criteria are conflicting and we discuss the decisions that had to be made and present flow quality results that were achieved. The 16-bladed axial fan with 13-blade stators is driven by a variable-speed electric motor. At the corners, 100 mm dense acoustic foam is installed on the vertical walls, floor and ceiling, and the turning vanes are filled with acoustic-absorbing material. The long settling chamber contains a 3.175 mm mesh hexagonal honeycomb and five fine mesh nylon screens, ending in a 7:1 area ratio short contraction. The 3-m long closed-working section has a $1\times 1\ {\rm m}^2$ cross-section area. At 15 m/s the working section wall boundary layer is less than 100 mm thick, providing an area of at least $800\times 800\ \mathrm{mm}^2$ where the streamwise flow uniformity was within 1%. In the 10–30 m/s flow speed range, the turbulence intensity ranged from 0.05% to 0.071% and the background acoustic noise level, obtained with an inflow microphone, ranged from 90 and 110 dB. A benchmark experiment on a flat plate boundary layer produced an almost perfect two-dimensional Blasius profile up to $Re_x \approx 2.5 \times 10^6$ . A beamforming benchmark experiment on aeroacoustics accurately identified the sound emitted by a cylinder immersed in the flow.

Pilot-Induced Oscillation (PIO), although an old issue, still poses a significant threat to aviation safety. The introduction of new systems in modern aircraft modifies the human–machine interaction and makes it necessary for research to revisit the subject from time to time. Given the need of aircraft manufacturers to constantly perform PIO tests, this study analysed the feasibility of using three different computational pilot models (Tustin, Crossover and Precision) to simulate PIO conditions. Three aircraft models with different levels of propensity to PIO (original, low propensity and high propensity) were tested, as well as two pilot gain conditions (normal and high). Data were collected for a purely longitudinal synthetic task through simulations conducted in MATLAB®. PIO conditions were detect using a tuned PIO detection algorithm (ROVER). Data were analysed in terms of both whether the pilot models triggered a PIO condition and for how long the condition was sustained. The results indicated that the three pilot models only provoked PIO conditions when high gain inputs were applied. Additionally, Crossover was the only pilot model to trigger a PIO for the three aircraft models. There were also significant differences between the pilot models in the total PIO time, as the Tustin model typically sustained the oscillatory condition for longer.

This paper discusses the utilisation of deep reinforcement learning algorithms to obtain optimal paths for an aircraft to avoid or minimise radar detection and tracking. A modular approach is adopted to formulate the problem, including the aircraft kinematics model, aircraft radar cross-section model and radar tracking model. A virtual environment is designed for single and multiple radar cases to obtain optimal paths. The optimal trajectories are generated through deep reinforcement learning in this study. Specifically, three algorithms, namely deep deterministic policy gradient, trust region policy optimisation and proximal policy optimisation, are used to find optimal paths for five test cases. The comparison is carried out based on six performance indicators. The investigation proves the importance of these reinforcement learning algorithms in optimal path planning. The results indicate that the proximal policy optimisation approach performed better for optimal paths in general.

Orbiting objects in space are exposed to the risk of collision with space debris over their lifetime. Space debris orbiting in space experiences orbital decay due to various orbital perturbations. This work considers only orbital perturbations due to aerodynamic forces, which spacecraft experience due to the presence of a rarefied atmosphere, causing tumbling motion and orbital decay. Analysis of the orbital decay of a spacecraft is carried out by considering the variation of the drag coefficient as a function of its shape, motion and angle-of-attack. An in-house Direct Simulation Monte Carlo (DSMC) solver is modified for aerodynamic analysis of a spacecraft orbiting in the free molecular regime in low Earth orbit. In addition, an orbital dynamics model is developed to simulate the tumbling motion of a spacecraft and its orbital decay. The orbital decay trajectory is predicted for two sample spacecrafts using the aerodynamic coefficients obtained from the in-house DSMC solver as inputs to the orbital decay model. This study analyses and explores in detail the effects of the aerodynamic coefficients and shape of a spacecraft on its orbital decay.