Search Results (5)

In the history of civil aircraft transportation, ice formation has been identified as a key factor in the safety of flight. Anti-icing and deicing systems have emerged through the years with the aim to prevent or to eliminate ice formation on wing airfoils, control surfaces and probes. Modern flying machines demand more efficiency in order to reduce the carbon footprint and increase the sustainability of flight transport. In order to achieve this goal, the need to have an efficient aircraft with an efficient and low power consuming system is fundamental. This paper proposes a new model for ice accretion using computational fluid dynamics (CFD). This model permits the simulation of the shape of the ice formed over a profile varying boundary condition (i.e., speed, liquid water content, and so on). The proposed model also takes into account the amount of heat transferred between the water and the surrounding environment and includes the effects of air turbulence on the ice formation process. The CFD simulations have been validated with NASA experimental outcome and show good agreement. The proposed model can be also used to investigate the effects of various parameters such as air speed, liquid water content, and air temperature on the ice formation process. The results evidence that the proposed model can accurately predict ice formation process and is suitable to optimize the design of anti-icing or deicing systems for aircraft and helicopters. This approach is not limited to aerospace but can also be exported to other applications such as transportation, wind turbine, energy management, and infrastructure. Full article

In the past decade, drones have become available and affordable for civil applications, including mapping and monitoring the Earth with geophysical sensors. In 2017 and 2019, the feasibility of executing frequency domain electromagnetic (FDEM) surveys using an off-the-shelf drone was investigated at Deltares Institute. This paper reports firstly the preparatory tests executed to determine the optimal instrumental configuration, flight path, data processing and inversion schemes and secondly the three field validation tests executed to demonstrate the feasibility of the drone-borne electromagnetic survey in real-scale applications. At several test sites, the optimal configuration of the drone and electromagnetic instruments, such as the mounting device and distance of the electromagnetic (EM) sensor with respect to the drone, the flight altitude, the coil separation and frequency of the EM source, efficiency and safety, and the assemblage of instrument and drone data were investigated. This has resulted in a robust method to acquire accurate and repeatable in-phase, quadrature and apparent resistivity data, and a workflow for data correction, processing and inversion scheme was developed. During those tests, three EM instruments were tested. The drone-borne electromagnetic (DR-EM) system has the ability and efficacy to fly over inaccessible areas and surface water. Compared to helicopter-borne electromagnetic surveys, the spatial resolution is much higher, which allows very detailed 3D mapping of subsurface targets, and the survey costs are relatively low. Repeated drone-borne electromagnetic (DR-EM) surveys allow low-cost monitoring of local changes in water saturation and salinity. Full article

Unmanned helicopter (UH) is often utilized for raid missions because it can evade radar detection by flying at ultra-low altitudes. Path planning is the key technology to realizing the autonomous action of UH. On the one hand, the dynamically changing radar coverage area and the mountains in the low airspace environment will seriously affect the flight safety of UH. On the other hand, the huge state space of the three-dimensional (3D) environment will also make traditional algorithms difficult to converge. To address the above problems, a memory-enhanced dueling deep Q-network (ME-dueling DQN) algorithm was proposed. First, a comprehensive reward function was designed, which can guide the algorithm to converge quickly and effectively improve the sparse reward problem. Then, we introduced a dual memory pool structure and proposed a memory-enhanced mechanism, which can reduce invalid exploration, further improve the learning efficiency of the algorithm, and make the algorithm more stable. Finally, the path planning ability of the proposed algorithm in multiple experimental environments was verified. Experiments showed that the proposed algorithm has good environmental adaptability and can help UH to accurately identify dangerous areas and plan a safe and reliable flight path. Full article

Unmanned helicopters (UH) can avoid radar detection by flying at ultra-low altitudes; thus, they have been widely used in the battlefield. The flight safety of UH is seriously affected by moving obstacles such as flocks of birds in low airspace. Therefore, an algorithm that can plan a safe path to UH is urgently needed. Due to the strong randomness of the movement of bird flocks, the existing path planning algorithms are incompetent for this task. To solve this problem, a state-coded deep Q-network (SC-DQN) algorithm with symmetric properties is proposed, which can effectively avoid randomly moving obstacles and plan a safe path for UH. First, a dynamic reward function is designed to give UH appropriate rewards in real time, so as to improve the sparse reward problem. Then, a state-coding scheme is proposed, which uses binary Boolean expression to encode the environment state to compress environment state space. The encoded state is used as the input to the deep learning network, which is an important improvement to the traditional algorithm. Experimental results show that the SC-DQN algorithm can help UH avoid the moving obstacles to unknown motion status in the environment safely and effectively and successfully complete the raid task. Full article

Being equipped with a millimeter-wave radar allows a low-flying helicopter to sense the surroundings in real time, which significantly increases its safety. However, nonhomogeneous clutter environments, such as a multiple target situation and a clutter edge environment, can dramatically affect the radar signal detection performance. In order to improve the radar signal detection performance in nonhomogeneous clutter environments, this paper proposes a new automatic censored cell averaging CFAR detector. The proposed CFAR detector does not require any prior information about the background environment and uses the hypothesis test of the first-order difference (FOD) result of ordered data to reject the unwanted samples in the reference window. After censoring the unwanted ranked cells, the remaining samples are combined to form an estimate of the background power level, thus getting better radar signal detection performance. The simulation results show that the FOD-CFAR detector provides low loss CFAR performance in a homogeneous environment and also performs robustly in nonhomogeneous environments. Furthermore, the measured results of a low-flying helicopter validate the basic performance of the proposed method. Full article