Investigation of hot jet on active control of oblique detonation waves

The hot jet injection is utilized to actively control the oblique detonation wave, such as initiating and stabilizing an oblique detonation wave at a desired position that is shorter than the length of induction zone, and adjust the height of the oblique detonation wave at the exit of combustor when the oblique detonation wave engine is working on off-design flight conditions. The fifth order Weighted Essentially Non-Oscillatory (WENO) scheme and a two-step reversible reaction mechanism of the stoichiometric H₂/Air are adopted in the simulations. With the help of hot jet injection, the transition from inert oblique shock wave to the oblique detonation wave immediately occurs near the position of hot jet injection, and consequently the length of combustor can be reduced. The angle of oblique detonation wave also decreases as the hot jet injection approaches the nose of wedge. Additionally, the height of the oblique detonation wave at the exit of combustor can be flexibly adjusted, and also depends on the injection position and the strength of the hot jet. If the velocity of the hot jet is too weak to directly trigger the overall oblique detonation wave at the position of injection, increasing the injection pressure will improve the strength of the hot jet and results in a successful transition.     

基于RDK-ELM 的航空发动机控制系统故障诊断

为保持较高诊断正确率,缩短训练时间,满足航空发动机故障诊断对于实时性和高诊断率的需求,提出1种对深度核极限学习机的简约改进方法。输入数据中随机选取部分数据作为支持向量,结合深度学习网络的多层结构,完成了对输入样本的特征提取,通过核函数实现了高维空间映射分类。数字仿真表明:算法分类正确率高,训练时间短,可应用于航空发动机控制系统的故障诊断。     

Efficient aerodynamic shape optimization using variable-fidelity surrogate models and multilevel computational grids

A variable-fidelity method can remarkably improve the efficiency of a design optimization based on a high-fidelity and expensive numerical simulation, with assistance of lower-fidelity and cheaper simulation(s). However, most existing works only incorporate “two” levels of fidelity, and thus efficiency improvement is very limited. In order to reduce the number of high-fidelity simulations as many as possible, there is a strong need to extend it to three or more fidelities. This article proposes a novel variable-fidelity optimization approach with application to aerodynamic design. Its key ingredient is the theory and algorithm of a Multi-level Hierarchical Kriging (MHK), which is referred to as a surrogate model that can incorporate simulation data with arbitrary levels of fidelity. The high-fidelity model is defined as a CFD simulation using a fine grid and the lower-fidelity models are defined as the same CFD model but with coarser grids, which are determined through a grid convergence study. First, sampling shapes are selected for each level of fidelity via technique of Design of Experiments (DoE). Then, CFD simulations are conducted and the output data of varying fidelity is used to build initial MHK models for objective (e.g. C_D) and constraint (e.g. C_L, C_m) functions. Next, new samples are selected through infill-sampling criteria and the surrogate models are repetitively updated until a global optimum is found. The proposed method is validated by analytical test cases and applied to aerodynamic shape optimization of a NACA0012 airfoil and an ONERA M6 wing in transonic flows. The results confirm that the proposed method can significantly improve the optimization efficiency and apparently outperforms the existing single-fidelity or two-level-fidelity method.     

基于全连接神经网络的分层旋流火焰燃烧振荡预报

燃气轮机贫油预混低排放燃烧室容易发生燃烧振荡,在宽工况极端条件下燃烧主动控制有潜力成为抑制燃烧振荡的有效方法。然而主动控制系统依赖于对燃烧振荡的快速监测和准确预报,因此有必要针对不同的燃烧振荡预报手段开展研究和验证。本文以甲烷预混同心分层旋流火焰的图像为基础,采用降低图像分辨率和提取火焰结构特征参数这两种不同的方式对火焰图像信息进行简化处理,并使用全连接神经网络对燃烧振荡进行预报研究。结果发现,两种方式都可以较为准确地预报燃烧振荡,精度均达到90%以上。预报精度随着图像分辨率的增加而升高,在极低的图像分辨率(3*3)下,预报精度也能达到90%以上。此外,对根据火焰平均图像提取的结构特征参数进行了敏感性分析,捕捉到了系统稳定性的转变,但参数变化范围受训练集限制。本文提出的基于数据驱动方法对燃烧振荡的预报时间小于2 ms,为实现燃烧振荡实时在线预报提供了支持。     

基于扩张状态观测器的高空台进气环境模拟控制技术研究

针对航空发动机高空台推力瞬变等过渡态试验对进气环境模拟控制系统所提出的强抗扰性、强鲁棒性的迫切需求,设计了一种基于扩张状态观测器（ESO）的高空台进气环境模拟主动抗扰控制技术方法。首先分析了现有高空台过渡态环境模拟的技术特点和高品质控制指标难于实现的原因;其次设计线性自抗扰控制器（LADRC）和一体化并行控制器（IPC）;最后通过仿真对高空台进气环境模拟主动抗扰控制方法进行了验证。结果表明,应用基于扩张状态观测器的主动抗扰控制技术能够大幅提高发动机过渡态试验中进气环境模拟的动态响应速度、控制精度和抗扰动能力。     

基于MEMD和ELM的飞机机翼健康状态预测技术

复合材料在现代飞机结构中的应用越来越广泛,为了有效地对飞机机翼健康状态进行预测,提出了基于多元经验模态分解(MEMD)和极限学习机(ELM)的飞机机翼健康状态预测方法。以某型飞机复合材料机翼盒段为具体研究对象,对其进行冲击与疲劳加载试验,利用光纤传感器及其采集系统募集飞机复合材料机翼盒段的原始应变信息,对其健康状态予以表征。对所采集的原始应变信息进行MEMD分解,提取分解后各频带信号的能量熵作为表征飞机复合材料机翼盒段健康状态的特征信息,采用动态主元分析法(DPCA)将所提取的能量熵特征信息进行融合,采用融合后所得到的能量熵构建ELM预测模型,对某型飞机复合材料机翼盒段健康状态进行预测。试验研究表明,本文方法可以有效实现飞机机翼的健康状态预测,具有很好的应用前景。     

基于神经网络的卫星控制系统故障识别方法

闭环控制系统的反馈调节机制,可能破坏开环系统的可辨识性,导致误报率和漏报率同时上升,还可能导致同一故障模式下的多个变量发生异常,给闭环控制系统故障识别带来难度。针对单输入单输出系统,分析了闭环控制对可辨识性、误报率和漏报率的影响,对多输入多输出系统,理论上推导了闭环控制系统传感器故障的传播机理,分析了闭环控制系统对系统变量的影响关系,利用神经网络构建了闭环故障识别算法。数值仿真结果验证了闭环对故障系统诊断的不利影响,而卫星姿态控制系统的仿真结果标明:与传统方法相比,提出的方法识别性能更高。     

Ground maneuver for front-wheel drive aircraft via deep reinforcement learning

The maneuvering time on the ground accounts for 10%–30% of their flight time, and it always exceeds 50% for short-haul aircraft when the ground traffic is congested. Aircraft also contribute significantly to emissions, fuel burn, and noise when taxiing on the ground at airports. There is an urgent need to reduce aircraft taxiing time on the ground. However, it is too expensive for airports and aircraft carriers to build and maintain more runways, and it is space-limited to tow the aircraft fast using tractors. Autonomous drive capability is currently the best solution for aircraft, which can save the maneuver time for aircraft. An idea is proposed that the wheels are driven by APU-powered (auxiliary power unit) motors, APU is working on its efficient point; consequently, the emissions, fuel burn, and noise will be reduced significantly. For Front-wheel drive aircraft, the front wheel must provide longitudinal force to tow the plane forward and lateral force to help the aircraft make a turn. Forward traction effects the aircraft’s maximum turning ability, which is difficult to be modeled to guide the controller design. Deep reinforcement learning provides a powerful tool to help us design controllers for black-box models; however, the models of related works are always simplified, fixed, or not easily modified, but that is what we care about most. Only with complex models can the trained controller be intelligent. High-fidelity models that can easily modified are necessary for aircraft ground maneuver controller design. This paper focuses on the maneuvering problem of front-wheel drive aircraft, a high-fidelity aircraft taxiing dynamic model is established, including the 6-DOF airframe, landing gears, and nonlinear tire force model. A deep reinforcement learning based controller was designed to improve the maneuver performance of front-wheel drive aircraft. It is proved that in some conditions, the DRL based controller outperformed conventional look-ahead controllers.     

What are the physical mechanisms of eruptions and CMEs?

CMEs are due to physical phenomena that drive both, eruptions and flares in active regions. Eruptions/CMEs must be driven from initially force-free current-carrying magnetic field. Twisted flux ropes, sigmoids, current lanes and pattern in photospheric current maps show a clear evidence of currents parallel to the magnetic field. Eruptions occur starting from equilibria which have reached some instability threshold. Revisiting several data sets of CME observations we identified different mechanisms leading to this unstable state from a force free field. Boundary motions related to magnetic flux emergence and shearing favor the increase of coronal currents leading to the large flares of November 2003. On the other hand, we demonstrated by numerical simulations that magnetic flux emergence is not a sufficient condition for eruptions. Filament eruptions are interpreted either by a torus instability for an event occurring during the minimum of solar activity either by the diffusion of the magnetic flux reducing the tension of the restraining arcade. We concluded that CME models (tether cutting, break out, loss of equilibrium models) are based on these basic mechanisms for the onset of CMEs.