基于强化学习的航空器机场智能静态路径规划

随着人工智能迅速发展以及“智慧机场”的提出,研究人工智能在机场如何有效地辅助机场管制人员,驾驶员指挥航空器在地面滑行具有重要意义。本文提出一种基于强化学习的滑行路径规划方法,构建航空器机场地面强化学习移动模型,并以海口美兰机场为案例采用 Python 内置工具包 Tkinter 进行场面仿真;在此基础上,考虑机场航空器滑行规则,采用 Off-Policy 中 Q-Learning 算法求解贝尔曼方程,实现航空器在 Model-based 环境中进行静态路径规划。结果表明：本文所提方法能够实现停机位到跑道出口智能静态路径规划     

基于雷诺平均Navier-Stokes方程的表面传热系数计算

采用有限体积法数值求解控制二维绕流的雷诺平均Navier-Stokes(RANS)方程组,计算了光滑和粗糙NACA0012翼型以及圆柱表面的局部表面传热系数.分析了近壁面网格间距、湍流模式和表面粗糙度模型对数值计算结果的影响.结果表明:切应力输运(SST)湍流模型能够区分层流和湍流边界层的对流传热特性,并能预测转捩的发生;采用Spalart-Allmaras(S-A) 扩展模型能够计算粗糙壁面的对流传热系数,但采用忽略转捩函数的S-A模型不能有效计算层流边界层的传热系数.当近壁面网格间距接近10-5量级的黏性子层时,在光滑和粗糙壁面都能得到准确的传热系数分布.结合合适的近壁面网格间距,湍流模式和表面粗糙度模型可以得到与实验数据十分接近的表面传热系数曲线.通过与求解不可压缩RANS方程得到的结果比较后发现,不可压缩RANS方程主要忽略了压缩和黏性耗散效应,这种效应可以通过绝热升温项的形式并入总体热分析.      

基于小波包分析与多核学习的滚动轴承故障诊断

为了更准确地诊断滚动轴承故障,提出了一种基于小波包分析与多核学习的滚动轴承故障诊断方法.该方法首先对振动信号进行3层小波包分解,将振动信号分解为不同频带的信号,提取各频带的相对能量特征,构建特征向量;然后采用多核学习算法从训练样本集中学习核函数与分类器;最后使用训练出的分类器识别滚动轴承故障类型.为了验证方法的有效性,进行了滚动轴承故障诊断实验,实验结果表明该方法的故障诊断准确率达到98.25%,与传统的基于小波包与支持向量机的滚动轴承故障诊断方法相比,其故障诊断准确率更高,同时由于避免了核函数的选择问题,该方法更便于实际应用.      

汽雾两相流强化冷却燃气轮机叶片内冷通道

实验研究了方形通道这一重型燃气轮机中常用叶片强制对流冷却通道结构.分析了雷诺数、壁面热流密度以及水雾质量流量比等关键参数对汽雾冷却通道的传热特性的影响,并建立了考虑离散相水雾的流动工况和通道壁面加热条件的实验关联式.结果表明:相对于纯蒸汽,汽雾两相流的传热系数显著提高,且传热性能提高的幅度随热流密度的增大而减小,随雷诺数和水雾质量流量比的增大而增大;通道上壁面平均传热系数低于下壁面,在高热流密度和低水雾质量流量比下,两者相差约13%,而在低热流密度与高水雾质量流量比的情况下,该比值增加到约25%.      

Recent active thermal management technologies for the development of energy-optimized aerospace vehicles in China

Recently, the development of modern vehicles has brought about aggressive integration and miniaturization of on-board electrical and electronic devices. It will lead to exponential growth in both the overall waste heat and heat flux to be dissipated to maintain the devices within a safe temperature range. However, both the total heat sinks aboard and the cooling capacity of currently utilized thermal control strategy are severely limited, which threatens the lifetime of the on-board equipment and even the entire flight system and shrink the vehicle’s flight time and range. Facing these thermal challenges, the USA proposed the program of “INVENT” to maximize utilities of the available heat sinks and enhance the cooling ability of thermal control strategies. Following the efforts done by the USA researchers, scientists in China fought their ways to develop thermal management technologies for Chinese advanced energy-optimized airplanes and spacecraft. This paper elaborates the available on-board heat sinks and aerospace thermal management systems using both active and passive technologies not confined to the technology in China. Subsequently, active thermal management technologies in China including fuel thermal management system, environment control system, non-fuel liquid cooling strategy are reviewed. At last, space thermal control technologies used in Chinese Space Station and Chang’e-3 and to be used in Chang’e-5 are introduced. Key issues to be solved are also identified, which could facilitate the development of aerospace thermal control techniques across the world.     

Design of nanofluid-cooled heat sink using topology optimization

The paper presented topology optimization of 2D and 3D Nanofluid-Cooled Heat Sink (NCHS). The flow and heat transfer problem in the NCHS was treated as a single-phase nanofluid based convective heat transfer model. The temperature-dependent fluid properties were taken into account in the model due to the strong temperature-dependent features of nanofluids. An average temperature minimum problem was studied subject to the fluid area and energy dissipation constraints by using the density method. In the method, the design variable is updated according to the gradient information obtained by an adjoint based sensitivity analysis process. The effects of the energy dissipation constraint, temperature-dependent fluid properties and nanofluid characteristics on optimal configurations of NCHS were numerically investigated with following conclusions. Firstly, branched flow channels in the optimal configuration increased with the rise of the allowed energy dissipation. Secondly, temperature-dependent fluid properties were significant for obtaining the appropriate optimal results with best cooling performance. Thirdly, heat transfer performances of optimal configurations were enhanced by reducing the nanoparticle diameter or increasing the nanoparticle volume fraction. Fourthly, the optimal configuration for nanofluid had better cooling performance than that for its base fluid.     

Probing the origin of the iron Kα line around stellar and supermassive black holes using X-ray polarimetry

Asymmetric, broad iron lines are a common feature in the X-ray spectra of both X-ray binaries (XRBs) and type-1 Active Galactic Nuclei (AGN). It was suggested that the distortion of the Fe Kα emission results from Doppler and relativistic effects affecting the radiative transfer close to the strong gravitational well of the central compact object: a stellar mass black hole (BH) or neutron star (NS) in the case of XRBs, or a super massive black hole (SMBH) in the case of AGN. However, alternative approaches based on reprocessing and transmission of radiation through surrounding media also attempt to explain the line broadening. So far, spectroscopic and timing analyzes have not yet convinced the whole community to discriminate between the two scenarios. Here we study to which extent X-ray polarimetric measurements of black hole X-ray binaries (BHXRBs) and type-1 AGN could help to identify the possible origin of the line distortion. To do so, we report on recent simulations obtained for the two BH flavors and show that the proposed scenarios are found to behave differently in polarization degree and polarization angle. A relativistic origin for the distortion is found to be more probable in the context of BHXRBs, supporting the idea that the same mechanism should lead the way also for AGN. We show that the discriminating polarization signal could have been detectable by several X-ray polarimetry missions proposed in the past.     

Accelerating NLTE radiative transfer by means of the Forth-and-Back Implicit Lambda Iteration: A two-level atom line formation in 2D Cartesian coordinates

State-of-the-art methods in multidimensional NLTE radiative transfer are based on the use of local approximate lambda operator within either Jacobi or Gauss–Seidel iterative schemes. Here we propose another approach to the solution of 2D NLTE RT problems, Forth-and-Back Implicit Lambda Iteration (FBILI), developed earlier for 1D geometry. In order to present the method and examine its convergence properties we use the well-known instance of the two-level atom line formation with complete frequency redistribution. In the formal solution of the RT equation we employ short characteristics with two-point algorithm. Using an implicit representation of the source function in the computation of the specific intensities, we compute and store the coefficients of the linear relations J=a+bS$J=a+\mathit{bS}$ between the mean intensity J and the corresponding source function S. The use of iteration factors in the ‘local’ coefficients of these implicit relations in two ‘inward’ sweeps of 2D grid, along with the update of the source function in other two ‘outward’ sweeps leads to four times faster solution than the Jacobi’s one. Moreover, the update made in all four consecutive sweeps of the grid leads to an acceleration by a factor of 6–7 compared to the Jacobi iterative scheme.     

利用神经元拓展正则极端学习机预测时间序列

为实现对于时间序列预测数据的准确预测,提出一种神经元拓展正则极端学习机(NERELM,Neuron-Expanding Regularized Extreme Learning Machine),并研究了其在时间序列预测中的应用.NERELM根据结构风险最小化原理权衡经验风险与结构风险,以逐次拓展隐层神经元的方式自动确定最佳的网络结构,以避免传统神经网络训练过程中需人为确定网络结构的弊端.应用于时间序列的仿真结果表明:NERELM可有效实现对于RELM最佳网络结构的自动确定,具有预测精度高与计算速度快的优点.