基于自适应随机优化的连续阵风关键载荷预测

连续阵风载荷是构成民用飞机设计工况的主要载荷之一，在设计阶段，任意一轮的模型更新都涉及到上万种载荷工况的计算，然而其中仅个别工况构成载荷包线，需进行强度校核。为此发展了一套阵风关键载荷的快速识别方法。首先，采用二水平全因子（2LFF）采样获取得到初始计算工况，基于已计算得到的载荷值，结合多元自适应回归样条（MARS）建立一个可靠的代理模型；然后，在此基础上，开创性地应用自适应随机优化技术，实现对阵风关键工况及载荷的主动搜索；最后，以适航条款规定的侧向连续阵风载荷进行方法验证及参数影响研究。计算结果表明，本文建立的方法可以高效且准确地实现连续阵风关键载荷的预测，针对本文算例，关键载荷的预测值与基准值相比误差小于1%。     

Effect of removing the common mode errors on linear regression analysis of noise amplitudes in position time series of a regional GPS network & a case study of GPS stations in Southern California

The analysis of the correlations between the noise in different components of GPS stations has positive significance to those trying to obtain more accurate uncertainty of velocity with respect to station motion. Previous research into noise in GPS position time series focused mainly on single component evaluation, which affects the acquisition of precise station positions, the velocity field, and its uncertainty. In this study, before and after removing the common-mode error (CME), we performed one-dimensional linear regression analysis of the noise amplitude vectors in different components of 126?GPS stations with a combination of white noise, flicker noise, and random walking noise in Southern California. The results show that, on the one hand, there are above-moderate degrees of correlation between the white noise amplitude vectors in all components of the stations before and after removal of the CME, while the correlations between flicker noise amplitude vectors in horizontal and vertical components are enhanced from un-correlated to moderately correlated by removing the CME. On the other hand, the significance tests show that, all of the obtained linear regression equations, which represent a unique function of the noise amplitude in any two components, are of practical value after removing the CME. According to the noise amplitude estimates in two components and the linear regression equations, more accurate noise amplitudes can be acquired in the two components.     

基于浸入与不变理论的航天器姿态跟踪自适应控制

针对惯性参数不确定的航天器姿态跟踪控制问题，基于浸入与不变（I&I）方法设计出了一种新的姿态跟踪控制器。研究结果表明，传统的浸入与不变方法运用到姿态跟踪模型，存在参数回归矩阵不可积进而导致偏微分方程无解析解的问题。针对该问题，提出了一种对回归矩阵改造使其满足可积条件的方法，通过动态放缩技术消除了回归矩阵改造前后的差异对闭环系统稳定性的影响，设计出了一种新的航天器姿态跟踪自适应控制器。通过李雅普诺夫稳定性分析方法证明了所设计的控制器能够保证闭环系统的全局渐近稳定性。相对于已有的基于动态放缩法的浸入与不变控制器，设计了一种全新的缩放因子，使得控制器的执行不需要缩放因子的信息，并且也不需要惯量矩阵的先验信息。最后，仿真对比实验进一步验证了所设计控制器的有效性和优越性。     

Estimating canopy LAI and chlorophyll of tropical forest plantation (North India) using Sentinel-2 data

With the free and full access to images from Sentinel-2 satellite, the interest to use this data for quantitative retrieval of vegetation parameters is ever-increasing. LAI and chlorophyll are two key variables which are desired for studying productivity, nutrient and stress status of vegetation. Studies carried out on croplands using simulated Sentinel-2 MSI and parametric approach have identified vegetation indices (VIs) with high sensitivity to LAI and chlorophyll. To test how Sentinel-2 red-edge based VIs perform for retrieval of LAI and Chlorophyll of tropical mixed forest canopies, this study has been performed. The field measurements of LAI and chlorophyll content were recorded in a total of 28 ESUs (Elementary Sampling Units) in Bhakra range in the Tarai Central Forest Division, Uttarakhand (India). The in-situ measurements were statistically correlated with Sentinel-2VIs and strength of correlation was validated using Predicted Residual Error Sum of Squares (PRESS) statistic. Field LAI corrected for foliage clumpiness effect improved correlation of VIs with LAI. Among all VIs tested, Normalized Difference Index (NDI) offered highest positive correlation (R² = 0.79, p < 0.05) with LAI while Red-Edge Chlorophyll Index (RECI) (R² = 0.83, RMSE = 0.24 g/m², p < 0.05) and Simple Ratio (SR) 740/705 (R² = 0.79, RMSE = 0.27 g/m², p < 0.05) were the most closely related to chlorophyll content. VIs with red-edge and NIR combinations offered best results.     

GDP spatialization in Ningbo City based on NPP/VIIRS night-time light and auxiliary data using random forest regression

Accurate spatial distribution information on gross domestic product (GDP) is of great importance for the analysis of economic development, industrial distribution and urbanization processes. Traditional administrative unit-based GDP statistics cannot depict the detailed spatial differences in GDP within each administrative unit. This paper presents a study of GDP spatialization in Ningbo City, China based on National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL) data and town-level GDP statistical data. The Landsat image, land cover, road network and topographic data were also employed as auxiliary data to derive independent variables for GDP modelling. Multivariate linear regression (MLR) and random forest (RF) regression were used to estimate GDP at the town scale and were assessed by cross-validation. The results show that the RF model achieved significantly higher accuracy, with a mean absolute error (MAE) of 109.46 million China Yuan (CNY)·km⁻² and a determinate coefficient (R² = 0.77) than the MLR model (MAE = 161.8 million CNY·km⁻², R² = 0.59). Meanwhile, by comparing with the estimated GDP data at the county level, the town-level estimated data showed a better performance in mapping GDP distribution (MAE decreased from 115.1 million CNY·km⁻² to 74.8 million CNY·km⁻²). Among all of the independent variables, NTL, land surface temperature (T_s) and plot ratio (PR) showed higher impacts on the GDP estimation accuracy than the other variables. The GDP density map generated by the RF model depicted the detailed spatial distribution of the economy in Ningbo City. By interpreting the spatial distribution of the GDP, we found that the GDP of Ningbo was high in the northeast and low in the southwest and formed continuous clusters in the north. In addition, the GDP of Ningbo also gradually decreased from the urban centre to its surrounding areas. The produced GDP map provides a good reference for the future urban planning and socio-economic development strategies.     

Location layout design of aircraft parts assembly based on MSVR

Location layout of aircraft assembly is an important factor affecting product quality. Most of the existing re-searches use the combination of finite element analysis and intelligent algorithm to optimize the location layout, which are limited by numerical simulation accuracy and the selection and improvement of intelligent algorithms. At present, the analysis and decision-making technology based on field data is gradually applied in aircraft manufacturing. Based on the perception data of intelligent assembly unit of aircraft parts, a regression model of multi-input and multi-output support vector machine with Gauss kernel function as radial basis function is established, and the hyperparameters of the model are optimized by hybrid particle swarm optimization genetic algorithm (PSO-GA). GA-MSVR, PSO-MSVR and PSOGA-MSVR model are constructed respectively, and their results show that PSOGA-MSVR model has the best performance. Finally, the design of the aircraft wing location layout is taken as an example to verify the effectiveness of the method.     

旋流燃烧室对固体燃料冲压发动机药柱表面传热以及燃速的影响

为研究旋流燃烧室对固体燃料冲压发动机(SFRJ)药柱表面传热以及燃速的影响,以高密度聚乙烯(HDPE)为燃料,对旋流和无旋工况下的固体燃料冲压发动机进行了连管实验研究,并且编制了二维轴对称湍流燃烧仿真程序,采用流固耦合传热的方法以及非定常时间推进方式,对实验工况进行了数值模拟。结果表明:①药柱表面热流密度对燃速有显著影响,在回流区与附着点处,药柱表面的对流换热能力要明显优于再发展区;②在旋流工况下,在离心力与切向速度的作用下,使热解产物在药柱表面附近区域停留时间更长,有助于热解产物的充分反应,并且明显增强药柱表面对流换热能力,与无旋工况相比,提高幅度可达100%,并且在旋流工况下发动机可更快建立自持燃烧;③通过实验研究发现,旋流的引入提高发动机的燃速有积极作用,增幅可达26%,但会导致固体燃料冲压发动机补燃室压强出现周期性振荡。      

Improving the estimation of zenith dry tropospheric delays using regional surface meteorological data

Global Navigation Satellite Systems (GNSS) are emerging as possible tools for remote sensing high-resolution atmospheric water vapour that improves weather forecasting through numerical weather prediction models. Nowadays, the GNSS-derived tropospheric zenith total delay (ZTD), comprising zenith dry delay (ZDD) and zenith wet delay (ZWD), is achievable with sub-centimetre accuracy. However, if no representative near-site meteorological information is available, the quality of the ZDD derived from tropospheric models is degraded, leading to inaccurate estimation of the water vapour component ZWD as difference between ZTD and ZDD. On the basis of freely accessible regional surface meteorological data, this paper proposes a height-dependent linear correction model for a priori ZDD. By applying the ordinary least-squares estimation (OLSE), bootstrapping (BOOT), and leave-one-out cross-validation (CROS) methods, the model parameters are estimated and analysed with respect to outlier detection. The model validation is carried out using GNSS stations with near-site meteorological measurements. The results verify the efficiency of the proposed ZDD correction model, showing a significant reduction in the mean bias from several centimetres to about 5 mm. The OLSE method enables a fast computation, while the CROS procedure allows for outlier detection. All the three methods produce consistent results after outlier elimination, which improves the regression quality by about 20% and the model accuracy by up to 30%.     

多生理参数管制负荷模型下的扇区BSP划分

为了合理评估管制员的主观（认知）工作负荷，实现均衡管制员工作负荷的空域扇区划设目标，在前期研究基础上，考虑航空器数量、冲突点数量以及进离场航班比例对管制员工作负荷的影响，运用回归分析的方法，建立了基于多生理参数的管制员工作负荷多元回归模型。应用二叉空间分割（ BSP ）算法实现了空域扇区划分，结合多元回归模型实现了均衡管制工作负荷的扇区最优划设。算例结果验证了管制员工作负荷模型的合理性，BSP法均衡了不同扇区间的管制工作负荷。     

基于SPSS的终端区交通流态势评估

空中交通管理工作的实施必须以准确掌握各航线运行状况及其发展趋势为基础和前提。对终端区交通流的运行态势进行评估，即是对交通流实时运行状态及发展趋势进行分析。对终端区空中交通流进行现状评估，确定各个交通流参数的时序分布，评价终端区交通流运行状态。借助SPSS统计软件，剖析参数变量之间存在的关系，以线性回归模型为基础模型，了解变量之间如何相互影响，获取终端区流量出现拥塞的时段，反映终端区交通流特性。