A new weighted mean temperature model in China

The Global Positioning System (GPS) has been applied in meteorology to monitor the change of Precipitable Water Vapor (PWV) in atmosphere, transformed from Zenith Wet Delay (ZWD). A key factor in converting the ZWD into the PWV is the weighted mean temperature ($T_m$), which has a direct impact on the accuracy of the transformation. A number of Bevis-type models, like $T_m-T_s$ and $T_m-T_s\text{,}{P}_s$ type models, have been developed by statistics approaches, and are not able to clearly depict the relationship between $T_m$ and the surface temperature, $T_s$. A new model for $T_m$, called weighted mean temperature norm model (abbreviated as norm model), is derived as a function of $T_s$, the lapse rate of temperature, δ, the tropopause height, $h_{\mathit{trop}}$, and the radiosonde station height, $h_s$. It is found that $T_m$ is better related to $T_s$ through an intermediate temperature. The small effects of lapse rate can be ignored and the tropopause height be obtained from an empirical model. Then the norm model is reduced to a simplified form, which causes fewer loss of accuracy and needs two inputs, $T_s$ and $h_s$. In site-specific fittings, the norm model performs much better, with RMS values reduced averagely by 0.45 K and the Mean of Absolute Differences (MAD) values by 0.2 K. The norm model is also found more appropriate than the linear models to fit $T_m$ in a large area, not only with the RMS value reduced from 4.3 K to 3.80 K, correlation coefficient $R^2$ increased from 0.84 to 0.88, and MAD decreased from 3.24 K to 2.90 K, but also with the distribution of simplified model values to be more reasonable. The RMS and MAD values of the differences between reference and computed PWVs are reduced by on average 16.3% and 14.27%, respectively, when using the new norm models instead of the linear model.     

旋转对固体火箭发动机两相流点火过程影响仿真研究

为研究高速旋转对内外燃管型装药固体火箭发动机凝聚相点火瞬态过程的影响规律，应用计算流体动力学（CFD）流体计算软件，使用用户定义函数（UDF）编程接口建立固体火箭发动机点火模型，对旋转条件下发动机凝聚相点火过程进行模拟。将数值计算结果与地面旋转实验内弹道进行对比分析，验证数值模型的正确性。计算结果表明：①点火药燃气颗粒因旋转做离心运动，大量粒子聚集在燃烧室头部上端，部分粒子附着在发动机壁面，且停留时间较长。②点火药燃气颗粒占比从20%增加到40%，点火压力峰值降低3.93%，发动机转速的升高会造成内弹道平衡压力升高，但点火压力峰会逐渐降低，且峰值出现时间发生延迟，转速达到15 000 r/min时点火压力峰消失。③转速增大，点火颗粒与推进剂传热增大，火焰传播期减小，但燃气填充期和点火延迟增大，点火药燃气颗粒占比为20%时，转速为15 000 r/min较静止条件下点火延迟增加了23.76%。     

基于数字孪生的航班链延误动态预测模型

针对复杂多变的航班运行环境，提出一种基于数字孪生的航班链延误动态预测模型，以改善传统预测方法的精度及自适应性。模型基于数字孪生航班链系统构建，采用滑动窗口下的多通道特征建模完成单元级航班延误预测，并提出一种混合优化策略进行模型参数的动态优化，最后通过孪生数据驱动的链式分析方法实现了全航班链的延误分析与修正。采用国内航班数据进行实验，得到在各个窗口下的航班延误平均绝对误差（Mean absolute error, MAE）为11.79 min，低于其他基线模型和静态模型；且引入孪生数据驱动分析和修正后，紧随其后的航班预测误差比此前进一步降低了6.44%。结果表明，模型有利于数字孪生航班链系统实现虚实交互，并具有优良的预测精度和自适应性。     

Locally generalised multi-agent reinforcement learning for demand and capacity balancing with customised neural networks

Reinforcement Learning (RL) techniques are being studied to solve the Demand and Capacity Balancing (DCB) problems to fully exploit their computational performance. A locally generalised Multi-Agent Reinforcement Learning (MARL) for real-world DCB problems is proposed. The proposed method can deploy trained agents directly to unseen scenarios in a specific Air Traffic Flow Management (ATFM) region to quickly obtain a satisfactory solution. In this method, agents of all flights in a scenario form a multi-agent decision-making system based on partial observation. The trained agent with the customised neural network can be deployed directly on the corresponding flight, allowing it to solve the DCB problem jointly. A cooperation coefficient is introduced in the reward function, which is used to adjust the agent’s cooperation preference in a multi-agent system, thereby controlling the distribution of flight delay time allocation. A multi-iteration mechanism is designed for the DCB decision-making framework to deal with problems arising from non-stationarity in MARL and to ensure that all hotspots are eliminated. Experiments based on large-scale high-complexity real-world scenarios are conducted to verify the effectiveness and efficiency of the method. From a statistical point of view, it is proven that the proposed method is generalised within the scope of the flights and sectors of interest, and its optimisation performance outperforms the standard computer-assisted slot allocation and state-of-the-art RL-based DCB methods. The sensitivity analysis preliminarily reveals the effect of the cooperation coefficient on delay time allocation.     

无人机集群组网高精度测距技术研究

针对无人机组网系统机间测距精度受限于时域采样周期时延估计的问题，本文提出一种联合正交频分复用（OFDM）前导序列和导频序列进行高精度测距估计的方法，利用最大似然估计和双向测距法将参与计算的序列测距估计结果按比例进行加权平均。同时，仿真分析了联合测距估计方法性能。结果表明：联合估计方法的前导与导频序列差分自相关间隔长度取典型值时测距精度能达到0.1 m以下，可在较低信噪比下实现厘米级测距精度，提升了集群组网测距的稳定性。     

基于组合方式的延时精确测量方法

研究了延时线的精确时间测量方法。首先介绍了传统群时延测量方法的误差，并分析了误差形成的机理，然后提出采用基于群时延和相位时延相结合的方式进行延时精确测量的方法。采用二次测量的方式，结合群时延的粗测量和相位时延的精测量，完成延时的精确测量，并通过仿真、试验验证了该方法在延时测量方面的精度更高。     

Periodic acoustic source tracking using propagation delayed measurements

There exist a large class of acoustic sources which have an underlying periodic phenomenon. Unlike the well-studied Bearings-Only Tracking(BOT) of an aperiodic acoustic source,this paper considers the problem of tracking a periodic acoustic source. For periodic acoustic tracking, the signal emission time is known. However, the true measurement reception time is unknown because it is corrupted by noise due to propagation delay. We augment the sensor’s signal reception time onto bearing measuremen...     

机动发射弹道导弹集群诸元快速规划

针对机动发射条件下弹道导弹集群的飞行诸元快速规划问题，将神经网络预测与最小二乘优化相结合，提出了一种弹道导弹发射诸元快速规划方法。首先分析了弹道导弹助推段飞行策略并选取适当的发射诸元，以发落点信息为输入，设计双隐藏层诸元预测网络，通过弹道仿真获取弹道数据建立数据集完成网络训练，利用该网络可以得到发射诸元迭代初值。在此基础上，为了消除数据集中样本数据不平衡对发射诸元规划精度的影响，以落点射程、横程、高程偏差最小为指标函数，结合最小二乘优化方法进行迭代获得发射诸元精确解。最后在典型发射场景下，进行了弹道导弹集群机动快速发射仿真验证。结果表明，该方法相较于传统方法可显著提高计算速度与精度，且在给定的大范围机动条件下，能够满足弹道导弹集群对远距离、多目标的快速精确打击。     

面向超燃数值模拟的正癸烷高温骨架机理

为了得到一个适用于超声速燃烧模拟的小规模正癸烷骨架机理，以现有的正癸烷燃烧机理（S709）为基础，通过机理简化和参数对比优化的方法，构建了包含27个物种和105个反应的高温骨架机理（S27）。在温度（T：1000–2000 K）、压力（p：0.1–0.3 MPa）、当量比（Φ：0.5–1.5）的超燃典型工况范围内，通过Chemkin-Pro软件计算了S27对于层流火焰速度、点火延迟时间、熄火拉伸率的预测值，在0.1 MPa富燃条件（Φ=1.7）下，计算了主要物种浓度分布，并与文献正癸烷骨架机理（S40，S96）、S709的模拟值和实验数据进行对比，以验证机理的合理性。结果表明S27的计算结果与文献实验数据和S709结果吻合良好。通过研究S27在高温条件下含C物种的反应途径以及影响层流火焰速度的关键反应，进一步证明了S27的合理性。相较于S709及其他正癸烷骨架机理，S27极大地提升了计算效率，展现了此机理应用于超燃流场数值模拟的良好前景。     

基于多智能体航空公司航班恢复协同决策方法

航空公司进行航班延误恢复时，各种资源之间会通过航班计划产生间接关联，此时各决策单元若独立地考虑本领域内的资源恢复问题，将难以保证恢复方案的整体可行性和全局优化性。为探究航空公司航班恢复过程中各决策部门的决策模型及其协同关系，本文提出了基于多智能体技术的航班恢复协同决策仿真方法。首先，基于航空公司实际组织架构构建了航班恢复多智能体决策系统框架；其次，对部门间协同决策的动态过程进行了分析，将延误恢复的全过程分为了预恢复、可行解协商、均衡解协商3个阶段，构建了三阶段协同决策机制；最后，根据不同资源的恢复特性建立各决策部门的核心决策模型与部门间自动协商模型，并基于多智能体系统进行仿真。仿真结果显示，基于多智能体的协同决策方法能够在3.8 s的极短时间内针对1天中包含3架飞机和12个航班的航班计划做出完整的延误恢复方案，并且能够在保障航空公司整体效益的情况下一定程度地平衡各决策主体的自身利益。