Conditional sea surface statistics and their impact on geophysical sea surface parameters retrieved from SAR altimetry signals

In this study we present an analytical formulation of synthetic-aperture radar (SAR) altimetry signals including narrow banded nonlinear wave fields and conditional statistics between wave elevation displacements, horizontal wave slopes and vertical wave particle velocities. Considering the wave elevation displacements coskewness with respect to horizontal slopes leads to an analytical formulation of the electromagnetic bias within a SAR-mode altimeter stack. This formulation can be either parametrized by the significant wave height (SWH) and mean wave steepness, or in terms of the variance of vertical wave velocities. The effect of conditional vertical wave particle velocity variances with respect to the observed horizontal wave slopes close to nadir incidence angles leads to an effective reduction of the azimuth blurring of SAR-mode stacks. We present here a formulation of this effect by examining JONSWAP ocean wave spectra. In most cases this effect reduces the azimuth blurring by 10% to 30%. Additionally we investigate the effect of a nonlinear wave elevation displacement probability density function (PDF) on estimated geophysical parameters. We were able to show that including an elevation displacement skewness of 0.13 improves significantly the SWH consistency between altimetry and ECMWF Reanalysis v5 ERA5 results.All of these effects are validated with respect to ERA5 model data in the North East Atlantic region and in situ data located in the German Bight and Baltic Sea.The developed model can be used in both SAR and conventional altimetry retrackers.     

Modelling ignition probability with pairwise mixing-reaction model for flame particle tracking

Reduced order models for ignition analysis can offer insights into ignition processes and facilitate the combustor optimization. In this study, a Pairwise Mixing-Reaction (PMR) model is formulated to model the interaction between the flame particle and the surrounding cell mixture during Lagrangian flame particle tracking. Specifically, the model accounts for the two-way coupling of mass and energy between the flame particle and the surrounding shell layer by modelling the corresponding turbulent mixing, chemical reaction and evaporation process if present. The state of a flame particle, e.g., burnt, hot gas or extinguished, is determined based on particle temperature. This model can properly describe the ignition process with a spark kernel being initiated in a nonflammable region, which is of practical importance in certain turbine engines and has not been rigorously accounted for by the existing models based on the estimation of local Karlovitz number. The model is integrated into an ignition probability analysis platform and is demonstrated for a methane/air bluff-body flame with the flow and fuel/air mixing characteristics being extracted from a non-reacting simulation. The results show that for the spark location being at the extreme fuel-lean outer shear layer of the recirculation zone, PMR can yield ignition events with a significant number of active flame particles. The mechanisms for the survival of the initial flame particles and the entrainment of the survived flame particles into the recirculation zone are analyzed. The results also show that the ignition probability map from PMR agrees well with the experimental observation: a high ignition probability in the shear layer of the recirculation zone near the mean stoichiometric surface, and low ignition probabilities inside the recirculation zone and the top stagnation region of the recirculation zone. The parametric study shows that the predicted shape of the ignition progress factor and ignition probability is in general insensitive to the model parameters and the model is adequate for quantifying the regions with high ignition probabilities.     

高速开关阀控电液位置伺服系统自适应鲁棒控制

为研究高精度的液压缸位置跟踪控制问题，设计了高速开关阀和换向阀组合控制液压缸的结构方案，并通过实验分析了高速开关阀的静态流量特性。建立了液压缸的连续可微摩擦模型，利用粒子群优化算法对其参数进行辨识。建立了系统的非线性数学模型，基于非连续参数映射和反步法设计了直接自适应鲁棒控制器，通过参数在线自适应调节来更新估计值和鲁棒反馈项支配参数不确定性，实验结果表明:在跟踪幅值为5mm，频率为0.4Hz的正弦信号时，最后一个周期的最大跟踪误差、平均跟踪误差及其标准差分别为0.638、0.25mm和0.405mm，与传统PID控制器相比，控制精度显著提升，旨在为实现高精度的数字阀控位置伺服技术提供有价值的参考。      

基于CMPSO算法的永磁同步电机转动惯量识别研究

永磁同步电机(PMSM)是一种具有很强非线性的动态系统,在工业驱动应用中发挥着重要作用。实时辨识转动惯量对于高精度PMSM系统控制和稳定性状态监测具有重要意义,但传统转动惯量识别方法精度较低。在传统离散模型参考自适应理论基础之上,用柯西变异粒子群优化(CMPSO)算法代替待辨识参数自适律设计环节,以实现PMSM转动惯量识别。该转动惯量辨识方法充分利用了CMPSO算法的快速高效收敛性,仿真结果和试验结果表明了可行性、正确性和准确性。     

基于IPSO-Elman神经网络的飞机客舱能耗预测

为了提高飞机客舱使用地面空调制冷时客舱能耗的预测精度，提出了一种改进的粒子群优化（IPSO）Elman神经网络的飞机客舱能耗预测模型。依据对算法中惯性权重与学习因子的收敛域分析，得出了二者合理的取值范围，将粒子到全局最优位置间距离与参数的取值范围相结合，构造了惯性权重与学习因子的动态调节函数，对其进行非线性的动态调节，并引入了变异因子，提出了一种跳出局部最优的策略，防止粒子群优化（PSO）陷入局部最优。将IPSO-Elman应用于Boeing738飞机客舱能耗预测中，与PSO-Elman、Elman算法进行性能比较，仿真结果表明基于IPSO-Elman的客舱能耗预测模型在预测精度和收敛速度方面均有一定的提升。该研究结果为飞机客舱能耗预测模型的建立提供了理论依据，对飞机地面空调的节能与机场电能合理调配提供了支持。     

Inverse identification of constitutive parameters of Ti2AlNb intermetallic alloys based on cooperative particle swarm optimization

Ti₂AlNb intermetallic alloy is a relatively newly developed high-temperature-resistant structural material, which is expected to replace nickel-based super alloys for thermally and mechanically stressed components in aeronautic and automotive engines due to its excellent mechanical properties and high strength retention at elevated temperature. The aim of this work is to present a fast and reliable methodology of inverse identification of constitutive model parameters directly from cutting experiments. FE-machining simulations implemented with a modified Johnson-Cook (TANH) constitutive model are performed to establish the robust link between observables and constitutive parameters. A series of orthogonal cutting experiments with varied cutting parameters is carried out to allow an exact comparison to the 2D FE-simulations. A cooperative particle swarm optimization algorithm is developed and implemented into the Matlab programs to identify the enormous constitutive parameters. Results show that the simulation observables (i.e., cutting forces, chip morphologies, cutting temperature) implemented with the identified optimal material constants have high consistency with those obtained from experiments, which illustrates that the FE-machining models using the identified parameters obtained from the proposed methodology could be predicted in a close agreement to the experiments. Considering the wide range of the applied unknown parameters number, the proposed inverse methodology of identifying constitutive equations shows excellent prospect, and it can be used for other newly developed metal materials.     

基于粒子群算法的多波位高度估计方法

为解决雷达斜视模式下对目标区域的高度估计问题,提出了一种基于粒子群算法的多波位高度估计方法。利用粒子群优化算法建立约束条件和目标函数,通过多次迭代使目标函数趋于一个最优值,同时得到高度的最优解。利用粒子群优化算法求解多波位测高方程组可减小方程组近似处理误差。此外,利用粒子群优化算法可随时更改波位数目,增强了该测高方法使用的灵活性,有效提高了高度估计精度。通过理论仿真和实测数据仿真分析,验证了粒子群优化算法在求解多波位测高方程组时的有效性。结果表明:该方法具有较高的高度估计精度。     

用于BVI噪声试验的新型涡发生器设计与分析

针对直升机特有的旋翼桨/涡干扰(Blade vortex interaction,BVI)噪声计算精度低且试验数据缺乏问题,也为了开展旋翼气动噪声特性分离方法的验证试验研究,本文设计了一种能够用于BVI噪声试验的新型多段翼型组合式涡发生器。首先通过CATIA软件建立涡发生器出口端翼型段在不同迎角下的试验模型,再使用FLUENT软件建立涡发生器的流场仿真计算模型,比较分析了不同翼型段迎角下的涡流流场。随后用粒子图像测速法(Particle image velocimetry,PIV)技术系统测量了不同翼型段迎角、距离涡发生器出口端的长度及流速等参数变化下的涡流流场,对不同试验状态下的涡核、涡强等参数进行了对比分析。针对涡量偏弱的缺点,对传统单级涡发生器进行了改进,设计研发了双级涡发生段。试验证明其能产生更强且稳定的涡,为BVI噪声试验提供了模拟的桨尖涡,试验结果表明了涡发生器的有效性。     

涡轮基组合循环发动机分布式控制系统通信网络混合拓扑结构优化方法

涡轮机组合循环(Turbine based combined cycle,TBCC)发动机控制系统通信网络拓扑结构是其分布式控制系统方案设计的重要部分,优化网络拓扑结构可提高发动机推重比和控制系统可靠性。本文基于智能优化算法提出TBCC分布式控制系统网络拓扑结构优化方法。基于图论建立TBCC几何模型和网格模型,以重量和可靠性为优化性能指标,同时考虑发动机表面高温区域以及控制节点的工作可靠性,分别采用粒子群算法和遗传算法优化星形结构中智能中央节点位置、中央节点的环形拓扑结构,获得星形-环形混合拓扑结构。仿真实例表明,基于本文方法优化所得的混合拓扑结构相较于星形集中式控制结构,系统重量降低了51.9%。     

新能源接入配电网经济性与可靠性优化研究

为寻求新能源配电网的可靠性和经济性最优,实现新能源电源的优化配置,本文提出将可靠性和经济性同时作为两个优化目标,运用自适应粒子群优化法,对新能源电源进行多目标优化配置。以IEEE-RBTS Bus 6主馈线F4为例,通过系统仿真找到新能源配电系统可靠性与经济较均衡的Pareto最优解集,可根据决策者的偏好选择最优方案。本文的研究为新能源电力系统可靠性和经济评估提供了一个新的研究思路。