An Uncertainty Analysis Method for Artillery Dynamics with Hybrid Stochastic and Interval Parameters

This paper proposes a non-intrusive uncertainty analysis method for artillery dynamics involving hybrid uncertainty using polynomial chaos expansion(PCE).The uncertainty parameters with sufficient information are regarded as stochastic variables,whereas the interval variables are used to treat the uncertainty parameters with limited stochastic knowledge.In this method,the PCE model is constructed through the Galerkin projection method,in which the sparse grid strategy is used to generate the integral points and the corresponding integral weights.Through the sampling in PCE,the original dynamic systems with hybrid stochastic and interval parameters can be transformed into deterministic dynamic systems,without changing their expressions.The yielded PCE model is utilized as a computationally efficient,surrogate model,and the supremum and infimum of the dynamic responses over all time iteration steps can be easily approximated through Monte Carlo simulation and percentile difference.A numerical example and an artillery exterior ballistic dynamics model are used to illustrate the feasibility and efficiency of this approach.The numerical results indicate that the dynamic response bounds obtained by the PCE approach almost match the results of the direct Monte Carlo simulation,but the computational efficiency of the PCE approach is much higher than direct Monte Carlo simulation.Moreover,the proposed method also exhibits fine precision even in high-dimensional uncertainty analysis problems.

Air target intention recognition and causal effect analysis combining uncertainty information reasoning and potential outcome framework

Recognizing target intent is crucial for making decisions on the battlefield.However,the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent recognition techniques.Facing with the challenge,a target intention causal analysis paradigm is proposed by combining with an Intervention Retrieval(IR)model and a Hybrid Intention Recognition(HIR)model.The target data acquired by the sensors are modelled as Basic Probability Assignments(BPAs)based on evidence theory to create uncertain datasets.Then,the HIR model is utilized to recognize intent for a tested sample from uncertain datasets.Finally,the intervention operator under the evidence structure is utilized to perform attribute intervention on the tested sample.Data retrieval is performed in the sample database based on the IR model to generate the intention distribution of the pseudo-intervention samples to analyze the causal effects of individual sample attributes.The simulation results demonstrate that our framework successfully identifies the target intention under the evidence structure and goes further to analyze the causal impact of sample attributes on the target intention.

混联机器人关节间隙误差建模及其定位精度可靠性分析

机器人末端定位精度可靠性是衡量其精度性能的重要指标,关节间隙是影响机器人定位精度的主要因素之一,建立准确的机器人定位精度误差模型是进行可靠性分析的前提。为探究空间关节间隙的随机不确定性对含有闭链机构混联机器人定位误差的影响规律,本文运用旋量理论和D-H参数法,提出一种考虑关节间隙的混联机器人定位精度误差模型;根据关节间隙模型参数的约束条件,量化间隙模型参数的不确定性,建立混联机器人定位精度的可靠性模型;采用双变量降维方法和鞍点近似方法,拟合机器人单坐标定位误差的概率密度函数,实现混联机器人定位精度高效可靠性分析。以含有平行四边形闭链机构的建筑机器人为例,验证所提模型的准确性和工程适用性,结果表明所提方法对比于传统的蒙特卡洛方法极大地提高了效率。

Uncertainty analysis and design optimization of hybrid rocket motor powered vehicle for suborbital flight

Abstract In this paper, we propose an uncertainty analysis and design optimization method and its applications on a hybrid rocket motor （HRM） powered vehicle. The multidisciplinary design model of the rocket system is established and the design uncertainties are quantified. The sensitivity anal- ysis of the uncertainties shows that the uncertainty generated from the error of fuel regression rate model has the most significant effect on the system performances. Then the differences between deterministic design optimization （DDO） and uncertainty-based design optimization （UDO） are discussed. Two newly formed uncertainty analysis methods, including the Kriging-based Monte Carlo simulation （KMCS） and Kriging-based Taylor series approximation （KTSA）, are carried out using a global approximation Kriging modeling method. Based on the system design model and the results of design uncertainty analysis, the design optimization of an HRM powered vehicle for suborbital flight is implemented using three design optimization methods： DDO, KMCS and KTSA. The comparisons indicate that the two UDO methods can enhance the design reliability and robustness. The researches and methods proposed in this paper can provide a better way for the general design of HRM powered vehicles.

Hybrid uncertainties-based analysis and optimization design of powertrain mounting systems

In this study,a hybrid uncertainties-based analysis and optimization method is presented for the designs of the powertrain mounting system(PMS)involving mixed uncertainties.In the presented method,the PMS parameters with sufficient data are treated as random variables,while those with limited information are defined as interval variables.Then,an uncertainty-based analysis method called as hybrid interval-random perturbation-central difference method(HIRP-CDM),is proposed to compute the hybrid interval-random outputs of the inherent characteristics of the PMS in concerned directions.In addition,the hybrid interval-random-Monte Carlo method(HIR-MCM)is developed to verify the computational accuracy of HIRP-CDM.Next,an optimization model mixed uncertainties is built up for the PMS design based on HIRP-CDM,in which the hybrid intervalrandom outputs of the concerned inherent characteristics are adopted to construct the design objective and constrains.The complex optimization problem can be effectively settled by means of HIRP-CDM.The effectiveness of the presented method is verified by a numerical example.

Uncertainty analysis and probabilistic design optimization of hybrid rocket motors for manned lunar landing

To obtain a conceptual design for a hybrid rocket motor(HRM)to be used as the Ascent Propulsion System in the Apollo lunar module,the deterministic design optimization(DDO)method is applied to the HRM design.Based on the results of an uncertainty analysis of HRMs,an uncertainty-based design optimization(UDO)method is also adopted to improve the design reliability.The HRM design process,which is a multidisciplinary system,is analyzed,and a mathematical model for the system design is established to compute the motor performance from the input parameters,including the input variables and model parameters.The input parameter uncertainties are quantified,and a sensitivity analysis of the model parameter uncertainties is conducted to identify the most important model parameter uncertainties for HRMs.The DDO and probabilistic UDO methods are applied to conceptual designs for an HRM to be used as a substitute for the liquid rocket motor(LRM)of the Ascent Propulsion System.The conceptual design results show that HRMs have several advantages as an alternative to the LRM of the Ascent Propulsion System,including nontoxic propellant combination,small motor volume,and comparable functions,such as restarting and throating.Comparisons of the DDO and UDO results indicate that the UDO method achieves more robust and reliable optimal designs than the DDO method.The probabilistic UDO method can be used to develop better conceptual designs for HRMs.

不确定扰动下交直流混合配电系统的可达性分析

交直流混合配电系统中高比例可再生能源并网引入了强不确定性,给系统的安全运行带来巨大挑战。文中提出一种交直流混合配电系统可达性分析方法,能够有效刻画不确定性下的系统动态特性。首先,说明了可达性分析在交直流混合配电系统的适用性。接着,阐述了可达性分析的基本原理,利用齐诺多面体表征系统中不确定扰动。然后,从主站电压源型换流器(VSC)出发,到直流网络,再到从站VSC,进一步构建了交直流混合配电系统的非线性微分代数方程,及其线性化处理后的系统微分包含式,并在此基础上提出了考虑直流负荷叠加可再生能源不确定扰动下交直流混合配电系统可达性分析方法。最后,结合MATLAB/Simulink仿真验证了所述方法的有效性,能够刻画不确定性扰动下直流电压等关键状态变量随时间变化的所有运行轨迹及其边界范围。

基于参数型指数混合熵模型的农业遥感分类不确定性评价

针对对像元尺度上独立于分类方法的不确定性评价的需要和对数混合熵函数在评价遥感影像分类不确定性中存在的不足,该文提出了一种基于参数型指数混合熵模型的农业遥感影像分类不确定性评价方法。研究首先对指数混合熵函数进行改进,推导出参数型指数混合熵函数并确定出适合于评价农作区遥感影像分类的参数;然后,使用该函数建立一种像元尺度上独立于分类的不确定性评价模型;最后,将该模型应用于空间分辨率退化10倍的SPOT-5影像中,并使用原始影像对评价结果进行验证。试验结果表明,当模型中参数型指数混合熵函数的参数分别为4和1时,该函数比对数混合熵函数更好地统一了模糊性和随机性,熵值范围提高了2.11倍。该模型不确定性评价结果与原始影像3种分类的不确定像元比例相关系数分别为0.60、0.66、0.70,评价结果较为准确。因此,该模型可以在像元尺度上独立于分类方法将地物类别相对复杂的农业遥感影像分类不确定性更为精确地表达出来,为确保农作物种植面积提取、区域产量遥感估测精度提供了有力支撑。

垂向混合模型在湫水河洪水预报预警中的应用

针对半干旱半湿润地区的产汇流特点,采用垂向混合模型进行了洪水模拟与预报研究;面向洪水等级预警需求,提出了洪水等级与相对误差相结合的洪峰预报精度等级评价标准,并结合置信区间洪峰包含比这一评估指标,构建了洪水预报预警可靠性的综合评价方法。以黄河中游湫水河流域为例,选取流域出口林家坪水文站1980—2012年间洪峰大于300 m3/s的20场洪水进行模型率定与验证。结果表明:洪峰预报精度等级评价合格率在70%以上,可靠度为95%,优于新安江模型或陕北模型,在一定程度上能够满足当地洪水预报预警需求。

用非概率理论分析混合不确定性结构的可靠性

针对不确定性结构可靠性分析中的输入变量分布参数具有不确定性和输入变量为区间模型的混合不确定性结构展开研究。考虑到可用信息最少的情况,将分布参数的不确定性描述为区间模型。通过等概率转换方法将随机变量与其分布参数进行分离,使问题转化为随机与区间变量混合的可靠性问题,建立了混合不确定性问题的可靠性分析模型。基于非概率可靠性理论,建立混合不确定结构分析模型的二级极限状态函数并结合Kriging代理模型建立了高效的求解方法。将所建立的混合不确定模型应用于飞行器结构的不确定性分析中,验证了所建模型的合理性和所提方法的高效性和准确性。

基于D-U空间的混合型多属性决策方法

针对混合型决策中不同类型属性值无法有效保留其不确定信息的问题,根据不确定原理,将联系数的D-U空间理论引入到混合型多属性决策问题中,提出一种基于D-U空间的混合型多属性决策方法。将确定性与不确定视为一个整体,提出不同类型属性值在D-U空间中的映射转换法则,使得不同类型属性值在空间中得以统一量化,并明确属性中的不确定信息,避免不确定信息丢失而造成的决策偏差。在决策过程中,通过计算空间中属性向量的模和幅角的值进行方案选择,以描述各方案的稳定性,使排序准则具有直观的意义。通过2个算例验证了该方法的适用性和实用性。

具有不确定性声振系统的中频动力学分析及实验研究（英文）

为考虑声-振结构中不确定性对系统动态响应的影响,提出了一种基于有限元方法(FEM)和统计能力分析法(SEA)相结合的混合方法.基于该方法,研究了具有不确定性的框架-板结构的中频动力学响应并以蒙特卡洛仿真结果为基准进行了对比分析.结果表明,框架-板结构的能量响应与蒙特卡洛仿真结果具有较好的一致性,验证了混合方法对具有不确定性复杂结构的中频声-振分析的有效性.基于混合FE-SEA方法,建立了具有随机特性的某工程机械驾驶室声-振模型,通过实验对模型的激励进行了测量.计算了驾驶室FE-SEA模型的声腔声压级响应与前风挡玻璃的振动功率谱密度响应,并通过实验进行了对比分析.在中频域内,二者有着较好的一致性,预测误差小于3.5dB.

SOFC-PEM联合发电系统的不确定性分析

以固体氧化物燃料电池与质子交换燃料电池(SOFC-PEM)联合发电系统为研究对象,以系统操作参数作为不确定参数,采用基于拉丁超立方体抽样法的不确定分析方法,即包括以适宜的概率分布函数来表述和量化不确定因素并将不确定性传播到确定性模型来建立输出结果的概率分布规律,进而分析系统操作变量的不确定性对系统净电功率的影响。不同于传统的灵敏度分析方法,考虑参数的概率结构,评价多个参数的相对重要性。结果表明:高SOFC电堆操作温度、低燃料流量和低SOFC燃料利用率有利于减少系统净电功率因不确定因素引起的偏差和波动,但同时以减少系统净电功率为代价。不确定性分析可为在不确定条件下设计参数的优化提供依据。

集对分析在不确定性智能决策中的应用

语言是思维的表达,智能决策是基于确定性与不确定性对立统一思维的一类高级决策。文章综述集对分析在纯自然语言决策,自然语言与数学混合语言决策,区间数决策和直觉模糊决策,集对分析粗糙集决策,联系数与马尔可夫链相结合的决策,赵森烽-克勤概率的贝叶斯决策,偏联系数的决策和同异反综合集成决策等方面的应用。特点是把基于确定性的决策建模与不确定性系统分析相结合,把系统宏观层次的分析与微观层次的分析相结合,把两种或多种决策方法综合集成,根据不确定性的具体情况给出决策建议,因而是一种立足于全局的智能决策,并认为集对分析的不确定性智能决策过程,在本质上是把决策系统中的信息能转换成智能的过程。

面向概率-区间混合可靠性分析的自适应共轭控制方法与应用

复杂工程结构中往往包含多源不确定性,而概率-区间混合不确定性理论是处理这些不确定性因素的有效手段。但是,经典的概率-区间混合可靠性计算模型由双层优化循环嵌套组成,对于复杂工程问题存在计算成本过高和鲁棒性不足的问题。为了改进算法性能,提出了面向概率-区间混合可靠性分析的自适应共轭控制方法,该方法将前两次迭代方向与当前迭代点处的负梯度方向组合,同时加入控制因子对组合后的方向进行修正,改善其迭代点的下降方向,提高相应收敛速度,并通过可行下降方向的定义证明了修正后方向仍为优化问题的下降方向。此外,为防止迭代过程中产生数值振荡,进一步引入了Wolfe-Powell准则,保证控制因子自适应更新的同时增强了算法的鲁棒性。为了获得随机参数的上下界,基于超参数凸模型构造了相应的显式迭代公式。计算结果表明,该方法在解决高非线性问题时能有效地避免迭代过程中出现的振荡现象,与传统方法相比,不仅能满足精度需求,而且可以高效、稳定地求解可靠度指标。

面向航天器在轨管理的混合智能诊断系统设计

针对目前航天器专家系统普遍存在的"知识获取瓶颈"、"处理效率低"、"知识结构直观性差"等问题,介绍了一种新的混合智能诊断系统。该系统在使用基于规则推理进行故障征兆识别的基础上,一方面利用案例推理技术实现故障的快速诊断;另一方面利用故障树分析法给出详细的推理过程和推理结果。文中介绍了这种混合智能诊断系统的系统构成、诊断流程和推理算法。

考虑周期划分特性和储能电池生命周期费用的混合供电系统优化

在风/柴/储混合供电系统中,风力发电的不确定性和用户负载的波动性对混合供电系统及其储能电池系统的优化设计和运行具有重要影响。针对混合供电系统,采用k-均值周期划分法考虑风力发电和用户负载的波动性,建立了混合供电系统及其储能电池系统的优化模型。在优化模型中将储能电池生命周期费用作为惩罚函数,将循环次数作为约束,以标准化能源费用为目标对混合供电系统进行优化,分析了风力发电不确定性对混合供电系统优化设计的影响。以能量需求为793 k W·h·d^(-1)的风/柴/储混合供电系统为例,研究了混合供电系统优化设计方法。研究表明,考虑风力发电和用户负载的周期划分后,储能电池系统循环次数显著降低,有利于储能电池使用寿命的延长。按风速特性划分周期时,储能电池系统的年循环次数最少,混合供电系统的标准化能源费用最低。当储能电池在其生命周期内的最大循环次数由2000次提升到10000次时,混合供电系统的标准化能源费用降低幅度可达8.3%~16.6%。

基于概率-非概率混合可靠性模型的结构地震易损性分析

在易损性分析中,存在对结构阈值的不确定性研究较少,对阈值的选择通常为规范中的数值,得到的破坏概率仅为固定数值等问题,文中同时考虑结构响应和阈值不确定性,建立一种基于概率-非概率混合模型的结构地震易损性分析方法。基于Opensees建立桥梁有限元模型,选择最大支座纵向位移、最大桥墩柱弯曲延性作为衡量结构性能的工程需求参数,并被视为符合二元对数正态分布的概率随机变量;以混凝土应变作为反映结构极限状态的指标,利用增量动力分析法,获得工程需求参数阈值的样本数据,利用灰度理论得到阈值的取值区间,并将阈值视为非概率凸集变量;建立基于概率-非概率混合模型的二维性能极限状态方程,利用混合模型的可靠性分析法求解破坏概率,建立易损性曲线。为对比计算结果,将阈值分别拟定为服从对数正态分布的随机变量和固定数值,并通过蒙特卡洛法求解破坏概率,研究表明:通过概率-非概率混合模型可有效得到破坏概率的区间估计,从而弥补了传统研究中只能得到固定数值破坏概率的问题;不考虑阈值的随机性会对分析结果产生较大影响。

Hybrid uncertainty-based design optimization and its application to hybrid rocket motors for manned lunar landing

Design reliability and robustness are getting increasingly important for the general design of aerospace systems with many inherently uncertain design parameters.This paper presents a hybrid uncertainty-based design optimization（UDO） method developed from probability theory and interval theory.Most of the uncertain design parameters which have sufficient information or experimental data are classified as random variables using probability theory,while the others are defined as interval variables with interval theory.Then a hybrid uncertainty analysis method based on Monte Carlo simulation and Taylor series interval analysis is developed to obtain the uncertainty propagation from the design parameters to system responses.Three design optimization strategies,including deterministic design optimization（DDO）,probabilistic UDO and hybrid UDO,are applied to the conceptual design of a hybrid rocket motor（HRM） used as the ascent propulsion system in Apollo lunar module.By comparison,the hybrid UDO is a feasible method and can be effectively applied to the general design of aerospace systems.