三维实体转子系统热-结构耦合响应分析

以某型航空发动机高压转子系统为研究对象,基于不均匀分布稳态温度场,建立了某高压转子系统三维实体单元有限元模型以及稳态温度场下转子系统热-结构耦合振动方程,利用热-结构-动力学耦合理论,采用间接耦合法,通过稳态温度场分析和静力分析生成热应力,然后进行预应力模态分析,最后利用模态叠加法进行不平衡量和热弯曲耦合响应分析,实现热-结构-动力学耦合计算.通过稳态温度场对典型级盘稳态响应影响的分析以及不平衡量与热弯曲耦合稳态响应分析,发现耦合响应对转子系统各级盘的振动响应有较大影响.     

基于啤酒发酵过程的时变轨迹输出反馈鲁棒模糊预测控制

考虑到实际生产中状态不易测量和设定值变化的情况以及系统本身的非线性特性,针对啤酒发酵过程温度控制系统提出了一种时变轨迹下输出反馈鲁棒模糊预测控制方法。在啤酒发酵罐温度系统的机理模型的基础上,建立包括不确定性和未知干扰的状态空间模型;通过设计模糊集,建立为具有加权系数的T-S模糊状态空间模型;并在状态变量的中引入输出跟踪误差,建立新型多自由度状态空间模型;并运用鲁棒模型预测控制方法优化参数不确定性问题,结合李雅普诺夫稳定性理论推导出线性矩阵不等式形式的稳定性条件,通过求解线性矩阵不等式中参数来计算对应子模型控制律,并对所设计的输出反馈控制器给定权值。通过仿真结果验证了提出方法的有效性和可行性。     

不确定性参数灵敏度分析的随机响应面法

动力学和控制系统中往往包含有不确定性参数,为此提出了一种基于随机响应面的不确定性参数灵敏度分析方法,以量化参数不确定性对响应变异性的影响.文中首先利用随机响应面建立不确定性参数和响应之间的表达式,然后通过求偏导方式推导参数的灵敏度系数,该系数综合反映了参数均值和标准差的影响.最后通过一根包含几何、材料不确定参数的数值梁来验证所提出方法,并与方差分析法结果进行了比较.     

烧蚀条件下C/C复合材料温度场的数值分析

研究复合材料温度场的变化规律是烧蚀热防护系统的重要内容.为了揭示在不同烧蚀率下炭/炭复合材料的热响应,采用虚拟失效、剔除单元的方法对烧蚀表面退缩条件下瞬态温度场的变化规律进行了数值分析和研究.建立了移动边界条件下烧蚀温度场计算的有限元数值计算模型.数值分析结果表明,烧蚀材料的壁面温度与材料的烧蚀率密切相关,壁面温度随着烧蚀率的增大而减小,材料的烧蚀有效的起到了热防护效果.数值仿真结果满足研究项目要求,提出的方法对任意的烧蚀热防护材料均具有适应性.     

非线性时延网络控制系统的模糊建模与控制

针对时变网络诱导时延小于一个采样周期的非线性时延网络控制系统,讨论系统的稳定性及控制器的设计方法.利用基于“IF-THEN”规则的模糊模型近似系统中的非线性,将时延的不确定性转化为系统参数的不确定性,从而将此类非线性网络控制系统建模为一类具有参数不确定性的离散Takagi-Sugeno（T-S）模糊模型.基于建立的模型,利用Lyapunov方法和线性矩阵不等式方法,分析了系统的稳定性及模糊状态反馈控制器的设计方法,最后通过仿真实例验证了所提出方法的有效性.     

多传感器融合的数控机床热误差回归建模新方法

热误差对机床的加工精度影响很大,高性能的补偿系统依赖于多传感器融合建立的三维模型的精度、鲁棒性和合适的温度进行反馈输入。本文使用温度与位移传感器的模糊聚类进行温度分类,基于评价模型比对分析最优的温度分类,从每个分类中选择具有代表性温度作为候选温度。归纳试验数据,使用分段逆回归SIR模型进行热误差建模,SIR模型将高维前移回归问题转化为多个一维的回归问题,并且进一步消除了候选温度之间的耦合。热误差试验表明,SIR模型具有泛化能力强、预测精度高及鲁棒性好的特点,能够准确地描述多种典型工况条件下的实际热误差特性。     

基于有限元法的滚珠丝杠传动过程中的温度场和热变形仿真

为减小精密机床进给传动过程中传动发热对机床进给精度的影响,考虑丝杠导程引起表面积变化及螺母移动对温度场的影响,建立滚珠丝杠传动过程中温度场和热变形的数学模型. 应用有限元法(Finite Element Method, FEM)对该模型进行数值模拟,得到滚珠丝杠传动过程中温度场的分布规律以及温度对丝杠变形的影响关系. 结果可为机床进给传动系统散热结构的设计和丝杠热变形误差补偿设计提供依据.     

基于DRFNN的共振频率自适应反推控制

针对共振破碎机频率控制系统的不确定性问题,提出基于动态递归模糊神经网络的自适应反推控制策略。建立了破碎机频率控制系统的数学模型,在忽略不确定性项的前提下,设计了基于自适应Back-stepping方法控制律。其次将电液系统中影响频率控制性能的不确定性因素定义为待估计项,采用动态递归模糊神经网络对其进行实时估计,给出了基于动态递归模糊神经网络的参数自适应律,并通过了Lyapunov的稳定性分析。仿真实验和车载测试结果表明,对于系统参数的不确定性,该方法具有较好地频率控制性能。     

基于FAHP-IE算法的尾矿库安全性三级评估研究及应用

从系统安全工程学的观点出发,针对尾矿库事故发生的随机性与不确定性特点,结合模糊层次分析法(Fuzzy Analytic Hierarchy Process,FAHP)和信息熵(Information Entropy,IE)建立尾矿库安全性三级综合评价模型。为解决专家评价分析过程中主观不确定性难以量化的问题,利用信息熵方法对FAHP确定的尾矿库安全评价指标权重进行优化,进而建立面向分层指标体系的尾矿库多主体安全评价模型。根据建立的尾矿库安全性综合评价模型,能够计算尾矿库安全综合评价指数,对于具体的尾矿库工程进行评价后分类定级,从而找出尾矿库存在的安全隐患。案例分析表明,FAHP-IE方法计算简便,用于系统安全状态评价不仅具有模糊综合法的特性,而且兼顾了评价主体认知歧异引起的不确定性,信息利用率高、评价结果可靠,可以为现场整改提供量化依据。     

基于云模型和SFT的可靠性数据不确定性评价

为了分析和评价系统可靠性数据中的不确定性,即随机性、离散性和模糊性,将云模型与空间故障树(Space Fault Tree,SFT)理论相结合,使用云化SFT作为基础对数据不确定性进行评价。首先使用云模型能表示数据不确定性的特点,将SFT的相关概念进行云化。将其中云化系统故障概率分布对在某因素影响下,可靠性数据生成的云模型特征参数Ex、En和He进行求导。根据在系统工作环境范围内的求导结果,并结合提出的模糊性 、离散性 和随机性 计算数据的不确定性。使用该方法对经典实例进行了分析,得到了一些定性和定量结果。但Q和 两个参数应该具体问题具体分析。     

预焙铝电解槽在线仿真槽况诊断专家系统研究

预焙铝电解槽是一个复杂的非线性系统,其工艺过程具有模型的不确定性;应用有限容积方法,根据槽壳温度以及相关工艺参数,基于软测量原理,建立了230KA预焙铝电解槽槽膛内形准三维仿真模型;开发了电解槽温度场的仿真程序,在线显示槽膛内形;根据槽壳温度值、槽膛内形仿真值及电解槽主要工艺参数,用对象—属性—值三元组模式建立了数据库;根据各种病槽的发生原因、现象及处理方法,用产生式方法建立了规则库;并在此基础上开发了铝电解槽槽况诊断专家系统,实现了230KA铝电解槽槽膛内形与槽况的实时诊断。     

综合孔径辐射计可见度采样测量不确定度估计

现有灵敏度指标在描述经过复杂校正之后的综合孔径辐射计的性能时存在困难。考虑到反演亮温分布与可见度采样的线性关系,提出了一种可见度采样测量不确定度估计方法来衡量综合孔径辐射计性能。首先,根据校正流程建立校正参数与可见度采样之间的数学模型;然后,分别对各校正参数的测量不确定度进行估计;最后,基于上述两项工作估计可见度采样的合成测量不确定度。对于综合孔径辐射计校正,有利于选择合理的校正参数并优化校正流程。     

TDLAS技术温度测量的不确定度分析方法研究

随着可调谐二极管激光吸收光谱（TDLAS）技术在工业测量与科学研究领域的广泛应用，对其测量结果的质量进行量化评定已成为迫切需要。以一套已集成的TDLAS测温系统为例，根据最新国家标准，基于不确定度传播律，理论分析了影响测温结果测量不确定度的因素，给出了评定TDLAS测温系统在线测量结果不确定度的一般方法。分析表明TDLAS系统测温结果的测量不确定度随所测温度值的变化而变化，并受所选谱线参数、吸收信号信噪比、系统响应情况等条件的影响。理论推导了针对测温系统的不确定度评定公式，结果表明对特定测温范围与测温系统，需对其进行温度标定，补偿系统误差。     

热轧带钢层流冷却系统的前馈-反馈控制及其优化

以热轧层流冷却系统为研究对象,对如何提高带钢卷取温度控制精度进行了研究;在带钢冷却模型的基础上,对预设定模块进行了前馈补偿;考虑模型的不确定性和大时滞特性,采用模糊Smith预估器提高了反馈控制的精度;结果证明所采用的这些措施大大提高了卷取温度的控制精度。     

自适应神经模糊-PID控制在电厂过热汽温控制中的应用

针对电厂过热蒸汽温度对象具有大迟延、不确定性等特点,本文设计了一种基于自适应神经模糊-PID（ANFIS-PID）的过热汽温控制系统。利用ANFISPID控制对本系统实现非线性建模和系统仿真,并与传统的PID控制在电厂过热汽温系统中的应用进行了比较。仿真结果表明,本过热汽温控制系统具有较高的控制精度、良好的动态特性和鲁棒性,系统的控制特性在超调量、快速性、抗干扰方面均有很大的提高。     

Uncertainty analysis in air dispersion modeling

The Taylor series approach for uncertainty analyses is advanced as an efficient method of producing a probabilistic output from air dispersion models. A probabilistic estimate helps in making better-informed decisions when compared to results of deterministic models. In this work, the Industrial Source Complex Short Term (ISCST) model is used as an analytical model to predict pollutant transport from a point source. First- and second-order Taylor series approximations are used to calculate the uncertainty in ground level concentrations of ISCST calculations. The results of the combined ISCST and uncertainty calculations are then validated with traditional Monte Carlo (MC) simulations. The Taylor series uncertainty estimates are a function of the variance in input parameters (wind speed and temperature) and the model sensitivities to input parameters. While the input variance is spatially invariant, sensitivity is spatially variable; hence the uncertainty in modeled output varies spatially. A comparison with the MC approach shows that uncertainty estimated by first-order Taylor series is found to be appropriate for ambient temperature, while second-order Taylor series is observed to be more accurate for wind speed. Since the Taylor series approach is simple and time-efficient compared to the MC method, it provides an attractive alternative.     

Noodles: a tool for visualization of numerical weather model ensemble uncertainty

Numerical weather prediction ensembles are routinely used for operational weather forecasting. The members of these ensembles are individual simulations with either slightly perturbed initial conditions or different model parameterizations, or occasionally both. Multi-member ensemble output is usually large, multivariate, and challenging to interpret interactively. Forecast meteorologists are interested in understanding the uncertainties associated with numerical weather prediction; specifically variability between the ensemble members. Currently, visualization of ensemble members is mostly accomplished through spaghetti plots of a single mid-troposphere pressure surface height contour. In order to explore new uncertainty visualization methods, the Weather Research and Forecasting (WRF) model was used to create a 48-hour, 18 member parameterization ensemble of the 13 March 1993 "Superstorm". A tool was designed to interactively explore the ensemble uncertainty of three important weather variables: water-vapor mixing ratio, perturbation potential temperature, and perturbation pressure. Uncertainty was quantified using individual ensemble member standard deviation, inter-quartile range, and the width of the 95% confidence interval. Bootstrapping was employed to overcome the dependence on normality in the uncertainty metrics. A coordinated view of ribbon and glyph-based uncertainty visualization, spaghetti plots, iso-pressure colormaps, and data transect plots was provided to two meteorologists for expert evaluation. They found it useful in assessing uncertainty in the data, especially in finding outliers in the ensemble run and therefore avoiding the WRF parameterizations that lead to these outliers. Additionally, the meteorologists could identify spatial regions where the uncertainty was significantly high, allowing for identification of poorly simulated storm environments and physical interpretation of these model issues.     

Multivariate approach to the thermal challenge problem

This paper presents an engineering approach to the thermal challenge problem defined by Dowding et al. (this issue). This approach to model validation is based on a multivariate validation metric that accounts for model parameter uncertainty and correlation between multiple measurement/prediction differences. The effect of model parameter uncertainty is accounted for through first-order sensitivity analysis for the ensemble/validation tests, and first-order sensitivity analysis and Monte-Carlo analysis for the regulatory prediction. While sensitivity based approaches are less computational expensive than Monte-Carlo approaches, they are less likely to capture the far tail behavior of even mildly nonlinear models.The application of the sensitivity based validation metric provided strong evidence that the tested model was not consistent with the experimental data. The use of a temperature dependent effective conductivity with the linear model resulted in model predictions that were consistent with the data. The correlation structure of the model was used to pool the prediction/measurement differences to evaluate the corresponding cumulative density function (CDF). Both the experimental CDF and the predicted CDFs indicated that the regulatory criterion was not met.     

Robust dynamic optimization in heterogeneous multiscale catalytic flow reactors using polynomial chaos expansion

This paper explores the application of optimal design and operational strategies under uncertainty to a transient multiscale catalytic flow reactor system. The catalytic reactor is modeled using a spatially-dependent multiscale model that comprises lattice-based kinetic Monte Carlo (kMC) models coupled with continuum partial differential equations (PDEs) to account for the fine-scale and the macroscale reactor behaviour, respectively. This work compares two uncertainty propagation techniques, power series expansion (PSE) and polynomial chaos expansion (PCE), to assess their performance in multiscale process systems. The analysis reveals that PCE provides accurate results at minimal computational cost for the multiscale catalytic reactor model under the conditions considered in this work. PCE is subsequently used to perform robust dynamic optimization studies on the catalytic reactor system under uncertainty. The first study determines the optimal temperature trajectories that maximize the reactor’s performance under uncertainty. The second study aims to identify the optimal design and operating policies that allow the reactor, under uncertainty in the multiscale model parameters, to meet targeted performance specifications within a level of confidence. Both studies illustrate the benefits of performing dynamic optimization studies to improve performance for multiscale process systems under uncertainty.