预测结构性能退化的混合粒子滤波方法

由于载荷,环境以及材料内部因素的作用,结构的性能一般随时间而逐渐退化. 为了评估结构服役期间的状态,常采用随机变量模型来描述结构性能的退化规律. 即,采用含不确定性模型参数的物理模型来逼近结构响应特性. 利用同类型结构的先知数据集信息可确定模型参数的先验分布. 结合结构服役期间的检测信息和贝叶斯原理,对模型参数进行更新,从而提高物理模型的准确性. 本文提出一种混合粒子滤波方法(particle filter-differential evolution adaptive Metropolis,PF-DREAM)用于模型更新,即:在确定参数先验分布时,采用证据理论(Dempster-shafer theory, DST)初始化模型参数;结合差分进化自适应 Metropolis 算法(differential evolution adaptive Metropolis, DREAM)和粒子滤波(particle filter, PF)算法,来计算更新公式中的复杂的高维积分. 相比于传统的 PF 算法,混合 PF-DREAM 方法可以有效提高样本粒子的多样性,解决重采样算法中粒子多样性匮乏的问题,从而得到更加合理的物理模型. 为了证明该方法的有效性,将提出的方法分别应用于电池性能退化和裂纹扩展规律预测. 算例表明采用本文提出的模型参数确定方法,使得物理模型更加合理,性能预测更加准确. 用于更新的数据越多,模型参数的分散性越小. 本文方法应用于高维问题或隐式函数问题时,计算原理和步骤不发生改变,但函数评价次数和计算时间会随之增大.      

Real-time Bayesian data assimilation with data selection,correction of model bias,and on-the-fly uncertainty propagation

The work introduces new advanced numerical tools for data assimilation in structural mechanics. Considering the general Bayesian inference context, the proposed approach performs real-time and robust sequential updating of selected parameters of a numerical model from noisy measurements, so that accurate predictions on outputs of interest can be made from the numerical simulator. The approach leans on the joint use of Transport Map sampling and PGD model reduction into the Bayesian framework. In addition, a procedure for the dynamical and data-based correction of model bias during the sequential Bayesian inference is set up, and a procedure based on sensitivity analysis is proposed for the selection of the most relevant data among a large set of data, as encountered for instance with full-field measurements coming from digital image/volume correlation (DIC/DVC) technologies. The performance of the overall numerical strategy is illustrated on a specific example addressing structural integrity on damageable concrete structures, and dealing with the prediction of crack propagation from a damage model and DIC experimental data.     

Analytical rheology of blends of linear and star polymers using a Bayesian formulation

A Bayesian data analysis technique is presented as a general tool for inverting linear viscoelastic models of branched polymers. The proposed method takes rheological data of an unknown polymer sample as input and provides a distribution of compositions and structures consistent with the rheology, as its output. It does so by converting the inverse problem of analytical rheology into a sampling problem, using the idea of Bayesian inference. A Markov chain Monte Carlo method with delayed rejection is proposed to sample the resulting posterior distribution. As an example, the method is applied to pure linear and star polymers and linear–linear, star–star, and star–linear blends. It is able to (a) discriminate between pure and blend systems, (b) accurately predict the composition of the mixtures, in the absence of degenerate solutions, and (c) describe multiple solutions, when more than one possible combination of constituents is consistent with the rheology.     

Bayesian System Identification using auxiliary stochastic dynamical systems

Bayesian approaches to statistical inference and system identification became practical with the development of effective sampling methods like Markov Chain Monte Carlo (MCMC). However, because the size and complexity of inference problems has dramatically increased, improved MCMC methods are required. Dynamical systems based samplers are an effective extension of traditional MCMC methods. These samplers treat the posterior probability distribution as the potential energy function of a dynamical system, enabling them to better exploit the structure of the inference problem. We present an algorithm, Second-Order Langevin MCMC (SOL-MC), a stochastic dynamical system based MCMC algorithm, which uses the damped second-order Langevin stochastic differential equation (SDE) to sample a posterior distribution. We design the SDE such that the desired posterior probability distribution is its stationary distribution. Since this method is based upon an underlying dynamical system, we can utilize existing work to develop, implement, and optimize the sampler's performance. As such, we can choose parameters which speed up the convergence to the stationary distribution and reduce temporal state and energy correlations in the samples. We then apply this sampler to a system identification problem for a non-linear hysteretic structure model to investigate this method under globally identifiable and unidentifiable conditions.     

基于Bayes方法的多元正态应力强度可靠度分析

实际工程中,应力和强度是多维相关的,且相关信息很难获得,使用传统的单维应力-强度干涉模型很难进行可靠度分析,需要研究多元应力-强度干涉模型的可靠度计算问题。描述了两种多元应力-强度干涉模型,针对应力、强度服从正态分布的情形,使用Bayes方法,充分利用验前信息,由多元应力-强度干涉模型获得分布参数验后分布,并基于多元统计学知识,采用仿真抽样方法,进行可靠度验后预报。巡航弹可用过载的仿真算例验证Bayes方法在应力、强度试验信息较少的情况下,可高精度地预测可靠度的验后分布。     

基于变量修正分布的混凝土结构长期变形预测Prediction of long-term deformation for concrete structure based on revised distribution of variables

由于随机因素的影响,混凝土结构长期变形通常表现出很强的离散性,Bayesian理论提供了改善这种离散性的方法。基于Bayesian理论的混凝土结构长期变形预测方法的核心是引入短期变形构造似然函数,通过修正先验概率得到长期变形的后验概率分布。但是对于实际结构而言,在施工之前短期变形及其标准差无法获取,这就使得这种方法在开展时机和实际应用方面存在一定的限制性。为了改善这种限制性,在随机变量修正分布的基础上,结合拉丁超立方抽样技术,采用CEB‐FIP（MC90）模型建立了钢筋混凝土梁长期变形的随机分析模型。采用该模型进行混凝土梁长期变形随机分析,得到基于变量修正分布的混凝土梁长期变形预测结果,并分别与先验预测结果和Bayesian预测结果进行比较。研究结果表明,基于变量修正分布的预测结果与Bayesian预测结果十分接近,比先验预测结果不确定性降低50％左右,与试验结果吻合良好。     

Variational data assimilation using targetted random walks

The variational approach to data assimilation is a widely used methodology for both online prediction and for reanalysis. In either of these scenarios, it can be important to assess uncertainties in the assimilated state. Ideally, it is desirable to have complete information concerning the Bayesian posterior distribution for unknown state given data. We show that complete computational probing of this posterior distribution is now within the reach in the offline situation. We introduce a Markov chain–Monte Carlo (MCMC) method which enables us to directly sample from the Bayesian posterior distribution on the unknown functions of interest given observations. Since we are aware that these methods are currently too computationally expensive to consider using in an online filtering scenario, we frame this in the context of offline reanalysis. Using a simple random walk‐type MCMC method, we are able to characterize the posterior distribution using only evaluations of the forward model of the problem, and of the model and data mismatch. No adjoint model is required for the method we use; however, more sophisticated MCMC methods are available which exploit derivative information. For simplicity of exposition, we consider the problem of assimilating data, either Eulerian or Lagrangian, into a low Reynolds number flow in a two‐dimensional periodic geometry. We will show that in many cases it is possible to recover the initial condition and model error (which we describe as unknown forcing to the model) from data, and that with increasing amounts of informative data, the uncertainty in our estimations reduces. Copyright © 2011 John Wiley & Sons, Ltd.     

基于显示连通贝叶斯网络的机场塔台地震易损性分析

将基于性能的多维易损性分析方法,结合显示连通贝叶斯网络,应用于机场塔台的多维易损性分析。考虑地震激励的不确定性,通过非线性时程分析获得结构响应数据;将塔台结构分为三个层次,每个层次按包含的层数分为相应的子层次。根据功能特性确定子层次的评价指标和极限状态,建立服从多元对数正态分布的概率地震需求模型;考虑各种极限状态之间的相关性,建立极限状态方程,确定失效域,通过蒙特卡洛法求得构件的超越概率;建立塔台结构的显示连通贝叶斯网络模型,利用层次分析法获得中间节点的条件概率表,利用MATLAB进行贝叶斯网络的推理计算,实现从单一层次的易损性到整体易损性的推理。     

Fuzzy knowledge-based model for prediction of soil loosening and draft efficiency in tillage

A knowledge-based system for assessing soil loosening and draft efficiency in tillage is presented. The knowledge-based system was built through expert opinion elicitation and available scientific data using fuzzy logic. It is expected that such a non-linear relationship includes some uncertainties. A fuzzy inference system employing fuzzy If-Then rules has an ability to deal with ill-defined and uncertain systems. Compared with traditional approaches, fuzzy logic is more efficient in linking the multiple inputs to a single output in a non-linear domain. The main purpose of this study is to investigate the relationship between cultivator shares working parameters to soil loosening and draft efficiency, and to illustrate how fuzzy expert system might play an important role in prediction of these. Experimental values were taken in soil bin. The trials were conducted in different working depths and forward velocities of cultivator shares. In this paper, a sophisticated intelligent model, based on Mamdani approach fuzzy modeling principles, was developed to predict the changes in soil loosening and draft efficiency of tool. The fuzzy model consists of 25 rules. In this research, a Mamdani max-min inference for inference mechanism and the center of gravity (Centroid) defuzzifier formula method for defuzzification were used as these operators assure a linear interpolation of the output between the rules. The verification of the proposed model is achieved via various numerical error criterias. For all parameters, the relative error of predicted values was found to be less than the acceptable limits (10%).     

A Bayesian Calibration–Prediction Method for Reducing Model-Form Uncertainties with Application in RANS Simulations

Model-form uncertainties in complex mechanics systems are a major obstacle for predictive simulations. Reducing these uncertainties is critical for stake-holders to make risk-informed decisions based on numerical simulations. For example, Reynolds-Averaged Navier-Stokes (RANS) simulations are increasingly used in the design, analysis, and safety assessment of mission-critical systems involving turbulent flows. However, for many practical flows the RANS predictions have large model-form uncertainties originating from the uncertainty in the modeled Reynolds stresses. Recently, a physics-informed Bayesian framework has been proposed to quantify and reduce model-form uncertainties in RANS simulations for flows by utilizing sparse observation data. However, in the design stage of engineering systems, when the system or device has not been built yet, measurement data are usually not available. In the present work we extend the original framework to scenarios where there are no available data on the flow to be predicted. In the proposed method, we first calibrate the model discrepancy on a related flow with available data, leading to a statistical model for the uncertainty distribution of the Reynolds stress discrepancy. The obtained distribution is then sampled to correct the RANS-modeled Reynolds stresses for the flow to be predicted. The extended framework is a Bayesian calibration–prediction method for reducing model-form uncertainties. The merits of the proposed method are demonstrated on two flows that are challenging to standard RANS models. By not requiring observation data on the flow to be predicted, the present calibration–prediction method will gain wider acceptance in practical engineering design and analysis compared to the original framework. While RANS modeling is chosen to demonstrate the merits of the proposed framework, the methodology is generally applicable to other complex mechanics models involving solids, fluids flows, or the coupling between the two (e.g., mechanics models for the cardiovascular systems), where model-form uncertainties are present in the constitutive relations.     

P-CS不确定性量化模型与其性能数据驱动更新方法

在实际工程中, 广泛存在大量的不确定性信息, 直接或间接影响着工程结构形式设计、结构性能评估与预测以及在役结构损伤识别等工作的开展与决策. 这些多源不确定性信息往往需要用多种不同的不确定性量化模型加以描述; 与此同时, 不确定性变量在使用过程中可能随时间变化且难以直接测量, 需要间接根据性能测试信息在使用工程中更新不确定性量化模型. 为兼顾上述两个问题, 本文基于等概率变换原则提出了一种P-CS (probability-convex set) 不确定性量化模型, 该模型将不确定性变量用概率随机变量与非概率凸集变量组合表征, 可统一表达概率模型、非概率模型以及非精确概率模型, 实现多源、多类型不确定性的统一量化. 本文进一步基于贝叶斯理论提出了一种针对该P-CS不确定性量化模型的性能数据驱动更新方法. 该更新方法根据性能测试数据信息更新P-CS不确定性量化模型参数取值的信度分布, 从而根据后验信度分布计算得出当前P-CS不确定性量化模型参数集合. 通过数值算例详述了P-CS不确定性量化模型的构建方法与其概率、非概率特性, 并验证了性能数据驱动更新P-CS模型方法的适用性.      

基于随机加权的捷联惯组历次测试数据验前分布研究

针对捷联惯性测量组合(捷联惯组)历次测试数据小样本的特点,在总体分布参数形式未知的情况下,根据已有的先验信息,提出了通过随机加权法,获得捷联惯组历次测试数据总体参数的验前分布。结合当前样本信息,利用贝叶斯方法得到捷联惯组历次测试数据的验后分布。将统计推断建立在验后分布基础之上,可以减小小样本情况下的统计分析误差。     

基于贝叶斯理论的恢复力模型参数识别方法

提出了基于贝叶斯理论的恢复力模型参数识别方法,该方法考虑了模型误差的影响,结合实测滞回曲线数据,不仅可以得到模型参数的最有可能值,而且可以得到模型参数的定量的不确定性。以密肋复合墙体在低周反复荷载作用下所得滞回曲线为例,提出了可考虑刚度降低、捏拢滑移及极限荷载后强度降低现象的恢复力模型,建立了基于贝叶斯理论的恢复力模型参数识别计算框架,推导得到了模型参数的负对数似然函数,据此可得到模型参数的最有可能值及协方差矩阵。对标准密肋复合墙体预制试件和现浇试件的恢复力模型参数进行了识别,将根据模型参数最有可能值得到的滞回曲线及根据模型参数最有可能值及协方差矩阵得到的骨架曲线,与相应的实测值进行了对比,验证了所提方法的可行性及识别结果的合理性,更新的模型参数概率分布可用于后续的抗震风险评估。     

压气机流动稳定性自适应控制方法研究进展

压气机流动稳定性自适应控制是未来智能航空发动机的一项关键技术. 基础研究需要回答3个关切: 如何描述系统的稳定性？如何改变系统的稳定性？如何监测系统的稳定性？为此, 本团队在压气机流动稳定性通用理论、壁面阻抗边界扩稳方法和在线实时失速预警技术等3个方面开展了系统深入的研究工作. (1)所发展的叶轮机流动稳定性通用理论既能包含流动非均匀性又能考虑叶片几何, 计算高效, 预测精度高, 为压气机气动/稳定性一体化设计提供了可靠的评估工具. (2)所发展的基于壁面阻抗边界调控策略的SPS (stall precursor-suppressed)机匣处理和泡沫金属机匣处理在扩稳、降噪和保持系统气动性能方面取得实质性进展, 采用等价分布源方法建立了包含机匣处理影响的压气机失速起始预测模型, 对SPS机匣处理和泡沫金属机匣处理关键结构参数进行敏感性分析, 使其具有明确的理论设计准则. 实验结果证实, SPS机匣处理通过抑制失速先兆波的非线性演化达到扩稳的目的, 在扩稳的同时可以保持压气机的压比和效率特性; 泡沫金属机匣处理可以实现扩稳和降噪的双重效果, 也具有良好的工程应用前景. (3)所发展的基于气动声学原理的实时失速预警方法将压气机失速预警时间提高到秒量级以上, 能够在线监测系统稳定性. 综合上述理论预测方法、扩稳技术和实时失速预警技术, 发展了闭环反馈自适应控制方法, 为未来智能航空发动机提供了一种自适应扩稳控制技术.      

Uncertainty quantification in LES of channel flow

In this paper, we present a Bayesian framework for estimating joint densities for large eddy simulation (LES) sub‐grid scale model parameters based on canonical forced isotropic turbulence direct numerical simulation (DNS) data. The framework accounts for noise in the independent variables, and we present alternative formulations for accounting for discrepancies between model and data. To generate probability densities for flow characteristics, posterior densities for sub‐grid scale model parameters are propagated forward through LES of channel flow and compared with DNS data. Synthesis of the calibration and prediction results demonstrates that model parameters have an explicit filter width dependence and are highly correlated. Discrepancies between DNS and calibrated LES results point to additional model form inadequacies that need to be accounted for. Copyright © 2016 John Wiley & Sons, Ltd.     

混沌时间序列预测的局域法在边坡变形分析中的应用

边坡作为一个复杂系统,其本身的各种参量是不确定的和随机的,在其演化过程中,表现出复杂的非线性行为,发生一系列的混沌现象。本文运用现代混沌理论,对边坡变形的预测问题进行探索性研究,把混沌时间序列理论引入到边坡工程研究中,对该理论的建立及预测方法进行系统地讨论,为该领域的研究提供完整的技术方法。通过对新滩滑坡的研究结果表明,混沌时间序列方法对混沌序列的预测较线性时间序列具有较高的精度。     

Inverse estimation for unknown fouling geometry on inner wall of forced-convection pipe

A conjugate gradient method (CGM) based on the inverse algorithm is used to estimate the unknown fouling-layer profile on the inner wall of a pipe system using simulated temperature measurements taken within the pipe wall. It is assumed that no prior information is available about the functional form of the unknown profile. Therefore, the procedure is classified as the function estimation in inverse calculation. The temperature data obtained from the direct problem are used to simulate the temperature measurements. The accuracy of the inverse analysis is examined using the simulated exact and inexact temperature measurements. The results show that the excellent estimation of the fouling-layer profile can be obtained for the test case considered in this study. The technique presented in this study can be used in a warning system to call for pipe maintenance when the thickness of fouling exceeds a predefined criterion.     

Asymptotic inverse scattering

Several inverse techniques are developed for determining the shape of an unknown scattering surface by analyzing backscattered acoustic or electromagnetic waves. These techniques are based on asymptotic high frequency representations of the fields and may be divided into three categories. The first one is the geometrical imaging method where the surface is reconstructed by means of a travel-time analysis which is here specified to the far field by utilizing Minkowski's support function. Furthermore, a geometrical method is developed for localizing edges from mid field data measured along a curve. The second category is called quasigeometrical imaging and uses geometric optics or higher order amplitudes for the reconstruction. It is shown that cross-polarized electromagnetic far field amplitude measurements permit one to deduce the complete quadratic approximation of the surface at the specular points from which the surface can be reconstructed pointwise. The third category may be subsumed under ‘asymptotic inverse scattering identities’. Here, asymptotic relations between scattered fields and distributions associated with the geometry of the scatterer are established. It is shown that the physical optics far field inverse scattering identity is only a leading order asymptotic relation but as such is also valid for non-convex scatterers. Furthermore, asymptotic inverse scattering identities are deduced which relate the singular function of a closed surface to the backscattered field data measured on a sphere enclosing the scatterer. This generalizes far field results of Cohen and Bleistein (Wave Motion 1 (1979), p. 153) to the mid field.     

基于链式贝叶斯网络的结构可靠性分析

将贝叶斯网络与传统可靠性方法结合,建立结构系统的可靠性贝叶斯网络模型,通过改进的分支限界法确定结构主要失效模式,并将贝叶斯网络链式化来提升计算效率。根据可靠性方法计算条件概率表;使用概率网络估算法来考虑主要失效模式之间的相关性,计算系统可靠性;当有新信息出现时,利用贝叶斯网络推理,对结构系统可靠性进行评估。以一桁架结构为研究对象,计算结构系统的可靠性,并在新信息出现的情况下对系统可靠性进行了更新。     

Parameter identification of dynamic models using a Bayes approach

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