An Efficient Reliability-Based Optimization Method Utilizing High-Dimensional Model Representation and Weight-Point Estimation Method

The objective of reliability-based design optimization(RBDO)is to minimize the optimization objective while satisfying the corresponding reliability requirements.However,the nested loop characteristic reduces the efficiency of RBDO algorithm,which hinders their application to high-dimensional engineering problems.To address these issues,this paper proposes an efficient decoupled RBDO method combining high dimensional model representation(HDMR)and the weight-point estimation method(WPEM).First,we decouple the RBDO model using HDMR and WPEM.Second,Lagrange interpolation is used to approximate a univariate function.Finally,based on the results of the first two steps,the original nested loop reliability optimization model is completely transformed into a deterministic design optimization model that can be solved by a series of mature constrained optimization methods without any additional calculations.Two numerical examples of a planar 10-bar structure and an aviation hydraulic piping system with 28 design variables are analyzed to illustrate the performance and practicability of the proposed method.

HIGH ACCURACY FINITE VOLUME ELEMENT METHOD FOR TWO-POINT BOUNDARY VALUE PROBLEM OF SECOND ORDER ORDINARY DIFFERENTIAL EQUATIONS

In this paper, a high accuracy finite volume element method is presented for two-point boundary value problem of second order ordinary differential equation, which differs from the high order generalized difference methods. It is proved that the method has optimal order error estimate O(h3) in H1 norm. Finally, two examples show that the method is effective.

Comparison of two Bayesian-point-estimation methods in multiple-source localization

Environmental uncertainty represents the limiting factor in matched-field localization. Within a Bayesian framework, both the environmental parameters, and the source parameters are considered to be unknown variables. However, including environmental parameters in multiple-source localization greatly increases the complexity and computational demands of the inverse problem. In the paper, the closed-form maximumlikelihood expressions for source strengths and noise variance at each frequency allow these parameters to be sampled implicitly, substantially reducing the dimensionality and difficulty of the inversion. This paper compares two Bayesian-point-estimation methods： the maximum a posteriori（MAP） approach and the marginal posterior probability density（PPD） approach to source localization. The MAP approach determines the sources locations by maximizing the PPD over all source and environmental parameters. The marginal PPD approach integrates the PPD over the unknowns to obtain a sequence of marginal probability distribution over source range or depth.Monte Carlo analysis of the two approaches for a test case involving both geoacoustic and water-column uncertainties indicates that：（1） For sensitive parameters such as source range, water depth and water sound speed, the MAP solution is better than the marginal PPD solution.（2） For the less sensitive parameters, such as,bottom sound speed, bottom density, bottom attenuation and water sound speed, when the SNR is low, the marginal PPD solution can better smooth the noise, which leads to better performance than the MAP solution.Since the source range and depth are sensitive parameters, the research shows that the MAP approach provides a slightly more reliable method to locate multiple sources in an unknown environment.

Prediction of Flash Point Temperature of Organic Compounds Using a Hybrid Method of Group Contribution ＋ Neural Network ＋ Particle Swarm Optimization

The flash points of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) with particle swarm optimization (PSO). Different topologies of a multilayer neural network were studied and the optimum architecture was determined. Property data of 350 compounds were used for training the network. To discriminate different substances the molecular structures defined by the concept of the classical group contribution method were given as input variables. The capabilities of the network were tested with 155 substances not considered in the training step. The study shows that the proposed GCM+ANN+PSO method represent an excellent alternative for the estimation of flash points of organic compounds with acceptable accuracy (AARD = 1.8%; AAE = 6.2 K).

An approximate point-based alternative for the estimation of variance under big BAF sampling

Background:A new variance estimator is derived and tested for big BAF(Basal Area Factor)sampling which is a forest inventory system that utilizes Bitterlich sampling(point sampling)with two BAF sizes,a small BAF for tree counts and a larger BAF on which tree measurements are made usually including DBHs and heights needed for volume estimation.Methods:The new estimator is derived using the Delta method from an existing formulation of the big BAF estimator as consisting of three sample means.The new formula is compared to existing big BAF estimators including a popular estimator based on Bruce’s formula.Results:Several computer simulation studies were conducted comparing the new variance estimator to all known variance estimators for big BAF currently in the forest inventory literature.In simulations the new estimator performed well and comparably to existing variance formulas.Conclusions:A possible advantage of the new estimator is that it does not require the assumption of negligible correlation between basal area counts on the small BAF factor and volume-basal area ratios based on the large BAF factor selection trees,an assumption required by all previous big BAF variance estimation formulas.Although this correlation was negligible on the simulation stands used in this study,it is conceivable that the correlation could be significant in some forest types,such as those in which the DBH-height relationship can be affected substantially by density perhaps through competition.We derived a formula that can be used to estimate the covariance between estimates of mean basal area and the ratio of estimates of mean volume and mean basal area.We also mathematically derived expressions for bias in the big BAF estimator that can be used to show the bias approaches zero in large samples on the order of 1n where n is the number of sample points.

ON THE EXISTENCE OF SOLUTION OF A NONLINEAR TWO-POINT BOUNDARY VALUE PROBLEM ARISING FROM A LIQUID METAL FLOW

In this paper, we discuss the existence of solution of a nonlinear two-point boundary value problem with a positive parameter Q arising in the study of surfacetension-induced flows of a liquid metal or semiconductor. By applying the Schauder's fixed-point theorem, we prove that the problem admits a solution for 0 ≤ Q ≤ 14.306.It improves the result of 0 ≤ Q ＜ 1 in [2] and 0 ≤ Q ≤ 13.213 in [3].

Optimal L_∞ Estimates for Galerkin Methods for Nonlinear Singular Two-point Boundary Value Problems

In this paper, we apply the symmetric Galerkin methods to the numerical solutions of a kind of singular linear two-point boundary value problems. We estimate the error in the maximum norm. For the sake of obtaining full superconvergence uniformly at all nodal points, we introduce local mesh refinements. Then we extend these results to a class of nonlinear problems. Finally, we present some numerical results which confirm our theoretical conclusions.

Nataf transformation based point estimate method

Structural probabilistic analysis quantifies the effect of input random variables, such as material proper- ties, geometrical parameters and loading conditions, on the structural responses. The point estimate method (PEM) is a direct and easy-used way to perform the structural probabilistic analysis in practice. In this paper, a novel and efficient point estimate method is proposed for computing the first four statistical moments of structural response which is a function of input random variables. The method adopts Nataf transformation to replace Rosenblatt transformation in conventional point estimate method. Because of the nature of engineering problems and limited statistical data, the joint probability density function (PDF) of all input random variables is hard to acquire, but it must be known in Rosenblatt transformation. A more common case is that the marginal PDF of each random variable and the correlation matrix are available, which just satisfy the service condition of Nataf transformation. Hence the Nataf transformation based point estimate method is particularly suitable for engineering applications. The comparison between the proposed method and the conventional point estimate method shows that (1) they are equivalent when all random variables are mutually independent; (2) if the marginal PDFs and the correlation matrix are known, the conventional PEM cannot be applicable, but the proposed method can give a rational approximation. Finally, the procedure is demonstrated in detail through a simple illustration.

An improved probabilistic load flow in distribution networks based on clustering and Point estimate methods

Clustering approaches are one of the probabilistic load flow(PLF)methods in distribution networks that can be used to obtain output random variables,with much less computation burden and time than the Monte Carlo simulation(MCS)method.However,a challenge of the clustering methods is that the statistical characteristics of the output random variables are obtained with low accuracy.This paper presents a hybrid approach based on clustering and Point estimate methods.In the proposed approach,first,the sample points are clustered based on the𝑙-means method and the optimal agent of each cluster is determined.Then,for each member of the population of agents,the deterministic load flow calculations are performed,and the output variables are calculated.Afterward,a Point estimate-based PLF is performed and the mean and the standard deviation of the output variables are obtained.Finally,the statistical data of each output random variable are modified using the Point estimate method.The use of the proposed method makes it possible to obtain the statistical properties of output random variables such as mean,standard deviation and probabilistic functions,with high accuracy and without significantly increasing the burden of calculations.In order to confirm the consistency and efficiency of the proposed method,the 10-,33-,69-,85-,and 118-bus standard distribution networks have been simulated using coding in Python®programming language.In simulation studies,the results of the proposed method have been compared with the results obtained from the clustering method as well as the MCS method,as a criterion.

一种基于蒙特卡洛方法的配电网概率可靠性快速计算方法

配电网的概率可靠性能够弥补传统可靠性指标的期望值仅从均值角度衡量系统可靠性的不足,但随着配电网规模扩大以及数据量的剧增,能够同时兼顾计算准确性与计算速度的概率可靠性计算方法是亟待研究的。为此,提出一种基于蒙特卡洛方法的配电网概率可靠性快速计算方法,利用改进三点估计法以及三阶多项式正态变换,在保留输入变量相关性的同时有效缩减输入样本点规模,并采用级数展开得到概率可靠性。首先采用改进三点估计法,在独立标准正态空间内选取样本点,再通过三阶多项式正态变换将其转换为原始变量空间的样本点;接着采用序贯蒙特卡洛方法,在考虑孤岛划分的情况下对样本点进行可靠性计算;最后通过Edgeworth级数展开,得到可靠性指标的概率分布。对改进的IEEE-RBTS Bus6的F4馈线进行算例分析,结果表明:该文所提方法与传统蒙特卡洛方法的配电网系统可靠性计算结果之间仅存在2.195%的最大偏差,而该文所提方法计算时间仅为传统蒙特卡洛方法的1.05%,证明该文所提方法在保证较高精度的同时可以显著提升计算速度。

考虑岩体性质空间变异的岩爆倾向性概率评估

为了探究更为符合工程实际的岩爆倾向性评判方法,对岩爆倾向性及其不确定性问题进行了研究.首先,建立改进的黏结强度弱化-摩擦强度强化模型,结合能量指标实现岩爆倾向性分析;依托埋深超千米的大红山铜矿工程,将点估计-有限元分析应用于岩爆倾向性评判,构建分析模型;进一步求得岩爆倾向性的概率模型,并得到直观的概率分布情况.结果表明:所用本构及指标可较好地进行岩爆倾向性分析,考虑参数变异性后,95%置信度的岩爆深度与现场记录深度吻合度较高,岩爆角度范围也基本吻合,比仅取定值更为准确,验证了所作不确定性分析的可行性与正确性.不同统计指标符合不同的分布函数,岩爆深度、范围角及完全能量释放值的最优分布分别为Normal分布、Gamma分布及Lognormal分布.基于深度及范围角指示,以80%、40%、20%为界限,可将倾向性概率划分为极大、大、中、小四个等级区间,岩爆区域的概率分布图可以更为直观合理地判断出岩爆破坏的区域和概率.研究结果对岩爆支护及风险评估具有参考意义.

基于点估计仿射可调鲁棒优化的含储能电力系统实时调度

为应对大规模风电的接入,建立一种机组与储能联合参与自动发电控制的电力系统实时调度模型,并提出一种点估计仿射可调鲁棒优化来处理风电不确定性。不同于传统仿射可调鲁棒调度优化基准运行成本,点估计仿射可调鲁棒调度优化期望运行成本以提高系统经济性。提出利用确定性的点估计法来实现对期望运行成本的快速、精确评估。所提模型为一混合整数双线性约束问题,采用一种“预估-矫正”的凸化方法来求解该难题,预估阶段对储能的状态变量进行松弛,而矫正阶段直接对其状态变量进行矫正。最后,引入一种凸函数差优化进一步凸化2个阶段的双线性约束问题,以提高含储能实时调度的求解质量。在IEEE39、118以及300节点3个系统的仿真验证了所提模型及方法的有效性。

计及SOP参数优化的柔性配电网风险评估方法

为分析智能软开关(soft open point,SOP)连续调节能力对柔性配电网(flexible distribution network,FDN)风险的影响。首先,实现基于三点估计的FDN风险评估方法;采用三点估计法结合交直流交替迭代法和Gram-Charlier级数展开法进行FDN概率潮流计算,获得节点电压与支路有功功率的概率密度函数,使用越限偏移量结合风险偏好型效用函数构建严重度函数,根据风险评估理论建立并计算风险评估指标。其次,在此基础上,提出一种计及SOP参数优化的FDN风险评估方法;以系统总风险最低为目标,建立计及SOP参数优化的FDN风险评估模型,采用粒子群优化算法结合基于三点估计的FDN风险评估方法对其进行求解,用得到的结果去配置SOP,并对此FDN进行风险评估。以3个IEEE 33节点网络通过三端口SOP互联形成的FDN为例,验证了所提风险评估方法的有效性,分析了SOP连续调节能力以及不同接入位置对FDN风险的影响。

基于M估计算法的三维点云平面拟合方法研究

通过激光传感器获取的三维点云难免混入噪声和异常点,导致点云平面的拟合精度降低。为解决该问题,本文提出了一种结合M估计样本一致性(MSAC)算法和主成分分析(PCA)法拟合点云平面的方法。该方法首先通过MSAC算法去除点云数据中的异常点,获得较为理想的点云平面,然后使用PCA方法对保留的点云数据进行平面拟合,以获取更加精确的点云平面参数。使用电池托盘作为被测物,应用3D线激光轮廓传感器扫描被测物并将点云数据传输到计算机进行处理。通过设定的仿真数据和电池托盘点云数据进行实验,发现本文方法与随机采样一致性(RANSAC)结合PCA、最小平方中值(LMedS)结合PCA的方法相比,在耗时接近的情况下,能够显著降低异常点对点云平面拟合的影响,获得更精确的平面拟合参数。对两个部分的电池托盘点云滤波处理后进行平面拟合时,能够发现本文方法与其他两种方法相比,标准差分别降低了28.6%和22.5%%、24.0%和29.0%,该方法具有较高的平面拟合精度和实用性。

农业社会化服务对农业面源污染的影响研究——基于塔里木河流域棉农微观调查数据的实证分析

在经济社会发展全面绿色转型背景下,探究农业社会化服务对农业面源污染的影响,旨在实现减污降碳的同时,为完善农业社会化服务体系提供有力支撑。基于新疆塔里木河流域5地州7县360名棉农的调查数据,通过普通最小二乘法和倾向得分匹配法分析农业社会化服务对农业面源污染的影响,进一步运用逆向概率加权法对农业社会化服务采纳程度与农业面源污染排放量的关系进行实证检验。实证结果表明:不同农业社会化服务类型对农业面源污染的影响存在差异。其中,农业技术服务和农业机械服务对农业面源污染具有显著抑制作用,分别降低农户每公顷农资投入费用约1070元和1170元,而农业保险服务对农业面源污染存在显著正向影响。控制变量中,务农年限、家庭非农劳动力比例、种植规模对农业面源污染具有显著正向影响,而土壤肥力具有负向影响。农业社会化服务采纳程度越高,农业面源污染排放程度越低,且农业机械服务与农业技术服务的协同作用能够进一步降低农业面源污染,相较于仅采纳机械服务或技术服务,同时购买两项服务的农户农资投入费用分别减少约640元和800元。据此,提出积极推进各方主体参与环境污染综合治理,完善环境污染防控协作共治机制;加强农业科技投入,研发农业面源污染防控先进技术;把握农户差异化诉求,提供针对性农业社会化服务的对策建议。

小流域面源污染负荷精细化估算与实证研究——以成都市阳化河流域为例

精准估算沱江支流阳化河小流域面源污染负荷是制定水质达标方案的基础。运用改进的输出系数法估算阳化河流域面源污染负荷总量,并基于当月降雨量占年降雨量的比例对面源污染负荷年输出总量进行月份分配,获得精细化估算数据。结果表明:2020年流域内COD污染最严重,污染负荷总量为6 042.61 t,污染负荷主要分布在转龙镇、竹篙镇、禾丰镇、云龙镇和施家镇;污染物COD、氨氮和总磷主要来源于农村分散生活面源和规模化畜禽养殖;估算得到的面源污染负荷年内月份的分配结果与实测数据比较吻合,平均误差都<10%。研究结果可为流域水环境精细化管理提供数据支持。

Existence and numerical approximation of a solution to frictional contact problem for electro-elastic materials

In this paper,a frictional contact problem between an electro-elastic body and an electrically conductive foundation is studied.The contact is modeled by normal compliance with finite penetration and a version of Coulomb’s law of dry friction in which the coefficient of friction depends on the slip.In addition,the effects of the electrical conductivity of the foundation are taken into account.This model leads to a coupled system of the quasi-variational inequality of the elliptic type for the displacement and the nonlinear variational equation for the electric potential.The existence of a weak solution is proved by using an abstract result for elliptic variational inequalities and a fixed point argument.Then,a finite element approximation of the problem is presented.Under some regularity conditions,an optimal order error estimate of the approximate solution is derived.Finally,a successive iteration technique is used to solve the problem numerically and a convergence result is established.

Seismic safety assessment with non-Gaussian random processes for train-bridge coupled systems

Extensive high-speed railway(HSR)network resembled the intricate vascular system of the human body,crisscrossing mainlands.Seismic events,known for their unpredictability,pose a significant threat to both trains and bridges,given the HSR’s extended operational duration.Therefore,ensuring the running safety of train-bridge coupled(TBC)system,primarily composed of simply supported beam bridges,is paramount.Traditional methods like the Monte Carlo method fall short in analyzing this intricate system efficiently.Instead,efficient algorithm like the new point estimate method combined with moment expansion approximation(NPEM-MEA)is applied to study random responses of numerical simulation TBC systems.Validation of the NPEM-MEA’s feasibility is conducted using the Monte Carlo method.Comparative analysis confirms the accuracy and efficiency of the method,with a recommended truncation order of four to six for the NPEM-MEA.Additionally,the influences of seismic magnitude and epicentral distance are discussed based on the random dynamic responses in the TBC system.This methodology not only facilitates seismic safety assessments for TBC systems but also contributes to standard-setting for these systems under earthquake conditions.

基于云计算的优化配网调度执行系统研究

为了优化配网调度执行工作,对配网调度进行了研究。设计了基于云计算的优化配网调度执行系统。在硬件设计中采用分布式结构执行配网调度。在系统软件部分,使用云计算技术,设计了主动配网云边协同计算体系。该体系实现了配电调度边缘计算节点的远程操作,提高了配网调度执行优化的能力。采用客户端/服务端(C/S)、浏览器/服务器(B/S)混合架构,实现了配网调度的人机可视化执行。利用两点估计法以提高配网调度模型的优化计算能力,进而实现配网调度的控制,提升了配网调度运行的稳定性与经济性。试验结果表明,在对配网调度执行的可靠性进行测试时,该系统的可靠性达到了96%。该系统可使配网始终处于安全、可靠、优质、经济、高效的运行状态,为配网调度执行提供了一种可行的方案。