RBF神经网络可靠度分析方法在边坡稳定性研究中的应用

针对边坡稳定性可靠度分析,当状态函数无法显式表达且传统计算方法求解复杂问题困难时,提出一种基于ABAQUS和粒子群优化径向基函数神经网络的可靠度分析方法。基于ABAQUS的强度折减方法计算所选随机变量对应的安全系数,利用径向基函数神经网络的数据拟合功能,建立模型并映射出安全系数和随机变量之间的关系,构造响应面功能函数;利用蒙特卡罗生成的大量随机样本代入功能函数得到相应的安全系数,进而计算边坡的失稳概率和可靠度指标来反映边坡稳定性。研究结果表明:相对于传统方法,本文方法计算效率更高、误差更小,适合实际工程应用。     

基于蒙特卡罗法的煤巷锚杆支护结构可靠性分析

应用工程结构可靠性理论,建立煤巷锚杆支护结构可靠性模型,针对其结构稳定性的极限状态功能函数高度非线性的特征,采用蒙特卡罗法在Matlab环境下直接产生服从各相应概率分布函数的随机变量数组计算其结构可靠度,编程过程简化,计算速度快,精度高,且不受极限状态方程非线性、随机变量非正态的限制,开辟了煤巷锚杆支护结构可靠度计算的新途径。建立了支护参数与可靠度的关系,指出了提高煤巷锚杆支护结构可靠度的方法和措施,为煤巷锚杆支护参数设计和优化提供了科学依据。     

随机桁架结构可靠性分析的完全概率方法

提出了一种求解随机参数桁架结构在随机荷载作用下反应的概率密度函数和结构精确可靠度的方法.通过对随机参数桁架结构在随机荷载作用下的有限元分析方法的研究,考虑了结构的物理参数,构件的几何尺寸和作用荷载幅值等的随机性.应用随机向量函数的概率分布函数表达式,通过确定积分区间、变量替换、交换积分顺序等一系列数学上的处理,获得所求结构反应的概率密度函数.由干涉理论得到了结构位移和应力的可靠度.通过算例的结果与Monte-Carlo模拟法结果比较,表明该方法具有较高的精度及良好的实用性.     

基于随机有限元可靠度的RC结构抗火设计参数优化方法

本文较详细地分析了各设计参数对RC结构抗火能力的影响。在已知结构抗火功能函数的基础上利用随机有限元可靠度的计算方法分析各参数对结构抗火能力的敏感性,根据各参数敏感性的不同,在设计时有意识地对该参数作一些较小的变动,结构的可靠度会明显的增加从而提高了结构的抗火能力。为RC结构抗火设计提供了一种参数优化的方法。     

桥式起重机主梁结构多源不确定性混合可靠性分析

为了预测桥式起重机金属结构的可靠度,采用凸模型非概率研究桥式起重机的可靠性,在只知道不确定参量的界限而不知其分布情况的条件下,即可求得各功能函数的可靠度。针对起重机金属结构中存在的不确定性因素并不单一,且是随机性、模糊性和非概率不确定性共同作用的结果,首先基于凸模型计算单一变量的结构可靠度;然后基于主梁结构的加工尺寸、起重量、材料边界条件等不确定性,建立了以主梁金属结构强度、刚度、整体稳定性失效模式的凸模型非概率可靠性分析模型,通过混合可靠性模型求和计算,得到主梁结构的混合可靠度。该模型更加切合实际,从而推导出一种桥式起重机主梁金属结构的混合可靠性计算方法,也建立了一些适用于复杂工程问题的结构可靠性分析方法;最后基于凸模型,针对工程实际中大量存在的"未知但有界"参数的结构可靠性分析问题,通过数值算例验证该混合模型的有效性和实用性,对现有某型号桥式起重机主梁金属结构工况进行了混合模型可靠性分析,结果表明,该可靠性模型意义明确,对模糊信息的处理也比较合理,可作为混合可靠性计算方法的一种补充,用该方法得到的桥式起重机强度、刚度、稳定性混合模型可靠度符合实际情况。     

基于模糊优化理论的生物质裂解装置转锥有限元安全分析

提出一种将模糊数学理论和有限元法相结合的结构优化分析方法,建立转锥模糊优化的数学模型,给出目标函数、设计变量和模糊约束条件;通过合理模拟转锥的边界条件和受力状况,建立生物质裂解装置转锥的有限元结构优化模型并划分网格,同时引入实际载荷及边界条件;根据约束的性质和设计要求,选取了模糊约束隶属函数,在优化过程中提出一种改进的水平截集法来考虑模糊约束的影响,从而调整最优解在空间的位置,可求得比传统优化方法更为合理的数学解;根据算法框图进行模糊优化运算,与普通优化设计方法所得结果相比,转锥的重量减轻5.2%。笔者为转锥及其他机械结构的优化设计和安全分析,提供了一种新的途径和依据。     

基于ANSYS的可靠度分析方法及应用

通过对可靠度分析的概述理解到现代工程分析中实体模型不确定因素众多,进而带来分析的不准确性。基于有限元分析软件ANSYS提供的概率设计系统(PDS)的概率分析功能,使对结构的概率分析非常容易。根据结构的失效模式来确定结构功能函数,由此建立结构极限状态方程,再运用结构可靠度分析中的蒙特卡洛法(MCS法)利用结构的失效频率来估算其失效概率。在本文中提出了利用ANSYS的概率分析功能结合MCS法进行结构的可靠性分析的方法,并通过一个实例具体说明了利用ANSYS的概率分析功能实现结构的可靠性分析的可行性。     

含风电的电力系统低碳经济调度

含风电场的电力系统调度是一个多目标优化问题。运用机会约束方法,建立了包括低碳化和发电成本两个目标函数的调度模型,在充分考虑环境保护效益的同时,提出了风险成本的概念,使得发电成本更切合工程实际。在改进自适应遗传算法的基础上,引入了pareto最优解集方法,充分发挥了遗传算法的全局搜索能力和pareto求解多目标问题的潜力。通过实际调度案例验证了所提算法的合理性。     

基于时变可靠度分析的桥梁使用寿命预测

为分析一座桥梁结构寿命,在现有混凝土桥梁抗力和荷载的时变性研究的基础上,建立桥梁时变可靠度计算模型。将结构抗力等效转换为车辆荷载作用下结构所产生的竖向位移限值,预测远景交通量,反映荷载的时变性;运用非线性有限单元增量分析方法,以递增特定荷载的方式,采用响应面法计算时变可靠度指标及累计失效概率。结果表明:桥梁结构初始阶段具有足够大的可靠性,但其会随时间推移迅速衰减,利用韦伯分布函数和最小二乘法计算桥梁累计失效概率,进而推算桥梁使用寿命。根据桥梁寿命可靠度计算结果,制定出桥梁最佳维修养护策略。     

改进的FOA算法在通用桥式起重机轻量化设计中的应用

针对传统FOA算法全局收敛能力差、易陷入局部极值的缺陷,提出了具有混沌映射及协同进化功能的改进果蝇算法。首先利用Logistic混沌映射功能在整个收敛域范围内搜索并初始化果蝇种群,保证算法的全局计算能力,然后根据当前果蝇个体的位置赋予搜索的方向与距离,以期全面提高算法的计算速度。采用两个优化函数测试改进后算法优化的特性,优化计算的结果显示了该算法具有良好的全局优化能力,在通用桥式起重机金属结构轻量化设计中的成功应用,体现了该算法在结构设计轻量化方面的优越性。     

A reliability evaluation method based on the weakest failure modes for side-by-side offloading mooring system of FPSO

Offshore structures are complex systems, and numerous failure modes must be taken into consideration when reliability analysis in different loads and environment conditions are conducted on them. It is difficult to obtain structural system reliability with respect to complicated systems with numerous failure modes and dependency consideration among them. This paper applies the combination of the weakest failure modes theory with structural reliability theory to conduct reliability analysis on of side-by-side offloading mooring system of FPSO. Firstly, the numerical simulation of the system in different conditions is addressed to acquire the statistical data of time-history stress of components including hawsers, fenders and yoke, based on which, the reliability indexes of all the failure modes and correlation coefficient matrix are derived. Then the weakest failure modes, i.e. the representative failure modes that have significant impact on the system, are located through Probability Network Evaluation Technique. The probability of structural system is estimated through the weakest failure modes by considering the system as series. The analysis results indicate that two environment conditions (0°&0°&0° and 0°&30°&45° in combinations of wave, wind and current) are relative dangerous, which is in good correspondence with the practical expertise. The method is verified to be an effective and convenient evaluation approach for structural reliability analysis in terms of complex systems. It is beneficial for the identification of structure indicators from the weakest failure mode group and conduct optimum of structural system configurations in the design stage.     

Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines

Rockburst possibility prediction is an important activity in many underground openings design and construction as well as mining production. Due to the complex features of rockburst hazard assessment systems, such as multivariables, strong coupling and strong interference, this study employs support vector machines (SVMs) for the determination of classification of long-term rockburst for underground openings. SVMs is firmly based on the theory of statistical learning algorithms, uses classification technique by introducing radial basis function (RBF) kernel function. The inputs of models are buried depth H, rocks’ maximum tangential stress σ_θ, rocks’ uniaxial compressive strength σ_c, rocks’ uniaxial tensile strength σ_t, stress coefficient σ_θ/σ_c, rock brittleness coefficient σ_c/σ_t and elastic energy index W_et. In order to improve predictive accuracy and generalization ability, the heuristic algorithms of genetic algorithm (GA) and particle swarm optimization algorithm (PSO) are adopted to automatically determine the optimal hyper-parameters for SVMs. The performance of hybrid models (GA + SVMs = GA-SVMs) and (PSO + SVMs = PSO-SVMs) have been compared with the grid search method of support vector machines (GSM-SVMs) model and the experimental values. It also gives variance of predicted data. A rockburst dataset, which consists of 132 samples, was employed to evaluate the current method for predicting rockburst grade, and the good results of overall success rate were obtained. The results indicated that the heuristic algorithms of GA and PSO can speed up SVMs parameter optimization search, the proposed method is robust model and might hold a high potential to become a useful tool in rockburst prediction research.     

基于个体脆弱性区域疏散最佳路径

针对危险化学品、生化制剂、放射性或核物质（ CBRN）事故情景,建立区域疏散路径优化模型。基于个体脆弱性模型,提出了适合求解模型算法,包括静态最优路径算法和动态最优路径算法。最后用随机路网测试算法的应用效果,结果表明,所提算法可以满足区域疏散路径双目标优化的需求,并能根据外部环境变化动态更新当前最优路径,是一种近似、快速的算法。区域疏散路径的确定可为CBRN事故救灾提供有力的技术支持。     

Uncertainty analysis framework for tubular connection sealability of underground gas storage wells

The seal failure of tubing and casing connections compromises underground gas storage well safety. This work proposes a systematic uncertainty analysis framework for connection sealability assessment. The framework covers reliability analysis and reliability sensitivity analysis and attempts to provide more effective support for the reliability design of connection seals. The reliability analysis introduces an adaptive Kriging with stopping criterion P-Monte Carlo simulation (AKP-MCS) method, which can provide a satisfactory estimate of failure probability with a small number of performance function evaluations. This metamodeling technology can effectively reduce the numerical efforts required for the reliability assessment of connection sealability. In the reliability sensitivity analysis, the refined metamodel obtained from the reliability analysis is coupled into a single-loop Monte Carlo simulation (MCS) method. The classifier attribute of this metamodel can meet the requirement of the single-loop MCS method to classify the signs of sampling points. This attribute enables sample matrices to be evaluated on this metamodel instead of the performance function, making the reliability sensitivity assessment more feasible. The proposed method is first demonstrated with four academic examples with promising results. Next, an illustrative tubular connection case is provided. The proposed scheme gives estimates of the failure probability and reliability sensitivity close to the classical model but requires less computational cost. The results of the analysis can provide useful information for the scheme decision-making and reliability optimization of connection seal design.     

多因素影响下系统故障预防的多方案比选方法研究*

为研究多因素影响下系统遭遇危险事件前预防方案的选优,在集对分析和空间故障树基础上提出比选方法。首先,论述集对分析与空间故障树理论结合的可行性;其次,研究系统故障预防的多因素多方案比选方法和步骤;最后,通过实例进行分析。研究结果表明:停运方案没有因素分量系数影响,其损失是停运带来的经济损失;无措施方案的系统常分量系数最小,因素分量系数最大;采取措施方案中,当常分量系数相差不大时,因素分量系数积最小者为最优方案。使用赵森烽-克勤概率表示危险事件的发生特征是可行的;常分量系数代表方案对系统故障预防效用的可靠性,因素分量系数代表该可靠性的稳定性;结合常分量系数和因素分量系数即可确定最优方案。该方法适合于故障发生对因素变化敏感的系统。     

模式搜索算法在毒气泄漏中的源强反算

基于泄漏源下风向的浓度监测数据并结合大气扩散模式建立反算模型,以确定泄漏源的位置和强度。以扩散模式仿真的浓度数据与监测数据的匹配度作为目标函数,将反演问题转化为优化问题,利用模式搜索算法迭代优化。以高斯模型为例验证了算法的可行性,结果表明利用探测器提供的测量浓度值,模式搜索算法能够在较短时间内搜索到最优解,在计算复杂性或时间上较梯度型算法和智能优化算法有一定优势。该算法能够及时而准确地反算出泄漏源强度和位置,为事故的应急响应与救援提供依据。     

Optimization of test interval for ageing equipment: A multi-objective genetic algorithm approach

Reducing the unavailability of safety systems at nuclear power plants, by utilizing the probabilistic safety assessment (PSA) methodology, is one of the prime goals in the nuclear industry. In that sense, optimization of test and maintenance activities, which are defined within the technical specifications, represents quite popular and interesting domain. Obtaining optimal test and maintenance schedule is of great significance for improving system availability and performance as well as plant availability in general.On the other side, equipment aging has gradually become a major concern in the nuclear industry since the number of safety systems components, that are approaching their wear-out stage, is rising fast. Nuclear power plants life management programs, considering safety components aging, are being developed and employed. The immense uncertainty associated to the available component aging rates databases poses significant difficulties in the process of incorporation and quantification of the aging effect within the PSA and, subsequently, in the decision-making process.In this paper, an approach for optimization of surveillance test interval of standby equipment with highly uncertain aging parameters, based on genetic algorithm technique and PSA, is presented. A standard standby safety system in nuclear power plant is selected as a case study. A Monte Carlo simulation-based approach is used to assess uncertainty propagation on system level. Optimal test interval is derived on the basis of minimal system unavailability and minimal impact of components aging parameters uncertainty. The results obtained in this application indicate the fact that risk-informed surveillance requirements differ from existing ones in technical specifications as well as show the importance of considering aging data uncertainties in component aging modeling.