高维模型表达(high dimensional model representation,HDMR)在描述系统输出量关于多输入量之间关系方面具有独特的性能,而电网潮流状态量与网络多个节点源流注入量间正好符合HDMR的相关属性。基于此,将HDMR应用于电网潮流概率评估与调...高维模型表达(high dimensional model representation,HDMR)在描述系统输出量关于多输入量之间关系方面具有独特的性能,而电网潮流状态量与网络多个节点源流注入量间正好符合HDMR的相关属性。基于此,将HDMR应用于电网潮流概率评估与调控问题:通过典型代表性的样本构建关键支路上传输的功率与电源和负荷间的HDMR关系,并替换传统潮流计算方式承担潮流概率评估过程中大规模的潮流计算任务,以极大地提高关键支路潮流累积概率分布生成及其相关特征求取的效率;对关键支路潮流阻塞问题,设计了一种利用HDMR提供的全局灵敏度信息并兼顾节能减排性能指标的概率调控策略。算例表明,HDMR的应用可显著提高电网潮流概率评估的计算效率和关键支路潮流阻塞概率调控的性能。展开更多
A new hybrid method is proposed to estimate the failure probability of a structure subject to random parameters. The high dimensional model representation(HDMR) combined with artificial neural network(ANN) is used to ...A new hybrid method is proposed to estimate the failure probability of a structure subject to random parameters. The high dimensional model representation(HDMR) combined with artificial neural network(ANN) is used to approximate implicit limit state functions in structural reliability analysis. HDMR facilitates the lower dimensional approximation of the original limit states function. For evaluating the failure probability, a first-order HDMR approximation is constructed by deploying sampling points along each random variable axis and hence obtaining the structural responses. To reduce the computational effort of the evaluation of limit state function, an ANN surrogate is trained based on the sampling points from HDMR. The component of the approximated function in HDMR can be regarded as the input of the ANN and the response of limit state function can be regarded as the target for training an ANN surrogate. This trained ANN surrogate is used to obtain structural outputs instead of directly calling the numerical model of a structure. After generating the ANN surrogate, Monte Carlo simulation(MCS) is performed to obtain the failure probability, based on the trained ANN surrogate. Three numerical examples are used to illustrate the accuracy and efficiency of the proposed method.展开更多
在工程问题中,尽管积累了大量的实验、仿真和设计经验,传统的设计方法仍然面临着知识利用率不高的挑战。为了有效利用知识信息来加速装备的设计与开发,一种基于知识挖掘的高维模型表示(High dimensional model representation,HDMR)优...在工程问题中,尽管积累了大量的实验、仿真和设计经验,传统的设计方法仍然面临着知识利用率不高的挑战。为了有效利用知识信息来加速装备的设计与开发,一种基于知识挖掘的高维模型表示(High dimensional model representation,HDMR)优化方法被提出。首先,引入了一种改进的多元模型筛选策略,以提高HDMR子项的构建效率和预测精度。之后,提出了一种基于知识挖掘的优化策略。该策略使用全局代理模型替代真实函数,以最优样本为中心点构建HDMR子项,并在每个子项代表的维度上分别寻找局部最优点,并通过置信度对比挖掘全局潜在较优点,以加速算法寻优。最终,利用该方法开展翼身融合水下翔机(Blended-wing-body underwater glider,BWBUG)外形优化设计。在满足体积约束的条件下,将滑翔机的升阻比提升了5.04%,优于无知识辅助下升阻比提升2.93%的优化结果,验证了所提方法中知识挖掘的作用。展开更多
文摘高维模型表达(high dimensional model representation,HDMR)在描述系统输出量关于多输入量之间关系方面具有独特的性能,而电网潮流状态量与网络多个节点源流注入量间正好符合HDMR的相关属性。基于此,将HDMR应用于电网潮流概率评估与调控问题:通过典型代表性的样本构建关键支路上传输的功率与电源和负荷间的HDMR关系,并替换传统潮流计算方式承担潮流概率评估过程中大规模的潮流计算任务,以极大地提高关键支路潮流累积概率分布生成及其相关特征求取的效率;对关键支路潮流阻塞问题,设计了一种利用HDMR提供的全局灵敏度信息并兼顾节能减排性能指标的概率调控策略。算例表明,HDMR的应用可显著提高电网潮流概率评估的计算效率和关键支路潮流阻塞概率调控的性能。
基金Project(U1533109)supported by the National Natural Science Foundation,ChinaProject supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions,China
文摘A new hybrid method is proposed to estimate the failure probability of a structure subject to random parameters. The high dimensional model representation(HDMR) combined with artificial neural network(ANN) is used to approximate implicit limit state functions in structural reliability analysis. HDMR facilitates the lower dimensional approximation of the original limit states function. For evaluating the failure probability, a first-order HDMR approximation is constructed by deploying sampling points along each random variable axis and hence obtaining the structural responses. To reduce the computational effort of the evaluation of limit state function, an ANN surrogate is trained based on the sampling points from HDMR. The component of the approximated function in HDMR can be regarded as the input of the ANN and the response of limit state function can be regarded as the target for training an ANN surrogate. This trained ANN surrogate is used to obtain structural outputs instead of directly calling the numerical model of a structure. After generating the ANN surrogate, Monte Carlo simulation(MCS) is performed to obtain the failure probability, based on the trained ANN surrogate. Three numerical examples are used to illustrate the accuracy and efficiency of the proposed method.
