A method which adopts the combination of least squares support vector machine（LS-SVM） and Monte Carlo（MC） simulation is used to calculate the foundation settlement reliability.When using LS-SVM,choosing the traini...A method which adopts the combination of least squares support vector machine（LS-SVM） and Monte Carlo（MC） simulation is used to calculate the foundation settlement reliability.When using LS-SVM,choosing the training dataset and the values for LS-SVM parameters is the key.In a representative sense,the orthogonal experimental design with four factors and five levels is used to choose the inputs of the training dataset,and the outputs are calculated by using fast Lagrangian analysis continua（FLAC）.The decimal ant colony algorithm（DACA） is also used to determine the parameters.Calculation results show that the values of the two parameters,and δ2 have great effect on the performance of LS-SVM.After the training of LS-SVM,the inputs are sampled according to the probabilistic distribution,and the outputs are predicted with the trained LS-SVM,thus the reliability analysis can be performed by the MC method.A program compiled by Matlab is employed to calculate its reliability.Results show that the method of combining LS-SVM and MC simulation is applicable to the reliability analysis of soft foundation settlement.展开更多
地震随机反演方法由于井间数据缺失,反演结果的横向连续性较差。且反演效率低、反演结果随机性强。为此,我们提出基于地震波形约束的地质统计学反演方法。用地震数据的相关系数来衡量地震波形的相似程度,代替传统的变差函数进行序贯高...地震随机反演方法由于井间数据缺失,反演结果的横向连续性较差。且反演效率低、反演结果随机性强。为此,我们提出基于地震波形约束的地质统计学反演方法。用地震数据的相关系数来衡量地震波形的相似程度,代替传统的变差函数进行序贯高斯模拟。在贝叶斯框架下,结合地震数据的约束,利用马尔科夫链-蒙特卡洛(Markov Chain Monte Carlo,MCMC)算法对模拟结果进行随机扰动和全局寻优,获得优化的参数反演结果。模型测试结果表明,基于地震波形约束的初始模型较为精确地刻画了地下储层的空间结构。对其进行迭代优化可以加快马尔科夫链的收敛速度,有效提高反演结果的精度。本文将提出的地质统计学反演方法用于某油田实际地震数据,在随机模拟过程和目标函数的约束中,充分挖掘了地震波形蕴含的地质信息,并为实现多数据联合约束地震反演提供了理论依据。展开更多
Several structural design parameters for the description of the geometric features of a hollow fan blade were determined.A structural design optimization model of a hollow fan blade which based on the strength constra...Several structural design parameters for the description of the geometric features of a hollow fan blade were determined.A structural design optimization model of a hollow fan blade which based on the strength constraint and minimum mass was established based on the finite element method through these parameters.Then,the sequential quadratic programming algorithm was employed to search the optimal solutions.Several groups of value for initial design variables were chosen,for the purpose of not only finding much more local optimal results but also analyzing which discipline that the variables according to could be benefit for the convergence and robustness.Response surface method and Monte Carlo simulations were used to analyze whether the objective function and constraint function are sensitive to the variation of variables or not.Then the robust results could be found among a group of different local optimal solutions.展开更多
针对航空弹药保障效率和可靠性要求高的问题,在分析保障流程的计划评审技术(PERT,Program Evaluation and Review Technique)网络图中各工序对任务完工影响程度的基础上,提取了工序关键度指标和重要度指标以及各方案下的按期完工概率,...针对航空弹药保障效率和可靠性要求高的问题,在分析保障流程的计划评审技术(PERT,Program Evaluation and Review Technique)网络图中各工序对任务完工影响程度的基础上,提取了工序关键度指标和重要度指标以及各方案下的按期完工概率,建立了保障人员配置方案评价指标体系,表示为评价函数形式.以基于蒙特卡洛方法的PERT网络仿真为核心,选择在遗传算法进化寻优框架下构建优化模型,该方法不仅得到了最优的保障人员配置方案,而且评估了需要注重的关键工序.算例实验证实了其有效性与实用性.展开更多
This study presents a robust design method for autonomous photovoltaic (PV)-wind hybrid power systems to obtain an optimum system configuration insensitive to design variable variations. This issue has been formulated...This study presents a robust design method for autonomous photovoltaic (PV)-wind hybrid power systems to obtain an optimum system configuration insensitive to design variable variations. This issue has been formulated as a constraint multi-objective optimization problem, which is solved by a multi-objective genetic algorithm, NSGA-II. Monte Carlo Simulation (MCS) method, combined with Latin Hypercube Sampling (LHS), is applied to evaluate the stochastic system performance. The potential of the proposed method has been demonstrated by a conceptual system design. A comparative study between the proposed robust method and the deterministic method presented in literature has been conducted. The results indicate that the proposed method can find a large mount of Pareto optimal system configurations with better compromising performance than the deterministic method. The trade-off information may be derived by a systematical comparison of these configurations. The proposed robust design method should be useful for hybrid power systems that require both optimality and robustness.展开更多
文摘A method which adopts the combination of least squares support vector machine（LS-SVM） and Monte Carlo（MC） simulation is used to calculate the foundation settlement reliability.When using LS-SVM,choosing the training dataset and the values for LS-SVM parameters is the key.In a representative sense,the orthogonal experimental design with four factors and five levels is used to choose the inputs of the training dataset,and the outputs are calculated by using fast Lagrangian analysis continua（FLAC）.The decimal ant colony algorithm（DACA） is also used to determine the parameters.Calculation results show that the values of the two parameters,and δ2 have great effect on the performance of LS-SVM.After the training of LS-SVM,the inputs are sampled according to the probabilistic distribution,and the outputs are predicted with the trained LS-SVM,thus the reliability analysis can be performed by the MC method.A program compiled by Matlab is employed to calculate its reliability.Results show that the method of combining LS-SVM and MC simulation is applicable to the reliability analysis of soft foundation settlement.
基金supported by the National Natural Science Foundation of China[Grant Nos.42174146,42074136,42174144]Innovation Fund Project for Graduate Students of China University of Petroleum(East China)[Grant No.23CX04015A].
文摘地震随机反演方法由于井间数据缺失,反演结果的横向连续性较差。且反演效率低、反演结果随机性强。为此,我们提出基于地震波形约束的地质统计学反演方法。用地震数据的相关系数来衡量地震波形的相似程度,代替传统的变差函数进行序贯高斯模拟。在贝叶斯框架下,结合地震数据的约束,利用马尔科夫链-蒙特卡洛(Markov Chain Monte Carlo,MCMC)算法对模拟结果进行随机扰动和全局寻优,获得优化的参数反演结果。模型测试结果表明,基于地震波形约束的初始模型较为精确地刻画了地下储层的空间结构。对其进行迭代优化可以加快马尔科夫链的收敛速度,有效提高反演结果的精度。本文将提出的地质统计学反演方法用于某油田实际地震数据,在随机模拟过程和目标函数的约束中,充分挖掘了地震波形蕴含的地质信息,并为实现多数据联合约束地震反演提供了理论依据。
文摘Several structural design parameters for the description of the geometric features of a hollow fan blade were determined.A structural design optimization model of a hollow fan blade which based on the strength constraint and minimum mass was established based on the finite element method through these parameters.Then,the sequential quadratic programming algorithm was employed to search the optimal solutions.Several groups of value for initial design variables were chosen,for the purpose of not only finding much more local optimal results but also analyzing which discipline that the variables according to could be benefit for the convergence and robustness.Response surface method and Monte Carlo simulations were used to analyze whether the objective function and constraint function are sensitive to the variation of variables or not.Then the robust results could be found among a group of different local optimal solutions.
文摘针对航空弹药保障效率和可靠性要求高的问题,在分析保障流程的计划评审技术(PERT,Program Evaluation and Review Technique)网络图中各工序对任务完工影响程度的基础上,提取了工序关键度指标和重要度指标以及各方案下的按期完工概率,建立了保障人员配置方案评价指标体系,表示为评价函数形式.以基于蒙特卡洛方法的PERT网络仿真为核心,选择在遗传算法进化寻优框架下构建优化模型,该方法不仅得到了最优的保障人员配置方案,而且评估了需要注重的关键工序.算例实验证实了其有效性与实用性.
文摘This study presents a robust design method for autonomous photovoltaic (PV)-wind hybrid power systems to obtain an optimum system configuration insensitive to design variable variations. This issue has been formulated as a constraint multi-objective optimization problem, which is solved by a multi-objective genetic algorithm, NSGA-II. Monte Carlo Simulation (MCS) method, combined with Latin Hypercube Sampling (LHS), is applied to evaluate the stochastic system performance. The potential of the proposed method has been demonstrated by a conceptual system design. A comparative study between the proposed robust method and the deterministic method presented in literature has been conducted. The results indicate that the proposed method can find a large mount of Pareto optimal system configurations with better compromising performance than the deterministic method. The trade-off information may be derived by a systematical comparison of these configurations. The proposed robust design method should be useful for hybrid power systems that require both optimality and robustness.
