Minimizing the impact of the mixed uncertainties(i.e.,the aleatory uncertainty and the epistemic uncertainty) for a complex product of compliant mechanism(CPCM) quality improvement signifies a fascinating research top...Minimizing the impact of the mixed uncertainties(i.e.,the aleatory uncertainty and the epistemic uncertainty) for a complex product of compliant mechanism(CPCM) quality improvement signifies a fascinating research topic to enhance the robustness.However, most of the existing works in the CPCM robust design optimization neglect the mixed uncertainties, which might result in an unstable design or even an infeasible design. To solve this issue, a response surface methodology-based hybrid robust design optimization(RSM-based HRDO) approach is proposed to improve the robustness of the quality characteristic for the CPCM via considering the mixed uncertainties in the robust design optimization. A bridge-type amplification mechanism is used to manifest the effectiveness of the proposed approach. The comparison results prove that the proposed approach can not only keep its superiority in the robustness, but also provide a robust scheme for optimizing the design parameters.展开更多
The traditional tangent impulse interception problem does not consider the influence of actual deviation.However,by taking the actual state deviation of the interceptor into the orbit design process,an interception or...The traditional tangent impulse interception problem does not consider the influence of actual deviation.However,by taking the actual state deviation of the interceptor into the orbit design process,an interception orbit that is more robust than the nominal orbit can be obtained.Therefore,we study the minimum time interception problem and the minimum terminal interception error problem under tangent impulse conditions and give an orbit optimization method that considers the interception time and the interception uncertainty.First,we express the interceptor's transfer time equation as a form of flight path angle,establish a global optimization model for solving the minimum time tangent impulse interception and give a hybrid optimization algorithm based on Augmented Lagrange Genetic Algorithm-Sequential Quadratic Programming(ALGA-SQP).Secondly,we use the universal time equation and Bootstrap resampling technology to calculate the interceptor's terminal error distribution and establish the relevant global optimization model by using the circumscribed cuboid volume of the interceptor's terminal position error ellipsoid as the optimization index.Finally,we combined the above two singleobjective optimization models to establish a global multi-objective optimization model that considers interception time and interception uncertainty and gave a hybrid multi-objective optimization algorithm based on Non-dominated Sorting Genetic Algorithm Ⅱ-Goal Achievement Method(NSGA2-GAM).The simulation example verifies the effectiveness of this method.展开更多
Underground mines require complex construction activities including the shaft, levels, raises, winzes and ore passes. In an underground mine based on stoping method, orebody part(s) maximizing profit should be determi...Underground mines require complex construction activities including the shaft, levels, raises, winzes and ore passes. In an underground mine based on stoping method, orebody part(s) maximizing profit should be determined. This process is called stope layout optimization (SLO) and implemented under site-specific geotechnical, operational and economic constraints. For practical purpose, the design obtained by SLO shows consecutive stopes in one path, which assists in defining the mining direction of these stopes. However, this direction may not accommodate the spatial distribution of the ore grade: if the orebody orientation and mining direction differ, the value of the mining operation may decrease. This paper proposes an approach whereby paths in the SLO are defined as decision variables to avoid the cost of mining in the wrong direction. Furthermore, in the genetic-based formulation, which accounts for orebody uncertainty, a robust cluster average design process is proposed to improve SLO’s performance regarding metal content. A case study in narrow gold vein deposit shows that the profit of an underground mining operation could be underestimated by 25%-48% if the algorithm ignores stope layout orientation.展开更多
A new strategy is presented to solve robust multi-physics multi-objective optimization problem known as improved multi-objective collaborative optimization （IMOCO） and its extension improved multi-objective robust c...A new strategy is presented to solve robust multi-physics multi-objective optimization problem known as improved multi-objective collaborative optimization （IMOCO） and its extension improved multi-objective robust collaborative （IMORCO）. In this work, the proposed IMORCO approach combined the IMOCO method, the worst possible point （WPP） constraint cuts and the Genetic algorithm NSGA-II type as an optimizer in order to solve the robust optimization problem of multi-physics of microstructures with uncertainties. The optimization problem is hierarchically decomposed into two levels： a microstructure level, and a disciplines levels, For validation purposes, two examples were selected： a numerical example, and an engineering example of capacitive micro machined ultrasonic transducers （CMUT） type. The obtained results are compared with those obtained from robust non-distributed and distributed optimization approach, non-distributed multi-objective robust optimization （NDMORO） and multi-objective collaborative robust optimization （McRO）, respectively. Results obtained from the application of the IMOCO approach to an optimization problem of a CMUT cell have reduced the CPU time by 44% ensuring a Pareto front close to the reference non-distributed multi-objective optimization （NDMO） approach （mahalanobis distance, D2M =0.9503 and overall spread, So=0.2309）. In addition, the consideration of robustness in IMORCO approach applied to a CMUT cell of optimization problem under interval uncertainty has reduced the CPU time by 23% keeping a robust Pareto front overlaps with that obtained by the robust NDMORO approach （D2M =10.3869 and So=0.0537）.展开更多
针对可再生能源出力和多能负荷的不确定性,提出一种综合能源系统多目标鲁棒优化规划方法。结合综合能源系统结构,构建其规划优化模型,在碳中和背景下将全寿期年化总成本最小和碳排放量最小作为两个规划目标。通过对源荷两侧不确定性因...针对可再生能源出力和多能负荷的不确定性,提出一种综合能源系统多目标鲁棒优化规划方法。结合综合能源系统结构,构建其规划优化模型,在碳中和背景下将全寿期年化总成本最小和碳排放量最小作为两个规划目标。通过对源荷两侧不确定性因素的刻画和对偶转换,建立综合能源系统多目标鲁棒优化规划模型,并融合启发式搜索和约束法的优点,提出基于拥挤度的约束生成算法,求解IES(Integrated Energy System)最优规划方案。算例结果证明了该规划方法的正确性和有效性。展开更多
随着“双碳”目标的提出,天然气作为火电转型过渡时期的重要能源,亟需通过绿色转型实现安全、稳定运行。碳捕集、利用和储存(carbon capture,utilization and storage,CCUS)技术的应用将为燃气电厂低碳发展提供可靠的技术支撑。文中提...随着“双碳”目标的提出,天然气作为火电转型过渡时期的重要能源,亟需通过绿色转型实现安全、稳定运行。碳捕集、利用和储存(carbon capture,utilization and storage,CCUS)技术的应用将为燃气电厂低碳发展提供可靠的技术支撑。文中提出一种发电机组(generator unit,GU)-电转气(power to gas,P2G)-CCUS系统,并构建一个考虑风电输出不确定性的数据驱动鲁棒优化(data-driven robust optimization,DDRO)模型。在此基础上,由于现有优化方法无法实现科学成本分配,考虑到主体间的合作博弈关系及系统运行的稳定性,建立了系统内部基于核仁法(nucleolus based cooperative game,NCG)的成本分配模型,以确保成本分配的科学性和合理性。结果表明:引入CCUS可以显著减少碳排放,并通过参与碳市场获得额外的利润;DDRO模型可以有效抵抗不确定风电输出的干扰,增强系统运行的安全性,降低传统优化模型的保守性;NCG模型可以实现GU、P2G和CCUS之间的合理分配,使得每个参与者都可以获得比其独立运行时更高的收益,提高参与者的合作意愿,进而增强合作的长期性与稳定性。展开更多
为解决含氢综合能源系统(hydrogen integrated energy system,HIES)在源荷出力和场景概率多重不确定性下难以兼顾鲁棒性和经济性的问题,提出了一种计及场景概率的分布鲁棒和两阶段鲁棒结合的三阶段四层随机鲁棒优化方法。首先,充分考虑...为解决含氢综合能源系统(hydrogen integrated energy system,HIES)在源荷出力和场景概率多重不确定性下难以兼顾鲁棒性和经济性的问题,提出了一种计及场景概率的分布鲁棒和两阶段鲁棒结合的三阶段四层随机鲁棒优化方法。首先,充分考虑系统运行的灵活性、低碳性,建立HIES,并引入碳捕集机组和阶梯式碳交易保证系统低碳运行;其次,用鲁棒优化法和随机规划中的场景法分别处理源荷出力不确定和场景概率不确定,建立min-max-max-min三阶段四层优化模型。采用变量交替迭代的列与约束生成算法求解得到最优鲁棒调度结果以及最恶劣场景概率分布。最后,通过算例分析表明所提方法兼顾了经济性和鲁棒性,并且系统具有较强的新能源消纳能力,保证了HIES系统的低碳、经济运行。展开更多
基金supported by the National Natural Science Foundation of China(71702072 71811540414+2 种基金 71573115)the Natural Science Foundation for Jiangsu Institutions(BK20170810)the Ministry of Education of Humanities and Social Science Planning Fund(18YJA630008)
文摘Minimizing the impact of the mixed uncertainties(i.e.,the aleatory uncertainty and the epistemic uncertainty) for a complex product of compliant mechanism(CPCM) quality improvement signifies a fascinating research topic to enhance the robustness.However, most of the existing works in the CPCM robust design optimization neglect the mixed uncertainties, which might result in an unstable design or even an infeasible design. To solve this issue, a response surface methodology-based hybrid robust design optimization(RSM-based HRDO) approach is proposed to improve the robustness of the quality characteristic for the CPCM via considering the mixed uncertainties in the robust design optimization. A bridge-type amplification mechanism is used to manifest the effectiveness of the proposed approach. The comparison results prove that the proposed approach can not only keep its superiority in the robustness, but also provide a robust scheme for optimizing the design parameters.
文摘The traditional tangent impulse interception problem does not consider the influence of actual deviation.However,by taking the actual state deviation of the interceptor into the orbit design process,an interception orbit that is more robust than the nominal orbit can be obtained.Therefore,we study the minimum time interception problem and the minimum terminal interception error problem under tangent impulse conditions and give an orbit optimization method that considers the interception time and the interception uncertainty.First,we express the interceptor's transfer time equation as a form of flight path angle,establish a global optimization model for solving the minimum time tangent impulse interception and give a hybrid optimization algorithm based on Augmented Lagrange Genetic Algorithm-Sequential Quadratic Programming(ALGA-SQP).Secondly,we use the universal time equation and Bootstrap resampling technology to calculate the interceptor's terminal error distribution and establish the relevant global optimization model by using the circumscribed cuboid volume of the interceptor's terminal position error ellipsoid as the optimization index.Finally,we combined the above two singleobjective optimization models to establish a global multi-objective optimization model that considers interception time and interception uncertainty and gave a hybrid multi-objective optimization algorithm based on Non-dominated Sorting Genetic Algorithm Ⅱ-Goal Achievement Method(NSGA2-GAM).The simulation example verifies the effectiveness of this method.
基金Project(488262-15)supported by the Natural Sciences and Engineering Research Council of Canada
文摘Underground mines require complex construction activities including the shaft, levels, raises, winzes and ore passes. In an underground mine based on stoping method, orebody part(s) maximizing profit should be determined. This process is called stope layout optimization (SLO) and implemented under site-specific geotechnical, operational and economic constraints. For practical purpose, the design obtained by SLO shows consecutive stopes in one path, which assists in defining the mining direction of these stopes. However, this direction may not accommodate the spatial distribution of the ore grade: if the orebody orientation and mining direction differ, the value of the mining operation may decrease. This paper proposes an approach whereby paths in the SLO are defined as decision variables to avoid the cost of mining in the wrong direction. Furthermore, in the genetic-based formulation, which accounts for orebody uncertainty, a robust cluster average design process is proposed to improve SLO’s performance regarding metal content. A case study in narrow gold vein deposit shows that the profit of an underground mining operation could be underestimated by 25%-48% if the algorithm ignores stope layout orientation.
基金Supported by National Natural Science Foundation of China （60496322）, Natural Science Foundation of Beijing （4083034）, and Scientific Research Common Program of Beijing Municipal Commission.of Education （KM200610005020）_ _ _
基金Supported by National High Technology Research and Development Program of China （863 Program） （2007AA809502C） National Natural Science Foundation of China （50979093） Program for New Century Excellent Talents in University （NCET-06-0877）
文摘A new strategy is presented to solve robust multi-physics multi-objective optimization problem known as improved multi-objective collaborative optimization （IMOCO） and its extension improved multi-objective robust collaborative （IMORCO）. In this work, the proposed IMORCO approach combined the IMOCO method, the worst possible point （WPP） constraint cuts and the Genetic algorithm NSGA-II type as an optimizer in order to solve the robust optimization problem of multi-physics of microstructures with uncertainties. The optimization problem is hierarchically decomposed into two levels： a microstructure level, and a disciplines levels, For validation purposes, two examples were selected： a numerical example, and an engineering example of capacitive micro machined ultrasonic transducers （CMUT） type. The obtained results are compared with those obtained from robust non-distributed and distributed optimization approach, non-distributed multi-objective robust optimization （NDMORO） and multi-objective collaborative robust optimization （McRO）, respectively. Results obtained from the application of the IMOCO approach to an optimization problem of a CMUT cell have reduced the CPU time by 44% ensuring a Pareto front close to the reference non-distributed multi-objective optimization （NDMO） approach （mahalanobis distance, D2M =0.9503 and overall spread, So=0.2309）. In addition, the consideration of robustness in IMORCO approach applied to a CMUT cell of optimization problem under interval uncertainty has reduced the CPU time by 23% keeping a robust Pareto front overlaps with that obtained by the robust NDMORO approach （D2M =10.3869 and So=0.0537）.
文摘针对可再生能源出力和多能负荷的不确定性,提出一种综合能源系统多目标鲁棒优化规划方法。结合综合能源系统结构,构建其规划优化模型,在碳中和背景下将全寿期年化总成本最小和碳排放量最小作为两个规划目标。通过对源荷两侧不确定性因素的刻画和对偶转换,建立综合能源系统多目标鲁棒优化规划模型,并融合启发式搜索和约束法的优点,提出基于拥挤度的约束生成算法,求解IES(Integrated Energy System)最优规划方案。算例结果证明了该规划方法的正确性和有效性。
文摘随着“双碳”目标的提出,天然气作为火电转型过渡时期的重要能源,亟需通过绿色转型实现安全、稳定运行。碳捕集、利用和储存(carbon capture,utilization and storage,CCUS)技术的应用将为燃气电厂低碳发展提供可靠的技术支撑。文中提出一种发电机组(generator unit,GU)-电转气(power to gas,P2G)-CCUS系统,并构建一个考虑风电输出不确定性的数据驱动鲁棒优化(data-driven robust optimization,DDRO)模型。在此基础上,由于现有优化方法无法实现科学成本分配,考虑到主体间的合作博弈关系及系统运行的稳定性,建立了系统内部基于核仁法(nucleolus based cooperative game,NCG)的成本分配模型,以确保成本分配的科学性和合理性。结果表明:引入CCUS可以显著减少碳排放,并通过参与碳市场获得额外的利润;DDRO模型可以有效抵抗不确定风电输出的干扰,增强系统运行的安全性,降低传统优化模型的保守性;NCG模型可以实现GU、P2G和CCUS之间的合理分配,使得每个参与者都可以获得比其独立运行时更高的收益,提高参与者的合作意愿,进而增强合作的长期性与稳定性。
文摘为解决含氢综合能源系统(hydrogen integrated energy system,HIES)在源荷出力和场景概率多重不确定性下难以兼顾鲁棒性和经济性的问题,提出了一种计及场景概率的分布鲁棒和两阶段鲁棒结合的三阶段四层随机鲁棒优化方法。首先,充分考虑系统运行的灵活性、低碳性,建立HIES,并引入碳捕集机组和阶梯式碳交易保证系统低碳运行;其次,用鲁棒优化法和随机规划中的场景法分别处理源荷出力不确定和场景概率不确定,建立min-max-max-min三阶段四层优化模型。采用变量交替迭代的列与约束生成算法求解得到最优鲁棒调度结果以及最恶劣场景概率分布。最后,通过算例分析表明所提方法兼顾了经济性和鲁棒性,并且系统具有较强的新能源消纳能力,保证了HIES系统的低碳、经济运行。
