To expand mission capabilities needed without a proportional increase in cost or risk for exploration of the solar system,the multiple objective trajectory using low-thrust propulsion and gravity-assist technique is c...To expand mission capabilities needed without a proportional increase in cost or risk for exploration of the solar system,the multiple objective trajectory using low-thrust propulsion and gravity-assist technique is considered.However,low-thrust,gravity-assist trajectories pose significant optimization challenges because of their large design space.Here,the planets are selected as primal scientific mission goals,while the asteroids are selected as secondary scientific mission goals,and a global trajectory optimization problem is introduced and formulated.This multi-objective decision making process is transformed into a bi-level programming problem,where the targets like planets with small subsamples but high weight are optimized in up level,and targets like asteroids with large subsamples but low weight are optimized in down level.Then,the selected solutions for bi-level programming are optimized thanks to a cooperative Differential Evolution(DE) algorithm that is developed from the original DE algorithm;in addition,an sequential quadratic programming(SQP) method is used in low-thrust optimization.This solution approach is successfully applied to the simulation case of the multi-objective trajectory design problem.The results obtained are presented and discussed.展开更多
In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （S...In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （SI） gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide （CO）, hydrocarbons （HC）, and nitrogen oxides （NOx）, which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks （ANNs） were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms： NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NOx, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NOx, respectively.展开更多
Urban energy systems(UESs)play a pivotal role in the consumption of clean energy and the promotion of energy cascade utilization.In the context of the construction and operation strategy of UESs with multiple compleme...Urban energy systems(UESs)play a pivotal role in the consumption of clean energy and the promotion of energy cascade utilization.In the context of the construction and operation strategy of UESs with multiple complementary energy resources,a comprehensive assessment of the energy efficiency is of paramount importance.First,a multi-dimensional evaluation system with four primary indexes of energy utilization,environmental protection,system operation,and economic efficiency and 21 secondary indexes is constructed to comprehensively portray the UES.Considering that the evaluation system may contain a large number of indexes and that there is overlapping information among them,an energy efficiency evaluation method based on data processing,dimensionality reduction,integration of combined weights,and gray correlation analysis is proposed.This method can effectively reduce the number of calculations and improve the accuracy of energy efficiency assessments.Third,a demonstration project for a UES in China is presented.The energy efficiency of each scenario is assessed using six operational scenarios.The results show that Scenario 5,in which parks operate independently and investors build shared energy-storage equipment,has the best results and is best suited for green and low-carbon development.The results of the comparative assessment methods show that the proposed method provides a good energy efficiency assessment.This study provides a reference for the optimal planning,construction,and operation of UESs with multiple energy sources.展开更多
Recently,reliability-based design is a universal method to quantify negative influence of uncertainty in geotechnical engineering.However,for deep foundation pit,evaluating the system safety of retaining structures an...Recently,reliability-based design is a universal method to quantify negative influence of uncertainty in geotechnical engineering.However,for deep foundation pit,evaluating the system safety of retaining structures and finding cost-effective design points are main challenges.To address this,this study proposes a novel system reliability-based robust design method for retaining system of deep foundation pit and illustrated this method via a simplified case history in Suzhou,China.The proposed method included two parts:system reliability model and robust design method.Back Propagation Neural Network(BPNN)is used to fit limit state functions and conduct efficient reliability analysis.The common source random variable(CSRV)model are used to evaluate correlation between failure modes and determine the system reliability.Furthermore,based on the system reliability model,a robust design method is developed.This method aims to find cost-effective design points.To solve this problem,the third generation non-dominated genetic algorithm(NSGA-III)is adopted.The efficiency and accuracy of whole computations are improved by involving BPNN models and NSGA-III algorithm.The proposed method has a good performance in locating the balanced design point between safety and construction cost.Moreover,the proposed method can provide design points with reasonable stiffness distribution.展开更多
Due to increased penetration of renewable energies,DC links and other emerging technologies,power system operation and planning have to cope with various uncertainties and risks.In order to solve the problems of excee...Due to increased penetration of renewable energies,DC links and other emerging technologies,power system operation and planning have to cope with various uncertainties and risks.In order to solve the problems of exceeding short circuit current and multi-infeed DC interaction,a coordinated optimization method is presented in this paper.Firstly,a branch selection strategy is proposed by analyzing the sensitivity relationship between current limiting measures and the impedance matrix.Secondly,the impact of network structure changes on the multi-infeed DC system is derived.Then the coordinated optimization model is established,which considers the cost and effect of current limiting measures,the tightness of network structure and the voltage support capability of AC system to multiple DCs.Finally,the non-dominated sorting genetic algorithm II combining with the branch selection strategy,is used to find the Pareto optimal schemes.Case studies on a planning power system demonstrated the feasibility and speediness of this method.展开更多
基金supported by the Open Research Foundation of Science and Technology on Aerospace Flight Dynamics Laboratory of China (Grant No. 2012afdl005)
文摘To expand mission capabilities needed without a proportional increase in cost or risk for exploration of the solar system,the multiple objective trajectory using low-thrust propulsion and gravity-assist technique is considered.However,low-thrust,gravity-assist trajectories pose significant optimization challenges because of their large design space.Here,the planets are selected as primal scientific mission goals,while the asteroids are selected as secondary scientific mission goals,and a global trajectory optimization problem is introduced and formulated.This multi-objective decision making process is transformed into a bi-level programming problem,where the targets like planets with small subsamples but high weight are optimized in up level,and targets like asteroids with large subsamples but low weight are optimized in down level.Then,the selected solutions for bi-level programming are optimized thanks to a cooperative Differential Evolution(DE) algorithm that is developed from the original DE algorithm;in addition,an sequential quadratic programming(SQP) method is used in low-thrust optimization.This solution approach is successfully applied to the simulation case of the multi-objective trajectory design problem.The results obtained are presented and discussed.
文摘In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （SI） gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide （CO）, hydrocarbons （HC）, and nitrogen oxides （NOx）, which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks （ANNs） were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms： NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NOx, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NOx, respectively.
基金supported by the National Natural Science Foundation of China under Grant 51567002 and Grant 50767001.
文摘Urban energy systems(UESs)play a pivotal role in the consumption of clean energy and the promotion of energy cascade utilization.In the context of the construction and operation strategy of UESs with multiple complementary energy resources,a comprehensive assessment of the energy efficiency is of paramount importance.First,a multi-dimensional evaluation system with four primary indexes of energy utilization,environmental protection,system operation,and economic efficiency and 21 secondary indexes is constructed to comprehensively portray the UES.Considering that the evaluation system may contain a large number of indexes and that there is overlapping information among them,an energy efficiency evaluation method based on data processing,dimensionality reduction,integration of combined weights,and gray correlation analysis is proposed.This method can effectively reduce the number of calculations and improve the accuracy of energy efficiency assessments.Third,a demonstration project for a UES in China is presented.The energy efficiency of each scenario is assessed using six operational scenarios.The results show that Scenario 5,in which parks operate independently and investors build shared energy-storage equipment,has the best results and is best suited for green and low-carbon development.The results of the comparative assessment methods show that the proposed method provides a good energy efficiency assessment.This study provides a reference for the optimal planning,construction,and operation of UESs with multiple energy sources.
基金The authors are grateful to the financial support from National Natural Science Foundation of China(No.52078086)Postdoctoral innovative talents support program,Chongqing(Grant No.CQBX2021022)Financial support from China Southwest Geotechnical Investigation&Design Institute Co.,Ltd(C2021-0264).
文摘Recently,reliability-based design is a universal method to quantify negative influence of uncertainty in geotechnical engineering.However,for deep foundation pit,evaluating the system safety of retaining structures and finding cost-effective design points are main challenges.To address this,this study proposes a novel system reliability-based robust design method for retaining system of deep foundation pit and illustrated this method via a simplified case history in Suzhou,China.The proposed method included two parts:system reliability model and robust design method.Back Propagation Neural Network(BPNN)is used to fit limit state functions and conduct efficient reliability analysis.The common source random variable(CSRV)model are used to evaluate correlation between failure modes and determine the system reliability.Furthermore,based on the system reliability model,a robust design method is developed.This method aims to find cost-effective design points.To solve this problem,the third generation non-dominated genetic algorithm(NSGA-III)is adopted.The efficiency and accuracy of whole computations are improved by involving BPNN models and NSGA-III algorithm.The proposed method has a good performance in locating the balanced design point between safety and construction cost.Moreover,the proposed method can provide design points with reasonable stiffness distribution.
基金This work was supported by State Grid Corporation of China,Major Projects on Planning and Operation Control of Large Scale Grid under Grant SGCC-MPLG020-2012.
文摘Due to increased penetration of renewable energies,DC links and other emerging technologies,power system operation and planning have to cope with various uncertainties and risks.In order to solve the problems of exceeding short circuit current and multi-infeed DC interaction,a coordinated optimization method is presented in this paper.Firstly,a branch selection strategy is proposed by analyzing the sensitivity relationship between current limiting measures and the impedance matrix.Secondly,the impact of network structure changes on the multi-infeed DC system is derived.Then the coordinated optimization model is established,which considers the cost and effect of current limiting measures,the tightness of network structure and the voltage support capability of AC system to multiple DCs.Finally,the non-dominated sorting genetic algorithm II combining with the branch selection strategy,is used to find the Pareto optimal schemes.Case studies on a planning power system demonstrated the feasibility and speediness of this method.
