Based on the characteristics of the internal structure of closed-cell aluminum foam, this paper attempts to illus- trate the process of reconstructing the internal structures of closed-cell aluminum foam in Monte-Carl...Based on the characteristics of the internal structure of closed-cell aluminum foam, this paper attempts to illus- trate the process of reconstructing the internal structures of closed-cell aluminum foam in Monte-Carlo method and the fractal characteristics of the reconstructed model. Furthermore, Binary Array Method is proposed by analyzing the reconstructed model and the thermal conductivity model of closed-cell aluminum foam is established. At the same time, the thermal conductivity of the foam materials with different porosity is calculated by Binary Array Method, and the calculated value coincides with the experimental results in the reference, which proves the correctness of these methods.展开更多
This paper proposes a new algorithm—binary glowworm swarm optimization(BGSO)to solve the unit commitment(UC)problem.After a certain quantity of initial feasible solutions is obtained by using the priority list and th...This paper proposes a new algorithm—binary glowworm swarm optimization(BGSO)to solve the unit commitment(UC)problem.After a certain quantity of initial feasible solutions is obtained by using the priority list and the decommitment of redundant unit,BGSO is applied to optimize the on/off state of the unit,and the Lambda-iteration method is adopted to solve the economic dispatch problem.In the iterative process,the solutions that do not satisfy all the constraints are adjusted by the correction method.Furthermore,different adjustment techniques such as conversion from cold start to hot start,decommitment of redundant unit,are adopted to avoid falling into local optimal solution and to keep the diversity of the feasible solutions.The proposed BGSO is tested on the power system in the range of 10–140 generating units for a 24-h scheduling period and compared to quantuminspired evolutionary algorithm(QEA),improved binary particle swarm optimization(IBPSO)and mixed integer programming(MIP).Simulated results distinctly show that BGSO is very competent in solving the UC problem in comparison to the previously reported algorithms.展开更多
Grassland fire is one of the most important disturbance factors in the natural ecosystems.This paper focuses on the spatial distribution of long-term grassland fire patterns in the Hulun Buir Grassland located in the ...Grassland fire is one of the most important disturbance factors in the natural ecosystems.This paper focuses on the spatial distribution of long-term grassland fire patterns in the Hulun Buir Grassland located in the northeast of Inner Mongolia Autonomous Region in China.The density or ratio of ignition can reflect the relationship between grassland fire and different ignition factors.Based on the relationship between the density or ratio of ignition in different range of each ignition factor and grassland fire events,an ignition probability model was developed by using binary logistic regression function and its overall accuracy averaged up to 81.7%.Meanwhile it was found that daily relative humidity,daily temperature,elevation,vegetation type,distance to county-level road,distance to town are more important determinants of spatial distribution of fire ignitions.Using Monte Carlo method,we developed a time-dependent stochastic ignition probability model based on the distribution of inter-annual daily relative humidity and daily temperature.Through this model,it is possible to estimate the spatial patterns of ignition probability for grassland fire,which will be helpful to the quantitative evaluation of grassland fire risk and its management in the future.展开更多
This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic(PV)installations.An accurate PV power generation forecasting tool essentially addresses the iss...This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic(PV)installations.An accurate PV power generation forecasting tool essentially addresses the issues resulting from the intermittent and uncertain nature of solar power to ensure efficient and reliable system operation.A day-ahead,hourly mean PV power generation forecasting method based on a combination of genetic algorithm(GA),particle swarm optimization(PSO)and adaptive neuro-fuzzy inference systems(ANFIS)is presented in this study.Binary GA with Gaussian process regression model based fitness function is used to determine important input parameters that significantly influence the amount of output power of a PV generation plant;and an integrated hybrid algorithm combining GA and PSO is used to optimize an ANFIS based PV power forecasting model for the plant.The proposed modeling technique is tested based on power generation data obtained from Goldwind microgrid system found in Beijing.Forecasting results demonstrate the superior performance of the proposed method as compared with commonly used forecasting approaches.The proposed approach outperformed existing artificial neural network(ANN),linear regression(LR),and persistence based forecasting models,validating its effectiveness.展开更多
Taking simultaneous variations in both particle volume and density into account, the radial mixing and segregation of binary granular bed in a rotating drum half loaded were investigated by a 3D discrete element metho...Taking simultaneous variations in both particle volume and density into account, the radial mixing and segregation of binary granular bed in a rotating drum half loaded were investigated by a 3D discrete element method. Then, based on the competition theory of condensation and percolation, radial segregation due to differences in particle volume and/or density was analyzed. The results show that if either percolation effect induced by volume difference or condensation effect induced by density difference dominates in the active layer of moving bed, separation will occur. Controlling the volume ratio or density ratio of the two types of particles can achieve an equilibrium state between percolation and condensation, and then homogenous mixture can be obtained. When the percolation balances with the condensation, the relationship between volume ratioand density ratiopresents nearly a power function. Scaling up a rotating drum will not affect the mixing degree of the granular bed so long as the volume ratio and density ratio are predefined.展开更多
文摘Based on the characteristics of the internal structure of closed-cell aluminum foam, this paper attempts to illus- trate the process of reconstructing the internal structures of closed-cell aluminum foam in Monte-Carlo method and the fractal characteristics of the reconstructed model. Furthermore, Binary Array Method is proposed by analyzing the reconstructed model and the thermal conductivity model of closed-cell aluminum foam is established. At the same time, the thermal conductivity of the foam materials with different porosity is calculated by Binary Array Method, and the calculated value coincides with the experimental results in the reference, which proves the correctness of these methods.
文摘This paper proposes a new algorithm—binary glowworm swarm optimization(BGSO)to solve the unit commitment(UC)problem.After a certain quantity of initial feasible solutions is obtained by using the priority list and the decommitment of redundant unit,BGSO is applied to optimize the on/off state of the unit,and the Lambda-iteration method is adopted to solve the economic dispatch problem.In the iterative process,the solutions that do not satisfy all the constraints are adjusted by the correction method.Furthermore,different adjustment techniques such as conversion from cold start to hot start,decommitment of redundant unit,are adopted to avoid falling into local optimal solution and to keep the diversity of the feasible solutions.The proposed BGSO is tested on the power system in the range of 10–140 generating units for a 24-h scheduling period and compared to quantuminspired evolutionary algorithm(QEA),improved binary particle swarm optimization(IBPSO)and mixed integer programming(MIP).Simulated results distinctly show that BGSO is very competent in solving the UC problem in comparison to the previously reported algorithms.
基金Under the auspices of National Science & Technology Support Program of China(No.2006BAD20B00)
文摘Grassland fire is one of the most important disturbance factors in the natural ecosystems.This paper focuses on the spatial distribution of long-term grassland fire patterns in the Hulun Buir Grassland located in the northeast of Inner Mongolia Autonomous Region in China.The density or ratio of ignition can reflect the relationship between grassland fire and different ignition factors.Based on the relationship between the density or ratio of ignition in different range of each ignition factor and grassland fire events,an ignition probability model was developed by using binary logistic regression function and its overall accuracy averaged up to 81.7%.Meanwhile it was found that daily relative humidity,daily temperature,elevation,vegetation type,distance to county-level road,distance to town are more important determinants of spatial distribution of fire ignitions.Using Monte Carlo method,we developed a time-dependent stochastic ignition probability model based on the distribution of inter-annual daily relative humidity and daily temperature.Through this model,it is possible to estimate the spatial patterns of ignition probability for grassland fire,which will be helpful to the quantitative evaluation of grassland fire risk and its management in the future.
文摘This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic(PV)installations.An accurate PV power generation forecasting tool essentially addresses the issues resulting from the intermittent and uncertain nature of solar power to ensure efficient and reliable system operation.A day-ahead,hourly mean PV power generation forecasting method based on a combination of genetic algorithm(GA),particle swarm optimization(PSO)and adaptive neuro-fuzzy inference systems(ANFIS)is presented in this study.Binary GA with Gaussian process regression model based fitness function is used to determine important input parameters that significantly influence the amount of output power of a PV generation plant;and an integrated hybrid algorithm combining GA and PSO is used to optimize an ANFIS based PV power forecasting model for the plant.The proposed modeling technique is tested based on power generation data obtained from Goldwind microgrid system found in Beijing.Forecasting results demonstrate the superior performance of the proposed method as compared with commonly used forecasting approaches.The proposed approach outperformed existing artificial neural network(ANN),linear regression(LR),and persistence based forecasting models,validating its effectiveness.
基金Projects(5137424151275531)supported by the National Natural Science Foundation of ChinaProject(CX2014B059)supported by the Innovation Foundation for Postgraduate of Hunan Province,China
文摘Taking simultaneous variations in both particle volume and density into account, the radial mixing and segregation of binary granular bed in a rotating drum half loaded were investigated by a 3D discrete element method. Then, based on the competition theory of condensation and percolation, radial segregation due to differences in particle volume and/or density was analyzed. The results show that if either percolation effect induced by volume difference or condensation effect induced by density difference dominates in the active layer of moving bed, separation will occur. Controlling the volume ratio or density ratio of the two types of particles can achieve an equilibrium state between percolation and condensation, and then homogenous mixture can be obtained. When the percolation balances with the condensation, the relationship between volume ratioand density ratiopresents nearly a power function. Scaling up a rotating drum will not affect the mixing degree of the granular bed so long as the volume ratio and density ratio are predefined.
