Sediment deposition in the pumping station has a huge negative impact on unit operation.The three-dimensional CFD method has been used to simulate inlet structure flow in pumping station based on the Eulerian solid- l...Sediment deposition in the pumping station has a huge negative impact on unit operation.The three-dimensional CFD method has been used to simulate inlet structure flow in pumping station based on the Eulerian solid- liquid two-phase flow model. The numerical results of the preliminary scheme show that sediment deposition occurs in the forebay of pumping station because of poor flow pattern therein. In order to improve hydraulic configuration in the forebay,one modified measure reconstructs water diversion weir shape,and another measure sets a water retaining sill in the approach channel. The simulation results of the modified scheme prove that back flow in the forebay has been eliminated and the sediment deposition region has also been reduced.展开更多
Bus rapid transit （BRT） systems have been shown to have many advantages including affordability, high capacity vehicles, and reliable service. Due to these attractive advantages, many cities throughout the world are...Bus rapid transit （BRT） systems have been shown to have many advantages including affordability, high capacity vehicles, and reliable service. Due to these attractive advantages, many cities throughout the world are in the process of planning the construction of BRT systems. To improve the performance of BRT systems, many researchers study BRT operation and control, which include the study of dwell times at bus/BRT stations. To ensure the effectiveness of real-time control which aims to avoid bus/BRT vehicles congestion, accurate dwell time models are needed. We develop our models using data from a BRT vehicle survey conducted in Changzhou, China, where BRT lines are built along passenger corridors, and BRT stations are enclosed like light rails. This means that interactions between passengers traveling on the BRT system are more frequent than those in traditional transit system who use platform stations. We statistically analyze the BRT vehicle survey data, and based on this analysis, we are able to make the following conclusions： （ I ） The delay time per passenger at a BRT station is less than that at a non-BRT station, which implies that BRT stations are efficient in the sense that they are able to move passengers quickly. （II） The dwell time follows a logarithmic normal distribution with a mean of 2.56 and a variance of 0.53. （III） The greater the number of BRT lines serviced by a station, the longer the dwell time is. （IV） Daily travel demands are highest during the morning peak interval where the dwell time, the number of passengers boarding and alighting and the number of passengers on vehicles reach their maximum values. （V） The dwell time is highly positively correlated with the total number of passengers boarding and alighting. （VI） The delay per passenger is negatively correlated with the total number of passengers boarding and alighting. We propose two dwell time models for the BRT station. The first proposed model is a linear model while the second is nonlinear. We introduce the conflict between passengers boarding and alighting into our models. Finally, by comparing our models with the models of Rajbhandari and Chien et al., and TCQSM （Transit Capacity and Quality of Service Manual）, we conclude that the proposed nonlinear model can better predict the dwell time at BRT stations.展开更多
为解决多基站定位模型中基站之间同步代价高的问题,提出了一种基于多根长馈线天线基站的到达时间差(Time Difference of Arrival,TDOA)定位模型,给出了模型方程和求解方法,该方法将复杂的3对距离差方程组转化为1个一元八次方程,然后采用...为解决多基站定位模型中基站之间同步代价高的问题,提出了一种基于多根长馈线天线基站的到达时间差(Time Difference of Arrival,TDOA)定位模型,给出了模型方程和求解方法,该方法将复杂的3对距离差方程组转化为1个一元八次方程,然后采用Aberth-Newton迭代法来迭代求解方程。通过计算机仿真验证了基于多根长馈线天线基站的TDOA定位模型和解法的有效性,并对该模型的多解问题进行了分析,用优化基站布局的方案,解决了定位模型的唯一解问题。本定位模型在覆盖范围数百米时,定位精度可达分米级。展开更多
电动汽车(electric vehicle,EV)充电行为存在强随机性与高波动性,使其充电站短期充电负荷预测精度较低,作为移动电力存储和负载资源参与车到网(vehicle to grid,V2G)服务中,其调度中心需要在短时间内预测EV的充电负荷来改善其对电网负...电动汽车(electric vehicle,EV)充电行为存在强随机性与高波动性,使其充电站短期充电负荷预测精度较低,作为移动电力存储和负载资源参与车到网(vehicle to grid,V2G)服务中,其调度中心需要在短时间内预测EV的充电负荷来改善其对电网负荷的影响。为了提高EV充电站短期充电负荷预测精度,提出一种冠豪猪优化器变分模态分解双向长短期记忆神经网络(crested porcupine optimizer variational mode decomposition bidirectional long short term memory,CPO VMD BiLSTM)组合模型进行EV充电站短期充电负荷预测的方法。首先,考虑影响EV充电负荷的多种因素和历史充电站充电负荷共同构成输入特征矩阵。然后利用CPO算法对VMD其核心参数进行优化搜索,实现参数自适应优化设置。之后采用CPO VMD对历史充电负荷数据进行分解,弱化负荷的非平稳性,捕捉其局部特征。最后在BiLSTM模型中输入分解后的特征矩阵来实现充电站短期充电负荷的预测目标。以美国ANN DATA公开数据集中位于加州理工大学校园内EV充电站的历史充电负荷数据作为实际算例,与独立模型、未优化组合模型、优化组合模型进行对比,均方根误差(root mean squared error,RMSE)和平均绝对误差(mean absolute error,MAE)平均降低了41.23%和59.04%。因此,验证了提出方法在充电站充电负荷短期预测中精度的提高和实用性。展开更多
基金Chinese National Foundation of Natural Science-Key Projects(51339005)
文摘Sediment deposition in the pumping station has a huge negative impact on unit operation.The three-dimensional CFD method has been used to simulate inlet structure flow in pumping station based on the Eulerian solid- liquid two-phase flow model. The numerical results of the preliminary scheme show that sediment deposition occurs in the forebay of pumping station because of poor flow pattern therein. In order to improve hydraulic configuration in the forebay,one modified measure reconstructs water diversion weir shape,and another measure sets a water retaining sill in the approach channel. The simulation results of the modified scheme prove that back flow in the forebay has been eliminated and the sediment deposition region has also been reduced.
基金supported by the National Scienceand Technology Support Program of China (No.2009BAG17B01)
文摘Bus rapid transit （BRT） systems have been shown to have many advantages including affordability, high capacity vehicles, and reliable service. Due to these attractive advantages, many cities throughout the world are in the process of planning the construction of BRT systems. To improve the performance of BRT systems, many researchers study BRT operation and control, which include the study of dwell times at bus/BRT stations. To ensure the effectiveness of real-time control which aims to avoid bus/BRT vehicles congestion, accurate dwell time models are needed. We develop our models using data from a BRT vehicle survey conducted in Changzhou, China, where BRT lines are built along passenger corridors, and BRT stations are enclosed like light rails. This means that interactions between passengers traveling on the BRT system are more frequent than those in traditional transit system who use platform stations. We statistically analyze the BRT vehicle survey data, and based on this analysis, we are able to make the following conclusions： （ I ） The delay time per passenger at a BRT station is less than that at a non-BRT station, which implies that BRT stations are efficient in the sense that they are able to move passengers quickly. （II） The dwell time follows a logarithmic normal distribution with a mean of 2.56 and a variance of 0.53. （III） The greater the number of BRT lines serviced by a station, the longer the dwell time is. （IV） Daily travel demands are highest during the morning peak interval where the dwell time, the number of passengers boarding and alighting and the number of passengers on vehicles reach their maximum values. （V） The dwell time is highly positively correlated with the total number of passengers boarding and alighting. （VI） The delay per passenger is negatively correlated with the total number of passengers boarding and alighting. We propose two dwell time models for the BRT station. The first proposed model is a linear model while the second is nonlinear. We introduce the conflict between passengers boarding and alighting into our models. Finally, by comparing our models with the models of Rajbhandari and Chien et al., and TCQSM （Transit Capacity and Quality of Service Manual）, we conclude that the proposed nonlinear model can better predict the dwell time at BRT stations.
文摘为解决多基站定位模型中基站之间同步代价高的问题,提出了一种基于多根长馈线天线基站的到达时间差(Time Difference of Arrival,TDOA)定位模型,给出了模型方程和求解方法,该方法将复杂的3对距离差方程组转化为1个一元八次方程,然后采用Aberth-Newton迭代法来迭代求解方程。通过计算机仿真验证了基于多根长馈线天线基站的TDOA定位模型和解法的有效性,并对该模型的多解问题进行了分析,用优化基站布局的方案,解决了定位模型的唯一解问题。本定位模型在覆盖范围数百米时,定位精度可达分米级。
文摘电动汽车(electric vehicle,EV)充电行为存在强随机性与高波动性,使其充电站短期充电负荷预测精度较低,作为移动电力存储和负载资源参与车到网(vehicle to grid,V2G)服务中,其调度中心需要在短时间内预测EV的充电负荷来改善其对电网负荷的影响。为了提高EV充电站短期充电负荷预测精度,提出一种冠豪猪优化器变分模态分解双向长短期记忆神经网络(crested porcupine optimizer variational mode decomposition bidirectional long short term memory,CPO VMD BiLSTM)组合模型进行EV充电站短期充电负荷预测的方法。首先,考虑影响EV充电负荷的多种因素和历史充电站充电负荷共同构成输入特征矩阵。然后利用CPO算法对VMD其核心参数进行优化搜索,实现参数自适应优化设置。之后采用CPO VMD对历史充电负荷数据进行分解,弱化负荷的非平稳性,捕捉其局部特征。最后在BiLSTM模型中输入分解后的特征矩阵来实现充电站短期充电负荷的预测目标。以美国ANN DATA公开数据集中位于加州理工大学校园内EV充电站的历史充电负荷数据作为实际算例,与独立模型、未优化组合模型、优化组合模型进行对比,均方根误差(root mean squared error,RMSE)和平均绝对误差(mean absolute error,MAE)平均降低了41.23%和59.04%。因此,验证了提出方法在充电站充电负荷短期预测中精度的提高和实用性。
