Computer networks and power transmission networks are treated as capacitated flow networks.A capacitated flow network may partially fail due to maintenance.Therefore,the capacity of each edge should be optimally assig...Computer networks and power transmission networks are treated as capacitated flow networks.A capacitated flow network may partially fail due to maintenance.Therefore,the capacity of each edge should be optimally assigned to face critical situations-i.e.,to keep the network functioning normally in the case of failure at one or more edges.The robust design problem(RDP)in a capacitated flow network is to search for the minimum capacity assignment of each edge such that the network still survived even under the edge’s failure.The RDP is known as NP-hard.Thus,capacity assignment problem subject to system reliability and total capacity constraints is studied in this paper.The problem is formulated mathematically,and a genetic algorithm is proposed to determine the optimal solution.The optimal solution found by the proposed algorithm is characterized by maximum reliability and minimum total capacity.Some numerical examples are presented to illustrate the efficiency of the proposed approach.展开更多
Analyzing rock mass seepage using the discrete fracture network(DFN)flow model poses challenges when dealing with complex fracture networks.This paper presents a novel DFN flow model that incorporates the actual conne...Analyzing rock mass seepage using the discrete fracture network(DFN)flow model poses challenges when dealing with complex fracture networks.This paper presents a novel DFN flow model that incorporates the actual connections of large-scale fractures.Notably,this model efficiently manages over 20,000 fractures without necessitating adjustments to the DFN geometry.All geometric analyses,such as identifying connected fractures,dividing the two-dimensional domain into closed loops,triangulating arbitrary loops,and refining triangular elements,are fully automated.The analysis processes are comprehensively introduced,and core algorithms,along with their pseudo-codes,are outlined and explained to assist readers in their programming endeavors.The accuracy of geometric analyses is validated through topological graphs representing the connection relationships between fractures.In practical application,the proposed model is employed to assess the water-sealing effectiveness of an underground storage cavern project.The analysis results indicate that the existing design scheme can effectively prevent the stored oil from leaking in the presence of both dense and sparse fractures.Furthermore,following extensive modification and optimization,the scale and precision of model computation suggest that the proposed model and developed codes can meet the requirements of engineering applications.展开更多
The presence of circles in the network maximum flow problem increases the complexity of the preflow algorithm.This study proposes a novel two-stage preflow algorithm to address this issue.First,this study proves that ...The presence of circles in the network maximum flow problem increases the complexity of the preflow algorithm.This study proposes a novel two-stage preflow algorithm to address this issue.First,this study proves that at least one zero-flow arc must be present when the flow of the network reaches its maximum value.This result indicates that the maximum flow of the network will remain constant if a zero-flow arc within a circle is removed;therefore,the maximum flow of each network without circles can be calculated.The first stage involves identifying the zero-flow arc in the circle when the network flow reaches its maximum.The second stage aims to remove the zero-flow arc identified and modified in the first stage,thereby producing a new network without circles.The maximum flow of the original looped network can be obtained by solving the maximum flow of the newly generated acyclic network.Finally,an example is provided to demonstrate the validity and feasibility of this algorithm.This algorithm not only improves computational efficiency but also provides new perspectives and tools for solving similar network optimization problems.展开更多
This article considers a class of bottleneck capacity expansion problems. Such problems aim to enhance bottleneck capacity to a certain level with minimum cost. Given a network G（V,A,C^-） consisting of a set of node...This article considers a class of bottleneck capacity expansion problems. Such problems aim to enhance bottleneck capacity to a certain level with minimum cost. Given a network G（V,A,C^-） consisting of a set of nodes V = {v1,v2,... ,vn}, a set of arcs A C {（vi,vj） ｜ i = 1,2,...,n; j = 1,2,...,n} and a capacity vector C. The component C^-ij of C is the capacity of arc （vi, vj）. Define the capacity of a subset A′ of A as the minimum capacity of the arcs in A, the capacity of a family F of subsets of A is the maximum capacity of its members. There are two types of expanding models. In the arc-expanding model, the unit cost to increase the capacity of arc （vi, vj） is ωij. In the node-expanding model, it is assumed that the capacities of all arcs （vi, vj） which start at the same node vi should be increased by the same amount and that the unit cost to make such expansion is wi. This article considers three kinds of bottleneck capacity expansion problems （path, spanning arborescence and maximum flow） in both expanding models. For each kind of expansion problems, this article discusses the characteristics of the problems and presents several results on the complexity of the problems.展开更多
The paper points out the relationship between the bottleneck and the minimum cutset of the network, and presents a capacity expansion algorithm of network optimization to solve the network bottleneck problem. The comp...The paper points out the relationship between the bottleneck and the minimum cutset of the network, and presents a capacity expansion algorithm of network optimization to solve the network bottleneck problem. The complexity of the algorithm is also analyzed. As required by the algorithm, some virtual sources are imported through the whole positive direction subsection in the network, in which a certain capacity value is given. Simultaneously, a corresponding capacity-expanded network is constructed to search all minimum cutsets. For a given maximum flow value of the network, the authors found an adjustment value of each minimum cutset arcs group with gradually reverse calculation and marked out the feasible flow on the capacity-extended networks again with the adjustment value increasing. All this has been done repeatedly until the original topology structure is resumed. So the algorithm can increase the capacity of networks effectively and solve the bottleneck problem of networks.展开更多
The minimum cost of capacity expansion for time-limited transportation problem on-demand (MCCETLTPD) is to find such a practicable capacity expansion transportation scheme satisfying the time-limited T along with all ...The minimum cost of capacity expansion for time-limited transportation problem on-demand (MCCETLTPD) is to find such a practicable capacity expansion transportation scheme satisfying the time-limited T along with all origins’ supply and all destinations’ demands as well as the expanding cost is minimum. Actually, MCCETLTPD is a balance transportation problem and a variant problem of minimum cost maximum flow problem. In this paper, by creating a mathematical model and constructing a network with lower and upper arc capacities, MCCETLTPD is transformed into searching feasible flow in the constructed network, and consequently, an algorithm MCCETLTPD-A is developed as MCCETLTPD’s solution method basing minimum cost maximum flow algorithm. Computational study validates that the MCCETLTPD-A algorithm is an efficient approach to solving the MCCETLTPD.展开更多
Accurate prediction of multiphase flowing bottom-hole pressure(FBHP)in wellbores is an important factor required for optimal tubing design and production optimization.Existing empirical correlations and mechanistic mo...Accurate prediction of multiphase flowing bottom-hole pressure(FBHP)in wellbores is an important factor required for optimal tubing design and production optimization.Existing empirical correlations and mechanistic models provide inaccurate FBHP predictions when applied to real-time field datasets because they were developed with laboratory-dependent parameters.Most machine learning(ML)models for FBHP prediction are developed with real-time field data but presented as black-box models.In addition,these ML models cannot be reproduced by other users because the dataset used for training the machine learning algorithm is not open source.These make using the ML models on new datasets difficult.This study presents an artificial neural network(ANN)visible mathematical model for real-time multiphase FBHP prediction in wellbores.A total of 1001 normalized real-time field data points were first used in developing an ANN black-box model.The data points were randomly divided into three different sets;70%for training,15%for validation,and the remaining 15%for testing.Statistical analysis showed that using the Levenberg-Marquardt training optimization algorithm(trainlm),hyperbolic tangent activation function(tansig),and three hidden layers with 20,15 and 15 neurons in the first,second and third hidden layers respectively achieved the best performance.The trained ANN model was then translated into an ANN visible mathematical model by extracting the tuned weights and biases.Trend analysis shows that the new model produced the expected effects of physical attributes on FBHP.Furthermore,statistical and graphical error analysis results show that the new model outperformed existing empirical correlations,mechanistic models,and an ANN white-box model.Training of the ANN on a larger dataset containing new data points covering a wider range of each input parameter can broaden the applicability domain of the proposed ANN visible mathematical model.展开更多
The transportation department relies on accurate traffic forecasting for effective decision-making.However,determining relevant parameters for existing traffic flow prediction models poses challenges.To address this i...The transportation department relies on accurate traffic forecasting for effective decision-making.However,determining relevant parameters for existing traffic flow prediction models poses challenges.To address this issue,this study proposes a hybrid model,sparrow search algorithm-gated recurrent unit-long short-term memory(SSA-GRU-LSTM),which leverages the SSA to optimize the GRUs and LSTM networks.The SSA is employed to identify the optimal parameters for the GRULSTM model,mitigating their impact on prediction accuracy.This model integrates the predictive efficiency of the GRU,LSTM’s capability in temporal data analysis,and the fast convergence and global search attributes of the SSA.Comprehensive experiments are conducted to validate the proposed method via traffic flow datasets,and the results are compared with those of baseline models.The numerical results demonstrate the superior performance of the SSA-GRU-LSTM model.Compared with the baselines,the proposed model results in reductions in the root mean square error(RMSE)of 4.632%-45.206%,the mean absolute error(MAE)of 2.608%-53.327%,the mean absolute percentage error(MAPE)of 1.324%-13.723%,and an increase in R^(2) of 0.5%-17.5%.Consequently,the SSA-GRU-LSTM model has high prediction accuracy and measurement stability.展开更多
Abstract:This paper addresses the problem of improving the optimal value of the Maximum Capacity Path(MCP)through expansion in a flexible network,and minimizing the involved costs.The only condition applied to the cos...Abstract:This paper addresses the problem of improving the optimal value of the Maximum Capacity Path(MCP)through expansion in a flexible network,and minimizing the involved costs.The only condition applied to the cost functions is to be non-decreasing monotone.This is a non-restrictive condition,reflecting the reality in practice,and is considered for the first time in the literature.Moreover,the total cost of expansion is a combination of max-type cost(e.g.,for supervision)and sum-type cost(e.g.for building infrastructures,price of materials,price of labor,etc.).For this purpose,two types of strategies are combined:(l)increasing the capacity of the existing arcs,and(l)adding potential new arcs.Two different problems are introduced and solved.Both the problems have immediate applications in Internet routing infrastructure.The first one is to extend the network,so that the capacity of an McP in the modified network becomes equal to a prescribed value,therefore the cost of modifications is minimized.A strongly polynomial-time algorithm is deduced to solve this problem.The second problem is a network expansion under a budget constraint,so that the capacity of an McP is maximized.A weakly polynomial-time algorithm is presented to deal with it.In the special case when all the costs are linear,a Meggido's parametric search technique is used to develop an algorithm for solving the problem in strongly polynomial time.This new approach has a time complexity of O(n^(4)),which is better than the time complexity of O(n4 log(n)of the previously known method from literature.展开更多
The discussions on the development of an electricity market model for accommodating cross-border cooperation remains active in Europe.The main interest is the establishment of market couplings without curtailing the f...The discussions on the development of an electricity market model for accommodating cross-border cooperation remains active in Europe.The main interest is the establishment of market couplings without curtailing the fair use of the scarce transmission capacity.However,it is difficult to gain mutual consensus on this subject because of the absence of convincing simulation results for the entire region.To achieve that,researchers need a common grid model(CGM)which is a simplified representation of the detailed transmission model which comprises aggregated buses and transmission lines.A CGM should sufficiently represent the inter-area power flow characteristics.Generally,it is difficult to establish a standard CGM that represents the actual transmission network with a suf-ficient degree of exactness because it requires knowledge on the details of the transmission network,which are undisclosed.This paper addresses the issue and reviews the existing approaches in transmission network approximation,and their shortcomings.Then,it proposes a new approach called the adaptive CGM approximation(ACA)for serving the purpose.The ACA is a datadriven approach,developed based on the direct current power flow theory.It is able to construct a CGM based on the published power flow data between the inter-connected market areas.This is done by solving the issue as a non-linear model fitting problem.The method is validated using three case studies.展开更多
The complex phenomena that occur during the plastic deformation process of aluminum alloys,such as strain rate hardening,dynamic recovery,recrystallization,and damage evolution,can significantly affect the properties ...The complex phenomena that occur during the plastic deformation process of aluminum alloys,such as strain rate hardening,dynamic recovery,recrystallization,and damage evolution,can significantly affect the properties of these alloys and limit their applications.Therefore,studying the high-temperature flow stress characteristics of these materials and developing accurate constitutive models has significant scientific research value.In this study,quasi-static tensile tests were conducted on 5754 aluminum alloy using an electronic testing machine combined with a hightemperature environmental chamber to explore its plastic flow behavior under main deformation parameters(such as deformation temperatures,strain rates,and strain).On the basis of true strain-stress data,a BP neural network constitutive model of the alloy was built,aiming to reveal the influence laws of main deformation parameters on flow stress.To further improve the model performance,the ant colony optimization algorithm is introduced to optimize the BP neural network constitutive model,and the relationship between the prediction stability of the model and the parameter settings is explored.Furthermore,the predictability of the two models was evaluated by the statistical indicators,including the correlation coefficient(R^(2)),RMSE,MAE,and confidence intervals.The research results indicate that the prediction accuracy,stability,and generalization ability of the optimized BP neural network constitutive model have been significantly enhanced.展开更多
In order to play a positive role of decentralised wind power on-grid for voltage stability improvement and loss reduction of distribution network,a multi-objective two-stage decentralised wind power planning method is...In order to play a positive role of decentralised wind power on-grid for voltage stability improvement and loss reduction of distribution network,a multi-objective two-stage decentralised wind power planning method is proposed in the paper,which takes into account the network loss correction for the extreme cold region.Firstly,an electro-thermal model is introduced to reflect the effect of temperature on conductor resistance and to correct the results of active network loss calculation;secondly,a two-stage multi-objective two-stage decentralised wind power siting and capacity allocation and reactive voltage optimisation control model is constructed to take account of the network loss correction,and the multi-objective multi-planning model is established in the first stage to consider the whole-life cycle investment cost of WTGs,the system operating cost and the voltage quality of power supply,and the multi-objective planning model is established in the second stage.planning model,and the second stage further develops the reactive voltage control strategy of WTGs on this basis,and obtains the distribution network loss reduction method based on WTG siting and capacity allocation and reactive power control strategy.Finally,the optimal configuration scheme is solved by the manta ray foraging optimisation(MRFO)algorithm,and the loss of each branch line and bus loss of the distribution network before and after the adoption of this loss reduction method is calculated by taking the IEEE33 distribution system as an example,which verifies the practicability and validity of the proposed method,and provides a reference introduction for decision-making for the distributed energy planning of the distribution network.展开更多
In this study, we simulated water flow in a water conservancy project consisting of various hydraulic structures, such as sluices, pumping stations, hydropower stations, ship locks, and culverts, and developed a multi...In this study, we simulated water flow in a water conservancy project consisting of various hydraulic structures, such as sluices, pumping stations, hydropower stations, ship locks, and culverts, and developed a multi-period and multi-variable joint optimization scheduling model for flood control, drainage, and irrigation. In this model, the number of sluice holes, pump units, and hydropower station units to be opened were used as decision variables, and different optimization objectives and constraints were considered. This model was solved with improved genetic algorithms and verified using the Huaian Water Conservancy Project as an example. The results show that the use of the joint optimization scheduling led to a 10% increase in the power generation capacity and a 15% reduction in the total energy consumption. The change in the water level was reduced by 0.25 m upstream of the Yundong Sluice, and by 50% downstream of pumping stations No. 1, No. 2, and No. 4. It is clear that the joint optimization scheduling proposed in this study can effectively improve power generation capacity of the project, minimize operating costs and energy consumption, and enable more stable operation of various hydraulic structures. The results may provide references for the management of water conservancy projects in complex river networks.展开更多
文中考虑人工驾驶小汽车、智能网联小汽车与人工驾驶卡车的空间分布特征,分析异质交通流中的9种跟驰情形与概率表达式,推导出此异质交通流的基本图模型,然后对不同车辆渗透率下的基本图模型进行分析研究.用SUMO仿真软件对于上述交通流...文中考虑人工驾驶小汽车、智能网联小汽车与人工驾驶卡车的空间分布特征,分析异质交通流中的9种跟驰情形与概率表达式,推导出此异质交通流的基本图模型,然后对不同车辆渗透率下的基本图模型进行分析研究.用SUMO仿真软件对于上述交通流设计实验,验证基本图模型的有效性.结果表明:智能网联车(connected and autonomous vehicle,CAV)渗透率的提高可一定程度的提高道路通行效率,但是提升幅度会因为卡车比例的提高而显著降低;并且相比于传统人工驾驶交通流,混有智能网联车的异质交通流会更容易受到卡车特性的影响.展开更多
文摘Computer networks and power transmission networks are treated as capacitated flow networks.A capacitated flow network may partially fail due to maintenance.Therefore,the capacity of each edge should be optimally assigned to face critical situations-i.e.,to keep the network functioning normally in the case of failure at one or more edges.The robust design problem(RDP)in a capacitated flow network is to search for the minimum capacity assignment of each edge such that the network still survived even under the edge’s failure.The RDP is known as NP-hard.Thus,capacity assignment problem subject to system reliability and total capacity constraints is studied in this paper.The problem is formulated mathematically,and a genetic algorithm is proposed to determine the optimal solution.The optimal solution found by the proposed algorithm is characterized by maximum reliability and minimum total capacity.Some numerical examples are presented to illustrate the efficiency of the proposed approach.
基金sponsored by the General Program of the National Natural Science Foundation of China(Grant Nos.52079129 and 52209148)the Hubei Provincial General Fund,China(Grant No.2023AFB567)。
文摘Analyzing rock mass seepage using the discrete fracture network(DFN)flow model poses challenges when dealing with complex fracture networks.This paper presents a novel DFN flow model that incorporates the actual connections of large-scale fractures.Notably,this model efficiently manages over 20,000 fractures without necessitating adjustments to the DFN geometry.All geometric analyses,such as identifying connected fractures,dividing the two-dimensional domain into closed loops,triangulating arbitrary loops,and refining triangular elements,are fully automated.The analysis processes are comprehensively introduced,and core algorithms,along with their pseudo-codes,are outlined and explained to assist readers in their programming endeavors.The accuracy of geometric analyses is validated through topological graphs representing the connection relationships between fractures.In practical application,the proposed model is employed to assess the water-sealing effectiveness of an underground storage cavern project.The analysis results indicate that the existing design scheme can effectively prevent the stored oil from leaking in the presence of both dense and sparse fractures.Furthermore,following extensive modification and optimization,the scale and precision of model computation suggest that the proposed model and developed codes can meet the requirements of engineering applications.
基金The National Natural Science Foundation of China(No.72001107,72271120)the Fundamental Research Funds for the Central Universities(No.NS2024047,NP2024106)the China Postdoctoral Science Foundation(No.2020T130297,2019M660119).
文摘The presence of circles in the network maximum flow problem increases the complexity of the preflow algorithm.This study proposes a novel two-stage preflow algorithm to address this issue.First,this study proves that at least one zero-flow arc must be present when the flow of the network reaches its maximum value.This result indicates that the maximum flow of the network will remain constant if a zero-flow arc within a circle is removed;therefore,the maximum flow of each network without circles can be calculated.The first stage involves identifying the zero-flow arc in the circle when the network flow reaches its maximum.The second stage aims to remove the zero-flow arc identified and modified in the first stage,thereby producing a new network without circles.The maximum flow of the original looped network can be obtained by solving the maximum flow of the newly generated acyclic network.Finally,an example is provided to demonstrate the validity and feasibility of this algorithm.This algorithm not only improves computational efficiency but also provides new perspectives and tools for solving similar network optimization problems.
基金This research is supported by National Natural Science Foundation(70471042)
文摘This article considers a class of bottleneck capacity expansion problems. Such problems aim to enhance bottleneck capacity to a certain level with minimum cost. Given a network G（V,A,C^-） consisting of a set of nodes V = {v1,v2,... ,vn}, a set of arcs A C {（vi,vj） ｜ i = 1,2,...,n; j = 1,2,...,n} and a capacity vector C. The component C^-ij of C is the capacity of arc （vi, vj）. Define the capacity of a subset A′ of A as the minimum capacity of the arcs in A, the capacity of a family F of subsets of A is the maximum capacity of its members. There are two types of expanding models. In the arc-expanding model, the unit cost to increase the capacity of arc （vi, vj） is ωij. In the node-expanding model, it is assumed that the capacities of all arcs （vi, vj） which start at the same node vi should be increased by the same amount and that the unit cost to make such expansion is wi. This article considers three kinds of bottleneck capacity expansion problems （path, spanning arborescence and maximum flow） in both expanding models. For each kind of expansion problems, this article discusses the characteristics of the problems and presents several results on the complexity of the problems.
基金supported by the Natural Science Foundation(Grant No.2001ABB013)the Key Science and Technology Foundation of Hubei Province(Grant No.2001AA104A05)
文摘The paper points out the relationship between the bottleneck and the minimum cutset of the network, and presents a capacity expansion algorithm of network optimization to solve the network bottleneck problem. The complexity of the algorithm is also analyzed. As required by the algorithm, some virtual sources are imported through the whole positive direction subsection in the network, in which a certain capacity value is given. Simultaneously, a corresponding capacity-expanded network is constructed to search all minimum cutsets. For a given maximum flow value of the network, the authors found an adjustment value of each minimum cutset arcs group with gradually reverse calculation and marked out the feasible flow on the capacity-extended networks again with the adjustment value increasing. All this has been done repeatedly until the original topology structure is resumed. So the algorithm can increase the capacity of networks effectively and solve the bottleneck problem of networks.
文摘The minimum cost of capacity expansion for time-limited transportation problem on-demand (MCCETLTPD) is to find such a practicable capacity expansion transportation scheme satisfying the time-limited T along with all origins’ supply and all destinations’ demands as well as the expanding cost is minimum. Actually, MCCETLTPD is a balance transportation problem and a variant problem of minimum cost maximum flow problem. In this paper, by creating a mathematical model and constructing a network with lower and upper arc capacities, MCCETLTPD is transformed into searching feasible flow in the constructed network, and consequently, an algorithm MCCETLTPD-A is developed as MCCETLTPD’s solution method basing minimum cost maximum flow algorithm. Computational study validates that the MCCETLTPD-A algorithm is an efficient approach to solving the MCCETLTPD.
文摘Accurate prediction of multiphase flowing bottom-hole pressure(FBHP)in wellbores is an important factor required for optimal tubing design and production optimization.Existing empirical correlations and mechanistic models provide inaccurate FBHP predictions when applied to real-time field datasets because they were developed with laboratory-dependent parameters.Most machine learning(ML)models for FBHP prediction are developed with real-time field data but presented as black-box models.In addition,these ML models cannot be reproduced by other users because the dataset used for training the machine learning algorithm is not open source.These make using the ML models on new datasets difficult.This study presents an artificial neural network(ANN)visible mathematical model for real-time multiphase FBHP prediction in wellbores.A total of 1001 normalized real-time field data points were first used in developing an ANN black-box model.The data points were randomly divided into three different sets;70%for training,15%for validation,and the remaining 15%for testing.Statistical analysis showed that using the Levenberg-Marquardt training optimization algorithm(trainlm),hyperbolic tangent activation function(tansig),and three hidden layers with 20,15 and 15 neurons in the first,second and third hidden layers respectively achieved the best performance.The trained ANN model was then translated into an ANN visible mathematical model by extracting the tuned weights and biases.Trend analysis shows that the new model produced the expected effects of physical attributes on FBHP.Furthermore,statistical and graphical error analysis results show that the new model outperformed existing empirical correlations,mechanistic models,and an ANN white-box model.Training of the ANN on a larger dataset containing new data points covering a wider range of each input parameter can broaden the applicability domain of the proposed ANN visible mathematical model.
基金supported by the Gansu Provincial Science and Technology Major Special Project-Enterprise Innovation Consortium Project(No.22ZD6GA010)the Natural Science Foundation of China(No.52062027)the Key Research and Development Project of Gansu Province(No.22YF7GA142).
文摘The transportation department relies on accurate traffic forecasting for effective decision-making.However,determining relevant parameters for existing traffic flow prediction models poses challenges.To address this issue,this study proposes a hybrid model,sparrow search algorithm-gated recurrent unit-long short-term memory(SSA-GRU-LSTM),which leverages the SSA to optimize the GRUs and LSTM networks.The SSA is employed to identify the optimal parameters for the GRULSTM model,mitigating their impact on prediction accuracy.This model integrates the predictive efficiency of the GRU,LSTM’s capability in temporal data analysis,and the fast convergence and global search attributes of the SSA.Comprehensive experiments are conducted to validate the proposed method via traffic flow datasets,and the results are compared with those of baseline models.The numerical results demonstrate the superior performance of the SSA-GRU-LSTM model.Compared with the baselines,the proposed model results in reductions in the root mean square error(RMSE)of 4.632%-45.206%,the mean absolute error(MAE)of 2.608%-53.327%,the mean absolute percentage error(MAPE)of 1.324%-13.723%,and an increase in R^(2) of 0.5%-17.5%.Consequently,the SSA-GRU-LSTM model has high prediction accuracy and measurement stability.
文摘Abstract:This paper addresses the problem of improving the optimal value of the Maximum Capacity Path(MCP)through expansion in a flexible network,and minimizing the involved costs.The only condition applied to the cost functions is to be non-decreasing monotone.This is a non-restrictive condition,reflecting the reality in practice,and is considered for the first time in the literature.Moreover,the total cost of expansion is a combination of max-type cost(e.g.,for supervision)and sum-type cost(e.g.for building infrastructures,price of materials,price of labor,etc.).For this purpose,two types of strategies are combined:(l)increasing the capacity of the existing arcs,and(l)adding potential new arcs.Two different problems are introduced and solved.Both the problems have immediate applications in Internet routing infrastructure.The first one is to extend the network,so that the capacity of an McP in the modified network becomes equal to a prescribed value,therefore the cost of modifications is minimized.A strongly polynomial-time algorithm is deduced to solve this problem.The second problem is a network expansion under a budget constraint,so that the capacity of an McP is maximized.A weakly polynomial-time algorithm is presented to deal with it.In the special case when all the costs are linear,a Meggido's parametric search technique is used to develop an algorithm for solving the problem in strongly polynomial time.This new approach has a time complexity of O(n^(4)),which is better than the time complexity of O(n4 log(n)of the previously known method from literature.
基金This work was funded by the Norwegian Centre of Offshore Wind Technologies(NOWITECH).
文摘The discussions on the development of an electricity market model for accommodating cross-border cooperation remains active in Europe.The main interest is the establishment of market couplings without curtailing the fair use of the scarce transmission capacity.However,it is difficult to gain mutual consensus on this subject because of the absence of convincing simulation results for the entire region.To achieve that,researchers need a common grid model(CGM)which is a simplified representation of the detailed transmission model which comprises aggregated buses and transmission lines.A CGM should sufficiently represent the inter-area power flow characteristics.Generally,it is difficult to establish a standard CGM that represents the actual transmission network with a suf-ficient degree of exactness because it requires knowledge on the details of the transmission network,which are undisclosed.This paper addresses the issue and reviews the existing approaches in transmission network approximation,and their shortcomings.Then,it proposes a new approach called the adaptive CGM approximation(ACA)for serving the purpose.The ACA is a datadriven approach,developed based on the direct current power flow theory.It is able to construct a CGM based on the published power flow data between the inter-connected market areas.This is done by solving the issue as a non-linear model fitting problem.The method is validated using three case studies.
基金funded by the Talent Introduction Project of Anhui Science and Technology University(RCYJ202105)Design and Key Technology Research of Multi Parameter Intelligent Control Instrument Junction Box(tzy202218)+3 种基金Natural Science Research Project of Higher Education Institutions in Anhui Province(2024AH050296)Research and Development of Fermentation Feed Drying Automatic Line(881314)Anhui Provincial Key Laboratory of Functional Agriculture and Functional Food,Anhui Science and Technology University(iFAST-2024-6)Key Technologies and Applications of Impinging Stream Based Plant Protection Hedge Spray System(2024AH050318).
文摘The complex phenomena that occur during the plastic deformation process of aluminum alloys,such as strain rate hardening,dynamic recovery,recrystallization,and damage evolution,can significantly affect the properties of these alloys and limit their applications.Therefore,studying the high-temperature flow stress characteristics of these materials and developing accurate constitutive models has significant scientific research value.In this study,quasi-static tensile tests were conducted on 5754 aluminum alloy using an electronic testing machine combined with a hightemperature environmental chamber to explore its plastic flow behavior under main deformation parameters(such as deformation temperatures,strain rates,and strain).On the basis of true strain-stress data,a BP neural network constitutive model of the alloy was built,aiming to reveal the influence laws of main deformation parameters on flow stress.To further improve the model performance,the ant colony optimization algorithm is introduced to optimize the BP neural network constitutive model,and the relationship between the prediction stability of the model and the parameter settings is explored.Furthermore,the predictability of the two models was evaluated by the statistical indicators,including the correlation coefficient(R^(2)),RMSE,MAE,and confidence intervals.The research results indicate that the prediction accuracy,stability,and generalization ability of the optimized BP neural network constitutive model have been significantly enhanced.
基金supported by the National Natural Science Foundation of China(52177081).
文摘In order to play a positive role of decentralised wind power on-grid for voltage stability improvement and loss reduction of distribution network,a multi-objective two-stage decentralised wind power planning method is proposed in the paper,which takes into account the network loss correction for the extreme cold region.Firstly,an electro-thermal model is introduced to reflect the effect of temperature on conductor resistance and to correct the results of active network loss calculation;secondly,a two-stage multi-objective two-stage decentralised wind power siting and capacity allocation and reactive voltage optimisation control model is constructed to take account of the network loss correction,and the multi-objective multi-planning model is established in the first stage to consider the whole-life cycle investment cost of WTGs,the system operating cost and the voltage quality of power supply,and the multi-objective planning model is established in the second stage.planning model,and the second stage further develops the reactive voltage control strategy of WTGs on this basis,and obtains the distribution network loss reduction method based on WTG siting and capacity allocation and reactive power control strategy.Finally,the optimal configuration scheme is solved by the manta ray foraging optimisation(MRFO)algorithm,and the loss of each branch line and bus loss of the distribution network before and after the adoption of this loss reduction method is calculated by taking the IEEE33 distribution system as an example,which verifies the practicability and validity of the proposed method,and provides a reference introduction for decision-making for the distributed energy planning of the distribution network.
基金supported by the Water Conservancy Science and Technology Project of Jiangsu Province(Grant No.2012041)the Jiangsu Province Ordinary University Graduate Student Research Innovation Project(Grant No.CXZZ13_0256)
文摘In this study, we simulated water flow in a water conservancy project consisting of various hydraulic structures, such as sluices, pumping stations, hydropower stations, ship locks, and culverts, and developed a multi-period and multi-variable joint optimization scheduling model for flood control, drainage, and irrigation. In this model, the number of sluice holes, pump units, and hydropower station units to be opened were used as decision variables, and different optimization objectives and constraints were considered. This model was solved with improved genetic algorithms and verified using the Huaian Water Conservancy Project as an example. The results show that the use of the joint optimization scheduling led to a 10% increase in the power generation capacity and a 15% reduction in the total energy consumption. The change in the water level was reduced by 0.25 m upstream of the Yundong Sluice, and by 50% downstream of pumping stations No. 1, No. 2, and No. 4. It is clear that the joint optimization scheduling proposed in this study can effectively improve power generation capacity of the project, minimize operating costs and energy consumption, and enable more stable operation of various hydraulic structures. The results may provide references for the management of water conservancy projects in complex river networks.
文摘文中考虑人工驾驶小汽车、智能网联小汽车与人工驾驶卡车的空间分布特征,分析异质交通流中的9种跟驰情形与概率表达式,推导出此异质交通流的基本图模型,然后对不同车辆渗透率下的基本图模型进行分析研究.用SUMO仿真软件对于上述交通流设计实验,验证基本图模型的有效性.结果表明:智能网联车(connected and autonomous vehicle,CAV)渗透率的提高可一定程度的提高道路通行效率,但是提升幅度会因为卡车比例的提高而显著降低;并且相比于传统人工驾驶交通流,混有智能网联车的异质交通流会更容易受到卡车特性的影响.
