Based on analyses of the spatio-temporal evolutionary characteristics of teleseismic response recorded by Fujian subsurface fluid network and in combination with earthquakes happened in Fujian province during the same...Based on analyses of the spatio-temporal evolutionary characteristics of teleseismic response recorded by Fujian subsurface fluid network and in combination with earthquakes happened in Fujian province during the same period, this paper points out that the step-like rising of water level after distant earthquakes may include some regional stress field information, and the area where water level step-like rises could be the position that the stress concentrated on and where the future earthquakes would occur. If combined with other impending precursors, the location of the events may be predicted to a certain degree.展开更多
This article discusses the development of the numerical methods of gas flow coupled with heat transfer,and introduces the fluid net-works method for rapid prediction of the performance of the composite cooling structu...This article discusses the development of the numerical methods of gas flow coupled with heat transfer,and introduces the fluid net-works method for rapid prediction of the performance of the composite cooling structures in turbine blade.The reliability of these methods is verified by comparing experimental data.For a HPT rotor blade,a rapid prediction on the internal cooling structures is first made by using the fluid network analysis,then an assessment of aerodynamic and heat transfer characteristics is conducted.Based on the network analysis results,three ways to improve the design of the cooling structures are tested,i.e.,adjusting the cooling gas flow mass ratios for different inner cooling cavities,reducing the flow resistances of the channel turning structures,and improving the local internal cooling structure geometries with high temperature distribution.Through the verification of full three-dimensional gas/solid/coolant conjugate heat transfer calculation,we conclude that the modified design can make the overall temperature distribution more even by significantly reducing the highest temperature of the blade surface,and reasonably matching the parameters of different coolant inlets.The results show that the proposed calculation methods can remarkably reduce the design cycle of complex turbine blade cooling structure.展开更多
The analysis of the solution of fluid network model is carried out to match the need of graphicallymodular autor-modelling for power plant simulators. Because of the symmetry and sparsity of thelinear system of equati...The analysis of the solution of fluid network model is carried out to match the need of graphicallymodular autor-modelling for power plant simulators. Because of the symmetry and sparsity of thelinear system of equations, a new method of improved Gauss elimination is presented for the solutionof large scale sparse matrices. Comparison of the new method with the classical Gauss eliminationmethod, the Gauss-Seidel iterative method are given. The results show that the algorithm provided isbetter than the others and is suitable for auto-modelling of fluid networks of power plants.展开更多
Energy consumption of the fluid machinery network in a circulating water system takes up a large part of energy consumption in the process industry, so optimization on the network will enhance the economic and environ...Energy consumption of the fluid machinery network in a circulating water system takes up a large part of energy consumption in the process industry, so optimization on the network will enhance the economic and environmental performance of the industry. In this paper, a synthesis approach is proposed to obtain the optimal network structure. The effective height curves are used as tools to perform energy analysis, so that the potential placement of water turbines and auxiliary pumps can be determined with energy benefit. Then economic optimization is carried out, by the mathematical model with the total cost as the objective function, to identify the branches for water turbines and auxiliary pumps with economic benefit. In this way, the optimal fluid machinery network structure can be obtained. The results of case study indicate that the proposed synthesis approach to optimize the fluid machinery network will obtain more remarkable benefits on economy, compared to optimizing only the water turbine network or pump network. The results under different flowrates of circulating water reveal that using a water turbine to recover power or adding an auxiliary pump to save energy in branches are only suitable to the flowrate in a certain range.展开更多
Multipe NSSS (Nuclear Steam Supply System) modules use the common feeding-water system to drive the common turbine power generation set. The SSFFN (secondary side fluid flow network) of MHTGR plant has features i.e. s...Multipe NSSS (Nuclear Steam Supply System) modules use the common feeding-water system to drive the common turbine power generation set. The SSFFN (secondary side fluid flow network) of MHTGR plant has features i.e. strong-coupling and nonlinearity. A wide range of power switching operation will cause unsteady flow, which may destroy the working elements and will be a threat for normal operation. To overcome those problems, a differential-algebraic model and PI controllers are designed for the SSFFN. In MATLAB\SIMULINK environment, a simulation platform is established and used to make a simulation of SSFFN of a MHTGR plant with two NSSS modules, which uses feedwater valves to control the mass flow rate in each module instead of feedwater pump. Results reflect good robustness of controllers.展开更多
The shortest path planning issure is critical for dynamic traffic assignment and route guidance in intelligent transportation systems. In this paper, a Particle Swarm Optimization (PSO) algorithm with priority-based e...The shortest path planning issure is critical for dynamic traffic assignment and route guidance in intelligent transportation systems. In this paper, a Particle Swarm Optimization (PSO) algorithm with priority-based encoding scheme based on fluid neural network (FNN) to search for the shortest path in stochastic traffic networks is introduced. The proposed algorithm overcomes the weight coefficient symmetry restrictions of the traditional FNN and disadvantage of easily getting into a local optimum for PSO. Simulation experiments have been carried out on different traffic network topologies consisting of 15-65 nodes and the results showed that the proposed approach can find the optimal path and closer sub-optimal paths with good success ratio. At the same time, the algorithms greatly improve the convergence efficiency of fluid neuron network.展开更多
The circulating water system is widely used as the cooling system in the process industry,which has the characteristics of high water and power consumption,and its energy consumption level has an important impact on t...The circulating water system is widely used as the cooling system in the process industry,which has the characteristics of high water and power consumption,and its energy consumption level has an important impact on the economic performance of the whole system.Pump network and water turbine network constitute the work network of the circulating water system,that is,the fluid machinery network.Based on the previous studies,this paper proposes a stepwise method to optimize the fluid machinery network,that is,to optimize the network structure by using the recoverable pressure-head curve of the branch,and consider the recovery of adjustable resistance at the valve of each branch,so as to further reduce energy consumption and water consumption.The calculation result of the case shows that the topology structure optimization can further reduce the operation cost and the annual capital cost on the basis of the fixed structure optimization,and the total annualized cost can be reduced by 30.04%.The optimization result of different flow shows that both the pump network and the water turbine network tend to series structure at a low flow rate whereas to parallel structure at a high flow rate.展开更多
AIM: To explore a segmentation algorithm based on deep learning to achieve accurate diagnosis and treatment of patients with retinal fluid.METHODS: A two-dimensional(2D) fully convolutional network for retinal segment...AIM: To explore a segmentation algorithm based on deep learning to achieve accurate diagnosis and treatment of patients with retinal fluid.METHODS: A two-dimensional(2D) fully convolutional network for retinal segmentation was employed. In order to solve the category imbalance in retinal optical coherence tomography(OCT) images, the network parameters and loss function based on the 2D fully convolutional network were modified. For this network, the correlations of corresponding positions among adjacent images in space are ignored. Thus, we proposed a three-dimensional(3D) fully convolutional network for segmentation in the retinal OCT images.RESULTS: The algorithm was evaluated according to segmentation accuracy, Kappa coefficient, and F1 score. For the 3D fully convolutional network proposed in this paper, the overall segmentation accuracy rate is 99.56%, Kappa coefficient is 98.47%, and F1 score of retinal fluid is 95.50%. CONCLUSION: The OCT image segmentation algorithm based on deep learning is primarily founded on the 2D convolutional network. The 3D network architecture proposed in this paper reduces the influence of category imbalance, realizes end-to-end segmentation of volume images, and achieves optimal segmentation results. The segmentation maps are practically the same as the manual annotations of doctors, and can provide doctors with more accurate diagnostic data.展开更多
The multi- layers feedforward neural network is used for inversion ofmaterial constants of fluid-saturated porous media. The direct analysis of fluid-saturated porousmedia is carried out with the boundary element meth...The multi- layers feedforward neural network is used for inversion ofmaterial constants of fluid-saturated porous media. The direct analysis of fluid-saturated porousmedia is carried out with the boundary element method. The dynamic displacement responses obtainedfrom direct analysis for prescribed material parameters constitute the sample sets training neuralnetwork. By virtue of the effective L-M training algorithm and the Tikhonov regularization method aswell as the GCV method for an appropriate selection of regu-larization parameter, the inversemapping from dynamic displacement responses to material constants is performed. Numerical examplesdemonstrate the validity of the neural network method.展开更多
Traditional methods for judging the degree of insufficient fluid supply in oil wells have low efficiency and limited accuracy. To address this problem, a method for intelligently identifying the degree of insufficient...Traditional methods for judging the degree of insufficient fluid supply in oil wells have low efficiency and limited accuracy. To address this problem, a method for intelligently identifying the degree of insufficient fluid supply in oil wells based on convolutional neural networks is proposed in this paper. Firstly, 5000 indicator diagrams with insufficient liquid supply were collected from the oilfield site, and a sample set was established after preprocessing;then based on the AlexNet model, combined with the characteristics of the indicator diagram, a convolutional neural network model including 4 layers of convolutional layers, 3 layers of down-pooling layers and 2 layers of fully connected layers is established. The backpropagation, ReLu activation function and dropout regularization method are used to complete the training of the convolutional neural network;finally, the performance of the convolutional neural network under different iteration times and network structure is compared, and the super parameter optimization of the model is completed. It has laid a good foundation for realizing the self-adaptive and intelligent matching of oil well production parameters and formation fluid supply conditions. It has certain application prospects. The results show that the accuracy of training and verification of the method exceeds 98%, which can meet the actual application requirements on site.展开更多
The occurrence of local circulating ventilation can be caused by many factors, such as the airflow reversion during mine fire,the improper arrangement of local fan or underground fan station and the man-made error inp...The occurrence of local circulating ventilation can be caused by many factors, such as the airflow reversion during mine fire,the improper arrangement of local fan or underground fan station and the man-made error input of raw data before network solving. Once circulating ventilations occur,the corresponding branches in the ventilation network corresponding to the relevant airways in ventilation system form circuits,and all the direc- tions of the branches in the circuits are identical,which is the unidirectional problem in ventilation network.Based on the properties of node adjacent matrix,a serial of mathe- matical computation to node adjacent matrix were performed,and a mathematical model for determining unidirectional circuits based on node adjacent matrix was put forward.展开更多
The continuum approach in fluid flow modeling is generally applied to porous geological media, but has limited applicability to fractured rocks. With the presence of a discrete fracture network relatively sparsely dis...The continuum approach in fluid flow modeling is generally applied to porous geological media, but has limited applicability to fractured rocks. With the presence of a discrete fracture network relatively sparsely distributed in the matrix, it may be difficult or erroneous to use a porous medium fluid flow model with continuum assumptions to describe the fluid flow in fractured rocks at small or even large field scales. A discrete fracture fluid flow approach incorporating a stochastic fracture network with numerical fluid flow simulations could have the capability of capturing fluid flow behaviors such as inhomogeneity and anisotropy while reflecting the changes of hydraulic features at different scales. Moreover, this approach can be implemented to estimate the size of the representative elementary volume (REV) in order to find out the scales at which a porous medium flow model could be applied, and then to determine the hydraulic conductivity tensor for fractured rocks. The following topics are focused on in this study: (a) conceptual discrete fracture fluid flow modeling incorporating a stochastic fracture network with numerical flow simulations; (b) estimation of REV and hydraulic conductivity tensor for fractured rocks utilizing a stochastic fracture network with numerical fluid flow simulations; (c) investigation of the effect of fracture orientation and density on the hydraulic conductivity and REV by implementing a stochastic fracture network with numerical fluid flow simulations, and (d) fluid flow conceptual models accounting for major and minor fractures in the 2 D or 3 D flow fields incorporating a stochastic fracture network with numerical fluid flow simulations.展开更多
基金supported jointly by the project from China Earthquake Admini-stration, the Chinese National Science and Technology Program (2006BAC01B02-03-02)the foundation from Administration Earthquake of Fujian province (200801)
文摘Based on analyses of the spatio-temporal evolutionary characteristics of teleseismic response recorded by Fujian subsurface fluid network and in combination with earthquakes happened in Fujian province during the same period, this paper points out that the step-like rising of water level after distant earthquakes may include some regional stress field information, and the area where water level step-like rises could be the position that the stress concentrated on and where the future earthquakes would occur. If combined with other impending precursors, the location of the events may be predicted to a certain degree.
基金supported by the National Natural Science Foundation of the innovative group of China(Grant No.51121004)the National Natural Science Foundation of China(Grant No.50706009)
文摘This article discusses the development of the numerical methods of gas flow coupled with heat transfer,and introduces the fluid net-works method for rapid prediction of the performance of the composite cooling structures in turbine blade.The reliability of these methods is verified by comparing experimental data.For a HPT rotor blade,a rapid prediction on the internal cooling structures is first made by using the fluid network analysis,then an assessment of aerodynamic and heat transfer characteristics is conducted.Based on the network analysis results,three ways to improve the design of the cooling structures are tested,i.e.,adjusting the cooling gas flow mass ratios for different inner cooling cavities,reducing the flow resistances of the channel turning structures,and improving the local internal cooling structure geometries with high temperature distribution.Through the verification of full three-dimensional gas/solid/coolant conjugate heat transfer calculation,we conclude that the modified design can make the overall temperature distribution more even by significantly reducing the highest temperature of the blade surface,and reasonably matching the parameters of different coolant inlets.The results show that the proposed calculation methods can remarkably reduce the design cycle of complex turbine blade cooling structure.
文摘The analysis of the solution of fluid network model is carried out to match the need of graphicallymodular autor-modelling for power plant simulators. Because of the symmetry and sparsity of thelinear system of equations, a new method of improved Gauss elimination is presented for the solutionof large scale sparse matrices. Comparison of the new method with the classical Gauss eliminationmethod, the Gauss-Seidel iterative method are given. The results show that the algorithm provided isbetter than the others and is suitable for auto-modelling of fluid networks of power plants.
基金Supported by the National Natural Science Foundation of China(21736008)
文摘Energy consumption of the fluid machinery network in a circulating water system takes up a large part of energy consumption in the process industry, so optimization on the network will enhance the economic and environmental performance of the industry. In this paper, a synthesis approach is proposed to obtain the optimal network structure. The effective height curves are used as tools to perform energy analysis, so that the potential placement of water turbines and auxiliary pumps can be determined with energy benefit. Then economic optimization is carried out, by the mathematical model with the total cost as the objective function, to identify the branches for water turbines and auxiliary pumps with economic benefit. In this way, the optimal fluid machinery network structure can be obtained. The results of case study indicate that the proposed synthesis approach to optimize the fluid machinery network will obtain more remarkable benefits on economy, compared to optimizing only the water turbine network or pump network. The results under different flowrates of circulating water reveal that using a water turbine to recover power or adding an auxiliary pump to save energy in branches are only suitable to the flowrate in a certain range.
文摘Multipe NSSS (Nuclear Steam Supply System) modules use the common feeding-water system to drive the common turbine power generation set. The SSFFN (secondary side fluid flow network) of MHTGR plant has features i.e. strong-coupling and nonlinearity. A wide range of power switching operation will cause unsteady flow, which may destroy the working elements and will be a threat for normal operation. To overcome those problems, a differential-algebraic model and PI controllers are designed for the SSFFN. In MATLAB\SIMULINK environment, a simulation platform is established and used to make a simulation of SSFFN of a MHTGR plant with two NSSS modules, which uses feedwater valves to control the mass flow rate in each module instead of feedwater pump. Results reflect good robustness of controllers.
文摘The shortest path planning issure is critical for dynamic traffic assignment and route guidance in intelligent transportation systems. In this paper, a Particle Swarm Optimization (PSO) algorithm with priority-based encoding scheme based on fluid neural network (FNN) to search for the shortest path in stochastic traffic networks is introduced. The proposed algorithm overcomes the weight coefficient symmetry restrictions of the traditional FNN and disadvantage of easily getting into a local optimum for PSO. Simulation experiments have been carried out on different traffic network topologies consisting of 15-65 nodes and the results showed that the proposed approach can find the optimal path and closer sub-optimal paths with good success ratio. At the same time, the algorithms greatly improve the convergence efficiency of fluid neuron network.
基金Financial support from Key Research and Development Projects of Shaanxi Province(2024GX-YBXM-508)Research Fund for Young Star of Science and Technology in Shaanxi Province(2023KJXX-124)are gratefully acknowledged.
文摘The circulating water system is widely used as the cooling system in the process industry,which has the characteristics of high water and power consumption,and its energy consumption level has an important impact on the economic performance of the whole system.Pump network and water turbine network constitute the work network of the circulating water system,that is,the fluid machinery network.Based on the previous studies,this paper proposes a stepwise method to optimize the fluid machinery network,that is,to optimize the network structure by using the recoverable pressure-head curve of the branch,and consider the recovery of adjustable resistance at the valve of each branch,so as to further reduce energy consumption and water consumption.The calculation result of the case shows that the topology structure optimization can further reduce the operation cost and the annual capital cost on the basis of the fixed structure optimization,and the total annualized cost can be reduced by 30.04%.The optimization result of different flow shows that both the pump network and the water turbine network tend to series structure at a low flow rate whereas to parallel structure at a high flow rate.
基金Supported by National Science Foundation of China(No.81800878)Interdisciplinary Program of Shanghai Jiao Tong University(No.YG2017QN24)+1 种基金Key Technological Research Projects of Songjiang District(No.18sjkjgg24)Bethune Langmu Ophthalmological Research Fund for Young and Middle-aged People(No.BJ-LM2018002J)
文摘AIM: To explore a segmentation algorithm based on deep learning to achieve accurate diagnosis and treatment of patients with retinal fluid.METHODS: A two-dimensional(2D) fully convolutional network for retinal segmentation was employed. In order to solve the category imbalance in retinal optical coherence tomography(OCT) images, the network parameters and loss function based on the 2D fully convolutional network were modified. For this network, the correlations of corresponding positions among adjacent images in space are ignored. Thus, we proposed a three-dimensional(3D) fully convolutional network for segmentation in the retinal OCT images.RESULTS: The algorithm was evaluated according to segmentation accuracy, Kappa coefficient, and F1 score. For the 3D fully convolutional network proposed in this paper, the overall segmentation accuracy rate is 99.56%, Kappa coefficient is 98.47%, and F1 score of retinal fluid is 95.50%. CONCLUSION: The OCT image segmentation algorithm based on deep learning is primarily founded on the 2D convolutional network. The 3D network architecture proposed in this paper reduces the influence of category imbalance, realizes end-to-end segmentation of volume images, and achieves optimal segmentation results. The segmentation maps are practically the same as the manual annotations of doctors, and can provide doctors with more accurate diagnostic data.
基金the National Natural Science Foundation of China (Nos.19872002 and 10272003)Climbing Foundation of Northern Jiaotong University
文摘The multi- layers feedforward neural network is used for inversion ofmaterial constants of fluid-saturated porous media. The direct analysis of fluid-saturated porousmedia is carried out with the boundary element method. The dynamic displacement responses obtainedfrom direct analysis for prescribed material parameters constitute the sample sets training neuralnetwork. By virtue of the effective L-M training algorithm and the Tikhonov regularization method aswell as the GCV method for an appropriate selection of regu-larization parameter, the inversemapping from dynamic displacement responses to material constants is performed. Numerical examplesdemonstrate the validity of the neural network method.
文摘Traditional methods for judging the degree of insufficient fluid supply in oil wells have low efficiency and limited accuracy. To address this problem, a method for intelligently identifying the degree of insufficient fluid supply in oil wells based on convolutional neural networks is proposed in this paper. Firstly, 5000 indicator diagrams with insufficient liquid supply were collected from the oilfield site, and a sample set was established after preprocessing;then based on the AlexNet model, combined with the characteristics of the indicator diagram, a convolutional neural network model including 4 layers of convolutional layers, 3 layers of down-pooling layers and 2 layers of fully connected layers is established. The backpropagation, ReLu activation function and dropout regularization method are used to complete the training of the convolutional neural network;finally, the performance of the convolutional neural network under different iteration times and network structure is compared, and the super parameter optimization of the model is completed. It has laid a good foundation for realizing the self-adaptive and intelligent matching of oil well production parameters and formation fluid supply conditions. It has certain application prospects. The results show that the accuracy of training and verification of the method exceeds 98%, which can meet the actual application requirements on site.
基金National Nature Science Foundation of China(50704019)Nature Science Foundation of Liaoning Province(20062204)
文摘The occurrence of local circulating ventilation can be caused by many factors, such as the airflow reversion during mine fire,the improper arrangement of local fan or underground fan station and the man-made error input of raw data before network solving. Once circulating ventilations occur,the corresponding branches in the ventilation network corresponding to the relevant airways in ventilation system form circuits,and all the direc- tions of the branches in the circuits are identical,which is the unidirectional problem in ventilation network.Based on the properties of node adjacent matrix,a serial of mathe- matical computation to node adjacent matrix were performed,and a mathematical model for determining unidirectional circuits based on node adjacent matrix was put forward.
基金ChinaCommitteeofEducation theUniver sityofArizonaandtheMetropolitanWaterDistrictofSouthernCaliforni a.
文摘The continuum approach in fluid flow modeling is generally applied to porous geological media, but has limited applicability to fractured rocks. With the presence of a discrete fracture network relatively sparsely distributed in the matrix, it may be difficult or erroneous to use a porous medium fluid flow model with continuum assumptions to describe the fluid flow in fractured rocks at small or even large field scales. A discrete fracture fluid flow approach incorporating a stochastic fracture network with numerical fluid flow simulations could have the capability of capturing fluid flow behaviors such as inhomogeneity and anisotropy while reflecting the changes of hydraulic features at different scales. Moreover, this approach can be implemented to estimate the size of the representative elementary volume (REV) in order to find out the scales at which a porous medium flow model could be applied, and then to determine the hydraulic conductivity tensor for fractured rocks. The following topics are focused on in this study: (a) conceptual discrete fracture fluid flow modeling incorporating a stochastic fracture network with numerical flow simulations; (b) estimation of REV and hydraulic conductivity tensor for fractured rocks utilizing a stochastic fracture network with numerical fluid flow simulations; (c) investigation of the effect of fracture orientation and density on the hydraulic conductivity and REV by implementing a stochastic fracture network with numerical fluid flow simulations, and (d) fluid flow conceptual models accounting for major and minor fractures in the 2 D or 3 D flow fields incorporating a stochastic fracture network with numerical fluid flow simulations.
