Coseismic effects recorded by Fujian subsurface fluid network and its meaning to earthquake prediction

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.

Retrofit design of composite cooling structure of a turbine blade by fluid networks and conjugate heat transfer methods

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.

A New Algorithm of Auto-Modelling for Fluid Network

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.

Synthesis of the fluid machinery network in a circulating water system

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.

Dynamic Characteristics Analysis on MHTGR Plant’s Secondary Side Fluid Flow Network

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.

Dynamic Shortest Path Algorithm in Stochastic Traffic Networks Using PSO Based on Fluid Neural Network

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.

Segmentation of retinal fluid based on deep learning:application of three-dimensional fully convolutional neural networks in optical coherence tomography images

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.

PARAMETERS INVERSION OF FLUID-SATURATED POROUS MEDIA BASED ON NEURAL NETWORKS

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.

Intelligent Recognition Method of Insufficient Fluid Supply of Oil Well Based on Convolutional Neural Network

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.

A new matrix-based mathematical model for determining unidirectional circuits in a ventilation network

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.

Application of Stochastic Fracture Network with Numerical Fluid Flow Simulations to Groundwater Flow Modeling in Fractured Rocks

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.

Advances in research on earthquake fluids hydrogeology in China:a review

Monitoring of subsurface fluid （underground fluid） is an important part of efforts for earthquake prediction in China. The nationwide network, which monitors groundwater level, water temperature, and radon and mercury in groundwater, has been constructed in the last decades. Large amounts of abnormal fluid changes before and after major earthquakes have been recorded, providing precious data for research in earthquake sciences. Many studies have been done in earthquake fluid hydrogeology in order to probe the nature of the earthquake. Much progress in earthquake fluid hydrogeology has been made in the last decades. The paper provides a review of the advances in research on earthquake fluid hydrogeology over the last 40 years in China. It deals with the following five aspects： （1） an introduction to the development history of monitoring networks construction; （2） cases of different subsurface fluid changes recorded before some major earthquakes which occurred in the last decades; （3） characteristics of subsurface fluid changes following major earthquakes; （4） mechanism of subsurface fluid changes before and following earthquakes; （5） application of earthquake fluids in the hydrogeology field.

A stepwise optimization method for topology structure of fluid machinery network

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.

Modeling and optimum operating conditions for FCCU using artificial neural network

A self-organizing radial basis function(RBF) neural network(SODM-RBFNN) was presented for predicting the production yields and operating optimization. Gradient descent algorithm was used to optimize the widths of RBF neural network with the initial parameters obtained by k-means learning method. During the iteration procedure of the algorithm, the centers of the neural network were optimized by using the gradient method with these optimized width values. The computational efficiency was maintained by using the multi-threading technique. SODM-RBFNN consists of two RBF neural network models: one is a running model used to predict the product yields of fluid catalytic cracking unit(FCCU) and optimize its operating parameters; the other is a learning model applied to construct or correct a RBF neural network. The running model can be updated by the learning model according to an accuracy criterion. The simulation results of a five-lump kinetic model exhibit its accuracy and generalization capabilities, and practical application in FCCU illustrates its effectiveness.

Particle dispersion modeling in ventilated room using artificial neural network

Due to insufficiency of a platform based on experimental results for numerical simulation validation using computational fluid dynamic method(CFD) for different geometries and conditions,in this paper we propose a modeling approach based on the artificial neural network(ANN) to describe spatial distribution of the particles concentration in an indoor environment.This study was performed for a stationary flow regime.The database used to build the ANN model was deducted from bibliography literature and composed by 261 points of experimental measurement.Multilayer perceptron-type neural network(MLP-ANN) model was developed to map the relation between the input variables and the outputs.Several training algorithms were tested to give a choice of the Fletcher conjugate gradient algorithm(TrainCgf).The predictive ability of the results determined by simulation of the ANN model was compared with the results simulated by the CFD approach.The developed neural network was beneficial and easy to predict the particle dispersion curves compared to CFD model.The average absolute error given by the ANN model does not reach 5%against 18%by the Lagrangian model and 28% by the Euler drift-flux model of the CFD approach.

基于仿真数据驱动的激光钻进气体喷嘴结构优化

激光钻进采用气体作为循环介质进行清孔,合理的气体流动特性是高效清孔的保障,气体喷嘴是影响气体流动特性的直接因素,其结构设计不合理会严重影响激光钻进的效率。针对激光钻进实验平台中的气体喷嘴,构建喷嘴基本型态,对影响气体清孔效率的喷嘴结构尺寸进行分析,制定仿真方案,通过Fluent模拟气体流场,对清孔效果进行分析,采用神经网络分析喷嘴结构及仿真结果,训练神经网络模型,得出最佳清孔效率时的喷嘴结构参数并进行验证,为喷嘴结构设计提供参考。

Review of Thoracic Endovascular Aneurysm Repair (TEVAR), Spinal Cord Ischemia (SCI), Cerebrospinal Fluid (CSF) Drainage and Blood Pressure (BP) Augmentation

The object of this review is to examine the role of TEVAR in causing SCI. The anatomy and physiology of blood flow to the spinal cord is examined. The role of auto regulation of blood flow within the spinal cord is also examined. This review examines the reported results from the scientific literature of the effect of thoracic aortic aneurysm repair on spinal cord blood flow. In the light of the-se findings several conclusions can reasonably be reached. These conclusions are that the development of SCI can reasonably be predicted based on complexity and extent of the TEVAR procedure performed and BP augmentation and CSF drainage can significantly reduce the impact of SCI.

汽轮发电机带有交替径向风道的转子流体与传热耦合分析

针对汽轮发电机带有交替径向通风道的转子发热冷却问题,以一台350 MW水氢氢冷汽轮发电机为研究对象,依据流体力学和传热学的基本理论,首先建立计及旋转的电机全域通风网络模型,采用逐次迭代法计算得到各支路流量和节点压力。其次,建立了带有交替径向风道的发电机转子流体-传热三维物理模型和数学模型,给出了基本假设和相应的边界条件,同时将通风网络计算得到的风速和压力作为转子求解域的耦合边界,采用有限体积法进行求解,计算结果与实测值吻合。然后分析了交替径向风道内流量分配和氢气流动情况,研究了转子内部氢气温度分布和槽楔出风口风温变化规律,探明了转子绕组和铁心轴向温度分布特性,讨论了副槽入口流量和槽楔出口直径对转子流体和温度的影响。得出副槽入口流量应控制在0.1~0.16 m^(3)/s范围内,且选择较小的槽楔出口直径,可以提高通风系统的效率与风量分配均匀性,降低转子轴向热不平衡。

广东地震地下流体监测网质量与效能分析

依据流体学科相关评价标准,从广东地下流体观测网观测环境、观测仪器、观测质量和预测应用方面着手,结合地下流体学科台网中心提供的台站网运行监控和质量监控情况,对地下流体观测网每个观测站的观测环境、仪器的运行情况、观测仪器产出的数据的质量及其预测应用情况进行了评估。结果显示,观测站观测环境满足二类台站居多;观测数据运行率为优秀者占比:水位、水温和氡各为77.8%、63.6%和100.0%;流体仪器数据完整率全部为优秀;预测应用效能井水位和氡均为良好,水温有88.9%为良好。通过评估,较全面地分析了广东地下流体观测网存在的问题,并针对存在的不足提出改进措施及建议。

编织知识网络提升《化工原理》学习效果

化工原理是化工类专业的基础课程,是学生在理科基础上建立工程思想的关键课程。该门课程的学习存在知识点多且杂、难度大、不好学等问题,文章提出编织单元知识网络的方法,帮助学生提高学习效率,拓展自学能力。让学生在掌握课程知识的同时,也获得提高效率的学习方法。