Development of an integrated dynamic model for supply security and resilience analysis of natural gas pipeline network systems

An integrated dynamic model of natural gas pipeline networks is developed in this paper.Components for gas supply,e.g.,pipelines,junctions,compressor stations,LNG terminals,regulation stations and gas storage facilities are included in the model.These components are firstly modeled with respect to their properties and functions and,then,integrated at the system level by Graph Theory.The model can be used for simulating the system response in different scenarios of operation,and evaluate the consequences from the perspectives of supply security and resilience.A case study is considered to evaluate the accuracy of the model by benchmarking its results against those from literature and the software Pipeline Studio.Finally,the model is applied on a relatively complex natural gas pipeline network and the results are analyzed in detail from the supply security and resilience points of view.The main contributions of the paper are:firstly,a novel model of a complex gas pipeline network is proposed as a dynamic state-space model at system level;a method,based on the dynamic model,is proposed to analyze the security and resilience of supply from a system perspective.

Gas liquid cylindrical cyclone flow regime identification using machine learning combined with experimental mechanism explanation

The flow regimes of GLCC with horizon inlet and a vertical pipe are investigated in experiments,and the velocities and pressure drops data labeled by the corresponding flow regimes are collected.Combined with the flow regimes data of other GLCC positions from other literatures in existence,the gas and liquid superficial velocities and pressure drops are used as the input of the machine learning algorithms respectively which are applied to identify the flow regimes.The choosing of input data types takes the availability of data for practical industry fields into consideration,and the twelve machine learning algorithms are chosen from the classical and popular algorithms in the area of classification,including the typical ensemble models,SVM,KNN,Bayesian Model and MLP.The results of flow regimes identification show that gas and liquid superficial velocities are the ideal type of input data for the flow regimes identification by machine learning.Most of the ensemble models can identify the flow regimes of GLCC by gas and liquid velocities with the accuracy of 0.99 and more.For the pressure drops as the input of each algorithm,it is not the suitable as gas and liquid velocities,and only XGBoost and Bagging Tree can identify the GLCC flow regimes accurately.The success and confusion of each algorithm are analyzed and explained based on the experimental phenomena of flow regimes evolution processes,the flow regimes map,and the principles of algorithms.The applicability and feasibility of each algorithm according to different types of data for GLCC flow regimes identification are proposed.

Quantitative analysis of planation surfaces of the upper Yangtze River in the Sichuan-Yunnan Region, Southwest China

Identification of the planation surfaces (PSs)is key for utilizing them as a reference in studying the long- term geomorphological evolution of the Upper Yangtze River Basin in the Sichuan-Yurman region,Southwest China.Using a combined method of DEM-based fuzzy logic and topographic and fiver profiles analysis and based on a comprehensive analysis of four morphometfic parameters:slope,curvature,terrain raggedness index, and relative height,we established the relevant fuzzy membership functions,and then calculated the membership degree (MD)of the study area.Results show that patches with a MD>80% and an area>0.4 km^2 correspond well to the results of Google Earth and field investigation,representing the PS remnants.They consist of 1764 patches with an altitude,area,mean slope,and relief of mostly 2000-2500 m above sea level (asl),0-10 km^2,4°-9°,0-500 m,respectively,covering 9.2% of the study area's landscape,dipping to southeast,decreasing progressively from northwest to southeast in altitude,and with no clear relation between each patch's altitude and slope,or relief.All these results indicate that they are remnants of once regionally continuous PSs which were deformed by both the lower crust flow and the faults in upper crust,and dissected by the network of Upper Yangtze River.Additionally,topographic and river profiles analysis show that three PSs (PS1-PS3)well developed along the main valleys in the Yongren-Huili region, indicating several phases of uplift then planation during the Late Cenozoic era.Based on the incision amount deduced from projection of relict river profiles on PSs, together with erosion rates,breakup times of the PS 1,PS2,and PS3 were estimated to be 3.47 Ma,2.19 Ma,and 1.45 Ma,respectively,indicating appearance of modem Upper Yangtze River valley started between the Pliocene to early Pleistocene.