Gas Flow In Pipeline Research Articles

MPNG: A MATPOWER-Based Tool for Optimal Power and Natural Gas Flow Analyses

MPNG is an open-source simulation package for solving Optimal Power and Natural Gas Flow (OPNGF) problems. It is coded in <small>Matlab</small> and developed using the widely-used tool for power systems research and education, <small>Matpower</small>. MPNG assembles a nonlinear steady state OPNGF formulation for analyzing the integration of electricity and natural gas networks with order-of-priority classes of consumers and gas nodal price-controlled storage facilities. This paper presents the details of the nonlinear optimization model behind MPNG, as well as several features that were designed into it for the purpose of achieving robustness. These include an approximation of the Weymouth equation for handling bi-directional gas flows in pipelines, a per-unit representation for the natural gas network, and penalized slack variables for power and gas balances. Simulation results using a test case and the Colombian power and natural gas networks are presented to highlight MPNG&#x0027;s capabilities to solve OPNGF problems.

Research on the hydraulic parameters and leak rate of natural gas flow in natural gas pipelines based on the methods of characteristic line and successive approximation

Pressure and flow rate are the most important hydraulic parameters in natural gas pipeline flow, and leak rate is the most crucial parameter after a leak accident occurs. The study of these parameters is vital to the safey of natural gas pipelines and risk assessment after accidents. In this research, based on the conservation laws universally applicable to the motion of objects, we establish the fundamental control equation group for natural gas flow. Then, for both normal and leak conditions of pipelines, we use the characteristic line method to derive the corresponding difference equations for the fundamental control equations, thereby providing calculation methods for pressure and flow rate. Finally, we investigate the calculation method of natural gas pipeline leak rate under different leak aperture sizes, and validate the accuracy of this method through simulation examples.

Enhanced Deep Learning Method for Natural Gas Pipeline Flow Prediction Based on Integrated Learning

Enhanced Deep Learning Method for Natural Gas Pipeline Flow Prediction Based on Integrated Learning

Feature extraction and pattern recognition of gas pipeline flow noise signals in a strong noisy background.

The purpose of this study is to put forward a feature extraction and pattern recognition method for the flow noise signal of natural gas pipelines in view of the complex situation brought by the rapid development and expansion of urban natural gas infrastructure in China, especially in the case that there are active and abandoned pipelines, metal and nonmetal pipelines, and natural gas, water and power pipelines coexist in the underground of the city. Because the underground situation is unknown, gas leakage incidents caused by natural gas pipeline rupture occur from time to time, posing a threat to personal safety. Therefore, the motivation of this study is to provide a feasible method to accelerate the aging, renewal and transformation of urban natural gas pipelines to ensure the safe operation of urban natural gas pipeline network and promote the high-quality development of urban economy. Through the combination of experimental test and numerical simulation, this study establishes a database of urban natural gas pipeline flow noise signals, and uses principal component analysis (PCA) to extract the characteristics of flow noise signals, and develops a mathematical model for feature extraction. Then, a classification and recognition model based on backpropagation neural network (BPNN) is constructed, which realizes the detection and recognition of convective noise signals. The research results show that the theoretical method based on acoustic feature analysis provides guidance for the orderly and safe construction of urban natural gas pipeline network and ensures its safe operation. The research conclusion shows that through the simulation analysis of 75 groups of gas pipeline flow noise under different working conditions. Combined with the experimental verification of ground flow noise signals, the feature extraction and pattern recognition method proposed in this study has a recognition accuracy of up to 97% under strong noise background, which confirms the accuracy of numerical simulation and provides theoretical basis and technical support for the detection and recognition of urban gas pipeline flow noise.

ALGORITHM FOR DETERMINING THE VOLUME OF NATURAL GAS LOSSES UNDER CONDITIONS OF MEASURING ITS PARAMETERS AT THE GAS PIPELINE OUTLET

Gas pipeline networks are complex distributed systems consisting of hundreds of kilometers of pipelines of various configurations. Equipping all sections of the gas pipelines with gas accounting and gas parameter metering devices that would allow for the quick detection of pipeline damages and the determination of gas loss volumes requires significant funds. As a result, many transportation and distribution pipelines are not equipped with metering devices. Therefore, it is important to develop algorithms that can estimate the volume of gas losses based on information from existing metering units without the reconstruction of gas networks.The paper proposes an improved mathematical model for the stationary flow of natural gas in pipelines which considers the flow velocity changes along the pipeline. This model is supplemented with an equation for calculating the flowrate of gas flowing into the atmosphere through a hole in the damaged above-ground gas pipeline. The authors have developed an equation to determine the discharge coefficient for a pressure range in the pipeline from 0.1 to 1.2 MPa with a methodological error of no more than 1.7%. Based on the obtained mathematical models, an algorithm has been developed to calculate the gas volume lost because of damage for pipeline configurations with gas parameter metering units at the pipeline outlet. The developed algorithm has been validated, and an example of its application for analyzing the distributions of gas pressure and temperature in a pipeline with existing damage is presented. The paper presents the simulation results for gas leakage through pipeline damage and the iterative determination of gas parameters at the damage point, as well as the results of calculating the gas flowrate through the damage.The application of the developed algorithm allows for increased accuracy in determining the volume of gas losses through pipeline damage. Its hardware implementation in the future will reduce the time for damage detection, localization, and elimination.

A Thermal Anemometry Method for Studying the Unsteady Gas Dynamics of Pipe Flows: Development, Modernisation, and Application.

A detailed study of the gas-dynamic behaviour of both liquid and gas flows is urgently required for a variety of technical and process design applications. This article provides an overview of the application and an improvement to thermal anemometry methods and tools. The principle and advantages of a hot-wire anemometer operating according to the constant-temperature method are described. An original electronic circuit for a constant-temperature hot-wire anemometer with a filament protection unit is proposed for measuring the instantaneous velocity values of both stationary and pulsating gas flows in pipelines. The filament protection unit increases the measuring system's reliability. The designs of the hot-wire anemometer and filament sensor are described. Based on development tests, the correct functioning of the measuring system was confirmed, and the main technical specifications (the time constant and calibration curve) were determined. A measuring system for determining instantaneous gas flow velocity values with a time constant from 0.5 to 3.0 ms and a relative uncertainty of 5.1% is proposed. Based on pilot studies of stationary and pulsating gas flows in different gas-dynamic systems (a straight pipeline, a curved channel, a system with a poppet valve or a damper, and the external influence on the flow), the applications of the hot-wire anemometer and sensor are identified.

Physics-regulated dynamic mode decomposition for natural gas pipeline flow

This study introduces a novel integration of dynamic mode decomposition (DMD) with physical regulations for natural gas pipeline flow. It aims to address the limitations of purely data-driven models and the importance of incorporating the physics of complex dynamic systems. By considering the mass conservation law, the proposed model ensures that the predictions generated via DMD with control adhere to the physical laws, resulting in a multi-objective optimization problem. To verify its performance, the proposed model was evaluated using real-world data of natural gas pipelines. The results demonstrate its superior accuracy and ability to avoid physically implausible predictions, particularly under data-limited conditions. Despite an increase in the overall computational cost by approximately 15%, the model achieved up to 50% error reduction with scarce training data, highlighting its robustness and effectiveness. This study represents a significant advancement in data-driven modeling techniques by fulfilling the critical need for accurate and reliable predictions that respect physical constraints, thus enhancing the interpretability and validity of the results.

Flow Conditioner Design and Analysis Based on Computational Fluid Dynamics

To address the issue of uneven gas flow velocity distribution upstream of natural gas pipeline flow meters, a wave-type fin flow conditioner was designed. The Computational Fluid Dynamics (CFD) numerical simulation method was used to analyze the velocity distribution inside the pipe, and the rectification effect of the flow conditioner was measured by using the offset degree and the minimum length of the straight pipe section. The offset degree at a specific downstream section was calculated when the fluid passed through a 90 degrees elbow spoiler component without a flow conditioner with an Etoile conditioner and wave-type flow conditioner. The results show that the wave-type flow conditioner has a better rectification effect, and the minimum straight pipe section length is shortened by 72% compared to without a flow conditioner, greatly reducing the pipeline design cost and improving the accuracy of measurement of the Ultrasonic flowmeter.

Constrained exact boundary controllability of a semilinear model for pipeline gas flow

AbstractWhile the quasilinear isothermal Euler equations are an excellent model for gas pipeline flow, the operation of the pipeline flow with high pressure and small Mach numbers allows us to obtain approximate solutions by a simpler semilinear model. We provide a derivation of the semilinear model that shows that the semilinear model is valid for sufficiently low Mach numbers and sufficiently high pressures. We prove an existence result for continuous solutions of the semilinear model that takes into account lower and upper bounds for the pressure and an upper bound for the magnitude of the Mach number of the gas flow. These state constraints are important both in the operation of gas pipelines and to guarantee that the solution remains in the set where the model is physically valid. We show the constrained exact boundary controllability of the system with the same pressure and Mach number constraints.

Gas Pipeline Flow Prediction Model Based on LSTM with Grid Search Parameter Optimization

Due to the abundant operation data (e.g., pressure, flow rate, and temperature) for natural gas (NG) gathering pipelines provided by the supervisory control and data acquisition (SCADA) system, the machine-learning-based real-time flow prediction has become a critical solution to enable the identification of the abnormality of pipelines, further to guarantee the safe operation of the pipelines. However, traditional machine-learning-based methods cannot always function well due to the temporal characteristics of the SCADA data often being ignored, resulting from a lack of time memory capability. Therefore, this paper proposes a method to automatically perform the feature mining of flow time series by considering the correlation of flow data at both ends of the pipeline, combined with the long short-term memory (LSTM) network. The current and historical data at both pipeline ends are used as input vectors of the LSTM network to predict the terminal output flow at the next moment. Furthermore, to solve the problem that the parameters of the LSTM model are configured with manual experience, a grid search algorithm (GSA) is introduced to optimize the parameters of the LSTM. Consequently, the effectiveness and superiority of the proposed method are carried out in a real-world NG gathering pipeline.

A liquid loading prediction method of gas pipeline based on machine learning

The liquid loading is one of the most frequently encountered phenomena in the transportation of gas pipeline, reducing the transmission efficiency and threatening the flow assurance. However, most of the traditional mechanism models are semi-empirical models, and have to be resolved under different working conditions with complex calculation process. The development of big data technology and artificial intelligence provides the possibility to establish data-driven models. This paper aims to establish a liquid loading prediction model for natural gas pipeline with high generalization ability based on machine learning. First, according to the characteristics of actual gas pipeline, a variety of reasonable combinations of working conditions such as different gas velocity, pipe diameters, water contents and outlet pressures were set, and multiple undulating pipeline topography with different elevation differences was established. Then a large number of simulations were performed by simulator OLGA to obtain the data required for machine learning. After data preprocessing, six supervised learning algorithms, including support vector machine (SVM), decision tree (DT), random forest (RF), artificial neural network (ANN), plain Bayesian classification (NBC), and K nearest neighbor algorithm (KNN), were compared to evaluate the performance of liquid loading prediction. Finally, the RF and KNN with better performance were selected for parameter tuning and then used to the actual pipeline for liquid loading location prediction. Compared with OLGA simulation, the established data-driven model not only improves calculation efficiency and reduces workload, but also can provide technical support for gas pipeline flow assurance.

Building a mathematical model to prevent hydrate formation in gas pipelines

Development of mathematical models of laminar gas flow in certainty and uncertainty conditions were considered. All factors that influence to character of flow of gas in pipeline and conditions of arising of hydrate inside of pipeline wall are analyzed. Results of analyze were used for development mathematical model of gas flow in pipeline that allow to calculate main parameters of hydrate deposition process. Model of gas flow consist of three non-linear differential equations that can be solved by exist soft wares. Two and three-dimension characteristic obtained, that describe of quantity of hydrate deposited at given process depending on time.&#x0D; Besides, the effectiveness of using DELPHI 7 software to determine the preparation of gas for transportation and the hydrates formed during transportation and its prevention based on the results of the application software was discussed. As a result, the change in cross-sectional area of the pipeline of hydrates formed on the inner surface of the pipeline is shown in 3D. Hydrate formation and elimination are visually represented by graphs. The results of theoretical and practical studies of changes in the inner surface of the pipeline depending on temperature and pressure have been identified. All this was investigated during quasi-stationary gas flow in the pipelines and the results were obtained.&#x0D; The assumes regarding calculation of parameters of gas flow were determined and necessary recommendations for applying of developed model in different conditions with taking account of temperature and pressure variation and depending on time of hydrate deposition were presented. The diagram of gas-hydrate separation boundary considered for detailed analysis of process

Operation optimization for gas-electric integrated energy system with hydrogen storage module

Hydrogen is gradually becoming one of the important carriers of global energy transformation and development. To analyze the influence of the hydrogen storage module (HSM) on the operation of the gas-electricity integrated energy system, a comprehensive energy system model consisting of wind turbines, gas turbines, power-to-hydrogen (P2H) unit, and HSM is proposed in this paper. The model couples the natural gas network and power grid bidirectionally, and establishes a mixed integer nonlinear programming problem considering HSM. The linearization model of the natural gas pipeline flow equation and the generator set equation is constructed by piecewise linearization method to improve the efficiency of solving the model. And the energy flow distribution in the gas-electricity integrated energy system is finally solved. In Model 1, compared with not considering the installation of P2H units, when the hydrogen doping ratio is 10%, the operating cost can be reduced by 6.63%, and the wind curtailment cost can be reduced by 17.54%, and the carbon emission can be reduced by 298.7 tons. The optimization results of Model 2 reveal that compared with no HSM, the system operating cost is reduced by 5.96%, the hydrogen content level in the natural gas pipeline network is increased by 42.12%, and the carbon emission of the system is reduced by 117.6 tons, and the fluctuation of wind power is suppressed. This study demonstrates the feasibility of large-scale absorption of renewable energy through HSM.

An Indirect Method for Determining the Local Heat Transfer Coefficient of Gas Flows in Pipelines.

An indirect method and procedure for determining the local heat transfer coefficient in experimental studies on the intensity of heat transfer at a gas–surface interface is described. The article provides an overview of modern approaches and technical devices for determining the heat flux or friction stresses on surfaces in the study of thermophysical processes. The proposed method uses a constant-temperature hot-wire anemometer and a sensor with a thread sensitive element fixed on the surface of a fluoroplastic substrate. A substrate with the sensor’s sensitive element was mounted flush with the wall of the investigated pipeline. This method is based on the Kutateladze–Leontiev approach (the laws of friction and heat transfer) and the hydrodynamic analogy of heat transfer (the Reynolds analogy): this is an assumption about the unity of momentum and heat transfer in a turbulent flow, which establishes a quantitative relationship between friction stresses on the heat exchange surface and heat transfer through this surface. The article presents a method for determining the speed of the developed measuring system. An example of a successful application of the proposed method in relation to the study of thermomechanical processes in the gas exchange systems of reciprocating internal combustion engines is described.

Frequency response function method for dynamic gas flow modeling and its application in pipeline system leakage diagnosis

Timely diagnosing leakages is significant for the safety and reliability of gas pipeline systems, where an efficient and accurate modeling and solution method is critical to identify the leakage rate and location. Here, a frequency response function-based modeling method for dynamic gas flow in pipelines is proposed. Based on the frequency-domain flow resistance of pipelines and Fourier Transform, original dynamic flow model is transformed into the frequency response function model consisting of linear lumped transmission constraints, nonlinear frequency response function, and nonlinear pipeline resistance characteristics, which are efficiently solved according to their different mathematical properties. Dynamic simulation cases show the proposed modeling and solution method greatly improves the computational efficiency keeping the accuracy. Besides, taking the difference between measurements and simulation results under leakage condition as objective, an iterative bilayer optimization algorithm is proposed based on the mathematical categorization of system constraints to efficiently diagnose leakages. Four leakage diagnosis cases in single pipeline and pipeline network with measurement noise and transient boundary conditions are studied to validate the leakage diagnosis method. Absolute location errors in the results are all within 1%, demonstrating the accuracy and robustness of the proposed method for single leakages and multiple leakages occurring successively or simultaneously.

Research on detection and recognition methods of gas pipelines based on acoustic signal feature analysis

In the process of reconstruction and expansion of gas pipeline, it is easy to destroy in-service gas pipeline and cause safety accidents. In order to realize the detection of in-service pipelines, based on the characteristics of low sound pressure level and easy attenuation of acoustic signals of gas pipelines, the detection and identification method of gas pipelines based on acoustic signal feature analysis was studied by using Hilebert–Huang transform algorithm and optimized Back Propagation (BP) neural network. This method takes the gas pipeline flow noise signals obtained by numerical simulation and experimental verification as the research object, and the underwater acoustic signals are collected for comparative analysis. Empirical Mode Decomposition (EMD) was used to decompose the two signals, and the time-domain waveform of Intrinsic Mode Functions (IMF) component was obtained, and the characteristic parameters of peak value and peak frequency were determined. The energy characteristic parameters of Hilbert marginal spectrum were calculated, and the characteristic database of gas pipeline flow noise signal was obtained. The optimized BP neural network was used for pattern recognition. The results show that the identification rate of gas pipeline acoustic signal can reach 97.5% by using this method, which verifies the effectiveness of the gas pipeline detection and identification method in this paper.

Gas pipeline safety management system based on neural network

AbstractThe risk of leakage poses a grave threat to natural gas pipeline safety. The high compressibility of gases combined with unsteady boundary conditions makes detecting leaks in pipelines a challenging endeavor. To date, in the literature, only a limited number of studies have focused on leak detection and diagnostics in gas mixture pipelines. The present study provides a system for detecting, locating, and estimating the size of small gas leaks from a compressible and dynamic natural gas flow in pipelines with improved accuracy. As a case study, a long natural gas pipeline of 80 km is simulated with leak sizes of 0%, 2%, and 5%. The safety system is developed using mass flow rate, temperature, and pressure measurements. Six classes for faulty cases and one class for no fault case were considered for the study. A shallow neural network classifier (SNNC) is trained to identify a specific fault class. The SNNC is based on a two‐layered network with 20 and 7 neurons. An input vector of 15 variables is provided to the system, and the output is one of the seven possible classes. Leakage as low as 2% at various locations are correctly diagnosed with more than 99% correct classification rate.

ANALYSIS OF SIMPLIFIED GAS TRANSPORTATION MODELS. CALCULATION OF THE GAS LEAK LOCATION IN A PIPELINE

An approach to a simplified calculation of the characteristics of a steady gas flow through a pipeline is proposed. In this approach, the pressure is calculated using an isothermal model; the temperature analysis is reduced to a cubic equation solving. A simple analytical gas leak point coordinate dependence on the pressure and gas flow values is obtained at outlet from the pipeline. The result of comparison of calculations for the general and simplified models of gas flow in pipelines is presented. A criterion has been determined for the fulfillment of which it is permissible to use a simplified approach to calculating the gas characteristics and the leak point coordinates. Using an example of practical interest, the high accuracy of the calculation according to the proposed approach of both pressure and temperature in the flow and the gas leak coordinate in the pipeline is demonstrated.

Influence of wall friction on flow parameters in natural gas transmission pipeline

A mathematical model and computational algorithm are derived for the prediction of natural gas pipeline flow. Non-isothermal and compressible steady-state flow is considered. Heat transfer between gas flow and surroundings is taken into account together with the heat generation due to the gas friction on the inner pipeline wall. The computational algorithm is based on the marching procedure with defined initial conditions. The predicted thermal effect of the wall friction is validated by the simulation of a case that is available in the open literature. The influence of heat generation by gas wall friction in the long transmission pipeline on gas pressure and temperature is evaluated. Differences between results obtained with and without the heat generation due to gas wall friction are analysed. The heat generation due to gas friction on the pipeline inner wall has an influence on the gas temperature change along the pipeline, while its influence on the pressure drop is negligible. These detailed results are novel since most of the previously published results on non-isothermal gas flow did not take into account the thermal effect of the gas wall friction or the influence of this effect was not evaluated. The presented results are a support to the gas pipeline design methods and operational analyses.

Nodal Stabilization of the Flow in a Network with a Cycle

In this paper we discuss an approach to the stability analysis for classical solutions of closed loop systems that is based upon the tracing of the evolution of the Riemann invariants along the characteristics. We consider a network where several edges are coupled through node conditions that govern the evolution of the Riemann invariants through the nodes of the network. The analysis of the decay of the Riemann invariants requires to follow backwards all the characteristics that enter such a node and contribute to the evolution. This means that with each nodal reflection/crossing the number of characteristics that contribute to the evolution increases. We show how for simple networks with a sufficient number of damping nodal controlers it is possible to keep track of this family of characteristics and use this approach to analyze the exponential stability of the system. The analysis is based on an adapted version of Gronwall’s lemma that allows us to take into account the possible increase of the Riemann invariants when the characteristic curves cross a node of the network. Our example is motivated by applications in the control of gas pipeline flow, where the graphs of the networks often contain many cycles.