Adaptive implicit finite difference method for natural gas pipeline transient flow

Wang, Peng; Yu, Bo; Han, Dongxu; Li, Jingfa; Sun, Dongliang; Xiang, Yue; Wang, Liyan

doi:10.2516/ogst/2018013

Cited by 14 publications

(14 citation statements)

References 34 publications

(55 reference statements)

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“…Dataset analysis indicates that the algorithms would have generated the rate of change alarm for the attacking event. This dataset does exercise the full capabilities of the application, as the simultaneous pressure drop off all inputs would be obvious to the gas controller using standard rate of change for attacking situation generation [8]; however, the application does ensure it will not be missed, and provides additional confirmation of the intrusion event. A first stage previous to the analysis, covers the data collection of the gas pipeline variation consumption of demand of gas and also the pipeline PSI and delta pipeline PSI characteristics, understanding how the data is presented and reshape it at convenience for the study.…”

Section: Methodsmentioning

confidence: 99%

Intrusion detection system in gas-pipeline industry using machine learning

Dakheel¹,

Dakheel

Abbas

2019

PEN

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In this paper, we study about the plausibility of building up a total intrusion identification framework for gas pipeline industry utilized in present day man-made AI based frameworks to tell a gas controller of unexpected changes in pipeline working qualities, for example, weight, time interim, delta pipeline PSI and stream rate. This examination assesses the possibility for utilizing AI example of cautions strategies utilizing three able AI calculations, for example, Decision tree, K-Nearest Neighbor and Neural Network to recognize breaks in gas frameworks, like the SCADA rate of progress blend philosophy utilized by the risky fluids pipeline industry. The highlights were extricated from the dataset by evacuating the repetitive information too cleaning the information. The significant commitment to this work is by utilizing choice tree in three distinct degrees for example randomized, advanced and timberland just as utilizing the neural system with 3 layers, 20 units for each concealed layer, 20 preparing rounds and with 2 layers, 50 preparing rounds as appeared in down to earth some portion of this work executed in Matlab R2019a to recognize and foresee the potential assault in the gas pipeline industry. The idea of AI examination considered here shows guarantee, in light of the aftereffects of gas pipeline burst checking under the conditions tried. It can possibly be formed into a compelling crack checking technique, yet additionally testing under genuine world, complex-framework setups, in participation with appropriate AI demonstrating specialists, is expected to all the more likely comprehend the genuine practicality of this adjustment mechanized innovation. Since gases and fluids display diverse physical practices under changing weight and stream conditions an immediate relationship between's the viability in gas versus fluids frameworks can't be accurately expected that why AI would give the answer for variety in Pipeline PSI and complete delta pipeline PSI. For example, bypassing and back-feeding, and various other framework explicit conditions requiring redid arrangements utilizing AI.

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Section: Methodsmentioning

confidence: 99%

Intrusion detection system in gas-pipeline industry using machine learning

Dakheel¹,

Dakheel

Abbas

2019

PEN

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“…The continuity and momentum equations are so-called flow equations, and the energy equation is a so-called thermodynamic equation. These equations can be written in a general form [24][25][26]31], as shown in Equation 1, and 50the parameters of the general form are given in Table 1. The detailed process of transformations and the parameter expressions of Equation (1) can be found in Appendix A:…”

Section: Pipeline Modelmentioning

confidence: 99%

“…In each multi-component interconnection node, the laws of mass conservation, the equality of pressure, and the equality of gas temperature must be observed [12,20,26,28,31]. The corresponding mathematical models are represented by Equations (9) to (10).…”

Section: Multi-component Interconnection Node Modelmentioning

confidence: 99%

An Efficiently Decoupled Implicit Method for Complex Natural Gas Pipeline Network Simulation

Wang

et al. 2019

Energies

Self Cite

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The simulation of a natural gas pipeline network allows us to predict the behavior of a gas network system under different conditions. Such predictions can be effectively used to guide decisions regarding the design and operation of the real system. The simulation is generally associated with a high computational cost since the pipeline network is becoming more and more complex, as well as large-scale. In our previous study, the Decoupled Implicit Method for Efficient Network Simulation (DIMENS) method was proposed based on the ‘Divide-and-Conquer Approach’ ideal, and its computational speed was obviously high. However, only continuity/momentum Equations of the simple pipeline network composed of pipelines were studied in our previous work. In this paper, the DIMENS method is extended to the continuity/momentum and energy Equations coupled with the complex pipeline network, which includes pipelines and non-pipeline components. The extended DIMENS method can be used to solve more complex engineering problems than before. To extend the DIMENS method, two key issues are addressed in this paper. One is that the non-pipeline components are appropriately solved as the multi-component interconnection nodes; the other is that the procedures of solving the energy Equation are designed based on the gas flow direction in the pipeline. To validate the accuracy and efficiency of the present method, an example of a complex pipeline network is provided. From the result, it can be concluded that the accuracy of the proposed method is equivalent to that of the Stoner Pipeline Simulator (SPS), which includes commercially available simulation core codes, while the efficiency of the present method is over two times higher than that of the SPS.

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“…La principal ventaja del MDF explícito es que para alcanzar la solución no requiere formar y solucionar sistemas de ecuaciones. Los MDFs implícitos (como el método de Preissmann MDFI y otros) son incondicionalmente estables, permitiendo, al menos en teoría, el uso de incrementos temporales mayores que los exigidos en los métodos explícitos, pudiendo trabajar con mallas espacio-tiempo relativamente gruesas, es decir, con ∆t y ∆x de mayor tamaño (Wang et al, 2018). Sin embargo, en el MDFI la necesidad de aproximar los diferenciales por diferencias finitas limita el tamaño de los incrementos de tiempo a valores comparables con los requeridos en los métodos explícitos (Chaudhry, 1979).…”

Section: Métodos De Diferencias Finitas (Mdf)unclassified

Discretización de la red para el análisis del flujo transitorio

Twyman¹

2019

ing.agua

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<p>Al aplicar el método de las características en el análisis transitorio se debe discretizar la red de tuberías de forma tal que se evite la aparición de errores debido al incumplimiento de Courant, lo cual puede ser difícil de conseguir ya que los sistemas generalmente están compuestos por tuberías diversas en cuanto a sus características físicas (longitud, material constituyente, etc.). Para solucionar esto existen técnicas como el método de ajuste de la velocidad de la onda (MAVO) que, pese a ser un método fácil de aplicar, debe cumplir con varias condiciones difíciles de verificar, aún en sistemas muy simples. Al analizar un sistema más complejo se concluye que el expediente de eliminar los tramos conflictivos o reemplazarlos por tuberías ficticias permite obtener una solución cercana a la exacta sin necesidad de aplicar el MAVO, en tiempos de ejecución bajos y sin afectar en forma sensible la calidad de los resultados.</p>

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Adaptive implicit finite difference method for natural gas pipeline transient flow

Cited by 14 publications

References 34 publications

Intrusion detection system in gas-pipeline industry using machine learning

Intrusion detection system in gas-pipeline industry using machine learning

An Efficiently Decoupled Implicit Method for Complex Natural Gas Pipeline Network Simulation

Discretización de la red para el análisis del flujo transitorio

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