Transient analysis of gas pipeline network

Tao, W.Q.; Ti, Hwei Chen

doi:10.1016/s1385-8947(97)00109-5

Cited by 43 publications

(12 citation statements)

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“…• The linearization and asymptotic assumptions described here need to be validated against direct dynamic (transient) simulations of gas flows in variety of situations. Most existing work on such validations [18], [12], [19], [20], [21], [22] uses single pipe models. The challenge is to develop fast computational algorithms for transient problems with mixed (initial and boundary) conditions over large and loopy gas networks.…”

Section: Discussionmentioning

confidence: 99%

Pressure Fluctuations in Natural Gas Networks Caused by Gas-Electric Coupling

Chertkov

Fisher

Backhaus

et al. 2015

2015 48th Hawaii International Conference on System Sciences

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Abstract-The development of hydraulic fracturing technology has dramatically increased the supply and lowered the cost of natural gas in the United States, driving an expansion of natural gas-fired generation capacity in several electrical interconnections. Gas-fired generators have the capability to ramp quickly and are often utilized by grid operators to balance intermittency caused by wind generation. The time-varying output of these generators results in time-varying natural gas consumption rates that impact the pressure and line-pack of the gas network. As gas system operators assume nearly constant gas consumption when estimating pipeline transfer capacity and for planning operations, such fluctuations are a source of risk to their system. Here, we develop a new method to assess this risk. We consider a model of gas networks with consumption modeled through two components: forecasted consumption and small spatio-temporarily varying consumption due to the gasfired generators being used to balance wind. While the forecasted consumption is globally balanced over longer time scales, the fluctuating consumption causes pressure fluctuations in the gas system to grow diffusively in time with a diffusion rate sensitive to the steady but spatially-inhomogeneous forecasted distribution of mass flow. To motivate our approach, we analyze the effect of fluctuating gas consumption on a model of the Transco gas pipeline that extends from the Gulf of Mexico to the Northeast of the United States.

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Section: Discussionmentioning

confidence: 99%

Pressure Fluctuations in Natural Gas Networks Caused by Gas-Electric Coupling

Chertkov

Fisher

Backhaus

et al. 2015

2015 48th Hawaii International Conference on System Sciences

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“…Different numerical methods are used to solve these equations. Tao and Ti 8 used the electronic analogy method by combining resistance and capacitance to convert PDEs to a set of ordinary differential equations which can be solved in a more simple manner. They improved their work by considering the kinetic energy of gas which is modeled by inductance.…”

Section: Introductionmentioning

confidence: 99%

A novel approach for dynamic flow simulation of gas pipelines using teaching–learning-based optimization algorithm

Pourfard

Khanmirza

Madoliat

2018

Proceedings of the Institution of Mechanical Engineers, Part C:

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Simulation of a natural gas network operation is a prerequisite for optimization and control tasks. Treating gas in a transient manner is necessary for accurate simulation of gas networks. However, solving the governing nonlinear partial differential equations of pipe flows is a challenging task. In this paper, a novel approach is proposed based on using an intelligent algorithm called teaching–learning-based optimization. This approach simplifies transient simulation of gas networks with a specified type of boundary conditions. Teaching–learning-based optimization estimates different values for network inlet flow rates. Then by knowing the inlet boundary conditions of the network, the discretized flow equations become linear and the flow equations of each pipe can be solved independently. Thus, the network outlet flow variables can be easily obtained. The differences of obtained and actual network outlet flow rates are considered as a cost function or error. Finally, this intelligent algorithm determines the optimum inlet flow rates at each time level, which minimize the error. The proposed approach is implemented on the in-service gas network. To validate the simulation results, a conventional gradient-based method called trust region dogleg is also used for simulation of the gas network. The comparison of numerical results confirms the accuracy and efficiency of this approach, while it is more computationally efficient. Moreover, the substitution of teaching–learning-based optimization with another powerful intelligent optimization algorithm would not improve the performance of the proposed approach.

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“…Mathematical models of compressors in pipeline networks are required for design and optimization of the network. Several works on compressors network modeling have been presented [7][8][9][10][11][12][13][14]. The models focused on stable operating area and assumed the compressors would not enter surge as the compressors were equipped with SAS.…”

Section: Introductionmentioning

confidence: 99%

Bond graph modeling of centrifugal compression systems

Uddin

Gravdahl

2015

SIMULATION

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A novel approach to model unsteady fluid dynamics in compressors network by using bond graph is presented. The model is intended in particular for compressor control system development. First, we develop a bond graph model of a single compression system. Bond graph modeling offers a different perspective to previous work by modeling the compression system based on energy flow instead of fluid dynamics. Analysing the bond graph model explains the energy flow during compressor surge. Two principal solutions for compressor surge problem are identified: upstream energy injection and downstream energy dissipation. Both principal solutions are verified in bond graph modelings of single compression system equipped with surge avoidance system (SAS) and single compression system equipped with active control system. Moreover, the bond graph model of single compressor equipped with SAS is able to show the effect of recycling flow to the compressor upstream states which improves the current available model. The bond graph model of single compression system is then used as the base model and combined to build compressors network models. Two compressor networks are modeled: serial compressors and parallel compressors. Simulation results show the surge conditions in both compressor networks.Keywords: bond graph, compressor modeling, compressor surge, surge avoidance system, active surge control system, serial compressors, parallel compressors. IntroductionA centrifugal compressor is basically used to increase gas pressures. The compressor operating area is described by a plot of the compressor pressure against the flow for different compressor speeds known as a compressor map. Figure 1 shows an example of a compressor map. The stable compressor operating area is limited by a surge line for lower flow. Operating the compressor at a flow lower than the surge line would cause the compressor to go into an unstable condition known as surge. It is an axisymmetric oscillation of the compressor flow and the compressor produced pressure followed by severe vibrations. The vibrations may reduce the reliability of the compression system and large amplitude vibrations may lead to compressor damage in particularly to the compressor blades and bearings, and also damages on pipe connections.Surge phenomena is of interest as higher compressor efficiency operating points are located near the surge line and some processes are requiring low mass flow at high pressure. However, operating in such points may endanger the compressor because a disturbance could bring the compressor into the surge area. There is a trade-off between operating the compressor at a higher efficiency and the stability. Two methods have been developed to overcome the surge problem:

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Transient analysis of gas pipeline network

Cited by 43 publications

References 1 publication

Pressure Fluctuations in Natural Gas Networks Caused by Gas-Electric Coupling

Pressure Fluctuations in Natural Gas Networks Caused by Gas-Electric Coupling

A novel approach for dynamic flow simulation of gas pipelines using teaching–learning-based optimization algorithm

Bond graph modeling of centrifugal compression systems

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