Numerical Simulation of Natural Gas Pipeline Transients

Alghlam, Abdoalmonaim; Stevanović, Vladimir D.; Elgazdori, Elmukhtar A.; Banjac, Miloš

doi:10.1115/1.4043436

Cited by 17 publications

(5 citation statements)

References 52 publications

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“…The pipeline outlet pressure simulated from the OLGA model is mentioned in Figure 5. It can be observed that the developed model is in good agreement with the experimental results [50] and other simulated studies [51][52][53]. It can be observed that pressure at the outlet is maintained constant in the start followed by constant increment while the mass flow rate was constantly decreased at the same time.…”

Section: Olga Model Validationsupporting

confidence: 82%

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Leak Detection in Gas Mixture Pipelines under Transient Conditions Using Hammerstein Model and Adaptive Thresholds

et al. 2020

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Conventional leak detection techniques require improvements to detect small leakage (<10%) in gas mixture pipelines under transient conditions. The current study is aimed to detect leakage in gas mixture pipelines under pseudo-random boundary conditions with a zero percent false alarm rate (FAR). Pressure and mass flow rate signals at the pipeline inlet were used to estimate mass flow rate at the outlet under leak free conditions using Hammerstein model. These signals were further used to define adaptive thresholds to separate leakage from normal conditions. Unlike past studies, this work successfully detected leakage under transient conditions in an 80-km pipeline. The leakage detection performance of the proposed methodology was evaluated for several leak locations, varying leak sizes and, various signal to noise ratios (SNR). Leakage of 0.15 kg/s—3% of the nominal flow—was successfully detected under transient boundary conditions with a F-score of 99.7%. Hence, it can be concluded that the proposed methodology possesses a high potential to avoid false alarms and detect small leaks under transient conditions. In the future, the current methodology may be extended to locate and estimate the leakage point and size.

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Section: Olga Model Validationsupporting

confidence: 82%

“…It can be observed that pressure at the outlet is maintained constant in the start followed by constant increment while the mass flow rate was constantly decreased at the same time. There was a delay of around 1.8 h between maximum pressure and minimum mass flow rate, this difference was due to the inertia effect [53].…”

Section: Olga Model Validationmentioning

confidence: 99%

Leak Detection in Gas Mixture Pipelines under Transient Conditions Using Hammerstein Model and Adaptive Thresholds

et al. 2020

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“…It is caused by the valve closure and a sudden decrease in the outlet mass flow rate (Figure 6A). The inertia effect resulted in a 1.8 h delay between the maximum pressure and the lowest mass flow rate 45 . Similarly, when the mass flow rate increased again, the pressure decreased, eventually reaching its lowest point in 15.2 h. The numerical investigation showed a progressive increase in pressure after roughly 16 h, whereas the measured pressure is practically constant.…”

Section: Resultsmentioning

confidence: 84%

Gas pipeline safety management system based on neural network

Mujtaba

Lemma

Vandrangi

2022

Process Safety Progress

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The 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.

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“…2. The code was validated for several transient conditions, such as: the main steam pipeline break [5], water hammers in two-phase systems [11], water hammer with gaseous cavitation [12] and natural gas pipeline transients [13].…”

Section: Calculation Methodologymentioning

confidence: 99%

Fluid dynamic forces in the main steam pipeline of thermal power plant upon stop valves closure

2020

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A steam turbine trip is followed by a prompt closure of stop valves in front of the turbine and consequently to a pressure rise in the main steam pipeline. This steam hammer transient leads to the generation of intensive fluid dynamic forces that act along the pipeline axis and induce additional dynamic loads on the main steam pipeline. It is a common practice to assume a simultaneous closure of all stop valves in the safety analysis of the main steam pipeline. In the present paper computer simulations and analyses of the fluid dynamic forces are performed for several scenarios that take into account the possibility of delayed closure of the stop valve in front of the turbine. The influence of the failure of the steam by-pass line opening is considered too. The results show that the delay of the stop valve closure increases the maximum intensity of fluid dynamic force in the pipeline segment in front of the stop valve and decreases the intensity of fluid dynamic forces in segments along the pipeline. The failure of the by-pass line to open leads to prolonged steam pressure and fluid dynamic forces oscillation in pipeline segments. The simulations were performed with the in-house computer code based on the method of characteristics for the solving of the hyperbolic system of PDE that represent the mass, momentum and energy balance equations of the 1-D, compressible and transient fluid-flow. The obtained results are a support to safety analyses of thermal power plants under transient conditions. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. TR-34028]

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Numerical Simulation of Natural Gas Pipeline Transients

Cited by 17 publications

References 52 publications

Leak Detection in Gas Mixture Pipelines under Transient Conditions Using Hammerstein Model and Adaptive Thresholds

Leak Detection in Gas Mixture Pipelines under Transient Conditions Using Hammerstein Model and Adaptive Thresholds

Gas pipeline safety management system based on neural network

Fluid dynamic forces in the main steam pipeline of thermal power plant upon stop valves closure

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