Analysis and Research on Pipeline Vibration of a Natural Gas Compressor Station and Vibration Reduction Measures

Liu, Enbin; Wang, Xingjie; Zhao, Wei; Su, Zhongya; Chen, Qikun

doi:10.1021/acs.energyfuels.0c03663

Cited by 57 publications

(21 citation statements)

References 26 publications

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“…With recent advances in computational intelligence, many scholars have replaced traditional methods with new generated machine learning [6][7][8][9][10][11], deep learning [12][13][14][15][16][17], decision making [18,19], and artificial intelligence-based tools [20][21][22]. These novel approximation techniques are well employed in various engineering fields such as in evaluating environmental concerns [19,[23][24][25][26][27][28][29][30][31], implications for natural environmental management [32][33][34][35][36][37][38][39], water resources management [28,[40][41][42][43][44], natural gas consumption [45][46][47][48], energy efficiency [49][50]…”

Section: Background Of Artificial Intelligencementioning

confidence: 99%

Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings

Moayedi

Mosavi

2021

Energies

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A reliable prediction of sustainable energy consumption is key for designing environmentally friendly buildings. In this study, three novel hybrid intelligent methods, namely the grasshopper optimization algorithm (GOA), wind-driven optimization (WDO), and biogeography-based optimization (BBO), are employed to optimize the multitarget prediction of heating loads (HLs) and cooling loads (CLs) in the heating, ventilation and air conditioning (HVAC) systems. Concerning the optimization of the applied algorithms, a series of swarm-based iterations are performed, and the best structure is proposed for each model. The GOA, WDO, and BBO algorithms are mixed with a class of feedforward artificial neural networks (ANNs), which is called a multi-layer perceptron (MLP) to predict the HL and CL. According to the sensitivity analysis, the WDO with swarm size = 500 proposes the most-fitted ANN. The proposed WDO-ANN provided an accurate prediction in terms of heating load (training (R2 correlation = 0.977 and RMSE error = 0.183) and testing (R2 correlation = 0.973 and RMSE error = 0.190)) and yielded the best-fitted prediction in terms of cooling load (training (R2 correlation = 0.99 and RMSE error = 0.147) and testing (R2 correlation = 0.99 and RMSE error = 0.148)).

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Section: Background Of Artificial Intelligencementioning

confidence: 99%

Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings

Moayedi

Mosavi

2021

Energies

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“…e detailed simulation of the process is computationally intensive. For simplicity, the following assumptions were made in the study [17][18][19][20]:…”

Section: Calculation Modelmentioning

confidence: 99%

Mathematical Model of Maximum Commutation Half Cycle for Thermal Countercurrent Oxidation of Low-Concentration Gas in Coal Mine Ventilation

Shi

Chen

2021

Shock and Vibration

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The Fluent computational fluid dynamics software was used to study the relevant factors affecting the maximum commutation half cycle for thermal countercurrent oxidation of low-concentration gas in coal mine ventilation. Based on orthogonal experiments, the maximum commutation half cycle for thermal countercurrent oxidation of the exhaust gas in the coal mine ventilation under 25 working conditions with the combination of different methane concentrations, inlet speeds, porosities, and oxidation bed filling lengths is investigated. SPSS data processing software was used to perform regression analysis on the numerical simulation data, and a mathematical model for predicting the maximum commutation half cycle under the influence of four factors was obtained. Through experiments, the mathematical model of the maximum commutation half cycle by the numerical simulation was verified. After introducing the wall heat loss correction coefficient, the complete prediction model of the maximum commutation half cycle was obtained. Comparing the experimental test value with the calculated value using the corrected model, the relative error was not more than 3%. The complete mathematical model corrected can be applied to the design calculation of the maximum commutation half cycle for thermal countercurrent oxidation of low-concentration gas in actual coal mine ventilation.

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“…e grid number affects the final result of numerical simulation, so different grid numbers are selected for grid independence test in this paper. When the drilling fluid flow rate is 4 m/s, the mesh independence of the numerical model is verified by taking the maximum equivalent stress of the drill string as the evaluation index [33].…”

Section: Data Transfer Between Fluid-structure Couplingmentioning

confidence: 99%

Effect of Annular Gas-Liquid Two-Phase Flow on Dynamic Characteristics of Drill String

Wang

et al. 2021

Shock and Vibration

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Natural gas hydrate (NGH) is a kind of new type green energy source with giant reserves which has been thought of highly by energy explorers in the world. However, NGH breaks down to produce some natural gas that enters the annulus and flows together with the drilling fluid. The gas-liquid two-phase flow can have an impact on the work of the drill string. Therefore, it is important to study gas-liquid two-phase flow in the annulus on the dynamic characteristics of the drill string. In this article, taking a single drill string as the research object, a fluid-structure coupled finite element mathematical model of two-phase flow in the annulus and drill string is established based on computational fluid dynamics and computational structural dynamics theory. The finite element numerical simulation method is used to analyze the influence of drilling fluid and natural gas in the annulus on the dynamic characteristics of the drill string. The simulation analysis shows the following: (1) The motion of drilling fluid or natural gas in the annulus will reduce the natural frequency of the drill string, and the drilling fluid has a greater impact on the natural frequency of the drill string. (2) When single-phase drilling fluid flows in the annulus, the displacement peak in different directions, maximum equivalent stress, and strain of the drill string increase with the increase of the drilling fluid flow velocity or pressure, and the drilling fluid pressure has a more significant effect. (3) When the gas-liquid two-phase fluid flows in the annulus, the displacement peak, maximum equivalent stress, velocity amplitude, and acceleration amplitude of the drill string all increase with the natural gas flow velocity and natural gas content increase, and the natural gas flow velocity has a more significant effect.

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Analysis and Research on Pipeline Vibration of a Natural Gas Compressor Station and Vibration Reduction Measures

Cited by 57 publications

References 26 publications

Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings

Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings

Mathematical Model of Maximum Commutation Half Cycle for Thermal Countercurrent Oxidation of Low-Concentration Gas in Coal Mine Ventilation

Effect of Annular Gas-Liquid Two-Phase Flow on Dynamic Characteristics of Drill String

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