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With economic development and the burgeoning need for energy, numerous underground gas pipelines have been deployed for operation. Buried gas pipelines often suffer failures as a result of adverse factors, including corrosion, uneven subsidence, and damage caused by third parties. To investigate and predict the diffusion behavior following a natural gas pipeline leakage, Fluent software is utilized to develop a numerical model for the leakage and diffusion of high-pressure natural gas within a confined space. This model incorporates the Soave–Redlich–Kwong equation of state, which is widely recognized for its exceptional precision in characterizing the behavior of natural gas under high-pressure conditions. The study focuses on the analysis of leakage and diffusion behavior as well as the examination of how pipeline operating pressure and leakage diameter have an impact on the dispersion of leaked gas. Further, the prediction model for the diffusion distance of the hazardous area is developed, employing the least-square method and finite element calculations. The results show that, during the leakage process, a vortex and velocity region emerge, extending along the confined space. The farther away from the leakage hole above the pipeline, the higher the overall concentration of the gas. Moreover, the horizontal diffusion distance of gas at the bottom of the pipeline is considerably smaller than that above it. However, gas tends to readily accumulate in a high concentration area at the bottom of the pipeline. Elevating the leakage diameter and the operation pressure leads to a significant rise in gas concentration and the horizontal diffusion of the hazardous area. It is worth noting that the leakage diameter has a more pronounced effect on gas diffusion than does the pressure. The prediction model proposed in this study effectively anticipates the horizontal diffusion of the hazardous area within confined spaces. [ABSTRACT FROM AUTHOR]

This paper proposes an improved matching pursuit (MP) method with a multicomponent interference dictionary (MCID) based on a pipeline guided wave reflection model. The proposed method separates the overlapped time domain reflections from the notch edges and extracts parameters such as amplitude, time of flight (TOF), and phase to identify the number and axial dimensions of notches in pipes. Firstly, the performance of the method in identifying parameters of overlapped components affected by noise and reverberation under different signal-to-noise ratios (SNRs) and frequencies is analyzed using four error metrics. Secondly, finite-element (FE) models of pipes with a single notch and double notches are established, and the accuracy of the reflection model is validated by comparing the predicted amplitudes, TOF, and phase of reflections with the FE results. Finally, experimental validation is conducted on aluminum pipes with multiple notches. The consistency between the experimental results, theoretical results, and FE results in wave packet parameters confirmed the accuracy of the proposed reflection models. The method accurately captures the parameters of the reflections from each notch edge in the experimental pipes, enabling the identification of the number and axial dimensions of the notches. The method holds the potential for identifying a greater number of notches in pipelines and characterizing their axial dimensions. This can provide a reliable reference for developing maintenance plans based on pipeline operational conditions, thereby preventing major safety incidents. [ABSTRACT FROM AUTHOR]

Pipeline condition assessment is a cost-effective method to determine the status of pipeline structure and predict failure probability. Although 100% inspection may not be feasible for decision makers, recent advancements in machine learning techniques have enabled more effective pipeline condition assessment. This paper provides a comprehensive review of machine learning applications in pipeline condition assessment, covering aspects such as fault diagnosis, risk prediction, parameter prediction, and visual defect recognition. The present study endeavors to make the following contributions: (1) extraction of the model, data size, and other relevant information from 91 papers, (2) in-depth analysis of the state of the art and frameworks of the models discussed in the 91 papers, (3) summary of the data characteristics, input variables, and accuracy of machine learning models, and (4) exploration of the potential avenues for future research in the use of machine learning for pipeline condition assessment. This review aims to serve as a practical reference for scholars engaged in related research. The review highlights the fact that the majority of the models employed in pipeline condition assessment are original, and the utilization of hybrid models remains limited. Transfer learning and reinforcement learning are identified as potential avenues for future research because they hold promise in facilitating the adaptive selection of model inputs and the transfer of models to similar projects. Furthermore, breaking down data barriers is deemed essential for advancing the use of machine learning in pipeline condition assessment. [ABSTRACT FROM AUTHOR]

Burst pressure is an essential parameter to measure the residual bearing capacity of pipelines with corrosion defects. Accurate prediction of burst pressure is beneficial to evaluate the remaining life of pipelines. The traditional estimation formulas mainly focus on single-point corrosion but seldom consider the case of multipoint corrosion. Although DNV-RP-F101 considers multipoint corrosion, this method only considers the influence of axial spacing of adjacent corrosion defects on burst pressure but does not consider the influence of circumferential spacing. This work refers to the pipe corrosion criterion (PCORRC) and DNV-RP-F101 methods and modifies the calculation formula of the PCORRC method by introducing (D−t). The average relative error is only 11.46%, which is 27.65% lower than the previous formula. In addition, this paper presents a method for estimating the burst pressure of pipelines with multipoint corrosion defects. This method considers not only the axial spacing between defects but also the circumferential spacing. The results show that the maximum relative error is 6.52%. A machine learning modeling framework for predicting burst pressure in multipoint corroded pipelines is proposed. The case using radial basis function neural network as the prediction tool shows that the average Pearson correlation coefficient of the prediction results in the test set is 0.903, indicating that the prediction performance is good. [ABSTRACT FROM AUTHOR]

The erosion of elbows by particles in shale gas pipelines poses a great threat to the production safety of pipelines. Accurate prediction of the erosion rate is critical to ensuring the safe operation of pipelines. Previous predictions of the erosion rate is based on experiments and numerical simulation. However, because many factors of affecting pipeline erosion lead to strong nonlinearity of the physical scene, these two methods are time-consuming and labor-intensive and have many assumptions. This paper presents a data-driven approach for efficiently predicting erosion rates in shale gas pipelines, which has a high prediction accuracy based on an amount of data without experiments and numerical simulation. The model mixes the adaptive t -distribution-based sparrow search algorithm and support vector machine. Through case studies, the following results were obtained: (1) the minimum mean square error (MSE) of the proposed model is less than 10%; (2) compared with the hybrid model without considering the adaptive t -distribution, the MSE of the proposed model is reduced by 32%; and (3) the MSE, Theil U statistic 1 (U1), and Theil U statistic 2 (U2) of the proposed model in the test set are 47%, 47%, and 34% lower than the average MSE, Theil U statistic 1 (U1), and Theil U statistic 2 (U2) of benchmark models, respectively. Two cases of multiphase flow and gas–solid conditions showed that the improved hybrid model has higher prediction accuracy than support vector machines with other optimizers and has strong generalization performance. This study may be helpful for pipeline maintenance and repair in practical engineering. [ABSTRACT FROM AUTHOR]

Because of their adverse impact on social infrastructure and economic property, gas explosions are among the most dangerous disasters that can befall utility tunnels. Flame propagation is the most intuitive manifestation during the explosion process. It can be significantly influenced by spatial obstacles. To analyze the propagation law of gas explosions in utility tunnels more realistically, an explosion model of the utility tunnel was established by considering actual spatial obstacles, including the pipeline support and reserved frame. The flame propagation characteristics, including the flame shape, temperature distribution, and propagation velocity, were analyzed systematically. Results demonstrate that spatial obstacles have a prominent influence on flame evolution laws. Flame propagation is disturbed but not completely blocked by spatial obstacles. Rather, irregular flame shapes and wrinkles of the flame front are easily developed. The explosion process is insufficient because of the existence of residual methane in the obstacle area. Flame propagation velocity is improved remarkably, whereas temperature distribution is affected slightly. In particular, the spread velocity increases with the increase of propagation distance. The maximum velocity of monitoring surfaces can be improved by 60% under conditions with obstacles. [ABSTRACT FROM AUTHOR]

Long-distance oil pipelines consume extensive energy during the operation. Accurate prediction of pipeline's energy consumption is the basis of intelligent management and energy-saving plan. This work demonstrates an effective data-driven model to predict a pipeline's energy consumption. The model first extracts features through correlation analyses and then uses a hybrid support vector machine for prediction. In the hybrid model, the fruit fly optimizer, simulated annealing algorithm, and cross factor are fused to form an improved fruit fly optimizer used to optimize the traditional support vector machine. The proposed model is tested on a pipeline in China, and the results indicate that the model has a high prediction accuracy (prediction accuracy is measured by one minus mean absolute percentage error, and the accuracy exceeds 80%). Compared with the three benchmark models, the accuracy of the proposed model is increased by 4.18%–25.47%. This paper discusses the influence of the kernel function on the prediction. The results show that the support vector machines with radial basis kernel function and polynomial kernel function perform well in Dataset I, while support vector machines with radial basis kernel function, polynomial kernel function, and linear kernel function perform almost the same in Dataset II. Moreover, this paper also discusses the input of the model on a deeper level. More experiments show that removing highly repetitive input can ensure higher prediction accuracy in engineering and reduce the complexity of the model. [ABSTRACT FROM AUTHOR]

With the rapid development of utility tunnels, fire safety after construction is increasingly important, especially in the cable compartment. An intelligent fire detection method based on the particle swarm optimization algorithm was proposed for fire state estimation of the utility tunnel, including the fire source location, the maximum temperature value, and the temperature attenuation coefficient. Additionally, a corresponding sensor optimization strategy was also established. The dispersion coefficient of the fire source location was defined as the judgment criteria of sensor optimization. The validity of the proposed algorithm and the sensor optimization strategy were demonstrated in the application of a full-scale experimental example. The maximum errors of the identified fire source location and the maximum temperature value after sensor optimization were 34.7164 m and 8.5403°C, respectively. The total number of temperature sensors was reduced by more than 50%. The proposed intelligent fire detection algorithm can provide precise guidance for fire protection and extinguishing plan. Particularly, the sensor optimization strategy can economize the cost of temperature sensors. [ABSTRACT FROM AUTHOR]

This document is a closure to a research paper titled "Predicting Solid-Particle Erosion Rate of Pipelines Using Support Vector Machine with Improved Sparrow Search Algorithm." The authors address three main issues raised by discussers. They acknowledge that overfitting occurred in the study, resulting in a smaller mean square error (MSE) for the test set compared to the training set. They also apologize for using the wrong terms for the training and testing sets in a table. The authors discuss the importance of hyperparameter optimization in support vector machines (SVM) and clarify a mistake in the steps mentioned by the discussers. Additionally, they apologize for the inconsistency in the name of a variable. The authors express gratitude for the questions and commit to improving their research. [Extracted from the article]

Guided wave technology shows excellent capacity in the damage identification of the pipeline. In practical inspection, the detection ability is greatly reduced due to the interference echo signal from the feature structure in the pipeline. In this paper, the propagation models of guided wave in straight pipe and pipe with feature structures are established, and the effect of feature structure on the guided wave echo signal is analyzed, including pipe with an elbow, ring joint, and accessory structure, and the corresponding damage identification strategies for pipe with different forms of features are proposed. The damage index of the echo signal difference and radial displacement signal is proposed to extract the signal from damage. Besides, to eliminate the noise effect caused by the actual detection situation, a novel guided wave denoising method based on the wavelet analysis is studied, and the analysis results indicate that the damage detection strategy based on the guided wave can accurately locate the damage region in both straight pipe and pipe with features and that the proposed denoising method can effectively eliminate the noise in the echo signal. [ABSTRACT FROM AUTHOR]

For the residual strength prediction of corroded pipelines, the existing standard has a small application range, and the finite-element method has too many assumptions. This paper proposes a new data-driven prediction framework. Firstly, principal component analysis (PCA) is used to reduce the dimensions of the existing data to determine the input-output structure of the prediction model. Secondly, support vector machine (SVM) based on multiobjective optimization is employed to predict the pipeline's residual strength. Compared with the traditional estimation methods, the model proposed in this paper is data-driven and combines data dimension reduction, multiobjective optimization, and a machine learning model. In addition, the accuracy and stability of the model are considered in the multiobjective optimization. The proposed framework is tested in a pipeline burst pressure data set. The results indicate that the mean absolute percentage error of the proposed models ranges from 1.353% to 3.220%, which has good prediction accuracy and stability. This paper also discusses the influence of the multiobjective optimization algorithm and dimension reduction on the prediction model. The following primary conclusions are drawn: (1) SVM optimized by multiobjective optimizer performs better than SVM optimized by the single-objective optimizer, and the original SVM performs worst, and (2) reducing the raw data dimensions can improve the residual strength prediction performance for corroded pipelines reduce the complexity of the model, and shorten the calculation time. [ABSTRACT FROM AUTHOR]

Pipelines are widely used as an economical transmission mode in petroleum transportation, ocean engineering, aerospace engineering, and nuclear industry. However, pipelines are potentially susceptible to vibration in horizontal and vertical directions under external excitation, which leads to a significant reduction in the service life of pipelines, or even the damage of pipelines. In this paper, a novel multidimensional vibration isolation and mitigation device (MVIMD) is proposed and optimized to mitigate the multidimensional vibration of pipelines. First, the dynamic characteristics and responses of pipelines with and without the devices are compared based on the modified equivalent standard solid model. Then the MVIMD is attached to a pipeline to verify its vibration mitigation effect in horizontal and vertical directions. Finally, the genetic algorithm (GA) is adopted to optimize the parameters of MVIMD. The numerical analysis results show that the novel MVIMD with parameters optimized by the genetic algorithm has better effects for controlling both horizontal and vertical vibration responses of pipelines. [ABSTRACT FROM AUTHOR]