Pipeline Size Research Articles

Quantitative analysis of hydrogen leakage flow measurement and calculation in the on-board hydrogen system pipelines

The mass flow rate of hydrogen leakage directly affects hydrogen diffusion and concentration distribution. In this paper, a test platform was constructed to measure mass flow rates during hydrogen leakage in the supply pipelines of the on-board hydrogen system. Hydrogen leakage tests were conducted under two different conditions. Mass flow rates were measured for tube fittings with various loosening angles and hydrogen supply pressures. The data were then fitted to extrapolate mass flow rates of hydrogen leakage beyond the tested conditions. Additionally, mass flow rates were measured for hydrogen supply pipelines with small holes of different shapes and sizes. The effects of pipeline size, hydrogen supply pressure, hole shape, and size on the hydrogen leakage mass flow rate were analyzed. Hydrogen leakage flow coefficients were corrected, and the accuracy of three calculation methods for hydrogen leakage flow was compared and analyzed based on the test results. The results provide a foundation for future numerical simulation models of hydrogen leakage diffusion.

Predicting critical flame quenching thickness using machine learning approach with ResNet and ANN

In the study of flame quenching, quenching thickness is one of the important parameters to determine the design of a flame arrester, and often determines the flame quenching performance of the arrester. In the study, residual network (ResNet) and artificial neural network (ANN) are used to predict the critical quench thickness of combustible gas in pipelines. The critical quench thickness is influenced by fuel concentration and density, pipeline size, inert gas type and concentration, porous media porosity, and thermal conductivity. The influence of different combinations of hyper-parameters on the prediction performance of the two models is explored. The results show that the prediction performance of both models reaches the best after hyper-parameter optimization. Compared with ANN, the ResNet model shows more stable and better prediction ability, and its optimal evaluation parameters are: MAE is 1.4679, MSE is 91.7431, R2 is 0.9216. The prediction errors of the two models on the same dataset are subjected to analysis, and the impact of the use of normalized data on the performance of the two models is compared. It is determined that the ResNet model demonstrated superior robustness and generalization ability in predicting the critical quenching thickness of combustible gases. The study is helpful for the safety protection of combustible gas and the safety design of pipeline arresters.

Proposing new correlations to improve the accuracy of hydrostatic tests for buried steel pipelines

This study proposes an efficient and effective method to estimate the water temperature inside buried gas pipelines during hydrostatic tests. The goal is to develop appropriate correlations that can easily calculate the pipeline’s water temperature, taking into account the adjacent soil temperature variations, pipeline size, thickness, and coating properties. The study first presents a mathematical model of the problem, introducing relevant non-dimensional parameters. Then, the unsteady problem is numerically analyzed, considering all significant variables, and next, the effects of each non-dimensional parameter on the water temperature are carefully examined and evaluated. The numerical simulations show that the difference between the pipeline water temperature and the adjacent soil temperature can be up to 80%, particularly for large-diameter pipelines. The proposed correlations are able to estimate the pipeline water temperature during the hydrostatic test with around 1% deviation from the direct numerical solutions. Additionally, the results indicate that for typical test cases, the pipeline size is the main influential parameter, while the pipeline thickness and material coating with conventional properties do not significantly affect the water temperature variations.

A novel optimization model for tackling capacity challenges in natural gas gathering systems

In the layout optimization of natural gas gathering pipeline network, a lot of work has been done, but few scholars focus on the capacity optimization of gas gathering station (short for station). The structural relationship parameters of dendritic pipeline network and the flow parameters of pipeline network are complicated, which makes it difficult to establish the model, and at the same time, the existence of nonlinear term causes the difficulty of solving the problem. In the current two stage star-dendritic natural gas gathering pipeline network, pipeline sizes and station capacity are not well taken into account for the optimization of pipeline network layout at the same time. In this paper, a consideration station capacity and pipeline sizes: Two-stage star-dendritic network layout optimization model (SCPSTSDLO-Model) is constructed by integrating the factors of site discrete characteristics, station capacity and pipeline sizes, and considering the hydraulic pressure drop conditions of the network, and a solution framework based on the nonlinear processing is proposed, which can obtain the optimization results of optimal affiliation, station type, station site and pipeline sizes at the same time. Five cases are used to verify the validity and accuracy of the model and algorithm, compare the sensitivity analysis and solution algorithm, compare the hydraulic pressure drop conditions, and analyze the application in different forms of pipeline networks. The results show that the SCPSTSDLO-Model and the solution method established in this paper can obtain the optimal solutions under different topologies according to the application requirements, taking into account the capacity of stations and pipeline sizes.

Load-independent creep constraint analysis and solutions for surface cracks in pressurized pipes

Using load-independent creep constraint parameters Q* and Qm*, the variations of crack front stress field and constraint level are analyzed for various crack sizes of pipelines containing internal surface cracks under high temperature conditions. The results show that the trends of stress changes in the axial and circumferential internal surface crack front for different crack sizes are similar. For axial internal surface cracks, when the crack depth to length ratio a/c is the same, the maximum values of crack front Q* and Qm* are shifted from the deepest part of the crack (2Φ/π = 1) to the vicinity of the free surface (2Φ/π = 0.2) as the crack depth a/t increases. As for the circumferential inner surface cracks, when the crack depth a/t exceeds 0.2, the locations where the maximum values of crack fronts Q* and Qm* appear are mainly concentrated in the deepest part of the crack (2Φ/π = 1). Based on the calculation results of the constraint parameters, the empirical formulas for the engineering calculation of the relationship between the average constraint parameters (Qavg* and Qm‐avg*) and the crack size are obtained by fitting.

Energy absorption characteristics of novel bionic H-type whip restraints for nuclear power plants

To enhance the efficiency of energy absorption in H-type whip restraints for nuclear power plants, this study takes inspiration from the asymmetric twisted lines observed in the Nautilus shell, introducing the Nautilus Bionic Hierarchical Multi-Cell (NBHMC) structure. For different high-energy pipeline sizes and energy absorption quantities, the proposed structures are miniaturized and can be combined into H-type whip restraint by multiple tubes to meet the requirements. The NBHMC structures are investigated based on the validated finite element method, the results demonstrate that the energy absorption per unit mass of the new structure can reach up to 180% of that of the hollow tube, with the maximum enhancement in energy absorption efficiency being 47%. Through parameter analysis of the cross-sectional wall thickness, the highest energy absorption efficiency obtained is 99.62%, with a specific energy absorption of 31.29J/g. Furthermore, utilizing the TOPSIS method, all proposed parameter combinations are ranked, leading to the determination of the optimal parameter combination. Additionally, a theoretical model predicting the mean crushing force is introduced, exhibiting good agreement with numerical simulations. The results presented in this work provide innovative insights for the design of efficient pipe whip restraints.

Modern approaches to determining the standardised parameters of vibration state of NPP steam pipelines

During the operation of NPP steam pipelines the main damaging factors are not only corrosion and erosion leading to thinning of the pipe wall but also fatigue damage resulting not only from temperature self-compensation efforts, but also high vibration load. As practice shows, the most promising direction of ensuring the vibration resistance of steam pipelines at present is the computational and experimental study of the stress-strain state of pipelines under vibration loading. This work shows that the strength of the most commonly used standard sizes of steel pipelines under the influence of vibration loads is determined in accordance with Russian State Standard R 59115.9-2021. Taking into account the stress concentration, the amplitudes of the conditional elastic reduced stresses were determined for the 4 variants of the calculation schemes accepted for consideration. The permissible amplitude of stresses from an operating condition of 60 years is determined. It is shown that the permissible voltage amplitude for the design temperature t = 350°C will be no more than 46.4 Mpa for a pipe made of 12X18H10T. As for NPP pipelines, the normalized vibration parameters in the regulatory documentation were not established until 2022, when the following vibration velocity limits were adopted for pipelines of nuclear power plants in Russian State Standard R 59115.11-2021, vibration resistance testing is not required: nmax up to 15 mm/s, nmsv up to 7 mm/s. Thus, currently in the Russian regulatory documentation there are no normalized values of vibration parameters (as a rule, vibration velocity) for pipelines selected depending on the frequency of vibration exposure. It is proposed to develop and introduce into the regulatory documentation frequency-dependent criteria for limiting vibration parameters of NPP pipelines, specified in accordance with the actual operating conditions of these pipelines.

A novel data center free radiating system driven by waste heat and wind energy system (DCFRWWs) and its operation performance analysis

Refrigeration equipment for data centers consumes a significant portion of global electricity, resulting in high Power Usage Effectiveness (PUE) values and increased entropy of the data center. To address these challenges, this study proposes a novel data center free radiating system driven by waste heat and wind energy (DCFRWWs). This approach recovers low-grade heat energy from the hot air heated by the central processing units (CPUs) and transform it into work. Unlike conventional cooling systems, the DCFRWWs do not require additional energy to meet CPU cooling demands and can effectively suppress the entropy increase of the data center. The Bernoulli equation was used to establish the mathematical model for DCFRWWs, which explained the underlying mechanisms of pipeline wind speed and the influence of ambient wind speed and pipeline inlet temperature on system operating characteristics. In addition, this study employed pipeline height and diameter as decision variables, while maximizing exergy efficiency served as the optimization objective. To obtain the optimal pipeline size (height and diameter) that is effective under real-world experimental conditions, the optimization calculations were conducted using weather data from Qinhuangdao. To verify the reliability and accuracy of the model, an experimental system was constructed, and the simulation results indicated that the relative error rates for the three key parameters are as follows: pipeline wind speed (33.09%), pipeline inlet temperature (0.59%), and CPU temperature (3.85%). A single factor analysis was performed on 14 different operating conditions to analyze the operating characteristics of the DCFRWWs. Moreover, the applicability verification conducted in three distinct cities across different regions suggest that the system is indeed suitable for diverse settings. The DCFRWW system, utilizing the dual forces of the chimney effect of waste hot air in pipelines and the extraction force generated by unpowered roof ventilator, achieves the objective of free heat dissipation. Furthermore, the DCFRWWs can not only be applied in the field of data center cooling but also serves as a thermodynamic reference for low-grade waste heat recovery in other sectors, thereby further expanding the scope of direct utilization of wind energy.

Optimal Sizing and Life-cycle Cost Modelling of Hydraulic Capsule Pipelines

Estimation of the total cost is essential for the planning and the designing of pipelines in a cost-effective way. As a result, the total cost modelling could go beyond the established objectives of least-cost design and optimization. This study incorporates a design methodology for pipelines transporting spherical capsules, which have variant sizes and densities. A methodology has been developed to determine the optimal size and lifetime of pipelines transporting solid–liquid mixtures (Capsule Flow). The methodology includes a model for the prediction of various life-cycle costs. The methodology provides a closed form solution to predict the optimal pipe diameter corresponding to the least total cost. Such solutions are obtained for different life-cycle costs, of which the one associated with minimum annual total cost provides the lifetime. The developed methodology has been used to obtain the optimal diameter of a pipeline for a practical case. The study reveals the interrelationship between the optimal pipe diameter and the corresponding minimum total cost.

An ultrasonic in-line inspection data processing method considering invalid data caused by sensor failure

Ultrasonic in-line inspection is one of the most widely adopted nondestructive testing methods for defect detection in pipelines. In practical industrial scenes, pipeline specification varies, sensor failure occurs frequently, and the number of pipeline defect samples is scarce. How to detect defects without false detection caused by invalid data and with limited labeled samples is a challenging problem in this area. An ultrasonic in-line inspection data processing method considering invalid data caused by sensor failure is proposed in this paper to enhance the accuracy of the defect and its profile detection. Firstly, the multi-channel data is aggregated according to sampling time. The data dimension is reduced, and the accuracy of the invalid waveform detection adopting isolation forest arithmetic is improved. The methods are adopted for the invalid waveforms replacement with two-dimensional cubic spline interpolation using adjacent sensors. Secondly, a natural breakpoint method is adopted to locate the echo peaks. Residual wall thickness is evaluated by calculating the time difference between the echo peaks. A fast pseudo-colorization method based on sliding windows and a morphological image processing method are proposed to detect defects and their profiles efficiently utilizing the residual wall thickness. Finally, practical in-line inspection data is utilized to evaluate the performance. The experiment results illustrate that the detection accuracy is enhanced on different sizes of pipelines without requiring labeled samples.

A metagenomic study of gut viral markers in amyloid-positive Alzheimer’s disease patients

BackgroundMounting evidence suggests the involvement of viruses in the development and treatment of Alzheimer’s disease (AD). However, there remains a significant research gap in metagenomic studies investigating the gut virome of AD patients, leaving gut viral dysbiosis in AD unexplored. This study aimed to fill this gap by conducting a metagenomics analysis of the gut virome in both amyloid-positive AD patients (Aβ + ADs) and healthy controls (HCs), with the objective of identifying viral signatures linked with AD.MethodWhole-genome sequence (WGS) data from 65 human participants, including 30 Aβ + ADs and 35 HCs, was obtained from the database NCBI SRA (Bio Project: PRJEB47976). The Metaphlan3 pipeline and linear discriminant analysis effect size (LEfSe) analysis were utilized for the bioinformatics process and the detection of viral signatures, respectively. In addition, the Benjamini–Hochberg method was applied with a significance cutoff of 0.05 to evaluate the false discovery rate for all biomarkers identified by LEfSe. The CombiROC model was employed to determine the discriminatory power of the viral signatures identified by LEfSe.ResultsCompared to HCs, the gut virome profiles of Aβ + ADs showed lower alpha diversity, indicating a lower bacteriophage richness. The Siphoviridae family was decreased in Aβ + ADs. Significant decreases of Lactococcus phages were found in Aβ + ADs, including bIL285, Lactococcus phage bIL286, Lactococcus phage bIL309, and Lactococcus phage BK5 T, Lactococcus phage BM13, Lactococcus phage P335 sensu lato, Lactococcus phage phiLC3, Lactococcus phage r1t, Lactococcus phage Tuc2009, Lactococcus phage ul36, and Lactococcus virus bIL67. The predictive combined model of these viral signatures obtained an area under the curve of 0.958 when discriminating Aβ + ADs from HCs.ConclusionThis is the first study to identify distinct viral signatures in the intestine that can be used to effectively distinguish individuals with AD from HCs.

Size estimation of underground targets from GPR frequency spectra: A deep learning approach

GPR (Ground Penetrating Radar) is a robust and effective device for identifying underground artefacts. Construction companies and civil engineers should be aware of the sizes of rebars and pipelines before and during construction work for various reasons. Most research efforts have typically concentrated on GPR signal analysis in the time domain, however recent studies have increasingly focused on analysis in the frequency domain. This paper proposes an artificial neural network (ANN) model for estimating the diameter of underground rods (solid) and pipes (hollow). GPR data captured in the time domain domain is transformed to the frequency domain using FFT, after which feature extraction is performed using ANN. An FPGA-based prototype GPR system is used to collect GPR A-Scan data for a variety of targets made of aluminium, stainless steel, rebar, and PVC. A mean absolute percentage error of 1.89% is achieved using the proposed model. The experimental results confirm the effectiveness of the proposed approach in extracting size-related information from GPR data.

Numerical Investigation of the Landslide Cover Thickness Effect on the Drag Forces Acting on Submarine Pipelines

Ranking among the most threatening and challenging marine geological disasters, submarine landslides of different magnitudes have destroyed various pipelines, attracting widespread attention from the scientific and engineering communities. However, the relative size and spatial relation of landslides and pipelines, especially the landslide cover thickness above the pipeline, have not been explored in previous studies. As a result, the conventional analysis methods continue to change, and the practical application of these methods is difficult when a general criterion is lacking. In this study, three parameters, namely, the landslide cover thickness HC, pipeline diameter D, and span height HS, are first proposed to clarify this problem, and a unified standard analysis model is established. Second, the drag forces on pipelines with five values of HC and two values of HS under four typical Reynolds numbers are systematically analyzed using a validated computational fluid dynamic method. These analyses indicate that with increasing HC, the drag force gradually increases; however, the growth law of the drag force deviates under different HS conditions. Notably, considering the effect of HC, the maximum drag force can be increased to as much as five times the original value, and therefore, this effect cannot be ignored. Furthermore, the cause of the drag force variation is revealed by the evolution of the flow fields (e.g., streamlines, velocity vectors, and pressure). Finally, a reference value of the drag force coefficient and an adjustment factor fψC are proposed, and a standard chart methodology is established to evaluate the drag forces.

An assessment of ensemble learning approaches and single-based machine learning algorithms for the characterization of undersaturated oil viscosity

BackgroundPrediction of accurate crude oil viscosity when pressure volume temperature (PVT) experimental results are not readily available has been a major challenge to the petroleum industry. This is due to the substantial impact an inaccurate prediction will have on production planning, reservoir management, enhanced oil recovery processes and choice of design facilities such as tubing, pipeline and pump sizes. In a bid to attain improved accuracy in predictions, recent research has focused on applying various machine learning algorithms and intelligent mechanisms. In this work, an extensive comparative analysis between single-based machine learning techniques such as artificial neural network, support vector machine, decision tree and linear regression, and ensemble learning techniques such as bagging, boosting and voting was performed. The prediction performance of the models was assessed by using five evaluation measures, namely mean absolute error, relative squared error, mean squared error, root mean squared error and root mean squared log error.ResultsThe ensemble methods offered generally higher prediction accuracies than single-based machine learning techniques. In addition, weak single-based learners of the dataset used in this study (for example, SVM) were transformed into strong ensemble learners with better prediction performance when used as based learners in the ensemble method, while other strong single-based learners were discovered to have had significantly improved prediction performance.ConclusionThe ensemble methods have great prospects of enhancing the overall predictive accuracy of single-based learners in the domain of reservoir fluid PVT properties (such as undersaturated oil viscosity) prediction.

A framework for assessing municipal high-growth high-technology entrepreneurship policy

This paper advances a framework for making rudimentary need, impact, and cost–benefit assessments of municipal high-growth high-tech entrepreneurship policy. The framework views ecosystem support organizations like accelerators , incubators, and hubs as components in a city’s venture pipeline. A component’s pipeline size, raise rate, and cost per raise measure its performance. In total, the framework consists of eight objective and reproducible measures based on quantities and qualities of venture capital investment and 16 definitions of related terms-of-the-art. These measures and definitions are illustrated in 26 real-world policy examples, which assess initiatives in Houston and St. Louis over the last 20 years. The examples reveal an enormous variation in welfare effects, and some policies appear welfare destroying. Many non-profit organizations claim success (and win awards and acclaim) using non-standard measures despite performing at less than half benchmark levels. Policy cartels , which control startup policy in many U.S. cities, also engage in non-market actions to protect their rents. • Inductive approach to find ‘best’ standardized framework for city startup policy. • Venture pipelines have sizes, raise rates, and costs per raise. • Economics of startup initiatives rooted in information problems and scarce expertise. • Policy cartels control startup policy and engage in non-market actions. • Enormous variation in policy effects and some startup policies may destroy welfare.

Evaluation and Optimization of an Export Gas Compressor in a Natural Gas Project

AbstractThe possibility of removing the export gas compressor from the central process facilities (CPF) in a gas project and enlarging the size of the gas export pipeline from the CPF to reduce the total capital expenditure (CAPEX) and operating expenditure (OPEX) is evaluated. Adding the export gas compressor to the CPF will increase the carbon footprint of the CPF, the power requirements of the CPF, and the emission of greenhouse gases. The CPF equipment would be designed for a lower operating pressure which leads to larger equipment sizes. PIPESIM and HYSYS software were used to simulate the CPF and different sizes of the export gas pipeline. With an export gas pipeline size smaller than 24”, an export gas compressor is required for the CPF, and as the pipeline size is reduced, the compressor power requirement will rise. CAPEX and OPEX comparisons were conducted between adding the export gas compressor or enlarge the size of the export gas pipeline. CAPEX and OPEX decline with larger pipeline size.

Vibro-Acoustic Distributed Sensing for Large-Scale Data-Driven Leak Detection on Urban Distribution Mains.

Non-surfacing leaks constitute the dominant source of water losses for utilities worldwide. This paper presents advanced data-driven analysis methods for leak monitoring using commercial field-deployable semi-permanent vibro-acoustic sensors, evaluated on live data collected from extensive multi-sensor deployments across a sprawling metropolitan city. This necessarily includes a wide variety of pipeline sizes, materials and surrounding soils, as well as leak sources and rates brought about by external factors. The novel proposition for structural pipe health monitoring shows that excellent leak/no-leak classification results (>94% accuracy) can be observed using Convolutional Neural Networks (CNNs) trained with Short-Time Fourier Transforms (STFTs) of the raw audio files. Most notably, it is shown how this can be achieved irrespective of the sensor used, with four models from different manufactures being part of the investigation, and over time across extended densely populated areas.

Design optimization and granularity analysis of district heating systems for distributed solar heating access

In the low-carbon transition process of district heating systems (DHS), the need for widespread access to renewable energy and lower carbon emissions conflict with the existing structure of large centralized DHS. This paper proposes an optimized design of the DHS based on granularity analysis methodology to harness distributed solar energy. This study applies a hierarchical clustering method to get the possible scheme set based on the given geometrical information of an area with distributed solar heating system (SHS). This study further carries out pipe network routings and the pipeline sizing optimization to implement the scheme with the best economic performance. To disclose the relationship between granularity and SHS access ratio, we propose a quantitative granularity estimation method considering the spatial distribution of heat in the sense of Manhattan distance. This study calculates optimal schemes' total annual cost (TAC) and potential operation instability (PRI) under a series of SHS access ratios. A real DHS covering a heating area of 164300 m2, and 21 buildings is selected for a case study. At a granularity of 4.50%, TAC is reduced by 16.04%, and PRI by 58.06% compared to the existing design where granularity is not considered and one substation covering the entire area. As the SHS access ratio increases from 0 to 12%, the optimal granularity value increases from 4.50% to 16.84%, indicating a higher granularity is necessary to utilize solar energy. Our work shows that a proper selection of granularity in the design stage is essential to the economic and stable operation of DHS with renewable energy access. The result from this study is also meaningful to the design and operation control of district energy systems and integrated energy systems.

Modeling and Dynamic Performance Analysis of Hydraulic Top Drive Main Transmission System with Long Hydraulic Pipelines

Hydraulic top drive is the main power component used to drive drilling tools rotating to drill a hole for drilling rigs. Its main transmission system was built with hydraulic power source and hydraulic actuators. During the drilling procession, the hydraulic top drive is mounted on a derrick, and moving along its vertical guide rails. The hydraulic power station is usually placed on the drilling platform or the ground. In that way, long pipelines are needed to connect the hydraulic pumps and hydraulic motors. Thus, the effects on the performance of hydraulic top drive should not be neglected. A mathematical model of hydraulic top drive main transmission with differential equation of hydraulic units and state space equation of hydraulic long pipelines was deduced in this research. A simulation model was built using the AMESim software. And field drilling test of the hydraulic top drive was carried out in Songliao Basin Drilling Project (SK-II) well site. To verify and modify the virtual model, a comparison analysis was performed by setting the parameters and load of the three methods at the same values. The result of comparison shows that the simulation results are very close to the theoretical results and field drilling test data. Thus, the dynamic characteristics could be analyzed by this simulation model. Dynamic characteristics of the hydraulic top drive with different length and various diameters of pipelines were studied. The simulation results demonstrate that pipeline lengths and pipeline diameters affect the system in different laws, and it needs to comprehensively consider the system response speed, overshoot, and steady-state characteristics when choosing the size of pipelines for hydraulic top drive.

Research on a model-based burst pressure prediction method for pipelines with corrosion defects

ABSTRACT The leakage of pipelines will cause serious harm to the human living environment. To meet the safety assessment requirement for pipelines, a pipeline burst pressure prediction method based on a parametric model is proposed. The parameters of the model include defect size, pipeline size and material properties. First, the relationship between the defect size and the burst pressure was studied by finite element analysis, and the parametric model was deduced according to the analysis results. Second, the undetermined coefficients of the model were obtained using the improved genetic algorithm. Last, comparisons of the test burst pressures with those predicted by the proposed model and three standard assessment methods were carried out. The results show that the predicted model is more reliable than the existing pipeline assessment codes.