Pipeline Model Research Articles

SIMPLIFIED HYDRAULIC CALCULATION OF THE CRYOGENIC PIPELINE OF EXTERNAL FACILITIES FOR FILLING LIQUEFIED NATURAL GAS

Natural gas is a mineral that is the most environmentally friendly energy fossil raw material. In recent decades, proven natural gas reserves have equaled oil reserves in terms of their indicators and, moreover, continue to grow actively. In this regard, the issue of transportation and storage of natural gas becomes relevant. One actively developing area has become the industry of natural gas liquefaction, transportation and distribution.The paper discusses the issues of shipment of liquefied natural gas (LNG) to gas tankers during its large-tonnage production. The authors propose the use of remote filling systems (VSN) for LNG in a similar way to similar devices for filling oil into tankers. However, the main feature of the VSN LNG is the cryogenic pipeline from the onshore facilities to the VSN. In this regard, it is necessary to make a simplified mathematical model of a cryogenic pipeline taking into account heat exchange with the environment and heat release during energy dissipation due to viscous friction forces.In the course of numerical simulation, the dependence of the maximum pumping distance was obtained for a given difference between the temperature of LNG on shore facilities and the maximum required temperature of LNG loading into a gas carrier. The influence of each component of the heat flow equation — heat exchange with the environment and LNG heating due to viscous friction forces was also evaluated.As an example, a section with a length of 10 km was selected and a simulation of the temperature distribution, as well as the required pressure on shore structures necessary to ensure a given loading speed of the gas carrier and the temperature of the supplied LNG was carried out.

World’s largest natural gas leak from nord stream pipeline estimated at 478,000 tonnes

Methane is a potent heat trapping gas believed to account for 30% of the observed global warming to-date. At a capacity of 110 bcm/year, the Nord Stream (NS) pipeline corridor measuring 1,153mm in internal diameter and stretching 1,224km from Russia to Germany is the biggest in the world. The explosions that NS sustained in September, 2022, in the Baltic Sea, have unleashed the largest single methane gas source in recent memory. Over the course of 7days, our transient multiphase pipeline model has estimated that the gas leaks from 3 lines pumped 478,000 tonnes of methane into the atmosphere. A range of pipeline shut-in pressures as a function of leakage time deduced an envelope of gas volume that matched the timeline of observed outflows. Interestingly, the methane gas that escaped from the damaged threads amounted to the CO2 equivalent emitted by concrete sufficient to build about 27 Burj Khalifa towers.

Study on the Leakage and Diffusion Characteristics of Buried Hydrogen-Blended Natural Gas Pipelines

Abstract Utilizing existing natural gas pipelines to transport hydrogen-blended natural gas is a primary strategy for achieving cost-effective, long-distance, and large-scale hydrogen transportation. However, blending hydrogen with natural gas alters its physical properties, resulting in changes in leakage and diffusion characteristics and the affected range. To illustrate this, we focus on the Jingxi Third Line natural gas long-distance pipeline and develop a buried hydrogen blended natural gas pipeline model to analyze the concentration distribution of hydrogen-blended natural gas and the temporal variation of gas velocity at the leakage point. We explore the influence of various factors, including pressure, leak orifice size, wind speed, and hydrogen-blending ratio, on the diffusion range of hydrogen-blended natural gas. The research findings demonstrate that in the vicinity of the leakage point, the methane concentration significantly exceeds the upper explosive limit while the hydrogen concentration remains within the explosive limit range. The hazardous range of hydrogen-blended natural gas leakage and diffusion is slightly larger than that of natural gas alone. Furthermore, both the vertical and horizontal hazardous ranges of hydrogen-blended natural gas leakage and diffusion exhibit positive correlations with pressure and leak orifice size. Additionally, as wind speed increases, the maximum impact distance in the vertical direction gradually decreases, while it gradually increases in the horizontal direction.

"Eat Enough"-A nurse-led intervention to enhance hospitalized older adults' protein and energy nutrition.

To develop an intervention enhancing hospitalized older adults' nutrition. For the first time, a mixed-methods design with data triangulation was applied according to the six-step model of Corry etal. to elaborate on a complex nursing intervention in the form of a logic model. Patients who were aged ≥80 years and hospitalized for at least 5 days were included. Sample size for quantitative practice analysis was 135 older adults, whereas 22 older inpatients participated in interviews and observations for needs analysis and generated data for key principles. The intervention "Eat Enough" encompasses nursing team culture and comprises six actionable targets to deliver needs-based support and reach required protein and energy intake for hospitalized older adults by sensitizing nurses and the interprofessional team. Facilitating nutritional intake would be supported by an advanced practice nurse who considers the medical and nursing care plan and therapy. The intervention "Eat Enough" demonstrates that nurses play a key role in interprofessional teams to enhance older adults' nutrition in hospital. The pipeline model displays how the actionable targets can be achieved, and how awareness raising can influence the context-leading to raised calories and protein requirement coverages and shorter length of stay. By identifying risk factors of malnutrition and strengthening nurses' responsibilities, the intervention "Eat Enough" could significantly enhance nutrition among hospitalized older adults. However, the logic model should be tested and implemented in future research.

Semi-analytical dynamic modeling and fluid-structure interaction analysis of L-shaped pipeline

The traditional semi-analytic modeling is mainly aimed at the straight pipeline with simple boundary conditions. In contrast, the actual aero-engine pipeline needs to consider the effects of complex configuration, boundary and multiple clamps, so proposing an improved semi-analytic method to model an arbitrary fluid-conveying L-shaped pipeline is challenging. Based on the Hamiltonian principle, the differential equations of the L-shaped pipeline are derived. Then, the modal shape functions are determined under the conditions that the elastic support points and the joints of the straight pipeline and the curved pipeline meet the deformation coordination requirements. The proposed semi-analytical model is verified by comparing the natural frequencies and mode shapes obtained from the finite element method using ANSYS and experiment; the error of each order natural frequency is less than 5 %. In addition, the model is verified under various working conditions: different center angle and boundary conditions (clamp position and pipeline end constraints). The effects of the fluid velocity and pressure on the natural characteristics of the L-shaped pipeline are also analyzed. The results show that under the critical pressure of 60.51 MPa and the critical velocity of 247 m/s, the natural frequency decreases with the increase of fluid velocity and pressure. The proposed method can provide theoretical support for efficient and accurate modeling of complex spatial pipeline systems.

Dynamic modeling and stress reduction optimization of parallel pipelines based on pipe-solid element coupling

In aero-engine, spatially parallel pipelines are prone to damage under the harmonic excitation produced by rotors, thus there is an urgent need for vibration reduction research. This paper presents a finite element modeling method for parallel pipelines based on pipe-solid element coupling and performs clamp layout optimization with the objective of reducing the stress response. The modeling idea can be described as follows: the regions of the pipeline with higher stress, such as single and dual clamps and bend sections, are modeled using solid elements, while other regions are modeled using pipe elements, and the different parts are coupled together to complete the overall modeling. An optimization model is created with clamp positions as design variables and the objective function is set as reducing stress response under the resonance condition of the fundamental frequency excitation. The procedure for solving the optimization model is provided based on genetic algorithms. A case study is conducted on a parallel pipeline system with one dual clamp and four single clamps. The rationality of the created spatial parallel pipeline model based on pipe-solid element coupling is demonstrated through a constructed pipeline test system. The model is shown to provide sufficient accuracy while having higher computational efficiency compared with a full 3D solid element model. Furthermore, the stress reduction optimization is performed, and a clamp layout that minimizes the maximum resonant stress of the parallel pipeline system is obtained.

Deep UV-excited fluorescence microscopy installed with CycleGAN-assisted image translation enhances precise detection of lymph node metastasis towards rapid intraoperative diagnosis

Rapid and precise intraoperative diagnosing systems are required for improving surgical outcomes and patient prognosis. Because of the poor quality and time-intensive process of the prevalent frozen section procedure, various intraoperative diagnostic imaging systems have been explored. Microscopy with ultraviolet surface excitation (MUSE) is an inexpensive, maintenance-free, and rapid imaging technique that yields images like thin-sectioned samples without sectioning. However, pathologists find it nearly impossible to assign diagnostic labels to MUSE images of unfixed specimens; thus, AI for intraoperative diagnosis cannot be trained in a supervised learning manner. In this study, we propose a deep-learning pipeline model for lymph node metastasis detection, in which CycleGAN translate MUSE images of unfixed lymph nodes to formalin-fixed paraffin-embedded (FFPE) sample, and diagnostic prediction is performed using deep convolutional neural network trained on FFPE sample images. Our pipeline yielded an average accuracy of 84.6% when using each of the three deep convolutional neural networks, which is a 18.3% increase over the classification-only model without CycleGAN. The modality translation to FFPE sample images using CycleGAN can be applied to various intraoperative diagnostic imaging systems and eliminate the difficulty for pathologists in labeling new modality images in clinical sites. We anticipate our pipeline to be a starting point for accurate rapid intraoperative diagnostic systems for new imaging modalities, leading to healthcare quality improvement.

Modeling Pipe to Soil Potentials From Geomagnetic Storms in Gas Pipelines in New Zealand

AbstractGas pipelines can experience elevated pipe to soil potentials (PSPs) during geomagnetic disturbances due to the induced geoelectric field. Gas pipeline operators use cathodic protection to keep PSPs between −0.85 and −1.2 V to prevent corrosion of the steel pipes and disbondment of the protective coating from the pipes. We have developed a model of the gas pipelines in the North Island of New Zealand to identify whether a hazard exists to these pipelines and how big this hazard is. We used a transmission line representation to model the pipelines and a nodal admittance matrix method to calculate the PSPs at nodes up to 5 km apart along the pipelines. We used this model to calculate PSPs resulting from an idealized 100 mVkm−1 electric field, initially to the north and east. The calculated PSPs are highest are at the ends of the pipelines in the direction of the applied electric field vector. The calculated PSP follows a characteristic curve along the length of the pipelines that matches theory, with deviations due to branchlines and changes in pipeline direction. The modeling shows that the PSP magnitudes are sensitive to the branchline coating conductance with higher coating conductances decreasing the PSPs at most locations. Enhanced PSPs produce the highest risk of disbondment and corrosion occurring, and hence this modeling provides insights into the network locations most at risk.

Comparison of tensile strain capacity models of oil and gas pipelines

In the field of strain-based pipeline design, the widely used pipeline tensile strain capacity models mainly include CSA Z662, PRCI-CRES, and EXXON MOBIL. In this paper, the methods for calculating the ultimate tensile strain, toughness, and other key parameters of various models are emphatically introduced, and their applicable ranges are compared. Ninety-six sets of full-scale test and curved wide plate test data are collected in this paper. The prediction accuracy of various tensile strain capacity models under different steel grades is compared and verified by the test results. The CSA Z662 model has few parameters to consider, the TSC prediction value is relatively low, and EXXON MOBIL has more nonconservative situations when the strain is less than 4%. The PRCI-CRES model has the highest prediction accuracy.

Single-Belt Versus Split-Belt: Intelligent Treadmill Control via Microphase Gait Capture for Poststroke Rehabilitation.

Stroke is the leading long-term disability and causes a significant financial burden associated with rehabilitation. In poststroke rehabilitation, individuals with hemiparesis have a specialized demand for coordinated movement between the paretic and the nonparetic legs. The split-belt treadmill can effectively facilitate the paretic leg by slowing down the belt speed for that leg while the patient is walking on a split-belt treadmill. Although studies have found that split-belt treadmills can produce better gait recovery outcomes than traditional single-belt treadmills, the high cost of split-belt treadmills is a significant barrier to stroke rehabilitation in clinics. In this article, we design an AI-based system for the single-belt treadmill to make it act like a split-belt by adjusting the belt speed instantaneously according to the patient's microgait phases. This system only requires a low-cost RGB camera to capture human gait patterns. A novel microgait classification pipeline model is used to detect gait phases in real time. The pipeline is based on self-supervised learning that can calibrate the anchor video with the real-time video. We then use a ResNet-LSTM module to handle temporal information and increase accuracy. A real-time filtering algorithm is used to smoothen the treadmill control. We have tested the developed system with 34 healthy individuals and four stroke patients. The results show that our system is able to detect the gait microphase accurately and requires less human annotation in training, compared to the ResNet50 classifier. Our system "Splicer" is boosted by AI modules and performs comparably as a split-belt system, in terms of timely varying left/right foot speed, creating a hemiparetic gait in healthy individuals, and promoting paretic side symmetry in force exertion for stroke patients. This innovative design can potentially provide cost-effective rehabilitation treatment for hemiparetic patients.

Studying Corrosion Failure Prediction Models and Methods for Submarine Oil and Gas Transport Pipelines

To predict the corrosion failure of carbon steel oil and gas pipelines more accurately, a new corrosion failure prediction model for submarine oil and gas transport pipelines was constructed. A corrosion failure prediction management system was also developed based on the constructed model. To construct the model, corrosion experiments were carried out to analyze the influences of temperature, partial pressure of CO2, pH value, and flow rate acting on the corrosion rate. Based on the analysis results and the corrosion experiment data, a new corrosion failure prediction model containing the time and flow rate for oil and gas pipelines was constructed. The model is based on the existing corrosion prediction model and has a determination coefficient, R2, of 0.9573, which indicates good prediction accuracy. A machine learning prediction model was also used to predict, and the prediction results are compared with that of the proposed model, which further verifies the accuracy and feasibility of the proposed model. A corrosion failure prediction management system for carbon steel oil and gas pipelines was developed based on the constructed model, which makes corrosion failure prediction more convenient and faster and provides a reference for the accurate prediction and efficient control of oil and gas pipeline corrosion failure.

Chinese Event Discourse Deixis Resolution: Design of the Dataset and Model

Anaphora resolution is a traditional task in the natural language processing community, defined as a cohesion phenomenon where one entity points back to a previous entity. Event discourse deixis (EDD) is a kind of more complex anaphora in which the anaphors refer to event descriptions such as sentences or clauses. Event discourse deixis resolution (EDDR) is able to help machines understand the richer linguistic and semantic information in the discourse. However, compared to anaphora resolution, EDDR has received relatively less research attention. In this work, we investigate the EDDR task by designing the corresponding dataset and model. First, we manually construct a high-quality Chinese corpus for EDDR, including 4,417 documents and 5,929 event chains that consist of event antecedents and anaphors. Second, we propose a deep neural network model for EDDR, which formulates the task into two subtasks, namely event anaphor recognition and event antecedent recognition. Our model is trained under the two subtasks jointly so that the EDDR task can be performed end to end. Besides our final model, we also build seven pipeline and joint models as baselines to build comprehensive benchmarks for follow-up research. Experimental results on our EDDR dataset show that our model outperforms all the baselines and achieves about 53%, 44%, 53%, and 63% F1s using standard anaphora resolution metrics such as CoNLL, MUC, B3, and Ceafe. The performances show that EDDR is a challenging task and worth researching in the future. Our dataset and model will be released to facilitate follow-up research.

Data Augmentation for Mask-Based Leaf Segmentation of UAV-Images as a Basis to Extract Leaf-Based Phenotyping Parameters

In crop protection, disease quantification parameters such as disease incidence (DI) and disease severity (DS) are the principal indicators for decision making, aimed at ensuring the safety and productivity of crop yield. The quantification is standardized with leaf organs, defined as individual scoring units. This study focuses on identifying and segmenting individual leaves in agricultural fields using unmanned aerial vehicle (UAV), multispectral imagery of sugar beet fields, and deep instance segmentation networks (Mask R-CNN). Five strategies for achieving network robustness with limited labeled images are tested and compared, employing simple and copy-paste image augmentation techniques. The study also evaluates the impact of environmental conditions on network performance. Metrics of performance show that multispectral UAV images recorded under sunny conditions lead to a performance drop. Focusing on the practical application, we employ Mask R-CNN models in an image-processing pipeline to calculate leaf-based parameters including DS and DI. The pipeline was applied in time-series in an experimental trial with five varieties and two fungicide strategies to illustrate epidemiological development. Disease severity calculated with the model with highest Average Precision (AP) shows the strongest correlation with the same parameter assessed by experts. The time-series development of disease severity and disease incidence demonstrates the advantages of multispectral UAV-imagery in contrasting varieties for resistance, as well as the limits for disease control measurements. This study identifies key components for automatic leaf segmentation of diseased plants using UAV imagery, such as illumination and disease condition. It also provides a tool for delivering leaf-based parameters relevant to optimize crop production through automated disease quantification by imaging tools.

Valuation Analysis of Innovative Pharmaceutical Companies on STAR Market: Evidence from Zhifei Biological

The bio-pharmaceutical industry has gained great opportunities of develop-ment under the COVID-19 epidemic. In order to fit the current market situa-tion better in the post-epidemic era, the valuation analysis of the innovative pharmaceutical industry is conducted, using a model with Zhifei Biological as an example. Based on the assumption that new drugs under the develop-ment phase can also be viewed as assets in the valuation of the company, the DCF and pipeline models are used in the process of valuation. The outbreak of the COVID-19 epidemic has caused considerable changes in the valuation system of innovative pharmaceutical companies in the STAR market, which requires further research and adjustment. This paper uses the method of pipeline valuation based on the DCF model to valuate the specific case, di-viding the companies products already on the market and in different stage of development into four different pipelines. The method is expected to have a higher applicability to the valuation of innovative pharmaceutical high-tech companies. These results shed light on guiding further exploration of valuation analysis.

Analytical fragility relation for buried cast iron pipelines with lead-caulked joints based on machine learning algorithms

A new numerical-based fragility relation for cast iron (CI) pipelines with lead-caulked joints subjected to seismic body-wave propagation is proposed in this article. Two-dimensional 1600-m-length finite element models for pipelines buried in sand are developed in OpenSees. Parametric analysis is performed to investigate the influence of various parameters on the damage estimates of the buried pipelines. Numerical analyses are conducted to estimate the repair rates ( RR) for CI pipelines subjected to wave propagation. The predictive model for RR is thus developed based on the numerical results and the Gaussian Process Regression approach. The model developed employs four predictor variables, namely, the peak particle velocity and wave propagation velocity along axial direction, the maximum soil shear force per unit length, and the outer diameter of pipelines, exhibiting desirable performance in terms of predictive efficiency and generalization. The performance of the developed relation is compared to several existing fragility relations. The new fragility relation can be used to estimate RR for CI pipelines with lead-caulked joints with outer diameters ranging from 169 to 1554 mm subjected to seismic body-wave propagation.

Development of software for the management of the maintenance of equipment of thermal power plants

The relevance of the use of thermal power plants for the generation of thermal and electric energy on the territory of the Russian Federation is revealed, taking into account its geographical and climatic features. The substantiation of the importance of the process of maintenance and repair in relation to boiler units, which are operated at thermal power plants, is given. Difficulties accompanying the practical implementation of this process are identified. The influence of the identified difficulties on the existing shortcomings in the process of maintenance and repair of boiler equipment and the technological pipelines connecting them and pipelines of steam and hot water is shown. The goal of the study is set, which is aimed at eliminating the identified objective difficulties and the tasks are formulated, the solution of which will contribute to the achievement of the goal. The necessity of developing specialized software for managing the process of maintenance and repair of equipment and pipelines of boiler units of combined heat and power plants is substantiated. The methodology of the work is based on the development of digital models of pipelines and boiler equipment, as well as algorithms aimed at processing the parameters of digital models. The software implementation of the developed models and algorithms is made by means of object-oriented programming in the form of a client-server application for the Windows operating system. The description of the results of software development aimed at solving the set tasks is given. Examples of some dialog boxes accompanying the functioning of the developed software are given, as well as examples of created digital models of boiler equipment and their individual elements. The practical application of the research results contributes to an increase in the degree of digitalization of industrial production in general and energy production in particular. The results obtained in the framework of this work, in the form of software, can be used to accompany the process of maintenance and repair of boiler equipment of combined heat and power plants. With appropriate adaptation, the results obtained can also be applied to boiler equipment, pipelines and apparatus that are used in the chemical, petrochemical and oil refining industries.

Seismic fragility analysis of water supply pipelines retrofitted with corrosion-protection liner buried in non-uniform site

The corrosion-protection liner (CPL) technology consists of installing flexible plastic liners with anchoring studs inside existing pipelines and subsequently filling cement mortar to the gap between the waterproof liner and the pipeline. With the excellent chemical resistance, impermeability and fast construction speed, CPL provides an economical and environmentally friendly alternative for pipeline rehabilitation without large-scale excavation. However, the seismic performance of water supply pipelines after being retrofitted with CPL has not been well studied yet. In this study, a series of full-scale quasi-static experiments were firstly conducted on ductile-iron push-on joints before and after reinforcement with CPL to investigate the nonlinear behavior of the joints under longitudinal load and transverse bending. Simplified numerical models of straight pipelines with joints before and after retrofitting in the non-uniform site were then developed in the OpenSees platform, and incremental dynamic analysis (IDA) were performed with consideration of nonlinear soil-pipeline interaction. Twenty-eight pairs of ground motions with the peak ground velocities (PGV) collectively scaled from 200 mm/s to 3000 mm/s were used as inputs. Seismic fragility curves of the pipelines before and after retrofitting were developed with respect to the optimum intensity measure, PGV. It can be seen from the experimental results that the longitudinal load and bending moment capacity of the push-on joint increased about 400% and 20% after CPL reinforcement, respectively, and the joint opening and rotation decreased about 20% and 18% after retrofitting. Numerical results show that the CPL reinforced pipelines exhibit better seismic performance and CPL effectively reduces the failure probability of segmented pipelines under earthquake ground excitations.

A Unique Pipeline Model to Improve Anomaly Detection in High Dimensional Data

This paper presents a comprehensive method for dimension reduction and detecting anomalies in high-dimensional data (on healthcare datasets) using R. Realizing that traditional linear methods such as Principal Component Analysis (PCA) often ignore the complexity of the non-linear manifold of the data, our approach exploits iterative learning, on the belief that high-dimensional data is largely based on a low-dimensional manifold. The methodology starts by preparing the data using R libraries like Keras, dplyr, and ggplot2, addressing challenges like missing values ??and visualizing meaningful information. Using the Mahalanobis distance, the paper identifies and removes country-specific outliers. The pipelined model integrates Principal Component Analysis (PCA) for data transformation and combines an Autoencoder with t-SNE for dimensionality reduction. This refined dataset is then used to train a Multi-Layer Perceptron (MLP) artificial neural network, which facilitates anomaly detection based on reconstruction errors, illustrated by the point cloud. Additionally, the paper explores metric multidimensional scaling using artificial neural networks, tests large datasets such as healthcare and wine, and compares the results of the work using conventional techniques. This study highlights the effectiveness of integrating various pre-processing, visualization, and artificial neural network strategies through R for effective anomaly detection.

Probabilistic burst pressure prediction model for pipelines with single crack-like defect

While several models have been developed to predict the failure pressure of pipelines with corrosion defects, relatively few models are available for pipelines with cracking defects. The goal of this study is to develop an unbiased and accurate probabilistic burst failure pressure model for thin-walled pipelines containing a single, longitudinally-oriented, crack-like defect. A comprehensive database is first established, consisting of experimental and numerical data collected from literature as well as numerical data obtained from the extended finite element method (XFEM) conducted in this study. The failure pressure prediction model is developed by adding a correction factor to the modified Ln-Secant method, a semi-empirical model previously developed. The correction factor is modeled using a multivariate linear regression that considers the pipe properties and defect characteristics as independent variables. The proposed model delivers unbiased and less variable failure pressure predictions. A case study of an example pipeline with a crack like defect shows that the failure pressure model plays a critical role in the risk management of pipelines through a life-cycle cost analysis.

A Machine Learning Pipeline and Application for Automatic Classification of Clinical Documents

Healthcare industry has many associated services including research on various trends or patterns in diseases and patients’ life style. With the emergence of Artificial Intelligence (AI), it is made possible that problems in healthcare domain can be solved by using Machine Learning (ML) techniques. One such problem considered in this paper is known as clinical document classification. Existing methods in this area lack a systematic approach in filtering out false positives. In this paper we proposed a ML framework that considers pipelining of ML models at multiple levels. In the first level, clinical documents that do not have any content related to smoking are discarded. In the second level, the documents that talk about known smoking cases are retained. In the third level clinical document are classified into two categories such as currently smoking and past smokers. We proposed an algorithm known as Learning based Clinical Document Classification (LbCDC). This algorithm makes use of three models in pipeline in order to perform classification of clinical documents at multiple levels of granularity. Our experimental results revealed that the proposed system is efficient in clinical document classification.