Pipeline Model Research Articles

Setup of PVT-models. Modeling of injection wells. Configuring models of collection system and pipelines and reservoir pressure maintenance system

Relevance. The ability to provide more accurate and efficient assessment and management of hydrocarbon production using software products for integrated field modeling. However, this approach requires an analysis of the source data quality, as well as the selection of the correct approach to creating models. Consistent and systematic work makes it possible to reproduce the actual situation with high accuracy in order to use the results obtained in further work. The integrated field model is a completely new approach to field development, when current work is based not on existing solutions, but on potential. Forecasting the behavior of the reservoir, wells and infrastructure, the collection system and the system for maintaining reservoir pressure allows for a comprehensive assessment of the possibilities of obtaining maximum oil and gas production while minimizing possible losses. Aim. Formation and analysis of an approach to setting up PVT-models of fluids, models of injection well stock, collection and transport systems and reservoir pressure maintenance systems by adapting parameters to actual data. Methods. Setting up and adapting injection well models; setting up and adapting collection system and pipeline models; approach to work with the reservoir pressure maintenance system. Results. The results obtained make it possible to adapt the components of the integrated model with an accuracy of over 95 %, which makes it possible to simulate the operating conditions of the field. Because of the analysis, tuning criteria were identified, and the minimum required set of parameters for high-quality adaptation of models was presented. Efficiency and accuracy of predictive calculations on adapted models has also been proven by comparing actual performance data with synthetic data. Based on the work done, we can conclude that the use of an integrated model shows us a high convergence with real data, which allows us to optimally approach the field development regime.

Research on damage assessment of buried pipelines with circular dent defects subjected to blast loading

In order to evaluate the damage characteristics of buried natural gas pipelines with circular dent defects subjected to blast loading, based on pressure–impulse damage theory, explosion experiment and numerical simulations were implemented to evaluate the damage of a natural gas with circular dent defects buried in soil under blast loading in this research. The ALE method was used to develop a coupled pipeline-soil simulation model using LS-DYNA software, and the validity of the established model was verified correctly compared with experimental results and empirical theory equations calculations. Furthermore, according to regulations in the Assessment and Management of Pipeline Dents (American Petroleum Institute Recommended Practice 1183), the effect with different circular dent defect diameter on the mechanical properties of natural gas was also investigated and analysed. The results showed that, under the blast loading, plastic deformation happened on the surfaces of the pipeline facing the explosive, and the high-stress zone appeared in the circumferential direction with 32.27°. The range of the high-stress zone firstly expanded along the axial direction of the pipelines and then along the circumferential direction. The larger diameter of the circular dent defects had, which resulted in a failure at the defects under the condition of the depth of the defects keeping constant. The effect of the size of defect diameter on the amount of pipeline deformation was further investigated, and the mathematical formula described the maximum plastic deformation with different defect diameter was established. Meanwhile, a finite element model of pipeline with a diameter of 457 mm was established for numerical analysis, which implied that the larger diameter of pipeline with the same defect had, the lower risk of pipeline in failure subjected to blast loading had. And through mathematical analysis and considering the feasibility of the actual situation, the curve described the maximum plastic deformation of the natural gas pipeline with three different defects varying in diameter were established respectively. In addition, a formula to express the relationship between pipeline defect diameter and maximum plastic deformation was established, which can effectively predict the critical plastic dent deformation of pipelines with different defect diameter subjected to blast loading. Besides, based on the pressure-pulse damage theory and with the damage assessment criterion of dent depth–dent length ratio of 0.072, the pressure–impulse diagrams of buried natural gas pipelines with defect diameters of 30 mm, 40 mm and 50 mm were defined, which can be used to predict the damage of pipelines with different defect diameters. Moreover, with the pressure–impulse damage evaluation curves, mathematical formula for the buried natural gas pipeline with the circular defects were established. Furthermore, the critical circular defect diameters with 30 mm was confirmed and the unique formula was also established, which could be effectively used to evaluate the safety of buried natural gas pipelines with the critical circular defects.

A demand-based three-stage seismic resilience assessment and multi-objective optimization method of community water distribution networks

This paper proposes a three-stage resilience concept that takes into account reliability, vulnerability and recovery resilience of water distribution networks (WDNs) from three phases, namely, pre-disaster preparation (PDP) phase, disaster response (DR) phase and the post-disaster recovery (PDR) phase. Based on the three-stage resilience concept, the initial performance, robustness and multi-objective optimization recovery method are proposed. In PDP phase, based on the reliability concept and the user demand, the initial performance of WDNs is defined, and the method is proposed based on Monte Carlo simulation method combined with seismic fragility of pipelines. In DR phase, a comprehensive quantitative index is proposed to quantify the robustness of WDNs by integrating the structural attributes, topological attributes and operational characteristics of WDNs. Finally, on the basis of the previous two stages, the disaster recovery process of failed pipelines of WDNs is further analyzed, and a multi-objective recovery optimization model of failed pipelines with consideration of users’ demands under the resources constraints is proposed to achieve the resilience enhancement. Through a case, the three-stage resilience assessment optimization enhancement of WDNs is implemented using the improved NSGA-II. The proposed method can be used as a basis for disaster prevention planning and seismic measures.

Nonlinear Characteristic Analysis of Gas-Interconnected Quasi-Zero Stiffness Pneumatic Suspension System: A Theoretical and Experimental Study

Because of significantly changed load and complex and variable driving road conditions of commercial vehicles, pneumatic suspension with lower natural frequencies is widely used in commercial vehicle suspension system. However, traditional pneumatic suspension system is hardly to respond the greatly changed load of commercial vehicles. To address this issue, a new Gas-Interconnected Quasi-Zero Stiffness Pneumatic Suspension (GIQZSPS) is presented in this paper to improve the vibration isolation performance of commercial vehicle suspension systems under frequent load changes. This new structure adds negative stiffness air chambers on traditional pneumatic suspension to reduce the natural frequency of the suspension. It can adapt to different loads and road conditions by adjusting the solenoid valves between the negative stiffness air chambers. Firstly, a nonlinear mechanical model including the dimensionless stiffness characteristic and interconnected pipeline model is derived for GIQZSPS system. By the nonlinear mechanical model of GIQZSPS system, the force transmissibility rate is chosen as the evaluation index to analyze characteristics. Furthermore, a testing bench simulating 1/4 GIQZSPS system is designed, and the testing analysis of the model validation and isolating performance is carried out. The results show that compared to traditional pneumatic suspension, the GIQZSPS designed in the article has a lower natural frequency. And the system can achieve better vibration isolation performance under different load states by switching the solenoid valves between air chambers.

A probabilistic framework for external pitting corrosion growth modelling for buried steel pipelines considering soil properties

This paper introduces a novel framework for developing reliable probabilistic predictive corrosion growth models for buried steel pipelines using pipeline inspection data. The framework adopts a power-law function of time model formulation, accounting for nonconstant damage growth rates, and considers the correlation between defect depth and length growth models. The proposed framework explicitly incorporates local influential soil properties in the model formulation; thus, it requires no segmentation and homogenous defect growth assumption and provides defect-specific growth models. The framework is applicable regardless of the availability of matched or non-matched defect data. For corrosion initiation time estimation, two different approaches are proposed: one is to use a Poisson process to account for defect occurrence, which can also predict newly generated defects since the last inspection, and the other is to use multivariate linear regression of soil and pipe properties. The statistics of unknown model parameters are assessed using a Bayesian updating framework in which the model error can be incorporated. The proposed framework is applied using two different sets of data: one set of inline inspection (ILI) data and one set of field excavation data. A case study is conducted, where time-dependent system reliability of an in-service pipeline is assessed considering small leak and burst failure modes using the developed defect growth models. The impact of the growth model accuracy on the probability of failure is investigated, and the importance analysis is performed to identify the most influential random variables to the probability of failure.

A pipeline model for research mentorship in the health professions

A pipeline model for research mentorship in the health professions

Research on Failure Pressure of API 5L X100 Pipeline with Single Defect

Background: With the continuous application of API 5L X100 high-strength grade steel pipeline and the development trend of increasing pipeline diameter and design pressure in China's long natural gas and petroleum pipeline, API 5L X100 high-strength grade steel pipeline steel is bound to become the aorta in long-distance pipeline construction in the future. Studying the failure pressure of API 5L X100 high-strength grade steel pipeline has high economic and social benefits. Objective: Exploring the corrosion mechanism of the two single corroded pipeline models with outer defect and inner defect. Methods: Using ANSYS Workbench 15.0 software to explore the two single corroded pipeline models with outer defect and inner defect and the influence of geometric parameters such as defect depth, defect length, defect width, etc., on the maximum equivalent stress and failure pressure investigated. Based on the finite element analysis database, the failure pressures of two single corroded pipeline models were fitted using Matlab software and the fitting formulas. Results: In the corrosion defect area of the pipeline, stress concentration occurs; Far away from the corrosion defect area, the stress distribution is uniform. For the pipeline model with outer defect and inner defect, as the defect depth and length increases, failure pressure shows a decreasing trend and is almost unaffected by defect width; As the ratio of diameter to thickness increases, failure pressure shows a decreasing trend; By fitting failure pressure formula of the pipeline models with outer defect and inner defect, a multivariate fitting function formula of failure pressure is obtained, with high fitting exactitude. Conclusion: The conclusion obtained have certain guiding values for the normal and stable operation of natural gas and petroleum transportation pipelines.

Limit state equation and failure pressure prediction model of pipeline with complex loading

Assessing failure pressure is critical in determining pipeline integrity. Current research primarily concerns the buckling performance of pressurized pipelines subjected to a bending load or axial compression force, with some also looking at the failure pressure of corroded pipelines. However, there is currently a lack of limit state models for pressurized pipelines with bending moments and axial forces. In this study, based on the unified yield criterion, we propose a limit state equation for steel pipes under various loads. The most common operating loads on buried pipelines are bending moment, internal pressure, and axial force. The proposed limit state equation for intact pipelines is based on a three-dimensional pipeline stress model with complex load coupling. Using failure data, we investigate the applicability of various yield criteria in assessing the failure pressure of pipelines with complex loads. We show that the evaluation model can be effectively used as a theoretical solution for assessing the failure pressure in such circumstances and for selecting appropriate yield criteria based on load condition differences.

How government policies promote transportation utilization in the national-level hydrogen supply chain: A case of China

Pipeline transportation is a low-cost option for delivering large volumes of hydrogen (H2), however, the high initial capital cost remains a major impediment to expanding pipeline delivery infrastructure. To quantitatively evaluate the impact of the H2 pipeline on the hydrogen supply chain (HSC), this paper develops an evaluation system for the development potential of the H2 pipeline and simulation models for H2 resource allocation. Furthermore, the HSC system in the southwest of China is used to validate the proposed method. The results demonstrate that the levelized cost range of a given 150–550 km H2 pipeline is 2.76–10.12 CNY/kg, which has price advantages over other transportation modes. Compared with the hydrogen supply chain resource allocation (HSCR) model, putting the H2 pipeline into use can optimize the layout and utilization of different transportation modes. It shows that the proportion of H2 transportation volume distribution in H2 pipelines has increased from 0 % to 73.44 %, while the proportion of HLT has first increased from 18.86 % to 54.27 %, and then decreased to 17.95 %. And then under different policy scenarios, the consideration of pipeline reduces the sensitivity of the H2 blending ratio of natural gas (NG) and NG demand to HSC. The impacts of policies on regional H2 development and regional policy implications are revealed. This approach provides a tool for policymakers to quantify the impacts of policy.

Automatic generation of pipelines from 2D CAD drawings to 3D IFC models using a parametric method

Creating 3D models of pipelines is essential for effective digital building management. However, generating 3D pipeline models from 2D CAD drawings still heavily depends on human-computer interaction at the design stage. This paper addresses this challenge by proposing a method to generate parametric 3D pipeline models from CAD drawings automatically. The framework includes three parts. Firstly, automatic layer recognition algorithms are used to extract data from CAD drawings, and algorithms are designed to match pipeline coordinates and diameters. Then, data on straight pipes is processed, including judging types of pipe fittings and calculating relevant parameters to realize the automatic generation of 3D BIM models. Finally, IFC is utilized to express the geometry and topology of models for BIM model generation. The process is implemented by writing a C++ program in Visio Studio 2019. Experimental results on a building water supply and drainage system demonstrate that the proposed method can significantly improve the generation efficiency and automation level of pipeline models.

Welding residual stress analysis of the X80 pipeline: simulation and validation

Abstract. In this work, a finite-element welding model of the X80 pipeline is established, and the residual stress is calculated using a direct thermal–mechanical coupling method through the User Material (UMAT) subroutine of the double-ellipsoid moving heat source. The effects of process parameters on the welding residual stress of the X80 pipelines are discussed. The ultrasonic longitudinal critical refraction (LCR) wave-detecting method is adopted to verify the simulation results. The results show that the residual stress at the inner surface is higher than that at the outer surface, and the peak Mises stress at the welding seam approaches the yield stress. With the increase in welding groove angle and heat input, the peak Mises stress increases at the inner surface and decreases at the outer surface, but the high-stress zone at the outer surface broadens. The residual stresses at the outer surface are more sensitive to the welding parameters. The comparison between the simulated results and ultrasonic LCR detection indicates that the finite-element method is feasible, and the simulation results are credible.

Continuous production and recirculation of plasma‐activated water bubbles under different flow regimes for mixed‐species bacterial biofilm inactivation inside pipelines

AbstractBiofilm formation in broiler drinking water systems is a public health concern. Bacterial detachment from the pipes into the drinking water subsequently increases the risk of waterborne transmission and has detrimental effects on animal and human health. The study evaluated the antimicrobial effectiveness of plasma‐activated water bubbles (PAWBs) recirculated under different flow regimes against the mixed‐species biofilms of Salmonella Typhimurium ATCC13311 and Aeromonas australiensis 03‐09 grown on the inner surfaces of polyvinyl chloride (PVC) pipes. A benchtop pipeline model representing broiler drinker lines was developed to compare the biofilm inactivation efficacy of PAWB recirculated at different flow rates, corresponding to Reynold's number of 1000, 2500, and 4000. The synergistic mechanical and oxidative recirculation using PAWB resulted in a higher biofilm inactivation from the pipe walls as compared to recirculation using distilled water alone. Apart from the flow regimes, various parameters including the volume of PAWB circulated, the concentration of the major plasma reactive species, and treatment time affected the susceptibility of the mixed‐species biofilms to PAWB treatment. Under all tested conditions, the bacterial cells were below the detection limit of 1 log CFU/mL in water after PAWB treatments. A better understanding of the hydrodynamic variations prevalent in the drinking water system is important for designing an effective disinfection protocol using PAWB. The results obtained from the study provide important information on the use of PAWB for biofilm control strategies.

DEEP LEARNING MODELS FOR PREDICTING THE RISK OF CARDIOVASCULAR INCIDENTS BASED ON THE WISCONSIN LONGITUDINAL STUDY

Objective: Existing models use only a limited number of well-known factors such as age, sex, cholesterol level, glucose level, and heart rate, while there is no general pipeline, which could give better overall prediction power, for an automatic pooling of the most correlated with heart disease predictors from the large-scale datasets. They often require preprocessing, and dimensionality reduction and has many missing data. Design and method: To address such challenges, we implemented data processing pipeline and several predictive models based on the Wisconsin Longitudinal Study dataset, which has both epidemiological and genetic data. We used univariate Cox Proportional Hazard models to estimate the effect of each variable, Hyperimpute to impute missing data, DeepSurv neural network and Autoprognosis 2.0 to develop final models. Fig. 1 The research pipeline, where MI – Myocardial Infarction, HA – Heart Attack. Results: The pipeline showed high predictive power (approx. 80% under the ROC curves) to predict risks of heart incidents such as heart attack, myocardial infarction, and stroke within a 5–10-year time window. Furthermore, we estimated the effect of various polygenic risk scores like subjective well-being, neuroticism, and depression on heart incidents. Conclusions: We implemented an effective pipeline of accurate model development to predict risks of heart incidents, which, as a result, can be used to build different models to estimate other risks correlated with heart incidents and could be used on other biobanks’ datasets. Therefore, this opens more ways to explore the causalities of heart diseases better and improve treatments of such conditions.

Advances of Pipeline Model Parallelism for Deep Learning Training: An Overview

Advances of Pipeline Model Parallelism for Deep Learning Training: An Overview

Personalized Machine Learning-Based Prediction of Wellbeing and Empathy in Healthcare Professionals.

Healthcare professionals are known to suffer from workplace stress and burnout, which can negatively affect their empathy for patients and quality of care. While existing research has identified factors associated with wellbeing and empathy in healthcare professionals, these efforts are typically focused on the group level, ignoring potentially important individual differences and implications for individualized intervention approaches. In the current study, we implemented N-of-1 personalized machine learning (PML) to predict wellbeing and empathy in healthcare professionals at the individual level, leveraging ecological momentary assessments (EMAs) and smartwatch wearable data. A total of 47 mood and lifestyle feature variables (relating to sleep, diet, exercise, and social connections) were collected daily for up to three months followed by applying eight supervised machine learning (ML) models in a PML pipeline to predict wellbeing and empathy separately. Predictive insight into the model architecture was obtained using Shapley statistics for each of the best-fit personalized models, ranking the importance of each feature for each participant. The best-fit model and top features varied across participants, with anxious mood (13/19) and depressed mood (10/19) being the top predictors in most models. Social connection was a top predictor for wellbeing in 9/12 participants but not for empathy models (1/7). Additionally, empathy and wellbeing were the top predictors of each other in 64% of cases. These findings highlight shared and individual features of wellbeing and empathy in healthcare professionals and suggest that a one-size-fits-all approach to addressing modifiable factors to improve wellbeing and empathy will likely be suboptimal. In the future, such personalized models may serve as actionable insights for healthcare professionals that lead to increased wellness and quality of patient care.

MTL-AraBERT: An Enhanced Multi-Task Learning Model for Arabic Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained type of sentiment analysis; it works on an aspect level. It mainly focuses on extracting aspect terms from text or reviews, categorizing the aspect terms, and classifying the sentiment polarities toward each aspect term and aspect category. Aspect term extraction (ATE) and aspect category detection (ACD) are interdependent and closely associated tasks. However, the majority of the current literature on Arabic aspect-based sentiment analysis (ABSA) deals with these tasks individually, assumes that aspect terms are already identified, or employs a pipeline model. Pipeline solutions employ single models for each task, where the output of the ATE model is utilized as the input for the ACD model. This sequential process can lead to the propagation of errors across different stages, as the performance of the ACD model is influenced by any errors produced by the ATE model. Therefore, the primary objective of this study was to investigate a multi-task learning approach based on transfer learning and transformers. We propose a multi-task learning model (MTL) that utilizes the pre-trained language model (AraBERT), namely, the MTL-AraBERT model, for extracting Arabic aspect terms and aspect categories simultaneously. Specifically, we focused on training a single model that simultaneously and jointly addressed both subtasks. Moreover, this paper also proposes a model integrating AraBERT, single pair classification, and BiLSTM/BiGRU that can be applied to aspect term polarity classification (APC) and aspect category polarity classification (ACPC). All proposed models were evaluated using the SemEval-2016 annotated dataset for the Arabic hotel dataset. The experiment results of the MTL model demonstrate that the proposed models achieved comparable or better performance than state-of-the-art works (F1-scores of 80.32% for the ATE and 68.21% for the ACD). The proposed SPC-BERT model demonstrated high accuracy, reaching 89.02% and 89.36 for APC and ACPC, respectively. These improvements hold significant potential for future research in Arabic ABSA.

The destructive form and response analysis of buried concrete pipes under the action of internal explosion loads

This research analyzes the damage mechanism of subterranean concrete pipelines subjected to internal explosive loads using a mix of field blasting tests and numerical simulations. The field tests reveal that, under the effect of internal explosion loads, axial and circumferential cracks occur in the concrete pipelines, with the crown, invert, and springline being damaged firstly, leading to penetrating cracks. In addition, an arbitrary Lagrangian Euler (ALE) method is used to establish a coupled model of underground concrete pipeline under the condition of internal explosion. The influence of the explosion mass on the destructive effects and dynamic characteristics of the pipeline is investigated, and the internal wall strain at the explosion center and in the central part of the pipeline is analyzed. Furthermore, the propagation and attenuation laws of shock waves in pipelines are investigated, and an attenuation law formula of shock waves inside the pipeline with two ends open is provided, which will be useful for future research.

Semi-analytic modeling and experimental verification of arbitrary aero-engine complex spatial pipeline

In-depth research is imperative for complex spatial pipelines with fluid-structure interaction. A comprehensive and universal three-dimensional semi-analytical modeling method is first established for complex fluid-conveying spatial pipelines under the base excitation with arbitrary connections and configurations under different boundary conditions. The virtual spring technology is used to connect the straight and curved pipeline segments with spatial angles and simulate boundary conditions. For a thorough analysis, the actual stiffness of the bracket-clamp system is experimentally measured for the first time. The multi-level coupling dynamic model of part, component, and system levels under actual boundary conditions is established. An efficient method for solving the fluid-structure coupling pipeline system dynamics considering the base excitation is proposed based on energy method. The mid-plane displacement function of the Fourier series is introduced and solved using the principle of modal superposition. Hammer impact modal experiments, ANSYS software and vibration response experiments are performed on spatial pipelines supported by flexible bracket-clamps; the numerical and experimental results support the semi-analytical results. The degenerate model is verified with the simple plane pipelines in other references, and the errors are less than 5 %. The proposed method exhibits broad applicability and generalizability, making it suitable for various configurations and boundary conditions.

Optimizing DNN training with pipeline model parallelism for enhanced performance in embedded systems

Deep Neural Networks (DNNs) have gained widespread popularity in different domain applications due to their dominant performance. Despite the prevalence of massively parallel multi-core processor architectures, adopting large DNN models in embedded systems remains challenging, as most embedded applications are designed with single-core processors in mind. This limits DNN adoption in embedded systems due to inefficient leveraging of model parallelization and workload partitioning. Prior solutions attempt to address these challenges using data and model parallelism. However, they lack in finding optimal DNN model partitions and distributing them efficiently to achieve improved performance.This paper proposes a DNN model parallelism framework to accelerate model training by finding the optimal number of model partitions and resource provisions. The proposed framework combines data and model parallelism techniques to optimize the parallel processing of DNNs for embedded applications. In addition, it implements the pipeline execution of the partitioned models and integrates a task controller to manage the computing resources. The experimental results for image object detection demonstrate the applicability of our proposed framework in estimating the latest execution time and reducing overall model training time by almost 44.87% compared to the baseline AlexNet convolutional neural network (CNN) model.

Refined VFIFE modeling on dynamic behaviours of deepwater S-lay pipeline in overbend section

The deepwater S-lay pipeline maintains high tension, bending moment, and stress in the overbend section, exhibiting substantially dynamic responses due to pipe-stinger roller interactions. This paper presents a refined S-lay pipeline model capable of accurately describing the forces and deformations on the pipeline section. In this model, the pipeline is discretized into beam particles and beam elements using the vector form intrinsic finite element (VFIFE) method. Meanwhile, the equivalent beam particle technique is developed to account for both the local forces and deformations of the pipeline as well as its overall static and dynamic responses. Through program coding, a dynamic time-domain simulation was accomplished for a 12-inch pipeline being laid to a water depth of 1000 m to provide a detailed illustration of the S-lay modeling process. Furthermore, dynamic behaviours of the pipeline in the overbend were systematically investigated under various groups of the pipe diameter and the stinger roller spacing. The results indicate that the stress and deformation on the upper section of the pipeline are more significant than those on the lower section. The equivalent beam particle method extends the capabilities of the VFIFE for the detailed analyses of local dynamic behaviours of deepwater S-lay pipelines.