Pipeline Model Research Articles

Electromechanical characteristics analysis of L-shaped pipelines with enhanced active constrained damping treatment

Abstract Fluid-conveying pipelines are widely employed in various engineering fields, such as aerospace, nuclear, and marine fields. These pipelines work in serious vibration environments, which can quickly damage the pipeline system. The vibration control of pipelines is a prominent challenge in the engineering field. This paper is aimed to investigate the electromechanical analysis characteristics of L-shaped pipelines with the Enhanced Active Constrained Layer Damping (EACLD) structure. A finite element model of the L-shaped pipeline with EACLD is established. The dynamic behaviour of an L-shaped pipeline with an EACLD structure was analysed in both the time and frequency domains. The influence of the voltage and the position, the length and the elastic modulus, the thickness and the edge element parameters are all considered. Additionally, the influence of the EACLD patch orientation on static displacement and stress are considered. Simulation results indicate that reasonable selection of the parameters for the EACLD patch and edge element can enhance vibration damping effectiveness, which can provide effective design guidance for active vibration control of the pipeline system. 

Parkinson’s Disease Interstage Prognostic Biomarkers for Early Detection through Hybrid Machine Learning Model

Background: Parkinson’s Disease (PD) is a neurodegenerative condition that takes 15 to 20 years to exhibit the symptoms. However, during the initial stages of the disease, there are microlevel changes in the body that are typically undetected. The pathogenesis of Parkinson’s disease requires understanding of these microlevel changes. Objectives: The objective of the present study is to develop a hybrid ensembled machine learning pipeline model for identifying inter-stage Parkinson’s Disease (PD) biomarkers for understanding disease progression as well as etiology. Methods: The proposed work was carried out on the dataset GSE202667 from Gene Expression Omnibus, containing time-resolved RNA signatures of CD4+ T cells at various stages. Differentiating genes were identified in different interstage groups. Two types of unsupervised learning methods- distance-based (K-means, Agglomerative, Density-Based Spatial Clustering of Application with Noise) and probability-based (Hidden Markov Model, Gaussian Mixture Model Latent Dirichlet Allocation) were applied. The best algorithms were selected and applied to optimize clusters. Enrichment analysis was conducted on the top 10 PD biomarkers in each category. Findings: The top 10 PD biomarkers in each category are identified and gene set enrichment analysis resulted into their enrichment in three KEGG Pathways and depletion in two GO molecular functions. These biomarkers’ depletion is observed in 215 Reactome pathways and enrichment in 18 Reactome pathways. Twelve GO-Cellular components had an enrichment whereas 111 GO-Cellular components had a depletion in the gene set. A total of 25 GO-Biological process components were enriched, while 339 GO-Biological process components were depleted. Novelty: The proposed hybrid ensembled machine learning pipeline model works as a tool to identify Parkinson’s Disease biomarkers from omics data. The model contributes to identify implicit patterns in omics data in order to unveil Parkinson’s disease progress mechanism through biomarkers discovery. Keywords: Gaussian Mixture Model clustering, Early Detection, k-means clustering, Machine learning, Parkinson’s Disease Interstage Biomarkers

Network-based modelling reveals cell-type enriched patterns of non-coding RNA regulation during human skeletal muscle remodelling

Abstract A majority of human genes produce non-protein-coding RNA (ncRNA), and some have roles in development and disease. Neither ncRNA nor human skeletal muscle is ideally studied using short-read sequencing, so we used a customised RNA pipeline and network modelling to study cell-type specific ncRNA responses during muscle growth at scale. We completed five human resistance-training studies (n = 144 subjects), identifying 61% who successfully accrued muscle-mass. We produced 288 transcriptome-wide profiles and found 110 ncRNAs linked to muscle growth in vivo, while a transcriptome-driven network model demonstrated interactions via a number of discrete functional pathways and single-cell types. This analysis included established hypertrophy-related ncRNAs, including CYTOR – which was leukocyte-associated (FDR = 4.9×10−7). Novel hypertrophy-linked ncRNAs included PPP1CB-DT (myofibril assembly genes, FDR = 8.15×10−8), and EEF1A1P24 and TMSB4XP8 (vascular remodelling and angiogenesis genes, FDR = 2.77×10−5). We also discovered that hypertrophy lncRNA MYREM shows a specific myonuclear expression pattern in vivo. Our multi-layered analyses established that single-cell-associated ncRNA are identifiable from bulk muscle transcriptomic data and that hypertrophy-linked ncRNA genes mediate their association with muscle growth via multiple cell types and a set of interacting pathways.

Real-Time Analysis and Digital Twin Modeling for CFD-Based Air Valve Control During Filling Procedures

Air exchange in pressurized water pipelines is an essential but complex aspect of pipeline modeling and operation. Implementing effective air management strategies can yield numerous benefits, enhancing the system’s energy efficiency, reliability, and safety. This paper comprehensively evaluates an irregular profile pipeline filling procedure involving air-release through an air valve. The analysis includes real-time data tests and numerical simulations using Computational Fluid Dynamics (CFD). A Digital Twin model was proposed and applied to filling maneuvers in water installations. In particular, this research considers an often-overlooked aspect, such as filling a pipe with an irregular profile rather than a simple straight pipe. CFD simulations have proven to capture the main features of the transient event, which are suitable for tracking the air-water interface, the unsteady water flow, and the evolution of the trapped air pocket. Thus, they provide thorough and reliable information for real-time operational processes in the industry, focusing on the filling pressure and geometry of the air-valve hydraulic system. Additionally, this study provides details regarding the application of an efficient Digital Twin CFD approach, demonstrating its feasibility in optimizing the filling procedure in pipes with irregular profiles.

Finite element analysis of rockfall impact on pipelines with different erosion resistant coatings

In this paper, the finite element analysis method is used to extensively study the response of rockfall impact on pipelines with different erosion resistant coating. Based on the numerical results, the safety of the pipeline is comprehensively evaluated. Firstly, through the establishment of detailed pipeline and rockfall models, the impact of different rockfall materials and speeds on the pipeline is simulated. The results of the finite element analysis indicate that rockfall impact can cause significant stress concentration and deformation in the pipelines and damage to the coating. With the increment of impact speed, the damage to the pipeline also increases significantly, and different rockfall materials exhibit varying damage conditions, and it is found that fibreglass reinforced epoxy is better than the polyethylene coating. By comparing the analysis results under different conditions, the safety threshold of the pipeline under various rockfall impact scenarios is obtained. This provides an important theoretical basis and reference for the protection design and safety maintenance of the pipeline. The research in this paper not only aids in deepening the understanding of the mechanism of rockfall impact on pipelines but also serves as a valuable reference for improving the safety and reliability of pipeline engineering.

Key Challenges for Internal Corrosion Modelling of Wet Gas Pipelines

Abstract The presence of CO2 and H2S in wet gas pipelines often creates the potential for high internal corrosion rates, which is typically mitigated by the injection of corrosion inhibitors. In practice, however, it is difficult to ensure that the inhibitor is always injected at the right level, while actual conditions in the pipeline may sometimes vary from those for which the inhibitor was qualified. Consequently, pipelines are also likely to be inspected from time to time using In-Line-Inspection tools. Various empirical and mechanistic models are used to estimate corrosion rates in such pipelines, both during the design phase to establish corrosion allowances and inhibitor availability requirements, and then during operation to help interpret inspection results and guide further operational decisions. These models can differ considerably in how they incorporate the effects of surface scaling, while the effects of inhibitors are generally not included in any mechanistic sense. This paper provides an overview of corrosion assessment for wet gas pipelines, with a particular focus on recent developments in modeling scale formation and the influence of inhibitors.

Experimental and Numerical Analysis of the Impact of Corrosion on the Failure Pressure of API 5L X65 Pipeline

This paper employs the Finite Element Method (FEM) to simulate and analyze the effects of corrosion defect parameters on the stress and failure pressure of pipelines. It investigates how the boundary conditions of the pipeline model influence stress and examines the sensitivity of failure pressure to corrosion defect parameters. A nonlinear regression equation has been developed from a dataset obtained through simulation experiments to predict the failure pressure of corroded pipelines. To validate the effects of corrosion defect parameters on failure pressure, a hydrostatic test platform for an API 5L X65 pipeline with corrosion defects was established to measure stress levels and failure pressures across varying corrosion defects. This study reveals that failure pressure is negatively correlated with corrosion length and depth, while positively correlated with corrosion width. Among these parameters, corrosion depth exerts a more significant influence on the pipeline’s failure pressure than corrosion length and width. Within the range of corrosion defect parameters examined, the maximum deviation of the prediction equation’s results from the simulation results is 8.71%, with an average deviation of 5.81%. The standard deviation of the fitted residuals is 0.01837. Additionally, the maximum deviation between the predicted results and experimental measurements is 8.39%, with an average deviation of 7.77%. The strong agreement between the predicted results from the equation and the actual measured data underscores the effectiveness of the nonlinear regression equation.

Corrosion prediction model for long-distance pipelines based on NLFE-NGO-ELM

Abstract To improve the accuracy of buried pipeline corrosion rate prediction, we developed a Northern Goshawk Optimization-based Extreme Learning Machine (NLFE-NGO-ELM) model enhanced by Non-linear Feature Expansion. Feature selection was used to expand the small dataset and reduce the dimensionality of factors affecting pipeline corrosion. The Northern Goshawk algorithm was used, and 106 sets of pipeline external corrosion detection data were simulated in MATLAB as a case study. The ELM optimizer was analyzed using Grey Wolf Optimization (GWO-ELM), Northern Goshawk Optimization (NGO-ELM), and BP optimization as comparison models. Using NLFE-NGO-ELM significantly improved the model's prediction accuracy and speed. The NGO-ELM model was applied to predict the corrosion rates of 21 pipelines, achieving an absolute error of only 2.40% and an R value of 0.97255, surpassing the predictive performance of NGO-BP and GWO-ELM models. These results indicate that the current model exhibits superior learning performance and stronger fitting capabilities.

From shallows to depths: unveiling hybrid synthetic data modeling for enhanced learning with privacy considerations in naturally imbalanced datasets

Data serves as the foundational element that drives model development and performance in machine learning and deep learning. The learning algorithms are as good as data on which they are trained on. Data constrained environments pose serious threat to the effectiveness of learning algorithms. Data limitations stem from a range of factors, including regulatory restrictions, privacy concerns, and the inherent scarcity of relevant data. This constrained availability of data often leads to class imbalance problem in the context of classification tasks. To address the challenges of limited data, the algorithmic generated data, also known as synthetic data, is gaining significant traction as a cost-effective, readily available, and secure alternative. Synthetic data generation techniques can be employed to enhance dataset size by augmenting data samples and to address class imbalance by increasing the number of minority class instances. These techniques generally fall into two main categories: distance-based shallow models and probability estimation-based deep generative models. Shallow interpolation-based models generate new data points within the local space between existing data points, while deep density estimation based models generate new data by learning the whole distribution of data. . In the context of smaller datasets, deep generative models often struggle to accurately estimate the probability distribution of whole data. To effectively represent the global data distribution, these deep models require initial starting data samples to guide their approximation. This paper examines the potential of integration of shallow and deep generative models in the data generation pipeline for effective synthetic data augmentation which furthers enhanced learning and generalization of downstream tasks. In this work, we present a hybrid approach of tabular data generation involving mixed type data attributes (continuous, discrete) and pay special attention to data imbalance and insufficient data problems. We introduce the Hybrid Data Balancing and Augmentation Approach for Mixed Tabular Data (HDBA-MTD), specifically designed to synthesize samples for underrepresented labels of output class and address issues of insufficient data instances. This approach enhances training data diversity, thereby paying special attention to the downstream classification and generalization performance. Experiments are carried out using benchmark datasets to assess the practicality of the presented hybrid model in real-world scenarios. This work has also attempted to quantify the privacy preservability for real data concerning ethical considerations and data security circumstances. The evaluation and analysis of these experiments show that the present hybrid model performs favorably compared to other current hybrid synthetic data generation methods.

An early warning method of pipeline leakage monitoring with limited leakage samples

Aiming at the problems of low computational efficiency of the real-time transient model (RTTM), and training difficulty of data-driven models with small leakage samples in existing pipeline leakage monitoring methods, this paper proposes a novel pipeline leakage detection model (Multiple Regression Modeling of Real-time Transients, MR-RTTM) based on the fusion of real-time transient mechanism model and data-driven inference. Firstly, the real-time transient mechanism model of pipelines is derived based on the Bernoulli equation, and the required eigenvalues and label values of the data-driven model are determined to achieve the integration of the mechanism and the data-driven model. Secondly, MR-RTTM is constructed by applying the data of pipelines in normal working conditions and combining with the multiple regression algorithm to fit the real-time transient mechanism model of pipelines. An optimal threshold interval selection method of the model is also proposed based on the probability of detection (POD) and the probability of false call (POFC). Experiments show that MR-RTTM can detect pipeline leakage by arranging common pressure and flow rate sensors in the pipeline inlets and outlets, and the minimum detectable leakage is 1 % with an acceptable interval of 80 %∼100 % for POD and 0 ∼ 10 % for POFC, and the leakage discrimination rate is up to 100 %.

An automatic method of siltation depth detection and 3D modeling in water-filled sewer pipelines based on sonar point clouds

Underground sewer pipeline inspection is essential for ensuring the safety and sustainability of urban infrastructure and improving residents’ quality of life. Traditional sewer pipeline inspection methods incur high manual costs. Therefore, this paper proposes an automated siltation depth identification and three-dimensional pipeline model reconstruction using an iterative farthest point removal fitting process based on a sonar point cloud. This method includes interpreting raw sonar data into point cloud data, point cloud preprocessing, iterative farthest point removal fitting process to fit pipeline profile and reverse interpolation of pipeline slices for three-dimensional model reconstruction. This method is validated in real-world applications, successfully calculating and annotating silt depth for pipeline slice profiles. The quantitative analysis shows an error in the estimated siltation proportion fluctuating within approximately 1%. This method also reconstructs the three-dimensional spatial model of siltation in the sewer pipeline, thereby providing effective technical support for engineering applications and reducing manual effort.

Optimization of Parallel Data Transfers in Multi-Tiered Persistent Storage

A logical model of multi-tiered persistent storage provides a view of data where all available storage resources are distributed over a number of levels depending on the data transfer parameters and capacities. The efficient parallelization of data transfers in multi-tiered persistent storage is a significant challenge for a pipelined data processing model. This work examines a category of database applications implemented as sequences of operations that transfer data between the levels of multi-tiered persistent storage. The concept of EPN: Extended Petri Nets represents how database applications can be processed in parallel. A proposed transformation involves converting EPN into sequences of parallel data transfers. Additionally, a method is demonstrated for partitioning these sequences of data transfers, with the goal of reducing the total number of conflicts when data transfers occur between the levels of multi-tiered persistent storage. The paper proposes new rule-based algorithms for scheduling parallel data transfers that minimize total data transfer time. The objectives of the new algorithms are to evenly distribute the workload among the data transfer processes and reduce their idle time. Several experiments have confirmed the effectiveness of the new algorithms in generating parallel data transfer plans.

Acoustic Detection of Pipeline Blockages in Gas Extraction Systems: A Novel Approach

Gas extraction is crucial for coal mine safety, yet pipeline blockages by solid slag and water severely hinder efficiency and pose risks. Traditional detection methods are limited by rapid signal attenuation and noise interference. In this study, an acoustic detection technology is introduced for pipeline blockages, utilizing sensors at potential blockage points to collect sound wave data. Experiments with a scaled pipeline model reveal that slag blockages produce characteristic peaks in the 1200 Hz–2000 Hz range, while water blockages show peaks in the 1 kHz–2 kHz and 3.5 kHz–4.5 kHz bands. The longitudinal blockage intensity and extraction pressure significantly affect the sound pressure levels. A reliable fitting model predicts the blockage intensity based on acoustic signals, achieving high accuracy. This novel method enhances blockage identification, offering a non-invasive, cost-effective solution that improves coal mine safety and efficiency.

Optimizing large-scale CO2 pipeline networks using a geospatial splitting approach

CO2 transport infrastructure is the backbone of carbon capture and storage (CCS) technology for the mitigation of carbon emissions and project deployment viability. In conventional large-scale CO2 pipeline network designs, the storage sites are generally assumed as the centroids of the major geologic basins, however, this approach might provide suboptimal solutions since the large extension of some storage formations significantly increases the length of the CO2 transportation networks. To address this situation and obtain optimal pipeline routes, we present a novel geospatial splitting framework that partitions large basins into multiple sub-sinks. In our approach, we used a large number of reservoir models varying petrophysical properties and CO2 injection rates to compute pressure plumes through numerical simulations, leading to the calculation of the number of subregions for each basin as a function of the extension of pressure interference areas and boundaries. Finally, we applied K-means clustering and Voronoi polygon algorithms to partition large basins into subregions and obtain their sink coordinates. To demonstrate the capability of the developed workflow, we investigated two CO2 pipeline network modeling case studies using our splitting approach: one regional case study focusing on the Intermountain West (I-West) region and one nationwide case study covering the lower 48 states in the U.S. In both case studies, we compared the optimal pipeline routes using the original and new storage locations and examined the major differences. The use of the developed geospatial approach resulted in both cases in a shortening of the total pipeline network length by 13% and 10%, compared to the pipeline modeling with the original basins, leading to cost reductions of 25% and 17%, respectively, demonstrating that the location of point sinks has a critical impact on the length and expenses of pipelines to efficiently transport CO2 to distant storage sites. Therefore, the workflow presented here contributes to the proper and realistic modeling of case studies that support decision-making in CCS deployment.

Investigation of the transportation characteristics of marine dredged-sediment in pipelines based on experimental pipeline models

Marine dredged-sediment(DS) is often transported through pipelines, where factors such as the solid volume concentration and transport rate can significantly influence the efficiency of the process. Inadequate selection of transport rates for a given solid volume concentration can lead to substantial energy losses. The pressure loss Δ P L or pressure loss gradient Δ P L / L are critical parameters for assessing energy loss during pipeline transport. In this study, large-scale indoor experiments on DS pipeline transport were conducted to investigate the effects of the cross-sectional mean flow velocity Vavg and solid concentration ϕ on the transport characteristics of DS. The results show that at higher solid volume concentrations, Δ P L / L increases rapidly with the square of Vavg and then plateaus, followed by a linear growth phase. In addition, a linear relationship is observed between lgf and lgVavg when the flow velocity is below critical velocity (Vavg ) critical ; above (Vavg ) critical , the friction factor f stabilizes at f 0 . Formulas for calculating (Vavg ) critical , f 0 and ϕ were developed. Additionally, a method for determining the optimal transport velocity of DS at varying volume concentrations was established, enabling the identification of the most efficient flow velocity for DS pipeline transportation across different concentrations. HIGHLIGHTS Pipeline model tests were conducted on marine dredged sediments. A formula for predicting pressure loss on the basis of solid concentration was derived. The impact of the solid concentration on the Darcy friction factor was determined. A method for determining the optimal transport velocity was established.

Modal analysis of Lamb waves of steel pipeline with calcium deposits

The relevance of the work lies in the study of the effect of calcium deposits of various thicknesses on the inner walls of the pipeline on the parameters of its own vibrations.OBJECTIVE: To construct a mathematical model of a steel pipeline without deposits and with calcium deposits of various thicknesses on its inner walls. To perform a modal analysis of the natural vibrations of the steel pipeline. Theoretically confirm the dependence of the change in the oscillation frequency of the pipeline when exposed to sediments.METHODS. The paper highlights a method for analyzing the natural frequencies of the pipeline using the acoustic method of non-destructive testing, since it can be implemented with one-way access and does not violate the integrity of the object of control. In solving this problem, the method of mathematical analysis in the software package of finite element analysis was used. results. The article describes the relevance of the topic, discusses the main methods of non-destructive testing, and defines Lamb waves. A mathematical model of a steel pipeline section with out internal deposits and with calcium deposits of 5 different thicknesses has been constructed.CONCLUSION. Calculations have shown that the natural vibration frequencies of the pipeline without deposits and with calcium deposits change in the direction of increasing values, the frequency increase occurs in waves. The highest frequency values are obtained when modeling calcium deposits with a thickness of 10 mm on the walls of the pipeline.

A study of neural network-based evaluation methods for pipelines with multiple corrosive regions

In recent years, significant developments have been made in methods for assessing the remaining strength of corroded pipelines. However, existing methods have limitations as they mainly focus on the local impact of corrosion defects. This study explores evaluation methods using neural networks to predict the ultimate resistance of pipelines containing multiple corrosive regions. Firstly, based on the validated method, the study generates a dataset comprising 3,000 corroded pipeline models and pixelates the corrosion information of these models via digital images. Then, three neural network evaluation frameworks are constructed: a Multilayer Perceptron (MLP) using the overall corrosion matrix, an MLP based on corrosion feature parameters, and Convolutional Neural Networks (CNN) based on corrosion images. Following this, the study analyzes the relationship between various corrosion parameters and failure pressure, compares the training effectiveness of the three neural network methods, and validates the accuracy and applicability of the proposed approach. The results indicated that various corrosion features should be considered when evaluating corroded pipelines, particularly depth. In addition, all three neural network-based methods show improved applicability and reliability compared to traditional evaluation methods, with CNN-image having the highest evaluation accuracy (correlation coefficient = 0.9564, average error = 3.46%).

Modeling of joint extraction of entity relationships in clinical electronic medical records

The advancement of medical informatization necessitates extracting entities and their relationships from electronic medical records. Presently, research on electronic medical records predominantly concentrates on single-entity relationship extraction. However, clinical electronic medical records frequently exhibit overlapping complex entity relationships, thereby heightening the challenge of information extraction. To rectify the absence of a clinical medical relationship extraction dataset, this study utilizes electronic medical records from 584 patients in a hospital to create a compact clinical medical relationship extraction dataset. To address the pipelined relationship extraction model’s limitation in overlooking the one-to-many correlation problem between entities and relationships, this paper introduces a cascading relationship extraction model. This model integrates the MacBERT pre-training model, gated recurrent network, and multi-head self-attention mechanism to enhance the extraction of text features. Simultaneously, adversarial learning is incorporated to bolster the model’s robustness. In scenarios involving one-to-many relationships between entities, a two-phase task is employed. Initially, the main entity is predicted, followed by predicting the associated object and their correspondences. Employing this cascade-structured approach enables the model to flexibly manage intricate entity relationships, thereby enhancing extraction accuracy. Experimental results demonstrate the model’s efficiency, yielding F1-scores of 82.8%, 76.8%, and 88.2% for fulfilling relational extraction requirements and tasks on DuIE, CHIP-CDEE, and private datasets, respectively. These scores represent improvements over the benchmark model. The findings indicate the model’s applicability in practical domains, particularly in tasks such as biomedical information extraction.

Clarifying urban flood response characteristics and improving interpretable flood prediction with sparse data considering the coupling effect of rainfall and drainage pipeline siltation

With climate warming and accelerated urbanisation, severe urban flooding has become a common problem worldwide. Frequent extreme rainfall events and the siltation of drainage pipes further increase the burden on urban drainage networks. However, existing studies have not fully considered the effects of rainfall and pipeline siltation on the response characteristics of flooding when constructing numerical models of urban flooding simulations. To solve this problem, a surface-subsurface coupling model was constructed by combining the Saint-Venant equation, Manning equation, a one-dimensional pipeline model (SWMM), and a two-dimensional surface overflow model (LISFLOOD-FP). Then, the SWMM model considering pipeline siltation and the two-dimensional surface overflow model (LISFLOOD-FP) were coupled with the flow exchange governing equation, and the urban flooding response characteristics considering the coupling effect of “rainfall and drainage pipeline siltation” were analysed. To enhance the solvability of waterlogging prediction, an intelligent prediction model of urban flooding based on Bayes-CNN-BLSTM was established by combining a convolutional neural network (CNN), bidirectional long short-term memory neural network (BLSTM), Bayesian optimisation (Bayes), and an interpretable loss function error correction method. The actual rainfall events and flooding processes recorded by the monitoring equipment at Huizhou University were used to calibrate and verify the model. The results show that in the Rainfall 1 and Rainfall 2 scenarios, the overload rates of the pipelines in the current siltation scenario were 60.06 % and 68.37 %, respectively, and the proportions of overflow nodes were 24.87 % and 25.89 %, respectively. When the drainage network was initially put into operation, the overload rates of the pipeline were 36.67 % and 41.16 %, and the overflow nodes accounted for 3.05 % and 4.06 %, respectively. The inundated area and volume of urban flooding increased when the combined siltation coefficient (CSC) was 0.2; therefore, two desilting schemes were determined. Under Rainfall 1, Rainfall 2, and the four rainfall recurrence periods, the Bayes-CNN-BLSTM model had clear advantages in terms of accuracy, reliability, and robustness.

Unified, Integral Approach to Modeling and Design of High-Pressure Pipelines: Assessment of Various Flow and Temperature Models for Supercritical Fluids

Abstract A unified one-dimensional, steady-state flow and heat transfer model is presented for the pipeline transport of fluids at high pressures, including the supercritical conditions. The model includes a generalized temperature equation, presented here for the first time, and accounts for all of the important effects, including the property variation, viscous dissipation, Joule-Thomson (J-T) cooling, and heat exchange with the surrounding. With appropriate approximations, this model can yield all isothermal and non-isothermal pipe flow solutions reported thus far. A generalized multi-zone integral method is developed which solves the two resulting algebraic equations for pressure and temperature in conjunction with a property database, such as the National Institute of Standard and Technology (NIST) Reference Fluid Thermodynamic and Transport Properties (REFPROP). With appropriately selected number and size of the zones and using property values at the mean temperature and pressure within each zone, this integral method can accurately predict the complex effects of the governing parameters, such as the pipe diameter and length, inlet and exit pressures, mass flow rate, J-T cooling, and inlet and surrounding temperatures. Its accuracy for small-to-large diameter pipes has been ascertained by a comparison with the numerical solutions of the differential form of governing equations that requires a large number of small grids along the pipe and the values of mean properties within each grid. Indeed, this integral model can be used for the pipeline transport at both subcritical and supercritical pressures as long as the fluid does not encounter its anomalous states and the phase-change.