bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparison of Pressure Decay Models for Liquid Pipelines
Authors: Zhang Shenwei , Huang Terry , Dooley Colin , Lai Roger , Conrad Brett 
Published: December 2022

Abstract
This paper presents a comparative study of various models in characterizing the reduction of pressure severity along the length of a given liquid pipeline section. Four models were considered in this study, namely the API RP1176 model, CEPA model, SSI Linear Interpolation model and Pressure Range Linear interpolation model. A total of 132 sets of pressure spectra from TC Energy’s liquid pipelines were collected. Each set includes three pressure spectra, namely one pressure spectrum from a given intermediate valve station and other two pressure spectra from the discharge end of its immediate upstream pump station and the suction end of its immediate downstream pump station. The SSI is used to characterize the severity of a given pressure spectrum. To quantify the uncertainties of the model error associated with the four pressure decay models, the calculated SSI based on the spectrum from the intermediate valve station is compared with the model-predicted SSI at the location of the intermediate valve station based on the SSIs of the upstream discharge and downstream suction pressure spectra. The comparative analyses indicate that the CEPA model is the most accurate and precise model in evaluating the pressure decay along the length of a given pipeline section, and in a descending order followed by Pressure Range Linear Interpolation model, API RP1176 model, and SSI Linear Interpolation model. A fitness-for-service assessment program for liquid pipeline can incorporate the probabilistic characteristics of the model error associated with each of the four models derived based on the 132 sets of pressure data.
Machine Learning Tools to Predict the Burst Capacity of Pipelines Containing Dent-Gouges
Authors: He Ziming , Zhou Wenxing 
Published: December 2022

Abstract
Dent-gouges as a result of the mechanical damage have serious implications for the burst capacity of oil and gas pipelines. The burst capacity of pipelines containing dent-gouges is lower than that of the same plain dented pipelines without gouges and that of the same gouged pipelines without dents. The well-known burst capacity prediction model adopted by the European Pipeline Research Group, i.e. the EPRG model, results in predictions of the burst capacity with high variability. In this study, a machine learning tool is employed to improve the predictive accuracy of the EPRG model for pipelines containing dent-gouges. To this end, a relatively large number of full-scale burst tests of pipe specimens containing dent-gouges are collected from the literature. The Gaussian process regression (GPR) technique, which is a class of non-parametric Bayesian model widely used in the machine learning, is employed to improve the EPRG model based on the collected full-scale burst test data. The full-scale burst tests are used to evaluate the hyper-parameters involved in the GPR analysis and validate the predictive accuracy of the improved EPRG model after the application of GPR. To facilitate the practical application of the improved EPRG model, a computer program with a graphic user interface (GUI) is further developed to compute the burst capacity of pipelines containing dent-gouges by inputting key parameters such as the pipe geometry and material properties as well as sizes of the dent and gouge through a GUI. This research will improve the fitness-for-service assessment of pipelines containing dent-gouges.
Hard Stamp, Paint, or Chemical DNA, How to Link the Steel Product to the Material Test Report
Authors: Krom Alfons , Huising Otto Jan 
Published: December 2022

Abstract
The material test report (MTR) provides proof of the mechanical properties of a steel product and confirms that the tests required have been performed and are satisfactory. For this purpose, test pieces that are representative of the steel product are destructively tested and the results are given on the MTR. The MTR is essential as one cannot determine the mechanical properties non-destructively. The standard way of linking the MTR and the product is done by a specific mark on the product, usually the batch/heat number which is given in the MTR. But what if there is no confidence the MTR relates to the steel product? The steel product will then have limited value because there is no proof of compliance with the requirements.
A chemical composition is usually mentioned on the MTR. This is the chemical composition of the ladle, also called the heat analysis. The chemical composition does not change during the process of manufacturing a steel product. Therefore it can be considered as the “DNA” of the steel.
This paper will explain the use of mobile optical emission spectrometry (OES) as a reliable method for measuring the chemical composition of a steel product. The measured chemical composition, the DNA of the steel, is compared with the heat analysis. If the material test report relates to the steel product, the differences between the two chemical compositions will be very small. If differences in the composition are large we can see that the MTR and steel product do not relate to each other. This does not mean that the steel product does not meet the specified mechanical properties. Only the relationship with the associated MTR is no longer there and thus proof that the steel product complies with requirements.
Investigation of Cost-Effective Braided and Wound Composite Pipelines for Hydrogen Applications
Authors: Grigat Niels , Mölling Tim , Torres Crooks Suyin Jireh , Vollbrecht Ben , Sackmann Johannes , Gries Thomas 
Published: December 2022

Abstract
In order to enable an emission-free society by 2050, the distribution of green hydrogen is a key element for a successful transformation of the energy supply. This paper presents the design and manufacturing of composite pipelines made of fiber-reinforced plastic (FRP) and its potential for the transport of high-pressurized gases such as hydrogen. Furthermore, the extent to which FRP-pipelines can be a potential complement to existing steel pipelines is being discussed.
The wet winding process is an established manufacturing process for FRP-pipes which, however, provide only a fraction of all necessary requirements. Oftentimes, a trade between the different factors cost, weight, performance and feasibility is made. By means of benchmarking the alternative manufacturing approaches such as multi-supply filament winding (MFW) and radial braiding, the potential for cost-effective high-pressure composite pipelines are investigated within this paper. For the aspired operational pressure of 350 bars, suitable lay-ups are derived and validated via simulation according to ISO 14692. As pre-impregnated fibers, so-called towpregs, enable elevated winding speeds and reduced resin content variance, the study focusses on this material. Additionally, MFW allows the processing of up to 48 towpregs simultaneously and therefore, increased productivity compared to single-filament winding. Using the generated data and based on the material combinations investigated, the productivity of the MFW process is examined. The most promising design is selected for the manufacturing of a demonstrator via MFW. Finally, recommendations for the industrial upscale of composite pipeline manufacturing are presented and the manufacturing approach via radial braiding as an alternative is discussed.
Machine Learning-Based Severity Assessment of Pipeline Dents
Authors: Tang Huang , Sun Jialin , Di Blasi Martin 
Published: December 2022

Abstract
One challenge to pipeline operators is to identify potentially injurious dents among thousands of reported deformation features using limited information (e.g., reported dent’s length, width, and depth) and to prioritize the efforts and allocate the resources to obtain additional more detailed information (e.g., dent profiles) for those potentially severe dents. An innovative approach based on machine learning predictions stemming from a representative dictionary of finite element analysis (FEA) generated prototypes was developed. The proposed approach predicts multiple severity-based indicators for each dent, then combines them in an overall severity score, which finally is used to prioritize the acquisition of dent profiles. Once the dent profiles are available, detailed level 3 FEA quantitative reliability analyses, following previously developed and published methodology (QuAD) [1], is performed allowing pipeline operators to confirm dent’s severity more accurately and perform an integrity risk informed decision (IRIDM) leading to a safer and more efficient integrity management.
Three severity indicators were considered herein and intended to address both formation-induced and service-induced failure mechanisms. The maximum dent formation plastic strain and accumulated ductile failure damage were used for evaluating the likelihood of forming a crack during indentation. The third indicator was the stress concentration factors (SCFs) to assess the potential of service-induced failure due to fatigue.
A machine learning model, as an emulator, trained and tested using ∼4000 FEA-based dent prototypes was shown to be able to effectively predict dent severity indicators previously referred to. These predicted dent severity indicators are combined to produce an overall severity score, which was finally used to prioritize the acquisition of the detailed dent profiles. Once profiles are obtained, detailed FEA quantitative reliability assessments will ultimately confirm the severity and hence drive repair/no repair decisions, enabling in this way an efficient and effective allocation of resources.
Onshore Pipeline Safety Consequence Modelling in Support of the Development of a Risk-Based Safety Class System
Authors: Stephens Mark , Yue Howard , Halford Ann , Foster Niall 
Published: December 2022

Abstract
A consequence-based safety class system was developed as an alternative to the class location system currently used as the basis for defining the maximum allowable hoop stress in Canadian Standard Association’s Standard Z662. Development of the safety class system required models to quantify the safety-related consequences of pipeline releases expressed as simple analytical functions of a limited number of parameters that are typically known at the pipeline design stage. These simple analytical formulas were developed using an empirical approach, in which validated numerical models were first used to estimate the hazard zone sizes for a matrix of cases representing the feasible input parameter ranges for pipelines associated with each service fluid. Subsequent work, described in a companion paper, employed regression analysis of the parametric analysis results to develop the required simplified analytical formulas.
This paper describes the safety hazards posed by releases of the various service fluids; the numerical models employed in the parametric analysis to determine the associated hazard areas as a function of key pipeline, service fluid, terrain, and weather-related parameters; and the hazard intensities adopted to delineate the size and extent of the hazard areas bound by both the 1% and 99% lethality contours. It also discusses the sensitivity analysis undertaken to establish the minimum set of pipeline and service fluid parameters that could be used to credibly estimate the size and extent of the applicable lethality zones. It concludes with a set of examples that illustrates the relative size and extent of the model-predicted lethality zones associated with the various hazards that could develop for each service fluid in a representative set of pipelines.
Characterization of Through-Wall Cracks and Resulting Leakage Rates in Pipelines
Authors: Ebrahimnia Elaheh , Prasad Shawn , Virk Amandeep , Shahzad Khurram , Kainat Muntaseer , Akonko Saheed 
Published: December 2022

Abstract
Major challenges are foreseen in quantitative risk assessment of ILI detected crack-related features for thin-wall pipelines due to disproportionate ILI sizing uncertainties relative to pipe wall thickness. Therefore, the likelihood of defects growing into through-wall cracks, leading to product leakage, even at relatively low operating pressures needs to be considered in thin-wall pipelines. To support quantifying the risk associated with operating such pipelines, leak rate simulations were conducted to help with the release consequence assessment and risk ranking of ILI reported crack features to design an appropriate mitigation plan.
Finite element analysis (FEA) and computational fluid dynamics (CFD) methods were used to determine the physical characteristics of through-wall cracks and the resulting leakage rates. The study highlights that, for a given fluid, the threshold for leak occurrence and the leakage rate depend primarily on the crack geometry and the operational pressure. CFD simulation results for the sensitivity cases modelled in this study showed that the leak rate can become very significant as the crack opening and internal pressure increase. These CFD results were then compared with the results obtained from a closed-form analytical model. It was determined that the analytical model started to deviate from the CFD results as the internal pressure increased and the crack opening became larger. This was explained by the fact that the analytical model was intended to be used for single-phase flow under laminar, isothermal conditions. Since its applicability to the turbulent flow regime has not been established, the deviation between the CFD results and the analytical results suggests that the use of the analytical model in the turbulent flow regime could greatly underestimate the leak rate. In addition, the importance of the design of experiment, and proper modeling of turbulence and crack surface roughness in the leakage rate estimation was demonstrated.
Evaluation of Higher Strength E8010 Pipe Root Pass Welding
Authors: Lee Kenneth , Bruce Bill , Gould Melissa 
Published: December 2022

Abstract
Recent girth weld failures in newly constructed pipelines in North America have highlighted the issue of the weld strength undermatching the actual pipe strength. The lowest strength region of a manual girth weld is usually the root pass. This project evaluated using higher strength E8010 shielded metal arc welding (SMAW) electrodes for root pass welding on modern API 5L Grade X70 (L485) line pipe, as part of a solution to increase overall weld strength.
The operability, weldability, and productivity of E8010 root pass welding were evaluated by welder operability trials, weldability testing, and welding procedure qualification testing and reviews.
Feedback from North American pipeline welders support that E8010 electrodes have satisfactory operability for pipeline open root pass welding. Welding Institute of Canada (WIC) weldability test results support that an E8010 root pass does not significantly increase weld cracking susceptibility when properly used. Furthermore, welding qualification tests and production welding procedures using E8010 electrodes for all weld passes were reviewed and found that productivity is similar to current industry practices using E6010 electrodes for root pass welding. Guidance with recommendations and precautions for using an E8010 root pass was developed. In summary, a higher strength E8010 root pass can be part of an effective solution to address pipeline girth weld strength undermatching.
Advanced Assessment of Pipeline River Crossings
Authors: Pathak Saurav , Dinovitzer Aaron , Fredj Abdelfettah , Song Peter , Fathi Ali , Tetteh-Wayoe Debra 
Published: December 2022

Abstract
This paper presents an overview of an advanced VIV assessment process and illustrates its ability to consider a range of river crossing scenarios not readily addressed by traditional analysis approaches such as river crossing with river weights and provides better pipeline response predictions for VIV to support integrity assessment. Additional details on the advanced river crossing assessment approach are also presented to show enhancement with respect to existing CFD analysis and empirical equations. FSI sample application case studies are used to demonstrate how the technique captures coupled changes in fluid dynamics and structural response simultaneously so that both stable and unstable vibration conditions are considered. These results are used to demonstrate how the advanced river flow induced vibration assessment can contribute to pipeline integrity water crossing management.
Optimizing the Prioritization of First-Time ILIs Using Quantitative Risk and Machine Learning
Authors: Snider Brenn , Xiang Wei , Zhang Billy , Lecut Sergiu 
Published: December 2022

Abstract
Inline inspections (ILIs) are one of the most effective methods for managing the integrity of pipelines. However, many older pipelines were not designed to accommodate ILI tools. Pipeline operators often prioritize which pipelines to make inspectable on a risk-basis. While this risk-based approach has many merits, it does not necessarily result in the maximum risk reduction for a given budget as the risk-reduction from completing the inspection is not considered. An optimized prioritization strategy should consider both the uninspected risk and amount of risk reduction.
Since post-ILI risk are calculated based on the detected imperfections, it is not possible to directly calculate the risk-reduction from performing a first-time ILI. To overcome this, TC Energy (TCE) completed an exploratory analysis of numerous first-time ILI results to identify key parameters and built machine learning models which predicts the risk impact of performing first-time ILIs.
Several machine learning algorithms (neural network, decision tree, etc.) were trained on data from pre and post-ILI risk results from TCE’s quantitative risk assessment. The models were trained at a dynamic segment level and aggregated to an ILI assessment path evaluation. The best-performing machine learning model was selected that accurately predicts the risk reduction achieved from a first-time ILI.
These results demonstrate the risk-reduction of a first-time ILI can be accurately predicted before the inspection is performed. Combining the traditional risk-based prioritization approach with the predictive abilities to estimate risk-reduction will allow TCE to optimize the selection of first-time inspections by maximizing the amount of risk reduction.
Optimization of Through Thickness Microstructural Grain Size, Homogeneity and Niobium Metallurgy Performance for Cost Effective Improved API X80 Heavy Gauge Large Diameter Pipe Body and Weld Mechanical Property Stability
Authors: Stalheim Douglas , Jinxing Jiang , Chunyong Huo , Yongqing Zhang , Bacalhau Jose , Han David 
Published: December 2022

Abstract
Today’s market for API pipeline steels requires higher strength and fracture toughness performance in heavier gauges and larger pipe diameters. Meeting basic average mechanical properties of strength, TCVN and DWTT performance has been achieved by many with newer powerful plate mills and cooling systems. However, stability/standard deviations of plate/pipe strength and toughness along with pipe seam/girth weld toughness continues to be challenging for many. Current focus has been on pipe seam and girth weld HAZ toughness stability which has proven to be difficult to achieve. Optimization of the alloy and processing design are critical components of creating the desired final through thickness microstructure, grain size/homogeneity and a volume fraction of proper size Nb precipitates in the hot rolled condition and corresponding final base and weld metal ductility properties. Optimized processing of two Nb levels, 0.055% and 0.075%, Nanjing Iron and Steel Company successfully improved base metal plate strength and toughness stability that translated into improved pipe strength and toughness stability including pipe seam/girth welds HAZ toughness. The optimized 0.075% Nb girth weld HAZ fusion line average was 339 J @ −80 °C with a 9.3 J standard deviation while the optimized 0.055% Nb girth weld HAZ fusion line was 307 J @ −80 °C with a 55.2 J standard deviation.
Incorporating Measurement Uncertainty Into Machine Learning-Based Grade Predictions
Authors: Anderson Joel , Switzner Nathan , Kornuta Jeffrey , Veloo Peter 
Published: December 2022

Abstract
As part of the regulations published in October of 2019, PHMSA requires operators that do not have reliable records to conduct material verification in accordance with §192.607. As part of the material verification process, §192.607(d)(2) compels the operator to “[c]onservatively account for measurement inaccuracy and uncertainty using reliable engineering tests and analyses” when utilizing nondestructive examination (NDE) methods. The Pacific Gas and Electric Company (PG&E) has completed extensive testing to develop approaches that utilize nondestructive measurements to estimate grade. As part of this work, a supervised classification machine learning (ML) model was developed to predict pipe grade using NDE chemical composition measurements as inputs. While using the ML-based model provides substantial improvement over yield strength (YS) in predicting pipe grade, measurement uncertainty from NDE tools must be considered per §192.607(d)(2). Moreover, some amount of uncertainty is present in any measurement regardless of precision, and this measurement uncertainty may ultimately affect the ML model’s pipe grade classification.
This paper presents a methodology for incorporating this variability into the authors’ ML classification model using a Monte Carlo-based simulation approach. In addition, this study will discuss the various metrics that were developed for interpreting the most probable pipe grade from the large number of simulation results, including the average probability, range of probability, and the number of simulations where each grade was identified as having the highest probability. Since any ML model can misclassify a sample and there are such slight differences between adjacent grades, it is necessary to have a method of systematically validating the results based on prior knowledge. Several case studies using field data will be presented to illustrate this approach, including validation cases where the pipe grade is known.