bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gas Diffusion in Flexible Pipes: A Comparison Between Two- and Three-Dimensional FE Models to Predict Annulus Composition
Authors: Vieira João Marcos Bastos , Mendes de Sousa José Renato 
Published: October 2022

Abstract
The Brazilian offshore oil and gas industry uses flexible pipes to transport water, oil, and/or gas. Currently, the high concentration of acid gases, such as carbon dioxide (CO2) and hydrogen sulfide (H2S), in Brazilian pre-salt wells has been introducing new challenges to these pipes. The acid gases may migrate from the bore to the annulus of these structures, forming a corrosive environment that can induce the steel armors’ failure by SCC (Stress Corrosion Cracking) or HIC (Hydrogen Induced Cracking). Hence, predicting the gas composition in the annulus is of fundamental importance to ensure the safe operation of flexible pipes. However, this prediction involves complex gas permeation analyses through the layers of these pipes. For instance, the permeation rate depends on temperature, gases partial pressures, and the free volume distribution. Therefore, new tools are required to understand better the fluid permeation between the flexible pipes’ layers.
Therefore, this paper presents and compares two finite element (FE) models to predict the annulus composition of flexible pipes. Both models consider the temperature gradient effects on the layers’ material properties. On the one hand, the first approach deals with a two-dimensional model that considers helical layers as rings. So, the shielding effect is simplified. On the other, the second develops a complete three-dimensional model of the cross-section geometry. The results indicate that, while being faster, the two-dimensional approach shows higher concentration results than the three-dimensional approach. Furthermore, the difference between the two approaches suggests that the shielding provided by the helicoidal wires is relevant.
Slamming Induced Fatigue in a Moonpool With a Recess
Authors: Chalkias Dimitris , Sulaiman Zana 
Published: October 2022

Abstract
This paper presents a method of assessing fatigue life due to slamming on the structural components inside drillship moonpools with a recess. The effect of free surface deformation in the moonpool, the resulting slamming pressure on the moonpool’s bulkheads, and the resulting stresses and fatigue life are studied for two typical drillship moonpools with a recess.
The fatigue life is predicted using a practical engineering approach. The natural period of the moonpool is determined by using a potential flow radiation/diffraction solver. The flow characteristics and slamming pressure in the moonpool are calculated using CFD. Subsequently, the slamming pressure results are used in a structural FEM model to calculate the stress on the structural elements. The fatigue life of the structure inside the moonpool is predicted using suitable S-N curves from classification society rules.
In this paper, a high-level description of the problem is made, the methodology for the prediction of fatigue life of the critical structural elements in the moonpool is described, and mitigation solutions are proposed to increase the fatigue life of the structural components.
Predicting the Dynamic Response of a Steel Lazy Wave Riser in the Time Domain Using Artificial Neural Networks
Authors: Cheng Ankang , Low Ying Min 
Published: October 2022

Abstract
Metamodeling (also referred to as surrogate modeling) has attracted increasing attention from researchers in the field of offshore engineering for its advantage in computation efficiency. Among the many metamodeling techniques, artificial neural network (ANN) has long been recognized as one of the most versatile and powerful. In this paper, metamodels of ANN type are constructed to predict time-domain dynamic responses of a steel lazy wave riser (SLWR) in irregular waves based on wave elevations. The work is first carried out with short-term variabilities in waves considered only. The capabilities of dynamic recurrent neural networks with exogenous inputs on the basis of nonlinear autoregression (NARX) is carefully studied. The prediction accuracy is evaluated by the coefficient of determination and the root mean squared error, and the possible overfitting is prevented using k-fold cross-validation. Then the work is expanded to include long-term variabilities in waves into consideration. The time series data of structural dynamic responses are generated from finite element method (FEM) based numerical simulations and are further used to train and validate the ANN. The results obtained suggest that time-domain dynamic responses of SLWRs subject to irregular waves can be effectively predicted by ANN type metamodels directly from wave elevations.
Well Path Design Using Q-Learning Algorithms and Bezier Curves With Obstacles Avoidance
Authors: Cao Jie , Ehsan Chowdhury Samiul , Wiktorski Tomasz , Sui Dan 
Published: October 2022

Abstract
Designing an optimal path has been considered as the one of the key challenges for drilling engineers. Even for a group of competent engineers, it takes several months to plan a well. A robust optimized path can influence the total cost of wellbore, wellbore quality and stability, transport efficiency, and drilling speed. A properly optimized path can be called if it is with a shortest path, avoids well-collisions, low touristy, and has maximum contact with the reservoir. Optimal design of wellbore trajectory is very demanding due to many complex and interacted drilling variables, models’ uncertainties, and design constraints. In drilling engineering, well path optimization is typically based on the objectives to minimize of the total length of wellbore, drilling cost and time in consideration of well-collision avoidance. Optimization algorithms such as genetic algorithm, dynamic programming, particle swarm optimization etc. might provide probable solutions to the optimization problem.
Recently, machine learning technology becomes more attractive in drilling engineering to deal with complicated cases, like regression, classification, control and optimization problems. The aim of this paper is to investigate the implementation feasibility and efficiency of Reinforcement Learning algorithms to design an optimal path with the obstacle detection and avoidance. Our work has applied Q-Learning of Reinforcement Learning to plan a path with minimum measure depth, and in the meanwhile to avoid different shapes of obstacles. The agent interacts with the environment, achieving maximum reward upon reaching the target with passing through the given obstacles or receiving penalties when it fails. This work shows the behavior of the algorithms to obey the main criterion of the trajectory design as an alternative solution. In addition, one engineering path plan method based on Bezier curve to modify the generated path from Q-Learning is proposed, where the minimum dog leg severity (DLS) is considered to make the designed trajectory close to the real ones. To the best of authors’ knowledge, it might be the first attempt to use Reinforcement Learning for well path design. The presented work will be a good example to introduce more and more artificial intelligent (AI) methods to petroleum engineering and explore a wise way to combine traditional engineering design methods with AI approaches.
An Artificial Neural Network Approach for Fatigue Analysis of Slender Marine Structures
Authors: Rodrigues Thiago Camargo , Gonzalez Gabriel Mattos , Sagrilo Luis Volnei Sudati 
Published: October 2022

Abstract
Fatigue life calculation of slender marine structures, such as risers and mooring lines, usually requires a high computational cost. This cost comes from using finite element-based numerical models to predict the stress response of such structures under the action of many fatigue-inducing environmental loadings that they will face during their operational life. Lately, alternative methods to reduce the computational cost of these predictions have been proposed. One consists of a hybrid method that combines the FEM (Finite Element Method) with Artificial Neural Networks (ANN). Most of the available hybrid FEM-ANN models are based on shallow neural networks, and the models are trained individually for each fatigue load case. The main goal of this work is to investigate a fatigue analysis methodology based on hybrid FEM-ANN models where the ANN modeling is developed using modern deep learning techniques, such as Long Short-Term Memory (LSTM). Besides, instead of using a case-to-case approach, this work presents a generalized deep learning model, i.e., the trained hybrid FEM-ANN model can make predictions in the top of the riser for various environmental loadings without the need for new training. The model is tested for a flexible lazy-wave riser and a free-hanging flexible riser. Results of the hybrid FEM-ANN model are compared to those from the complete FEM-based numerical analyses to show the former model’s accuracy.
Effect of Low Pretension on the Fatigue Performance of Large Bolts
Authors: Johnston Carol 
Published: October 2022

Abstract
Large bolts are used in applications which are subjected to fatigue loads, such as the M72 bolts used in ring flanges of wind turbine towers. Pretension is used as a means of protecting bolts against fatigue loading. Design standards require particular levels of pretension to be applied. For example, 70% of the bolt’s proof strength is often specified. However, in practice, bolts may not be experiencing the required level of pretension. Pretensioning procedure, joint face distortion and in-service relaxation can all influence resulting bolt pretension. However, no guidance is available on the fatigue performance of pretensioned bolts when the pretension is less than the specified value. In this work, a series of tests was carried out using a specially designed test jig, which allowed bolts to be pretensioned and then a fatigue load to be applied to the joint faces. Pretension was applied to the bolts using a hydraulic tensioning tool, and the resulting bolt tension was measured using strain gauges. The pretensions used were intentionally less than the recommended level. The fatigue test results showed that it was necessary to apply a significantly higher level of tension (an overtension) with the hydraulic tool prior to tightening the nut in order to achieve a particular target level of pretension. This overtension was more than two times the target tension. In terms of bolt fatigue performance, as expected, increasing the pretension reduced the load range experienced by the bolts and so increased fatigue life. The mean S-N curve for the pretensioned bolts was similar to and slightly higher than the mean S-N curve obtained from bolts tested without pretension. A relationship was derived, which allows bolt load range to be calculated for a given bolt pretension and applied load range.
Development of a Stress Intensity Factor Solution for Mechanically Lined Pipe
Authors: Johnston Carol , London Tyler 
Published: October 2022

Abstract
Mechanically lined pipe (MLP) is a lower cost alternative to clad pipe, and is used to carry corrosive fluids in oil and gas applications. The pipe includes a liner which is made from corrosion resistant alloy (CRA). The liner is fixed at both ends using seal welds and a length of weld overlay. This results in a junction between the backing steel, weld overlay and liner, which is called the transition point or the triple point. When lined pipe is subjected to fatigue loading, the triple point has been shown to be location where fatigue cracks initiate. Flaws present at the triple point will grow in service. When a flaw breaches the liner, the backing steel will be exposed to the corrosive fluid, limiting the remaining life of the pipeline. It is therefore of interest to perform fracture mechanics calculations to understand fatigue crack growth from the triple point, so that the operational service life of MLP can be calculated if a flaw is present at the triple point. In order to do this, a stress intensity factor, K, is needed which describes the stress intensity at a crack tip at the triple point. However, owing to the unique geometry of this location, no stress intensity factor solution currently exists for the triple point location. To overcome this challenge, parametric finite element models were developed to calculate stress intensity factors for triple point crack-like flaws. This paper presents results from a work package from a joint industry project whose aim was to understand the fatigue performance of mechanically lined pipe. Finite element analysis was used to develop K solutions for the triple point in mechanically lined pipe by developing geometry-specific Y-factors for the MLP triple point geometry. An engineering critical assessment was then performed. The calculated fatigue life agreed well with results from full scale resonance fatigue tests and demonstrates the value of using FEA to derive geometry-specific K solutions for this geometry.
Automatic Clustering of Metocean Conditions in the Brazilian Coast
Authors: M. Moreno Felipe , G. Cozman Fabio , A. Tannuri Eduardo 
Published: October 2022

Abstract
This paper introduces a pipeline that, first, assembles a dataset of metocean conditions consisting of wind, wave and surface currents, and, second, clusters this data to find the characteristic environmental conditions of each region in the Brazilian coast and the associated Exclusive Economic Zone. Clustering uses the Partitioning Around Medoids algorithm with the silhouette coefficient. As examples, we first present an analysis for the whole Exclusive Economic Zone and then a focused analysis around the Santos port in Southeastern Brazil.
Genetic Algorithms Applied to the Concept of Risk Based Inspection (RBI): Optimization of Inspection Plans
Authors: Morais Carlos Henrique Bittencourt , Abrahão Elcio , Maturana Marcos Coelho , Martins Marcelo Ramos , Rossi André Luis Debiaso , Schleder Adriana Miralles , Moura Fernanda Marques de , Barros Leonardo Oliveira de , Orlowski Rene Thiago Capelari 
Published: October 2022

Abstract
Cost optimization of asset management is a central issue in the offshore Oil and Gas strategy, and risk-based approaches, such as Risk Based Inspection (RBI), have been more and more employed to assist the segment in the concretization of that goal. The inspection procedure aims mitigating the uncertainty related to the asset degradation state, enabling a better quantification of the actual damage, and, consequently, increases the accuracy of remaining life projections. Since the costs involved in offshore subsea inspections are considerable, inspection plans optimization plays a crucial role in the balance of an asset management program. The present paper discusses the use of genetic algorithms, GA, an optimization technique inspired in the concepts of evolutionary genetics, in the development of inspection plans for subsea equipment. The genes are defined in terms of two variables: the type of inspection to be done and in which period it occurs, considering a finite window of opportunities for inspection occurrences. The optimization process considers a multicriteria objective function, consisting in the dimensions time, cost, and risk. Finally, the application of the proposed methodology is illustrated by means of the elaboration of the inspection plans for a subsea Christmas-tree.
Navigational Simulation of Cruising AUVs
Authors: Fujiwara Toshifumi , Umeda Jun , Sato Takumi , Kim Kangsoo , Sasano Masahiko 
Published: October 2022

Abstract
Navigational simulation of autonomous underwater vehicles (AUVs) is important for the navigational planning of the AUVs to confirm operational safety and navigational quality.
This paper presents examples of the situation method of cruising AUVs, which is based on the maneuvering mathematical modeling group (MMG) model proposed for estimating ship maneuvering. The AUV motion equations of external forces in the simulation method are consist of the sum of thrust forces, hydrodynamic ones of the main hull and rudders. Hydrodynamic coefficients in the motion equations were decided by towing and forced oscillation tests using a small AUV model, and the computational fluid dynamics (CFD) calculation.
The calculated results were compared with sea trial test ones conducted at Suruga Bay in Japan. The validity and effectiveness of the simulation method are enough shown by the comparison, in which the simulation and sea trial results have coincided with each other. As a result, it was confirmed that the presented simulation method using simple linear mathematical formulation is useful and has no large problem.
Life Cycle Assessment of Different Marine Fuel Types and Powertrain Configurations for Financial and Environmental Impact Assessment in Shipping
Authors: Taghavifar Hadi , Perera Lokukaluge P. 
Published: October 2022

Abstract
The fuel type selection according to optimal pathway from extraction of a raw material (feedstock) to its processing to transportation and finally its use in marine engines (well to wheel) based on the cost and emission criteria is the main motivation factor to conduct the current investigation. The undertaken procedure has been customized based on the available data (ship/bunker route and mileage, the ship powertrain system, etc.) of the shipping industry under the SeaTech H2020 project (seatech2020.eu). The selected modeling platform is utilized for the life cycle assessment of three potential fuels of diesel, methanol, and liquefied natural gas (LNG). Different fuel production pathways and powertrain dual-fuel technologies have been taken into account as the main variables, while the subsidiary factors such as transportation parameters (fuel economy and Avg. speed) are included in the calculations. The economic aspect and emission reduction trade-off for various scenarios are conducted to introduce the optimal solution based on the stakeholder interest in the shipping industry. The study also considers the fuel transport to the respective ports for a selected vessel from diverse fuel export locations and travelled routes according to datasets available for the same project. The results provide a guideline to the shipping industry on selecting possible conventional/renewable fuel resources to use in marine engines with emission content during each adopted pathway, where the respective subsequent expenditure per 1 MJ of each fuel sample as the functional unit has been evaluated.
Small Sizes of Molybdenum Disulfide Nanosheets As Heavy Oil Viscosity Reducers
Authors: Wu Guodong , Liu Yanchi , Anwaier Maieryemuguli , Yao Erdong , Ren Hongda , Li Yuan 
Published: October 2022

Abstract
The high content of asphaltenes and colloids in heavy oil lead to their high viscosity, making it difficult to exploit and transport. Nanomaterials have become an important additive in reducing viscosity of crude oil. In this work, a series of amphiphilic molybdenum dioxide (MoS2) nanosheets are developed to reduce the viscosity of crude oil by an ultralow concentration aqueous solution.
Firstly, nanosheets were synthesized by the hydrothermal methods. The influence factors such as raw material ratio, temperature and time were investigated. Then, SEM are used to systemically characterize the morphology and structure of MoS2 nanosheets. Then, the properties (i.e., size, viscosity reduction, and interfacial activity) of MoS2 nanosheets are tested. The relationship between these properties and viscosity reduction abilities were studied. Finally, based on viscosity reduction testing results, optimal MoS2 nanosheets formulation and dosage are determined.
By controlling the stirring and temperature, hydrophilic MoS2 nanosheets with sizes from 40 nm to 160 nm were synthesized, respectively. Modifying them with oleyl amine, the amphiphilic nanosheets can be obtained. SEM show that they are materials with layered nano-structure and commonly composed of 8–10 layers. These nanosheets have good interfacial activity, wetting and emulsifying ability. By adding 100 ppm of MoS2 nanosheets to the mixture of heavy oil and water, the viscosity of these system can fall from &gt; 880 mPa·s to 9 mPa·s. The smaller the size of the MoS2 nanosheets is, the better the viscosity reduction ability will be. The optimal using dosage of these nanosheets is 300–400 ppm, and the optimal size is 40–60 nm. It is predicted that nanosheets will gradually become a new field for the development of heavy oil.
Investigations on Hull-Ice Frictional Effects
Authors: Hisette Quentin , Myland Daniela 
Published: October 2022

Abstract
A study is performed to establish and enlarge the knowledge on the factors influencing the friction between ice and ship hulls. A set of experimental friction tests with a newly designed testing apparatus with computer controlled stroke, speed and acceleration is conducted. The testing involves the determination of the friction coefficient between an ice sample and a reference plate representing a ship hull with ice resistant coating. Results show a characterized, mostly decreasing evolution of the friction coefficient with an increasing number of testing cycles, especially at medium to large testing speed values. The influence of the normal load is significantly smaller and varies with the sliding speed. The friction coefficient tends to increase with a decreasing ice temperature. Finally the paper gives the basis to transfer the influence of these parameters (or others) at the laboratory level to the total ship resistance in ice.
Transitions in Charpy Energy and Splitting for X70 and X80 Pipe Steel
Authors: Mohr William , McGaughy Tom 
Published: October 2022

Abstract
Using data obtained for a standard production 42-inch X80 pipe with a 19.6-mm wall thickness produced around 2006 and a 24-inch diameter X70 pipe having a 0.5-inch (12.5-mm) wall thickness produced in 2016, the transition between fully brittle and fully ductile fracture behavior has been examined to assess any effects of transverse splits or delaminations. This is compared with a 48-inch X80 pipe from recent production. The 24-inch diameter pipe split more than the others, but all three showed splitting in the transition. The additional surface area formed at the splits can increase or decrease the Charpy energy by expending energy to form more fracture surface but also reducing constraint on the propagating crack as it extends through the unnotched Charpy ligament. Fitting of full Charpy energy transition curves using standard hyperbolic tangent functions does not represent the behavior well. An important transition is noted in the behavior between splitting that initiates at the initial notch tip and splitting that occurs further down in the ligament. Spitting across the notch tip increases Charpy energy by releasing constraint on crack initiation at the machined notch and occurs at temperatures just above the lower shelf temperature, where brittle fracture dominates. Splitting across the notch tip also decreases Charpy energy as compared to splitting across the ligament only. The Charpy splitting behavior also correlates to similar effects in crack tip opening displacement (CTOD) specimens for the same specimen orientation in the base material, both for conditions where unstable fracture and fully ductile response occurs.
Comparative Verification of Numerical Analysis and Model Experiments for Oyster Farming
Authors: Tamura Hiroki , Nihei Yasunori 
Published: October 2022

Abstract
Motion analysis of the oyster hanging ream using the numerical analysis software Orcaflex was conducted to prevent typhoon damage in oyster farming facilities. First, a model experiment was performed, and the calculation accuracy of Orcaflex was then verified by comparison with the result. Consequently, the amount of horizontal displacement was in good agreement. Therefore, it is considered that the motion of the hanging ream could be evaluated by Orcaflex. In addition, in order to verify the phenomenon of entanglement of adjacent hanging reams, the motion of multiple hanging reams is evaluated by numerical analysis. Consequently, it is suggested that there may be a wave-periodic zone in which the hanging reams are more likely to be entangled.