bluuebunny/crossref_metadata_2025_split huggingface dataset:
Depth Control Modelling and Analysis of a Subsea Shuttle Tanker
Authors: Ma Yucong , Sui Dan , Xing Yihan , Ong Muk Chen , Hemmingsen Tor Henning 
Published: October 2021

Abstract
A novel subsea shuttle tanker (SST) concept was recently proposed as a cost-effective alternative to subsea pipelines and tanker ships for liquid CO2 transportation between a source facility and a subsea well. It is envisioned that the SST will be deployed to transport CO2 to marginal subsea fields with an annual CO2 storage capacity less than 1 million metric tons; volumes that do not justify a full subsea field development. The SST is designed to be a fully autonomous underwater vessel with a cargo capacity of over 17,000 metric tons. It is 155 m long and it has a 17 m diameter hull. The vessel may operate at a water depth of between 50 to 200 m in a weather-independent environment. Furthermore, it travels at a slow speed for minimal energy consumption and maximal range. During the offloading process, the SST will approach the subsea well and land on the seabed just outside the safety radius of the well. After that, a remotely operated vehicle (ROV) will mate the offloading flowline to the SST, and the offloading process will start. The landing sequence is technically challenging for various reasons and warrants detailed analysis. First, the SST would have limited manoeuvrability due to the large inertia of the vessel and low effectiveness of the hydroplanes to provide steering at low speeds. Second, during the final phase before the SST lands, seabed boundary effects will intensify and lead to increased non-uniform, time-varying and drag-dominated load-effects. Third, the impact forces during landing should be minimised to allow for the lowest design load. Solving these technical challenges is crucial to meet SST’s design goals of having the least possible control appendices for maximum efficiency/range, and minimal structural weight for the largest cargo capacity. This paper will describe the development of a fully coupled 2D planar model that considers the most relevant load-effects. This model is developed with the feasibility to implement any control schemes and has the potential to plug observers or control modules in future study. This paper performs open loop test and applies simple control cases to explore the depth control in landing sequence. A feed-forward heading control method that achieves the fastest control response and best path following ability is then proposed based on the results obtained.
Numerical Prediction of 3-D Vortex-Induced Vibration of Catenary Riser In Planar and Non-Planar Flows
Authors: Ma Bowen , Srinil Narakorn 
Published: October 2021

Abstract
Vortex-induced vibration (VIV) is one of the most critical issues in deepwater developments due to its resultant fatigue damage to subsea structures such as risers, pipelines and jumpers. Although VIV effects on slender bodies have been comprehensively studied over decades, very few studies have dealt with VIV modelling and prediction of catenary risers in current flows with varying directions leading to complex fluid-structure interactions. This study advances a numerical model to simulate and predict 3-D VIV responses of a catenary riser in three flow orientations, relative to the riser curvature plane, including concave/convex (planar) and perpendicular (non-planar) flows. The model is described by equations of cross-flow and in-line responses of the catenary riser coupled with the hydrodynamic forces modelled by the distributed nonlinear wake oscillators. A finite difference method is applied to solve the coupled fluid-structure dynamic system. To consider the approaching flow in different directions, the vortex-induced lift and drag forces are formulated by accounting for the effect of flow angle of attack and the riser-flow relative velocities. Results show VIV features of a long catenary riser exhibiting a standing and travelling wave response pattern. VIV response amplitudes and oscillation frequencies are predicted and compared with experimental results in the literature for both straight and catenary risers. Overall results highlight the model capability in capturing the effect of approaching flow direction on 3-D VIV of the curved inclined flexible riser.
Bivariate Regional Frequency Analysis of Sea State Conditions
Authors: Vanem Erik 
Published: October 2021

Abstract
This paper presents a bivariate regional frequency analysis applied to data of extreme significant wave heights and concurrent wave period over an area in the North Atlantic Ocean. It extends previous regional frequency analysis on significant wave height to the bivariate case where the joint distribution of significant wave height and zero up-crossing wave period are analysed. This is believed to be an important extension, as the joint distribution is typically needed for marine design and other ocean engineering applications. The analysis presented in this paper is based on a bivariate index-wave/period approach and assumes a common regional growth curve within homogeneous regions of the overall area. One of the main benefits of performing a regional frequency analysis as opposed to at-site analysis based on data from one location only is that more accurate predictions of extreme conditions can be obtained, due to the increased number of observations that will effectively be available. Moreover, results for locations within the regions where no observations are available can be obtained by interpolation of the index wave/period and utilizing the regional growth curve. This paper outlines the various steps and modelling choices involved in a bivariate regional frequency analysis and presents the results of such an analysis applied to 30 years of data covering the North Atlantic Ocean. Moreover, it is shown how environmental contours can be constructed based on the outcome of the bivariate RFA, corresponding to one particular definition of a bivariate return period.
Experimental Investigation of Aspects Influencing the Level Ice Resistance of Ships With Non-Typical Icebreaking Bow Shapes
Authors: Myland Daniela , Hisette Quentin , Cilkaya Emre , Sefa Özhan Yusuf 
Published: October 2021

Abstract
For non-typical icebreaking ships the hull-ice interaction process in level ice comprises a combination of many different phenomena which is difficult to be described by existing straightforward approaches. In order to gain knowledge about the operability of such non-typical hull shapes in level ice, a study has been carried out to identify and evaluate the level ice resistance as well as its distribution along the hull of ships with non-typical icebreaking bow shapes with high stem and/or small waterline angles. For this purpose, one ship model has been manufactured and instrumented with several multi-axis load cells in the bow region of the waterline as well as with one large six-component load scale between the bow and the stern. Performing resistance model tests at several loading conditions in model ice sheets of different thickness and at multiple speed values allows obtaining relevant information to meet the goals of the study. The developed methodology and the analysis of the measured loads have been described in previous publications. As direct continuation, the present paper focuses on investigation of the ice floe characteristics and its linkage to the ice properties. Moreover, analysis results related to the crushing portion of the total resistance in ice, the friction between ice and ship model hull as well as the ship model motions during ice model testing are presented within the paper.
Machine Learning Approach to Estimate the Diffusion Coefficient of CO2 in Hydrocarbons
Authors: Bagalkot Nikhil , Keprate Arvind 
Published: October 2021

Abstract
Diffusion of the gas into the liquids is a critical part in understanding multiphase systems and engineering applications associated with these multiphase systems. The study couples multiphase pendant drop experiments and computational modelling to calculate the CO2 diffusion coefficient in n-decane. Experiments were carried out at a varied range of pressure and temperature 25–45°C and 25–65 bar. During the experiments, the change in the volume of the hydrocarbon drop due to CO2 diffusion was dynamically measured, and numerical model was developed which used the experimental data to estimate the diffusion coefficient. The current study brings in the capability of machine learning as a replacement of the computational part for prediction of the diffusion coefficient of the process. The feasibility of various machine learning models such as Gradient boosting, Gaussian Process Regression (GPR), k-NN, Decision tree etc. are checked. Firstly different algorithms were trained on the dataset and finally evaluated on the test dataset, using various statistical metrics). Finally, the most accurate algorithm is used as a surrogate model for predicting the diffusion coefficient. The chosen ML algorithm was fairly accurate in predicting the diffusion coefficient with a maximum inaccuracy of 7.5%. Therefore, ML may then be employed as an alternative to experiments and numerical methods. A case study is performed to demonstrate the proposed methodology.
FPSO Mooring Line Failure Detection Based on Predicted Motion
Authors: Saad Amir Muhammed , Schopp Florian , Queiroz Filho Asdrubal N. , Cunha Rodrigo Da Silva , Santos Ismael H. F. , Barreira Rodrigo Augusto , Tannuri Eduardo Aoun , Gomi Edson Satoshi , Costa Anna Helena Reali 
Published: October 2021

Abstract
A failure in the mooring line of a platform, if not detected quickly, can cause a riser system failure, extended production downtime, or even environmental damages. Therefore, integrity management and timely detection of mooring failure for floating platforms are critical. In this paper, we propose a new model for an ANN-based mooring failure detection system. The proposal’s idea is to train a Multilayer Perceptron (MLP) to estimate the platform’s future motion based on its motion’s temporal data without failure. A classifier then indicates whether or not there is a failure in the mooring system based on the difference between the predicted and the measured motion. The results with several tests of the implemented system show that our proposal can correctly predict the motion of the platform in most environmental conditions. The system shows a precision, accuracy and F1-score of 99.88%, 99.99% and 99.94%, respectively, for detecting changes in platform motion in near real-time, quickly signaling a possible breakage of mooring lines.
Evolution of the Stress-Induced Residual Magnetic Field of X70 Pipeline Steel With Multiple Corrosion Defects
Authors: Bao Sheng , Luo Qiang , Zhao Zhengye , Yang Jian 
Published: October 2021

Abstract
The aim of this research is to investigate the correlation between the residual magnetic field (RMF) and multiple corrosion defects in ferromagnetic steels. Specimens of X70 pipeline steel were machined into standard bars with a single corrosion defect, double corrosion defects and triple corrosion defects, respectively. Tensile tests were carried out to detect the RMF signals on the surface of these specimens. The variations of abnormal magnetic changes of the RMF signals with the external loads were investigated and the results showed that the tangential component and the normal component of the RMF signals of three defect patterns presented different shapes. It was found that the RMF signals were capable of capturing the location and the number of defects in pipeline steels. The peak values of the tangential component and the slopes of the normal component were not influenced by the defects number. This research will promote the investigation on the corrosion defect cluster of ferromagnetic steels based on the metal magnetic memory (MMM) technique.
Efficient Harbor Craft Monitoring System: Time-Series Data Analytics and Machine Learning Tools to Achieve Fuel Efficiency by Operational Scoring System
Authors: Tay Zhi Yung , Hadi Januwar , Konovessis Dimitrios , Loh De Jin , Tan David Kong Hong , Chen Xiaobo 
Published: October 2021

Abstract
This paper presents the analysis to reduce carbon emission from tugboat operations by utilizing a proposed unsupervised machine learning operational scoring system. The time-series analysis is performed by transforming data into a common domain for clustering. The data are collected from a tugboat to investigate the correlation between environmental and location data with fuel consumption to achieve fuel efficiency. The relevant parameters that influence the fuel consumption of the tugboat, such as fuel consumption, vessel route, vessel speed and wind metrics are collected from sensors installed onboard the ship and data provider to monitor and to gauge the vessel’s performance. The raw readings are conditioned (data cleaning and data pre-processing) before transformation to Score Dataset: the Raw mass-flowrate readings are cleaned by using the Haar wavelet; the wind raw reading is converted to wind effect data; the Location data is converted to vessel speed data. Together, they form a Score Dataset by applying the time series K-means clustering. The subsequent unsupervised learning identifies the activity labels that describe qualitatively the operations of the vessels and are obtained by using the non-time series K-mean clustering. By using the Hidden Markov Model approach, this paper attempts to explain the stochastic correlation among parameters explained earlier. The correlation is the information of newly discovered knowledge in terms of likelihood matrices, also known as the knowledge base (KB). The KB may be consumed to perform predictions. Hence, it is possible to suggest the optimal ship operation, i.e., speed that produces the optimum fuel consumption. The Score Dataset and clustering that are produced in this paper could also be used in the Artificial Neural Network for future work.
Comparison of the Fatigue Performance of Galvanised M72 Bolts With Design Standard Recommendations
Authors: Johnston Carol , Doré Matthew 
Published: October 2021

Abstract
Now that bolted flanges rather than grouted connections are used to join the transition piece to the monopile in offshore wind turbine towers, many large bolts are being used in applications which subject them to fatigue loads. The bolts in these ring flanges are typically M64 or M72 in size (ie 64mm of 72mm nominal diameter). The fatigue design codes, BS 7608, DNVGL-RP-C203 and Eurocode 3 do provide S-N curves for threaded fasteners, but the reference diameter in those documents is 25mm or 30mm. A thickness correction is provided, to account for larger diameter bolts, but this was originally derived by analysis of the performance of welded joints. It is unclear whether the S-N curves and the recommended thickness correction are appropriate for larger diameter threaded fasteners.
The offshore wind industry usually specifies hot dip galvanised bolts, to provide some corrosion protection in the offshore environment. Again, there is uncertainty over whether the S-N curves in fatigue design standards apply to bolts with a galvanised coating. Since the fatigue design codes provide S-N curves for air, free corrosion or seawater with cathodic protection, it is also unclear which of these should be used to predict the fatigue performance of bolts with a galvanised coating.
In order to provide data to address these uncertainties, hot-dip galvanised, grade 10.9, M72 bolts from two manufacturers were tested in both air and a seawater environment. In order to represent the conditions experienced by bolts in internal ring flanges, the artificial seawater was sprayed onto the bolts during testing. Tests were conducted with a mean stress corresponding to 70% of the specified minimum 0.2% proof strength of the bolts. Tests were also performed in air, on uncoated M72 bolts, and uncoated M64 bolts for comparison.
The results suggest that the current thickness correction in DNVGL RP C203 and BS 7608 is appropriate for M72 bolts.
The results in air from the galvanised bolts were below those from uncoated bolts. Although the galvanised results were above the thickness corrected in-air standard design curves (BS7608 Class X -20%, DNVGL Class G and DNVGL ST 0126 FAT 50), they were below the mean curves, suggesting that the performance of galvanised bolts is slightly lower than the existing recommendations.
Bubbler System Testing in Controlled Environment for Harsh Environment Oil Spill Recovery
Authors: Thodi Premkumar , Talimi Vandad , Burton Robert , Abdi Majid , Bruce Jonathon , Lui Lei , Brown Robert 
Published: October 2021

Abstract
Mechanical recovery techniques are used to clean up oil spills in marine environments; however, their efficiency is challenged when dealing with heavy oil, ice covered water and high sea states. Current mechanical recovery techniques are based on removing oil from the water surface, however, a significant amount of oil could remain in the water column below the surface due to turbulent ocean conditions, the density of heavy oil and oil escaping underneath the booms when the sweeping speed increases. To enhance the oil recovery effectiveness, oil particles in the water column need to be guided to the surface to be recovered by the skimmers. This paper focusses on the development of a test protocol and physical testing in C-CORE’s lab of a bubbler system for enhancing the harsh environment oil spill recovery. Air bubbles produce an upward flow in the water body, which guides the submerged particles to the surface. The air bubbles also attach to the oil particles, leading to an increase in the buoyancy and rate at which oil droplets rise to the surface. By adopting this technique for oil recovery, additional oil particles can be brought to the surface. In the study, the bubbler system was tested in both stationary and advancing conditions with medium and heavy oils. The results of the stationary and advancing tests indicate that the oil recovery ratios can be significantly enhanced by using an optimized bubbler system. Different types and configurations of bubblers were tested by varying the airflow rates and bubbler advancing speeds to determine the optimal solution. The optimal bubbler system has been observed to enhance the recovery ratio from 41.5% to 84.8% with airflow rates ranging from 0.05 to 0.20 CFM/foot. Furthermore, the effective integration of the bubbler system with a mechanical recovery system, its deployment and retrieval in a near field condition were demonstrated during tests in an outdoor tank.
Data Driven Prediction of the Minimum Miscibility Pressure (MMP) Between Mixtures of Oil and Gas Using Deep Learning
Authors: Pham Quynh C. , Trinh Trung Q. , James Lesley A. 
Published: October 2021

Abstract
Knowing the minimum miscibility pressure (MMP) between different oil and gas compositions is important to predict reservoir performance for gas-based injection as a secondary gas flood or tertiary technique such as water alternating gas (WAG). Machine Learning (ML) has been used widely and has been proven efficient in estimating these properties. In this work, the development of ML as well as commonly used algorithms in predicting bubble point pressure and oil formation volume factor is reviewed. Just a few studies are found before 2000. From 2001 to 2010, the use of ML increased steadily. However, a sharp augmentation in number of articles is observed from 2011 up to now. More than that, Artificial Neural Networks (ANN) is the most employed algorithm with 23 applications out of 38 studied papers.
In addition, for the first time, deep learning- multiple fully connected networks algorithm is implemented to predict the MMP for oil and gas through 250 datasets covering a wide range of CO2 concentration from 0 to 100% in the injected gas. The wide range of CO2 concentrations is to cover all modes of gas injection from a pure CO2 flood to CO2 being negligibly present when injecting a sweet gas. The model is then optimized using Early Stopping and K-Fold Cross Validation techniques, showing the average result of k splitting data sets. The eight input parameters are as follows: reservoir temperature, oil characteristics (molecular weight, ratio of volatile components, and intermediate components), and gas characteristics (mole percentage of CO2, Cl, N2, H2S, C2+). The proposed model is compared with other Machine Learning Techniques such as Decision Tree and Random Forest Regression. The results show that reservoir temperature, the amount of CO2 and Cl in the gas source were the parameters to affect MMP the most significantly. The presence of CO2 in the gas stream will lower the MMP significantly. The Deep Learning model obtained an R2 = 0.96 and a Root Mean Square Error (RMSE) of 5.4%. Through Early Stopping technique, the proposed model reach the R2 result of 0.97 in 7 epochs. An R2 value of 0.954 was found using K-Fold Cross Validation technique, resulting in a good model generated by five folds data set. The model built by Deep Learning algorithm was more accurate than these ones built by Decision Tree and Random Forest Regression, which had an R2 value below 0.9 and RMSE larger than 10%. This work goes beyond other prior research by adding a ‘stopping point’ concept, increasing the overall performance of the methods for general applications, and considering the full range of CO2 in the gas stream.
Development of a Real-Time Simulation Model for an ASD Tug
Authors: Jin Yuting , Zheng Yingying , Yiew Lucas J. , Magee Allan R. 
Published: October 2021

Abstract
A hydrodynamic digital twin of vessel can be used to replicate the behaviour and response of the vessel in a virtual environment. In this paper, a real-time simulation model (RTSM) for an azimuth stern-drive (ASD) tug has been developed for simulating the hydrodynamic performance of the vessel under a range of environmental conditions. Based on the framework of a 4-DoF MMG manoeuvring model, the RTSM comprises manoeuvring, propulsion and environmental loads which are parameterised using numerical results from a combination of computational fluid dynamics (CFD) modelling work, including virtual planar motion mechanism (vPMM), seakeeping analysis, wind drag prediction and propulsion modelling. The RTSM is used to demonstrate the manoeuvrability of the vessel in calm water and under external loads from waves, winds and currents.
On State-of-the-Art and Alternative Energy-Efficient Lifting Technologies for Deep Sea Mining
Authors: Berge Vegard , Helgesen Christoffer R. , Ivanov Dimitar Z. , Jahren Marianne H. , Opstad Pauline S. , Sletten Petter S. , Nejad Amir R. 
Published: October 2021

Abstract
The main aim of this article is to study and evaluate existing and potential lifting technologies used in deep sea mining. The lifting is an energy intensive operation and can be decisive if a mining operation is feasible or not. An additional goal for this study was to see if it can be rewarding to utilise the potential energy in the returned masses, because the excess material has to be returned to the ocean bottom so that no microorganisms would be released on the ocean surface. After a general study of possible solutions, regulations and existing projects, the technologies further explored in this study include an in-line pump system, a tubular-disc conveyor and a bucket conveyor, all with modifications to suit deep sea mining. To compare different lifting technologies an estimate for power consumption to lift the mined material from 1000 m depth at three different rates, namely 75, 150, 300 tons/hour, is considered. To calculate the power requirements realistic system parameters are considered and internal system resistance are also taken into account. The results show that the power consumption for the tubular and bucket conveyor are almost in the same range, while the pump system requires about two to three times more power than them. This indicates that there are feasible alternatives to hydraulic lifting by adapting existing onshore based technologies for deep sea mining.