bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quasi-Static Fluid Structure Interactions for Ship Advancing in Waves With Constant Forward Speed
Authors: Nguyen Minh Quan , De Hauteclocque Guillaume , De Lauzon Jerome , Malenica Sime , Chen Xiao Bo 
Published: August 2024

Abstract
Evaluation of structural stresses for ships advancing with constant forward speed U in waves, represents a particularly important practical problem. Depending on the wave conditions and the structural properties of the ship, different types of fluid structure interaction procedures might be needed to evaluate these stresses. In the present work, the accent is on the so-called quasi static structural response which is relevant when structural vibrations are not likely to occur. Due to relatively high structural natural frequencies of the hull structure, when compared to wave frequencies, the quasi-static structural response is most often of major practical concern. The exceptions are some particular problems such as for example whipping where the structural vibrations might occur due to important impulsive loading induced by slamming or other sources. For those cases, a more complex coupling procedures, based on full hydro-elastic coupling principles, are necessary. These issues are not discussed here but will be considered in the future.
Within the quasi-static assumptions for the structural response, the main technical difficulty concerns the pressure transfer from the hydrodynamic model to structural model. In the present work, Boundary Integral Equation Method (BIEM) is used to solve the hydrodynamic problem and the Finite Element Method (FEM) is used to solve for the structural response. When compared to the seakeeping problem without forward speed, the solution of the hydrodynamic Boundary Value Problems (BVP) is much more complex and the different coupling strategies are required.
Intelligent Identification Workflow of Drilling Conditions Combining Deep Learning and Drilling Knowledge
Authors: Liu Zihao , Song Xianzhi , Ye Shanlin , Ma Baodong , Zhang Chengkai , Wang Zheng , Yao Xuezhe , Zhu Zhaopeng , Wang Yifan 
Published: August 2024

Abstract
Oil and gas drilling is a complex construction process, drilling conditions as the key parameters of the construction process, efficient and accurate identification of drilling conditions is the basis for statistical drilling efficiency and analysis of drilling status. With the continuous development of integrated logging technology and sensor technology, field operators and researchers have access to large amounts of realtime data. The existing methods mainly include logical judgment and manual judgment, which have the problems of insufficient accuracy and low efficiency, respectively. In recent years, more and more scholars have adopted data-driven methods such as machine learning to identify drilling conditions, but pure data-driven models have the problem of high false positive rate. This paper proposes an intelligent identification method for drilling conditions based on a combination of deep learning and drilling experience knowledge. First, abnormal fluctuations in the logging data are removed through an efficient real-time data cleaning process, and then the most critical parameters that characterize the drilling conditions during the drilling process are selected. Three deep learning algorithms, Gate Recurrent Unit (GRU), Long short-term memory (LSTM), and Convolutional Neural Networks (CNN), are used to build the intelligent identification model for drilling conditions and compare them. Then, according to the expert knowledge of drilling condition judgment, the logical constraint is formed, and it is added to the identification process. The final workflow can effectively realize real-time intelligent identification of 10 drilling conditions. The recognition precision and recall rate of various conditions are both above 94%, providing a basis for drilling efficiency analysis and risk prevention.
An Upgraded Direct Forcing Immersed Boundary Method With Integrated Mooring Algorithm for Floating Offshore Wind Turbines
Authors: Soydan Ahmet , Wang Widar Weizhi , Bihs Hans 
Published: August 2024

Abstract
An upgraded direct forcing immersed boundary method is implemented in the open-source hydrodynamic framework REEF3D::CFD for simulating the six-degrees-of-freedom (6DOF) motions of a floating offshore wind turbine (FOWT) based on the OC5 semi-submersible design. The direct forcing method is enhanced with a new density interpolation method across the fluid-structure interface that removes unphysical spurious phenomena and ensures stable and accurate wave load calculations on floating objects. A quasi-static algorithm is used for modeling the mooring system of the OC5 platform and restraining its motions in waves. The Navier-Stokes equations are solved on a staggered structured rectilinear grid for the hydrodynamic simulations. The level-set method is used to capture the free surface of the ocean waves. A ray-casting algorithm is employed to get inside-outside information near the fluid-solid interface while maintaining the underlying Cartesian grid in the hydrodynamic domain. The performance and accuracy of the mooring algorithm are compared to the widely-used mooring model MoorDyn, which is coupled to the hydrodynamic solver in REEF3D::CFD. The study demonstrates that the enhanced direct forcing method with the integrated quasi-static mooring algorithm in REEF3D::CFD provides a robust and accurate tool, suitable for the numerical analysis of the state-of-the-art FOWT in ocean waves.
Risk Monitors Applied to Offshore Installations
Authors: Maturana Marcos C. , M. Schleder Adriana , Frutuoso e Melo Paulo F. F. , O. de Barros Leonardo , Orlowski Thiago C. , R. Martins Marcelo 
Published: August 2024

Abstract
In recent decades, regulation of the offshore sector has migrated from a prescriptive normative base to a risk-based one. This paradigm shift brought to operators the need to adapt the risk management of their current plants and operations, and to deepen their analysis and risk assessments for new ventures (in compliance with regulatory requirements). Experiences in risk analysis, assessment and management and their advantages in other industries can be useful to accelerate and deepen the improvement of this topic in the offshore industry. As an example of what can be used (from the experience of other industries), one can mention the application of Risk Monitors (RMs) in the nuclear industry. A risk monitor can be based on a Top Logic Model (TLM), obtained from the event trees and fault trees that make up probabilistic models of the plant (e.g., focused on systems that are considered important for safety or that may have significant impact on operating costs). In this sense, this paper presents a discussion on how RMs are applied in the nuclear industry, and how they can be adapted and explored to improve risk management in offshore installations, including remotely controlled or automated installations, and provides a simple example of obtaining a model to calculate the point risk curve (a possible variable monitored by means of a RM).
Environmental Load on an Ocean Semisubmersible Structure in the Presence of Rogue Waves With Currents
Authors: Zaman Hasanat , Akinturk Ayhan 
Published: August 2024

Abstract
Renewable and low-carbon energy sources are critical to helping to reduce greenhouse gas emissions in the fight against global warming. One method for generating this renewable, low-carbon energy is offshore wind farms. A consistent wind source is always dependable for carbon-free energy production, equipment performance, and operational safety. In contrast to on land, offshore wind velocities are often steady. All structures in the offshore areas are subjected to a variety of environmental loadings due to the presence of very large waves and strong currents that can have significant impacts on the operation cycles of such structures. The presence of large waves and strong currents, as well as their interactions with the base structures and mooring lines, might produce hazardous operational circumstances. The operation and safety of such structures may be more vulnerable especially when a large wave travels against a strong current and produces abnormally high rogue waves. In this study, the tensions in the mooring lines of a tethered semisubmersible structure due to the presence of large waves, strong currents, various incident angles, and their consequent interactions are considered. The semisubmersible structure often serves as a fundamental framework capable of supporting energy-generating equipment such as wind turbines. Numerical simulations were conducted using an in-house developed 3D tool that incorporates mass, momentum, and energy-balanced equations. Additionally, two commercial codes, OrcaFlex™ and STAR-CCM+™, were employed for result comparisons. OrcaFlex™ is an implicit and explicit commercial code that uses lumped mass elements to streamline equations and the STAR-CCM+™ uses Navier Stokes equations. To explore numerous instances, settings for incident wave and current conditions were systematically changed in the simulations. The semisubmersible structure’s geometry and still water depth were maintained constant. The semisubmersible was anchored to the sea bottom with four mooring lines, one from each column’s bottom, in all simulations.
The model is used for the computation of the hydrodynamic loads on a semisubmersible and its mooring lines in a wave and current co-existence field.
Active Magnetic Ranging in Geothermal Drilling: Identifying Ghost Targets
Authors: Husby Karsten , Ytrehus Jan David , Saasen Arild , Eriksen Tor Jan 
Published: August 2024

Abstract
To achieve proper efficiency, for some geothermal applications it is necessary to drill deep connected closed loop wells. These wells are connected by a series of parallel horizontal well sections pairs for heat transfer from the formations. The separation of these wells, and the distance between them, must be controlled in the drilling operation to ensure efficient heat transfer.
Active Magnetic Ranging (AMR) is an oilfield drilling measurement technique developed for efficient relief well drilling and to drill well sections within a conglomerate of old wells without hitting any existing wells. Different to traditional ranging systems where the ranging is performed using a wireline operation, an AMR system integrate the logging system onto the drill string bottom hole assembly. Such method would save several rig days as the trip-out operations can be skipped.
We have conducted test rig experiments using a full-size drilling rig. Several test wells are drilled on this location for testing different types of drilling equipment. At the depth used in the tests the formation consists of a gneiss. A gneiss would represent typical low porosity and low permeable formations with poor electrical conductivity that is a representative model formation for geothermal heat transfer formations. Possibly because of the low conductivity false measurements can be obtained — so-called Ghost Targets.
In this paper we present an analysis of how ghost targets can look like. Furthermore, it is outlined how to distinguish ghost targets from real targets and how to design systems to reduce the impact of such ghosts.
Sealing Ability of Cement Plugs: Investigation of Critical Testing Parameters
Authors: Opedal Nils , Feneuil Blandine , Wiggen Even , Haug Jakob , N'Gouamba Elie , Skorpa Ragnhild 
Published: August 2024

Abstract
Determination of a sealant material’s ability to provide zonal isolation is important. Any potential well barrier material should ideally be tested and optimized under controlled, reproducible, and relevant conditions. Recent advances in experimental methods for qualification of materials beyond the API-10 standard have been published. For further development, the critical testing parameters relevant for these test methods should be investigated. The objective of this paper was to study the effect of test conditions such as cement curing time and scale dimensions. The curing and test conditions were found to be potentially important factors to ensure reproducible data and for understanding what is actually measured from the setup. For instance, the mechanical strength of cement might not be an adequate proxy to determine the hydraulic bond strength of a material. Also, the experimental determination of sealability of cement to a steel casing is dependent on several factors, such as curing time, presence of water during the cement hydration process, the cement plug dimensions, and the testing protocol itself.
Reinforcement Learning for Fishing Route Planning and Optimization
Authors: Zhu Tiantian , Naseri Masoud , Dhar Sushmit , Ashrafi Behrooz 
Published: August 2024

Abstract
Pelagic fishing is one of the primary economic activities in the Arctic, especially in the Barents Sea region. Optimizing fishing vessel voyage planning is a feasible avenue for enhancing efficiency, profitability, and sustainability while mitigating accidental risks posed by adverse weather conditions and reducing environmental impact through decreased fuel consumption. Currently, fishing voyage planning and route optimization rely heavily on the accumulated experience of skippers. Thus, it is a challenging task for inexperienced skippers. This paper presents an application of the reinforcement learning algorithm for fishing route planning and optimization. Reinforcement learning is a type of machine learning technique to learn an optimal behavior through a trial-and-error search in an environment to discover the sequence of actions under uncertainty that maximizes the defined reward. In this study, several optimization agents for different objectives, including maximizing catch, minimizing distance, maximizing catch while minimizing risk, etc., are trained from a programmed fishing environment to test out the capabilities of reinforcement earning for fishing route optimization. Future research can be developed by building upon the programmed fishing environment by incorporating a more realistic geographical map, uncertain fish stock distribution, a more sophisticated ship model, sound route risk function, fuel consumption function, etc. . Customized agents can be trained and further utilized as a decision support tool by skippers for their fishing trip planning and route optimization.
Hydrodynamic Assessment of the CorPower C4 Point Absorber Wave Energy Converter in Extreme Wave Conditions
Authors: Venugopal Vengatesan , Tan Tian 
Published: August 2024

Abstract
This study focussed on the evaluation of the motion responses exhibited by the CorPower C4 wave energy converter (WEC) when subjected to extreme wave conditions. The hydrodynamic performance of the device is examined through experiments conducted with a 1:25 scale model of the CorPower WEC at the FloWave Ocean Energy Research Facility, University of Edinburgh. The investigation encompasses a range of sea conditions, employing both regular and random waves to comprehensively understand the hydrodynamics of the CorPower WEC, with a particular focus on scenarios representative of the WEC’s survivability. The experimental setup involves the utilization of a Qualisys camera system to measure the six-component motion responses of the WEC at its waterline. Additionally, a 6-component load cell is employed to measure forces and moments acting on the entire model, including its mooring system.
This article provides a detailed overview of the model setup, experimental procedures, data analysis techniques, and thorough discussions of the results obtained. Drawing insights from the Response Amplitude Operators (RAOs) derived through the experiments, this work asserts that the CorPower WEC exhibits remarkable stability, demonstrating its capability to safely endure extreme wave conditions similar to those simulated in this study. The findings contribute valuable knowledge to the understanding of the CorPower WEC’s performance and resilience in challenging marine environments.
An Equivalent Wind State Method for Efficient Fatigue Screening Assessments
Authors: Jia Junbo 
Published: August 2024

Abstract
A dedicated wind induced fatigue analysis using full scatter diagram is desirable for reducing the design uncertainties in the assessment, even though it is demanding regarding engineering efforts, especially for large structural engineering projects that require the wind buffeting fatigue calculation for many different substructures. To conquer this challenge, the current paper proposes a method by condensing wind scatter diagrams to much fewer wind states for calculating wind induced fatigue damage, which can dramatically reduce the calculation efforts. By adopting a nonlinear time domain dynamic analysis method and applying the proposed method into the fatigue damage calculation of two types of structures, with different dynamic characteristics from each other, the obtained fatigue damage using the proposed method is compared with that using full wind scatter diagram. The comparisons show that the proposed method can reach a satisfied engineering accuracy and efficient for concept and preliminary engineering design. To further reduce the assessment efforts, by adopting the equivalent wind state method, a method to calculate the fatigue damage based on a static analysis is proposed. The method is especially suitable for relevant engineering projects requiring a large amount of screening assessment efforts for wind induced fatigue.
Machine Learning and Deep Learning for Enhanced Spatio-Temporal Wave Parameters Prediction
Authors: Tan Tian , Venugopal Vengatesan 
Published: August 2024

Abstract
Traditional methods of wave prediction, which are mainly reliant on extensive numerical simulations, such as the utilization of spectral wave models SWAN, WaveWatch III, or TOMAWAC, have prompted the question: Can faster wave prediction be achieved? The answer, as demonstrated by this study, lies in the advancements of machine learning and deep neural networks.
In this research, the spatio-temporal relationship between wind and wave conditions is established using the XGBoost machine learning method and Informer deep neural networks. This approach enables effective predictions of wave height and wave period within the waters of the North Atlantic and northern Scotland. Ten years of hourly wind data from ECMWF ERA5 (2012–2021) is used as training data, while field measured wave parameters from CEFAS WaveNet buoys are employed for model training and verification. The final output enable a comparison that ultimately leads to wave predictions for the year 2022. Building upon this foundation, a versatile model for typical weather conditions and a specialized model for extreme weather scenarios are devised, facilitating more precise predictions.
The data-driven model, rooted in wind data, proves adept at predicting wave characteristics across different times and locations. Notably, the trained machine learning and deep learning model delivers significant efficiency gains compared to traditional numerical models. One year’s worth of data can be predicted within a few seconds by machine learning, whereas over 24 hours (on 16 logical CPUs) are required for the same prediction by TOMAWAC spectra wave model. This leap in training efficiency is a crucial development in the realm of wave prediction.
Free Span Assessment: Benchmarking and Parametric Study
Authors: Abdelsalam Mohammed , Karrech Ali , Bransby Fraser , Watson Phil , An Hongwei 
Published: August 2024

Abstract
The assessment of pipeline free-spans may involve non-linearities, including those arising from the mechanical behaviour of the pipeline, soil properties and hydrodynamic loading; as well as the interaction between these factors. Failure to properly quantify and account for these factors may lead to inadequate design outcomes, which can lead to failure (if unconservative) or result in costly remediation when unnecessary (if conservative). This paper is part of an ongoing study into the vibration response of free spanning subsea pipelines, including the development of a numerical model that highlights the value of modal and full fatigue analyses as tools for understanding span behaviour. For quality control and enhancing the reliability of assessment methodologies, the model has been benchmarked against published data in terms of modal analysis, and against industrial fatigue assessment packages in terms of fatigue analysis — and shows excellent agreement with both. Since verification, the model was used to conduct a sensitivity study on a single free span, to explore how pipeline response and fatigue damage is affected by the value of dynamic soil stiffness and damping.
Development of an Experimental System to Investigate Viscous Damping of Oscillatory Flow Between Two Vessels
Authors: Yang Yuhang , Zhao Wenhua , Draper Scott , Wolgamot Hugh 
Published: August 2024

Abstract
An experimental design is proposed for investigating viscous damping in oscillatory flow between two rectangular boxes with sharp corners. It is anticipated that a relationship between Reynolds number and damping coefficient can be established through a series of tests with a wide range of Reynolds numbers, which would be of great significance for extending the database from lab scale to full scale. This experiment considers viscous damping induced by both wall friction and flow separation. The experiment is designed to allow global damping to be determined through measurements of flow rate and differential pressure, while wall friction can be estimated through an analytical solution. Particle image velocimetry technology has been adopted to visualize the flow field. In the paper, detailed design of the experimental approach is presented, together with validation of the instrumentation, demonstrating the reliability of the experimental set-up.
Overview of the Dilemma of Left in Situ: Structures Coated With Weathered Coal Tar
Authors: Candido Cordeiro Marcelle , Couto Thiago , Carneiro Paulo R. F. , Fasca Hanna , Santos Luan , de Souza Marcelo I. L. , Salomon Paulo S. 
Published: August 2024

Abstract
Coal Tar coatings were extensively utilized from the 1960s to around 1990 to waterproof offshore oil facilities, such as pipelines, and shield them from corrosion. However, in the 2000s, Coal Tar was found to be linked with a significant release of polycyclic aromatic hydrocarbons (PAHs) into the environment, prompting a decline in its usage. Nevertheless, numerous aging oil structures coated with Coal Tar persist on the seabed. As these facilities near the end of their service life, a pivotal question emerges: does this mass of weathered Coal Tar coating still pose a risk to marine life? In response to these concerns, this overview explores the scientific understanding of Coal Tar and its toxic components degradation in seawater. Our objective is to foster discussions regarding decommissioning options that mitigate environmental harm, underscoring the crucial necessity of evaluating the effects of prolonged exposure to Coal Tar in marine ecosystems, particularly within the context of the left in situ (LIS) approach. We seek to heighten awareness among the academic community and project operators responsible for decommissioning initiatives about the gravity and complexity of this issue.