Maritime Autonomous Surface Ships (MASSs) will reshape the fast-evolving ecosystem for their attractive socio-economic benefits and potential to improve safety. However, their new systems and technology need thorough verification to identify unintended components of risk. The interaction between MASS cyber-physical systems and the existing regulatory framework is currently unpredictable; AI-powered intelligent situation awareness and autonomous navigation algorithms must safely and efficiently adhere to the regulations which are only designed for human interpretation without MASSs consideration. This paper contributes to algorithmic regulations and particularly algorithmic COLREGs in real-world MASS applications. It focuses on codifying COLREGs into a machine-executable system applicable to MASSs, then analyzing their performance in dynamic and mixed interactions between multiple vessels in complex scenarios. Based on fullest pairwise COLREGs criteria, this paper considers Decision-Making (DM) and complexity analysis in multi-collision-conflict scenarios. Complexity influential factors are an interplay between the characteristics of COLREGs, traffic scenarios, MASS interactions, and the environment. Participant vessels are the decision-makers forming a decentralized uncertain DM process, casted into a multi-participant multi-conflict multi-criteria DM problem. This is tackled through the technique of graph models for conflict resolution, using risk graph models and fuzzy preferences over alternative collision-avoidance states. The presented work is validated on a database of historical scenarios extracted from multiple data sources.
海上自主水面舰艇（MASSs）将重塑快速发展的生态系统，带来诱人的社会经济效益和提高安全性的潜力。然而，它们的新系统和新技术需要彻底验证，以识别意外风险因素。目前，MASS 网络物理系统与现有监管框架之间的互动是不可预测的；人工智能驱动的智能态势感知和自主导航算法必须安全、高效地遵守仅为人类解释而设计、不考虑 MASS 的法规。本文将对算法法规，特别是真实世界 MASS 应用中的算法 COLREGs 做出贡献。本文的重点是将 COLREGs 编纂成适用于 MASS 的机器可执行系统，然后分析其在复杂场景下多船动态混合互动中的性能。基于最完整的成对 COLREGs 标准，本文考虑了多碰撞冲突场景中的决策（DM）和复杂性分析。复杂性影响因素是 COLREGs 特性、交通场景、MASS 相互作用和环境之间的相互作用。参与船只是决策者，形成一个分散的不确定 DM 流程，并将其转化为一个多参与者多冲突多标准 DM 问题。该问题通过图模型技术解决冲突，使用风险图模型和对备选避碰状态的模糊偏好。所介绍的工作在从多个数据源提取的历史情景数据库中得到了验证。
{"title":"Complexity analysis using graph models for conflict resolution for autonomous ships in complex situations","authors":"Azzeddine Bakdi, Erik Vanem","doi":"10.1115/1.4066198","DOIUrl":"https://doi.org/10.1115/1.4066198","url":null,"abstract":"\u0000 Maritime Autonomous Surface Ships (MASSs) will reshape the fast-evolving ecosystem for their attractive socio-economic benefits and potential to improve safety. However, their new systems and technology need thorough verification to identify unintended components of risk. The interaction between MASS cyber-physical systems and the existing regulatory framework is currently unpredictable; AI-powered intelligent situation awareness and autonomous navigation algorithms must safely and efficiently adhere to the regulations which are only designed for human interpretation without MASSs consideration. This paper contributes to algorithmic regulations and particularly algorithmic COLREGs in real-world MASS applications. It focuses on codifying COLREGs into a machine-executable system applicable to MASSs, then analyzing their performance in dynamic and mixed interactions between multiple vessels in complex scenarios. Based on fullest pairwise COLREGs criteria, this paper considers Decision-Making (DM) and complexity analysis in multi-collision-conflict scenarios. Complexity influential factors are an interplay between the characteristics of COLREGs, traffic scenarios, MASS interactions, and the environment. Participant vessels are the decision-makers forming a decentralized uncertain DM process, casted into a multi-participant multi-conflict multi-criteria DM problem. This is tackled through the technique of graph models for conflict resolution, using risk graph models and fuzzy preferences over alternative collision-avoidance states. The presented work is validated on a database of historical scenarios extracted from multiple data sources.","PeriodicalId":509714,"journal":{"name":"Journal of Offshore Mechanics and Arctic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141924785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Veronica Jaramillo Jimenez, Z. H. Munim, Hyungju Kim, Prasad Perera
The maritime industry is urged to reduce greenhouse gas emissions and improve the energy efficiency of ships. A potential and relatively inexpensive solution is to implement data analytics as an aid to identify areas of improvement to optimize ship performance and fuel consumption. This study investigates barriers to data analytics for maritime organizations intending to utilize data as a means of operational enhancement. This study used the DELPHI – Best Worst Method (BWM) hybrid approach to identify and rank the barriers to data analytics for energy efficiency. The results revealed a total 20 sub-barriers grouped into five main barriers. These barriers fall into two overarching categories: Organizational barriers, including Cultural, Managerial, and Economic, and Technological barriers, comprising Data Management and Data Analysis. This study also highlights the most critical barriers within each category, revealing inadequate data governance, multiple suppliers needed to implement a comprehensive system and contracts and restrictive clauses as the dominant barriers that hamper the adoption of big data analytics in the maritime domain.
{"title":"Barriers to data analytics for energy efficiency in the maritime industry","authors":"Veronica Jaramillo Jimenez, Z. H. Munim, Hyungju Kim, Prasad Perera","doi":"10.1115/1.4066199","DOIUrl":"https://doi.org/10.1115/1.4066199","url":null,"abstract":"\u0000 The maritime industry is urged to reduce greenhouse gas emissions and improve the energy efficiency of ships. A potential and relatively inexpensive solution is to implement data analytics as an aid to identify areas of improvement to optimize ship performance and fuel consumption. This study investigates barriers to data analytics for maritime organizations intending to utilize data as a means of operational enhancement. This study used the DELPHI – Best Worst Method (BWM) hybrid approach to identify and rank the barriers to data analytics for energy efficiency. The results revealed a total 20 sub-barriers grouped into five main barriers. These barriers fall into two overarching categories: Organizational barriers, including Cultural, Managerial, and Economic, and Technological barriers, comprising Data Management and Data Analysis. This study also highlights the most critical barriers within each category, revealing inadequate data governance, multiple suppliers needed to implement a comprehensive system and contracts and restrictive clauses as the dominant barriers that hamper the adoption of big data analytics in the maritime domain.","PeriodicalId":509714,"journal":{"name":"Journal of Offshore Mechanics and Arctic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141922019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Henry Schwartz, Tomi Solakivi, Jonas Spohr, Magnus Gustafsson
The shipping sector must reduce its greenhouse gas emissions. Consumers demand green travel and transport services, but shipping is still to a large extent powered by fossil fuels. One of the key factors affecting the released emissions onboard the ship is the type of fuel utilized. Technological development is making new kinds of engines running with carbon-neutral fuels available. However, the new build and retrofit projects encountering the green transition have to be planned in detail. We analyze future cash flows of ropax ships operating with different types of fuel systems on an identical route. We base our findings on calculated asset values and internal rates of return. Based on our analysis of the given scenarios, the transition to carbon-neutral fuels comes with a cost for the shipping companies. In the forthcoming shipping competition, ships using fossil fuels will be more likely than their less polluting counterparts to be categorized as stranded assets.
{"title":"CAPITAL DESTRUCTION – WHAT IS THE COST OF CARBON-NEUTRALITY IN SHIPPING COMPETITION?","authors":"Henry Schwartz, Tomi Solakivi, Jonas Spohr, Magnus Gustafsson","doi":"10.1115/1.4066065","DOIUrl":"https://doi.org/10.1115/1.4066065","url":null,"abstract":"\u0000 The shipping sector must reduce its greenhouse gas emissions. Consumers demand green travel and transport services, but shipping is still to a large extent powered by fossil fuels. One of the key factors affecting the released emissions onboard the ship is the type of fuel utilized. Technological development is making new kinds of engines running with carbon-neutral fuels available. However, the new build and retrofit projects encountering the green transition have to be planned in detail. We analyze future cash flows of ropax ships operating with different types of fuel systems on an identical route. We base our findings on calculated asset values and internal rates of return. Based on our analysis of the given scenarios, the transition to carbon-neutral fuels comes with a cost for the shipping companies. In the forthcoming shipping competition, ships using fossil fuels will be more likely than their less polluting counterparts to be categorized as stranded assets.","PeriodicalId":509714,"journal":{"name":"Journal of Offshore Mechanics and Arctic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, we aim to evaluate the technical and economic requirements of electrofuel production through Fischer-Tropsch synthesis using green hydrogen and CO2. We find that a plant with a 1 000-barrel per day capacity necessitates 60 tH2 and 424 tCO2 daily, achieving a 36% hydrogen-to-electrofuel conversion rate. The initial capital expenditure (CAPEX) is projected at 295 million USD, with an additional 139 million USD for electrolyzer replacement. The hydrogen production unit accounts for 61% of the CAPEX, followed by the direct air capture unit at 22%. The annual operating expense (OPEX) is estimated at 49.3 million USD, with hydrogen production and direct air capture constituting 58% and 25% of this expense, respectively, largely due to substantial electricity requirements. The synthesis and refining units comprise 17% of the OPEX. The levelized cost of electrofuel is calculated at 1881 USD/t, which is 2.5 times the cost of marine gas oil and 3.8 times that of very low sulfur fuel oil. However, electrofuels emission factor is found to be significantly lower, nearly 14 times less carbon-intensive than traditional fuels. We conclude that electrofuels cost competitiveness with fossil fuels depends on favorable conditions such as low electricity costs, low discount rates, and high carbon prices. While the EU's ETS inclusion for maritime transport will lessen the cost disparity, it is unlikely to make electrofuels cost-competitive given the current carbon prices.
{"title":"Techno-economic analysis of electrofuel as a shipping fuel","authors":"F. Tassew, Hendrik Brinks, H. A. Tvete","doi":"10.1115/1.4066064","DOIUrl":"https://doi.org/10.1115/1.4066064","url":null,"abstract":"\u0000 In this study, we aim to evaluate the technical and economic requirements of electrofuel production through Fischer-Tropsch synthesis using green hydrogen and CO2. We find that a plant with a 1 000-barrel per day capacity necessitates 60 tH2 and 424 tCO2 daily, achieving a 36% hydrogen-to-electrofuel conversion rate. The initial capital expenditure (CAPEX) is projected at 295 million USD, with an additional 139 million USD for electrolyzer replacement. The hydrogen production unit accounts for 61% of the CAPEX, followed by the direct air capture unit at 22%. The annual operating expense (OPEX) is estimated at 49.3 million USD, with hydrogen production and direct air capture constituting 58% and 25% of this expense, respectively, largely due to substantial electricity requirements. The synthesis and refining units comprise 17% of the OPEX. The levelized cost of electrofuel is calculated at 1881 USD/t, which is 2.5 times the cost of marine gas oil and 3.8 times that of very low sulfur fuel oil. However, electrofuels emission factor is found to be significantly lower, nearly 14 times less carbon-intensive than traditional fuels. We conclude that electrofuels cost competitiveness with fossil fuels depends on favorable conditions such as low electricity costs, low discount rates, and high carbon prices. While the EU's ETS inclusion for maritime transport will lessen the cost disparity, it is unlikely to make electrofuels cost-competitive given the current carbon prices.","PeriodicalId":509714,"journal":{"name":"Journal of Offshore Mechanics and Arctic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141801732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Global whipping responses contribute to a significant increase in Vertical Bending Moments (VBM), making their accurate prediction crucial for ship safety. In this study, a Long Short-Term Memory (LSTM) based encoder-decoder model is established to predict the whipping responses under varying sea states. The model is trained on a comprehensive dataset, which includes motion data and VBM history of a cruise ship under various sea conditions. This dataset is established via numerical simulation, ensuring a wide range of scenarios for the model to learn from. The efficacy of the LSTM encoder-decoder model in capturing global whipping responses is initially verified under a single sea condition case. This step confirms the model's ability to accurately predict vertical bending moments under known conditions. Subsequently, the model’s performance under unprecedented sea conditions is examined. Given that the distribution of training data significantly influences the model's performance and the data from diverse sea conditions typically exhibit distinct data distribution, a mixed data training strategy is employed during the training process in this scenario. The results indicate that the LSTM encoder-decoder model effectively captures whipping responses. Furthermore, the mixed data training strategy significantly improves the model's prediction accuracy for global whipping responses under unprecedented sea conditions.
{"title":"PREDICTION FOR GLOBAL WHIPPING RESPONSES OF A LARGE CRUISE SHIP UNDER UNPRECEDENTED SEA CONDITIONS USING AN LSTM BASED ENCODER-DECODER MODEL","authors":"Ruixiang Liu, Hui Li, M. Ong, Jian Zou","doi":"10.1115/1.4066063","DOIUrl":"https://doi.org/10.1115/1.4066063","url":null,"abstract":"\u0000 Global whipping responses contribute to a significant increase in Vertical Bending Moments (VBM), making their accurate prediction crucial for ship safety. In this study, a Long Short-Term Memory (LSTM) based encoder-decoder model is established to predict the whipping responses under varying sea states. The model is trained on a comprehensive dataset, which includes motion data and VBM history of a cruise ship under various sea conditions. This dataset is established via numerical simulation, ensuring a wide range of scenarios for the model to learn from. The efficacy of the LSTM encoder-decoder model in capturing global whipping responses is initially verified under a single sea condition case. This step confirms the model's ability to accurately predict vertical bending moments under known conditions. Subsequently, the model’s performance under unprecedented sea conditions is examined. Given that the distribution of training data significantly influences the model's performance and the data from diverse sea conditions typically exhibit distinct data distribution, a mixed data training strategy is employed during the training process in this scenario. The results indicate that the LSTM encoder-decoder model effectively captures whipping responses. Furthermore, the mixed data training strategy significantly improves the model's prediction accuracy for global whipping responses under unprecedented sea conditions.","PeriodicalId":509714,"journal":{"name":"Journal of Offshore Mechanics and Arctic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The characteristics of flow-induced oscillation (FIO) of cylinders in tandem change due to interference between cylinders. Identifying positive and negative interferences is vital for designing marine structures involving cylinders on elastic supports. Experiments on a single cylinder, two tandem-cylinder and three tandem-cylinder cases were conducted to study the interference due to placing an identical oscillating cylinder upstream, downstream, and both upstream and downstream. Critical parameters (damping, stiffness) and Reynolds number (Re) are varied in the tests. Both vortex-induced vibration (VIV) and galloping in these cases are identified and analyzed. The onset inflow velocity of oscillation for the cylinder is reduced and back-to-back VIV and galloping is achieved, when placing interference cylinder/s in the vicinity of the basic oscillator. The interference due to the downstream cylinder enhances FIO of the basic oscillator. In the VIV region, one interference downstream cylinder is more effective than two interference downstream cylinders. In the galloping region, the enhancement improves for two interference cylinders for lower stiffness and damping ratios. The interference upstream cylinder tends to suppress FIO of the basic oscillator for higher stiffness. The suppression due to one upstream cylinder is more vigorous than that due to two upstream cylinders in the VIV region and the opposite is true in the galloping region. When two interference cylinders are placed both downstream and upstream of the basic oscillator, its FIO is enhanced for the lower stiffness whilst it is suppressed for the higher stiffness.
{"title":"Interference effects on flow-induced oscillation of three tandem cylinders with passive turbulence control","authors":"Huaijun Li, M. Bernitsas, Hai Sun","doi":"10.1115/1.4065972","DOIUrl":"https://doi.org/10.1115/1.4065972","url":null,"abstract":"\u0000 The characteristics of flow-induced oscillation (FIO) of cylinders in tandem change due to interference between cylinders. Identifying positive and negative interferences is vital for designing marine structures involving cylinders on elastic supports. Experiments on a single cylinder, two tandem-cylinder and three tandem-cylinder cases were conducted to study the interference due to placing an identical oscillating cylinder upstream, downstream, and both upstream and downstream. Critical parameters (damping, stiffness) and Reynolds number (Re) are varied in the tests. Both vortex-induced vibration (VIV) and galloping in these cases are identified and analyzed. The onset inflow velocity of oscillation for the cylinder is reduced and back-to-back VIV and galloping is achieved, when placing interference cylinder/s in the vicinity of the basic oscillator. The interference due to the downstream cylinder enhances FIO of the basic oscillator. In the VIV region, one interference downstream cylinder is more effective than two interference downstream cylinders. In the galloping region, the enhancement improves for two interference cylinders for lower stiffness and damping ratios. The interference upstream cylinder tends to suppress FIO of the basic oscillator for higher stiffness. The suppression due to one upstream cylinder is more vigorous than that due to two upstream cylinders in the VIV region and the opposite is true in the galloping region. When two interference cylinders are placed both downstream and upstream of the basic oscillator, its FIO is enhanced for the lower stiffness whilst it is suppressed for the higher stiffness.","PeriodicalId":509714,"journal":{"name":"Journal of Offshore Mechanics and Arctic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141644175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Grindheim, Morten Stakkeland, Ingrid Glad, Erik Vanem
This paper tests two data-driven approaches for predicting the State of Health (SoH) of Lithium-ion-batteries (LIBs) for the purpose of monitoring maritime battery systems. First, nonsequential approaches are investigated and various models are tested: Ridge, Lasso, Support vector regression, and Gradient boosted trees. Binning is proposed for feature engineering for these types of models to capture the temporal structure in the data. Such binning creates histograms for the accumulated time the LIB has been within various voltage, temperature, and current ranges. Further binning to combine these histograms into 2D or 3D histograms is explored in order to capture relationships between voltage, temperature and current. Secondly, a sequential approach is explored where different deep learning architectures are tried out: long short-term memory (LSTM), Transformer, and Temporal convolutional network (TCN). Finally, the various models and the two approaches are compared in terms of their SoH prediction ability. Results indicate that the binning with ridge regression models performed best. The same publicly available sensor data from laboratory cycling tests are used for both approaches.
{"title":"COMPARATIVE STUDY OF MACHINE LEARNING METHODS FOR STATE OF HEALTH ESTIMATION OF MARITIME BATTERY SYSTEMS","authors":"C. Grindheim, Morten Stakkeland, Ingrid Glad, Erik Vanem","doi":"10.1115/1.4065967","DOIUrl":"https://doi.org/10.1115/1.4065967","url":null,"abstract":"\u0000 This paper tests two data-driven approaches for predicting the State of Health (SoH) of Lithium-ion-batteries (LIBs) for the purpose of monitoring maritime battery systems. First, nonsequential approaches are investigated and various models are tested: Ridge, Lasso, Support vector regression, and Gradient boosted trees. Binning is proposed for feature engineering for these types of models to capture the temporal structure in the data. Such binning creates histograms for the accumulated time the LIB has been within various voltage, temperature, and current ranges. Further binning to combine these histograms into 2D or 3D histograms is explored in order to capture relationships between voltage, temperature and current. Secondly, a sequential approach is explored where different deep learning architectures are tried out: long short-term memory (LSTM), Transformer, and Temporal convolutional network (TCN). Finally, the various models and the two approaches are compared in terms of their SoH prediction ability. Results indicate that the binning with ridge regression models performed best. The same publicly available sensor data from laboratory cycling tests are used for both approaches.","PeriodicalId":509714,"journal":{"name":"Journal of Offshore Mechanics and Arctic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jialun Chen, David Gunawan, Paul Taylor, Yunzhuo Chen, Ian A. Milne, Wenhua Zhao
Phase-resolved wave prediction capability, even if only over two wave periods in advance, is of value for optimal control of wave energy converters (WECs), resulting in a dramatic increase in power generation efficiency. Previous studies on wave-by-wave predictions have shown that an Artificial Neural Network (ANN) model can outperform the traditional linear wave theory-based model in terms of both prediction accuracy and prediction horizon when using synthetic wave data. However, the prediction performance of ANN models is significantly reduced by the varying wave conditions and buoy positions that occur in the field. To overcome these limitations, a novel wave prediction method is developed based on the neural network with an attention mechanism. This study validates the new model using wave data measured at sea. The model utilizes past time histories of three Sofar Spotter wave buoys at upwave locations to predict the vertical motion of a Datawell Waverider-4 at a downwave location. The results show that the attention-based neural network model is capable of capturing the slow variation in the displacement of the buoys, which reduces the prediction error compared to a standard ANN and Long Short-Term Memory (LSTM) model.
{"title":"An attention-based deep learning model for phase-resolved wave prediction","authors":"Jialun Chen, David Gunawan, Paul Taylor, Yunzhuo Chen, Ian A. Milne, Wenhua Zhao","doi":"10.1115/1.4065969","DOIUrl":"https://doi.org/10.1115/1.4065969","url":null,"abstract":"\u0000 Phase-resolved wave prediction capability, even if only over two wave periods in advance, is of value for optimal control of wave energy converters (WECs), resulting in a dramatic increase in power generation efficiency. Previous studies on wave-by-wave predictions have shown that an Artificial Neural Network (ANN) model can outperform the traditional linear wave theory-based model in terms of both prediction accuracy and prediction horizon when using synthetic wave data. However, the prediction performance of ANN models is significantly reduced by the varying wave conditions and buoy positions that occur in the field. To overcome these limitations, a novel wave prediction method is developed based on the neural network with an attention mechanism. This study validates the new model using wave data measured at sea. The model utilizes past time histories of three Sofar Spotter wave buoys at upwave locations to predict the vertical motion of a Datawell Waverider-4 at a downwave location. The results show that the attention-based neural network model is capable of capturing the slow variation in the displacement of the buoys, which reduces the prediction error compared to a standard ANN and Long Short-Term Memory (LSTM) model.","PeriodicalId":509714,"journal":{"name":"Journal of Offshore Mechanics and Arctic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141642833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ed Mackay, Callum Murphy-Barltrop, Philip Jonathan
This paper presents the application of a new multivariate extreme value model for the estimation of metocean variables. The model requires fewer assumptions about the forms of the margins and dependence structure compared to existing approaches, and provides a flexible and rigorous framework for modelling multivariate extremes. The method involves a transformation of variables to polar coordinates. The tail of the radial variable is then modelled using the generalised Pareto distribution, with parameters conditional on angle, providing a natural extension of univariate theory to multivariate problems. The resulting model is referred to as the semi-parametric angular-radial (SPAR) model. We consider the estimation of the joint distributions of (1) wave height and wave period, and (2) wave height and wind speed. We show that the SPAR model provides a good fit to the observations in terms of both the marginal distributions and dependence structures. The use of the SPAR model for estimating long-term extreme responses of offshore structures is discussed, using some simple response functions for floating structures and an offshore wind turbine with monopile foundation. We show that the SPAR model is able to accurately reproduce response distributions, and provides a realistic quantification of uncertainty.
{"title":"THE SPAR MODEL: A NEW PARADIGM FOR MULTIVARIATE EXTREMES. APPLICATION TO JOINT DISTRIBUTIONS OF METOCEAN VARIABLES","authors":"Ed Mackay, Callum Murphy-Barltrop, Philip Jonathan","doi":"10.1115/1.4065968","DOIUrl":"https://doi.org/10.1115/1.4065968","url":null,"abstract":"\u0000 This paper presents the application of a new multivariate extreme value model for the estimation of metocean variables. The model requires fewer assumptions about the forms of the margins and dependence structure compared to existing approaches, and provides a flexible and rigorous framework for modelling multivariate extremes. The method involves a transformation of variables to polar coordinates. The tail of the radial variable is then modelled using the generalised Pareto distribution, with parameters conditional on angle, providing a natural extension of univariate theory to multivariate problems. The resulting model is referred to as the semi-parametric angular-radial (SPAR) model. We consider the estimation of the joint distributions of (1) wave height and wave period, and (2) wave height and wind speed. We show that the SPAR model provides a good fit to the observations in terms of both the marginal distributions and dependence structures. The use of the SPAR model for estimating long-term extreme responses of offshore structures is discussed, using some simple response functions for floating structures and an offshore wind turbine with monopile foundation. We show that the SPAR model is able to accurately reproduce response distributions, and provides a realistic quantification of uncertainty.","PeriodicalId":509714,"journal":{"name":"Journal of Offshore Mechanics and Arctic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sotaro Masanobu, Satoru Takano, Marcio Yamamoto, Hyun Jin Park, Y. Tasaka, Y. Murai
The pressure loss due to the hydraulic transport of large solid particles should be predicted for the design of subsea mining systems. The mixture flow in a flexible jumper is expected to be unsteady during lifting operations in an actual mining system. The authors develop a one-dimensional mathematical model that predicts such pressure loss under pulsating mixture flows in a static inclined pipe assuming that the flow in the jumper is fully developed. An experiment is performed on the hydraulic transport of solid particles to obtain data for model validation. In this experiment, several kinds of solid particles are used: alumina beads, glass beads, and gravel. The experimental parameters are mixture velocity, solid concentration, pulsation period and amplitude of water velocity, and pipe inclination angle. The proposed model is validated through a comparison with experimental data. Furthermore, we calculate the pressure loss due to the hydraulic transport of polymetallic sulfide ores using the proposed model. The calculation results show that the time-averaged pressure loss drastically varies with the pipe inclination angle, reaching its maximum value between the pipe inclination angles of 30° and 60°, at which the flow is inclined upward. The results also show that the amplitude of pressure loss pulsation differs little between pipe inclination angles and that the pulsation component of pressure loss should be considered in designing lifting systems.
{"title":"Hydraulic Transport of Large Solid Particles in Inclined Pipes under Pulsating Flow Conditions","authors":"Sotaro Masanobu, Satoru Takano, Marcio Yamamoto, Hyun Jin Park, Y. Tasaka, Y. Murai","doi":"10.1115/1.4065970","DOIUrl":"https://doi.org/10.1115/1.4065970","url":null,"abstract":"\u0000 The pressure loss due to the hydraulic transport of large solid particles should be predicted for the design of subsea mining systems. The mixture flow in a flexible jumper is expected to be unsteady during lifting operations in an actual mining system. The authors develop a one-dimensional mathematical model that predicts such pressure loss under pulsating mixture flows in a static inclined pipe assuming that the flow in the jumper is fully developed. An experiment is performed on the hydraulic transport of solid particles to obtain data for model validation. In this experiment, several kinds of solid particles are used: alumina beads, glass beads, and gravel. The experimental parameters are mixture velocity, solid concentration, pulsation period and amplitude of water velocity, and pipe inclination angle. The proposed model is validated through a comparison with experimental data. Furthermore, we calculate the pressure loss due to the hydraulic transport of polymetallic sulfide ores using the proposed model. The calculation results show that the time-averaged pressure loss drastically varies with the pipe inclination angle, reaching its maximum value between the pipe inclination angles of 30° and 60°, at which the flow is inclined upward. The results also show that the amplitude of pressure loss pulsation differs little between pipe inclination angles and that the pulsation component of pressure loss should be considered in designing lifting systems.","PeriodicalId":509714,"journal":{"name":"Journal of Offshore Mechanics and Arctic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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