Mooring Integrity Management (MIM) is a key operational aspect for FPSO operators, mooring system designers and Recognized Classification Societies (RCS). MIM programs should include the capability to detect a mooring line failure. When direct or indirect measurement of line tension is available continuously and reliably over time, this requirement can be readily fulfilled. Current industry-wide experience shows it has been challenging to develop robust cost-effective real-time anchor line load monitoring systems for the offshore environment. To remedy this situation, Artificial Intelligence (AI) techniques are used to extract an intact or damage diagnosis for a spread moored unit offshore Brazil based on data readily available in situ. The Intelligent Agent (IA) developed has been reviewed by the Classification Society against Ref. [1] and [2]. Following this review a statement of maturity has been issued Ref. [3] qualifying the new technology.
{"title":"Mooring Line Failure Detection in the Absence of Load Monitoring","authors":"M. Naciri, M. Viola, Z. Wang, R. Yam","doi":"10.1115/omae2022-79591","DOIUrl":"https://doi.org/10.1115/omae2022-79591","url":null,"abstract":"\u0000 Mooring Integrity Management (MIM) is a key operational aspect for FPSO operators, mooring system designers and Recognized Classification Societies (RCS). MIM programs should include the capability to detect a mooring line failure. When direct or indirect measurement of line tension is available continuously and reliably over time, this requirement can be readily fulfilled. Current industry-wide experience shows it has been challenging to develop robust cost-effective real-time anchor line load monitoring systems for the offshore environment. To remedy this situation, Artificial Intelligence (AI) techniques are used to extract an intact or damage diagnosis for a spread moored unit offshore Brazil based on data readily available in situ. The Intelligent Agent (IA) developed has been reviewed by the Classification Society against Ref. [1] and [2]. Following this review a statement of maturity has been issued Ref. [3] qualifying the new technology.","PeriodicalId":23502,"journal":{"name":"Volume 1: Offshore Technology","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89338259","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}
Jonathan Fernández, A. Arredondo, Beatriz Albisu, Aintzane Expósito, E. Rodríguez, J. Arana
Corrosion-fatigue is the main damage mechanism of chains in permanent mooring systems. Fatigue loading is unavoidable, but corrosion can be mitigated or prevented; so, the impact of its reduction in the corrosion-fatigue damage mechanism is of great interest. TSA (Thermal Spray Aluminium) coating has been applied for corrosion mitigation in mooring chains, typically at the splash zone, where higher corrosion rates are expected compared to those in submerged condition. TSA coating was first applied in mooring chains in 2001 at the Gulf of Mexico. This early experience of TSA demonstrated its effectiveness in preventing corrosion of top chains and led to several projects applying TSA in the same location. However, TSA’s effect on the fatigue performance of mooring chains had not been investigated so far. The paper presents full scale fatigue test results of TSA coated mooring chains. Six fatigue tests have been carried out on 76 mm chain, which results show increased fatigue endurance relative to comparable fatigue test data of freely corroding chains in seawater. The results are statistically analysed and compared with all available test data of uncoated chains from Joint Industry Projects, in order to assess the effect of the coating in the fatigue strength of mooring chains.
{"title":"Fatigue Performance of Thermal Spray Aluminium Coated Mooring Chains","authors":"Jonathan Fernández, A. Arredondo, Beatriz Albisu, Aintzane Expósito, E. Rodríguez, J. Arana","doi":"10.1115/omae2022-80919","DOIUrl":"https://doi.org/10.1115/omae2022-80919","url":null,"abstract":"\u0000 Corrosion-fatigue is the main damage mechanism of chains in permanent mooring systems. Fatigue loading is unavoidable, but corrosion can be mitigated or prevented; so, the impact of its reduction in the corrosion-fatigue damage mechanism is of great interest.\u0000 TSA (Thermal Spray Aluminium) coating has been applied for corrosion mitigation in mooring chains, typically at the splash zone, where higher corrosion rates are expected compared to those in submerged condition. TSA coating was first applied in mooring chains in 2001 at the Gulf of Mexico. This early experience of TSA demonstrated its effectiveness in preventing corrosion of top chains and led to several projects applying TSA in the same location.\u0000 However, TSA’s effect on the fatigue performance of mooring chains had not been investigated so far. The paper presents full scale fatigue test results of TSA coated mooring chains. Six fatigue tests have been carried out on 76 mm chain, which results show increased fatigue endurance relative to comparable fatigue test data of freely corroding chains in seawater. The results are statistically analysed and compared with all available test data of uncoated chains from Joint Industry Projects, in order to assess the effect of the coating in the fatigue strength of mooring chains.","PeriodicalId":23502,"journal":{"name":"Volume 1: Offshore Technology","volume":"155 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80628058","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}
Jonathan Bailey, R. Bamford, Suvabrata Das, Soma S. Maroju, R. J. Barker
A Digital Twin has been developed for the Glen Lyon FPSO to maintain vessel integrity and ensure operation within the allowable design limits. At the core of this Digital Twin are two components: the Integrated Marine Monitoring System (IMMS) installed on the FPSO, and BMT DEEP, a cloud-based platform that stores, manages, integrates, post-processes and displays the vast data sets collected by the IMMS as well as other data sources. This paper focuses on harnessing the benefits of Digital Twin Technology, by bringing in data from all sources and enabling to synthesize and monitor the FPSO in near real-time from any remote location. This Digital Twin is designed to allow rapid query of the data by filtering with any time window in terms of hour, day, month, quarter, and year of data collection for the life of the asset. Several sensors feed data to the Glen Lyon IMMS. The sensors include FPSO motion, stress response monitoring, and metocean monitoring. In addition to the FPSO based measurements, metocean data is also available from Met Office weather buoy K7, and wind measurements from the nearby Clair platform. A composite of the measured metocean parameters is generated from the quality control of the data. This quality-controlled data is visualized on DEEP as a time series, as well as comparisons with the basis of design data for the facility in terms of several statistical charts that form the metocean and structural dashboards. Some key insights and findings from these comparisons are presented.
{"title":"Generating a Digital Twin of the Glen Lyon FPSO","authors":"Jonathan Bailey, R. Bamford, Suvabrata Das, Soma S. Maroju, R. J. Barker","doi":"10.1115/omae2022-80547","DOIUrl":"https://doi.org/10.1115/omae2022-80547","url":null,"abstract":"\u0000 A Digital Twin has been developed for the Glen Lyon FPSO to maintain vessel integrity and ensure operation within the allowable design limits. At the core of this Digital Twin are two components: the Integrated Marine Monitoring System (IMMS) installed on the FPSO, and BMT DEEP, a cloud-based platform that stores, manages, integrates, post-processes and displays the vast data sets collected by the IMMS as well as other data sources. This paper focuses on harnessing the benefits of Digital Twin Technology, by bringing in data from all sources and enabling to synthesize and monitor the FPSO in near real-time from any remote location. This Digital Twin is designed to allow rapid query of the data by filtering with any time window in terms of hour, day, month, quarter, and year of data collection for the life of the asset.\u0000 Several sensors feed data to the Glen Lyon IMMS. The sensors include FPSO motion, stress response monitoring, and metocean monitoring. In addition to the FPSO based measurements, metocean data is also available from Met Office weather buoy K7, and wind measurements from the nearby Clair platform. A composite of the measured metocean parameters is generated from the quality control of the data. This quality-controlled data is visualized on DEEP as a time series, as well as comparisons with the basis of design data for the facility in terms of several statistical charts that form the metocean and structural dashboards. Some key insights and findings from these comparisons are presented.","PeriodicalId":23502,"journal":{"name":"Volume 1: Offshore Technology","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81117532","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}
Yining He, S. Hirabayashi, S. Tabeta, T. Nakajima, Yoshihiko Yamashita, Yuuki Yamashita, Motoko Imai
This paper presents an experimental study of a new floating platform, being supported by air-cushion modules. The platform consists of six hexahedron air cushion units in which their bottom is open to the water surface. A moonpool is placed in the middle of the platform. A 1:47 scale model was used for the measurement of heave, pitch and surge motions in regular wave conditions. To evaluate the effect of a hexahedron air cushion unit, that of barge-type model was tested. The results show that the motion behaviors of the hexagonal air-cushion-type platform are better than those of the barge-type platform in short wave conditions, while behaviors in long wavelength are almost the same. To evaluate the stability of the hexagonal air-cushion-type platform in windy conditions, a wind turbine of a circular disc-shape was installed on the platform. The thrust acting on the wind turbine and the wind velocity were measured simultaneously. Further, the inclination test of a three-blade wind turbine model was carried out. The results show that the tested hexagonal air-cushion-type platform is sufficiently stable for the practical use of wind turbines of a 20MW capacity. Similarly, platforms of larger size could be used for wind turbines larger than 20MW.
{"title":"Hydrodynamic Motion Behavior of Air-Cushion-Supported Hexagonal Floating Platform for Offshore Wind Turbine","authors":"Yining He, S. Hirabayashi, S. Tabeta, T. Nakajima, Yoshihiko Yamashita, Yuuki Yamashita, Motoko Imai","doi":"10.1115/omae2022-80564","DOIUrl":"https://doi.org/10.1115/omae2022-80564","url":null,"abstract":"\u0000 This paper presents an experimental study of a new floating platform, being supported by air-cushion modules. The platform consists of six hexahedron air cushion units in which their bottom is open to the water surface. A moonpool is placed in the middle of the platform. A 1:47 scale model was used for the measurement of heave, pitch and surge motions in regular wave conditions. To evaluate the effect of a hexahedron air cushion unit, that of barge-type model was tested. The results show that the motion behaviors of the hexagonal air-cushion-type platform are better than those of the barge-type platform in short wave conditions, while behaviors in long wavelength are almost the same.\u0000 To evaluate the stability of the hexagonal air-cushion-type platform in windy conditions, a wind turbine of a circular disc-shape was installed on the platform. The thrust acting on the wind turbine and the wind velocity were measured simultaneously. Further, the inclination test of a three-blade wind turbine model was carried out. The results show that the tested hexagonal air-cushion-type platform is sufficiently stable for the practical use of wind turbines of a 20MW capacity. Similarly, platforms of larger size could be used for wind turbines larger than 20MW.","PeriodicalId":23502,"journal":{"name":"Volume 1: Offshore Technology","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82671477","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}
Q. Delivré, Jery Rajaobelina, Mengchen Kang, J. McConochie, Y. Drobyshevski
Prelude Floating Liquefied Natural Gas (FLNG) facility is moored with an internal turret allowing it to free weathervane (FW), i.e. by leaving the unit to rotate according to environmental loads. During the engineering phase, the FLNG FW heading is estimated by the heading analysis (i.e. physics-based approach), and results are then used as input for other studies. Therefore, a good estimation of the various environmental effects (waves, current and wind) and their contributions in terms of loads on the FLNG is critical to ensure a correct prediction of the FW heading. For the predominant contributions (wind and current), the force coefficients have been initially derived from wind tunnel tests during the engineering phase. However, Prelude FLNG being now installed on-site, measurements over recent years have shown slight discrepancies with the numerical predictions by the heading analysis. Preliminary investigations were carried out and were aimed to improve some parameters of the numerical model. Nevertheless, it appeared that even with these improvements, discrepancies between numerical predictions and measurements were not always resolved. These discrepancies may have several origins, such as inadequacy of the numerical model, variability of the metocean data, uncertainties in measurements, etc. In order to overcome the aforementioned uncertainties and unknowns, it has been decided to set-up a machine learning model (i.e. data-based approach). This machine learning model (RBF ANN - Radial Basis Function Artificial Neural Network) was trained with the recorded metocean data (input) and measured FLNG FW heading (output). Considering the amount of the measured data available (two years with a time step of 10 minutes), the necessity to optimize the model’s hyperparameters and the computer capability, a stepwise approach has been applied to ensure an accurate model can be built in a reasonable timeframe. Finally, the machine learning model calculation shows a significant improvement in the prediction capability when compared to the measured FLNG FW heading. The resulting surrogate model is hence used to predict the FW heading and to derive the associated prediction intervals, which define the range of error with certain probability (for instance 95%). This paper describes the machine learning model used, the methodology and challenges of the approach, and discusses the results. The main conclusions and lessons learnt are also shared.
Prelude浮式液化天然气(FLNG)设施系泊在一个内部转塔上，允许它释放风向标(FW)，即让装置根据环境负载旋转。在工程阶段，通过航向分析(即基于物理的方法)估计FLNG FW航向，然后将结果作为其他研究的输入。因此，对各种环境影响(波浪、水流和风)及其对FLNG负载的贡献进行良好的估计对于确保正确预测FW航向至关重要。对于主要贡献(风和水流)，力系数最初是在工程阶段从风洞试验中得出的。然而，目前在现场安装的Prelude FLNG，近年来的测量结果显示与航向分析的数值预测略有差异。为了改进数值模型的一些参数，进行了初步的研究。然而，即使有了这些改进，数值预测和测量之间的差异似乎也并不总是得到解决。这些差异可能有几个原因，例如数值模式的不充分、海洋气象数据的变率、测量的不确定性等。为了克服上述的不确定性和未知数，我们决定建立一个机器学习模型(即基于数据的方法)。该机器学习模型(RBF ANN - Radial Basis Function Artificial Neural Network)以记录的海洋数据(输入)和测量的FLNG FW航向(输出)进行训练。考虑到可获得的测量数据量(两年，时间步长为10分钟)，优化模型超参数的必要性和计算机能力，采用逐步方法确保在合理的时间范围内建立准确的模型。最后，与实测的FLNG FW航向相比，机器学习模型计算的预测能力有了显著提高。由此产生的代理模型用于预测FW航向并推导相关的预测区间，该预测区间定义了具有一定概率的误差范围(例如95%)。本文描述了所使用的机器学习模型、方法和挑战，并讨论了结果。还分享了主要结论和吸取的教训。
{"title":"Prelude FLNG Free Weathervaning Heading Prediction and Uncertainties, Based on Machine Learning Model","authors":"Q. Delivré, Jery Rajaobelina, Mengchen Kang, J. McConochie, Y. Drobyshevski","doi":"10.1115/omae2022-79924","DOIUrl":"https://doi.org/10.1115/omae2022-79924","url":null,"abstract":"\u0000 Prelude Floating Liquefied Natural Gas (FLNG) facility is moored with an internal turret allowing it to free weathervane (FW), i.e. by leaving the unit to rotate according to environmental loads. During the engineering phase, the FLNG FW heading is estimated by the heading analysis (i.e. physics-based approach), and results are then used as input for other studies.\u0000 Therefore, a good estimation of the various environmental effects (waves, current and wind) and their contributions in terms of loads on the FLNG is critical to ensure a correct prediction of the FW heading. For the predominant contributions (wind and current), the force coefficients have been initially derived from wind tunnel tests during the engineering phase. However, Prelude FLNG being now installed on-site, measurements over recent years have shown slight discrepancies with the numerical predictions by the heading analysis.\u0000 Preliminary investigations were carried out and were aimed to improve some parameters of the numerical model. Nevertheless, it appeared that even with these improvements, discrepancies between numerical predictions and measurements were not always resolved. These discrepancies may have several origins, such as inadequacy of the numerical model, variability of the metocean data, uncertainties in measurements, etc.\u0000 In order to overcome the aforementioned uncertainties and unknowns, it has been decided to set-up a machine learning model (i.e. data-based approach). This machine learning model (RBF ANN - Radial Basis Function Artificial Neural Network) was trained with the recorded metocean data (input) and measured FLNG FW heading (output). Considering the amount of the measured data available (two years with a time step of 10 minutes), the necessity to optimize the model’s hyperparameters and the computer capability, a stepwise approach has been applied to ensure an accurate model can be built in a reasonable timeframe.\u0000 Finally, the machine learning model calculation shows a significant improvement in the prediction capability when compared to the measured FLNG FW heading. The resulting surrogate model is hence used to predict the FW heading and to derive the associated prediction intervals, which define the range of error with certain probability (for instance 95%). This paper describes the machine learning model used, the methodology and challenges of the approach, and discusses the results. The main conclusions and lessons learnt are also shared.","PeriodicalId":23502,"journal":{"name":"Volume 1: Offshore Technology","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74148625","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}
There is a growing interest in the applications of real-time wave forecasting (RTWF), which consists in predicting physical quantities directly related to waves, such as the free-surface elevation, wave loads, or the motion of a ship, from a few seconds to several minutes in advance, and using measurements updated in real time. Unlike comparable RTWF methods found in the literature, which are based on the solution of the physical wave propagation equations, the present approach, known as SBP (Spectrum-Based Predictor), adopts a rigorous probabilistic view on the wave prediction problem, based on well-established, standard oceanographic assumptions. This paper presents an application of the SBP method to real wave field data coming from a stereoscopic camera system. To the best of the authors’ knowledge, this is the first time stereo wave data are employed to test RTWF algorithms. The data, recorded at a location in Korea, in the Yellow Sea, present some considerable challenges, such as strong current in excess of 1 m/s, steep waves with substantial non-linear components, and large directional spread in the high-frequency range. With some adjustments to the original SBP approach to account for current, several prediction configurations are tested, showing excellent agreement between the experimental prediction performance curves, and those expected from the SBP theory. With an observation range in the order of 100m, and in the wave conditions studied, reasonably accurate predictions can be achieved up to 20s ahead (approximately 3.5 peak wave periods).
{"title":"A Stochastic Approach to Short-Term Ocean Wave Forecasting: Preliminary Results Using Data From a Remote Sensing Imaging System","authors":"Alexis Mérigaud, P. Tona","doi":"10.1115/omae2022-81067","DOIUrl":"https://doi.org/10.1115/omae2022-81067","url":null,"abstract":"\u0000 There is a growing interest in the applications of real-time wave forecasting (RTWF), which consists in predicting physical quantities directly related to waves, such as the free-surface elevation, wave loads, or the motion of a ship, from a few seconds to several minutes in advance, and using measurements updated in real time. Unlike comparable RTWF methods found in the literature, which are based on the solution of the physical wave propagation equations, the present approach, known as SBP (Spectrum-Based Predictor), adopts a rigorous probabilistic view on the wave prediction problem, based on well-established, standard oceanographic assumptions. This paper presents an application of the SBP method to real wave field data coming from a stereoscopic camera system. To the best of the authors’ knowledge, this is the first time stereo wave data are employed to test RTWF algorithms. The data, recorded at a location in Korea, in the Yellow Sea, present some considerable challenges, such as strong current in excess of 1 m/s, steep waves with substantial non-linear components, and large directional spread in the high-frequency range. With some adjustments to the original SBP approach to account for current, several prediction configurations are tested, showing excellent agreement between the experimental prediction performance curves, and those expected from the SBP theory. With an observation range in the order of 100m, and in the wave conditions studied, reasonably accurate predictions can be achieved up to 20s ahead (approximately 3.5 peak wave periods).","PeriodicalId":23502,"journal":{"name":"Volume 1: Offshore Technology","volume":"101 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89611285","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}
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.
{"title":"Slamming Induced Fatigue in a Moonpool With a Recess","authors":"D. Chalkias, Z. Sulaiman","doi":"10.1115/omae2022-78395","DOIUrl":"https://doi.org/10.1115/omae2022-78395","url":null,"abstract":"\u0000 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.\u0000 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.\u0000 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.","PeriodicalId":23502,"journal":{"name":"Volume 1: Offshore Technology","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77595561","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}
S. S. Virdi, Yong Thiang Ng, Yisi Liu, Kelvin Tan, Daniel Zhang
Situation Awareness (SA) is the perception of the current situation, comprehension of its meaning, and projection of what is going to happen in the near future. It is crucial for navigators to possess high SA in a navigational Bridge to mitigate the risk of human errors and to improve navigational safety. However, the current methodology to assess SA mainly rely on human experts, which might bring in potential problems such as bias, work overload, and it is also hard for the human experts to capture every fine detail of the behaviour of the seafarers being assessed. To overcome these, an objective and automated way to assess Situation Awareness is needed. In this work, eye-tracking data is used for the assessment of SA. With the eye tracking device, it is possible to localize where the navigator is looking at, and by applying computer vision with deep learning algorithm, the ongoing activity being executed by the navigator could be identified. In total 7 activities (using RADAR, ECDIS, checking of ship’s heading, and speed, checking data on Echo Sounder, and data related to ships maneuvering, and others) can be recognized which are used as indicators of SA. A set of training data was recorded using Tobii Pro Glasses 3 to train the deep learning algorithm and test the classification accuracy. To further verify the proposed eye-tracking based assessment, a preliminary experiment has been designed and carried out. Five subjects were recruited for data collection. A full-mission Advanced Navigation Research Simulator (ANRS) was used to provide scenarios for both training data collection and preliminary experiment. From the initial results, it shows that a recognition accuracy of > 99% can be achieved, which gives positive support to the eye-tracking based recognition. The analytics results using data from preliminary experiment also show great potential in using eye-tracking to assess SA of navigators. The proposed assessment could be used in both simulator and on-board and for multiple purposes such as performance evaluation, promotion to the next rank, and Continuing Professional Development.
情境感知(SA)是对当前情境的感知，对其含义的理解，以及对近期将要发生的事情的预测。在航行桥梁中，具有较高的安全系数对于降低人为失误的风险，提高航行安全性至关重要。然而，目前评估SA的方法主要依赖于人类专家，这可能会带来偏见、工作过载等潜在问题，而且人类专家也很难捕捉到被评估海员行为的每一个细节。为了克服这些问题，需要一种客观和自动化的方法来评估态势感知。在这项工作中，眼动追踪数据被用于SA的评估。使用眼动追踪设备，可以定位导航员正在看的地方，并且通过应用具有深度学习算法的计算机视觉，可以识别导航员正在执行的活动。总共可以识别出7项活动(使用RADAR、ECDIS、检查船舶航向、航速、检查回声测深仪数据、船舶操纵相关数据等)作为SA的指标。使用Tobii Pro Glasses 3记录一组训练数据，训练深度学习算法并测试分类准确率。为了进一步验证提出的基于眼动追踪的评估方法，设计并进行了初步实验。招募5名受试者进行数据收集。利用全任务高级导航研究模拟器(ANRS)提供训练数据采集和初步实验场景。从初步结果来看，该方法的识别准确率可以达到> 99%，为基于眼动追踪的识别提供了积极的支持。使用初步实验数据的分析结果也显示了使用眼动追踪来评估导航员SA的巨大潜力。建议的评估可以在模拟器和在职中使用，并有多种目的，如绩效评估、晋升下一级和持续专业发展。
{"title":"Assessment of Situation Awareness for Seafarers Using Eye-Tracking Data","authors":"S. S. Virdi, Yong Thiang Ng, Yisi Liu, Kelvin Tan, Daniel Zhang","doi":"10.1115/omae2022-80754","DOIUrl":"https://doi.org/10.1115/omae2022-80754","url":null,"abstract":"\u0000 Situation Awareness (SA) is the perception of the current situation, comprehension of its meaning, and projection of what is going to happen in the near future. It is crucial for navigators to possess high SA in a navigational Bridge to mitigate the risk of human errors and to improve navigational safety. However, the current methodology to assess SA mainly rely on human experts, which might bring in potential problems such as bias, work overload, and it is also hard for the human experts to capture every fine detail of the behaviour of the seafarers being assessed. To overcome these, an objective and automated way to assess Situation Awareness is needed. In this work, eye-tracking data is used for the assessment of SA. With the eye tracking device, it is possible to localize where the navigator is looking at, and by applying computer vision with deep learning algorithm, the ongoing activity being executed by the navigator could be identified. In total 7 activities (using RADAR, ECDIS, checking of ship’s heading, and speed, checking data on Echo Sounder, and data related to ships maneuvering, and others) can be recognized which are used as indicators of SA. A set of training data was recorded using Tobii Pro Glasses 3 to train the deep learning algorithm and test the classification accuracy. To further verify the proposed eye-tracking based assessment, a preliminary experiment has been designed and carried out. Five subjects were recruited for data collection. A full-mission Advanced Navigation Research Simulator (ANRS) was used to provide scenarios for both training data collection and preliminary experiment. From the initial results, it shows that a recognition accuracy of > 99% can be achieved, which gives positive support to the eye-tracking based recognition. The analytics results using data from preliminary experiment also show great potential in using eye-tracking to assess SA of navigators. The proposed assessment could be used in both simulator and on-board and for multiple purposes such as performance evaluation, promotion to the next rank, and Continuing Professional Development.","PeriodicalId":23502,"journal":{"name":"Volume 1: Offshore Technology","volume":"202 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77986926","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 modal state responses of very large floating structures subjected to sea waves are addressed in this paper. Semi-analytical approximations for their second order statistics are first provided. They are derived by using the multiple timescale spectral analysis and they aim at calculating the variances of the nodal state responses much more rapidly than with the traditional time and frequency domain methods. Based on these approximate formulas, such expressions are developed for the correlation coefficients in this paper. They allow to understand in which cases the covariances between two modal state responses are significant and cannot legitimately be neglected. They are for instance important to consider when the natural frequencies of the corresponding modes are close to one another or when their shapes are similar. The accuracy of the proposed expressions is then verified on a realistic example inspired by an actual floating pontoon bridge. The results are shown to be less precise, although still acceptable, when the peak frequency of the loading and the natural frequencies of the structure are of the same order of magnitude. This is to be expected since the validity of the proposed approximations is conditioned upon the separation of these timescales.
{"title":"Importance of the Inertial Components in Modal State Covariances","authors":"M. Geuzaine, A. Fenerci, O. Øiseth, V. Denoël","doi":"10.1115/omae2022-78644","DOIUrl":"https://doi.org/10.1115/omae2022-78644","url":null,"abstract":"\u0000 The modal state responses of very large floating structures subjected to sea waves are addressed in this paper. Semi-analytical approximations for their second order statistics are first provided. They are derived by using the multiple timescale spectral analysis and they aim at calculating the variances of the nodal state responses much more rapidly than with the traditional time and frequency domain methods. Based on these approximate formulas, such expressions are developed for the correlation coefficients in this paper. They allow to understand in which cases the covariances between two modal state responses are significant and cannot legitimately be neglected. They are for instance important to consider when the natural frequencies of the corresponding modes are close to one another or when their shapes are similar. The accuracy of the proposed expressions is then verified on a realistic example inspired by an actual floating pontoon bridge. The results are shown to be less precise, although still acceptable, when the peak frequency of the loading and the natural frequencies of the structure are of the same order of magnitude. This is to be expected since the validity of the proposed approximations is conditioned upon the separation of these timescales.","PeriodicalId":23502,"journal":{"name":"Volume 1: Offshore Technology","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73835380","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}
Jang-Whan Kim, Sewan Park, J. Kyoung, Aldric Baquet, Zhi-rong Shen, Y. Ha, Kyong-Hwan Kim
Numerical wave model that predicts realistic and correct wave kinematics such as wave elevation and particle velocity for the ocean waves become an essential part of the offshore platform design. A recent joint-industry effort, ‘Reproducible Offshore CFD JIP’, presented a systematic procedure to qualify the wave models and several wave models that satisfy the proposed qualification criteria. To name a few, Higher-Order Spectral Method (HOS, HOSM), HAWASSI, IGN, Reef3D::FNPF and TPNWT wave models have been verified for moderate to extreme sea states. They are all based on potential theory with an empirical wave-breaking model implemented. In this paper, the numerical methods of the existing wave models are compared to derive a new numerical wave model combining superior features of them. The new model uses pseudo-spectral method that is used in the HOS / HOSM and HAWASSI for the time evolution of the surface variables — surface elevation and free-surface velocity potential, but the variational method for the Laplace equation for fluid volume that is used in IGN and TPNWT. The new hybrid model shows efficiency and accuracy of the spectral method and the robustness of the variational method. The new numerical model is verified following the qualification criteria proposed in the ‘Reproducible Offshore CFD JIP’. The verification results of the new model show both robust convergence behavior and efficiency in practical numerical setup.
{"title":"A Hybrid Numerical Wave Model for Extreme Wave Kinematics","authors":"Jang-Whan Kim, Sewan Park, J. Kyoung, Aldric Baquet, Zhi-rong Shen, Y. Ha, Kyong-Hwan Kim","doi":"10.1115/omae2022-87901","DOIUrl":"https://doi.org/10.1115/omae2022-87901","url":null,"abstract":"\u0000 Numerical wave model that predicts realistic and correct wave kinematics such as wave elevation and particle velocity for the ocean waves become an essential part of the offshore platform design. A recent joint-industry effort, ‘Reproducible Offshore CFD JIP’, presented a systematic procedure to qualify the wave models and several wave models that satisfy the proposed qualification criteria. To name a few, Higher-Order Spectral Method (HOS, HOSM), HAWASSI, IGN, Reef3D::FNPF and TPNWT wave models have been verified for moderate to extreme sea states. They are all based on potential theory with an empirical wave-breaking model implemented.\u0000 In this paper, the numerical methods of the existing wave models are compared to derive a new numerical wave model combining superior features of them. The new model uses pseudo-spectral method that is used in the HOS / HOSM and HAWASSI for the time evolution of the surface variables — surface elevation and free-surface velocity potential, but the variational method for the Laplace equation for fluid volume that is used in IGN and TPNWT. The new hybrid model shows efficiency and accuracy of the spectral method and the robustness of the variational method.\u0000 The new numerical model is verified following the qualification criteria proposed in the ‘Reproducible Offshore CFD JIP’. The verification results of the new model show both robust convergence behavior and efficiency in practical numerical setup.","PeriodicalId":23502,"journal":{"name":"Volume 1: Offshore Technology","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74777565","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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