Rick Cole, Scot Duncan, F. Jose, Anju Kaur, Jeffery Kinder
Abstract Florida has a rich coastal and offshore biodiversity and ecology, and its low-lying geography with three dynamic coastlines is unique in many respects. Millions of people are attracted to visit, live, and work in the region. The same unique qualities make Florida highly exposed to impact-weather events, climate change, sea level rise, and environmental interference from exploding population growth over the last few decades. Environmental conditions must be monitored, baselines formed, and advanced circulation and ecosystem models created and verified (in-situ). The SeaWARRDD team discusses the proposed implementation of a comprehensive “Florida Coastal Ocean Observing System” beginning with a pilot study along the inner-West Florida Shelf. Our SeaWARRDD team brings decades of experience to the ocean-observing community, from the federal, state, academic, and private sectors including designing, developing, installing, and maintaining ocean (bay and estuary) monitoring and data collection systems. The SeaWARRDDobserving technologies are described in their application to monitor impact-weather, the structure of water-column density (conductivity, temperature, depth/ocean heat content), water-quality parameters, harmful algal blooms, acidification, and met-ocean physical components. Also discussed is the engagement with new ocean technologies and artificial intelligence, machine learning, and neural networks as they progress from concept, to prototype, and onto operational status. SeaWARRDD takes ocean-data processing to higher levels within the observing community and opens new avenues to provide both direct and indirect benefits to the millions of people who live along the Florida coast.
{"title":"“SeaWARRDD”: Coastal Warning and Rapid Response Data Density: Rethinking Coastal Ocean Observing, Intelligence, Resilience, and Prediction","authors":"Rick Cole, Scot Duncan, F. Jose, Anju Kaur, Jeffery Kinder","doi":"10.4031/mtsj.56.6.4","DOIUrl":"https://doi.org/10.4031/mtsj.56.6.4","url":null,"abstract":"Abstract Florida has a rich coastal and offshore biodiversity and ecology, and its low-lying geography with three dynamic coastlines is unique in many respects. Millions of people are attracted to visit, live, and work in the region. The same unique qualities make Florida\u0000 highly exposed to impact-weather events, climate change, sea level rise, and environmental interference from exploding population growth over the last few decades. Environmental conditions must be monitored, baselines formed, and advanced circulation and ecosystem models created and verified\u0000 (in-situ). The SeaWARRDD team discusses the proposed implementation of a comprehensive “Florida Coastal Ocean Observing System” beginning with a pilot study along the inner-West Florida Shelf. Our SeaWARRDD team brings decades of experience to the ocean-observing community, from\u0000 the federal, state, academic, and private sectors including designing, developing, installing, and maintaining ocean (bay and estuary) monitoring and data collection systems. The SeaWARRDDobserving technologies are described in their application to monitor impact-weather, the structure of\u0000 water-column density (conductivity, temperature, depth/ocean heat content), water-quality parameters, harmful algal blooms, acidification, and met-ocean physical components. Also discussed is the engagement with new ocean technologies and artificial intelligence, machine learning, and neural\u0000 networks as they progress from concept, to prototype, and onto operational status. SeaWARRDD takes ocean-data processing to higher levels within the observing community and opens new avenues to provide both direct and indirect benefits to the millions of people who live along the Florida coast.","PeriodicalId":49878,"journal":{"name":"Marine Technology Society Journal","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44188193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract With the growth of the Blue Economy, the volume of data collection within the ocean environment has been rapidly increasing. Larger numbers of oceanographic, meteorological, and floating Light Detection And Ranging (LiDAR) buoys have been collecting high fidelity measurements while pushing against power budget limits. Power limitations lead to infrequent transmission of reduced data sets or recording data to local storage that must be physically collected when the buoy is serviced. Triton Systems, Inc. and its partners are developing a retrofittable wave energy converter (WEC) to provide auxiliary power to these observation buoys to increase mission duration and power budget, improve reliability, and reduce the need for service trips. One of the greatest challenges has been developing a method to interface Triton's WEC with these buoys without impacting measurement fidelity. This is especially critical for inertial wave and LiDAR wind measurements collected with sensors that could be adversely affected by additional buoy dynamics introduced by an integrated WEC. To address this, Triton and EOM Offshore developed a compliant tether to pair an observation buoy with a floating WEC while decoupling relative motion. Based on EOM's proven stretch hose technology, this compliant tether transmits power and data between the buoy-WEC system. Modeling shows that the system has the potential to minimally adversely affect oceanographic, meteorological, wind resource characterization, and other measurements, with future testing scheduled to validate modeling efforts.
{"title":"Use of a Compliant Tether to Decouple Observation Buoy Motion for Auxiliary Wave Power","authors":"T. Robertson, D. Aubrey, Alicia M. Mahon","doi":"10.4031/mtsj.56.6.9","DOIUrl":"https://doi.org/10.4031/mtsj.56.6.9","url":null,"abstract":"Abstract With the growth of the Blue Economy, the volume of data collection within the ocean environment has been rapidly increasing. Larger numbers of oceanographic, meteorological, and floating Light Detection And Ranging (LiDAR) buoys have been collecting high fidelity\u0000 measurements while pushing against power budget limits. Power limitations lead to infrequent transmission of reduced data sets or recording data to local storage that must be physically collected when the buoy is serviced. Triton Systems, Inc. and its partners are developing a retrofittable\u0000 wave energy converter (WEC) to provide auxiliary power to these observation buoys to increase mission duration and power budget, improve reliability, and reduce the need for service trips. One of the greatest challenges has been developing a method to interface Triton's WEC with these buoys\u0000 without impacting measurement fidelity. This is especially critical for inertial wave and LiDAR wind measurements collected with sensors that could be adversely affected by additional buoy dynamics introduced by an integrated WEC. To address this, Triton and EOM Offshore developed a compliant\u0000 tether to pair an observation buoy with a floating WEC while decoupling relative motion. Based on EOM's proven stretch hose technology, this compliant tether transmits power and data between the buoy-WEC system. Modeling shows that the system has the potential to minimally adversely affect\u0000 oceanographic, meteorological, wind resource characterization, and other measurements, with future testing scheduled to validate modeling efforts.","PeriodicalId":49878,"journal":{"name":"Marine Technology Society Journal","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43081052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Biswajit Haldar, Abhishek Tandon, K. J. Joseph, M. Muthiah, P. Senthilkumar, R. Venkatesan
Abstract The OMNI (Ocean Moored Buoy Network for northern Indian Ocean) buoy network comprises 12 buoy systems that measure surface meteorological parameters along with temperature and salinity profile measurements at discrete levels up to 500 m. All the OMNI buoy systems are deployed with slack-line moorings, which respond more to wind, wave, and current forcing compared to taut-line mooring. Subsurface temperature measurements are subject to change depending on both environmental condition and mooring design. The standard sensor fit of the OMNI buoy systems has only one pressure sensor fixed at 500 m, which shows significant depth variability. In order to see the spatial and seasonal variability in the vertical movement of the mooring line and the associated temperature variability, four deployments with additional pressure measurements at 200 m are analyzed. It is observed that the depth/temperature variability exhibits significant seasonality with maximum variability during pre-monsoon season. Also, the effect of this movement in the shallower depths is analyzed with four more pressure sensors in the mooring line for a 1-year period in the central Bay of Bengal. The analysis shows that the maximum value of average and root mean square (RMS) temperature deviations is 0.38 °C and 0.48 °C in the deepest interpolated depth at 400 m where the mooring line experiences a greater range of motion and the actual temperature variability in shallower depths is negligible particularly up to 75 m (<0.01°C). The study reveals the necessity of additional pressure measurements for better remapping of temperature profile measurements.
{"title":"Remapping of Temperature Profile Measurements in OMNI Buoy Systems","authors":"Biswajit Haldar, Abhishek Tandon, K. J. Joseph, M. Muthiah, P. Senthilkumar, R. Venkatesan","doi":"10.4031/mtsj.56.6.3","DOIUrl":"https://doi.org/10.4031/mtsj.56.6.3","url":null,"abstract":"Abstract The OMNI (Ocean Moored Buoy Network for northern Indian Ocean) buoy network comprises 12 buoy systems that measure surface meteorological parameters along with temperature and salinity profile measurements at discrete levels up to 500 m. All the OMNI buoy systems\u0000 are deployed with slack-line moorings, which respond more to wind, wave, and current forcing compared to taut-line mooring. Subsurface temperature measurements are subject to change depending on both environmental condition and mooring design. The standard sensor fit of the OMNI buoy systems\u0000 has only one pressure sensor fixed at 500 m, which shows significant depth variability. In order to see the spatial and seasonal variability in the vertical movement of the mooring line and the associated temperature variability, four deployments with additional pressure measurements at 200\u0000 m are analyzed. It is observed that the depth/temperature variability exhibits significant seasonality with maximum variability during pre-monsoon season. Also, the effect of this movement in the shallower depths is analyzed with four more pressure sensors in the mooring line for a 1-year\u0000 period in the central Bay of Bengal. The analysis shows that the maximum value of average and root mean square (RMS) temperature deviations is 0.38 °C and 0.48 °C in the deepest interpolated depth at 400 m where the mooring line experiences a greater range of motion and the actual\u0000 temperature variability in shallower depths is negligible particularly up to 75 m (<0.01°C). The study reveals the necessity of additional pressure measurements for better remapping of temperature profile measurements.","PeriodicalId":49878,"journal":{"name":"Marine Technology Society Journal","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48894439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract There is a strong connection and interrelationship between ocean observing and technology development. Technology helps us observe, and observing applications help refine and drive technological advances. This unique relationship creates mutual benefits across sectors and communities.
{"title":"Creating Synergies Through Advancing Technology and Ocean Observing","authors":"M. Heupel","doi":"10.4031/mtsj.56.5.6","DOIUrl":"https://doi.org/10.4031/mtsj.56.5.6","url":null,"abstract":"Abstract There is a strong connection and interrelationship between ocean observing and technology development. Technology helps us observe, and observing applications help refine and drive technological advances. This unique relationship creates mutual benefits across sectors\u0000 and communities.","PeriodicalId":49878,"journal":{"name":"Marine Technology Society Journal","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42634253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Recently, research concerning the navigation of autonomous surface vehicles (ASVs) has been increasing. However, a large-scale implementation of these vessels is still held back by several challenges such as multi-object tracking. Attaining accurate object detection plays a big role in achieving successful tracking. This article presents the development of a detection model with an image-based Convolutional Neural Network trained through transfer learning, a deep learning technique. To train, test, and validate the detector module, data were collected with the SENSE ASV by sailing through two nearby ports, Leixões and Viana do Castelo, and recording video frames through its on-board cameras, along with a Light Detection And Ranging, GPS, and Inertial Measurement Unit data. Images were extracted from the collected data, composing a manually annotated dataset with nine classes of different vessels, along with data from other open-source maritime datasets. The developed model achieved a class mAP@[.5 .95] (mean average precision) of 89.5% and a clear improvement in boat detection compared to a multi-purposed state-of-the-art detector, YOLO-v4, with a 22.9% and 44.3% increase in the mAP with an Intersection over Union threshold of 50% and the mAP@[.5 .95], respectively. It was integrated in a detection and tracking system, being able to continuously detect nearby vessels and provide sufficient information for simple navigation tasks.
摘要近年来，对自动水面车辆(asv)导航的研究日益增多。然而，这些船只的大规模实施仍然受到多目标跟踪等挑战的阻碍。获得准确的目标检测是实现成功跟踪的重要因素。本文介绍了一种基于图像的卷积神经网络检测模型的开发，该模型通过迁移学习(一种深度学习技术)进行训练。为了训练、测试和验证探测器模块，SENSE ASV通过附近的两个港口Leixões和Viana do Castelo收集数据，并通过其机载摄像机记录视频帧，以及光探测和测距、GPS和惯性测量单元的数据。从收集的数据中提取图像，与来自其他开源海事数据集的数据一起，组成一个包含九类不同船只的手动注释数据集。所开发的模型实现了类映射@[。5.95](平均精度)为89.5%，与最先进的多用途探测器YOLO-v4相比，船舶检测方面有明显改善，mAP的交叉点超过联合阈值为50%，mAP@的交叉点超过联合阈值为22.9%和44.3%。5.95]。它被集成在探测和跟踪系统中，能够持续探测附近的船只，并为简单的导航任务提供足够的信息。
{"title":"Multiple Vessel Detection in Harsh Maritime Environments","authors":"D. Duarte, M. Pereira, A. Pinto","doi":"10.4031/mtsj.56.5.07","DOIUrl":"https://doi.org/10.4031/mtsj.56.5.07","url":null,"abstract":"Abstract Recently, research concerning the navigation of autonomous surface vehicles (ASVs) has been increasing. However, a large-scale implementation of these vessels is still held back by several challenges such as multi-object tracking. Attaining accurate object detection\u0000 plays a big role in achieving successful tracking. This article presents the development of a detection model with an image-based Convolutional Neural Network trained through transfer learning, a deep learning technique. To train, test, and validate the detector module, data were collected\u0000 with the SENSE ASV by sailing through two nearby ports, Leixões and Viana do Castelo, and recording video frames through its on-board cameras, along with a Light Detection And Ranging, GPS, and Inertial Measurement Unit data. Images were extracted from the collected data, composing\u0000 a manually annotated dataset with nine classes of different vessels, along with data from other open-source maritime datasets. The developed model achieved a class mAP@[.5 .95] (mean average precision) of 89.5% and a clear improvement in boat detection compared to a multi-purposed state-of-the-art\u0000 detector, YOLO-v4, with a 22.9% and 44.3% increase in the mAP with an Intersection over Union threshold of 50% and the mAP@[.5 .95], respectively. It was integrated in a detection and tracking system, being able to continuously detect nearby vessels and provide sufficient information for simple\u0000 navigation tasks.","PeriodicalId":49878,"journal":{"name":"Marine Technology Society Journal","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45701096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract High-resolution optical imaging systems are quickly becoming universal tools to characterize and quantify microbial diversity in marine ecosystems. Automated classification systems such as convolutional neural networks (CNNs) are often developed to identify species within the immense number of images (e.g., millions per month) collected. The goal of our study was to develop a CNN to classify phytoplankton images collected with an Imaging FlowCytobot for the Palmer Antarctica Long-Term Ecological Research project. A relatively small CNN (~2 million parameters) was developed and trained using a subset of manually identified images, resulting in an overall test accuracy, recall, and f1-score of 93.8, 93.7, and 93.7%, respectively, on a balanced dataset. However, the f1-score dropped to 46.5% when tested on a dataset of 10,269 new images drawn from the natural environment without balancing classes. This decrease is likely due to highly imbalanced class distributions dominated by smaller, less differentiable cells, high intraclass variance, and interclass morphological similarities of cells in naturally occurring phytoplankton assemblages. As a case study to illustrate the value of the model, it was used to predict taxonomic classifications (ranging from genus to class) of phytoplankton at Palmer Station, Antarctica, from late austral spring to early autumn in 2017‐2018 and 2018‐2019. The CNN was generally able to identify important seasonal dynamics such as the shift from large centric diatoms to small pennate diatoms in both years, which is thought to be driven by increases in glacial meltwater from January to March. This shift in particle size distribution has significant implications for the ecology and biogeochemistry of these waters. Moving forward, we hope to further increase the accuracy of our model to better characterize coastal phytoplankton communities threatened by rapidly changing environmental conditions.
{"title":"A Convolutional Neural Network to Classify Phytoplankton Images Along the West Antarctic Peninsula","authors":"S. Nardelli, P. Gray, O. Schofield","doi":"10.4031/mtsj.56.5.8","DOIUrl":"https://doi.org/10.4031/mtsj.56.5.8","url":null,"abstract":"Abstract High-resolution optical imaging systems are quickly becoming universal tools to characterize and quantify microbial diversity in marine ecosystems. Automated classification systems such as convolutional neural networks (CNNs) are often developed to identify species\u0000 within the immense number of images (e.g., millions per month) collected. The goal of our study was to develop a CNN to classify phytoplankton images collected with an Imaging FlowCytobot for the Palmer Antarctica Long-Term Ecological Research project. A relatively small CNN (~2 million parameters)\u0000 was developed and trained using a subset of manually identified images, resulting in an overall test accuracy, recall, and f1-score of 93.8, 93.7, and 93.7%, respectively, on a balanced dataset. However, the f1-score dropped to 46.5% when tested on a dataset of 10,269 new images drawn from\u0000 the natural environment without balancing classes. This decrease is likely due to highly imbalanced class distributions dominated by smaller, less differentiable cells, high intraclass variance, and interclass morphological similarities of cells in naturally occurring phytoplankton assemblages.\u0000 As a case study to illustrate the value of the model, it was used to predict taxonomic classifications (ranging from genus to class) of phytoplankton at Palmer Station, Antarctica, from late austral spring to early autumn in 2017‐2018 and 2018‐2019. The CNN was generally able\u0000 to identify important seasonal dynamics such as the shift from large centric diatoms to small pennate diatoms in both years, which is thought to be driven by increases in glacial meltwater from January to March. This shift in particle size distribution has significant implications for the\u0000 ecology and biogeochemistry of these waters. Moving forward, we hope to further increase the accuracy of our model to better characterize coastal phytoplankton communities threatened by rapidly changing environmental conditions.","PeriodicalId":49878,"journal":{"name":"Marine Technology Society Journal","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46627178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fausto Ferreira, Igor Kvasić, Đ. Nađ, Luka Mandić, N. Mišković, Christopher Walker, D. O. Antillon, Iain Anderson
Abstract Diver‐robot interaction is an exciting and recent field of study. There are different ways a diver and robot can interact, such as using tablets or detecting divers with cameras or sonars. A novel approach presented in this paper uses direct diver‐robot communication. To facilitate communication for humans, we use typical diver gestures, which are transmitted to a robot using a wearable glove and acoustic communications. Following previous work by the University of Zagreb and the University of Auckland, a collaboration to control an autonomous underwater vehicle based on a wearable diver glove has been made possible through the EU Marine Robots project. Under this project, Trans-National Access trials allow Laboratory for Underwater Systems and Technologies, University of Zagreb, to offer its robots and infrastructure to external partners. Initial trials with the University of Auckland, which were planned to take place on site, were transformed into remote access trials. This paper reports on these challenging trials and collaboration given the distance and time zone difference. The key point is to demonstrate the possibility of having a diver remotely controlling a robot using typical gestures recognized by a wearable glove and transmitted via acoustic modems (and the Internet for the remote connection).
{"title":"Diver‐Robot Communication Using Wearable Sensing: Remote Pool Experiments","authors":"Fausto Ferreira, Igor Kvasić, Đ. Nađ, Luka Mandić, N. Mišković, Christopher Walker, D. O. Antillon, Iain Anderson","doi":"10.4031/mtsj.56.5.5","DOIUrl":"https://doi.org/10.4031/mtsj.56.5.5","url":null,"abstract":"Abstract Diver‐robot interaction is an exciting and recent field of study. There are different ways a diver and robot can interact, such as using tablets or detecting divers with cameras or sonars. A novel approach presented in this paper uses direct diver‐robot\u0000 communication. To facilitate communication for humans, we use typical diver gestures, which are transmitted to a robot using a wearable glove and acoustic communications. Following previous work by the University of Zagreb and the University of Auckland, a collaboration to control an autonomous\u0000 underwater vehicle based on a wearable diver glove has been made possible through the EU Marine Robots project. Under this project, Trans-National Access trials allow Laboratory for Underwater Systems and Technologies, University of Zagreb, to offer its robots and infrastructure to external\u0000 partners. Initial trials with the University of Auckland, which were planned to take place on site, were transformed into remote access trials. This paper reports on these challenging trials and collaboration given the distance and time zone difference. The key point is to demonstrate the\u0000 possibility of having a diver remotely controlling a robot using typical gestures recognized by a wearable glove and transmitted via acoustic modems (and the Internet for the remote connection).","PeriodicalId":49878,"journal":{"name":"Marine Technology Society Journal","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49230760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The author reflects on a fulfilling career in the marine industry, from his childhood fascination with shipbuilding, to challenging and exciting positions in several dynamic positioning and offshore energy companies. He notes appreciation for a career that enabled him to travel the world and work with outstanding marine technology and business professionals, and for his involvement with the Marine Technology Society, which has enabled him to expand his technical expertise and his network of marine technology colleagues and friends.
{"title":"The Art of Being Professionally Lucky: A Career in the Marine Industry","authors":"S. Browne","doi":"10.4031/mtsj.56.5.4","DOIUrl":"https://doi.org/10.4031/mtsj.56.5.4","url":null,"abstract":"Abstract The author reflects on a fulfilling career in the marine industry, from his childhood fascination with shipbuilding, to challenging and exciting positions in several dynamic positioning and offshore energy companies. He notes appreciation for a career that enabled\u0000 him to travel the world and work with outstanding marine technology and business professionals, and for his involvement with the Marine Technology Society, which has enabled him to expand his technical expertise and his network of marine technology colleagues and friends.","PeriodicalId":49878,"journal":{"name":"Marine Technology Society Journal","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48301401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract A full-featured wave tank was set up to evaluate a wave energy converter-bearing platform. A two-layer overset mesh was implemented to solve the complex two-body floating structure hydrodynamic problem. One validation case was conducted to verify the reliability of the wave tank and the box-shape structure excited by regular waves, which showed good consistency through experimental results. A wavelength-based parameterized mesh size and time-step setting method and a computing cost indicator were presented. A set of parameters generating regular waves with less than 1% errors was found. A complex-shaped, novel concept, wave energy converter was assessed in the wave tank. A series of regular wave tests was conducted to observe the frequency domain response of the wave energy converter platform and to reproduce the vortex shedding at the edges of the plate. The mooring cases were compared with the width experiment results, and the response amplitude operators of heave and pitch of the platform were obtained.
{"title":"Optimization of a Numerical Wave Tank and Its Application to a Wave Energy Converter Platform Response Test","authors":"Fei Pei, YanSong Lin, Yunlong Wang","doi":"10.4031/mtsj.56.4.10","DOIUrl":"https://doi.org/10.4031/mtsj.56.4.10","url":null,"abstract":"Abstract A full-featured wave tank was set up to evaluate a wave energy converter-bearing platform. A two-layer overset mesh was implemented to solve the complex two-body floating structure hydrodynamic problem. One validation case was conducted to verify the reliability\u0000 of the wave tank and the box-shape structure excited by regular waves, which showed good consistency through experimental results. A wavelength-based parameterized mesh size and time-step setting method and a computing cost indicator were presented. A set of parameters generating regular waves\u0000 with less than 1% errors was found. A complex-shaped, novel concept, wave energy converter was assessed in the wave tank. A series of regular wave tests was conducted to observe the frequency domain response of the wave energy converter platform and to reproduce the vortex shedding at the\u0000 edges of the plate. The mooring cases were compared with the width experiment results, and the response amplitude operators of heave and pitch of the platform were obtained.","PeriodicalId":49878,"journal":{"name":"Marine Technology Society Journal","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47211207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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