Data-Driven and Hybrid Methods for Naval Applications

The goal of this PhD thesis is to study, design and develop data analysis methods for naval applications. Data analysis is improving our ways to understand complex phenomena by profitably taking advantage of the information laying behind a collection of data. In fact, by adopting algorithms coming from the world of statistics and machine learning it is possible to extract valuable information, without requiring specific domain knowledge of the system generating the data. The application of such methods to marine contexts opens new research scenarios, since typical naval problems can now be solved with higher accuracy rates with respect to more classical techniques, based on the physical equations governing the naval system. During this study, some major naval problems have been addressed adopting state-of-the-art and novel data analysis techniques: condition-based maintenance, consisting in assets monitoring, maintenance planning, and real-time anomaly detection; energy and consumption monitoring, in order to reduce vessel consumption and gas emissions; system safety for maneuvering control and collision avoidance; components design, in order to detect possible defects at design stage. A review of the state-of-the-art of data analysis and machine learning techniques together with the preliminary results of the application of such methods to the aforementioned problems show a growing interest in these research topics and that effective data-driven solutions can be applied to the naval context. Moreover, for some applications, data-driven models have been used in conjunction with domain-dependent methods, modelling physical phenomena, in order to exploit both mechanistic knowledge of the system and available measurements. These hybrid methods are proved to provide more accurate and interpretable results with respect to both the pure physical or data-driven approaches taken singularly, thus showing that in the naval context it is possible to offer new valuable methodologies by either providing novel statistical methods or improving the state-of-the-art ones

Data-driven Ship Performance Models - - Emphasis on Energy Efficiency and Fatigue Safety

Due to digitalization in the maritime industry, a huge amount of ship operation-related data has been collected. The main objective of this thesis is to exploit machine learning/big data analytics to build data-driven ship performance models, focusing on speed-power relationship modeling, and fatigue accumulation assessment during a ship’s operation at sea.The speed-power performance models are established in three different ways: 1) semi-empirical white-box models, 2) machine learning black-box methods, and 3) physics-informed grey-box models. The white-box models include improved semi-empirical formulas for ship added resistance due to head waves, and further developed formulas in arbitrary wave headings. Validation studies using three case study ships show good agreement between the speed predictions by the white-box models and the long-term averages of full-scale measurements. Different supervised machine learning methods’ capabilities have been compared for black-box modeling. The XGBoost algorithm is found to have the most reliable predictive ability, with the highest efficiency suitable for onboard devices. The novel grey-box models are proposed by considering the physical principles in model tests and big data information from real sailing. It has been demonstrated that the proposed grey-box models can improve prediction accuracy by approximately 30% for ship speed estimation and provides 50% less cumulative error of sailing time than the black-box methods.The impact of voyage optimization-aided operations on the encountered wave conditions and ship fatigue damage is investigated in this thesis. By recommending appropriate routes, voyage optimization can greatly extend the fatigue life of a ship by at least 50%. The machine learning techniques are also applied to a ship’s fatigue assessment. The results indicate that the proposed data-driven fatigue assessment model could increase accuracy by approximately 70% for the case study vessel compared to other prominent spectral methods

Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities

With the increasing amount of spatial-temporal~(ST) ocean data, numerous spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, e.g., climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated with some unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models. Unfortunately, an overview of these studies is still missing, hindering computer scientists to identify the research issues in ocean while discouraging researchers in ocean science from applying advanced STDM techniques. To remedy this situation, we provide a comprehensive survey to summarize existing STDM studies in ocean. Concretely, we first summarize the widely-used ST ocean datasets and identify their unique characteristics. Then, typical ST ocean data quality enhancement techniques are discussed. Next, we classify existing STDM studies for ocean into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate the techniques for these tasks. Finally, promising research opportunities are highlighted. This survey will help scientists from the fields of both computer science and ocean science have a better understanding of the fundamental concepts, key techniques, and open challenges of STDM in ocean

Modelling of Harbour and Coastal Structures

As the most heavily populated areas in the world, coastal zones host the majority and some of the most important human settlements, infrastructures and economic activities. Harbour and coastal structures are essential to the above, facilitating the transport of people and goods through ports, and protecting low-lying areas against flooding and erosion. While these structures were previously based on relatively rigid concepts about service life, at present, the design—or the upgrading—of these structures should effectively proof them against future pressures, enhancing their resilience and long-term sustainability. This Special Issue brings together a versatile collection of articles on the modelling of harbour and coastal structures, covering a wide array of topics on the design of such structures through a study of their interactions with waves and coastal morphology, as well as their role in coastal protection and harbour design in present and future climates

Proceedings of the 39th International Workshop on Water Waves and Floating Bodies

The International Workshop on Water Waves and Floating Bodies (IWWWFB) is anannual meeting of engineers and scientists with a particular emphasis on waterwaves and their effects on floating and fixed marine structures. The Workshop wasinitiated by Professor D. V. Evans (University of Bristol) and Professor J. N. Newman(MIT) following informal meetings between their research groups in 1984. Firstintended to promote communication between researchers in the UK and the USA,the interest and participation quickly spread to include researchers from many othercountries around the world.The Workshop enhances the basic and applied scientific knowledge on water wavesand their interaction with floating and fixed bodies with various applications andfacilitates the advancement and transfer of knowledge between research groupsacross the globe, and between senior and early career researchers. The workshopproceedings are freely accessible through the dedicated internet addresswww.iwwwfb.org where all contributions from 1986 on can be found.Individual papers from the 2024 conference can be found on the IWWWFB website here: http://www.iwwwfb.org/Workshops/39.htm 

Design and analysis of a hybrid timber-steel floating substructure for a 15 MW semisubmersible-type FWT

Wind energy has developed to be among the most promising sources of renewable energy. Furthermore, floating offshore wind turbines have presented the opportunity for higher power production in intermediate (45-150 m) and deep water (> 150 m). However, the manufacturing, installation, and operation of wind turbines in general, and floating wind turbines in particular, can result in significant amounts of greenhouse gas emissions (GHG). This thesis proposes a novel design of a hybrid timber-steel floating substructure for the IEA 15 MW floating wind turbine. The new design presents a modified version of the UMaine VolturnUS-S semisubmersible platform that was initially developed for the same turbine. The main objective of the new design is to reduce the turbine’s overall CO2 footprint. This objective is achieved by replacing structural steel with glued laminated timber, a more sustainable material known for its environmental benefits. Firstly, a robust design methodology is introduced. Secondly, Ansys workbench 2020 R1 is utilized to compare and then select between three preliminary hybrid timber-steel models based on a set of criteria that are extracted from relevant standards for both timber and steel. Compared to the UMaine VolturnUS-S semisubmersible platform, the selected hybrid configuration provides a considerable reduction in the steel mass (around 590 t). Subsequently, fully coupled aero-hydro-servo-elastic dynamic analysis is carried out using OpenFAST to validate the selected model. Only the ultimate limit state design (ULS) for the turbine under extreme and normal operating conditions is considered. The results from the numerical analysis show that the selected model fulfills all design criteria with a utilization factor that varies between 74- 94% for the different design load cases. In the end, the work concludes that the glulam-based supporting structure offers an effective load-bearing solution for the IEA 15 MW turbine, contributing to the development of floating wind energy with minimal cost and CO2 footprint. However, a series of tasks and suggestions are proposed to enhance the process of developing an optimal timber-steel design

Proceedings of the 39th International Workshop on Water Waves and Floating Bodies

The International Workshop on Water Waves and Floating Bodies (IWWWFB) is anannual meeting of engineers and scientists with a particular emphasis on waterwaves and their effects on floating and fixed marine structures. The Workshop wasinitiated by Professor D. V. Evans (University of Bristol) and Professor J. N. Newman(MIT) following informal meetings between their research groups in 1984. Firstintended to promote communication between researchers in the UK and the USA,the interest and participation quickly spread to include researchers from many othercountries around the world.The Workshop enhances the basic and applied scientific knowledge on water wavesand their interaction with floating and fixed bodies with various applications andfacilitates the advancement and transfer of knowledge between research groupsacross the globe, and between senior and early career researchers. The workshopproceedings are freely accessible through the dedicated internet addresswww.iwwwfb.org where all contributions from 1986 on can be found.Individual papers from the 2024 conference can be found on the IWWWFB website here: http://www.iwwwfb.org/Workshops/39.htm 

Data-driven solutions to enhance planning, operation and design tools in Industry 4.0 context

This thesis proposes three different data-driven solutions to be combined to state-of-the-art solvers and tools in order to primarily enhance their computational performances. The problem of efficiently designing the open sea floating platforms on which wind turbines can be mount on will be tackled, as well as the tuning of a data-driven engine's monitoring tool for maritime transportation. Finally, the activities of SAT and ASP solvers will be thoroughly studied and a deep learning architecture will be proposed to enhance the heuristics-based solving approach adopted by such software. The covered domains are different and the same is true for their respective targets. Nonetheless, the proposed Artificial Intelligence and Machine Learning algorithms are shared as well as the overall picture: promote Industrial AI and meet the constraints imposed by Industry 4.0 vision. The lesser presence of human-in-the-loop, a data-driven approach to discover causalities otherwise ignored, a special attention to the environmental impact of industries' emissions, a real and efficient exploitation of the Big Data available today are just a subset of the latter. Hence, from a broader perspective, the experiments carried out within this thesis are driven towards the aforementioned targets and the resulting outcomes are satisfactory enough to potentially convince the research community and industrialists that they are not just "visions" but they can be actually put into practice. However, it is still an introduction to the topic and the developed models are at what can be defined a "pilot" stage. Nonetheless, the results are promising and they pave the way towards further improvements and the consolidation of the dictates of Industry 4.0

하역중단 예보를 위한 자료기반 모델 : 포항신항에 적용

학위논문 (박사)-- 서울대학교 대학원 공과대학 건설환경공학부, 2017. 8. 서경덕.Unexpected downtime due to harbor agitation has been a serious problem in Pohang New Port. Such downtime occurrences have not well been predicted by field wave observation or numerical modeling around the port. Hence it is required to make an effort to enhance understanding intrinsic causes of downtime and to develop an efficient forecasting model that can be incorporated into daily port operation procedures. Considering inefficiency of predicting downtime by conventional wave simulation models, a data-based model is developed in this study based on extensive wave observed data over years and corresponding downtime records available at multiple locations inside and outside the port. The main structure of the downtime forecasting model consists of Neural Network (NN) that predict wave parameters inside and outside the port from simulated wave data at outside the port and a classification model that predicts downtime occurrences based on information about wave field produced by the NN. The overall predictive performance of the model was good, showing more than 80% of correct identification of the downtime occurrences on average. In addition, spatio-temporal changes in spectral energies of various wave components during downtime events were examined by using Hilbert-Huang Transform (HHT) analysis, the details of which have never been studied so far. By conducting this analysis, a better understanding was obtained about the influence of each of gravity wave (GW), infragravity wave (IGW), and natural oscillation period (NOP) on the downtime occurrences inside the port. It also significantly contributed to check and improve the quality of the manually-written downtime records, which consequently enhance the eventual performance of the forecasting model.CHAPTER 1. INTRODUCTION 1 1.1 Background and Motivation of the Study 1 1.2 Research Objectives 3 1.3 Structure of the Thesis 4 CHAPTER 2. LITERATURE REVIEW 6 2.1 Agitation in Harbor 6 2.1.1 Generation of Waves by Wind 6 2.1.2 Infragravity Waves 9 2.1.3 Oscillations in Harbor 12 2.2 Data-based Modeling Method 15 2.2.1 Hilbert-Huang Transform for Data Analysis 15 2.2.2 Neural Network for Data Prediction 19 2.2.3 Classification as a Branch of Machine Learning 25 CHAPTER 3. DATA 28 3.1 Pohang New Port 28 3.2 Collection of Data 31 3.2.1 Observed Wave Data 31 3.2.2 Simulated Wave Data 35 3.2.3 Recorded Downtime Data 39 3.3 Preliminary Analysis of Wave Data 43 3.3.1 Time Series of Wave Data 43 3.3.2 Correlations Between Wave Data 46 CHAPTER 4. HILBERT-HUANG TRANSFORM ANALYSIS 49 4.1 Hilbert-Huang Transform 49 4.1.1 Hilbert Transform 49 4.1.2 Empirical Mode Decomposition 51 4.1.3 Hilbert Spectral Analysis for IMFs 54 4.2 HHT Analysis of Sea Surface Elevation 58 4.2.1 Temporal Variation of the HHT spectra 60 4.2.2 Comparison of HHT Spectra at Multiple Stations 67 4.3 Quality Control of Downtime Data by Using HHT Analysis 71 4.3.1 Necessity of Examining Quality of Downtime Data 71 4.3.2 Modification of Downtime Data 73 CHAPTER 5. WAVE PREDICTION WITH NEURAL NETWORKS 78 5.1 Conventional Approach for Wave Prediction 79 5.2 Neural Network Prediction 82 5.3 Model Selection and Ensemble of Neural Networks 88 5.3.1 Model Selection Strategy 88 5.3.2 Ensemble Neural Networks 95 5.4 Discussion of the Prediction Models 103 CHAPTER 6. CLASSIFICATION MODEL 105 6.1 Classification as a Downtime Forecasting Model 105 6.2 Use of Predicted Wave and Modified Downtime Data 108 6.3 Test of Downtime Forecasting Model 115 CHAPTER 7. CONCLUSION 130 References 134 국문초록 143Docto