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The objective of this study is to design and optimize the layout of the offshore wind farms to maximize the power at a specific location. The energy production of the downstream wind turbines decreases because of the reduced wind speed and increased level of turbulence caused by the wakes formed by the upstream wind turbines. Therefore, the overall power efficiency is lowered due to the wake interference among wind turbines. This paper focuses on using the application of a Gaussian-based wake model and different optimization algorithms like the differential evolution particle swarm optimization (DPSO). The Gaussian wake model uses an exponential function to evaluate the velocity deficit, in contrast to the Jensen wake model that assumes a uniform velocity profile inside the wake. The layout optimization framework has been created for the energy production in order to provide reference for specific conditions and constraints at the Gulf of Maine and other typical projects in the future.
With the growing requirement of energy and environmental protection, the sustainable energy like wind energy has been significantly concerned in recent years. In this case, the investigations about wind farm optimization have been concerned by lots of researchers. In wind farms, one of the most critical power reduction is caused by the wake and turbulence from the blades of previous turbines. Generally, this phenomenon would drop the power production and mechanical performance of turbines. The layout optimization of wind farms according to the wake has been an essential concern for both onshore and offshore wind energy applications.
Figure 1 indicates the annual average offshore wind speeds (m/s) in the United States. From this diagram, the Gulf of Maine have one of the greatest wind energy potential on the east coast. The Gulf of Maine locates very close to the cities such as Portland and Boston with magnificent electricity requirement. So, it is considerably valuable to investigate how to develop wind power in the Gulf of Maine.
He, Zechen (The State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology ) | Ning, Dezhi (The State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology ) | Gou, Ying (The State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology )
An optimization model of buoy dimension of wave energy converter is established by using differential evolution algorithm. The linear potential flow method is used in hydrodynamic calculation. Taking the vertical oscillating cylindrical buoy as the research object, the radius and draft of the buoy are optimized under each specified volume. Through the comparison of different volume optimization results, it is found that there is an optimal buoy volume for a specific wave condition. With the increase of the volume, the optimal draft tends to a fixed value, and the optimal radius tends to be an asymptote. In addition, the influence of different damping of power take-off systems on the optimization results is also studied.
Wave energy is a kind of renewable and clean energy. The development and utilization of wave energy is attracting the attention of many scholars and research institutions around the world, which may make a significant contribution to the world' power consumption. For the commercial feasibility of wave energy, it is very important to improve the production efficiency of wave energy device and reduce its construction, installation and operation costs. Obviously, the volume of the Wave Energy Converter (WEC) is a key factor affecting both the efficiency and the cost. De Andres et al. (2015) discussed that small equipment is usually more economical due to reduced material costs and deployment. Göteman et al. (2014) and Göteman (2017) showed that the total power production can be improved if the wave energy array consists of devices of different dimensions that are similar to the WECs that have been developed at Uppsala University since 2006 (Leijon et al.,2009). Most previous optimal studies focus on the buoy dimensions instead of the buoy volume. For example, Giassi and Göteman (2017) optimized the parameters of the single wave energy converter by parameter sweep optimization of the variables and genetic algorithm, in which the radius, draft and damping of the Power Take Off (PTO) systems are optimized simultaneously in discrete parameter space. Because there are many combinations of radius and draft under a certain volume even for a truncated cylinder buoy, it' difficult to get the relationship between the volume and the efficiency directly. That means the designer couldn't balance the cost and the efficiency with the optimal dimensions.
Liu, Zhiqiang (Computational Marine Hydrodynamics Lab (CMHL), State Key Laboratory of Ocean Engineering. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University) | Liu, Xinwang (Computational Marine Hydrodynamics Lab (CMHL), State Key Laboratory of Ocean Engineering. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University) | Wan, Decheng (Computational Marine Hydrodynamics Lab (CMHL), State Key Laboratory of Ocean Engineering. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University)
To improve the ship hull optimization efficiency and take full advantage of the non-linear fitting capability of neural networks and the fast random search capability of genetic algorithms, the Wigley hull optimization based on artificial network and genetic algorithm is investigated in the present paper. The in-house hull form optimization software OPTShip-SJTU is firstly applied to obtain a series of new hull form and to calculate these hull resistances. Then a surrogate model of 3-layer BP neural network is constructed based on the sample data and a genetic algorithm is used to optimize the design of the Wigley ship with the total resistance minimum as the optimization objective function. During the calculation of hull hydrodynamics, potential flow solver NMShip-SJTU combined with ITTC formula is adopted to efficiently obtain the total resistance of the Wigley hull. The verification is also carried out to ensure the reliability of the optimization result. The results show that the resistance performance of the Wigley hull can be improved by designing the hull form reasonably. Besides, the form of bow bulbous is essential for the decreasing of total resistance according to the parameters sensitivity analysis. The design method—artificial network and the genetic algorithm can accurately work out the minimum resistance hull form and can be taken as a practical and efficient design tool.
With the implementation of the green ship and Ship Energy Efficiency Design Index (EEDI), how to reduce fuel consumption and carbon emission becomes the focus of the attention of shipyards and ship owners. One way to alleviate this problem is to optimize ship-shape curves. Based on the original ship, the ship-shape curves are optimized to reduce the wave-making resistance of the hull, and the ship-shape line also can be optimized with multiple objectives considering the ship's 6-DOF motion index.
The method of combining neural networks and genetic algorithm is used widely in different fields. Wang, Han, Sun, and Guo (2020) combined the elliptic basis (EBF) neural network approximation model and genetic algorithm to optimize the KP505 propeller, obtained the optimal design scheme theoretically and improved the optimization efficiency. Zeng, Ding, and Tang (2010) used the BP neural network and genetic algorithm to establish a new method for the optimal design of ship propeller based on the original map design method. Koushan (2003) used the genetic algorithm and neural networks to optimize the resistance and wave-making of a high-speed ship, and the optimization effect was obvious. Xu, Zhou, and Wang (2017) used the neural networks and genetic algorithm to optimize the ship's mooring system, and the optimization result is well. Yan, Liu, Xu, and Feng (2013) used the BP neural network and genetic algorithm to obtain the seaworthiness layout of trimaran ships with different layouts at different speeds. Wang, Lu, and Wang (2020) applied neural network and genetic algorithm to the airfoil optimization, optimized FFAW3- 301 airfoil have better aerodynamic performance. The optimization results showed that the optimization method was feasible. Lv, and Wang (2018) use the RBF neural network and genetic algorithm to optimize the strength of the ship hull after the broken. Chen and Ye (2009) firstly used the genetic algorithm to optimize the weights of the neural network, and then used the optimized neural network to predict the resistance of series 60 ship types. The neural network is simple and fast to calculate the resistance of ships, which can be applied to the calculation of ship resistance. Lin, Chen, Luo, and Wang (2019) analyzed a large number of data collected during the operation of a bulk cargo ship and used BP artificial neural network for training under the condition of considering fuel consumption. The fuel consumption rate optimization model is based on the neural network and the genetic algorithm is established. Xu (2012) used the BP neural network to optimize the layout of the trimaran with static water resistance as the target. From above all, we can realize that the BP neural network surrogate model is applied in optimization. But for hull optimization, it doesn’t been applied for the wide hull.
Wang, Tiange (Shandong Provincial Key Lab of Ocean Engineering, Ocean University of China) | Zhang, Min (Shandong Provincial Key Lab of Ocean Engineering, Ocean University of China) | Ji, Hao (China Renewable Energy Engineering Institute ) | Liao, Qichen (Key Laboratory of Far-shore Wind Power Technology of Zhejiang Province, POWERCHINA Huadong Engineering Corporation Limited )
In this paper, the damage identification of offshore floating platform mooring system is investigated. Firstly, based on the change of the axial stiffness of the mooring line, different damage severities of the mooring system are simulated and the static analysis of the platform and mooring system are calculated under a series of sea states. The Radial Basis Function (RBF) neural network is applied for damage identification to deal with the complex behavior of floater and mooring system. The numerical results show that RBF neural network has a good performance on damage identification of mooring lines.
Mooring line is the key component of offshore floating system as providing the station keeping function. During the service life, damages of the mooring system are unavoidable as a result of the action of various loads including operational and environmental forces. The structural health monitoring (SHM) system is very necessary to ensure the safety of the structures, lower the maintenance cost and prolong the service lives. A SHM system is defined as the process of implementing a damage detection strategy for engineering infrastructure related to aerospace, civil and mechanical engineering (Farrar and Sohn, 2000).
For damage detection of offshore structures, Mangal
The emergence of artificial neural networks has greatly improved this situation. The neural network has a good nonlinear mapping ability, and converts the inverse problems such as damage identification and positioning of the engineering structure into the positive problem. The earliest use of neural networks for structural damage identification was the Venkatasubramanian and Chan of Purdue University in the United States. In 1989, they first used neural networks for damage identification of large structures (Venkatasubramanian and Chan, 1989).
Hao, Jian (State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology) | Li, Jinxuan (State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology) | Liu, Shuxue (State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology) | Wang, Lei (State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology)
A numerical model for wave propagation over three-dimensional (3D) bathymetry was developed based on High Order Spectral (HOS) method, considering the wave-maker boundary and variable bathymetry. The numerical model was validated by the comparison of numerical results and the 3D elliptical mound's experiment results. Based on this model, wave spatial transformation over a submerged shoal was investigated. Strong wave diffractions were observed after the shoal when incident waves passed across this terrain. Moreover, the effects of incident wave length, topographic height and topographic slope on the spatial evolution of waves over the shoal were analyzed respectively. The results show that the wave focusing effect, that is, the concentration of wave energy and the maximum wave height, decreases with the increase of incident wave length. And it increases with the increase of topographic height and the decrease of topographic slope.
Offshore wave propagation, a key issue in port design and construction, is generated by shallow water deformation, refraction, diffraction and reflection because of seabed topography in the process of wave propagating from deep sea to offshore area. As seabed topography determines wave-propagation direction, wavelength and wave height, it deserves a full consideration for the safety of engineering structures in offshore area. Therefore, the interaction between waves and topography has attracted intensive interest. It is significant to investigate the interaction between waves and topography.
For the study of the correlation between waves and topography, multiple approaches have been proposed in the last a few decades. The physical experiment is one of the main means to study the effect of wave and topography. For the researchers of numerical simulation, the comparison with the experimental results is an effective verification method. In the physical experiments, the submerged bar (Beji, 1993) and waves Bragg reflection experiment (Davis and Heathershaw, 1984) are typical cases for the study of two-dimensional wave and topography. In the numerical models, Teng (2010) and Liu et al. (2005) simulated the wave propagation on submerged bar by VOF method. Zheng (2004) and Liu (2005) simulated the wave propagation over submerged bar through the improved Boussinesq equation, which can fairly analyze nonlinear variation of waves. However, the real sea area is three-dimensional. In three-dimensional sea area, wave refraction and diffraction over topography had attracted many scholars' attention (Vincent and Briggs, 1989; Chawla and Kirby, 1998). Mile-slope equation was one of the main methods to study wave refraction and diffraction over topography. It was derived from Berkhoff's (1972) perturbation expansion under the assumption that the seabed topography changed slowly. Based on this, a new analytical solution of the mile-slope long wave equation was presented (Yu and Zhang, 2003). Then Liu and Li (2007) analyzed simple harmonic waves scattered over a submerged circular truncated shoal by an analytical solution of longwave equation in closed-form. Zhu and Harun (2009) and Niu and Yu (2011) further explored wave transformation over a submerged circular hump by an analytical solution of the shallow water wave equation. However, the mile-slope equation has certain restrictions for slope, and the computational error is large when the seabed topography changes dramatically. Most numerical models (Chen and Kirby, 2000; Zhang, 2014) can achieve effective simulation results, but the time-consuming of three-dimensional model is relatively large.
LIU, Junping (College of Civil Engineering, Zhejiang University of Technology) | HE, Lulu (College of Civil Engineering, Zhejiang University of Technology) | Han, Wei (College of Civil Engineering, Zhejiang University of Technology) | Cao, Feifeng (College of Civil Engineering, Zhejiang University of Technology) | Chen, Wei (School of Port and Transportation Engineering, Zhejiang Ocean University)
Because of the complex terrain and frequent typhoon, Zhujiajian Island, located in the southeast of Zhoushan Archipelogo is prone to flooding disasters. MIKE FLOOD is applied to study the flood disaster of Zhujiajian Island by analyzing water level, inundated water depth, inundated area and inundated duration of the flood. The results show that the reasonable measures of widening the river width or reducing the elevation of the riverbed can effectively reduce the river water level, decrease the inundated area and the inundated water depth of the study area, and cut down the flood duration of the key areas.
As a key component of the hydrological cycle in nature, urban river not only offers sufficient water for urban development, but also plays an important role in transportation, climate regulation, ecological landscape as well as flood control and water storage. Because of the development of urbanization in China, the increasing demand for land resources and the influence of natural conditions, the environment of urban river is getting worse with the decreasing number of urban rivers (Yin et al., 2015), the change of river structure, and the weakening flood control capacity of urban river networks (Du et al. 2019; Lyu et al., 2018). The resulting urban floods and the corresponding disasters also threat the safety of people's life and the stability of society development (Jiang et al., 2018; Kundzewicz et al., 2019). In view of this, study on the protection of urban rivers and the relationship between river network conditions and functions attracts more and more attention.
In order to improve the city's ability to deal with flood disasters and reduce its harm to society and people, researchers both at home and abroad have successively carried out simulations on urban flood disasters through a variety of models and have achieved good social and economic effects. The modeling approaches for urban flood can be divided into three categories according to their calculation method, i.e., the hydrodynamic method, hydrologic method, and simplified method (Meng et al., 2019), among which the hydrodynamic method is commonly applied due to the clear physical laws (Rubinato et al., 2013). Some typical hydrodynamic models are MIKE-Urban (DHI, Denmark) (Lowe et al., 2017; Wang et al., 2017) or InfoWorks CS (Wallingford Software, UK) (Archetti et al., 2011; Hurford et al., 2010). Storm water management model (SWMM) (Zhou et al., 2018; Zhu et al., 2018; Babaei et al., 2018) is a popular urban hydrological model and suitable to generate floodwater in large catchment (Zhao et al., 2019). The well-known Soil Conservation Service-Curve Number (SCS-CN) method (Li et al., 2019) is also rapidly applied to calculate the surface runoff. Beside these models, GIS (Ozkan et al., 2016) is particularly useful in flood hazard mapping as it can incorporate both the spatial and physical dimension of the floods (Kourgialas et al., 2017), from which models such as UFIM (Urban Flood Inundation Model) (Chen et al., 2009), TSR (Tokyo Storm Runoff) model (Amaguchi, et al., 2011) are derived. For the areas lack of hydrological data, artificial neural network (ANN) technique (Panda, et al., 2009; Han et al., 2011) is always applied in order to get more accurate simulation results.
Hunt, Jasper Joseph (University of Western Australia) | Lubis, Michael Binsar (Oceans Graduate School, University of Western Australia) | Kimiaei, Mehrdad (Oceans Graduate School, University of Western Australia)
Extreme response analysis of flexible offshore facilities under current loads is an engineering challenge mainly for deep and ultra-deep waters. In this study, an improved method for defining characteristic current profiles for application in extreme response analysis is presented. It utilises unsupervised dimensionality reduction algorithms including Principle Component Analysis (PCA) and AutoEncoders (AE) followed by application of clustering through K-Means Algorithm (KMA) for two different deep-water locations to identify the current profile corresponding to extreme response of a typical ROV umbilical line. Additionally, a combination of dimensionality reduction and clustering method, known as Embedded Clustering (EC), is also explored alongside various pre-processing techniques. The unsupervised methods presented are demonstrated as an effective approach to scaling the clustering approach to a higher-class resolution.
Knowledge and understanding of ocean current profiles are essential for the design and operation of offshore structures and devices. Umbilicals between surface vessels and Remote Operated Vehicles (ROVs) are examples of flexible structural components exposed to hydrodynamic loads across the vertical expanse of the water column. The effects of ocean current on the umbilicals are becoming ever more prominent in deeper water. As a result, tools that can be used to effectively analyse this phenomenon is becoming increasingly necessary.
In ROV operation, hydrodynamic loads on the umbilical greatly impact the ROV-umbilical motion which results in a time-varying tension load in the umbilical under different current profiles. For design and operation of ROVs, it is important to identify the maximum tension in the umbilical as the extreme response to these current profiles. This is usually carried out through time-domain analysis using the current time history datasets. This is a time-consuming approach with excessive computational efforts.
Within offshore engineering industries, methods such as Current Profile Characterisation (CPC) are used to simplify current profile data. CPC is the process of reducing current time series datasets to smaller characteristic subset while retaining the key information needed for analysis (Prevosto, et al., 2011). This is a useful approach for reducing the computation time required in applications such as extreme response and fatigue analysis. CPC has been implemented in previous studies by clustering vertical current profiles using unsupervised learning (Prevosto et al., 2011; Jeans et al., 2015). Jeans et al. (2015) identified the K-Means Algorithm (KMA) as an effective form of CPC at low profile numbers. However, these studies applied clustering methods to the data in its original feature space only.
Wang, Nina (PowerChina Huadong Engineering Corporation Limited, Key Laboratory of Far-Shore Wind Power Technology of Zhejiang Province) | Liu, Xinwang (Computational Marine Hydrodynamics Lab (CMHL), State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University) | Wan, Decheng (Computational Marine Hydrodynamics Lab (CMHL), State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University)
Having good resistance and seakeeping performances, a trimaran usually consists of a main hull and two side hulls, which has already been one of the most noticed high-performance vessels. In order to optimize the total drag of the trimaran, hydrodynamic evaluation is an unavoidable and vital part. Using technology of Computational Fluid Dynamics, the evaluation of ship's hydrodynamic performances is mainly based on the potential-flow or viscous-flow theory. In order to reduce its total drag at Froude number 0.3, the hull form optimization solver OPTShip-SJTU is used to deform the spacings of the side hulls, which are regarded as optimization design variables. After evaluations of new sample hulls created by Optimal Latin Hypercube Sampling method, Kriging surrogate models are then constructed to save the computational cost. Finally, using Genetic Algorithm, the optimal hulls are obtained by two evaluation methods, and the optimal trimarans are further compared and analyzed to see the differences of two evaluation methods and its effect on the obtained optimal hulls.
Apart from catamaran, trimaran has become one of the most growing high-performance ships in these days due to their good performances including stability, resistance and seakeeping. In the early stage of the trimaran design, the resistance performance should be considered at first in order to save energy and reduce pollution.
A trimaran usually consists of a main hull and two same side hulls. The total drag of trimaran is mainly determined by the hull shape and spacings of the main and side hulls. Since the spacings including lateral spacing and longitudinal spacing of the side hulls relative to the main hull have a great impact on the wave-making interference between the demihulls, in order to optimize the total drag of the trimaran at the very first phase of the optimization design, the two spacings should be considered as the design variables.
Supply-chain solutions provider Würth Industry North America (WINA) and Baker Hughes created a joint service offering for their design, digital inventory, and customized 3D printing services to expand into different industrial sectors. The companies will work on design and additive manufacturing opportunities in the oil and gas, renewables, power generation, maritime, automotive, and aerospace industrial sectors. The collaboration improves WINA’s automation with no infrastructure change, allowing it to take customer prototype ideas to small-batch production and mass production at accelerated rates. Würth will offer Baker Hughes’ additive manufacturing services, and Baker Hughes gains access to Würth’s global customer base of 80,000 clients. The expanded service includes access to Baker Hughes’ digital inventory capabilities.