Analytical convolution model for shipping water evolution on a fixed structure

Shipping water events that propagate over the decks of marine structures can generate significant loads on them. As the configuration of the structure may affect the loading behaviour, investigation of shipping water loads in different structural conditions is required. This paper presents a numerical investigation of the effect of deck roughness and deck length on vertical and horizontal loads caused by shipping water on a fixed structure. Systematic analyses were carried out of isolated shipping water events generated with the wet dam-break method and simulated with OpenFoam Computational Fluid Dynamics toolbox. The numerical approach was validated and then the shipping water loads were examined. It was found that, as roughness increased, the maximum vertical and horizontal loads showed a delay. As the deck length reduced, the vertical backflow loads tended to increase. These results suggest it may be worthwhile examining the behaviour of shipping water as it propagates over rough surfaces caused by fouling, corrosion, or those with small structural elements distributed on them. Moreover, the effect of deck length is important in understanding the order of magnitude of loads on structures with variable deck lengths, and those which have forward and backflow loading stages.

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Capturing Two Consecutive Green Water Events by Convolution

Volume 7B: Ocean Engineering ◽

10.1115/omae2019-96747 ◽

2019 ◽

Cited By ~ 1

Author(s):

Jassiel V. Hernández-Fontes ◽

Rodolfo Silva-Casarín ◽

Edgar Mendoza

Keyword(s):

Time Series ◽

Green Water ◽

Analytical Models ◽

Regular Waves ◽

Convolution Model ◽

Water Elevations ◽

Convolution Approach ◽

Fixed Structure ◽

Hydrodynamic Effects ◽

Selection Of

Abstract Capturing the propagation of green water events on in ships and other marine structures is of importance when studying the hydrodynamic effects on their motion and the structure’s behavior. Analytical models used to predict green water elevations, such as dam-break models, have been considered to represent time series of water elevations of single green water events. This paper presents the use of a convolution approach to represent the time series of water elevations of two consecutive green water events on deck of a fixed structure. The procedure is described considering green water events, generated with regular waves, on a barge-type fixed structure. Its application is compared with results available elsewhere in the literature. With the assumptions related with the selection of input parameters of the convolution model, and considering only the first green water event, the results show that this methodology allows two consecutive green water events to be captured acceptably. It is hoped that this methodology will be useful in further time-domain applications which study the dynamic behavior of structures subjected to green water.

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Generalized Fixed-Structure Optimality Conditions for H2 Optimal Control

1993 American Control Conference ◽

10.23919/acc.1993.4793327 ◽

1993 ◽

Author(s):

Emmanuel G. Collins ◽

Wassim M. Haddad ◽

Sidney S. Ying

Keyword(s):

Optimal Control ◽

Optimality Conditions ◽

Fixed Structure

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Adaptive Wavelet Estimator for a Function and its Derivatives in an Indirect Convolution Model

Journal of Statistical Theory and Practice ◽

10.1080/15598608.2011.10412030 ◽

2011 ◽

Vol 5 (2) ◽

pp. 303-326 ◽

Cited By ~ 2

Author(s):

Christophe Chesneau

Keyword(s):

Adaptive Wavelet ◽

Wavelet Estimator ◽

Convolution Model

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Gradient Methods for Fixed Structure Controller Synthesis and System Identification of Decomposable Systems

IEEE Control Systems Letters ◽

10.1109/lcsys.2021.3085427 ◽

2021 ◽

pp. 1-1

Author(s):

Simon Heinke ◽

Herbert Werner

Keyword(s):

System Identification ◽

Gradient Methods ◽

Controller Synthesis ◽

Fixed Structure

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A Graph Convolution model considering label co-occurrence LC_GCN

2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence ◽

10.1145/3446132.3446155 ◽

2020 ◽

Author(s):

Chaoshun Chang ◽

Xiaoyong Li ◽

Yali Gao

Keyword(s):

Convolution Model

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Chinese Character Image Completion Using a Generative Latent Variable Model

Applied Sciences ◽

10.3390/app11020624 ◽

2021 ◽

Vol 11 (2) ◽

pp. 624

Author(s):

In-su Jo ◽

Dong-bin Choi ◽

Young B. Park

Keyword(s):

Latent Variable ◽

Chinese Character ◽

Image Inpainting ◽

Latent Variable Model ◽

Chinese Characters ◽

Image Completion ◽

Variable Model ◽

Variational Autoencoder ◽

Convolution Model ◽

Detection Technologies

Chinese characters in ancient books have many corrupted characters, and there are cases in which objects are mixed in the process of extracting the characters into images. To use this incomplete image as accurate data, we use image completion technology, which removes unnecessary objects and restores corrupted images. In this paper, we propose a variational autoencoder with classification (VAE-C) model. This model is characterized by using classification areas and a class activation map (CAM). Through the classification area, the data distribution is disentangled, and then the node to be adjusted is tracked using CAM. Through the latent variable, with which the determined node value is reduced, an image from which unnecessary objects have been removed is created. The VAE-C model can be utilized not only to eliminate unnecessary objects but also to restore corrupted images. By comparing the performance of removing unnecessary objects with mask regions with convolutional neural networks (Mask R-CNN), one of the prevalent object detection technologies, and also comparing the image restoration performance with the partial convolution model (PConv) and the gated convolution model (GConv), which are image inpainting technologies, our model is proven to perform excellently in terms of removing objects and restoring corrupted areas.

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