Nonlinear Ship Loads: Stochastic Models for Extreme Response

In this study we focus on stochastic analysis methods for selective simulations, and we consider the extreme midspan moment of a fast-moving ship subjected to random Gaussian waves. We concentrate on analysis within a stationary sea state and our purpose is to accurately estimate hourly maximum ship response (compared with the correct result per hour) within a sea state with as little computational resources as possible. We consider how the use of a limited number of short simulations with "critical wave episodes" (short wave segments which are likely candidates to produce extreme response in the simulated hour-long history) reduces the cost of nonlinear time-domain ship response analysis.

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Design Seastates for Extreme Response of Jackup Structures

Journal of Offshore Mechanics and Arctic Engineering ◽

10.1115/1.2829523 ◽

1998 ◽

Vol 120 (2) ◽

pp. 103-108 ◽

Cited By ~ 1

Author(s):

S. R. Winterstein ◽

R. Torhaug ◽

S. Kumar

Keyword(s):

Earthquake Engineering ◽

Time Domain ◽

Flexible Structures ◽

Wave Model ◽

Response Prediction ◽

Wave Crest ◽

Response Analysis ◽

New Wave ◽

Extreme Response ◽

Wave Methods

The extreme response of a jackup structure is studied. We consider how design seastate histories can be introduced to reduce the cost of time-domain response analysis. We first identify critical wave characteristics for extreme response prediction. In quasi-static cases, the maximum wave crest height, ηmax, is shown to best explain extreme deck sway. For more flexible structures we introduce a new wave characteristic, SD, based on response spectral concepts from earthquake engineering. Finally, we show how accurate response estimates can require fewer time-domain analyses, provided design seastates are pre-selected to ensure that ηmax or SD is near its average value. With respect to standard Monte-Carlo simulation, these design seastates achieve at least a 50-percent reduction in response variability, and hence at least a fourfold savings in needed simulation cost. These results may lend insight, not only into time-domain simulation, but also into more fundamental questions of jackup behavior. They also suggest that, at least in quasi-static cases, still simpler design wave methods based on ηmax may suffice. We illustrate and evaluate some such design wave methods here (e.g., the “new wave” model and others based on Slepian theory).

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Time Domain Simulation of Jack-up Dynamics With the Extremes of a Gaussian Process

Journal of Vibration and Acoustics ◽

10.1115/1.2889772 ◽

1997 ◽

Vol 119 (4) ◽

pp. 624-628 ◽

Cited By ~ 33

Author(s):

P. H. Taylor ◽

P. Jonathan ◽

L. A. Harland

Keyword(s):

Time Domain ◽

Random Sequence ◽

Time Domain Simulation ◽

Sea State ◽

Efficient Manner ◽

Large Wave ◽

Random Occurrence ◽

Statistical Limit ◽

Extreme Response ◽

Jack Up

Random simulations are often used to simulate the statistics of storm-driven waves. Work on Gaussian linear random signals has lead to a method for embedding a large wave into a random sequence in such a way that the composite signal is virtually indistinguishable (in a rigorous statistical limit) from a purely random occurrence of a large wave. We demonstrate that this idea can be used to estimate the extreme response of a jack-up in a severe sea-state in a robust and efficient manner. Results are in good agreement with those obtained from a full random time-domain simulation.

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Enabling Service Cache in Edge Clouds

ACM Transactions on Internet of Things ◽

10.1145/3456564 ◽

2021 ◽

Vol 2 (3) ◽

pp. 1-24

Author(s):

Chih-Kai Huang ◽

Shan-Hsiang Shen

Keyword(s):

Virtual Machines ◽

Replacement Policy ◽

Cache Replacement ◽

Cache Replacement Policy ◽

Average Latency ◽

Hit Rates ◽

Iot Devices ◽

Multi Access ◽

Computational Resources ◽

The Cost

The next-generation 5G cellular networks are designed to support the internet of things (IoT) networks; network components and services are virtualized and run either in virtual machines (VMs) or containers. Moreover, edge clouds (which are closer to end users) are leveraged to reduce end-to-end latency especially for some IoT applications, which require short response time. However, the computational resources are limited in edge clouds. To minimize overall service latency, it is crucial to determine carefully which services should be provided in edge clouds and serve more mobile or IoT devices locally. In this article, we propose a novel service cache framework called S-Cache , which automatically caches popular services in edge clouds. In addition, we design a new cache replacement policy to maximize the cache hit rates. Our evaluations use real log files from Google to form two datasets to evaluate the performance. The proposed cache replacement policy is compared with other policies such as greedy-dual-size-frequency (GDSF) and least-frequently-used (LFU). The experimental results show that the cache hit rates are improved by 39% on average, and the average latency of our cache replacement policy decreases 41% and 38% on average in these two datasets. This indicates that our approach is superior to other existing cache policies and is more suitable in multi-access edge computing environments. In the implementation, S-Cache relies on OpenStack to clone services to edge clouds and direct the network traffic. We also evaluate the cost of cloning the service to an edge cloud. The cloning cost of various real applications is studied by experiments under the presented framework and different environments.

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Survey of stochastic models for wind and sea state time series

Probabilistic Engineering Mechanics ◽

10.1016/j.probengmech.2006.08.003 ◽

2007 ◽

Vol 22 (2) ◽

pp. 113-126 ◽

Cited By ~ 62

Author(s):

V. Monbet ◽

P. Ailliot ◽

M. Prevosto

Keyword(s):

Time Series ◽

Stochastic Models ◽

Sea State ◽

State Time

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Optimized Mooring Line Simulation Using a Hybrid Method Time Domain Scheme

Volume 1B: Offshore Technology ◽

10.1115/omae2014-23939 ◽

2014 ◽

Cited By ~ 3

Author(s):

Niels Hørbye Christiansen ◽

Per Erlend Torbergsen Voie ◽

Jan Høgsberg ◽

Nils Sødahl

Keyword(s):

Hybrid Method ◽

Time Domain ◽

Computation Time ◽

Mooring Line ◽

Mooring Lines ◽

Fem Model ◽

Input Variables ◽

The Time Domain ◽

The Cost ◽

Short Time

Dynamic analyses of slender marine structures are computationally expensive. Recently it has been shown how a hybrid method which combines FEM models and artificial neural networks (ANN) can be used to reduce the computation time spend on the time domain simulations associated with fatigue analysis of mooring lines by two orders of magnitude. The present study shows how an ANN trained to perform nonlinear dynamic response simulation can be optimized using a method known as optimal brain damage (OBD) and thereby be used to rank the importance of all analysis input. Both the training and the optimization of the ANN are based on one short time domain simulation sequence generated by a FEM model of the structure. This means that it is possible to evaluate the importance of input parameters based on this single simulation only. The method is tested on a numerical model of mooring lines on a floating off-shore installation. It is shown that it is possible to estimate the cost of ignoring one or more input variables in an analysis.

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