Development of Closed Formula of Wave Load Based Upon Long-Term Prediction: Heave Acceleration and Pitch Angle

2020 ◽  
Author(s):  
Kyohei Shinomoto ◽  
Sadaoki Matsui ◽  
Kei Sugimoto ◽  
Shinsaku Ashida
Keyword(s):  
Pitch Angle ◽  
Irregular Waves ◽  
Structural Safety ◽  
Closed Formula ◽  
Wave Load ◽  
Structural Rules ◽  
Term Prediction ◽  
Long Term Prediction ◽  
Classification Society

Abstract In order to ensure the structural safety of a ship, the most severe sea states she is expected to encounter throughout her service life need to be given consideration. This is the reason why the maximum loads corresponding to such sea states are typically specified in classification society structural rules such as the Common Structural Rules (CSR) of the International Association of Classification Societies (IACS). The maximum loads used for the structural design of a ship can have a significant impact on not only her structural safety, but also her hull construction cost; therefore, it is very important that the loads be accurately estimated. The linear term of the maximum loads typically specified in some classification society rules is equivalent to a long-term predicted value with an exceedance probability of 10−8. Since the maximum loads specified in classification society rules such as the CSR were developed specifically for specific ship types, their effective application to other ship types may be somewhat limited. Aim of our larger study is to develop a closed formula of long-term prediction for maximum loads. The formula has high accuracy and can be applied to any ship size and type. This paper focused on the heave acceleration and pitch angle, which are used for the calculation of internal loads and so on. A formula which takes into account such as the standard deviation of the hull response in irregular waves and the directional distribution of irregular waves was proposed. Main ship parameters such as ship length L, breadth B, draft d, block coefficient Cb, and water line area coefficient Cw were used for formulating the long-term prediction. The accuracy and effectiveness of the proposed formula were confirmed through various numerical calculations using a linear seakeeping analysis code developed by ClassNK. The calculation covers 154 ship models (77 existing ships × 2 loading conditions per ship).

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

Structural Safety Assessment of a 1100 TEU Container Ship, Based on a Enhanced Long Term Fatigue Analysis

2014 ◽  
Vol 1036 ◽  
pp. 935-940
Author(s):  
Leonard Domnisoru ◽  
Ionica Rubanenco ◽  
Mihaela Amoraritei
Keyword(s):  
Safety Assessment ◽  
Safety Evaluation ◽  
Irregular Waves ◽  
Structural Safety ◽  
Random Wave ◽  
Wave Loads ◽  
Strength Assessment ◽  
Integrated Method ◽  
Hydroelastic Response

This paper is focused on an enhanced integrated method for structural safety assessment of maritime ships under extreme random wave loads. In this study is considered an 1100 TEU container test ship, with speed range 0 to 18 knots. The most comprehensive criteria for ships structural safety evaluation over the whole exploitation life is based on the long term ship structures analysis, that includes: stress hot-spots evaluation by 3D/1D-FEM hull models, computation of short term ship dynamic response induced by irregular waves, long term fatigue structure assessment. The analysis is enhanced by taking into account the ships speed influence on hydroelastic response. The study includes a comparative analysis on two scenarios for the correlation between the ships speed and waves intensity. The standard constant ship speed scenario and CENTEC scenario, with total speed loss at extreme waves condition, are considered. Instead of 20 years ship exploitation life estimated by classification societies rules from the long term structural safety criteria, the enhanced method has predicted more restrictive values of 14.4-15.7 years. The numerical analyses are based on own software and user subroutines. The study made possible to have a more realistic approach of ships structural strength assessment, for elastic and faster ships as container carriers, in compare to the standard one based only on naval rules, delivering a method with higher confidence in the designed structural safety.

Long term prediction of forest resources

1985 ◽  
Vol 12 ◽  
pp. 176
Author(s):  
H Kurth ◽  
B Anders ◽  
G Lucas
Keyword(s):  
Forest Resources ◽  
Term Prediction ◽  
Long Term Prediction
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