Scale effects in AR model real-time ship motion prediction

Abstract Real-time prediction of ship motion is essential for decision making in shipborne maritime operations. Differences in ship hulls render different ship motion characteristics, which consequently affects the performance of real-time prediction models. In this study, the ship hull scale effects in real-time motion prediction are investigated using the AR model. The ship datasets are generated by applying the strip theory. These ship motions datasets with various spectral characteristics are used in real-time prediction simulations. This study explores how the spectrum bandwidth, peak frequency, and ship hull scale influence prediction performance, and conclusions are drawn based on numerical simulation results. Prediction accuracy shows a negative relation to spectrum bandwidth and peak frequency. The AR model performance is better for ships with larger principal dimensions where ship hulls are the same. A preliminary empirical formulation for evaluating the maximum predictable time duration is developed based on the above regularities.

[1]  Yuntao Han,et al.  A hybrid EMD-SVR model for the short-term prediction of significant wave height , 2016 .

[2]  H. Akaike.  A new look at the statistical model identification , 1974 .

[3]  Stergios John Liapis,et al.  Time-Domain Analysis of Ship Motions , 1986 .

[4]  C Bil,et al.  Real time prediction of ship motion for the aid of helicopter and aircraft deployment and recovery , 2006 .

[5]  W. Cummins.  THE IMPULSE RESPONSE FUNCTION AND SHIP MOTIONS , 2010 .

[6]  Shi Ai-guo,et al.  Empirical mode decomposition based LSSVM for ship motion prediction , 2013, ISNN 2013.

[7]  Joel T. Johnson,et al.  A Real-Time System for Forecasting Extreme Waves and Vessel Motions , 2015 .

[8]  Gu Mi.  Extreme short-term prediction of ship motion based on chaotic theory and RBF neural network , 2013 .

[9]  Atilla Incecik,et al.  Comparative study on steady wave-making problem using viscous and potential-flow methods , 2018 .

[10]  H. Akaike.  A Bayesian extension of the minimum AIC procedure of autoregressive model fitting , 1979 .

[11]  C. Soares,et al.  Time-Domain Analysis of Large-Amplitude Vertical Ship Motions and Wave Loads , 1998 .

[12]  Xilin Yang,et al.  Displacement motion prediction of a landing deck for recovery operations of rotary UAVs , 2013 .

[13]  V. Panchang,et al.  One-Day Wave Forecasts Based on Artificial Neural Networks , 2006 .

[14]  C. Bil,et al.  Ship motion prediction for launch and recovery of air vehicles , 2005, Proceedings of OCEANS 2005 MTS/IEEE.

[15]  Paul Kaplan,et al.  A PRELIMINARY STUDY OF PREDICTION TECHNIQUES FOR AIRCRAFT CARRIER MOTIONS AT SEA , 1965 .

[16]  I. Yumori,et al.  Real Time Prediction of Ship Response to Ocean Waves Using Time Series Analysis , 1981 .

[17]  Michael S. Triantafyllou,et al.  Real time prediction of marine vessel motions using Kalman filtering techniques , 1982 .

[18]  Jie Ma,et al.  Comparison of Representative Method for Time Series Prediction , 2006, 2006 International Conference on Mechatronics and Automation.

[19]  Michael S. Triantafyllou,et al.  Real Time Estimation of the Heaving and Pitching Motions of a Ship, Using a Kalman Filter , 1981 .

[20]  Harry B. Bingham,et al.  Simulating ship motions in the time domain , 1994 .

[21]  Wang Rui,et al.  IRF - AR Model for Short-Term Prediction of Ship Motion , 2015 .

[22]  Makarand Deo,et al.  Real time wave forecasting using neural networks , 1998 .

[23]  W. S. Ra,et al.  Real-time long-term prediction of ship motion for fire control applications , 2006 .

[24]  Michael S. Triantafyllou,et al.  Real time estimation of ship motions using Kalman filtering techniques , 1983 .