Back to Search
Start Over
Response predictions using the observed autocorrelation function
- Source :
- Nielsen, U D, H. Brodtkorb, A & Jensen, J J 2018, ' Response predictions using the observed autocorrelation function ', Marine Structures, vol. 58, pp. 31–52 . https://doi.org/10.1016/j.marstruc.2017.10.012
- Publication Year :
- 2018
- Publisher :
- Elsevier BV, 2018.
-
Abstract
- This article studies a procedure that facilitates short-time, deterministic predictions of the wave-induced motion of a marine vessel, where it is understood that the future motion of the vessel is calculated ahead of time. Such predictions are valuable to assist in the execution of many marine operations (crane lifts, helicopter landings, etc.), as a specific prediction can be used to inform whether it is safe, or not, to carry out the particular operation within the nearest time horizon. The examined prediction procedure relies on observations of the correlation structure of the wave-induced response in study. Thus, predicted (future) values ahead of time for a given time history recording are computed through a mathematical combination of the sample autocorrelation function and previous measurements recorded just prior to the moment of action. Importantly, the procedure does not need input about the exciting wave system, and neither does it rely on off-line training. In the article, the prediction procedure is applied to experimental data obtained through model-scale tests, and the procedure's predictive performance is investigated for various irregular wave scenarios. The presented results show that predictions can be successfully made in a time horizon corresponding to about 8–9 wave periods ahead of current time (the moment of action).
- Subjects :
- Determinstic motion prediction
Stationary process
Computer science
020101 civil engineering
Ocean Engineering
Time horizon
02 engineering and technology
01 natural sciences
010305 fluids & plasmas
0201 civil engineering
Conditional process
Sample autocorrelation function
0103 physical sciences
General Materials Science
SDG 14 - Life Below Water
business.industry
Autocorrelation technique
Mechanical Engineering
Measurements
Autocorrelation
Pattern recognition
Function (mathematics)
Moving-average model
Partial autocorrelation function
Moment (mathematics)
Mechanics of Materials
Artificial intelligence
business
Real-time
Algorithm
Subjects
Details
- ISSN :
- 09518339
- Volume :
- 58
- Database :
- OpenAIRE
- Journal :
- Marine Structures
- Accession number :
- edsair.doi.dedup.....cd7ac9b666a10863e62917d8337d47f2
- Full Text :
- https://doi.org/10.1016/j.marstruc.2017.10.012