Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Wind simulation in the context of ships motions is used to estimate the effect of the wind on large containerships, sailboats and yachts. Wind models are typically based on a sum of harmonics with random phases and different amplitudes. In this paper we propose to use autoregressive model to simulate the wind. This model is based on autocovariance function that can be estimated from the real-world data collected by anemometers. We have found none of the data that meets our resolution requirements, and decided to produce the dataset ourselves using three-axis anemometer. We built our own anemometer based on load cells, collected the data with the required resolution, verified the data using well-established statistical distributions, estimated autocovariance functions from the data and simulated the wind using autoregressive model. We have found that the load cell anemometer is capable of recording wind speed for statistical studies, but autoregressive model needs further calibration to reproduce the wind with the same statistical properties.
Original language | English |
---|---|
Title of host publication | Computational Science and Its Applications – ICCSA 2021 - 21st International Conference, Proceedings |
Editors | Osvaldo Gervasi, Beniamino Murgante, Sanjay Misra, Chiara Garau, Ivan Blečić, David Taniar, Bernady O. Apduhan, Ana Maria A. C. Rocha, Eufemia Tarantino, Carmelo Maria Torre |
Place of Publication | Cham |
Publisher | Springer Nature |
Pages | 471-485 |
Number of pages | 15 |
ISBN (Print) | 9783030870096 |
DOIs | |
State | Published - 2021 |
Event | 21st International Conference on Computational Science and Its Applications, ICCSA 2021 - Virtual, Online, Italy Duration: 13 Sep 2021 → 16 Sep 2021 |
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
---|---|
Volume | 12956 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference | 21st International Conference on Computational Science and Its Applications, ICCSA 2021 |
---|---|
Abbreviated title | ICCSA 2021 |
Country/Territory | Italy |
City | Virtual, Online |
Period | 13/09/21 → 16/09/21 |
ID: 85910272