Standard
Wind Simulation Using High-Frequency Velocity Component Measurements. / Гавриков, Антон Александрович; Gankevich, Ivan.
Computational Science and Its Applications – ICCSA 2021 - 21st International Conference, Proceedings. ред. / 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. Cham : Springer Nature, 2021. стр. 471-485 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 12956 LNCS).
Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Harvard
Гавриков, АА & Gankevich, I 2021,
Wind Simulation Using High-Frequency Velocity Component Measurements. в O Gervasi, B Murgante, S Misra, C Garau, I Blečić, D Taniar, BO Apduhan, AMAC Rocha, E Tarantino & CM Torre (ред.),
Computational Science and Its Applications – ICCSA 2021 - 21st International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Том. 12956 LNCS, Springer Nature, Cham, стр. 471-485, 21st International Conference on Computational Science and Its Applications, ICCSA 2021, Virtual, Online, Италия,
13/09/21.
https://doi.org/10.1007/978-3-030-87010-2_35
APA
Гавриков, А. А., & Gankevich, I. (2021).
Wind Simulation Using High-Frequency Velocity Component Measurements. в O. Gervasi, B. Murgante, S. Misra, C. Garau, I. Blečić, D. Taniar, B. O. Apduhan, A. M. A. C. Rocha, E. Tarantino, & C. M. Torre (Ред.),
Computational Science and Its Applications – ICCSA 2021 - 21st International Conference, Proceedings (стр. 471-485). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 12956 LNCS). Springer Nature.
https://doi.org/10.1007/978-3-030-87010-2_35
Vancouver
Гавриков АА, Gankevich I.
Wind Simulation Using High-Frequency Velocity Component Measurements. в Gervasi O, Murgante B, Misra S, Garau C, Blečić I, Taniar D, Apduhan BO, Rocha AMAC, Tarantino E, Torre CM, Редакторы, Computational Science and Its Applications – ICCSA 2021 - 21st International Conference, Proceedings. Cham: Springer Nature. 2021. стр. 471-485. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
https://doi.org/10.1007/978-3-030-87010-2_35
Author
Гавриков, Антон Александрович ; Gankevich, Ivan. /
Wind Simulation Using High-Frequency Velocity Component Measurements. Computational Science and Its Applications – ICCSA 2021 - 21st International Conference, Proceedings. Редактор / 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. Cham : Springer Nature, 2021. стр. 471-485 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
BibTeX
@inproceedings{b47983879b984dae8cc1e6ccbfcbb562,
title = "Wind Simulation Using High-Frequency Velocity Component Measurements",
abstract = "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.",
keywords = "Anemometer, Autoregressive model, Load cell, Strain gauge, Three-dimensional ACF, Turbulence, Wind velocity PDF, DISTRIBUTIONS, SPEED",
author = "Гавриков, {Антон Александрович} and Ivan Gankevich",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 21st International Conference on Computational Science and Its Applications, ICCSA 2021, ICCSA 2021 ; Conference date: 13-09-2021 Through 16-09-2021",
year = "2021",
doi = "10.1007/978-3-030-87010-2_35",
language = "English",
isbn = "9783030870096",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "471--485",
editor = "Osvaldo Gervasi and Beniamino Murgante and Sanjay Misra and Chiara Garau and Ivan Ble{\v c}i{\'c} and David Taniar and Apduhan, {Bernady O.} and Rocha, {Ana Maria A. C.} and Eufemia Tarantino and Torre, {Carmelo Maria}",
booktitle = "Computational Science and Its Applications – ICCSA 2021 - 21st International Conference, Proceedings",
address = "Germany",
}
RIS
TY - GEN
T1 - Wind Simulation Using High-Frequency Velocity Component Measurements
AU - Гавриков, Антон Александрович
AU - Gankevich, Ivan
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Anemometer
KW - Autoregressive model
KW - Load cell
KW - Strain gauge
KW - Three-dimensional ACF
KW - Turbulence
KW - Wind velocity PDF
KW - DISTRIBUTIONS
KW - SPEED
UR - http://www.scopus.com/inward/record.url?scp=85115676829&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/69429700-db39-312b-a066-7edac5995469/
U2 - 10.1007/978-3-030-87010-2_35
DO - 10.1007/978-3-030-87010-2_35
M3 - Conference contribution
SN - 9783030870096
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 471
EP - 485
BT - Computational Science and Its Applications – ICCSA 2021 - 21st International Conference, Proceedings
A2 - Gervasi, Osvaldo
A2 - Murgante, Beniamino
A2 - Misra, Sanjay
A2 - Garau, Chiara
A2 - Blečić, Ivan
A2 - Taniar, David
A2 - Apduhan, Bernady O.
A2 - Rocha, Ana Maria A. C.
A2 - Tarantino, Eufemia
A2 - Torre, Carmelo Maria
PB - Springer Nature
CY - Cham
T2 - 21st International Conference on Computational Science and Its Applications, ICCSA 2021
Y2 - 13 September 2021 through 16 September 2021
ER -
