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Wind Energy
Published in Frank Jackson, Dilwyn Jenkins, Renewable Energy Systems, 2013
Less vital, but also a factor (since different turbines start generating electricity at different cut-in wind speeds), is that wind speeds and cut-in speeds should be analysed together to work out the likely yearly output on any wind installation. In this book, wind speed is represented in metres per second (1 m/s = approximately 2.99 ft/s or 1.944 knots). In North America the wind is strongest (mainly between 7 m/s and 9 m/s) in the Rocky Mountains, particularly the eastern edge and down onto the Great Plains, where water pumping has been going on for well over 100 years. There are also strong wind areas just to the south of Chicago and to the northwest of Minneapolis (both areas achieving speeds of up to 7.5 m/s). This can be seen quite clearly on the Wind Powering America website (www.windpoweringamerica.gov/wind_maps.asp).
Ocean Environment/Sea States
Published in Sukumar Laik, Offshore Petroleum Drilling and Production, 2018
Similarly, it also is to be noted that wind speed is conventionally reported in knots (1 knot = 1 nautical mile/h = 1.15 statute mile/h = 1.852 km/h). Wind speeds are categorised as Beaufort numbers (devised in 1805 by an Irish Royal Navy officer named Rear Admiral Sir Francis Beaufort and adopted in 1830), which are presented in tabular form known as the Beaufort Scale (Table 2.1). Beaufort numbers between 7 and 10 are known as gales in which 7 indicates moderate gale (28–33 knots), 8 indicates a fresh gale (34–40 knots), 9 indicates a strong gale (41–47 knots) and 10 indicates a whole gale (48–55 knots).
Equations of motion
Published in Mohammad H. Sadraey, Aircraft Performance, 2017
In terms of speed, one knot is equal to one nautical mile per hour. For cars and trains, statute mile is used in the United States, since statute mile is different from nautical mile. Relationships between various units of speed are as follows: Knot=Nautical mileHour
A deep learning approach for port congestion estimation and prediction
Published in Maritime Policy & Management, 2023
Wenhao Peng, Xiwen Bai, Dong Yang, Kum Fai Yuen, Junfeng Wu
When matching the waiting time of other ports, we also consider time lags to account for the propagation time of congestion. Since ships which are delayed in the preceding port need to undertake a voyage to arrive the next port, the propagation time of congestion from one port to the other should be consistent with the voyage time. Therefore, we calculate the lag time among ports based on their nautical mile distances over an average speed of 24 knots. Based on the result from Figure 10, the identified propagation effect only exists between nearby ports, i.e., congestion propagation time between two ports is less than 2 days, which means that after taking the time lag, there are about 48 observations at most deleted from one month observation, and the left observations are enough to identify the congestion propagation effect.
Distributed output feedback control of decomposable LPV systems with delay and switching topology: application to consensus problem in multi-agent systems
Published in International Journal of Control, 2021
A multi-agent system composed of VTOL helicopters subject to parameter varying time delay and switching topology can be described as where the state variables are horizontal velocity, vertical velocity, pitch rate and pitch angle, respectively. The matrices in (47) are given as and the parameter is defined as , where is the airspeed in knots. This example is inspired by Zakwan (2020).
Quantile regression analysis of time-space variation characteristics of tropical cyclones in the west North Pacific basin under global warming
Published in Coastal Engineering Journal, 2022
X. J. Wang, J. W. Yang, B. Huang, J. F. Cao
The historical TC data and sea surface temperature (SST) anomaly used in the present study are obtained from the National Center for Atmospheric Research (NCAR), National Oceanic and Atmospheric Administration (NOAA) (Knapp et al. 2010), and China Meteorological Administration (Lu et al. 2021; Ying et al. 2014). The information in these datasets includes the central location (latitude and longitude), maximum 10-min sustained wind speed and minimum sustained atmospheric pressure in knots for each 6-h interval. The SST anomaly data are from NCAR reanalysis dataset (Kistler et al. 2001). Note that, in the period before 1970, aircraft observations were the mainstream method to observe the TC intensity. The Dvorak method was employed in the period after 1970. Indeed, the reliability of the data used in the present study has been discussed by Ying et al. (2014). For the TC datasets, data quality control was conducted, and the analyzed dataset drafts were submitted to the Working Group of Typhoons and Marine Meteorology Experts for checking after the season (Lu et al. 2021). Moreover, detailed analysis of any difficult cases was carried out by the temporary working group of experts formed at the end of each year. Final examination at the beginning of the following year was provided to ensure the accuracy and reliability of TC data. However, the data inhomogeneity contributed to the real trend are incompletely calculated and considered in present paper, because the separation of the real climate signal and data artifacts within observed trends is almost impossible (Schreck, Knapp, and Kossin 2014; Moon, Kim, and Chan 2019). All the same, the analysis method and results will provide an effective reference for trend prediction of TCs.