WAVE PREDICTION BY NEURAL NETWORK USING ATMOSPHERIC PRESSURE AND WIND SPEEDS

2018 ◽  
Vol 74 (2) ◽  
pp. I_691-I_696 ◽  
Author(s):  
Tracey H. A. TOM ◽  
Hajime MASE ◽  
Ai IKEMOTO ◽  
Takehisa SAITOH ◽  
Koji KAWASAKI ◽  
...  
Keyword(s):  
Neural Network ◽  
Atmospheric Pressure ◽  
Wind Speeds ◽  
Wave Prediction

scholarly journals Viscosity–Temperature–Pressure Relationship of Extra-Heavy Oil (Bitumen): Empirical Modelling versus Artificial Neural Network (ANN)

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2390 ◽  
Author(s):  
Olalekan Alade ◽  
Dhafer Al Shehri ◽  
Mohamed Mahmoud ◽  
Kyuro Sasaki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heavy Oil ◽  
Atmospheric Pressure ◽  
Predictive Accuracy ◽  
Empirical Models ◽  
Viscosity Data ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The viscosity data of two heavy oil samples X and Y, with asphaltene contents 24.8% w/w and 18.5% w/w, respectively, were correlated with temperature and pressure using empirical models and the artificial neural network (ANN) approach. The viscosities of the samples were measured over a range of temperatures between 70 °C and 150 °C; and from atmospheric pressure to 7 MPa. It was found that the viscosity of sample X, at 85 °C and atmospheric pressure (0.1 MPa), was 1894 cP and that it increased to 2787 cP at 7 MPa. At 150 °C, the viscosity increased from 28 cP (at 0.1 MPa) to 33 cP at 7 MPa. For sample Y, the viscosity at 70 °C and 0.1 MPa increased from 2260 cP to 3022 cP at 7 MPa. At 120 °C, the viscosity increased from 65 cP (0.1 MPa) to 71 cP at 7 MPa. Notably, using the three-parameter empirical models (Mehrotra and Svrcek, 1986 and 1987), the correlation constants obtained in this study are very close to those that were previously obtained for the Canadian heavy oil samples. Moreover, compared to other empirical models, statistical analysis shows that the ANN model has a better predictive accuracy (R2 ≈ 1) for the viscosity data of the heavy oil samples used in this study.

scholarly journals Novel Modified Elman Neural Network Control for PMSG System Based on Wind Turbine Emulator

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Chih-Hong Lin
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Permanent Magnet ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Generator ◽  
Power Converter ◽  
Network Control ◽  
Elman Neural Network ◽  
Wind Speeds ◽  
Ac Power

The novel modified Elman neural network (NN) controlled permanent magnet synchronous generator (PMSG) system, which is directly driven by a permanent magnet synchronous motor (PMSM) based on wind turbine emulator, is proposed to control output of rectifier (AC/DC power converter) and inverter (DC/AC power converter) in this study. First, a closed loop PMSM drive control based on wind turbine emulator is designed to generate power for the PMSG system according to different wind speeds. Then, the rotor speed of the PMSG, the voltage, and current of the power converter are detected simultaneously to yield better power output of the converter. Because the PMSG system is the nonlinear and time-varying system, two sets online trained modified Elman NN controllers are developed for the tracking controllers of DC bus power and AC power to improve output performance of rectifier and inverter. Finally, experimental results are verified to show the effectiveness of the proposed control scheme.

scholarly journals Sea Surface Wind Correction Using HF Ocean Radar and Its Impact on Coastal Wave Prediction

2017 ◽  
Vol 34 (9) ◽  
pp. 2001-2020 ◽  
Author(s):  
Yukiharu Hisaki
Keyword(s):  
Wind Direction ◽  
Surface Wind ◽  
Hf Radar ◽  
Sea Surface ◽  
Wave Parameter ◽  
Simple Method ◽  
Wind Speeds ◽  
Wave Prediction ◽  
Sea Surface Wind ◽  
The Impact

AbstractBoth wind speeds and wind directions are important for predicting wave heights near complex coastal areas, such as small islands, because the fetch is sensitive to the wind direction. High-frequency (HF) radar can be used to estimate sea surface wind directions from first-order scattering. A simple method is proposed to correct sea surface wind vectors from reanalysis data using the wind directions estimated from HF radar. The constraints for wind speed corrections are that the corrections are small and that the corrections of horizontal divergences are small. A simple algorithm for solving the solution that minimizes the weighted sum of the constraints is developed. Another simple method is proposed to correct sea surface wind vectors. The constraints of the method are that corrections of wind vectors and horizontal divergences from the reanalysis wind vectors are small and that the projection of the corrected wind vectors to the direction orthogonal to the HF radar–estimated wind direction is small. The impact of wind correction on wave parameter prediction is large in the area in which the fetch is sensitive to wind direction. The accuracy of the wave prediction is improved by correcting the wind in that area, where correction of wind direction is more important than correction of wind speeds for the improvement. This method could be used for near-real-time wave monitoring by correcting forecast winds using HF radar data.

Use BP Neural Network Set up the Corrosion Prediction Model of Low Temperature Parts of Atmospheric Pressure Device

2012 ◽  
Vol 152-154 ◽  
pp. 1138-1142 ◽  
Author(s):  
Yu Guang Fan ◽  
Zai Dong Piao ◽  
Bing Chen ◽  
Hong Xian Lin ◽  
Yang Yang
Keyword(s):  
Neural Network ◽  
Low Temperature ◽  
Prediction Model ◽  
Corrosion Protection ◽  
Bp Neural Network ◽  
Atmospheric Pressure ◽  
Ph Value ◽  
Set Up

In research of the low temperature parts of atmospheric pressure device, by using BP neural network, the connection of PH value, Cl-, H2S and Fe+2 was setup which can predict Fe+2 content accurately, and obtain the requirement accuracy, hence more accurate corrosion can be predicted and providing more suggests for corrosion protection.

scholarly journals Development of a smart system for diagnosing the operating conditions of a helicopter prototype via vibrations analysis

2021 ◽  
Vol 10 (12) ◽  
pp. e304101220546
Author(s):  
João Manoel de Oliveira Neto ◽  
Andersson Guimarães Oliveira ◽  
João Vitor Lira de Carvalho Firmino ◽  
Marcelo Cavalcanti Rodrigues ◽  
Antônio Almeida Silva ◽  
...  
Keyword(s):  
Neural Network ◽  
Fundamental Frequency ◽  
Vibration Analysis ◽  
Vibration Amplitude ◽  
Operating Conditions ◽  
The Other ◽  
Smart System ◽  
Wind Speeds ◽  
Hover Flight ◽  
Artificial Neural Network Ann

In the forward flight, wind loads affect the helicopters and cause vibration. This paper analyzes the behavior of a helicopter prototype composed by two blades when subjected to a front wind load, similar to the forwarding flight condition. An Artificial Neural Network (ANN) processes the experimental data in order to identify the pattern of its dynamic behavior. The tests led to Vibration analysis for different wind speeds. Also, the data indicates that vibration amplitude increases when the blades are subjected to the fundamental frequency and its first harmonic on tests conducted without rotor plane tilt (hover flight). On the other hand, the second test performs a 5-degree tilt on the rotor disc. In this test, the vibration amplitude decreased in the fundamental frequency, and the amplitude related to the first harmonic increased. The ANN achieved 100% efficiency in recognizing the flight conditions of the prototype.

scholarly journals A Neural Network-Based Rain Effect Correction Method for HY-2A Scatterometer Backscatter Measurements

2020 ◽  
Vol 12 (10) ◽  
pp. 1648
Author(s):  
Xuetong Xie ◽  
Jing Wang ◽  
Mingsen Lin
Keyword(s):  
Neural Network ◽  
Systematic Error ◽  
Surface Wind ◽  
Correction Method ◽  
Reanalysis Data ◽  
Systematic Deviation ◽  
Wind Speeds ◽  
The Neural Network ◽  
Rain Effect ◽  
Ku Band

The backscattering coefficients measured by Ku-band scatterometers are strongly affected by rainfall, resulting in a systematic error in sea surface wind field retrieval. In rainy conditions, the radar signals are subject to absorption by the raindrops in their round-trip propagation through the atmosphere, while the backscatter of raindrops raises the echo energy. In addition, raindrops give rise to roughness by impinging the ocean surface, resulting in an increase in the echo energy measured by a scatterometer. Under moderate wind conditions, the comprehensive impact of rainfall causes the wind speeds retrieved by the scatterometer to be higher than their actual values. The HY-2A scatterometer is a Ku-band, pencil-beam, conically scanning scatterometer. To correct the systematic error of the HY-2A scatterometer measurement in rainy conditions, a neural network model is proposed according to the characteristics of the backscatter coefficients measured by the HY-2A scatterometer in the presence of rain. With the neural network, the wind fields of the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data were used as the reference to correct the deviation in backscatter coefficients measured by the HY-2A scatterometer in rainy conditions, and the accuracy in wind speeds retrieved using the corrected backscatter coefficients was significantly improved. Compared with the cases of wind retrieval without rain effect correction, the wind speeds retrieved from the corrected backscatter coefficients by the neural network show a much lower systematic deviation, which indicates that the neural network can effectively remove the systematic deviation in the backscatter coefficients and the retrieved wind speeds caused by rain.

scholarly journals Wind Conditions in a Fjordlike Bay and Predictions of Wind Speed Using Neighboring Stations Employing Neural Network Models

2014 ◽  
Vol 53 (6) ◽  
pp. 1525-1537 ◽  
Author(s):  
Jens J. Currie ◽  
Pierre J. Goulet ◽  
Andry W. Ratsimandresy
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wind Speed ◽  
Network Performance ◽  
South Coast ◽  
General Description ◽  
The South ◽  
Neural Network Models ◽  
Absolute Minimum ◽  
Wind Speeds

AbstractThis paper evaluates the applicability of neural networks for estimating wind speeds at various target locations using neighboring reference locations along the south coast of Newfoundland, Canada. The stations were chosen to cover a variety of topographic features and span distances in excess of 100 km. The goal of the study is to provide a general description of the summer wind conditions along the south coast of Newfoundland and to assess the potential application of neural networks for wind speed predictions. Analysis of wind data from July to October showed the wind going dominantly toward the northeast with speeds ranging from 0 to 45 m s−1. The efficacy of neural networks to predict wind speeds varied among stations and was largely influenced by the presence/absence of wind barriers. Sensitivity analysis on neural network performance concluded that an absolute minimum of 3000 h of continuous monitoring is needed to effectively train neural networks to predict wind speeds. The conclusions of this study have implications for future work utilizing wind speed data where a generalization of uniform wind speeds is assumed.

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