scholarly journals Variation of diurnal Turbulence Kinetic Energy using sonic anemometer observation: a case study Ledeng, Bandung

2018 ◽  
Author(s):  
Sandy Hardian Susanto Herho ◽  
Dasapta Erwin Irawan
Keyword(s):  
Kinetic Energy ◽  
Diurnal Variation ◽  
Wind Velocity ◽  
Sonic Anemometer ◽  
Diurnal Variations ◽  
Turbulence Kinetic Energy ◽  
Atmospheric Conditions

Sonic anemometer observation was performed on 29 - 30 September 2014 in Ledeng, Bandung to see diurnal variations of Turbulence Kinetic Energy (TKE) that occurred in this area. The measured sonic anemometer was the wind velocity components u, v, and w. From the observation result, it can be seen that the diurnal variation that happened was quite significant. The maximum TKE occurs during the daytime when atmospheric conditions tend to be unstable. TKE values were small at night when atmospheric conditions are more stable than during the daytime.

scholarly journals HUBUNGAN VARIASI HARIAN NILAI TURBULENCE KINETIC ENERGY DAN FLUKS PANAS DI WILAYAH KOTA BANDUNG

2015 ◽  
Vol 10 (2) ◽  
Author(s):  
Fani Setyawan
Keyword(s):  
Boundary Layer ◽  
Heat Flux ◽  
Kinetic Energy ◽  
Sonic Anemometer ◽  
Turbulence Kinetic Energy ◽  
Major Influence ◽  
The West ◽  
Daily Cycle ◽  
Boundary Layer Turbulence

Abstrak : Lapisan Batas Meteorologi merupakan suatu lapisan yang berada di bagian terbawah dari Troposfer. Salah satu fenomena yang terjadi di lapisan batas ini adalah Turbulensi yang memiliki durasi singkat namun memiliki pengaruh yang besar terhadap pembentukan cuaca di Troposfer. Dalam aktifitas Turbulensi terdapat nilai Turbulence Kinetic Energy (TKE) dan mempengaruhi transfer panas atau Fluks Panas di atmosfer. Observasi meteorologi menggunakan Sonic Anemometer dapat digunakan untuk menghitung nilai-nilai tersebut. Komponen pengamatan yang dihasilkan merupakan variabel-variabel yang digunakan untuk menghitung nilai TKE dan Fluks Panas, yaitu komponen u, v, w, dan T. Dari hasil pengamatan yang dilakukan di wilayah kota Bandung (studi kasus tanggal 26 september 2014), didapatkan variasi angin harian yang berhembus dari arah Barat hingga Utara dengan kecepatan rata-rata 4 knots. Namun pada siang hari, angin berhembus secara bergantian dari arah Barat dan dari arah Timur. Siklus harian dari nilai TKE menunjukkan adanya variasi dengan nilai yang besar saat matahari mulai muncul dan berakhir sebelum tengah hari. Sedangkan untuk siklus harian dari Fluks Panas menunjukkan fluktuasi yang signifikan saat tengah hari hingga matahari terbenam. Hal ini menunjukkan bahwa proses konveksi saat pagi hari dipengaruhi oleh Turbulence Kinetic Energy dan nilai Fluks Panas mempengaruhi proses konveksi setelah nilai TKE turun hingga matahari terbenam.Kata Kunci : Lapisan batas, Turbulence Kinetic Energy (TKE), Fluks PanasAbstrack : Meteorological Boundary Layer is a layer at lowest part of troposphere. One of phenomena at this layer is Turbulence which has a short duration but has a major influence in the weather at the Troposphere. Turbulence activities consists of a Turbulence Kinetic Energy (TKE) value and affect heat flux at the atmosphere. Meteorological observation using Sonic Anemometer can be used to calculate these values. The variables used to calculate TKE value and Heat Flux, the variable u, v, w, and T, obtained from these observation. From the observations at Bandung region (case study was september, 26, 2014), found variations in the daily wind blows from the West and North with an average speed of 4 knots. But during the day, wind blows alternately from the West and from the East. The daily cycle from TKE value indicate a variation with a great value when the sun began and endeed before noon. As for the daily cycle of heat flux show significant fluctuations at noon to sunset. This suggests that the process of convection during the morning influenced by Turbulence Kinetic Energy and value of Heat Flux affects the convection process after TKE value decline until the sunset.Keywords : Boundary layer, Turbulence Kinetic Energy (TKE), Heat Flux

scholarly journals Evaluation of turbulence measurement techniques from a single Doppler lidar

2017 ◽  
Vol 10 (8) ◽  
pp. 3021-3039 ◽  
Author(s):  
Timothy A. Bonin ◽  
Aditya Choukulkar ◽  
W. Alan Brewer ◽  
Scott P. Sandberg ◽  
Ann M. Weickmann ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Kinetic Energy ◽  
Planetary Boundary Layer ◽  
Turbulence Intensity ◽  
Measurement Techniques ◽  
Sonic Anemometer ◽  
Shear Velocity ◽  
Turbulence Kinetic Energy ◽  
Doppler Lidar ◽  
Turbulence Measurement

Abstract. Measurements of turbulence are essential to understand and quantify the transport and dispersal of heat, moisture, momentum, and trace gases within the planetary boundary layer (PBL). Through the years, various techniques to measure turbulence using Doppler lidar observations have been proposed. However, the accuracy of these measurements has rarely been validated against trusted in situ instrumentation. Herein, data from the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) are used to verify Doppler lidar turbulence profiles through comparison with sonic anemometer measurements. For 17 days at the end of the experiment, a single scanning Doppler lidar continuously cycled through different turbulence measurement strategies: velocity–azimuth display (VAD), six-beam scans, and range–height indicators (RHIs) with a vertical stare.Measurements of turbulence kinetic energy (TKE), turbulence intensity, and stress velocity from these techniques are compared with sonic anemometer measurements at six heights on a 300 m tower. The six-beam technique is found to generally measure turbulence kinetic energy and turbulence intensity the most accurately at all heights (r2  ≈  0.78), showing little bias in its observations (slope of  ≈  0. 95). Turbulence measurements from the velocity–azimuth display method tended to be biased low near the surface, as large eddies were not captured by the scan. None of the methods evaluated were able to consistently accurately measure the shear velocity (r2 =  0.15–0.17). Each of the scanning strategies assessed had its own strengths and limitations that need to be considered when selecting the method used in future experiments.

Multi-step wind variability prediction based on deep learning neural network

2021 ◽  
Author(s):  
Sayahnya Roy
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Kinetic Energy ◽  
Wind Velocity ◽  
Turbulence Kinetic Energy ◽  
Discrete Wavelet ◽  
Wind Variability ◽  
Velocity Prediction ◽  
Deep Learning Neural Network

<p>Wind energy is widely used in renewable energy systems but the randomness and the intermittence of the wind make its accurate prediction difficult. This study develops an advanced and reliable model for multi-step wind variability prediction using long short-term memory (LSTM) network based on deep learning neural network (DLNN). A 20 Hz Ultrasonic anemometer was positioned in northern France (LOG site) to measure the random wind variability for the duration of thirty-four days. Real-time turbulence kinetic energy is computed from the measured wind velocity components, and multi-resolution features of wind velocity and turbulent kinetic energy are used as input for the prediction model. These multi-resolution features of wind variability are extracted using one-dimensional discrete wavelet transformation. The proposed DLNN is framed to implement multi-step prediction ranging from 10 min to 48 h. For velocity prediction, the root mean square error, mean absolute error and mean absolute percentage error are 0.047 m/s, 0.19 m/s, and 11.3% respectively. These error values indicate a good reliability of the proposed DLNN for predicting wind variability. We found that the present model performs well for mid-long-term (6-24h) wind velocity prediction. The model is also good for the long-term (24-48h) turbulence kinetic energy prediction.</p>

scholarly journals Evaluation of Turbulence Measurement Techniques from a Single Doppler Lidar

2017 ◽  
Author(s):  
Timothy A. Bonin ◽  
Aditya Choukulkar ◽  
W. Alan Brewer ◽  
Scott P. Sandberg ◽  
Ann M. Weickmann ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Kinetic Energy ◽  
Planetary Boundary Layer ◽  
Turbulence Intensity ◽  
Measurement Techniques ◽  
Sonic Anemometer ◽  
Shear Velocity ◽  
Turbulence Kinetic Energy ◽  
Doppler Lidar ◽  
Turbulence Measurement

Abstract. Measurements of turbulence are essential to understand and quantify the transport and dispersal of heat, moisture, momentum, and trace gases within the planetary boundary layer. Through the years, various techniques to measure turbulence using Doppler lidar observations have been proposed. However, the accuracy of these measurements has rarely been validated against trusted in situ instrumentation. Herein, data from the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) are used to verify Doppler lidar turbulence profiles through comparison with sonic anemometer measurements. For 17 days at the end of the experiment, a single scanning Doppler lidar continuously cycled through different turbulence measurement strategies: velocity azimuth display, six-beam, and range height indicators with a vertical stare. Measurements of turbulence kinetic energy, turbulence intensity, and shear velocity from these techniques are compared with sonic anemometer measurements at six heights on a 300-m tower. The six-beam technique is found to generally measure turbulence kinetic energy and turbulence intensity the most accurately at all heights, showing little bias in its observations. Turbulence measurements from the velocity azimuth display method tended to biased low near the surface, as large eddies were not captured by the scan. None of the methods evaluated were able to consistently accurately measure the shear velocity. Each of the scanning strategies assessed had its own strengths and limitations that need to be considered when selecting the method used in future experiments.

scholarly journals Study on the characteristics of the pressure fluctuations and their contribution to turbulence kinetic energy

2021 ◽  
pp. 105634
Author(s):  
Zhuorui Wei ◽  
Hongsheng Zhang ◽  
Yan Ren ◽  
Qianhui Li ◽  
Xuhui Cai ◽  
...  
Keyword(s):  
Kinetic Energy ◽  
Pressure Fluctuations ◽  
Turbulence Kinetic Energy
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