scholarly journals Turbulence for different background conditions using fuzzy logic and clustering

2010 ◽  
Vol 28 (8) ◽  
pp. 1475-1481 ◽  
Author(s):  
K. Satheesan ◽  
S. Kirkwood
Keyword(s):  
Fuzzy Logic ◽  
Wind Speed ◽  
Data Clustering ◽  
Good Prediction ◽  
Root Mean Square Velocity ◽  
Radar Observations ◽  
Mst Radar ◽  
Vertical Wind Speed ◽  
Using Data ◽  
Different Altitudes

Abstract. Wind and turbulence estimated from MST radar observations in Kiruna, in Arctic Sweden are used to characterize turbulence in the free troposphere using data clustering and fuzzy logic. The root mean square velocity, νfca, a diagnostic of turbulence is clustered in terms of hourly wind speed, direction, vertical wind speed, and altitude of the radar observations, which are the predictors. The predictors are graded over an interval of zero to one through an input membership function. Subtractive data clustering has been applied to classify νfca depending on its homogeneity. Fuzzy rules are applied to the clustered dataset to establish a relationship between predictors and the predictant. The accuracy of the predicted turbulence shows that this method gives very good prediction of turbulence in the troposphere. Using this method, the behaviour of νfca for different wind conditions at different altitudes is studied.

scholarly journals Atmospheric temperature profiles estimated by the vertical wind speed observed by MST radar

2014 ◽  
Vol 63 (9) ◽  
pp. 094301
Author(s):  
Qing Hai-Yin ◽  
Zhang Yuan-Nong ◽  
Zhou Chen ◽  
Zhao Zheng-Yu ◽  
Chen Gang
Keyword(s):  
Wind Speed ◽  
Atmospheric Temperature ◽  
Vertical Wind ◽  
Temperature Profiles ◽  
Mst Radar ◽  
Vertical Wind Speed

THE INFLUENCE OF THE WIND SPEED PREDICTION ERROR ON THE SIZE OF THE STORAGE CONTROLLED OPERATION ZONE IN THE SYSTEM WITH THE WIND GENERATOR

2020 ◽  
Vol 2020 (57) ◽  
pp. 35-41
Author(s):  
K.S. Klen ◽  
◽  
M.K. Yaremenko ◽  
V.Ya. Zhuykov ◽  
◽  
...  
Keyword(s):  
Wind Speed ◽  
Prediction Error ◽  
Average Energy ◽  
Interpolation Formula ◽  
Wind Speed Prediction ◽  
Wind Generator ◽  
Prediction Horizon ◽  
Speed Prediction ◽  
Using Data ◽  
Predictor Corrector

The article analyzes the influence of wind speed prediction error on the size of the controlled operation zone of the storage. The equation for calculating the power at the output of the wind generator according to the known values of wind speed is given. It is shown that when the wind speed prediction error reaches a value of 20%, the controlled operation zone of the storage disappears. The necessity of comparing prediction methods with different data discreteness to ensure the minimum possible prediction error and determining the influence of data discreteness on the error is substantiated. The equations of the "predictor-corrector" scheme for the Adams, Heming, and Milne methods are given. Newton's second interpolation formula for interpolation/extrapolation is given at the end of the data table. The average relative error of MARE was used to assess the accuracy of the prediction. It is shown that the prediction error is smaller when using data with less discreteness. It is shown that when using the Adams method with a prediction horizon of up to 30 min, within ± 34% of the average energy value, the drive can be controlled or discharged in a controlled manner. References 13, figures 2, tables 3.

scholarly journals Comorbidity index for predicting mortality at 6 months after reperfusion therapy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hyo Suk Nam ◽  
Young Dae Kim ◽  
Joonsang Yoo ◽  
Hyungjong Park ◽  
Byung Moon Kim ◽  
...  
Keyword(s):  
Prospective Cohort ◽  
Multivariate Logistic Regression Analysis ◽  
Area Under The Curve ◽  
Reperfusion Therapy ◽  
Good Prediction ◽  
Multivariate Logistic Regression ◽  
Stroke Patients ◽  
Number Of Patients ◽  
Comorbidity Index ◽  
Using Data

AbstractThe eligibility of reperfusion therapy has been expanded to increase the number of patients. However, it remains unclear the reperfusion therapy will be beneficial in stroke patients with various comorbidities. We developed a reperfusion comorbidity index for predicting 6-month mortality in patients with acute stroke receiving reperfusion therapy. The 19 comorbidities included in the Charlson comorbidity index were adopted and modified. We developed a statistical model and it was validated using data from a prospective cohort. Among 1026 patients in the retrospective nationwide reperfusion therapy registry, 845 (82.3%) had at least one comorbidity. As the number of comorbidities increased, the likelihood of mortality within 6 months also increased (p < 0.001). Six out of the 19 comorbidities were included for developing the reperfusion comorbidity index on the basis of the odds ratios in the multivariate logistic regression analysis. This index showed good prediction of 6-month mortality in the retrospective cohort (area under the curve [AUC], 0.747; 95% CI, 0.704–0.790) and in 333 patients in the prospective cohort (AUC, 0.784; 95% CI, 0.709–0.859). Consideration of comorbidities might be helpful for the prediction of the 6-month mortality in patients with acute ischemic stroke who receive reperfusion therapy.

scholarly journals An empirical-analytical model of the vertical wind speed profile above and within an Amazon forest site

2015 ◽  
Vol 23 (1) ◽  
pp. 158-164 ◽  
Author(s):  
Cledenilson Mendonça de Souza ◽  
Cléo Quaresma Dias-Júnior ◽  
Júlio Tóta ◽  
Leonardo Deane de Abreu Sá
Keyword(s):  
Wind Speed ◽  
Analytical Model ◽  
Vertical Wind ◽  
Forest Site ◽  
Amazon Forest ◽  
Speed Profile ◽  
Wind Speed Profile ◽  
Vertical Wind Speed

scholarly journals Characteristics of the Concentric Eyewall Structure of Super Typhoon Muifa during Its Formation and Replacement Processes

2019 ◽  
Author(s):  
Tonggui Bo ◽  
Yudi Liu ◽  
Dawei Li ◽  
Lang Huang ◽  
Yi Yu
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Lower Troposphere ◽  
Super Typhoon ◽  
Early Signal ◽  
Tangential Wind ◽  
Radar Echo ◽  
Vertical Wind Speed ◽  
Potential Vortex ◽  
Physical Quantities

To explore the characteristics of the concentric eyewall of a typhoon during its formation and replacement processes, with Super Typhoon Muifa in 2011 as the example case, the Weather Research and Forecast (WRF) mode was used to carry out a numerical simulation to reproduce the entire formation and replacement processes of the concentric eyewall. The physical quantities such as the tangential wind speed, radar echo, radial wind speed, vertical wind speed, and potential vortex were diagnosed and analyzed. The results of the analysis show that the outward expansion of the isovelocity in the lower troposphere was the early signal of the formation of the outer eyewall. After the outer eyewall formed, there was a center of second-highest tangential wind speed in the corresponding area. The second-highest wind speed increased as the strength of the outer eyewall increased, and the position of the second-highest wind speed center was retracted with the retraction of the outer eyewall. The tangential wind speed of the moat area was smaller than that corresponding to the concentric eyewall and this feature gradually disappeared with the increase of the height. The echo in the moat area was weak, and this characteristic was particularly evident when the moat area was relatively wide and the outer eyewall was relatively strong. With the formation and development of the outer eyewall, the intensity of the inflow in the boundary layer corresponding to the inner eyewall was reduced, the intensity of the outflow in the upper layers declined, and the intensities of the inflow and outflow corresponding to the outer eyewall were enhanced. After the second outer eyewall matured, there was a significant inflow in the upper layer of the moat area. Once the outer eyewall formed, a large amount of hydrometeors appeared in the corresponding area, and there was a strong ascending motion inside that area. The strength of the ascending motion and the content of hydrometeors increased as the outer eyewall increased. When the moat area was relatively wide, the divergent airflow generated by the developed outer eyewall in the upper layer would produce a significant descending motion in the moat area.

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