The Prediction Method Research of Wavelet and Fuzzy Combined with Statistical Correlation

2011 ◽  
Author(s):  
Peng Yong ◽  
Xue Zhi-chun
Keyword(s):  
Prediction Method ◽  
Statistical Correlation

Selection of a leading edge noise prediction method for PNoise

2018 ◽  
pp. 214-223
Author(s):  
AM Faria ◽  
MM Pimenta ◽  
JY Saab Jr. ◽  
S Rodriguez
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Prediction Method ◽  
Leading Edge ◽  
Wind Farms ◽  
Broadband Noise ◽  
Turbulence Energy ◽  
Noise Prediction ◽  
Migration Routes ◽  
Turbulent Inflow

Wind energy expansion is worldwide followed by various limitations, i.e. land availability, the NIMBY (not in my backyard) attitude, interference on birds migration routes and so on. This undeniable expansion is pushing wind farms near populated areas throughout the years, where noise regulation is more stringent. That demands solutions for the wind turbine (WT) industry, in order to produce quieter WT units. Focusing in the subject of airfoil noise prediction, it can help the assessment and design of quieter wind turbine blades. Considering the airfoil noise as a composition of many sound sources, and in light of the fact that the main noise production mechanisms are the airfoil self-noise and the turbulent inflow (TI) noise, this work is concentrated on the latter. TI noise is classified as an interaction noise, produced by the turbulent inflow, incident on the airfoil leading edge (LE). Theoretical and semi-empirical methods for the TI noise prediction are already available, based on Amiet’s broadband noise theory. Analysis of many TI noise prediction methods is provided by this work in the literature review, as well as the turbulence energy spectrum modeling. This is then followed by comparison of the most reliable TI noise methodologies, qualitatively and quantitatively, with the error estimation, compared to the Ffowcs Williams-Hawkings solution for computational aeroacoustics. Basis for integration of airfoil inflow noise prediction into a wind turbine noise prediction code is the final goal of this work.

Effect of age on outcome in patients with cerebral stroke

2020 ◽  
pp. 41-45
Author(s):  
G. R. Kuchava ◽  
E. V. Eliseev ◽  
B. V. Silaev ◽  
D. A. Doroshenko ◽  
Yu. N. Fedulaev
Keyword(s):  
Ischemic Stroke ◽  
Neurological Deficit ◽  
Neurological Status ◽  
Statistical Correlation ◽  
Multiple Organ ◽  
The Body ◽  
Social Adaptation ◽  
Cerebral Stroke ◽  
Septic Complications ◽  
Risk Of Death

The aim of the study was to assess the course and outcome of cerebral infarction, depending on the age factor and duration of stay in the neuroblock. Materials and methods: a dynamic observation of 494 patients, men and women, aged 38–84 years with acute ischemic stroke of hemispheric localization, which were divided into the three groups depending on age, was performed. Group 1 – younger than 60 years old, group 2–60–70 years old, group 3 – older than 60 years. All patients underwent standard therapy, according to the recommendations for the treatment of ischemic stroke. The patients underwent comprehensive clinical and instrumental monitoring, which included assessment of somatic and neurological status according to the NIH‑NINDS scales at 1st, 3rd, 10th days and at discharge or death; assessment of the level of social adaptation according to the Bartel scale on 1st, 3rd, 10th days and at discharge, clinical and biochemical blood tests, computed tomography of the brain. Assessment of the quality of therapy was carried out according to specially developed maps using methods of statistical correlation analysis. Results: the most pronounced positive dynamics of neurological status was in the 1st group of patients. The regression of neurological deficit in the 2nd group was worse. The minimal dynamics of neurological deficit was in the 3rd group of patients with cerebral stroke. Most often, the death of patients with cerebral stroke occurred from the development of multiple organ disorders. Conclusions: patients over 70 years of age have the greatest risk of death, due to: a decrease in the reactivity of the body, the presence of initially severe concomitant somatic pathology in patients with admission to hospital; accession of secondary somatic and purulent‑septic complications.

Prediction Method of a System with Continuous Change Parameters Considering SNR Fluctuation

2018 ◽  
Vol 138 (9) ◽  
pp. 1075-1081
Author(s):  
Yasuhide Kobayashi ◽  
Mitsuyuki Saito ◽  
Yuki Amimoto ◽  
Wataru Wakita
Keyword(s):  
Prediction Method ◽  
Continuous Change

Validation of a Belt Model for Prediction of Hub Forces from a Rolling Tire4

2009 ◽  
Vol 37 (2) ◽  
pp. 62-102 ◽  
Author(s):  
C. Lecomte ◽  
W. R. Graham ◽  
D. J. O’Boy
Keyword(s):  
Internal Pressure ◽  
Equations Of Motion ◽  
Three Dimensional ◽  
Prediction Method ◽  
Analytical Models ◽  
Beam Model ◽  
Impedance Boundary ◽  
Bending Waves ◽  
Tire Rotation ◽  
Three Dimensional Models

Abstract An integrated model is under development which will be able to predict the interior noise due to the vibrations of a rolling tire structurally transmitted to the hub of a vehicle. Here, the tire belt model used as part of this prediction method is first briefly presented and discussed, and it is then compared to other models available in the literature. This component will be linked to the tread blocks through normal and tangential forces and to the sidewalls through impedance boundary conditions. The tire belt is modeled as an orthotropic cylindrical ring of negligible thickness with rotational effects, internal pressure, and prestresses included. The associated equations of motion are derived by a variational approach and are investigated for both unforced and forced motions. The model supports extensional and bending waves, which are believed to be the important features to correctly predict the hub forces in the midfrequency (50–500 Hz) range of interest. The predicted waves and forced responses of a benchmark structure are compared to the predictions of several alternative analytical models: two three dimensional models that can support multiple isotropic layers, one of these models include curvature and the other one is flat; a one-dimensional beam model which does not consider axial variations; and several shell models. Finally, the effects of internal pressure, prestress, curvature, and tire rotation on free waves are discussed.

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