A Theoretically Based Valve Noise Prediction Method for Compressible Fluids

1986 ◽  
Vol 108 (3) ◽  
pp. 329-338 ◽  
Author(s):  
G. Reethof ◽  
W. C. Ward
Keyword(s):  
Transmission Loss ◽  
Prediction Method ◽  
Low Noise ◽  
Compressible Fluids ◽  
Noise Prediction ◽  
Duct Acoustics ◽  
Proper Design ◽  
Predicted Values ◽  
Mode Generation ◽  
Wide Pressure

Noise generated by control valves in power generation, chemical and petrochemical plants must be predictable so that proper design measures can be taken to conform to OSHA’s noise regulation. Currently available noise prediction methods are empirically based and not sufficiently accurate. The method proposed is based on jet noise theory for both subcritical and choked conditions, duct acoustics theory in terms of higher order mode generation and propagation, and the theory of acoustics-structure interaction in the development of the transmission loss values for the pipe. One third octave values are calculated over the audio spectrum by incorporating spectral aspects of noise generation, propagation, transmission, and radiation. The predicted values of noise for several size cage globe valves over wide pressure ranges compare well with measured results by two prominent valve manufacturers. The method, at present, is restricted to conventional valve styles, as opposed to the special low noise valve types with their very complicated orificial elements.

Study of the road traffic noise prediction method applicable to low-noise road surfaces

2010 ◽  
Vol 31 (1) ◽  
pp. 102-112 ◽  
Author(s):  
Keisuke Tsukui ◽  
Yasuo Oshino ◽  
Gijsjan van Blokland ◽  
Hideki Tachibana
Keyword(s):  
Road Traffic ◽  
Traffic Noise ◽  
Prediction Method ◽  
Low Noise ◽  
Road Traffic Noise ◽  
Noise Prediction ◽  
The Road ◽  
Road Surfaces

scholarly journals The Analysis of Low Noise Protection Barriers Influence on Tram Traffic Noise Levels

2013 ◽  
Vol 2 (1) ◽  
pp. 1-9
Author(s):  
Maja Ahac ◽  
Stjepan Lakušić ◽  
Saša Ahac ◽  
Vesna Dragčević
Keyword(s):  
Traffic Noise ◽  
Prediction Method ◽  
Field Measurements ◽  
Low Noise ◽  
Capital City ◽  
Noise Prediction ◽  
Residential Areas ◽  
Noise Mitigation ◽  
Protection Measure ◽  
Noise Models

Abstract The paper describes the analysis of tram traffic noise situation in residential areas in the vicinity of Drzic Avenue, one of the major routes between the northern and southern part of the Croatian capital city Zagreb, and the effect of low barriers placed by the tracks on tram noise mitigation. In order to evaluate the effect of planned protection measure, noise models were produced and verified with short-term field measurements. Calculations were conducted by means of noise prediction software, using European interim noise prediction method and 3D model of analyzed area. Finally, the results of noise calculations for existing tram traffic situation and planned measure of protection are presented on noise maps.

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.

Abstract 2917: Outcome Prediction With A Population-based Ischemic Stroke Atlas

Stroke ◽  
2012 ◽  
Vol 43 (suppl_1) ◽  
Author(s):  
Wieslaw L Nowinski ◽  
Varsha Gupta ◽  
Guoyu Qian ◽  
Wojciech Ambrosius ◽  
Jie He ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Outcome Prediction ◽  
Three Dimensional ◽  
Prediction Method ◽  
Population Based ◽  
Novel Approach ◽  
History Of ◽  
High Prediction ◽  
Predicted Values ◽  
One Year

Outcome prediction is critical in stroke patient management. We propose a novel approach combining imaging with parameters (including history, hospitalization, demographics, clinical and outcome) for a population of patients in the Probabilistic Stroke Atlas (PSA) along with prediction engine. The PSA aggregates multiplicity of data for a population of stroke patients and presents them in image format. The PSA is composed from a series of three-dimensional (3D) image volumes including scans and parameters. A cohort of over 700 ischemic stroke generally treated patients with 176 parameters per patient, and CT scan performed at admission and on day 7 was acquired. Outcome measurements were assessed up to one year after stroke onset. Cases with old infarcts, infarcts in both hemispheres, and hemorrhagic transformations were rejected. This data was post-processed to build the PSA and then the PSA was used for prediction. The infarcts were delineated on CT scans and their 3D surface models constructed and normalized. The PSA was calculated from the normalized 3D infarct models as frequency of stroke occurrence. Similar maps were calculated for the following parameters: Age; Sex; Survival; NIH Stroke Scale (NIHSS); Barthel Index (BI) at 30, 90, 180, 360 days; modified Rankin Scale (mRS) at 7, 30, 90, 180, 360 days; White blood cell count; C-reative protein; Glucose at emergency department; History of hypertension; and History of diabetes. The PSA was used for prediction of mRS and BI for 50 stroke subjects. For a given case to be predicted, the infarct was delineated and analyzed by the PSA mapped on the scan. The predicted values of the parameters from the PSA were compared with the actual values of the parameters measured in up to 1-year neurological follow up. The accuracy was defined as 100*(1-(actual value-predicted value)/actual value)%. The mean prediction accuracy of mRS at (7, 30, 90, 180, 360) days is (89.7, 90.7, 92.1, 87.0, 83.3)% and that for BI at (30, 90, 180, 360) days is (90.0, 95.4, 94.4, 92.2)% respectively. This novel prediction method has high prediction rates. It can be applied to any other parameters. The PSA is dynamic and its power can increase with additional cases.

scholarly journals Autonomous Sensor Data Cleaning in Stream Mining Setting

2018 ◽  
Vol 9 (2) ◽  
pp. 69-79 ◽  
Author(s):  
Klemen Kenda ◽  
Dunja Mladenić
Keyword(s):  
Big Data ◽  
Real World ◽  
Data Cleaning ◽  
Concept Drift ◽  
Prediction Method ◽  
Low Noise ◽  
Sensor Data ◽  
Grid Data ◽  
Stream Mining ◽  
Real Time Processing

Abstract Background: Internet of Things (IoT), earth observation and big scientific experiments are sources of extensive amounts of sensor big data today. We are faced with large amounts of data with low measurement costs. A standard approach in such cases is a stream mining approach, implying that we look at a particular measurement only once during the real-time processing. This requires the methods to be completely autonomous. In the past, very little attention was given to the most time-consuming part of the data mining process, i.e. data pre-processing. Objectives: In this paper we propose an algorithm for data cleaning, which can be applied to real-world streaming big data. Methods/Approach: We use the short-term prediction method based on the Kalman filter to detect admissible intervals for future measurements. The model can be adapted to the concept drift and is useful for detecting random additive outliers in a sensor data stream. Results: For datasets with low noise, our method has proven to perform better than the method currently commonly used in batch processing scenarios. Our results on higher noise datasets are comparable. Conclusions: We have demonstrated a successful application of the proposed method in real-world scenarios including the groundwater level, server load and smart-grid data

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