A Versatile Implementation of Acoustic Analogy Based Noise Prediction Method in a General-Purpose CFD Code

2003 ◽  
Author(s):  
Sung-Eun Kim ◽  
Yi Dai ◽  
Evangelos Koutsavdis ◽  
Sandeep Sovani ◽  
Nitin Kadam ◽  
...  
Keyword(s):  
Prediction Method ◽  
General Purpose ◽  
Noise Prediction ◽  
Acoustic Analogy

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.

Application of acoustic analogy to automotive engine-cooling fan noise prediction

AIAA Journal ◽  
2000 ◽  
Vol 38 ◽  
pp. 1095-1098 ◽  
Author(s):  
Jeonghan Lee ◽  
Kyungseok Cho ◽  
Soogab Lee
Keyword(s):  
Noise Prediction ◽  
Acoustic Analogy ◽  
Fan Noise ◽  
Automotive Engine ◽  
Engine Cooling ◽  
Cooling Fan

scholarly journals An information theoretic approach to link prediction in multiplex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seyed Hossein Jafari ◽  
Amir Mahdi Abdolhosseini-Qomi ◽  
Masoud Asadpour ◽  
Maseud Rahgozar ◽  
Naser Yazdani
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Large Scale ◽  
Similarity Measures ◽  
Prediction Method ◽  
General Purpose ◽  
Fast Method ◽  
Theoretic Approach ◽  
Multiplex Networks ◽  
Wide Range

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

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.

scholarly journals Anisotropic source modelling for turbulent jet noise prediction

2019 ◽  
Vol 377 (2159) ◽  
pp. 20190075 ◽  
Author(s):  
Xihai Xu ◽  
Xiaodong Li
Keyword(s):  
Large Scale ◽  
Noise Source ◽  
Jet Noise ◽  
Source Model ◽  
Noise Prediction ◽  
Reynolds Stresses ◽  
Acoustic Analogy ◽  
Mean Flow ◽  
Navier Stokes Equation ◽  
Anisotropic Source

An anisotropic component of the jet noise source model for the Reynolds-averaged Navier–Stokes equation-based jet noise prediction method is proposed. The modelling is based on Goldstein's generalized acoustic analogy, and both the fine-scale and large-scale turbulent noise sources are considered. To model the anisotropic characteristics of jet noise source, the Reynolds stress tensor is used in place of the turbulent kinetic energy. The Launder–Reece–Rodi model (LRR), combined with Menter's ω -equation for the length scale, with modified coefficients developed by the present authors, is used to calculate the mean flow velocities and Reynolds stresses accurately. Comparison between predicted results and acoustic data has been carried out to verify the accuracy of the new anisotropic source model. This article is part of the theme issue ‘Frontiers of aeroacoustics research: theory, computation and experiment’.

CRiSP: accurate structure prediction of disulfide-rich peptides with cystine-specific sequence alignment and machine learning

2020 ◽  
Vol 36 (11) ◽  
pp. 3385-3392
Author(s):  
Zi-Lin Liu ◽  
Jing-Hao Hu ◽  
Fan Jiang ◽  
Yun-Dong Wu
Keyword(s):  
Machine Learning ◽  
Sequence Alignment ◽  
Structure Prediction ◽  
High Throughput Sequencing ◽  
Prediction Method ◽  
General Purpose ◽  
Supplementary Information ◽  
Model Quality ◽  
Specific Sequence ◽  
Structure Information

Abstract Motivation High-throughput sequencing discovers many naturally occurring disulfide-rich peptides or cystine-rich peptides (CRPs) with diversified bioactivities. However, their structure information, which is very important to peptide drug discovery, is still very limited. Results We have developed a CRP-specific structure prediction method called Cystine-Rich peptide Structure Prediction (CRiSP), based on a customized template database with cystine-specific sequence alignment and three machine-learning predictors. The modeling accuracy is significantly better than several popular general-purpose structure modeling methods, and our CRiSP can provide useful model quality estimations. Availability and implementation The CRiSP server is freely available on the website at http://wulab.com.cn/CRISP. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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