Determination of CFD Turbulence Scales for Lobed Mixer Jet Noise Prediction

2007 ◽  
Author(s):  
Sid-Ali Meslioui ◽  
Mark Cunningham ◽  
Patrick Germain
Keyword(s):  
Jet Noise ◽  
Prediction Method ◽  
New Method ◽  
Noise Model ◽  
Excess Noise ◽  
Scale Model ◽  
Frequency Noise ◽  
Noise Prediction ◽  
Single Stream ◽  
Noise Data

Many turbofan engine exhaust designs feature internal forced mixers to rapidly mix the hot core flow with the cold bypass flow before the nozzle exit, primarily to enhance mixing and thus improve Specific Fuel Consumption (SFC). Although the design is intended for performance improvement, it may also considerably reduce low frequency noise because of the lower relative mixed jet velocity compared to a confluent nozzle. In reality, the presence of the mixer adds complexity to the jet flow fields and additional high frequency source noise commonly labeled “mixer excess noise”. There is no industry standard on predicting such jet noise contribution. As a remedy to this, a new method was recently developed by the Institute of Sound and Vibration Research (ISVR), UK, and Purdue University, USA, under the AeroAcoustics Research Consortium (AARC) contract to predict jet noise of lobed mixers. The method essentially relies on SAE ARP876D or ESDU98019 far field noise spectra predicted for single stream jets, with appropriate filtering to decompose the spectrum into an enhanced jet spectrum and a fully mixed jet spectrum. The process is similar to the four source model earlier developed for the coplanar separate flow jets. In addition to mixer flow parameters, the prediction method requires the knowledge of two parameters related to mixer excess noise: a turbulence factor Fm, defined as the ratio of the turbulence in a forced mixer to the ‘normal’ turbulence in a single-stream mixed jet at equal distances downstream of the nozzle; and LenJ that represents the axial length of the effective jet over which Fm exceeds unity. Extensive analysis of NASA scale model lobed mixers noise data showed that the method is promising. RANS CFD was also performed to numerically determine equivalent turbulence scales based on the turbulent kinetic energy in forced mixer jets relative to confluent mixer jets. The present paper extends this work, refining the prediction method and providing validation of the new method with full-scale engine noise data. In addition, the potential of CFD to enhance noise prediction for lobed mixer jets by providing the turbulence scales needed for the empirical model is further investigated. A new definition of the equivalent CFD turbulence parameters is proposed that agrees well with those derived from empirical jet noise model. Comparison of the CFD results with NASA PIV data for a confluent mixer configuration showed that the CFD methodology is not yet fully mature and additional work is required. However, the resolution of the mixer turbulence scales predicted by CFD analysis is sufficient to identify noise trends between two mixer designs. As a result, CFD is seen as a tool with the potential to identify mixer designs that result in lower jet noise.

scholarly journals Low Frequency Noise Contamination in Fan Model Testing

2008 ◽  
Author(s):  
Clifford A. Brown ◽  
Nicholas A. Schifer
Keyword(s):  
Power Level ◽  
Pressure Ratio ◽  
Jet Noise ◽  
Scale Model ◽  
Frequency Noise ◽  
Fan Noise ◽  
Noise Component ◽  
Least Squares Fit ◽  
Noise Contamination ◽  
The Impact

Aircraft engine noise research and development depends on the ability to study and predict the noise created by each engine component in isolation. The presence of a downstream pylon for a model fan test, however, may result in noise contamination through pylon interactions with the free stream and model exhaust airflows. Additionally, there is the problem of separating the fan and jet noise components generated by the model fan. A methodology was therefore developed to improve the data quality for the 9 × 15 Low Speed Wind Tunnel (LSWT) at the NASA Glenn Research Center that identifies three noise sources: fan noise, jet noise, and rig noise. The jet noise and rig noise were then measured by mounting a scale model of the 9 × 15 LSWT model fan installation in a jet rig to simulate everything except the rotating machinery and in duct components of fan noise. The data showed that the spectra measured in the LSWT has a strong rig noise component at frequencies as high as 3 kHz depending on the fan and airflow fan exit velocity. The jet noise was determined to be significantly lower than the rig noise (i.e. noise generated by flow interaction with the downstream support pylon). A mathematical model for the rig noise was then developed using a multi-dimensional least squares fit to the rig noise data. This allows the rig noise to be subtracted or removed, depending on the amplitude of the rig noise relative to the fan noise, at any given frequency, observer angle, or nozzle pressure ratio. The impact of isolating the fan noise with this method on spectra, overall power level (OAPWL), and Effective Perceived Noise Level (EPNL) is studied.

Comparison of Measured Low-Frequency Engine Noise With Combustion and Jet Noise Predictions for a Turbofan Engine With an Internal Lobed Mixer Nozzle

2007 ◽  
Author(s):  
Lysbeth S. Lieber ◽  
Donald S. Weir
Keyword(s):  
Low Frequency ◽  
Jet Noise ◽  
Combustion Noise ◽  
Noise Model ◽  
Engine Noise ◽  
Turbofan Engine ◽  
Technology Program ◽  
Modeling Techniques ◽  
Noise Data ◽  
Static Conditions

This paper presents an examination of the low-frequency engine noise of a turbofan engine with an internal lobed mixer nozzle, and identifies the contributions of the combustion and exhaust jet component noise sources within the low frequency portion of the spectrum by applying recently developed modeling techniques. This investigation was performed as part of the NASA Quiet Aircraft Technology Program. Because the mixer reduces the total jet noise, the combustion noise source becomes a significant contributor. In addition, the character of the jet noise for the mixer nozzle is different from that for a single-stream or coannular nozzle. Although the internal mixer reduces the low-frequency shear-induced jet noise, it also produces an additional higher frequency contribution to the jet noise due to enhanced turbulence levels produced by the mixing process. Therefore, the modeling techniques that predict the low-frequency component source noise must capture sufficient physics of the noise generation process for the combustor and mixer nozzle to accurately represent the component spectral distributions. The improved modeling of component source noise for both combustor and jet sources was addressed as part of the NASA Quiet Aircraft Technology Program. This activity included development of a new narrowband combustion noise model, as well as the application of a recent jet noise model for nozzles with internal forced mixers. The noise data used in this study was taken during the NASA Engine Validation of Noise Reduction Concepts (EVNRC) Program. Both static and flight noise measurements were made at a range of power settings using the Honeywell TFE731-60 turbofan engine. The engine configuration of interest for this study employed a nozzle with an internal lobed mixer. Comparison of static and flight data with predictions from the combustion and jet noise models indicates that combustor noise has a significant contribution to lower-frequency engine noise (in the 400–1000 Hz range), particularly for flight conditions, where the jet noise is reduced due to flight effects, and also for lower power settings at static conditions. However, further calibration of the combustion and jet noise prediction techniques will be required, with isolated component noise data, before these models may be applied with certainty to model total engine noise in the low-frequency range.

Single-Stream Jet Noise Prediction Using Empirical Methodology for a Newly Designed Turbojet Engine

2013 ◽  
Author(s):  
João Roberto Barbosa ◽  
Daniel Jonas Dezan
Keyword(s):  
Sound Pressure Level ◽  
Performance Prediction ◽  
Gas Turbines ◽  
Sound Pressure ◽  
Jet Noise ◽  
Pressure Level ◽  
High Fidelity ◽  
Noise Prediction ◽  
Single Stream ◽  
Turbojet Engine

The Center for Reference on Gas Turbines (CRTG), at the Technological Institute of Aeronautics, carries out research in relevant areas of gas turbines, to provide the support for teaching and the ability to design high performance gas turbines. Noise prediction, by means of theoretical and empirical methods, is among such areas. Emphasis is given to the prediction of noise from new engines, to anticipate problems at very early design stage and to take the necessary actions to guarantee that the engine noise is below the recommended limits. Noise prediction is part of a high fidelity gas turbine performance prediction computer program, which provides the designer, at any time during the design phases, with information on the noise levels generated by each component and by the engine. This paper presents results obtained with such methodology incorporated to the high fidelity engine performance prediction computer code, and in the format usually used in the literature. The SPL — far-field one-third octave band sound pressure level — and the OASPL — overall sound pressure level — for single-stream jet were calculated for several engine rotational speeds and observer positions. Two methods have been for the single-stream jet noise prediction, namely: ESDU item 98019 and SAE ARP 876D. Nozzle details were taken from a 5 kN turbojet engine, designed at the CRTG, and which is being installed in the test rig for the preliminary evaluation. In this paper the influence of the observer position on the calculated SPL is presented, and the corresponding OASPL for steady engine operation, combined with the effect of the engine rotational speeds on exhaust jet noise. It is shown that they are in agreement with the noise of similar operating conditions. Ground reflection and atmospheric attenuation were not considered in this work. The results indicate that the noise prediction is adequate for use during the design phase and that the model derived in the SAE ARP 876D paper provides better single-stream jet noise prediction than ones predicted using the ESDU Item 98019.

scholarly journals Similarity scaling of jet noise sources for low-order jet noise modelling based on the Goldstein generalised acoustic analogy

2017 ◽  
Vol 16 (6) ◽  
pp. 476-490 ◽  
Author(s):  
Vasily A Semiletov ◽  
Sergey A Karabasov
Keyword(s):  
Jet Noise ◽  
Field Measurements ◽  
Noise Prediction ◽  
Far Field ◽  
Acoustic Analogy ◽  
Noise Sources ◽  
Modelling Framework ◽  
Noise Data ◽  
Field Noise ◽  
Low Order

As a first step towards a robust low-order modelling framework that is free from either calibration parameters based on the far-field noise data or any assumptions about the noise source structure, a new low-order noise prediction scheme is implemented. The scheme is based on the Goldstein generalised acoustic analogy and uses the Large Eddy Simulation database of fluctuating Reynolds stress fields from the CABARET MILES solution of Semiletov et al. corresponding to a static isothermal jet from the SILOET experiment for reconstruction of effective noise sources. The sources are scaled in accordance with the physics-based arguments and the corresponding sound meanflow propagation problem is solved using a frequency domain Green’s function method for each jet case. Results of the far-field noise predictions of the new method are validated for the two NASA SHJAR jet cases, sp07 and sp03 from and compared with the reference predictions, which are obtained by applying the Lighthill acoustic analogy scaling for the SILOET far-field measurements and using an empirical jet-noise prediction code, sJet.

OCDs Reliability Screening Using Normalization Variance of Noise Spectrum

2011 ◽  
Vol 130-134 ◽  
pp. 72-75
Author(s):  
Qiu Zhan Zhou ◽  
Jian Gao ◽  
Na Lei ◽  
Dan’e Wu
Keyword(s):  
Screening Method ◽  
Low Frequency ◽  
Noise Spectrum ◽  
Previous Method ◽  
New Method ◽  
Excess Noise ◽  
Frequency Noise ◽  
Related Characteristic ◽  
The Relationship ◽  
Large Frequency

Considering the inflexibility and limitations of the traditional frequency-domain OCDs reliability screening method using noise parameters at fixed frequency-points, we put forward a new method using the normalization variance of a large frequency-band noise spectrum in this paper. The possible sources of excess noise in OCDs and the relationship between low-frequency noise and reliability are studied at first, and then we describe the detailed process of the method for reliability screening, including the normalization method of noise spectrum, the calculations of variance and related characteristic quantities. At last, compare the screening results obtained by both of the previous method and this new method, and it demonstrated that this method is much more accurate and reliable.

scholarly journals Noise prediction of chemical industry park based on multi-station Prophet and multivariate LSTM fitting model

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Qingtian Zeng ◽  
Yu Liang ◽  
Geng Chen ◽  
Hua Duan ◽  
Chunguo Li
Keyword(s):  
Chemical Industry ◽  
Prediction Method ◽  
Environmental Data ◽  
Industrial Safety ◽  
Noise Prediction ◽  
Production Environment ◽  
Main Research ◽  
Flow Information ◽  
Noise Data ◽  
Weighted Combination

AbstractWith the gradual transformation of chemical industry park to digital and intelligent, various types of environmental data in the park are extremely rich. It has high application value to provide safe production environment by deeply mining environmental data law and providing data support for industrial safety and workers’ health in the park through prediction means. This paper takes the noise data of the chemical industry park as the main research object, and innovatively applies the 3σ principle to the zero-value processing of the noise data, and builds an LSTM model that integrates multivariate information based on the characteristics of the wind direction classification noise data combined with the wind speed and vehicle flow information. The Prophet model integrating multi-site noise information was adopted, and the Multi-PL model was constructed by fitting the above two models to predict the noise. This paper designs and implements a comparative experiment with Kalman filter, BP neural network, Prophet, LSTM, Prophet + LSTM weighted combination prediction model. R2 was used to evaluate the fitting effect of single model in Multi-PL, RMSE and MAE that were used to evaluate the prediction effect of Multi-PL on noise time series. The experimental results show that the RMSE and MAE of the data processed by the 3σ principle are reduced by 32.2% and 23.3% in the multi-station ordered Prophet method, respectively. Compared with the above comparison models, the Multi-PL model prediction method is more stable and accurate. Therefore, the Multi-PL method proposed in this paper can provide a new idea for noise prediction in digital chemical parks.

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