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Results for “Noise Modeling”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Including Finite Surface Span Effects in Empirical Jet-Surface Interaction Noise Models

The effect of finite span on the jet-surface interaction noise source and the jet mixing noise shielding and reflection effects is considered using recently acquired experimental data. First, the experimental setup and resulting data are presented with particular attention to the role of surface span on far-field noise. These effects are then included in existing empirical models that have previously assumed that all surfaces are semi-infinite. This extended abstract briefly describes the experimental setup and data leaving the empirical modeling aspects for the final paper.

Aircraft↗
NASA NTRS2020

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

This paper investigates using machine learning to rapidly develop empirical models suitable for system-level aircraft noise studies. In particular, machine learning is used to train a neural network to predict the noise spectra produced by a round jet near a surface over a range of surface lengths, surface standoff distances, jet Mach numbers, and observer angles. These spectra include two sources, jet-mixing noise and jet-surface interaction (JSI) noise, with different scale factors as well as surface shielding and reflection effects to create a multi- dimensional problem. A second model is then trained using data from three rectangular nozzles to include nozzle aspect ratio in the spectral prediction. The training and validation data are from an extensive jet-surface interaction noise database acquired at the NASA Glenn Research Center's Aero-Acoustic Propulsion Laboratory. Although the number of training and validation points is small compared a typical machine learning application, the results of this investigation show that this approach is viable if the underlying data are well behaved.

Brown, Cliff↗
NASA NTRS2016

Including Finite Surface Span Effects in Empirical Jet-Surface Interaction Noise Models

The effect of finite span on the jet-surface interaction noise source and the jet mixing noise shielding and reflection effects is considered using recently acquired experimental data. First, the experimental setup and resulting data are presented with particular attention to the role of surface span on far-field noise. These effects are then included in existing empirical models that have previously assumed that all surfaces are semi-infinite. This extended abstract briefly describes the experimental setup and data leaving the empirical modeling aspects for the final paper.

Aircraft↗
NASA NTRS2014

Enhanced Fan Noise Modeling for Turbofan Engines

This report describes work by consultants to Diversitech Inc. for the NASA Glenn Research Center (GRC) to revise the fan noise prediction procedure based on fan noise data obtained in the 9- by 15 Foot Low-Speed Wind Tunnel at GRC. The purpose of this task is to begin development of an enhanced, analytical, more physics-based, fan noise prediction method applicable to commercial turbofan propulsion systems. The method is to be suitable for programming into a computational model for eventual incorporation into NASA's current aircraft system noise prediction computer codes. The scope of this task is in alignment with the mission of the Propulsion 21 research effort conducted by the coalition of NASA, state government, industry, and academia to develop aeropropulsion technologies. A model for fan noise prediction was developed based on measured noise levels for the R4 rotor with several outlet guide vane variations and three fan exhaust areas. The model predicts the complete fan noise spectrum, including broadband noise, tones, and for supersonic tip speeds, combination tones. Both spectra and directivity are predicted. Good agreement with data was achieved for all fan geometries. Comparisons with data from a second fan, the ADP fan, also showed good agreement.

aircraft↗
NASA NTRS1985

Propeller aircraft interior noise model: User's manual for computer program

A computer program entitled PAIN (Propeller Aircraft Interior Noise) has been developed to permit calculation of the sound levels in the cabin of a propeller-driven airplane. The fuselage is modeled as a cylinder with a structurally integral floor, the cabin sidewall and floor being stiffened by ring frames, stringers and floor beams of arbitrary configurations. The cabin interior is covered with acoustic treatment and trim. The propeller noise consists of a series of tones at harmonics of the blade passage frequency. Input data required by the program include the mechanical and acoustical properties of the fuselage structure and sidewall trim. Also, the precise propeller noise signature must be defined on a grid that lies in the fuselage skin. The propeller data are generated with a propeller noise prediction program such as the NASA Langley ANOPP program. The program PAIN permits the calculation of the space-average interior sound levels for the first ten harmonics of a propeller rotating alongside the fuselage. User instructions for PAIN are given in the report. Development of the analytical model is presented in NASA CR 3813.

Wilby, E. G.↗
NASA NTRS1977

Theoretical jet exhaust noise model for the duct burning turbofan

A model was developed to predict the acoustic power spectrum for a Duct Burning Turbofan (DBTF) whose cycle results in a high velocity, high temperature fan stream and a lower velocity, lower temperature primary stream. Model tests have demonstrated that a DBTF may produce substantially less noise than a conventional turbojet with the same thrust and weight flow. Predictions from the model agreed well with measured data from the model tests. The characteristics of the DBTF acoustic power spectrum were shown to be related to the rapid mixing of the fan and primary streams. The effects of fan to primary velocity ratio, temperature ratio, area ratio, and the effect of nozzle geometry on DBTF jet noise were determined.

Larson, R. S.↗
DOE OSTI2023

Modeling noise in global Mølmer-Sørensen interactions applied to quantum approximate optimization

Many-qubit Mølmer-Sørensen (MS) interactions applied to trapped ions offer unique capabilities for quantum information processing, with applications including quantum simulation and the quantum approximate optimization algorithm (QAOA). Here, we develop a physical model to describe many-qubit MS interactions under four sources of experimental noise: vibrational mode frequency fluctuations, laser power fluctuations, thermal initial vibrational states, and state preparation and measurement errors. The model parametrizes these errors from simple experimental measurements, without free parameters. Herein, we validate the model in comparison with experiments that implement sequences of MS interactions on two 171 Yb + ions. The model shows reasonable agreement after several MS interactions as quantified by the reduced chi-squared statistic $χ^{2}_{red}$≈2. As an application we examine MaxCut QAOA experiments on three and six ions. The experimental performance is quantified by approximation ratios that are 91% and 83% of the optimal theoretical values. Our model predicts $0.93^{+0.03}_{-0.02}$ and $0.95^{+0.04}_{-0.03}$, respectively, with disagreement in the latter value attributable to secondary noise sources beyond those considered in our analysis. With realistic experimental improvements to reduce measurement error and radial trap frequency variations, the model achieves approximation ratios that are 99% of the optimal. Incorporating these improvements into future experiments is expected to reveal new aspects of noise for future modeling and experimental improvements.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1984

Propeller aircraft interior noise model utilization study and validation

Utilization and validation of a computer program designed for aircraft interior noise prediction is considered. The program, entitled PAIN (an acronym for Propeller Aircraft Interior Noise), permits (in theory) predictions of sound levels inside propeller driven aircraft arising from sidewall transmission. The objective of the work reported was to determine the practicality of making predictions for various airplanes and the extent of the program's capabilities. The ultimate purpose was to discern the quality of predictions for tonal levels inside an aircraft occurring at the propeller blade passage frequency and its harmonics. The effort involved three tasks: (1) program validation through comparisons of predictions with scale-model test results; (2) development of utilization schemes for large (full scale) fuselages; and (3) validation through comparisons of predictions with measurements taken in flight tests on a turboprop aircraft. Findings should enable future users of the program to efficiently undertake and correctly interpret predictions.

Pope, L. D.↗
NASA NTRS1987

Propeller aircraft interior noise model. II - Scale-model and flight-test comparisons

A program for predicting the sound levels inside propeller driven aircraft arising from sidewall transmission of airborne exterior noise is validated through comparisons of predictions with both scale-model test results and measurements obtained in flight tests on a turboprop aircraft. The program produced unbiased predictions for the case of the scale-model tests, with a standard deviation of errors of about 4 dB. For the case of the flight tests, the predictions revealed a bias of 2.62-4.28 dB (depending upon whether or not the data for the fourth harmonic were included) and the standard deviation of the errors ranged between 2.43 and 4.12 dB. The analytical model is shown to be capable of taking changes in the flight environment into account.

Willis, C. M.↗
NASA NTRS1979

Shot-noise in resistive-diode mixers and the attenuator noise model

The representation of a pumped exponential diode, operating as a mixer, by an equivalent lossy network, is reexamined. It is shown that the model is correct provided the network has ports for all sideband frequencies at which (real) power flow can occur between the diode and its embedding. The temperature of the equivalent network is eta/2 times the physical temperature of the diode. The model is valid only if the series resistance and nonlinear capacitance of the diode are negligible. Expressions are derived for the input and output noise temperature and the noise-temperature ratio of ideal mixers. Some common beliefs concerning noise-figure and noise-temperature ratio are shown to be incorrect.

Kerr, A. R.↗
NASA NTRS2002

Slat Cove Noise Modeling: A Posteriori Analysis of Unsteady RANS Simulations

A companion paper by Khorrami et al demonstrates the feasibility of simulating the (nominally) self-sustained, large-scale unsteadiness within the leading-edge slat-cove region of multi-element airfoils using unsteady Reynolds-Averaged Navier-Stokes (URANS) equations, provided that the turbulence production term in the underlying two-equation turbulence model is switched off within the cove region. In conjunction with a FfowesWilliams-Hawkings solver, the URANS computations were shown to capture the dominant portion of the acoustic spectrum attributed to slat noise, as well as reproducing the increased intensity of slat cove motions (and, correspondingly, far-field noise as well) at the lower angles of attack. This paper examines that simulation database, augmented by additional simulations, with the objective of transitioning this apparent success to aeroacoustic predictions in an engineering context. As a first step towards this goal, the simulated flow and acoustic fields are compared with experiment and simplified analytical model. Rather intense near-field fluctuations in the simulated flow are found to be associated with unsteady separation along the slat bottom surface, relatively close to the slat cusp. Accuracy of the laminar-cove simulations in this near-wall region is raised to be an open issue. The adjoint Green's function approach is also explored in an attempt to identify the most efficient noise source locations.

Choudhari, Meelan↗
DOE OSTI2022

Fiber Uncertainty Visualization for Bivariate Data With Parametric and Nonparametric Noise Models

Visualization and analysis of multivariate data and their uncertainty are top research challenges in data visualization. Constructing fiber surfaces is a popular technique for multivariate data visualization that generalizes the idea of level-set visualization for univariate data to multivariate data. Here, in this paper, we present a statistical framework to quantify positional probabilities of fibers extracted from uncertain bivariate fields. Specifically, we extend the state-of-the-art Gaussian models of uncertainty for bivariate data to other parametric distributions (e.g., uniform and Epanechnikov) and more general nonparametric probability distributions (e.g., histograms and kernel density estimation) and derive corresponding spatial probabilities of fibers. In our proposed framework, we leverage Green's theorem for closed-form computation of fiber probabilities when bivariate data are assumed to have independent parametric and nonparametric noise. Additionally, we present a nonparametric approach combined with numerical integration to study the positional probability of fibers when bivariate data are assumed to have correlated noise. For uncertainty analysis, we visualize the derived probability volumes for fibers via volume rendering and extracting level sets based on probability thresholds. We present the utility of our proposed techniques via experiments on synthetic and simulation datasets.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1977

DSN system performance test Doppler noise models; noncoherent configuration

The newer model for variance, the Allan technique, now adopted for testing, is analyzed in the subject mode. A model is generated (including considerable contribution from the station secondary frequency standard), and rationalized with existing data. The variance model is definitely sound; the Allan technique mates theory and measure. The mean-frequency model is an estimate; this problem is yet to be rigorously resolved. The unaltered defining expressions are noncovergent, and the observed mean is quite erratic.

Bunce, R.↗