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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 307 records · Page 17

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NASA NTRS1979

Interior noise control prediction study for high-speed propeller-driven aircraft

An analytical model was developed to predict the noise levels inside propeller-driven aircraft during cruise at M = 0.8. The model was applied to three study aircraft with fuselages of different size (wide body, narrow body and small diameter) in order to determine the noise reductions required to achieve the goal of an A-weighted sound level which does not exceed 80 dB. The model was then used to determine noise control methods which could achieve the required noise reductions. Two classes of noise control treatments were investigated: add-on treatments which can be added to existing structures, and advanced concepts which would require changes to the fuselage primary structure. Only one treatment, a double wall with limp panel, provided the required noise reductions. Weight penalties associated with the treatment were estimated for the three study aircraft.

Rennison, D. C.↗

NASA NTRS1968

Predicted and measured XB-70 ground-to-ground engine noise

Predicted and measured ground noise effects of B-70 aircraft engine

Lasagna, P. L.↗

NASA NTRSDate not supplied

Developing and Testing a Physics Guided Machine Learning NeuralNetwork to Predict Tonal Noise Emitted by a Propeller

Artificial neural networks offer a highly nonlinear and adaptive model for predicting complex interactions between input-output parameters. However, these networks require large datasets which often exceed practical considerations in modeling experimental results. To alleviate the dataset size requirement, a method known as physics guided machine learning has been applied to construct several neural networks for predicting propeller tonal noise in the time domain over a broad range of flight conditions. Three space-filling designs, namely, Latin-Hypercube, Sphere-Packing, and Grid-Space, were used to distribute points throughout the input parameter space encompassing nondimensional flight conditions and observer geometry. Each neural network’s performance was validated by conditions outside of the training set and compared to the Propeller Analysis System tool from the NASA Aircraft Noise Prediction Program. Compared to the Grid-Space input design, the Latin-Hypercube and the Sphere-Packing designs provided a better representation of the domain for training. Regarding the network archetype, a fully connected perceptron was found to outperform the partially connected perceptron in their ability to predict tonal noise for small datasets. The black-box nature of these neural networks was also explored to understand how the networks constructed the waveform and understand why some network designs produce better models.

Propeller noise↗

NASA NTRS1988

The prediction of en route noise levels for a DC-9 aircraft

En route noise for advanced propfan powered aircraft has become an issue of concern for the Federal Aviation Administration. The NASA Aircraft Noise Prediction Program (ANOPP) is used to demonstrate the source noise and propagation effects for an aircraft in level flight up to 35,000 feet altitude. One-third octave band spectra of the source noise, atmospheric absorption loss, and received noise are presented. The predicted maximum A-weighted sound pressure level is compared to measured data from the Aeronautical Research Institute of Sweden. ANOPP is shown to be an effective tool in evaluating the en route noise characteristics of a DC-9 aircraft.

Weir, Donald S.↗

NASA NTRS2011

Acoustic Prediction Methodology and Test Validation for an Efficient Low-Noise Hybrid Wing Body Subsonic Transport

This investigation was conducted to: (1) Develop a hybrid wing body subsonic transport configuration with noise prediction methods to meet the circa 2007 NASA Subsonic Fixed Wing (SFW) N+2 noise goal of -52 dB cum relative to FAR 36 Stage 3 (-42 dB cum re: Stage 4) while achieving a -25% fuel burned compared to current transports (re :B737/B767); (2) Develop improved noise prediction methods for ANOPP2 for use in predicting FAR 36 noise; (3) Design and fabricate a wind tunnel model for testing in the LaRC 14 x 22 ft low speed wind tunnel to validate noise predictions and determine low speed aero characteristics for an efficient low noise Hybrid Wing Body configuration. A medium wide body cargo freighter was selected to represent a logical need for an initial operational capability in the 2020 time frame. The Efficient Low Noise Hybrid Wing Body (ELNHWB) configuration N2A-EXTE was evolved meeting the circa 2007 NRA N+2 fuel burn and noise goals. The noise estimates were made using improvements in jet noise shielding and noise shielding prediction methods developed by UC Irvine and MIT. From this the Quiet Ultra Integrated Efficient Test Research Aircraft #1 (QUIET-R1) 5.8% wind tunnel model was designed and fabricated.

Kawai, Ronald T.↗

NASA NTRS1976

Measurement, analysis, and prediction of aircraft interior noise

Considerations of comfort of passengers and crew in light aircraft and helicopters indicate substantial benefits may be obtained by the reduction of interior noise levels. This paper discusses an ongoing research effort to reduce interior noise in such vehicles. Data from both field and laboratory studies for a light aircraft are presented. The laboratory data indicate that structural vibration is an efficient source of interior noise and should be considered in the reduction of interior noise. Flight data taken on a helicopter before and after installation of acoustic treatment demonstrate that over 30 dB of noise reduction can be obtained in certain portions of the spectra. However, subjective evaluations of the treated vehicle indicate that further reductions in interior noise are desirable. An existing interior noise prediction method which was developed for large jet transports was applied to study low-frequency noise in a light aircraft fuselage. The results indicate that improvements in the analytical model may be necessary for the prediction of interior noise of light aircraft.

Howlett, J. T.↗

NASA NTRS2005

Empirical Prediction of Aircraft Landing Gear Noise

This report documents a semi-empirical/semi-analytical method for landing gear noise prediction. The method is based on scaling laws of the theory of aerodynamic noise generation and correlation of these scaling laws with current available test data. The former gives the method a sound theoretical foundation and the latter quantitatively determines the relations between the parameters of the landing gear assembly and the far field noise, enabling practical predictions of aircraft landing gear noise, both for parametric trends and for absolute noise levels. The prediction model is validated by wind tunnel test data for an isolated Boeing 737 landing gear and by flight data for the Boeing 777 airplane. In both cases, the predictions agree well with data, both in parametric trends and in absolute noise levels.

Golub, Robert A.↗

NASA NTRS2016

Modeling and Prediction of Krueger Device Noise

This paper presents the development of a noise prediction model for aircraft Krueger flap devices that are considered as alternatives to leading edge slotted slats. The prediction model decomposes the total Krueger noise into four components, generated by the unsteady flows, respectively, in the cove under the pressure side surface of the Krueger, in the gap between the Krueger trailing edge and the main wing, around the brackets supporting the Krueger device, and around the cavity on the lower side of the main wing. For each noise component, the modeling follows a physics-based approach that aims at capturing the dominant noise-generating features in the flow and developing correlations between the noise and the flow parameters that control the noise generation processes. The far field noise is modeled using each of the four noise component's respective spectral functions, far field directivities, Mach number dependencies, component amplitudes, and other parametric trends. Preliminary validations are carried out by using small scale experimental data, and two applications are discussed; one for conventional aircraft and the other for advanced configurations. The former focuses on the parametric trends of Krueger noise on design parameters, while the latter reveals its importance in relation to other airframe noise components.

Guo, Yueping↗

NASA NTRSDate not supplied

Refined Predictions Compared with the Propulsion Airframe Aeroacoustics and Aircraft System Noise Flight Research Test Data

In a collaboration between NASA and The Boeing Company, the Propulsion Airframe Aeroacoustics and Aircraft System Noise Flight Research Test was executed by the Boeing ecoDemonstrator Program in 2020 with an Etihad Airways Boeing 787-10 aircraft. This ambitious flight research successfully accomplished many objectives and constitutes the most comprehensive and highest quality acoustic flight data available to NASA for a modern commercial subsonic transport aircraft. One purpose of these data is to be the measure of accuracy for the aircraft system noise prediction capabilities of NASA. This research reviews the impact of the major improvements in prediction methods implemented up to this point and tested in the Research version of the NASA Aircraft Noise Prediction Program. The improvements have been to the prediction of jet source and jet-flap interaction, to both fan broadband and tone source prediction, and to the prediction of propulsion airframe aeroacoustic scattering effects. In general, over the engine power range, comparisons between prediction and flight data are within 2 EPNdB including for the intentional sideline-to-sideline asymmetries as implemented in the flight test by flying the aircraft with only one engine at power. Considerable progress has been shown here in the continuing effort to advance the fidelity of NASA aircraft noise prediction capabilities for subsonic aircraft flight acoustics, modern transport aircraft and future aircraft concepts.

aircraft system noise prediction↗

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 NTRS2005

Technologies for Turbofan Noise Reduction

An overview presentation of NASA's engine noise research since 1992 is given for subsonic commercial aircraft applications. Highlights are included from the Advanced Subsonic Technology (AST) Noise Reduction Program and the Quiet Aircraft Technology (QAT) project with emphasis on engine source noise reduction. Noise reduction goals for 10 EPNdB by 207 and 20 EPNdB by 2022 are reviewed. Fan and jet noise technologies are highlighted from the AST program including higher bypass ratio propulsion, scarf inlets, forward-swept fans, swept/leaned stators, chevron nozzles, noise prediction methods, and active noise control for fans. Source diagnostic tests for fans and jets that have been completed over the past few years are presented showing how new flow measurement methods such as Particle Image Velocimetry (PIV) have played a key role in understanding turbulence, the noise generation process, and how to improve noise prediction methods. Tests focused on source decomposition have helped identify which engine components need further noise reduction. The role of Computational AeroAcoustics (CAA) for fan noise prediction is presented. Advanced noise reduction methods such as Hershel-Quincke tubes and trailing edge blowing for fan noise that are currently being pursued n the QAT program are also presented. Highlights are shown form engine validation and flight demonstrations that were done in the late 1990's with Pratt & Whitney on their PW4098 engine and Honeywell on their TFE-731-60 engine. Finally, future propulsion configurations currently being studied that show promise towards meeting NASA's long term goal of 20 dB noise reduction are shown including a Dual Fan Engine concept on a Blended Wing Body aircraft.

Huff, Dennis↗

NASA NTRSDate not supplied

AIRNOISEUAM: An Urban Air Mobility Noise-Exposure Prediction Tool

A new software tool called AIRNOISEUAM is introduced that models the noise exposure of Urban Air Mobility (UAM) operations. Given relevant UAM aircraft performance models, mission profiles, and Noise-Power-Distance data, AIRNOISEUAM predicts the noise exposure footprint for receptors on the ground. The performance of AIRNOISEUAM using a Robinson R66 helicopter model and a six-passenger quadrotor model, and a diverse set of scenarios from NASA’s UAM human-in-the-loop simulations is compared to that of the industry-standard tool with the same inputs. The predicted noise exposure results from both tools are found to be nearly identical. AIRNOISEUAM offers a fast-time, flexible interface and modular design to facilitate the dynamic requirements of the aviation research community.

Urban air mobility↗

NASA NTRSDate not supplied

AIRNOISEUAM: An Urban Air Mobility Noise-Exposure Prediction Tool

Urban Air Mobility↗

NASA NTRS2016

ANOPP2 User's Manual: Version 1.2

This manual documents the Aircraft NOise Prediction Program 2 (ANOPP2). ANOPP2 is a toolkit that includes a framework, noise prediction methods, and peripheral software to aid a user in predicting and understanding aircraft noise. This manual includes an explanation of the overall design and structure of ANOPP2, including a brief introduction to aircraft noise prediction and the ANOPP2 background, philosophy, and architecture. The concept of nested acoustic data surfaces and its application to a mixed-fidelity noise prediction are presented. The structure and usage of ANOPP2, which includes the communication between the user, the ANOPP2 framework, and noise prediction methods, are presented for two scenarios: wind-tunnel and flight. These scenarios serve to provide the user with guidance and documentation references for performing a noise prediction using ANOPP2.

Lopes, L. V.↗

NASA NTRS1982

Analytical prediction of the interior noise for cylindrical models of aircraft fuselages for prescribed exterior noise fields. Phase 2: Models for sidewall trim, stiffened structures and cabin acoustics with floor partition

An airplane interior noise prediction model is developed to determine the important parameters associated with sound transmission into the interiors of airplanes, and to identify apropriate noise control methods. Models for stiffened structures, and cabin acoustics with floor partition are developed. Validation studies are undertaken using three test articles: a ring stringer stiffened cylinder, an unstiffened cylinder with floor partition, and ring stringer stiffened cylinder with floor partition and sidewall trim. The noise reductions of the three test articles are computed using the heoretical models and compared to measured values. A statistical analysis of the comparison data indicates that there is no bias in the predictions although a substantial random error exists so that a discrepancy of more than five or six dB can be expected for about one out of three predictions.

Pope, L. D.↗

NASA NTRS1997

TBIEM3D: A Computer Program for Predicting Ducted Fan Engine Noise

This document describes the usage of the ducted fan noise prediction program TBIEM3D (Thin duct - Boundary Integral Equation Method - 3 Dimensional). A scattering approach is adopted in which the acoustic pressure field is split into known incident and unknown scattered parts. The scattering of fan-generated noise by a finite length circular cylinder in a uniform flow field is considered. The fan noise is modeled by a collection of spinning point thrust dipoles. The program, based on a Boundary Integral Equation Method (BIEM), calculates circumferential modal coefficients of the acoustic pressure at user-specified field locations. The duct interior can be of the hard wall type or lined. The duct liner is axisymmetric, locally reactive, and can be uniform or axially segmented. TBIEM3D is written in the FORTRAN programming language. Input to TBIEM3D is minimal and consists of geometric and kinematic parameters. Discretization and numerical parameters are determined automatically by the code. Several examples are presented to demonstrate TBIEM3D capabilities.

Dunn, M. H.↗

NASA NTRS2008

The Aeroacoustics of Turbulent Flows

Aerodynamic noise prediction has been an important and challenging research area since James Lighthill first introduced his Acoustic Analogy Approach over fifty years ago. This talk attempts to provide a unified framework for the subsequent theoretical developments in this field. It assumes that there is no single approach that is optimal in all situations and uses the framework as a basis for discussing the strengths weaknesses of the various approaches to this topic. But the emphasis here will be on the important problem of predicting the noise from high speed air jets. Specific results will presented for round jets in the 0.5 to 1.4 Mach number range and compared with experimental data taken on the Glenn SHAR rig. It is demonstrated that nonparallel mean flow effects play an important role in predicting the noise at the supersonic Mach numbers. The results explain the failure of previous attempts based on the parallel flow Lilley model (which has served as the foundation for most jet noise analyses during past two decades).

Goldstein, M. E.↗

NASA NTRS2016

Perceived Noise Analysis for Offset Jets Applied to Commercial Supersonic Aircraft

A systems analysis was performed with experimental jet noise data, engine/aircraft performance codes and aircraft noise prediction codes to assess takeoff noise levels and mission range for conceptual supersonic commercial aircraft. A parametric study was done to identify viable engine cycles that meet NASA's N+2 goals for noise and performance. Model scale data from offset jets were used as input to the aircraft noise prediction code to determine the expected sound levels for the lateral certification point where jet noise dominates over all other noise sources. The noise predictions were used to determine the optimal orientation of the offset nozzles to minimize the noise at the lateral microphone location. An alternative takeoff procedure called "programmed lapse rate" was evaluated for noise reduction benefits. Results show there are two types of engines that provide acceptable mission range performance; one is a conventional mixed-flow turbofan and the other is a three-stream variable-cycle engine. Separate flow offset nozzles reduce the noise directed toward the thicker side of the outer flow stream, but have less benefit as the core nozzle pressure ratio is reduced. At the systems level for a three-engine N+2 aircraft with full throttle takeoff, there is a 1.4 EPNdB margin to Chapter 3 noise regulations predicted for the lateral certification point (assuming jet noise dominates). With a 10% reduction in thrust just after clearing the runway, the margin increases to 5.5 EPNdB. Margins to Chapter 4 and Chapter 14 levels will depend on the cumulative split between the three certification points, but it appears that low specific thrust engines with a 10% reduction in thrust (programmed lapse rate) can come close to meeting Chapter 14 noise levels. Further noise reduction is possible with engine oversizing and derated takeoff, but more detailed mission studies are needed to investigate the range impacts as well as the practical limits for safety and takeoff regulations.

Turbofans↗