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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

Effects of forward velocity and acoustic treatment on inlet fan noise

Flyover and static noise data from several engines are presented that show inlet fan noise measured in flight can be lower than that projected from static tests for some engines. The differences between flight and static measurements appear greatest when the fan fundamental tone due to rotor-stator interaction or to the rotor alone field is below cutoff. Data from engine and fan tests involving inlet treatment on the walls only are presented that show the attenuation from this treatment is substantially larger than expected from previous theories or flow duct experience. Data showing noise shielding effects due to the location of the engine on the airplane are also presented. These observations suggest that multiringed inlets may not be necessary to achieve the desired noise reduction in many applications.

Feiler, C. E.↗
NASA NTRS1974

Effects of forward velocity and acoustic treatment on inlet fan noise

Flyover and static noise data from several engines are presented that show inlet fan noise measured in flight can be lower than that projected from static tests for some engines. The differences between flight and static measurements appear greatest when the fan fundamental tone due to rotor-stator interaction or to the rotor-alone field is below cutoff. Data from engine and fan tests involving inlet treatment on the walls only are presented that show the attenuation from this treatment is substantially larger than expected from previous theories or flow duct experience. Data showing noise shielding effects due to the location of the engine on the airplane are also presented. These observations suggest that multiringed inlets may not be necessary to achieve the desired noise reduction in many applications.

Feiler, C. E.↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
NASA NTRS1980

A comparison between an existing propeller noise theory and wind tunnel data

The noise of three supersonic helical tip speed propellers was compared with the noise predicted by an existing noise theory. Comparisons of the peak blade passage tones showed fairly good agreement beween theory and experiment at the lowest helical tip Mach numbers tested, 0.86 and 1.00, while at higher numbers, the theory predicted higher noise levels than measured. When the differences among the propellers were considered the theory and measurement showed fairly good agreement. Directivity measurements in general showed that the measured blade passage tone data peaked further downstream than the theory predicted. At the cruise design condition the harmonics appeared to fall off faster in the data than the theory indicated.

Dittmar, J. H.↗
NASA NTRS2011

Enhanced Core Noise Modeling for Turbofan Engines

This report describes work performed by MTC Technologies (MTCT) for NASA Glenn Research Center (GRC) under Contract NAS3-00178, Task Order No. 15. MTCT previously developed a first-generation empirical model that correlates the core/combustion noise of four GE engines, the CF6, CF34, CFM56, and GE90 for General Electric (GE) under Contract No. 200-1X-14W53048, in support of GRC Contract NAS3-01135. MTCT has demonstrated in earlier noise modeling efforts that the improvement of predictive modeling is greatly enhanced by an iterative approach, so in support of NASA's Quiet Aircraft Technology Project, GRC sponsored this effort to improve the model. Since the noise data available for correlation are total engine noise spectra, it is total engine noise that must be predicted. Since the scope of this effort was not sufficient to explore fan and turbine noise, the most meaningful comparisons must be restricted to frequencies below the blade passage frequency. Below the blade passage frequency and at relatively high power settings jet noise is expected to be the dominant source, and comparisons are shown that demonstrate the accuracy of the jet noise model recently developed by MTCT for NASA under Contract NAS3-00178, Task Order No. 10. At lower power settings the core noise became most apparent, and these data corrected for the contribution of jet noise were then used to establish the characteristics of core noise. There is clearly more than one spectral range where core noise is evident, so the spectral approach developed by von Glahn and Krejsa in 1982 wherein four spectral regions overlap, was used in the GE effort. Further analysis indicates that the two higher frequency components, which are often somewhat masked by turbomachinery noise, can be treated as one component, and it is on that basis that the current model is formulated. The frequency scaling relationships are improved and are now based on combustor and core nozzle geometries. In conjunction with the Task Order No. 10 jet noise model, this core noise model is shown to provide statistical accuracy comparable to the jet noise model for frequencies below blade passage. This model is incorporated in the NASA FOOTPR code and a user s guide is provided.

Stone, James R.↗
NASA NTRS1976

Selected methods for quantification of community exposure to aircraft noise

A review of the state-of-the-art for the quantification of community exposure to aircraft noise is presented. Physical aspects, people response considerations, and practicalities of useful application of scales of measure are included. Historical background up through the current technology is briefly presented. The developments of both single-event and multiple-event scales are covered. Selective choice is made of scales currently in the forefront of interest and recommended methodology is presented for use in computer programing to translate aircraft noise data into predictions of community noise exposure. Brief consideration is given to future programing developments and to supportive research needs.

Edge, P. M., Jr.↗
NASA NTRS1979

Validation of aircraft noise prediction program

Predictions made with NASA's aircraft noise prediction program (ANOPP) were compared with flyover noise data. A data base of six flyover noise runs for the DC-10-40/JT9D-59A configuration was used. For all power settings, ANOPP consistently underpredicted the low frequency spectral levels, overpredicted high frequency spectral levels and, consequently, overpredicted the inlet and aft PNLT time histories.

Kapper, C. Y.↗
NASA NTRS2001

Large Engine Technology (LET) Task XXXVII Low-Bypass Ratio Mixed Turbofan Engine Subsonic Jet Noise Reduction Program Test Report

NASA Glenn Research Center supported a three year effort to develop the technology for reducing jet noise from low-bypass ratio engines. This effort concentrated on both analytical and experimental approaches using various mixer designs. CFD and MGB predictions are compared with LDV and noise data, respectively. While former predictions matched well with data, experiment shows a need for improving the latter predictions. Data also show that mixing noise can be sensitive to engine hardware upstream of the mixing exit plane.

Hauser, Joseph R.↗
NASA NTRS2015

Conflict Detection Performance Analysis for Function Allocation Using Time-Shifted Recorded Traffic Data

The performance of the conflict detection function in a separation assurance system is dependent on the content and quality of the data available to perform that function. Specifically, data quality and data content available to the conflict detection function have a direct impact on the accuracy of the prediction of an aircraft's future state or trajectory, which, in turn, impacts the ability to successfully anticipate potential losses of separation (detect future conflicts). Consequently, other separation assurance functions that rely on the conflict detection function - namely, conflict resolution - are prone to negative performance impacts. The many possible allocations and implementations of the conflict detection function between centralized and distributed systems drive the need to understand the key relationships that impact conflict detection performance, with respect to differences in data available. This paper presents the preliminary results of an analysis technique developed to investigate the impacts of data quality and data content on conflict detection performance. Flight track data recorded from a day of the National Airspace System is time-shifted to create conflicts not present in the un-shifted data. A methodology is used to smooth and filter the recorded data to eliminate sensor fusion noise, data drop-outs and other anomalies in the data. The metrics used to characterize conflict detection performance are presented and a set of preliminary results is discussed.

Guerreiro, Nelson M.↗
NASA NTRS2021

Medium-Sized Helicopter Noise Abatement Flight Test Data Report

Helicopter noise has consistently hindered operations within urban areas and surrounding communities. To combat this, an extensive flight test campaign, described herein, was conducted to gather acoustic data to support helicopter noise research (e.g., physics-based modeling, operational planning, etc.). Acoustic measurements were collected from four helicopters in the medium-sized weight class with maximum takeoff gross weights between 7,400 and 14,200 lb: a Bell 205 “Huey”, a Sikorsky S-76D, a Leonardo AW139, and a US Coast Guard MH-65 Dolphin (a variant of the commercial Airbus AS365 Dauphin). Each aircraft per- formed a subset of level flyovers, steady descents, turns with various initial conditions, and noise abatement approaches. A distributed ground microphone array spanning approximately 2,000 by 4,000 ft collected measurements of each flyover event. Additionally, a dedicated semicircular microphone array was used for gathering acoustic measurements of static conditions (e.g., hover). This report provides an overview of the flight test methodology, conditions tested, equipment used, and details of the accompanying distribution dataset. Figures of sample results generated for each test point are included to enable comparison and selection of the data at the discretion of the user. Specifically, source noise hemispheres of the steady flight conditions illustrate directivity of over- all levels. Noise contours over the array provide insight into the ground levels associated with approaches and turning events. These data figures are derived from the original acoustic pressure-time histories and vehicle tracking data that are provided in the distribution dataset.

Rotorcraft↗
NASA NTRS2000

Wavelet-Based Interpolation and Representation of Non-Uniformly Sampled Spacecraft Mission Data

A well-documented problem in the analysis of data collected by spacecraft instruments is the need for an accurate, efficient representation of the data set. The data may suffer from several problems, including additive noise, data dropouts, an irregularly-spaced sampling grid, and time-delayed sampling. These data irregularities render most traditional signal processing techniques unusable, and thus the data must be interpolated onto an even grid before scientific analysis techniques can be applied. In addition, the extremely large volume of data collected by scientific instrumentation presents many challenging problems in the area of compression, visualization, and analysis. Therefore, a representation of the data is needed which provides a structure which is conducive to these applications. Wavelet representations of data have already been shown to possess excellent characteristics for compression, data analysis, and imaging. The main goal of this project is to develop a new adaptive filtering algorithm for image restoration and compression. The algorithm should have low computational complexity and a fast convergence rate. This will make the algorithm suitable for real-time applications. The algorithm should be able to remove additive noise and reconstruct lost data samples from images.

Bose, Tamal↗
NASA NTRS1969

Towards an optimal inversion method for remote atmospheric sensing

The inference of atmospheric structure from satellite radiometric observations requires an inversion algorithm. A variety of techniques was spawned to meet these demands. One class, the nonlinear inversion methods, copes with the problem of data noise. Unlike linear techniques which require a priori data smoothing, the nonlinear method can be applied directly to raw data. The algorithm discriminates the noise input by resolving the inferences into two types of solution, associating the real roots with atmospheric structure while ascribing the imaginary roots to noise.

King, J. I. F.↗
DOE OSTI2025

Distributed Acoustic Sensing for Whale Vocalization Monitoring: A Vertical Deployment Field Test

Abstract There is growing interest in floating offshore wind turbine (FOWT) technology, where turbines are installed on floating structures anchored to the seabed, allowing wind energy development in areas unsuitable for traditional fixed-platform turbines. Responsible development requires monitoring the impact of FOWTs on marine wildlife, such as whales, throughout the operational lifecycle of the turbines. Distributed acoustic sensing (DAS)—a technology that transforms fiber-optic cables into vibration sensor arrays—has been demonstrated for acoustic monitoring of whales using seafloor telecommunications cables. However, no studies have yet evaluated DAS performance in dynamic, engineered environments, such as floating platforms or moving vessels with complex, dynamic strain loads, despite their relevance to FOWT settings. This study addresses that gap by deploying DAS aboard a boat in Monterey Bay, California, where a fiber-optic cable was lowered using a weighted and suspended mooring line, enabling vertical deployment. Humpback whale vocalizations were captured and identified in the DAS data, noise sources were identified, and DAS data were compared to audio captured by a standalone hydrophone attached to the mooring line and a nearby hydrophone on a cabled observatory. This study is unique in: (1) deploying DAS in a vertical deployment mode, where noise from turbulence, cable vibrations, and other sources posed additional challenges compared to seafloor DAS applications; (2) demonstrating DAS in a dynamic, nonstationary setup, which is uncommon for DAS interrogators typically used in more stable environments; and (3) leveraging looped sections of the cable to reduce the noise floor and mitigate the effects of excessive cable vibrations and strain. This research demonstrates DAS’s ability to capture whale vocalizations in challenging environments, highlighting its potential to enhance underwater acoustic monitoring, particularly in the context of renewable energy development in offshore environments.

Saw, Jaewon↗
NASA NTRS1976

Noise reduction tests of large-scale-model externally blown flap using trailing-edge blowing and partial flap slot covering

Noise data were obtained with a large-scale cold-flow model of a two-flap, under-the-wing, externally blown flap proposed for use on future STOL aircraft. The noise suppression effectiveness of locating a slot conical nozzle at the trailing edge of the second flap and of applying partial covers to the slots between the wing and flaps was evaluated. Overall-sound-pressure-level reductions of 5 db occurred below the wing in the flyover plane. Existing models of several noise sources were applied to the test results. The resulting analytical relation compares favorably with the test data. The noise source mechanisms were analyzed and are discussed.

Mckinzie, D. J., Jr.↗
NASA NTRS2000

The Alternative Low Noise Fan

A 106 bladed fan with a design takeoff tip speed of 1100 ft/sec was hypothesized as reducing perceived noise because of the shift of the blade passing harmonics to frequencies beyond the perceived noise rating range. A 22 in. model of this Alternative Low Noise Fan, ALNF, was tested in the NASA Glenn 9x 15 Wind Tunnel. 'Me fan was tested with a 7 vane long chord stator assembly and a 70 vane conventional stator assembly in both hard and acoustically treated configurations. In addition a partially treated 7 vane configuration was tested wherein the acoustic material between the 7 long chord stators was made inactive. The noise data from the 106 bladed fan with 7 long chord stators in a hard configuration was shown to be around 4 EPNdB quieter than a low tip speed Allison fan at takeoff and around 5 EPNdB quieter at approach. Although the tone noise behaved as hypothesized, the majority of this noise reduction was from reduced broadband noise related to the large number of rotor blades. This 106 bladed ALNF is a research fan designed to push the technology limits and as such is probably not a practical device with present materials technology. However, a low tip speed fan with around 50 blades would be a practical device and calculations indicate that it could be 2 to 3 EPNdB quieter at takeoff and 3 to 4 EPNdB quieter at approach than the Allison fan. 7 vane data compared with 70 vane data indicated that the tone noise was controlled by rotor wake-stator interaction but that the broadband noise is probably controlled by the interaction of the rotor with incoming flows. A possible multiple pure tone noise reduction technique for a fan/acoustic treatment system was identified. The data from the fully treated configuration showed significant noise reductions over a large frequency range thereby providing a real tribute to this bulk absorber treatment design. The tone noise data with the partially treated 7 vane configuration indicated that acoustic material in the source noise generation region may be more effective than similar material outside of the generation region.

Dittmar, James H.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-18315-DND

NASA used towers until 2009 for gathering community noise data. It was thought that the integration of the data into the metrics masked the reflection issues. We now use narrow band data for research and when evaluating community noise. Reflections are visible in narrow band and 1/3 octave tower data.

Charlotte Whitfield↗
NASA NTRS1977

Development of a procedure to model high-resolution wind profiles from smoothed or low-frequency data

The derivation of simulated Jimsphere wind profiles from low-frequency rawinsonde data and a generated set of white noise data are presented. A computer program is developed to model high-resolution wind profiles based on the statistical properties of data from the Kennedy Space Center, Florida. Comparison of the measured Jimsphere data, rawinsonde data, and the simulated profiles shows excellent agreement.

Camp, D. W.↗
NASA NTRS1975

Stepwise Iterative Fourier Transform: The SIFT

A program, designed specifically to study the respective effects of some common data problems on results obtained through stepwise iterative Fourier transformation of synthetic data with known waveform composition, was outlined. Included in this group were the problems of gaps in the data, different time-series lengths, periodic but nonsinusoidal waveforms, and noisy (low signal-to-noise) data. Results on sinusoidal data were also compared with results obtained on narrow band noise with similar characteristics. The findings showed that the analytic procedure under study can reliably reduce data in the nature of (1) sinusoids in noise, (2) asymmetric but periodic waves in noise, and (3) sinusoids in noise with substantial gaps in the data. The program was also able to analyze narrow-band noise well, but with increased interpretational problems. The procedure was shown to be a powerful technique for analysis of periodicities, in comparison with classical spectrum analysis techniques. However, informed use of the stepwise procedure nevertheless requires some background of knowledge concerning characteristics of the biological processes under study.

Benignus, V. A.↗