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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Data Quality Assurance for Supersonic Jet Noise Measurements

The noise created by a supersonic aircraft is a primary concern in the design of future high-speed planes. The jet noise reduction technologies required on these aircraft will be developed using scale-models mounted to experimental jet rigs designed to simulate the exhaust gases from a full-scale jet engine. The jet noise data collected in these experiments must accurately predict the noise levels produced by the full-scale hardware in order to be a useful development tool. A methodology has been adopted at the NASA Glenn Research Center s Aero-Acoustic Propulsion Laboratory to insure the quality of the supersonic jet noise data acquired from the facility s High Flow Jet Exit Rig so that it can be used to develop future nozzle technologies that reduce supersonic jet noise. The methodology relies on mitigating extraneous noise sources, examining the impact of measurement location on the acoustic results, and investigating the facility independence of the measurements. The methodology is documented here as a basis for validating future improvements and its limitations are noted so that they do not affect the data analysis. Maintaining a high quality jet noise laboratory is an ongoing process. By carefully examining the data produced and continually following this methodology, data quality can be maintained and improved over time.

Brown, Clifford A.↗
NASA NTRS1991

Neuromorphic learning of continuous-valued mappings from noise-corrupted data

The effect of noise on the learning performance of the backpropagation algorithm is analyzed. A selective sampling of the training set is proposed to maximize the learning of control laws by backpropagation, when the data have been corrupted by noise. The training scheme is applied to the nonlinear control of a cart-pole system in the presence of noise. The neural computation provides the neurocontroller with good noise-filtering properties. In the presence of plant noise, the neurocontroller is found to be more stable than the teacher. A novel perspective on the application of neural network technology to control engineering is presented.

Troudet, T.↗
NASA NTRS1981

Airframe noise of a small model transport aircraft and scaling effects

Airframe noise of a 0.01 scale model Boeing 747 wide-body transport was measured in the Langley Anechoic Noise Facility. The model geometry simulated the landing and cruise configurations. The model noise was found to be similar in noise characteristics to that possessed by a 0.03 scale model 747. The 0.01 scale model noise data scaled to within 3 dB of full scale data using the same scaling relationships as that used to scale the 0.03 scale model noise data. The model noise data are compared with full scale noise data, where the full scale data are calculated using the NASA aircraft noise prediction program.

Shearin, J. G.↗
NASA NTRS1985

A Preliminary Investigation of Systematic Noise in Data Acquired with the Airborne Imaging Spectrometer

Systematic noise is present in Airborne Imaging Spectrometer (AIS) data collected on October 26, 1983 and May 5, 1984 in grating position 0 (1.2 to 1.5 microns). In the October data set the noise occurs as 135 scan lines of low DN's every 270 scan lines. The noise is particularly bad in bands nine through thirty, restricting effective analysis to at best ten of the 32 bands. In the May data the regions of severe noise have been eliminated, but systematic noise is present with three frequencies (3, 106 and 200 scan lines) in all thirty two bands. The periodic nature of the noise in both data sets suggests that it could be removed as part of routine processing. This is necessary before classification routines or statistical analyses are used with these data.

Masuoka, E.↗
NASA NTRS1975

Analysis and prediction of Doppler noise during solar conjunctions

The results of a study of Doppler data noise during solar conjunctions were presented. During the first half of 1975, a sizeable data base of Doppler data noise (estimates) for the Pioneer 10, Pioneer 11, and Helios 1 solar conjunctions was accumulated. To analyze this data, certain physical assumptions are made, leading to the development of a geometric parameter ("ISI") which correlates strongly with Doppler data noise under varying sun-earth-spacecraft geometries. Doppler noise models are then constructed from this parameter, resulting in the newfound ability to predict Doppler data noise during solar conjunctions, and hence to additionally be in a position to validate Doppler data acquired during solar conjunctions.

Berman, A. L.↗
NASA NTRS1975

Observations of magnetospheric ionization enhancements using upper-hybrid resonance noise band data from the RAE-1 satellite

Noise bands associated with the upper-hybrid resonance were used to provide direct evidence for the existence of regions of enhanced density in the equatorial magnetosphere near L = 2. Density enhancements ranging from several percent to as high as 45 percent are observed with radial dimensions of several hundred kilometers. The enhancement characteristics strongly suggest their identification as magnetospheric whistler ducts.

Mosier, S. R.↗
DOE OSTI2025

Bayesian Optimized Deep Ensemble for Uncertainty Quantification of Deep Neural Networks: a System Safety Case Study on Sodium Fast Reactor Thermal Stratification Modeling

Deep neural networks (DNNs) are increasingly important to scientific computing and engineering system simulations. Accurate uncertainty quantification (UQ) for DNNs is critical in safety-sensitive engineering domains. Traditional Deep Ensemble (DE) methods, while easy to implement, frequently suffer from poorly calibrated uncertainty estimates and limited predictive accuracy due to reliance on fixed architectures with varied weight initializations. To address these issues, we introduce a workflow that combines Bayesian Optimization (BO) and DE. The workflow is modular, scalable, and integrates parallel BO initialized with Sobol sequences to individually optimize the hyperparameters of each ensemble member. This method enhances ensemble diversity, improves predictive accuracy, and provides reliable uncertainty estimates. We evaluate the proposed BODE approach in a sodium fast reactor thermal stratification modeling case study, where we used a densely connected convolutional neural network to predict turbulent viscosity during the reactor transient with consideration of data noise. We benchmark its performance against several optimization approaches, including baseline deep ensemble, evolutionary algorithm-optimized ensemble, ensemble formed via random search combined with greedy selection, and a BO ensemble using random initialization. Here, our results demonstrate superior performance of the developed BODE approach. In noise-free scenarios, BODE notably reduces incorrect aleatoric uncertainty and significantly enhances predictive accuracy. Under conditions of 5% and 10% Gaussian noise, BODE adaptively quantifies uncertainty proportional to data noise, achieving up to an 80% reduction in root mean square error compared to baseline methods and producing well-calibrated prediction intervals.

Bayesian optimization↗
NASA NTRS1972

EBF noise tests with engine under-the-wing and over-the-wing configurations

Noise tests of externally blown flaps with the engine under the wing and engine over the wing configurations were conducted. Flap noise data obtained on a TF-34 aircraft are discussed. Noise data obtained during a free-jet forward-speed-effect analysis are presented. Noise sources associated with upper surface flap blowing are described. Results of a small scale configuration screening study and some large scale model test data are analyzed. The noise data for the engine over wing configurations are compared with the engine under the wing configurations.

Dorsch, R. G.↗
NASA NTRS1976

The Pioneer 11 1976 solar conjunction: A unique opportunity to explore the heliographic latitudinal variations of the solar corona

The 1976 Pioneer II Solar Conjunction provided the opportunity to accumulate a substantial quantity of doppler noise data over a dynamic range of signal closest approach point heliographic latitudes. The observed doppler noise data were fit to the doppler noise model ISED, and the deviations of the observed doppler noise data from the model were used to construct a (multiplicative) function to describe the effect of heliographic latitude. This expression was then incorporated into the ISED model to produce a new doppler noise model-ISEDB.

Berman, A. L.↗
NASA NTRSDate not supplied

Prediction-Based Approaches for Generation of Noise-Power-Distance Data with Application to Urban Air Mobility Vehicles

In contrast to most commercial air traffic today, vehicles serving the urban air mobility (UAM) market are anticipated to operate within communities and be close to the public at large. The approved model for assessing environmental impact of air traffic actions in the United States, the Federal Aviation Administration (FAA) Aviation Environmental Design Tool (AEDT), does not directly support analysis of such operations due to a combined lack of UAM aircraft flight performance model data and aircraft noise data. This paper addresses the latter by offering two prediction-based approaches for generation of noise-power-distance (NPD) data for use within AEDT. One utilizes AEDT’s fixed-wing aircraft modeling approach and the other utilizes the rotary-wing aircraft modeling approach.

noise-power-distance↗
NASA NTRS2023

Prediction of Noise-Power-Distance Data for Urban Air Mobility Vehicles

In contrast to most commercial air traffic today, vehicles serving the urban air mobility (UAM) market are anticipated to operate within communities and be close to the public at large. The approved model for assessing environmental impact of air traffic actions in the United States, the Federal Aviation Administration (FAA) Aviation Environmental Design Tool (AEDT), does not directly support analysis of such operations due to a combined lack of UAM aircraft flight performance model data and aircraft noise data. This paper addresses the latter by offering two prediction-based approaches for generation of noise-power-distance (NPD) data for use within AEDT. One utilizes the AEDT fixed-wing aircraft modeling approach and the other utilizes the AEDT rotary-wing aircraft modeling approach.

noise-power-distance↗
NASA NTRS1972

Comparison of wind tunnel and flyover noise measurements of the YOV-10A STOL aircraft

The YOV-10A Research Aircraft was flown to obtain flyover noise data that could be compared to noise data measured in the 40- by 80- foot wind tunnel at NASA Ames Research Center. The flyover noise measurements were made during the early morning hours on runway 32L at Moffett Field, California. A number of passes were made at 50 ft altitude in level flight with an airplane configuration closely matching that tested in the wind tunnel. Two passes were selected as prime and were designated for full data reduction. The YOV-10A was flown over a microphone field geometrically similar to the microphone array set up in the wind tunnel. An acoustic center was chosen as a matching point for the data. Data from the wind tunnel and flyover were reduced and appropiate corrections were applied to compare the data. Results show that wind tunnel and flight test acoustic data agreed closely.

Atencio, A., Jr.↗
NASA NTRS1985

Data on noise environments at different times of day around airports

Sources of information about noise environments at different times of the day at civilian and military airports are identified. Information about movements of scheduled flights are available in machine readable form from the Official Airline Guide. Information about permanent noise monitoring sites is readily obtained from individual airports. Limited data on the timing of flights are available at centralized locations for military airports. An examination of scheduled flights at commercial airports leads to the conclusion that differences between daytime and nighttime noise levels (measured in Equivalent Continuous Noise Level, LEQ) vary from 7 to 15 decibels. Data from 128 permanent noise monitoring sites at 11 airports are also examined. Differences between daytime and nighttime noise levels at these 128 noise monitoring sites vary from 3 to 17 decibels (LEQ). Preliminary analyses suggest that accurate estimates of time-of-day weights could not be obtained from conventional social surveys at existing airports.

Fields, J. M.↗
NASA NTRS1990

Neuromorphic learning of continuous-valued mappings from noise-corrupted data. Application to real-time adaptive control

The ability of feed-forward neural network architectures to learn continuous valued mappings in the presence of noise was demonstrated in relation to parameter identification and real-time adaptive control applications. An error function was introduced to help optimize parameter values such as number of training iterations, observation time, sampling rate, and scaling of the control signal. The learning performance depended essentially on the degree of embodiment of the control law in the training data set and on the degree of uniformity of the probability distribution function of the data that are presented to the net during sequence. When a control law was corrupted by noise, the fluctuations of the training data biased the probability distribution function of the training data sequence. Only if the noise contamination is minimized and the degree of embodiment of the control law is maximized, can a neural net develop a good representation of the mapping and be used as a neurocontroller. A multilayer net was trained with back-error-propagation to control a cart-pole system for linear and nonlinear control laws in the presence of data processing noise and measurement noise. The neurocontroller exhibited noise-filtering properties and was found to operate more smoothly than the teacher in the presence of measurement noise.

Troudet, Terry↗
DOE OSTI2021

Novel Data Driven Noise Emulation Framework using Deep Neural Network for Generating Synthetic PMU Measurements

Sensors play a critical role in supporting day-to-day grid operations and they are essential to operator’s decision-making process. Furthermore, sensors and sensor behaviors need to be emulated with grid simulations to perform modeling studies and to design cutting edge power systems applications. Ensuring the accurate behavior of these applications requires accurate emulation of sensors and pertinent signals. However, most grid simulators and modeling tools assume either zero error scenarios or simplistic noise models that may not always correlate to real-world sensors. To address the above issue, this work presents an initial study on the noise characteristics of phasor measurement units (PMUs), along with models for recreating their unique noise signatures. The proposed methods (both analytical and machine-learning-based) provide a substantial increase in a sensor’s model fidelity, a feature that can be leveraged by an end-user application to yield more accurate system representations. The proposed methods were then applied to micro PMU data from the EPFL microgrid campus to extract sensor noise profiles. This data was used to train a deep learning model, which was tested to emulate the noise characteristics present in actual signals. Based on the observed results and the employed data-driven methodology, the proposed methods may be adapted to replicate the behavior of other grid sensors and power new applications capable of detecting sensor degradation and eventual device failures in near real-time.

PMU, noise emulation, synthetic measurements, deep↗
DOE OSTI2020

Evaluating the Potential and Challenges of an Uncertainty Quantification Method for Long Short–Term Memory Models for Soil Moisture Predictions

Recently, recurrent deep networks have shown promise to harness newly available satellite–sensed data for long–term soil moisture projections. However, to be useful in forecasting, deep networks must also provide uncertainty estimates. Here we evaluated Monte Carlo dropout with an input–dependent data noise term (MCD+N), an efficient uncertainty estimation framework originally developed in computer vision, for hydrologic time series predictions. MCD+N simultaneously estimates a heteroscedastic input–dependent data noise term (a trained error model attributable to observational noise) and a network weight uncertainty term (attributable to insufficiently constrained model parameters). Although MCD+N has appealing features, many heuristic approximations were employed during its derivation, and rigorous evaluations and evidence of its asserted capability to detect dissimilarity were lacking. To address this, we provided an in–depth evaluation of the scheme's potential and limitations. We showed that for reproducing soil moisture dynamics recorded by the Soil Moisture Active Passive (SMAP) mission, MCD+N indeed gave a good estimate of predictive error, provided that we tuned a hyperparameter and used a representative training data set. The input–dependent term responded strongly to observational noise, while the model term clearly acted as a detector for physiographic dissimilarity from the training data, behaving as intended. However, when the training and test data were characteristically different, the input–dependent term could be misled, undermining its reliability. Additionally, due to the data–driven nature of the model, data noise also influences network weight uncertainty, and therefore the two uncertainty terms are correlated. Altogether, this approach has promise, but care is needed to interpret the results.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1981

Jet noise results from static, wind tunnel, and flight tests of conical and mechanical suppressor nozzles

Results of jet noise suppression tests conducted on a Rolls-Royce Viper 601 turbojet engine are reported. Seven exhaust nozzle configurations are tested, including two conical nozzles, two suppressor mixers, and three treated ejector configurations with different ejector inlets. Tests are conducted at the NASA Ames outdoor static test facility and the 40- by 80-ft wind tunnel facility at minimum tunnel flow velocity and normal flow velocities of 230 and 290 ft/sec. Near-field multiple sideline noise levels are projected to the far fields to compare far-field fixed microphone outdoor static noise levels, and wind tunnel near-field noise data are projected to the far field and flight distances to compare with noise levels recorded from an Hs-125 aircraft. Near-field outdoor noise data duplicate the far-field data recorded from fixed microphones within 2 PNdB, and the Douglas mechanical jet noise suppressor/treated ejector exhaust system achieves a noise reduction of 12 EPNdB relative to a conic reference nozzle at equal thrust in flight.

Mckinnon, R. A.↗