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NASA NTRS2002

General Aviation Interior Noise: Noise Control Measure Evaluation - Part 3

The work reported herein is an extension to the work accomplished under NASA Grant NAG1-2091 on the development of noise/source/path identification techniques for single engine propeller driven General Aviation aircraft. The previous work developed a Conditioned Response Analysis (CRA) technique to identify potential noise sources that contributed to the dominating tonal responses within the aircraft cabin. The objective of the present effort was to improve and verify the findings of the CRA and develop and demonstrate noise control measures for single engine propeller driven General Aviation aircraft.

Unruh, James F.↗

NASA NTRS2019

Identification and Prediction of Broadband Noise for a Small Quadcopter

The growing interest in the noise of small unmanned aircraft systems (sUAS) in operation has motivated this study to characterize and predict broadband rotor noise sources. This paper analyzes the performance and acoustic data collected from two sets of wind tunnel experiments for hover and forward flight operating conditions. The first set of data is a result of testing a representative vehicle configuration of the Straight Up Imaging (SUI) Endurance quadcopter, while the second set of data represents an isolated rotor from the same vehicle. Following the analysis of the empirical data, a broadband noise prediction methodology is employed to compare with the experiments. This methodology uses NASA’s ROTONET and BARC tools to predict blade loading and self-noise at conditions matching the wind tunnel experiments. This methodology is effective at predicting the broadband noise at certain conditions. Furthermore, some predictions are repeated with the inflow conditions calculated by the rotorcraft analysis tool CAMRAD II, which shows improved prediction results for cases with nonuniform inflow. This work confirms some of the unique challenges associated with testing small rotor configurations, explores the limitations of this prediction methodology, and suggests improvements that can be made for future studies.

Nicole A Pettingill↗

NASA NTRS2011

An Accuracy Assessment of the CALIOP/CALIPSO Version 2/Version 3 Daytime Aerosol Extinction Product Based on a Detailed Multi-Sensor, Multi-Platform Case Study

The Cloud Aerosol LIdar with Orthogonal Polarization (CALIOP), on board the CALIPSO platform, has measured profiles of total attenuated backscatter coefficient (level 1 products) since June 2006. CALIOP s level 2 products, such as the aerosol backscatter and extinction coefficient profiles, are retrieved using a complex succession of automated algorithms. The goal of this study is to help identify potential shortcomings in the CALIOP version 2 level 2 aerosol extinction product and to illustrate some of the motivation for the changes that have been introduced in the next version of CALIOP data (version 3, released in June 2010). To help illustrate the potential factors contributing to the uncertainty of the CALIOP aerosol extinction retrieval, we focus on a one-day, multi-instrument, multiplatform comparison study during the CALIPSO and Twilight Zone (CATZ) validation campaign on 4 August 2007. On that day, we observe a consistency in the Aerosol Optical Depth (AOD) values recorded by four different instruments (i.e. spaceborne MODerate Imaging Spectroradiometer, MODIS: 0.67 and POLarization and Directionality of Earth s Reflectances, POLDER: 0.58, airborne High Spectral Resolution Lidar, HSRL: 0.52 and ground-based AErosol RObotic NETwork, AERONET: 0.48 to 0.73) while CALIOP AOD is a factor of two lower (0.32 at 532 nm). This case study illustrates the following potential sources of uncertainty in the CALIOP AOD: (i) CALIOP s low signal-to-noise ratio (SNR) leading to the misclassification and/or lack of aerosol layer identification, especially close to the Earth s surface; (ii) the cloud contamination of CALIOP version 2 aerosol backscatter and extinction profiles; (iii) potentially erroneous assumptions of the aerosol extinction-to-backscatter ratio (Sa) used in CALIOP s extinction retrievals; and (iv) calibration coefficient biases in the CALIOP daytime attenuated backscatter coefficient profiles. The use of version 3 CALIOP extinction retrieval for our case study seems to partially fix factor (i) although the aerosol retrieved by CALIOP is still somewhat lower than the profile measured by HSRL; the cloud contamination (ii) appears to be corrected; no particular change is apparent in the observation-based CALIOP Sa value (iii). Our case study also showed very little difference in version 2 and version 3 CALIOP attenuated backscatter coefficient profiles, illustrating a minor change in the calibration scheme (iv).

Kacenelenbogen, M.↗

NASA NTRS2018

Electric Motor Noise from Small Quadcopters: Source Characteristics - Part II

The increased interest in electric motors for propulsion systems has driven interest in quantifying the contribution of electric motor noise to the overall sound levels and possible human annoyance of the propulsion system. This work presents acoustic measurements of electric motors used for small quadcopters to quantify the sound produced by a number of outrunning motors with different types of controllers. Results are presented for loaded and unloaded motors as installed and uninstalled configurations. Motor resonance frequencies were measured and computed. Current probe measurements showed that the supply current from the controllers contained significant harmonic content for the conventional and sinewave controllers. Acoustic results showed motor noise is typically radiated at frequencies near the mode 2 vibration frequency at roughly 5000 Hz. Electric motor noise was evident in the spectra produced by many of the motor-controller combinations for motors loaded with propellers with levels often greater than those for the motor alone due to increase in the stator magnetic flux density with increased current. An installed configuration produced increases in acoustic radiation over that of the uninstalled motor in a frequency range near the mode 1 vibration frequency near 1200 Hz. A companion paper (Part I - Acoustic Measurements), focuses on source identification using a phased array and directivity characteristics for a baseline configuration.

Motor Resonance Frequencies↗

NASA NTRS2009

Porting Gravitational Wave Signal Extraction to Parallel Virtual Machine (PVM)

Laser Interferometer Space Antenna (LISA) is a planned NASA-ESA mission to be launched around 2012. The Gravitational Wave detection is fundamentally the determination of frequency, source parameters, and waveform amplitude derived in a specific order from the interferometric time-series of the rotating LISA spacecrafts. The LISA Science Team has developed a Mock LISA Data Challenge intended to promote the testing of complicated nested search algorithms to detect the 100-1 millihertz frequency signals at amplitudes of 10E-21. However, it has become clear that, sequential search of the parameters is very time consuming and ultra-sensitive; hence, a new strategy has been developed. Parallelization of existing sequential search algorithms of Gravitational Wave signal identification consists of decomposing sequential search loops, beginning with outermost loops and working inward. In this process, the main challenge is to detect interdependencies among loops and partitioning the loops so as to preserve concurrency. Existing parallel programs are based upon either shared memory or distributed memory paradigms. In PVM, master and node programs are used to execute parallelization and process spawning. The PVM can handle process management and process addressing schemes using a virtual machine configuration. The task scheduling and the messaging and signaling can be implemented efficiently for the LISA Gravitational Wave search process using a master and 6 nodes. This approach is accomplished using a server that is available at NASA Ames Research Center, and has been dedicated to the LISA Data Challenge Competition. Historically, gravitational wave and source identification parameters have taken around 7 days in this dedicated single thread Linux based server. Using PVM approach, the parameter extraction problem can be reduced to within a day. The low frequency computation and a proxy signal-to-noise ratio are calculated in separate nodes that are controlled by the master using message and vector of data passing. The message passing among nodes follows a pattern of synchronous and asynchronous send-and-receive protocols. The communication model and the message buffers are allocated dynamically to address rapid search of gravitational wave source information in the Mock LISA data sets.

Thirumalainambi, Rajkumar↗

DOE OSTI2020

Development of a Convolutional Neural Network Classifier for Data Starved Spectra - 20199

The Institute for Clean Energy Technology (ICET) at Mississippi State University is exploring the utility of machine learning in augmenting its mobile radiation surveying platforms, which are currently being developed as means to survey depleted uranium contaminated areas in support of remediation and decommissioning efforts. Mobile survey platforms provide a means to efficiently scan large areas of interest while reducing human exposure to radiation and other hazards. The survey platforms can also be used for scanning for any gamma emitting isotope in addition to depleted uranium. The spectral data that the platforms collect may be data starved with relatively low counts and poorly defined spectral features depending on the speed of the platforms and scintillation detector selection. Such data-starved spectra are difficult to use for isotope identification, requiring advanced knowledge of the possible radionuclides that could be present and environmental factors that could attenuate signals or introduce background noise. These factors in combination with the volume of survey data increases the time it takes to perform analysis of survey data when the source type is unknown. There are a number of algorithms in the field of machine learning that can be used to classify data that would be challenging and time-consuming for a human to identify. Supervised machine learning algorithms train models based on extensive amounts of human-labeled training data. Once sufficiently trained, these models can be used to quickly make high-fidelity predictions on new data. Convolutional neural networks are machine learning algorithms that excel in learning representations of 'shapes'. They do this by taking numerical input data and convolving them with spatial feature detectors referred to as filters. These filters are incrementally adjusted to reduce the prediction error on the data during the backpropagation step of training. Discussed in this paper is the development of a convolutional neural network classifier (CNNC) that can utilize spectral survey data for source discrimination and isotope identification. Bench-top laboratory experiments data using LaBr{sub 3}(Ce) scintillation detectors were used to train and evaluate the performance of the developed CNNC. The CNNC is capable of discriminating a variety of gamma emitting source types, differentiating different forms of uranium (depleted vs. natural), and estimating the amount of uranium for a known geometry. The discussed CNNC may be useful in scenarios where survey systems are deployed in situations where hazardous radioactive material maybe present, but the type is unknown. When used in remediation applications the CNNC can be used to screen-out false positives, helping reduce remediation costs. (authors)

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2021

Detection of Anomalies in Environmental Gamma Radiation Background with Hopfield Artificial Neural Network - Consortium on Nuclear Security Technologies (CONNECT) Q3 Report

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Detection of Anomalies in Gamma Background Radiation Data with K-Means and Self-Organizing Map Clustering Algorithms (Consortium on Nuclear Security Technologies (CONNECT) Q1 Report)

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2021

Neural Network Approaches for Mobile Spectroscopic Gamma-Ray Source Detection

Artificial neural networks (ANNs) for performing spectroscopic gamma-ray source identification have been previously introduced, primarily for applications in controlled laboratory settings. To understand the utility of these methods in scenarios and environments more relevant to nuclear safety and security, this work examines the use of ANNs for mobile detection, which involves highly variable gamma-ray background, low signal-to-noise ratio measurements, and low false alarm rates. Simulated data from a 2” × 4” × 16” NaI(Tl) detector are used in this work for demonstrating these concepts, and the minimum detectable activity (MDA) is used as a performance metric in assessing model performance.In addition to examining simultaneous detection and identification, binary spectral anomaly detection using autoencoders is introduced in this work, and benchmarked using detection methods based on Non-negative Matrix Factorization (NMF) and Principal Component Analysis (PCA). On average, the autoencoder provides a 12% and 23% improvement over NMF- and PCA-based detection methods, respectively. Additionally, source identification using ANNs is extended to leverage temporal dynamics by means of recurrent neural networks, and these time-dependent models outperform their time-independent counterparts by 17% for the analysis examined here. The paper concludes with a discussion on tradeoffs between the ANN-based approaches and the benchmark methods examined here.

Bilton, Kyle J. (ORCID:0000000184553689)↗

NASA NTRS2009

Using the Chandra Source-Finding Algorithm to Automatically Identify Solar X-ray Bright Points

This poster details a technique of bright point identification that is used to find sources in Chandra X-ray data. The algorithm, part of a program called LEXTRCT, searches for regions of a given size that are above a minimum signal to noise ratio. The algorithm allows selected pixels to be excluded from the source-finding, thus allowing exclusion of saturated pixels (from flares and/or active regions). For Chandra data the noise is determined by photon counting statistics, whereas solar telescopes typically integrate a flux. Thus the calculated signal-to-noise ratio is incorrect, but we find we can scale the number to get reasonable results. For example, Nakakubo and Hara (1998) find 297 bright points in a September 11, 1996 Yohkoh image; with judicious selection of signal-to-noise ratio, our algorithm finds 300 sources. To further assess the efficacy of the algorithm, we analyze a SOHO/EIT image (195 Angstroms) and compare results with those published in the literature (McIntosh and Gurman, 2005). Finally, we analyze three sets of data from Hinode, representing different parts of the decline to minimum of the solar cycle.

Adams, Mitzi L.↗

NASA NTRS1982

Experiments on identification and control of inflow disturbances in contracting streams

Vorticity from all surfaces and isolated objects in the vicinity of the fan intake, including the outside surfaces of the fan housing, were identified as the major sources for disturbances leading to blade passing frequency noise. The previously proposed mechanism based on atmospheric turbulence is refuted. Flow visualization and hot wire techniques were used in three different facilities to document the evolution of various types of disturbances, including the details of the mean flow and turbulence characteristics. The results suggest that special attention must be devoted to the design of the inlet and that geometric modeling may not lead to adequate simulation of the in flight characteristics. While honeycomb type flow manipulators appear to be effective in reducing some of the disturbances, higher pressure drop devices that generate adequate turbulence, for mixing of isolated nonuniformities, may be necessary to suppress the remaining disturbances. The results are also applicable to the design of inlets of open return wind tunnels and similar flow facilities.

Lee, A. H.↗

DOE OSTI2024

Design of radial interferometer–polarimeter for internal magnetic and density fluctuation measurements at multiple space–time scales in the National Spherical Torus Experiment-Upgrade (NSTX-U)

A Faraday-effect radial interferometer–polarimeter is designed for the National Spherical Torus Experiment-Upgrade (NSTX-U) to measure multiscale magnetic and density fluctuations critical to understanding fusion plasma confinement and stability, including those originating from magnetohydrodynamic instabilities, energetic particle-driven modes, and turbulence. The diagnostic will utilize the three-wave technique with 5 MHz bandwidth to simultaneously measure line-integrated magnetic and density fluctuations up to the ion-cyclotron frequency. Probe beams will be launched radially from the low-field side at the NSTX-U midplane, where the measured Faraday fluctuations mainly correspond to radial magnetic fluctuations that directly link to magnetic transport. A correlation technique will be employed to reduce the measurement noise to below 0.01° enabling detection of small amplitude fluctuations. Two toroidally displaced chords with 7° separation will be installed to measure toroidal mode numbers up to n = 25 for mode identification. Finally, solid-state microwave sources operating at 321 μm (935 GHz) will be used to minimize the impact of the Cotton–Mouton effect.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2011

The Effects of Ambient Conditions on Helicopter Rotor Source Noise Modeling

A new physics-based method called Fundamental Rotorcraft Acoustic Modeling from Experiments (FRAME) is used to demonstrate the change in rotor harmonic noise of a helicopter operating at different ambient conditions. FRAME is based upon a non-dimensional representation of the governing acoustic and performance equations of a single rotor helicopter. Measured external noise is used together with parameter identification techniques to develop a model of helicopter external noise that is a hybrid between theory and experiment. The FRAME method is used to evaluate the main rotor harmonic noise of a Bell 206B3 helicopter operating at different altitudes. The variation with altitude of Blade-Vortex Interaction (BVI) noise, known to be a strong function of the helicopter s advance ratio, is dependent upon which definition of airspeed is flown by the pilot. If normal flight procedures are followed and indicated airspeed (IAS) is held constant, the true airspeed (TAS) of the helicopter increases with altitude. This causes an increase in advance ratio and a decrease in the speed of sound which results in large changes to BVI noise levels. Results also show that thickness noise on this helicopter becomes more intense at high altitudes where advancing tip Mach number increases because the speed of sound is decreasing and advance ratio increasing for the same indicated airspeed. These results suggest that existing measurement-based empirically derived helicopter rotor noise source models may give incorrect noise estimates when they are used at conditions where data were not measured and may need to be corrected for mission land-use planning purposes.

Schmitz, Frederic H.↗

NASA NTRS2002

Measurement of Correlation Between Flow Density, Velocity, and Density*velocity(sup 2) with Far Field Noise in High Speed Jets

To locate noise sources in high-speed jets, the sound pressure fluctuations p', measured at far field locations, were correlated with each of radial velocity v, density rho, and phov(exp 2) fluctuations measured from various points in jet plumes. The experiments follow the cause-and-effect method of sound source identification, where correlation is related to the first, and correlation to the second source terms of Lighthill's equation. Three fully expanded, unheated plumes of Mach number 0.95, 1.4 and 1.8 were studied for this purpose. The velocity and density fluctuations were measured simultaneously using a recently developed, non-intrusive, point measurement technique based on molecular Rayleigh scattering. It was observed that along the jet centerline the density fluctuation spectra S(sub rho) have different shapes than the radial velocity spectra S(sub v), while data obtained from the peripheral shear layer show similarity between the two spectra. Density fluctuations in the jet showed significantly higher correlation, than either rhov(sub 2) or v fluctuations. It is found that a single point correlation from the peak sound emitting region at the end of the potential core can account for nearly 10% of all noise at 30 to the jet axis. The correlation, representing the effectiveness of a longitudinal quadrupole in generating noise 90 to the jet axis, is found to be zero within experimental uncertainty. In contrast rhov(exp 2) fluctuations were better correlated with sound pressure fluctuation at the 30 location. The strongest source of sound is found to lie at the centerline and beyond the end of potential core.

Panda, Jayanta↗

NASA NTRSDate not supplied

Identification and Computation of Individual Propeller Acoustics of the Joby Aviation Aircraft

The individual propeller sources of the Joby Aviation aircraft are separated using an order tracking filter on measurement data and computed using high- fidelity computational fluid dynamics (CFD) to quantify the relative contributions to the total noise. Computational results are verified against experimental data for hover and steady level flight at 60 knots and demonstrate very good agreement of both overall sound pressure level magnitude and directivity. The propeller source separation for hover shows good agreement between the methods except for the inboard propellers, and it is expected that the assumptions in the CFD model do not accurately capture the flight test condition. Both methods are able to separate the higher tonal levels generated by the tail propellers due to the aerodynamic interaction. Overall, considering the discrepancies between the CFD model and flight test, the agreement in Vold-Kalman-filtered results and prediction yields confidence that the separation method is successful.

eVTOL↗

NASA NTRS2014

Application of Machine Learning Algorithms to the Study of Noise Artifacts in Gravitational-Wave Data

The sensitivity of searches for astrophysical transients in data from the Laser Interferometer Gravitationalwave Observatory (LIGO) is generally limited by the presence of transient, non-Gaussian noise artifacts, which occur at a high-enough rate such that accidental coincidence across multiple detectors is non-negligible. Furthermore, non-Gaussian noise artifacts typically dominate over the background contributed from stationary noise. These "glitches" can easily be confused for transient gravitational-wave signals, and their robust identification and removal will help any search for astrophysical gravitational-waves. We apply Machine Learning Algorithms (MLAs) to the problem, using data from auxiliary channels within the LIGO detectors that monitor degrees of freedom unaffected by astrophysical signals. Terrestrial noise sources may manifest characteristic disturbances in these auxiliary channels, inducing non-trivial correlations with glitches in the gravitational-wave data. The number of auxiliary-channel parameters describing these disturbances may also be extremely large; high dimensionality is an area where MLAs are particularly well-suited. We demonstrate the feasibility and applicability of three very different MLAs: Artificial Neural Networks, Support Vector Machines, and Random Forests. These classifiers identify and remove a substantial fraction of the glitches present in two very different data sets: four weeks of LIGO's fourth science run and one week of LIGO's sixth science run. We observe that all three algorithms agree on which events are glitches to within 10% for the sixth science run data, and support this by showing that the different optimization criteria used by each classifier generate the same decision surface, based on a likelihood-ratio statistic. Furthermore, we find that all classifiers obtain similar limiting performance, suggesting that most of the useful information currently contained in the auxiliary channel parameters we extract is already being used. Future performance gains are thus likely to involve additional sources of information, rather than improvements in the MLAs themselves.

gravitational-wave data↗

DOE OSTI2022

BayesMT: A Probabilistic Bayesian Framework for the Seismic Moment Tensor

Moment tensors (MTs) have long been used in earthquake and explosion source analysis, and there has been a renewed interest in how they can inform us about the seismic source, particularly in the geophysical monitoring community due to its application in event identification and yield analysis. However, parameter uncertainties in seismic MT inversion are rarely available. The inverse procedure often does not quantify MT model errors such as event location, data noise and Earth model that are essential for estimating solution robustness. To address this need, we propose to adopt the Bayesian probabilistic framework to incorporate uncertainties in MT inversions. In this study, we present the theoretical background of a probabilistic Bayesian framework for MT inversion accounting for model and measurements errors and illustrate the implementation of the method using a synthetic example.

58 GEOSCIENCES↗

DOE OSTI2020

Development of Gamma Background Radiation Digital Twin with Machine Learning Algorithms: Application of Unsupervised Machine Learning to Detection of Anomalies and Nuisances in Gamma Background Radiation Environmental Screening Data

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for development of a digital twin of gamma radiation background, and for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. In one segment of work, we developed a gamma background estimation model using a Longshort term memory (LSTM) network for one-step CPS time series prediction. The LSTM model was validated with two data sets of measurements from two independent NaI detectors positioned on a mobile platform. The data sets contained background radiation only and no orphan isotope sources. The LSTM model was constructed and tested using data from one of the detectors. Performance of the LSTM model was validate through one-step prediction of CPS time series of another NaI detector without re-training. This approach allows to create a digital twin for nuclear background estimation. Using LSTM, it could be possible to detect a source through subtraction of the estimated counts from the measured background. In another segment of work, we investigated detection of gamma emitting sources in the presence of complex background using unsupervised machine learning. Spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.

54 ENVIRONMENTAL SCIENCES↗