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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 NTRS1986

A study of helicopter main rotor noise in hover

Some fundamental aspects of rotor noise generation and radiation are presented. Data on which the study is based were obtained during a hover test at the NASA Ames Outdoor Aerodynamic Research Facility of a one-sixth-scale, four-bladed, helicopter rotor. The test site provided an open environment ideal for acquiring good acoustic data. Information is presented on the delineation between the acoustic near-field and far-field, and on the effect of a simple boundary-layer trip device. Data obtained at near-zero thrust conditions indicate that it is possible to isolate thickness noise with minimum contamination by loading noise effects. An abrupt change in the nature of the emitted noise at high-thrust conditions is investigated and is related to unsteady wake/support interaction.

Kitaplioglu, C.↗
NASA NTRS2019

Automated Classification of Transient Contamination in Stationary Acoustic Data

An automated procedure for the classification of transient contamination of stationary acoustic data is proposed and analyzed. The procedure requires the assumption that the stationary acoustic data of interest can be modeled as a band-limited, Gaussian random process. It also requires that the transient contamination be of higher variance than the acoustic data of interest. When these assumptions are satisfied, it is a blind separation procedure, aside from the initial input specifying how to subdivide the time series of interest. No a priori threshold criterion is required. Simulation results show that for a sufficient number of blocks, the method performs well, as long as the occasional false positive or false negative is acceptable. The effectiveness of the procedure is demonstrated with an application to experimental wind tunnel acoustic test data which are contaminated by hydrodynamic gusts.

binary classification↗
DOE OSTI2024

Optimal 1D Ly α forest power spectrum estimation – III. DESI early data

ABSTRACT The 1D power spectrum P1D of the Ly α forest provides important information about cosmological and astrophysical parameters, including constraints on warm dark matter models, the sum of the masses of the three neutrino species, and the thermal state of the intergalactic medium. We present the first measurement of P1D with the quadratic maximum likelihood estimator (QMLE) from the Dark Energy Spectroscopic Instrument (DESI) survey early data sample. This early sample of 54 600 quasars is already comparable in size to the largest previous studies, and we conduct a thorough investigation of numerous instrumental and analysis systematic errors to evaluate their impact on DESI data with QMLE. We demonstrate the excellent performance of the spectroscopic pipeline noise estimation and the impressive accuracy of the spectrograph resolution matrix with 2D image simulations of raw DESI images that we processed with the DESI spectroscopic pipeline. We also study metal line contamination and noise calibration systematics with quasar spectra on the red side of the Ly α emission line. In a companion paper, we present a similar analysis based on the Fast Fourier Transform estimate of the power spectrum. We conclude with a comparison of these two approaches and discuss the key sources of systematic error that we need to address with the upcoming DESI Year 1 analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRSDate not supplied

Acoustic Improvements to the 9- by 15-Foot Low Speed Wind Tunnel

A major refurbishment of the 9- by 15-Foot Low Speed Wind Tunnel at NASA Glenn Research Center was undertaken between 2016 and 2019 to improve the acoustic measurement capability of the facility. The principal objective was to lower the background noise in the test section so quiet aircraft propulsors could be tested with good signal-to-noise ratio. The secondary objective was to improve the anechoic quality of the facility so that the measurement uncertainty would be reduced and the data collected would be more accurate. The results of the project were a vastly improved anechoic wind tunnel, with one-third octave sound levels reduced by 8 to 18 dB in the frequency range of interest. The anechoic quality of the test section was improved substantially, reducing the effect of unwanted echoes contaminating the noise measurements, with the error in an impulse response measurement reduced from 0.8 dB to 0.2 dB. This report describes the main acoustic findings and is intended to provide guidance for other facilities requiring a low noise environment

Wind tunnel↗
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

A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks

We employ and adapt the image-to-image translation concept based on conditional generative adversarial networks (cGAN) for learning a forward and an inverse solution operator of partial differential equations (PDEs). We focus on steady-state solutions of coupled hydromechanical processes in heterogeneous porous media and present the parameterization of the spatially heterogeneous coefficients, which is exceedingly difficult using standard reduced-order modeling techniques. We show that our framework provides a speed-up of at least 2,000 times compared to a finite-element solver and achieves a relative root-mean-square error (r.m.s.e.) of less than 2% for forward modeling. For inverse modeling, the framework estimates the heterogeneous coefficients, given an input of pressure and/or displacement fields, with a relative r.m.s.e. of less than 7%, even for cases where the input data are incomplete and contaminated by noise. The framework also provides a speed-up of 120,000 times compared to a Gaussian prior-based inverse modeling approach while also delivering more accurate results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Deep Learning Denoising Applied to Regional Distance Seismic Data in Utah

Seismic waveform data are generally contaminated by noise from various sources. Suppressing this noise effectively so that the remaining signal of interest can be successfully exploited remains a fundamental problem for the seismological community. To date, the most common noise suppression methods have been based on frequency filtering. These methods, however, are less effective when the signal of interest and noise share similar frequency bands. Inspired by source separation studies in the field of music information retrieval (Jansson et al., 2017) and a recent study in seismology (Zhu et al., 2019), we implemented a seismic denoising method that uses a trained deep convolutional neural network (CNN) model to decompose an input waveform into a signal of interest and noise. In our approach, the CNN provides a signal mask and a noise mask for an input signal. The short-time Fourier transform (STFT) of the estimated signal is obtained by multiplying the signal mask with the STFT of the input signal. To build and test the denoiser, we used carefully compiled signal and noise datasets of seismograms recorded by the University of Utah Seismograph Stations network. Results of test runs involving more than 9000 constructed waveforms suggest that on average the denoiser improves the signal-to-noise ratios (SNRs) by ~5 db, and that most of the recovered signal waveforms have high similarity with respect to the target waveforms (average correlation coefficient of ~0.80) and suffer little distortion. Application to real data suggests that our denoiser achieves on average a factor of up to ~2-5 improvement in SNR over band-pass filtering and can suppress many types of noise that band-pass filtering cannot. For individual waveforms, the improvement can be as high as ~15 db.

58 GEOSCIENCES↗
DOE OSTI2025

Mathematical Morphological Filtering with a Self-Adaptive Reconstruction Technique and Application to Local Seismic Data

Recorded seismic data are generally contaminated by noise from different sources, which masks the signals of interest. In the seismology community, frequency filtering (FF) is the standard method for noise suppression. However, when the signal of interest and noise share the same frequency band, the latter cannot be filtered out without infringing on the former. We implemented a noise suppression approach based on the mathematical morphology theorem. The method involves compound operations of dilation and erosion using structuring elements of varying lengths and decomposes an input noisy waveform into several time functions with differing characteristics. Further, the filtered waveform is constructed from the time functions using a self-adaptive reconstruction technique. Application to a data set of >4700 local waveforms suggests that the implemented mathematical morphological filtering (MMF) approach is efficient for data with low signal-to-noise ratio (SNR) and significantly outperforms FF in that SNR range. For most of the dataset, FF, machine learning (ML) denoising, and continuous wavelet transform (CWT) thresholding result in higher SNR values compared with the MMF method. However, for ~42% of the waveforms, MMF outperforms FF, and the SNR gain achieved with MMF is as large as ~23 dB. Compared to ML denoising and CWT thresholding, this proportion drops to only ~10%–14%. Our results suggests that in an operational setting, MMF cannot replace the other noise suppression methods; however, signal detection can be improved if MMF is used to supplement them in some scenarios. MMF could help detect signals in problematic low-SNR data, which are currently being missed particularly when using FF alone.

58 GEOSCIENCES↗
NASA NTRS1969

Environmental health program activities

Activities reported include studies on toxic air contaminants, excessive noise, poor lighting, food sanitation, water pollution, and exposure to nonionizing radiation as health hazards. Formulations for a radiological health manual provide guidance to personnel in the procurement and safe handling of radiation producing equipment and Apollo mission planning. A literature search and development of a water analysis laboratory are outlined to obtain information regarding microbiological problems involving potable water, waste management, and personal hygiene.

Bergtholdt, C. P.↗
NASA NTRS1974

Two-dimensional signal processing with application to image restoration

A recursive technique for modeling and estimating a two-dimensional signal contaminated by noise is presented. A two-dimensional signal is assumed to be an undistorted picture, where the noise introduces the distortion. Both the signal and the noise are assumed to be wide-sense stationary processes with known statistics. Thus, to estimate the two-dimensional signal is to enhance the picture. The picture representing the two-dimensional signal is converted to one dimension by scanning the image horizontally one line at a time. The scanner output becomes a nonstationary random process due to the periodic nature of the scanner operation. Procedures to obtain a dynamical model corresponding to the autocorrelation function of the scanner output are derived. Utilizing the model, a discrete Kalman estimator is designed to enhance the image.

Assefi, T.↗
NASA NTRS1974

Computer program CORDET

A FORTRAN 4 computer program provides convenient simulation of an all-digital phase-lock loop (DPLL). The DPLL forms the heart of the Omega navigation receiver prototype. Through the DPLL, the phase of the 10.2 KHz Omega signal is estimated when the true signal phase is contaminated with noise. This investigation has provided a convenient means of evaluating loop performance in a variety of noise environments, and has proved to be a useful tool for evaluating design changes. The goals of the simulation are to: (1) analyze the circuit on a bit-by-bit level in order to evaluate the overall design; (2) see easily the effects of proposed design changes prior to actual breadboarding; and (3) determine the optimum integration time for the DPLL in an environment typical of general aviation conditions.

Palkovic, R. A.↗
NASA NTRS1975

A simulation analysis of phase processing circuitry in the Ohio University Omega receiver prototype

A FORTRAN IV simulation study of the all-digital phase-processing circuitry is described. A digital phase-lock loop (DPLL) forms the heart of the Omega navigation receiver prototype, and through the DPLL, the phase of the 10.2 KHz Omega signal was estimated when the true signal phase is contaminated with noise. The DPLL uses a frequency synthesizer as the reference oscillator. The synthesizer is composed of synchronous rate multipliers (SRM's) driven by a temperature-compensated crystal oscillator, and the use of the SRM's in this application introduces phase jitter which degrades system performance. Simulation of the frequency synthesizer discussed was to analyze the circuits on a bit-by-bit level in order to evaluate the overall design, to see easily the effects of proposed design changes prior to actual breadboarding, to determine the optimum integration time for the DPLL in an environment typical of general aviation conditions, and to quantify the phase error introduced by the SRM synthesizer and examine its effect on the system.

Palkovic, R. A.↗
NASA NTRS1975

Minimum acquisition time detection

Two different methods of target detection when the return signal is contaminated with noise are discussed and compared. The first method uses Neyman-Pearson detection philosophy and selects the threshold level to give a desired false alarm probability. The maximum probability of false alarm is constrained by the target cross scan velocity component. The second method (minimum acquisition time detection), which is similar to the ideal observer, selects the threshold level to minimize the expected target acquisition time. The probabilities of false alarm and missed detection are selected so that the errors produced by these effects produce the minimum acquisition time. Three different scan techniques - linear, spiral and two-mode scan - are studied and compared.

Brock, H. I.↗
NASA NTRS1983

Refinement and application of acoustic impulse technique to study nozzle transmission characteristics

An improved acoustic impulse technique was developed and was used to study the transmission characteristics of duct/nozzle systems. To accomplish the above objective, various problems associated with the existing spark-discharge impulse technique were first studied. These included (1) the nonlinear behavior of high intensity pulses, (2) the contamination of the signal with flow noise, (3) low signal-to-noise ratio at high exhaust velocities, and (4) the inability to control or shape the signal generated by the source, specially when multiple spark points were used as the source. The first step to resolve these problems was the replacement of the spark-discharge source with electroacoustic driver(s). These included (1) synthesizing on acoustic impulse with acoustic driver(s) to control and shape the output signal, (2) time domain signal averaging to remove flow noise from the contaminated signal, (3) signal editing to remove unwanted portions of the time history, (4) spectral averaging, and (5) numerical smoothing. The acoustic power measurement technique was improved by taking multiple induct measurements and by a modal decomposition process to account for the contribution of higher order modes in the power computation. The improved acoustic impulse technique was then validated by comparing the results derived by an impedance tube method. The mechanism of acoustic power loss, that occurs when sound is transmitted through nozzle terminations, was investigated. Finally, the refined impulse technique was applied to obtain more accurate results for the acoustic transmission characteristics of a conical nozzle and a multi-lobe multi-tube supressor nozzle.

Salikuddin, M.↗
NASA NTRS1986

Tone calibration technique: A digital signaling scheme for mobile applications

Residual carrier modulation is conventionally used in a communication link to assist the receiver with signal demodulation and detection. Although suppressed carrier modulation has a slight power advantage over the residual carrier approach in systems enjoying a high level of stability, it lacks sufficient robustness to be used in channels severely contaminated by noise, interference and propagation effects. In mobile links, in particular, the vehicle motion and multipath waveform propagation affect the received carrier in an adverse fashion. A residual carrier scheme that uses a pilot carrier to calibrate a mobile channel against multipath fading anomalies is described. The benefits of this scheme, known as tone calibration technique, are described. A brief study of the system performance in the presence of implementation anomalies is also given.

Davarian, F.↗
NASA NTRS1987

A model-free method for mass spectrometer response correction

A new method for correction of mass spectrometer output signals is described. Response-time distortion is reduced independently of any model of mass spectrometer behavior. The delay of the system is found first from the cross-correlation function of a step change and its response. A two-sided time-domain digital correction filter (deconvolution filter) is generated next from the same step response data using a regression procedure. Other data are corrected using the filter and delay. The mean squared error between a step response and a step is reduced considerably more after the use of a deconvolution filter than after the application of a second-order model correction. O2 consumption and CO2 production values calculated from data corrupted by a simulated dynamic process return to near the uncorrupted values after correction. Although a clean step response or the ensemble average of several responses contaminated with noise is needed for the generation of the filter, random noise of magnitude not above 0.5 percent added to the response to be corrected does not impair the correction severely.

Shykoff, Barbara E.↗
NASA NTRS1991

Algorithm For Solution Of Subset-Regression Problems

Reliable and flexible algorithm for solution of subset-regression problem performs QR decomposition with new column-pivoting strategy, enables selection of subset directly from originally defined regression parameters. This feature, in combination with number of extensions, makes algorithm very flexible for use in analysis of subset-regression problems in which parameters have physical meanings. Also extended to enable joint processing of columns contaminated by noise with those free of noise, without using scaling techniques.

Verhaegen, Michel↗