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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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System of analog and digital signal-processing equipment, computers, and programs detects faults in ball bearings in turbomachines. System operates in real time, extracting information from vibrations sensed by accelerometers, strain gauges, and/or acoustical sensors. Vibrations caused when pits in balls contact races, and pits in races contact balls.
A characterization test was conducted in the NASA Langley Research Center 14- by 22-Foot Subsonic Tunnel to assess recent acoustic modifications in the facility. Test section reflectivity, background noise and shear layer effects were evaluated by taking measurements of known noise sources. Both conventional speakers and a high-pressure air source were employed as noise generation mechanisms. These were mounted to a pole or fairing at a consistent location in the test section. The source waveforms were used to represent the expected frequency content of scaled Urban Air Mobility vehicles. Acoustic measurements were taken using two unique microphone arrays placed outside the core flow, one of which was a 54-element streamwise-traversing phased array. The other was an 11-element linear tower array. The applicability of phased array acoustic beamforming techniques to mitigate shear layer effects and reject background noise is presented. Additionally, beamforming results are compared to results from single-microphone processing techniques. Flow speeds investigated ranged from Mach 0.00 (static) to Mach 0.12. The tunnel has been partially acoustically treated but still exhibits flow noise, reducing the signal-to-noise ratio of the source under investigation. Conventional beamforming was able to capture high frequency content passing through the shear layer better than single microphone measurements. CLEAN deconvolution improves source mapping at low frequencies.
A development status evaluation is given for aerospace applications of AE location, detection, and source characterization. Attention is given to the neural-like processing of AE signals for graphite/epoxy. It is recommended that development efforts for AE make connections between the material failure process and source dynamics, and study the effects of composite material anisotropy and inhomogeneity on the propagation of AE waves. Broadband, as well as frequency- and wave-mode selective sensors, need to be developed.
Infrasound data from arrays can be used to detect, locate, and quantify a variety of natural and anthropogenic sources from local to remote distances. However, many array processing methods use a single broad frequency range to process the data, which can lead to signals of interest being missed due to the choice of frequency limits or simultaneous clutter sources. In this work, we introduce a new open-source Python code that processes infrasound array data in multiple sequential narrow frequency bands using the least-squares approach. We test our algorithm on a few examples of natural sources (volcanic eruptions, mass movements, and bolides) for a variety of array configurations. Our method reduces the need to choose frequency limits for processing, which may result in missed signals, and it is parallelized to decrease the computational burden. Improvements of our narrow-band least-squares algorithm over broad-band least-squares processing include the ability to distinguish between multiple simultaneous sources if distinct in their frequency content (e.g., microbarom or surf vs. volcanic eruption), the ability to track changes in frequency content of a signal through time, and a decreased need to fine-tune frequency limits for processing. We incorporate a measure of planarity of the wavefield across the array (sigma tau, στ) as well as the ability to utilize the robust least trimmed squares algorithm to improve signal processing and insight into array performance. Our implementation allows for more detailed characterization of infrasound signals recorded at arrays that can improve monitoring and enhance research capabilities.
Geoscience instrumentation systems are considered along with questions of geoscience environment, signal processing, data processing, and design problems. Instrument platforms are examined, taking into account ground platforms, airborne platforms, ocean platforms, and space platforms. In situ and laboratory sensors described include acoustic wave sensors, age sensors, atmospheric constituent sensors, biological sensors, cloud particle sensors, electric field sensors, electromagnetic field sensors, precision geodetic sensors, gravity sensors, ground constituent sensors, horizon sensors, humidity sensors, ion and electron sensors, magnetic field sensors, tide sensors, and wind sensors. Remote sensors are discussed, giving attention to sensing techniques, acoustic echo-sounders, gamma ray sensors, optical sensors, radar sensors, and microwave radiometric sensors.
The detection and localization of damage in metallic structures using acoustic emission (AE) monitoring and artificial intelligence technology such as deep learning has been widely studied. However, a current challenge of this approach is the difficulty of obtaining sufficient labeled historical AE signals for the training process of deep learning models. This problem can be approached through the implementation of transfer learning. The innovation of this paper lies in the development of a transfer learning approach for AE source localization on a stainless-steel structure when no historical labeled AE signals are available for training. A finite element model is developed to generate numerical AE signals for the training. Unsupervised domain adaptation (UDA) technology is utilized to reduce the distribution difference between the numerical and the realistic AE signals and to derive the localization results of the unlabeled realistic AE signals. Finally, the results suggest that the proposed approach is capable of localizing AE signals with high accuracy in the absence of labeled training data.
Measuring the moisture content in flowing biomass is critical to processes such as liquid biofuel conversion, such as biogasoline, biodiesel, bio jet kerosene, etc. However, biomass tends to flow in aggregates, which results in significant inhomogeneities in the amount of biomass flowing in front of a sensor at a given time, and there can be significant overlap in the material properties of dry vs wet biomass, leading to poor signal-to-noise ratio. We present a technique for identifying biomass moisture content using a series of acoustic pitch-catch measurements to quantify the sound speed and acoustic amplitude through the biomass, in conjunction with classical machine learning techniques, including Naive Bayes, Random Forest, and K-Nearest Neighbors classification. We amplify the differences between the acoustic measurements in different moisture levels by collecting a series of pulse-echo measurements, which we sort in order of ascending sound speed. We test the accuracy of the technique on experimentally-prepared batches of corn stover biomass with specified moisture levels and measure the average error in the estimated moisture level as a function of the number of pitch-catch measurements used. We observe average estimation errors as low as 6.7% by increasing the number of measurements and optimizing the hyperparameters. This work presents a novel method determining moisture content in flowing biomass with inhomogeneous flow. Additionally, this technique has application in optimizing biomass conversion processes, as well as other fields including, paper production, natural fiber processing, and mineral extraction.
Wave propagation and diffusive transport phenomena in a geological rock sample undergoing crack evolution process are expected to interact with the mechanical discontinuities in the medium. The measurements of the signals associated with these phenomena can be used to assess and monitor the crack-driven micromechanical alterations in the rock. Different wave/diffusion phenomena, such as sonic propagation, pressure diffusion, and acoustic emission (AE), are sensitive to different elements of the mechanical discontinuities generated during the evolution of the crack clusters from initiation to coalescence. Sonic propagation, AE, and pressure diffusion monitoring have the potential to map the crack evolution because the transmitter-receiver arrays can be designed, arranged and tuned to (1) achieve maximum recovery of the scattered waveforms and travel times, (2) capture the later arrivals and multiple reflections, and (3) illuminate large rock volume. However, the structural/topological complexities of the mechanical discontinuities, complex distribution of the stress fields, complex mechanical alterations in media, and fluid redistribution in the crack system pose serious challenges for the detection and modeling of the crack evolution process (from here on, we will use the term ‘crack evolution process’ to mean that the crack evolution occurred under shallow crustal conditions). For purposes of accurately accounting such complexities and heterogeneities in the absence of reliable physical laws, simulation methods, and signal processing techniques, my early-career research proposal will develop and apply novel data-driven machine learning methods to: (1) extract signal components relevant to the various phases of crack evolution and (2) generate a 2D visual map of the crack evolution process.
A new nuclear fuel cycle test bed is being built at Idaho National Laboratory to support the purification of special nuclear material recovered from used fuel. The test bed provides an opportunity to research process flow and the application of computational tools in solvent extraction processes. A deeper understanding of process and equipment behavior coupled with real time data collection can indicate whether a process failure is accidental or purposeful. The goal of this project is to develop a system that utilizes non-traditional measurement sources such as vibration, acoustics, current, light, flow, and temperature in conjunction with data-based, machine learning techniques that will allow for signal discovery. This multi-sensor data can support the development of safeguards by design and security by design measures for such a facility. Additionally, it can aid in early detection and identification of removed materials indicating diversion, which is essential for initiating material recovery and actor identification. This overview encompasses the current research and testing of sensors to develop a spectrum of process signatures. To be followed by planned experiments aimed to characterize said signatures and study potential feature extraction techniques to identify a fault in the system (i.e. flow diversion).
The acoustic source strength distribution in a turbulent flow field was measured for two far field microphones at 45 deg above and below the plane of the flap surface. A processed signal from an inclined hot-film anemometry probe was cross correlated with the signal from the appropriate far field microphone. The contribution made by the sources associated with the fluctuating pressure on the flap surface to the sound received at far field microphone was estimated by cross correlating the processed signals of microphones which were embedded in the flap surface with the far field microphone signals. In addition, detailed fluid dynamic measurements were made in the flow field of the jet flap using dual sensor hot-film anemometry probes.
A method for detecting defects in an object of interest comprises applying an ultrasonic signal including a tone burst having a predetermined frequency and number of cycles into an object of interest, receiving a return signal reflected from the object of interest, and processing the return signal to detect defects in at least one inner material. The object may have an outer material and the at least one inner material that have different acoustic impedances. An ultrasonic sensor system includes an ultrasonic sensor configured to generate an ultrasonic signal having a tone burst at a predetermined frequency corresponding to a resonant frequency of an outer material of an object of interest.
Guided wave ultrasonic testing has been widely used for nondestructive and long-range evaluation of diverse mechanical structures, such as pipes and plates. In general, a large number of guided wave modes exist even in the simplest geometries, and they exhibit highly dispersive characteristics. Thus, sophisticated excitation methods and elaborate modal analyses need to be implemented to excite, acquire, and process well-defined pure modes as part of such a measurement method. If the interrogated structure has complex geometry, for example, with cross-sectional variations or multiple joints, the utilization of pure modes becomes significantly more difficult. To overcome this difficulty, this paper proposes the use of multi-mode, multi-frequency guided waves for long-range inspection of structures with arbitrarily complex geometries. Furthermore, using broadband chirp signals and multi-mode time–frequency analysis in a prototypical complex pipe assembly that includes multiple pipe diameters, elbows, and flange connections, we experimentally show sensitive detection of mechanical changes (both material addition and loss). In addition, we demonstrate that the complexity of the structure can be modeled in the digital domain with sufficient detail to allow accurate numerical simulation of the acoustic response using this method.
An acoustic impulse technique using a loudspeaker driver is developed to measure the acoustic properties of a duct/nozzle system. A signal synthesis method is used to generate a desired single pulse with a flat spectrum. The convolution of the desired signal and the inverse Fourier transform of the reciprocal of the driver's response are then fed to the driver. A signal averaging process eliminates the jet mixing noise from the mixture of jet noise and the internal noise, thereby allowing very low intensity signals to be measured accurately, even for high velocity jets. A theoretical analysis is carried out to predict the incident sound field; this is used to help determine the number and locations of the induct measurement points to account for the contributions due to higher order modes present in the incident tube method. The impulse technique is validated by comparing experimentally determined acoustic characteristics of a duct-nozzle system with similar results obtained by the impedance tube method. Absolute agreement in the comparisons was poor, but the overall shapes of the time histories and spectral distributions were much alike.
Several sources of interest often generate both low‐frequency acoustic and seismic signals due to energy propagation through the atmosphere and the solid Earth. Seismic and acoustic observations are associated with a wide range of sources, including earthquakes, volcanoes, bolides, chemical and nuclear explosions, ocean noise, and others. The fusion of seismic and acoustic observations contributes to a better understanding of the source, both in terms of constraining source location and physics, as well as the seismic to acoustic coupling of energy. Here in this review, we summarize progress in seismoacoustic data processing, including recent developments in open‐source data availability, low‐cost seismic and acoustic sensors, and large‐scale deployments of collocated sensors from 2010 to 2022. Similarly, we outline the recent advancements in modeling efforts for both source characteristics and propagation dynamics. Finally, we highlight the advantages of fusing multiphenomenological signals, focusing on current and future techniques to improve source detection, localization, and characterization efforts. This review aims to serve as a reference for seismologists, acousticians, and others within the growing field of seismoacoustics and multiphenomenology research.
The Wilkinson Microwave Anisotropy Probe (WMAP) is a Medium-Class Explorer (MIDEX) satellite aimed at elucidating cosmology through full-sky observations of the cosmic microwave background (CMB). The WMAP full-sky maps of the temperature and polarization anisotropy in five frequency bands provide our most accurate view to date of conditions in the early universe. The multi-frequency data facilitate the separation of the CMB signal from foreground emission arising both from our Galaxy and from extragalactic sources. The CMB angular power spectrum derived from these maps exhibits a highly coherent acoustic peak structure which makes it possible to extract a wealth of information about the composition and history of the universe. as well as the processes that seeded the fluctuations. WMAP data have played a key role in establishing ACDM as the new standard model of cosmology (Bennett et al. 2003: Spergel et al. 2003; Hinshaw et al. 2007: Spergel et al. 2007): a flat universe dominated by dark energy, supplemented by dark matter and atoms with density fluctuations seeded by a Gaussian, adiabatic, nearly scale invariant process. The basic properties of this universe are determined by five numbers: the density of matter, the density of atoms. the age of the universe (or equivalently, the Hubble constant today), the amplitude of the initial fluctuations, and their scale dependence. By accurately measuring the first few peaks in the angular power spectrum, WMAP data have enabled the following accomplishments: Showing the dark matter must be non-baryonic and interact only weakly with atoms and radiation. The WMAP measurement of the dark matter density puts important constraints on supersymmetric dark matter models and on the properties of other dark matter candidates. With five years of data and a better determination of our beam response, this measurement has been significantly improved. Precise determination of the density of atoms in the universe. The agreement between the atomic density derived from WMAP and the density inferred from the deuterium abundance is an important test of the standard big bang model. Determination of the acoustic scale at redshift z = 1090. Similarly, the recent measurement of baryon acoustic oscillations (BAO) in the galaxy power spectrum (Eisenstein et al. 2005) has determined the acoustic scale at redshift z approx. 0.35. When combined, these standard rulers accurately measure the geometry of the universe and the properties of the dark energy. These data require a nearly flat universe dominated by dark energy consistent with a cosmological constant. Precise determination of the Hubble Constant, in conjunction with BAO observations. Even when allowing curvature (Omega(sub 0) does not equal 1) and a free dark energy equation of state (w does not equal -1), the acoustic data determine the Hubble constant to within 3%. The measured value is in excellent agreement with independent results from the Hubble Key Project (Freedman et al. 2001), providing yet another important consistency test for the standard model. Significant constraint of the basic properties of the primordial fluctuations. The anti-correlation seen in the temperature/polarization (TE) correlation spectrum on 4deg scales implies that the fluctuations are primarily adiabatic and rule out defect models and isocurvature models as the primary source of fluctuations (Peiris et al. 2003).
Single-wire (hot-wire) measurements are presented of the mean velocity and turbulence quantities near the impact plate for a 45-degree impingement flow responsible for acoustic noise production. These results are summarized along with the conclusions which shaped the subsequent direction of the study of large angle oblique jet impingement flow. The development of a unique signal processing circuit for finding the instantaneous transverse vorticity from an array of four hot-wires is discussed.
A device and method of rapidly quantifying the relative distention of the bladder in a human subject are disclosed. The ultrasonic transducer which is positioned on the subject in proximity to the bladder is excited by a pulser under the command of a microprocessor to launch an acoustic wave into the patient. This wave interacts with the bladder walls and is reflected back to the ultrasonic transducer, when it is received, amplified and processed by the receiver. The resulting signal is digitized by an analog-to-digital converter under the command of the microprocessor and is stored in the data memory. The software in the microprocessor determines the relative distention of the bladder as a function of the propagated ultrasonic energy; and based on programmed scientific measurements and individual, anatomical, and behavioral characterists of the specific subject as contained in the program memory, sends out a signal to turn on any or all of the audible alarm, the visible alarm, the tactile alarm, and the remote wireless alarm.
A new method of separating the contributions of helicopter main and tail rotor noise sources is presented, making use of ground-based acoustic measurements. The method employs time-domain de-Dopplerization to transform the acoustic pressure time-history data collected from an array of ground-based microphones to the equivalent time-history signals observed by an array of virtual inflight microphones traveling with the helicopter. The now-stationary signals observed by the virtual microphones are then periodically averaged with the main and tail rotor once per revolution triggers. The averaging process suppresses noise which is not periodic with the respective rotor, allowing for the separation of main and tail rotor pressure time-histories. The averaged measurements are then interpolated across the range of directivity angles captured by the microphone array in order to generate separate acoustic hemispheres for the main and tail rotor noise sources. The new method is successfully applied to ground-based microphone measurements of a Bell 206B3 helicopter and demonstrates the strong directivity characteristics of harmonic noise radiation from both the main and tail rotors of that helicopter.