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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Platform for Post-Processing Waveform-Based NDE

Signal- and image-processing methods are commonly needed to extract information from the waves, improve resolution of, and highlight defects in an image. Since some similarity exists for all waveform-based nondestructive evaluation (NDE) methods, it would seem that a common software platform containing multiple signal- and image-processing techniques to process the waveforms and images makes sense where multiple techniques, scientists, engineers, and organizations are involved. NDE Wave & Image Processor Version 2.0 software provides a single, integrated signal- and image-processing and analysis environment for total NDE data processing and analysis. It brings some of the most useful algorithms developed for NDE over the past 20 years into a commercial-grade product. The software can import signal/spectroscopic data, image data, and image series data. This software offers the user hundreds of basic and advanced signal- and image-processing capabilities including esoteric 1D and 2D wavelet-based de-noising, de-trending, and filtering. Batch processing is included for signal- and image-processing capability so that an optimized sequence of processing operations can be applied to entire folders of signals, spectra, and images. Additionally, an extensive interactive model-based curve-fitting facility has been included to allow fitting of spectroscopy data such as from Raman spectroscopy. An extensive joint-time frequency module is included for analysis of non-stationary or transient data such as that from acoustic emission, vibration, or earthquake data.

Roth, Don J.↗
NASA NTRS2022

Novel Processing, Testing and Characterization of Copper/Carbon Nanotube (Cu/CNT) Yarn Composite Conductor

Sustainable electrified aircraft propulsion (EAP) is likely to lead to an increase in the electrical wiring contained within a single aircraft. Since the electrical resistance and mass of copper (Cu) conductors are associated with power losses, it is desirable to design high-conductivity lightweight conductor materials, thus reducing the mass of components like motor windings, low-voltage signal cables, and transmission cables for data and power to improve the overall energy efficiency. This paper describes a unique framework for manufacturing metalized carbon nanotube (CNT) composite conductors, measuring their electrical conductivity and strength, and modeling the overall conductivity and current sharing within such composites. Tensile testing was conducted on the processed composite conductor cables with the use of acoustic emission and electrical resistivity to determine stress-dependent-failure mechanisms while monitoring the electrical conductivity. The average of measured electrical conductivities of annealed Cu/CNT samples from batch 5 was greater than theoretical predictions by 9.8 percent and was also greater than the conductivity of pure annealed Cu by 4.8 percent and had comparable ultimate tensile strengths. Additionally, those Cu/CNT samples provide a 13.5% weight saving over current state of the art copper wires. Theories explaining improved intrinsic conductivity are discussed.

Metalized CNT↗
DOE OSTI2026

Spatiotemporal Learning in Power Modules: Wavelet-Enhanced Forecasting of Thermomechanical Degradation

Detecting internal defects in power electronics packages is critical for their performance and reliability, especially under extreme operating conditions, as these defects can lead to catastrophic failure if not properly addressed. Confocal scanning acoustic microscopy (C-SAM) plays a key role in the nondestructive evaluation of bond layer degradation within a power electronics package by detecting defects such as delamination, voids, and cracks. However, accurately quantifying and predicting these defects from C-SAM images remains a significant challenge due to the low noise-to-signal ratio, which typically arises from both imaging process and bond patterns itself. In this paper, we explore machine learning strategies for processing C-SAM images and providing predictive models of defect growth. We use C-SAM images of sintered copper and sintered silver samples, which are obtained under accelerated thermal experiments, as the representative dataset for our study. We investigate the effect of Fourier transforms and wavelet transforms on these datasets to remove high-frequency noise and address noise across multiple scales with histogram equalization to enhance the contrast and improve the visibility of defects. As a result, defect boundaries can be clearly distinguished, enabling more accurate tracking of their growth over time. We then employ different time-series forecasting algorithms on the denoised images to formulate an image-based lifetime prediction model. Statistical models and deep-learning techniques are trained on images obtained in the early stages of thermal shock, and defect growth in the later stages is predicted. Our work serves as a preliminary attempt to improve the accuracy of lifetime prediction models of power electronics packages, which is critical under extreme operating environments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2005

Small Vocabulary Recognition Using Surface Electromyography in an Acoustically Harsh Environment

This paper presents results of electromyographic-based (EMG-based) speech recognition on a small vocabulary of 15 English words. The work was motivated in part by a desire to mitigate the effects of high acoustic noise on speech intelligibility in communication systems used by first responders. Both an off-line and a real-time system were constructed. Data were collected from a single male subject wearing a fireghter's self-contained breathing apparatus. A single channel of EMG data was used, collected via surface sensors at a rate of 104 samples/s. The signal processing core consisted of an activity detector, a feature extractor, and a neural network classifier. In the off-line phase, 150 examples of each word were collected from the subject. Generalization testing, conducted using bootstrapping, produced an overall average correct classification rate on the 15 words of 74%, with a 95% confidence interval of [71%, 77%]. Once the classifier was trained, the subject used the real-time system to communicate and to control a robotic device. The real-time system was tested with the subject exposed to an ambient noise level of approximately 95 decibels.

Betts, Bradley J.↗
DOE OSTI2022

Sensor fusion of pyrometry and acoustic measurements for localized keyhole pore identification in laser powder bed fusion

We report in-situ process monitoring as an aid to part qualification for laser powder-bed fusion (L-PBF) technology is a topic of increasing interest to the additive manufacturing community. In this work, airborne acoustic and inline pyrometry measurements were recorded simultaneously with the laser position. X-ray radiography imaging was used to spatio-temporally register keyhole pore locations to the pyrometry and acoustic signals, enabling binary labeling of data partitions based on pore formation. These labeled partitions were subsequently featurized using a highly comparative time-series analysis toolbox. Acoustic data was found to be much more effective than the pyrometry data for keyhole pore identification. However, when the information contained in both sensing modalities was combined in a sensor fusion strategy, the error rates of the top performing models were significantly reduced.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Distributed fiber sensing systems for 3D combustion temperature field monitoring in coal-fired boilers using optically generated acoustic waves (Final Report)

In this project, we have developed and tested three kinds of fiber optic sensing systems for real time monitoring of temperature variations within an industrial scale boiler furnace. The fiber optic sensing systems target spatial and temporal distributions of high temperature profiles in a boiler furnace in fossil power plants. The reconstructed temperature profile will provide critical input for the control mechanisms to optimize the combustion process. This temperature profile will address the essential problem for fossil power plants in achieving higher efficiency and fewer pollutant emissions. Acoustic pyrometer systems have been used to reconstruct temperature field of power plant boilers based on measuring TOF (times-of-flight) of sound waves along some straight paths in a 2D cross-section of the boiler. In this project, optically generated acoustic signals from a fiber optic sensing system have replaced the acoustic signals generated from an electrical transducer. A 3D reconstruction algorithm replaced the previous 2D model. In this project, three kinds of fiber optic sensing systems have been developed and tested. They are fiber optic sensing system I, fiber optic sensing system II (Distributed Sensing System I) and fiber optic sensing system III (Distributed Sensing System II). For fiber optic sensing system I, the fiber optic ultrasound generator acts as a signal generator. A microphone, hydrophone or other electronic devices serve as a signal receiver. In this system, there are one generator and one receiver. Distance test, water temperature test, air temperature test, air temperature reconstruction, and GE ISBF pilot test were performed by Fiber optic sensing system I. The fiber optic sensing system I successfully detected temperature in all these tests. We got 2D temperature reconstruction results by using the fiber optic sensing system I and it matched the reference data. The fiber optic sensing system I successfully survived in GE ISBF boiler environment (480 °F). For fiber optic sensing system II (Distributed Sensing System I), it is an all optical ultrasound system. The fiber optic ultrasound generator acts as a signal generator. Fiber Bragg Grating (FBG) and Fabry-Perot (FP) sensor act as a signal receiver. In this system, there is one generator and one receiver. Aluminum plate temperature test, furnace high temperature test, and GE ISBF pilot test were performed by the fiber optic sensing system II. Fiber optic sensing system II successfully detected the temperature in all these tests. The fiber optic sensing system II successfully survived at up to 700 °C furnace environment and 320 °C GE ISBF boiler environment. For fiber optic sensing system III (Distributed Sensing System II), it is also an all optical ultrasound system. The fiber optic ultrasound generator acts as a signal generator. Multiple FP fiber sensors act as signal receivers. In this system, there is one generator and three receivers. Three GE ISBF pilot tests were performed by fiber optic sensing system III. The fiber optic sensing system III survived in the cold flow tests in GE’s ISBF pilot test facility. However, we didn’t get high temperature data by using this system since the nanosecond laser issues. During the period of the project, test trials, data simulation and algorithm optimization was performed successfully. For real time temperature field construction, the sampling rate must be fast enough to capture the field variations. The technology of Code-division multiple access (CDMA) is well studied which could allow parallel multiplexing, even if signals overlap in time or frequencies. Moreover, it has been known that extending the length of signal significantly improves SNR. For acoustic signals, these multiplexing techniques have also been widely used, mainly for sonar and acoustic communications. The CDMA modulation technique has been proposed and studied to guarantee high network throughput, low channel access delay and low energy consumption. We have studied the temperature field reconstruction using Gaussian Radial Basis Functions (GRBF)-based approximation approach. Reconstruction of 3D temperature field using Neural Networks with measured TOF and known propagation paths is feasible. 2D and 3D temperature field reconstruction simulation results are achieved. The milestone status is shown in Table 1. We finished milestone 1-8 and milestone 10. For milestone 9, we did three pilot tests by using the fiber optic sensing system III (Distributed Sensing System II) at GE Power. However, due to the failure of the ns laser, we did not get the temperature results. We conducted some additional tasks that were not originally proposed: 1) We fabricated a fiber optic sensing system I and did a pilot test based on this system. 2) In the proposal, we proposed two pilot tests at GE Power. In reality, we finished at least seven pilot tests at GE Power. GE Power has made a lot of efforts for supporting the pilot tests. 3) We got a simulation results based on CDMA. In summary, most of the tasks have been accomplished. The outcome of this project removed a few barriers that hinder the achievement of the final product of the distributed sensing systems. With the successful accomplishment of this project, a prototype of the fiber optic sensing system can be fabricated to attract more interests from companies and other funding agencies.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

SFAI and Related Technologies

Researchers at Los Alamos have developed Swept-Frequency Acoustic Interferometry (SFAI) a technology that enables businesses to have greater control over their industrial processes, allowing for significantly improved efficiency by giving operators vital composition and flow rate information in real-time. This technology fills the information gap by providing this data non-invasively and at a fraction of the cost of traditional composition meters. SFAI uses engineered ultrasound and advanced signal processing to characterize multiphase flow (e.g., liquids, gasses, multi-phase mixtures) and other flows inside sealed containers and flow pipes. By being both low-cost and non-invasive, this technology is set to disrupt the current oil field monitoring state-of-the-art by providing individual well-level composition data, compared to the multi-well macroscopic information that is currently available. We are seeking a commercialization partner to license the technology or fund us through a Cooperative Research and Development Agreement (CRADA) to a further develop a usable product for commercial purposes.

03 NATURAL GAS↗
DOE OSTI2022

Fully Distributed Multi-Material Magnetic Sensing Structures for Multiparameter DAS Applications

This dissertation demonstrates the first of its kind distributed magnetic field sensor based on a fiber optic distributed acoustic sensing (DAS) scheme. Ferromagnetic nickel and Metglas® were dispersed internally within a fiber optic preform and then drawn on an in-house fiber optic draw tower to lengths in the kilometers. Due to the close proximity of the ferromagnetic metals and fiber optic core, the magnetostrictive strain response of the ferromagnetic materials when exposed to a magnetic field would perturbate within the fiber cladding and transfer that strain, internally, to the fiber optic core. Strain resulting from the magnetostrictive effect allows the DAS based sensor to accurately translate strain into readable magnetic field data. Due to the high sensitivity seen in this sensor design, multiparameter sources, acoustic and magnetic fields, were tested and validated and a three dimensional magnetic-field vector sensor was proposed. Numerical analysis of the novel sensor design was first implemented using COMSOL Multiphysics, where inputs such as magnetostrictive element shape, size, distance, and number were first investigated. Upon optimizing system constraints, the sensor design was further modified such that single mode operation was consistent across multiple fiber draws while retaining high strain transfer from the ferromagnetic elements to the fiber optic core. Ferromagnetic material selection was evaluated as a function of the saturation magnetostriction constants and a total of 4 modules were used to fully characterize the complex physics involved in this sensor design. All fabrication and testing were performed in-house using a full scale 3-story fiber draw tower and custom environmental testing stations to imitate naturally occurring events such as magnetic or acoustic point sources. A unique stacking method was used to embed ferromagnetic nickel and Metglas® into a fiber optic preform which when combined with a custom fiber draw process resulted in consistent multi-material fibers drawn to lengths of 1-km. In-house testing facilities included different types of electromagnetic generators, in addition to a soil test bed, and an outdoor test bed which allowed 100 meters of fiber to be tested simultaneously. All tested sensors demonstrated high strain transfer capabilities on the order of 0.01-10 μϵ depending on the materials used, ferromagnetic rod number, and core to metal spacing. Due to the sensitivity of the system the difference between AC and DC was distinct, and directional magnetostriction was studied. Transverse and longitudinal magnetic wave propagation was controlled through a solenoid and rectangular Helmholtz coil, both built in-house. A three-dimensional magnetic field vector sensor was proposed due to the success of the magnetic field sensor, and a design was proposed and initially tested to validate direction as a function of field strength and distance. To summarize, this dissertation explores the first fully distributed magnetic field sensor using DAS based techniques and one of the first multi-material fiber draw processes which can produce consistent single mode fiber up to 1-km. Due to extensive FEA modeling, multiple iterations of the magnetic sensor were fully characterized and an equation describing the relationship between sensor design and strain transfer has been created and validated experimentally. Multi-parameter tests including acoustic and magnetic fields were implemented and an algorithm was developed to separate the mixed signals. Finally, a test was performed to demonstrate the feasibility of sensing magnetic fields directionally. Cumulative results demonstrate a high-quality sensor alternative to current designs which may surpass other magnetic sensors due to innate multi-parameter capabilities, in addition to the inexpensive production cost and extremely long operating lengths.

02 PETROLEUM↗
NASA NTRS2016

Large Area Nondestructive Evaluation of a Fatigue Loaded Composite Structure

Large area nondestructive evaluation (NDE) inspections are required for fatigue testing of composite structures to track damage initiation and growth. Of particular interest is the progression of damage leading to ultimate failure to validate damage progression models. In this work, passive thermography and acoustic emission NDE were used to track damage growth up to failure of a composite three-stringer panel. Fourteen acoustic emission sensors were placed on the composite panel. The signals from the array were acquired simultaneously and allowed for acoustic emission location. In addition, real time thermal data of the composite structure were acquired during loading. Details are presented on the mapping of the acoustic emission locations directly onto the thermal imagery to confirm areas of damage growth leading to ultimate failure. This required synchronizing the acoustic emission and thermal data with the applied loading. In addition, processing of the thermal imagery which included contrast enhancement, removal of optical barrel distortion and correction of angular rotation before mapping the acoustic event locations are discussed.

Joseph N Zalameda↗
DOE OSTI2025

Building Datasets and Training Methods for ML Based Magnet Quench Detection

Detecting quenches in superconducting (SC) magnets during training is a challenging process that involves capturing physical events that occur at different frequencies and appear as various signal features. These events may be correlated across instrumentation type, thermal cycle, and ramp. These events together build a more complete picture of continuous processes occurring in the magnet, and may allow us to flag potential precursors for quench detection. We present our work on building an automatic machine learning (ML) based quench detection system. We build upon our existing work on unsupervised auto-encoders for acoustic sensors and quench antenna (QA) by first establishing a supervised ML training pipeline. We show the results of an event tagging, analysis, and simulation framework on our QA and acoustic data which are used concurrently to build a training dataset for a supervised implementation. We then show how this supervised training can be used as a prior in a semi-supervised framework and compare this to the unsupervised neural network auto-encoder performance.This allows us to have a more concrete understanding of the performance of our algorithms relative to physical events occurring in the magnet, and also provides a baseline software tool to generically evaluate our quench prediction autoencoders under completely unsupervised, supervised, and semi-supervised training conditions.

Khan, Maira [Fermilab]↗
NASA NTRS2021

Development and Characterization of Lightweight Durable Composite Conductor for Cables

Sustainable aviation in the form of electrified propulsion is likely to lead to an increase in the electrical assets contained within a single aircraft. As a result, it is desirable to design light weight conductor materials, thus reducing the payload of components like motor windings, low voltage signal cables, and transmission cables for data and power. One approach involves replacing pure Cu wiring with metalized CNT composites. This paper evaluates a framework for modeling overall conductivity and current sharing within such composites. Processing methods and parameters were refined. Select electroplated samples outperformed pure Cu in conductivity and had comparable ultimate tensile strengths. Tensile strengths are expected to improve with an additional densification step during processing. Acoustic emission data was used to predict the failure mechanism of the composite. Theories explaining improved intrinsic conductivity are discussed, with a focus on chemical and mechanical interactions at the Cu-CNT interface. These include Cu infiltration of CNT crevices, CNT oxidation, activity at defects in CNT walls, and the release of carbide-forming metals from CNT walls. Validating any or all these theories will require further work replicating data, collecting electron micrographs and conducting chemical analyses.

Metalized CNT↗
NASA NTRS2004

Passive Wake Acoustics Measurements at Denver International Airport

From August to September 2003, NASA conducted an extensive measurement campaign to characterize the acoustic signal of wake vortices. A large, both spatially as well as in number of elements, phased microphone array was deployed at Denver International Airport for this effort. This paper will briefly describe the program background, the microphone array, as well as the supporting ground-truth and meteorological sensor suite. Sample results to date are then presented and discussed. It is seen that, in the frequency range processed so far, wake noise is generated predominantly from a very confined area around the cores.

Wang, Frank Y.↗
DOE OSTI2025

Characterization of contaminants in the Lyman-alpha forest auto-correlation with DESI

Baryon Acoustic Oscillations can be measured with sub-percent precision above redshift two with the Lyman-α (Lyα) forest auto-correlation and its cross-correlation with quasar positions. This is one of the key goals of the Dark Energy Spectroscopic Instrument (DESI) which started its main survey in May 2021. We present in this paper a study of the contaminants to the Lyα forest which are mainly caused by correlated signals introduced by the spectroscopic data processing pipeline as well as astrophysical contaminants due to foreground absorption in the intergalactic medium. Notably, an excess signal caused by the sky background subtraction noise is present in the Lyα auto-correlation in the first line-of-sight separation bin. We use synthetic data to isolate this contribution, we also characterize the effect of spectro-photometric calibration noise, and propose a simple model to account for both effects in the analysis of the Lyα forest. We then measure the auto-correlation of the quasar flux transmission fraction of low redshift quasars, where there is no Lyα forest absorption but only its contaminants. We demonstrate that we can interpret the data with a two-component model: data processing noise and triply ionized Silicon and Carbon auto-correlations. This result can be used to improve the modeling of the Lyα auto-correlation function measured with DESI.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Bolide Infrasound Signal Morphology and Yield Estimates: A Case Study of Two Events Detected by a Dense Acoustic Sensor Network

Two bolides (2016 June 2 and 2019 April 4) were detected at multiple regional infrasound stations, with many of the locations receiving multiple detections. Analysis of the received signals was used to estimate the yield, location, and trajectory, as well as the type of shock that produced the received signal. The results from the infrasound analysis were compared with ground-truth information that was collected through other sensing modalities. This multimodal framework offers an expanded perspective on the processes governing bolide shock generation and propagation. The majority of signal features showed reasonable agreement between the infrasound-based interpretation and the other observational modalities, though the yield estimate from the 2019 bolide was significantly lower using the infrasound detections. There was also evidence suggesting that one of the detections was from a cylindrical shock that was initially propagating upward, which is unusual though not impossible.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2025

Source-Time Dominant Modeling of the Doppler Shift for the Auralization of Moving Sources

When developing an auralization for acoustic scenarios involving moving sources and receivers, one key feature is the ability to simulate the Doppler shift, i.e., the changing frequency content from the receiver’s perspective. As the time-varying delay between a source and receiver is what accounts for the Doppler shift, an approximation of this delay is required to successfully render the changes in frequency content at the receiver. Depending on the signal-processing strategy chosen to accomplish this task, there is, how-ever, a potential to introduce audible artifacts due to frequency folding (aliasing), frequency replication (imaging), and broadband noise. In this paper we dis-cuss the manifestation of such artifacts and propose a method to eliminate them, which can be integrated into the digital signal processing chain of larger auralization schemes. The method is built upon a source-time dominant approach and uses a combination of oversampling, interpolation, and time-varying filtering to predict and eliminate frequency regions at the receiver that are vulnerable to aliasing and imaging. We demonstrate the strengths and weaknesses of the method using a circularly moving source with a fixed receiver.

Auralization↗
NASA NTRS2004

Experimental Test-Bed for Intelligent Passive Array Research

This document describes the test-bed designed for the investigation of passive direction finding, recognition, and classification of speech and sound sources using sensor arrays. The test-bed forms the experimental basis of the Intelligent Small-Scale Spatial Direction Finder (ISS-SDF) project, aimed at furthering digital signal processing and intelligent sensor capabilities of sensor array technology in applications such as rocket engine diagnostics, sensor health prognostics, and structural anomaly detection. This form of intelligent sensor technology has potential for significant impact on NASA exploration, earth science and propulsion test capabilities. The test-bed consists of microphone arrays, power and signal distribution modules, web-based data acquisition, wireless Ethernet, modeling, simulation and visualization software tools. The Acoustic Sensor Array Modeler I (ASAM I) is used for studying steering capabilities of acoustic arrays and testing DSP techniques. Spatial sound distribution visualization is modeled using the Acoustic Sphere Analysis and Visualization (ASAV-I) tool.

Solano, Wanda M.↗
NASA NTRS2004

Generation of Higher Order Modes in a Rectangular Duct

Advanced noise control methodologies to reduce sound emission from aircraft engines take advantage of the modal structure of the noise in the duct. This noise is caused by the interaction of rotor wakes with downstream obstructions such as exit guide vanes. Mode synthesis has been accomplished in circular ducts and current active noise control work has made use of this capability to cancel fan noise. The goal of the current effort is to examine the fundamental process of higher order mode propagation through an acoustically treated, curved duct. The duct cross-section is rectangular to permit greater flexibility in representation of a range of duct curvatures. The work presented is the development of a feedforward control system to generate a user-specified modal pattern in the duct. The multiple-error, filtered-x LMS algorithm is used to determine the magnitude and phase of signal input to the loudspeakers to produce a desired modal pattern at a set of error microphones. Implementation issues, including loudspeaker placement and error microphone placement, are discussed. Preliminary results from a 9-3/8 inch by 21 inch duct, using 12 loudspeakers and 24 microphones, are presented. These results demonstrate the ability of the control system to generate a user-specified mode while suppressing undesired modes.

Gerhold, Carl H.↗
NASA NTRS1991

Two-Dimensional Acousto-Optical Spectrum Analyzer

State-of-the-art two-dimensional acousto-optical spectrum analyzer processes input radio-frequency signal in real time into components in any number of spectral channels up to about 10(Sup5). Input radio-frequency signal to be analyzed launched via transducer into acousto-optical device along x axis. Acousto-optical device becomes Bragg cell. Pulsed plane waves of light from laser aimed at Bragg cell, which spatially modulates phases of plane waves and diffracts waves according to pattern of acoustic signal.

Ansari, Homayoon↗