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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

TEAMER - Acoustic Particle Velocity Measurements - CRADA 601 (Abstract)

With relatively few deployments of tidal turbines, the extent and effect of underwater sounds generated from these turbines is not well understood. The University of Washington (UW) is deploying a cross-flow turbine system, the Turbine Lander, in the entrance channel to Sequim Bay. The deployment of this system provides an opportunity to understand the noise radiated by the turbine and its sources. There are three hypothesized sound sources associated with operation of the turbine: 1) a continuous tone associated with energized power electronics; 2) sound associated with the generator when the turbine is rotating; and 3) sound associated with the bearing pack that supports the rotor. PNNL is collaborating with UW and Integral Consulting Inc. (Integral) to simultaneously measure sound sources using three different devices. The NoiseSpotter®, an acoustic sensor system designed by Integral, measures acoustic pressure and a three-dimensional particle velocity vector. The NoiseSpotter, along with a commercial-off-the-shelf acoustic particle motion and pressure sensor (M20-105, Geospectrum Technologies Inc.) owned by PNNL will be deployed on the seabed approximately 50-100 m from the Turbine Lander. UW will concurrently deploy Drifting Acoustic Instrumentation SYstems (DAISYs) to characterize acoustic pressure near the Turbine Lander and localize sounds using a Time Delay of Arrival (TDOA) algorithm. Integral, UW, and PNNL will collaborate on data analysis and interpretation, with the intention of jointly authoring an archival paper on the results. The noise generated from the Turbine Lander is not expected to be significant, yet this experiment will help to evaluate the efficacy of combining technologies to characterize noise and provide insights for approaches to consider for future turbine deployments at other locations.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗
DOE OSTI2020

Fluid characterization using acoustics

A measurement system and a method for determining steam quality (i.e. vapor mass fraction) measurements of multiphase fluid flowing through pipes are described. An acoustic sensor device consists of an acoustic transmitter and an acoustic receiver that are designed to be attached to a pipe. The acoustic transmitter and the acoustic receiver are exposed to an interior space of the pipe through openings in a wall of the pipe. Acoustic waves generated by the transmitter and captured by the receiver traverse the multiphase fluid flowing into the pipe. Swept-frequency acoustic interferometry (SFAI) technique is used to measure ultrasonic acoustic properties of a fluid. Machine-learning techniques based on principal component analysis, support vector machine regression and support vector machine classification are used for determining steam quality.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Lab Scale Demonstration of Pipeline Third-Party Damage Classification Using Convolutional Neural Networks

This research aims to propose a simple experiment for third party damage classification problem by generating a dataset of third-party damage events on a laboratory scale utilizing single mode-multi mode-single mode (SMS) fiber acoustic sensor. The sound samples representative of various third-party activities, such as vehicle movements, excavation, and digging, were sourced from open-source databases. These samples were then played through a speaker in proximity to an SMS sensor, and the resultant fiber acoustic vibration data were recorded for each event. This process yielded a collection of 200 samples across 13 distinct third-party events. Convolutional Neural Networks (CNNs) were employed to classify these samples into their respective categories, and an accuracy exceeding 97% was obtained from our results.

Bukka, Sandeep Reddy↗
DOE OSTI2024

Lab-Scale Demonstration of Pipeline Third-Party Damage Classification Using Convolutional Neural Networks

This research aims to mitigate the challenges of field tests for classification of third-party damages by generating a dataset of third-party damage events on a laboratory scale utilizing single mode-multi mode-single mode (SMS) fiber acoustic sensor. The sound samples representative of various third-party activities, such as vehicle movements, excavation, and digging, were sourced from open-source databases. These samples were then played through a speaker in proximity to an SMS sensor, and the resultant fiber acoustic vibration data were recorded for each event. This process yielded a collection of 200 samples across 13 distinct third-party events. Convolutional Neural Networks (CNNs) were employed to classify these samples into their respective categories, and an accuracy exceeding 97% was obtained from our results.

Bukka, Sandeep Reddy↗
DOE OSTI2024

Acoustic Measurements of Solvent Extraction Processes in Support of Safeguards

The proposed poster focuses on using acoustic monitoring to advance detection techniques for reprocessing equipment in support of nuclear safeguards. The usage of free air acoustic monitoring has been previously demonstrated at Idaho National Laboratory (INL) at facilities such as the Advanced Test Reactor and the National Security Test Range. However, this work focuses on a deployment environment dedicated to monitoring separation processes, using solvent extraction equipment such as centrifugal contactors. This environment offers an opportunity for signal discovery and in characterizing acoustic signatures of the equipment in operation. This data can support the development of safeguards by design and security by design measures for aqueous reprocessing facilities. Furthermore, this type of monitoring can aid in early detection and identification of removed materials indicating diversion, which is essential for initiating material recovery and actor identification. The results of this work include data from nine low-frequency acoustic sensors used to investigate potential acoustic characteristics of centrifugal contactors used in multi-stage processes. The results also discuss the correlation between the signatures and the operation of the contactor banks.

98 - NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL↗
DOE OSTI2024

Next Generation High-Temperature Optical Fibers

To advance the development of geothermal energy sources, research teams at the Lawrence Livermore National Laboratory and the University of Colorado/Boulder investigated novel techniques for applying metal coatings to optical fibers, and methods to overcome the quantum noise limits of optical fiber-based acoustic sensors.

15 GEOTHERMAL ENERGY↗
NASA NTRS1991

New Sensors For Flow Velocity And Acoustics

Paper describes two sensor-development programs at Fluid Mechanics Laboratory at NASA Ames Research Center. One program for digital image velocimetry (DIV) sensors, and other program, for advanced acoustic sensors for wind tunnels. DIV measures, in real time, instantaneous velocity fields of time-varying flow or of collection of objects moving with varying velocities. Advanced acoustic sensors for wind tunnels being developed to reduce effects of interference from wind noise, noise from interactions between flows and sensors, flow-induced vibrations of sensors, deflections of accoustic waves by boundary layers induced by sensors, and reflections from walls and sensor supports.

Cho, Y. C.↗
NASA NTRS1983

An all fiber-optic sensor for surface acoustic wave measurements

A surface acoustic wave (SAW) sensor constructed from single-mode fiber-optic components is described. An analysis of reciprocal and nonreciprocal modes of operation of the sensor is presented. Results from measurements on a variety of SAW devices illustrate the use of the sensor. The amplitude sensitivity is 0.0003 A for an integration time of 0.1 s.

Bowers, J. E.↗
NASA NTRS1990

Effect of atmospherics on beamforming accuracy

Two mathematical representations of noise due to atmospheric turbulence are presented. These representations are derived and used in computer simulations of the Bartlett Estimate implementation of beamforming. Beamforming is an array processing technique employing an array of acoustic sensors used to determine the bearing of an acoustic source. Atmospheric wind conditions introduce noise into the beamformer output. Consequently, the accuracy of the process is degraded and the bearing of the acoustic source is falsely indicated or impossible to determine. The two representations of noise presented here are intended to quantify the effects of mean wind passing over the array of sensors and to correct for these effects. The first noise model is an idealized case. The effect of the mean wind is incorporated as a change in the propagation velocity of the acoustic wave. This yields an effective phase shift applied to each term of the spatial correlation matrix in the Bartlett Estimate. The resultant error caused by this model can be corrected in closed form in the beamforming algorithm. The second noise model acts to change the true direction of propagation at the beginning of the beamforming process. A closed form correction for this model is not available. Efforts to derive effective means to reduce the contributions of the noise have not been successful. In either case, the maximum error introduced by the wind is a beam shift of approximately three degrees. That is, the bearing of the acoustic source is indicated at a point a few degrees from the true bearing location. These effects are not quite as pronounced as those seen in experimental results. Sidelobes are false indications of acoustic sources in the beamformer output away from the true bearing angle. The sidelobes that are observed in experimental results are not caused by these noise models. The effects of mean wind passing over the sensor array as modeled here do not alter the beamformer output as significantly as expected.

Alexander, Richard M.↗
DOE OSTI2024

An Impedance-Loaded Surface Acoustic Wave Corrosion Sensor for Infrastructure Monitoring

Passive surface acoustic wave (SAW) devices are attractive candidates for continuous wireless monitoring of corrosion in large infrastructures. However, acoustic loss in the aqueous medium and limited read range usually create challenges in their widespread use for monitoring large systems such as oil and gas (O&G) pipelines, aircraft, and processing plants. This paper presents the investigation of impedance-loaded reflective delay line (IL-RDL) SAW devices for monitoring metal corrosion under O&G pipeline-relevant conditions. Specifically, we studied the effect of change in resistivity of a reflector on the backscattered signal of an RDL and investigated an optimal range through simulation. This was followed by the experimental demonstrations of real-time monitoring of Fe film corrosion in pressurized (550 psi) humid CO 2 conditions. Additionally, remote monitoring of Fe film corrosion in an acidic solution inside a 70 m carbon steel pipe was demonstrated using guided waves. This paper also suggests potential ways to improve the sensing response of IL-RDLs.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

An automated system for continuous monitoring of CO 2 geosequestration using multi-well offset VSP with permanent seismic sources and receivers: Stage 3 of the CO2CRC Otway Project

Time-lapse seismic is an essential tool for monitoring CO 2 injection into the subsurface but suffers from high cost and long intervals between surveys. An alternative approach is continuous monitoring using permanent sources and receivers capable of mapping CO 2 plume evolution on an almost daily basis and drastically reduce human effort, cost and survey turnaround. As part of the CO2CRC Otway project, a continuous monitoring system is employed to monitor a small (15,000 tonnes) CO 2 injection within the CO2CRC Otway Project. The permanent monitoring system employs distributed acoustic sensors (DAS) installed in boreholes as seismic receivers and seismic orbital vibrators (SOV) as seismic sources. The monitoring approach is based on vertical seismic profiling using five wells equipped with DAS cemented behind the casing and nine SOVs and is acquiring the data continuously in an autonomous manner. The data is then automatically processed on-site producing a seismic image every 2.5 days, which is then transmitted to a remote office. The data has good repeatability with NRMS of about ~10-15%.

58 GEOSCIENCES↗
NASA NTRS1992

A survey and analysis of experimental hydrogen sensors

In order to ascertain the applicability of hydrogen sensors to aerospace applications, a survey was conducted of promising experimental point-contact hydrogen sensors and their operation was analyzed. The techniques discussed are metal-oxide-semiconductor or MOS based sensors, catalytic resistor sensors, acoustic wave detectors, and pyroelectric detectors. All of these sensors depend on the interaction of hydrogen with Pd or a Pd-alloy. It is concluded that no single technique will meet the needs of aerospace applications but a combination of approaches is necessary. The most promising combination is an MOS based sensor with a catalytic resistor.

Hunter, Gary W.↗