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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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13 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Enhanced Design of Radiation Tolerant High-Temperature Structural Health Monitoring Sensors

Acoustic emission sensors are vital in the nuclear industry for real-time structural health monitoring and early detection of material degradation. By capturing high-frequency stress waves emitted from defects like cracks, corrosion, or fatigue, acoustic emission sensors enable non-invasive monitoring of critical components such as reactor vessels, piping, and containment structures. This technology supports predictive maintenance, enhances safety, and ensures regulatory compliance by providing early warnings of potential failures. It is also instrumental in research, particularly in material testing reactors, where it is used to monitor the behavior of fuels and materials under irradiation, by allowing the detection of cracking or other acoustic signals in real time. This enables the evaluation of performance and accident behavior of advanced fuel concepts.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2023

Unsupervised acoustic detection of fatigue-induced damage modes from wind turbine blades

This paper proposes a new in-situ damage detection approach for wind turbine blades, which leverages blade-internal non-stationary acoustic pressure fluctuations caused by the mechanical loading as the main source of excitation. This acoustic excitation was leveraged for the detection of fatigue-related damage modes on a full-scale wind turbine blade undergoing edgewise fatigue testing. An unsupervised, data-driven structural health monitoring strategy was developed to learn the normal cavity-internal acoustic sequences generated by the blade’s load cycles and to detect damage-related anomalies in the context of those sequences. A linear cepstral-coefficient based feature set was used to characterize the cavity-internal acoustics and LSTM-autoencoders were trained to accurately reconstruct healthy-case sequences. The reconstruction error was then used to characterize anomalous acoustic patterns within the blade cavity. The technique was able to detect a damage event earlier than a strain-based system by 120,000 load cycles.

17 WIND ENERGY↗
DOE OSTI2020

Characterization of the Vertical Evolution of the Three-Dimensional Turbulence for Fatigue Design of Tidal Turbines

A system of two coupled four-beam acoustic Doppler current profilers was used to collect turbulence measurements over a 36-h period at a highly energetic tidal energy site in Alderney Race. This system enables the evaluation of the six components of the Reynolds stress tensor throughout a large proportion of the water column. The present study provides mean vertical profiles of the velocity, the turbulence intensity and the integral lengthscale along the streamwise, spanwise and vertical direction of the tidal current.

Alderney Race↗
DOE OSTI2024

Comparison of Loads and Aeroacoustics Between Upwind and Downwind Wind Turbine Rotors

This presentation discusses the motivation, preparation, risk mitigation, execution, and results of a full-scale experiment where the rotor of a 1.5 megawatt wind turbine was operated in a downwind configuration. The experiment took place at the National Renewable Energy Laboratory Flatirons Campus in Colorado, USA, and collected conventional loads and power together with acoustic measurements from an array of four microphones. Results from load simulations complement the experiment. Fatigue loads and performance are compared between 410 minutes of downwind operation with 960 minutes of conventional upwind operations. Downwind operations cause an increase in damage equivalent loads in a few load channels. Damage equivalent load of blade root flapwise and edgewise moment and the damage equivalent load of tower base fore aft moment increase between 2% and 20% depending on the wind speed. The comparison in active power is less conclusive, but experimental results show more power in downwind than predicted by simulations. In terms of acoustics, the overall sound pressure levels are similar between upwind and downwind, but downwind worsens the metrics describing amplitude modulation.

aeroacoustics↗
DOE OSTI2020

Mutually Reinforced Polymer–Graphene Bilayer Membranes for Energy‐Efficient Acoustic Transduction

Abstract Graphene holds promise for thin, ultralightweight, and high‐performance nanoelectromechanical transducers. However, graphene‐only devices are limited in size due to fatigue and fracture of suspended graphene membranes. Here, a lightweight, flexible, transparent, and conductive bilayer composite of polyetherimide and single‐layer graphene is prepared and suspended on the centimeter scale with an unprecedentedly high aspect ratio of 10 5 . The coupling of the two components leads to mutual reinforcement and creates an ultrastrong membrane that supports 30 000 times its own weight. Upon electromechanical actuation, the membrane pushes a massive amount of air and generates high‐quality acoustic sound. The energy efficiency is ≈ 10–100 times better than state‐of‐the‐art electrodynamic speakers. The bilayer membrane's combined properties of electrical conductivity, mechanical strength, optical transparency, thermal stability, and chemical resistance will promote applications in electronics, mechanics, and optics.

Khan, Assad U.↗
DOE OSTI2026

Correlation of Surface Acoustic Wave (SAW) force myography sensor output with elbow joint torque

Accurate assessment of skeletal muscle forces and net joint torque is essential for preventing fatigue-related injuries, optimizing physical training, and monitoring disease progression in neuromuscular conditions. However, existing joint torque evaluation techniques are hindered by limited portability and high operational costs, confining their use to controlled laboratory or clinical settings. Despite substantial advances in wearable joint torque estimation systems, ongoing challenges such as power constraints, bulky wired setups, and susceptibility to environmental or motion artifacts underscore the urgent need for truly batteryless, wireless solutions deployable in real-world settings. This paper proposes a novel surface acoustic wave (SAW)-based force myography (FMG) system for noninvasive measurement of joint torque, validated against a gold-standard electromechanical dynamometer. The approach uses a single SAW sensor embedded in an armband to detect volumetric biceps brachii changes, with a second-order polynomial mapping sensor output and elbow angle to torque. Seven participants were tested in both isometric (15°–90°) and isokinetic (10°/s and 20°/s) supinated elbow flexion tasks. Under isometric conditions, subject-specific calibration achieved a normalized root-mean-square error (NRMSE) of 13.6% ± 6.0% and R 2 = 0.834 ± 0.180, while a group-level model yielded 14.4% ± 6.8% and 0.808 ± 0.208, respectively. For isokinetic trials, the group model produced an NRMSE of 24.1% ± 6.6% at 10°/s and 24.9% ± 08.9% at 20°/s, highlighting the feasibility of using a single-sensor SAW-FMG setup across different speeds. Because SAW devices support wireless, battery-free operation, the proposed system offers a pathway to portable, real-time monitoring for sports medicine, rehabilitation, and clinical diagnostics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Upwind vs. downwind: loads and acoustics of a 1.5 MW wind turbine

This paper discusses the motivation, preparation, risk mitigation, execution, and results of a full-scale experiment where the conventional upwind rotor of a 1.5 MW wind turbine was operated in a downwind configuration. The experiment took place at the National Renewable Energy Laboratory Flatirons Campus in Colorado, USA, and involved the collection of loads and power together with acoustic measurements from an array of four microphones. To validate the numerical predictions of the aeroelastic solver OpenFAST in terms of loads and performance, 410 min of downwind operation and 960 min of conventional upwind operations were used. In the wind speed range from 4.5 to 12.5 m s−1, the downwind rotor generates higher damage equivalent loads for the blade root flapwise moment, blade root edgewise moment, and tower-base fore-aft moment compared to the upwind rotor. For these metrics of fatigue loads, numerical predictions match the experimental observations well. OpenFAST is, however, also seen underpredicting a power gain in the downwind rotor. In terms of acoustics, the overall sound pressure levels recorded in the field are similar between the upwind and downwind cases, but downwind operation worsens the metrics describing amplitude modulation.

17 WIND ENERGY↗
DOE OSTI2020

Data-driven Detection and Early Prediction of Thermoacoustic Instability in a Multi-nozzle Combustor

Thermoacoustic instability (TAI) is a critical issue in modern lean-burn gas-turbine combustors, which is induced by a strong coupling between the resonant combustor acoustics and fluctuations in the heat release rate. This instability may lead to high-amplitude pressure waves that generate undesirable noise levels as well as fatigue stresses in mechanical structures of the combustor. The intense pressure fluctuations due to TAI may also cause large flow perturbations and possibly flow reversal that may lead to flame oscillations, flame liftoff, and even flame blow-out. Hence, there is a strong need for exercising control actions in a timely fashion to mitigate the TAI phenomena. Anomaly detection is an essential prerequisite to the design of a good controller and such a detector must be able to reliably predict a forthcoming TAI. To detect and predict the onset of a TAI from an ensemble of pressure time series, this paper investigates three data-driven methods: Fast Fourier transform (FFT), symbolic time series analysis (STSA), and hidden Markov modeling (HMM). The main focus of the paper is to make a comparative evaluation of these three anomaly detection methods for classification of the current regime of operation into stable and unstable categories as well as for real-time identification of precursors to impending instabilities with short-length time series of measured variables (e.g., pressure oscillations). Finally, the results, generated on experimental data from a multi-nozzle combustor apparatus, have been compared to evaluate the performance of FFT, STSA, and HMM methods for TAI analysis.

42 ENGINEERING↗
DOE OSTI2022

A Novel Approach for Real-Time Quality Monitoring in Machining of Aerospace Alloy through Acoustic Emission Signal Transformation for DNN

Gamma titanium aluminide (γ-TiAl) is considered a high-performance, low-density replacement for nickel-based superalloys in the aerospace industry due to its high specific strength, which is retained at temperatures above 800 °C. However, low damage tolerance, i.e., brittle material behavior with a propensity to rapid crack propagation, has limited the application of γ-TiAl. Any cracks introduced during manufacturing would dramatically lower the useful (fatigue) life of γ-TiAl components, making the workpiece surface’s quality from finish machining a critical component to product quality and performance. To address this issue and enable more widespread use of γ-TiAl, this research aims to develop a real-time non-destructive evaluation (NDE) quality monitoring technique based on acoustic emission (AE) signals, wavelet transform, and deep neural networks (DNN). Previous efforts have opted for traditional approaches to AE signal analysis, using statistical feature extraction and classification, which face challenges such as the extraction of good/relevant features and low classification accuracy. Hence, this work proposes a novel AI-enabled method that uses a convolutional neural network (CNN) to extract rich and relevant features from a two-dimensional image representation of 1D time-domain AE signals (known as scalograms), subsequently classifying the AE signature based on pedigreed experimental data and finally predicting the process-induced surface quality. The results of the present work show good classification accuracy of 80.83% using scalogram images, in-situ experimental data, and a VGG-19 pre-trained neural network, establishing the significant potential for real-time quality monitoring in manufacturing processes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

An implantable biomechanical energy harvester for animal monitoring devices

Insufficient service life and the resulting need for battery replacements have been a great challenge for implantable electronic devices. This is particularly true for animal tracking applications, because recapturing animals is often unlikely once they are released to the wild. To tackle this problem, we developed a biomechanical energy harvester that uses a Macro Fiber Composite™ (MFC) piezoelectric beam to harvest the mechanical energy from animals’ body bending movements as the power source for implantable and wearable devices. Prototypes of an underwater acoustic transmitter using this technology were subdermally implanted into juvenile white sturgeon and their energy harvesting performance was evaluated through the device’s transmissions. Additionally, the fish successfully recovered from the implantation surgery and freely swam inside a tank. The transmitter prototypes in the fish continually transmitted signals for a period up to 5 weeks. A benchtop test setup was also created to emulate the fish’s body bending, estimate the device’s energy harvesting performance in the live fish, and perform accelerated fatigue testing of the energy harvester by applying test parameters learned from a video study of the fish’s body movement and behavior characteristics. The gradual depolarization of the piezoelectric ceramic material in the MFC under cyclic mechanical loading was the main limiting factor for the life span of the energy harvester. Pathways for improvement are proposed to achieve long-term efficacy of powering implantable and wearable electronic devices.

42 ENGINEERING↗
DOE OSTI2020

Critical instability at moving keyhole tip generates porosity in laser melting

Laser powder bed fusion is a dominant metal 3D printing technology. However, porosity defects remain a challenge for fatigue-sensitive applications. Some porosity is associated with deep and narrow vapor depressions called keyholes, which occur under high-power, low–scan speed laser melting conditions. High-speed x-ray imaging enables operando observation of the detailed formation process of pores in Ti-6Al-4V caused by a critical instability at the keyhole tip. We found that the boundary of the keyhole porosity regime in power-velocity space is sharp and smooth, varying only slightly between the bare plate and powder bed. The critical keyhole instability generates acoustic waves in the melt pool that provide additional yet vital driving force for the pores near the keyhole tip to move away from the keyhole and become trapped as defects.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Exploring 3D elastic-wave scattering at interfaces using high-resolution phased-array system

The elastic-wave scattering at interfaces, such as cracks, is essential for nondestructive inspections, and hence, understanding the phenomenon is crucial. However, the elastic-wave scattering at cracks is very complex in three dimensions since microscopic asperities of crack faces can be multiple scattering sources. We propose a method for exploring 3D elastic-wave scattering based on our previously developed high-resolution 3D phased-array system, the piezoelectric and laser ultrasonic system (PLUS). We describe the principle of PLUS, which combines a piezoelectric transmitter and a 2D mechanical scan of a laser Doppler vibrometer, enabling us to resolve a crack into a collection of scattring sources. Subsequently, we show how the 3D elastic-wave scattering in the vicinity of each response can be extracted. Here, we experimentally applied PLUS to a fatigue-crack specimen. We found that diverse 3D elastic-wave scattering occurred in a manner depending on the responses within the fatigue crack. This is significant because access to such information will be useful for optimizing inspection conditions, designing ultrasonic measurement systems, and characterizing cracks. More importantly, the described methodology is very general and can be applied to not only metals but also other materials such as composites, concrete, and rocks, leading to progress in many fields.

36 MATERIALS SCIENCE↗