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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Vibration-Based Method Developed to Detect Cracks in Rotors During Acceleration Through Resonance

In recent years, there has been an increasing interest in developing rotating machinery shaft crack-detection methodologies and online techniques. Shaft crack problems present a significant safety and loss hazard in nearly every application of modern turbomachinery. In many cases, the rotors of modern machines are rapidly accelerated from rest to operating speed, to reduce the excessive vibrations at the critical speeds. The vibration monitoring during startup or shutdown has been receiving growing attention (ref. 1), especially for machines such as aircraft engines, which are subjected to frequent starts and stops, as well as high speeds and acceleration rates. It has been recognized that the presence of angular acceleration strongly affects the rotor's maximum response to unbalance and the speed at which it occurs. Unfortunately, conventional nondestructive evaluation (NDE) methods have unacceptable limits in terms of their application for online crack detection. Some of these techniques are time consuming and inconvenient for turbomachinery service testing. Almost all of these techniques require that the vicinity of the damage be known in advance, and they can provide only local information, with no indication of the structural strength at a component or system level. In addition, the effectiveness of these experimental techniques is affected by the high measurement noise levels existing in complex turbomachine structures. Therefore, the use of vibration monitoring along with vibration analysis has been receiving increasing attention.

Sawicki, Jerzy T.↗
NASA NTRS2013

Optical Strain and Crack-Detection Measurements on a Rotating Disk

The development of techniques for the in-situ measurement and structural health monitoring of the rotating components in gas turbine engines is of major interest to NASA. As part of this on-going effort, several experiments have been undertaken to develop methods for detecting cracks and measuring strain on rotating turbine engine like disks. Previous methods investigated have included the use of blade tip clearance sensors to detect the presence of cracks by monitoring the change in measured blade tip clearance and analyzing the combined disk-rotor system's vibration response. More recently, an experiment utilizing a novel optical Moiré based concept has been conducted on a subscale turbine engine disk to demonstrate a potential strain measurement and crack detection technique. Moiré patterns result from the overlap of two repetitive patterns with slightly different spacing. When this technique is applied to a rotating disk, it has the potential to allow for the detection of very small changes in spacing and radial growth in a rotating disk due to a flaw such as a crack. This investigation was a continuation of previous efforts undertaken in 2011 to 2012 to validate this optical concept. The initial demonstration attempted on a subscale turbine engine disk was inconclusive due to the minimal radial growth experienced by the disk during operation. For the present experiment a new subscale Aluminum disk was fabricated and improvements were made to the experimental setup to better demonstrate the technique. A circular reference pattern was laser etched onto a subscale engine disk and the disk was operated at speeds up to 12 000 rpm as a means of optically monitoring the Moiré created by the shift in patterns created by the radial growth due the presence of the simulated crack. Testing was first accomplished on a clean defect free disk as a means of acquiring baseline reference data. A notch was then machined in to the disk to simulate a crack and testing was repeated for the purposes of demonstrating the concept. Displacement data was acquired using external blade tip clearance and shaft displacement sensors as a means of confirming the optical data and for validating other sensor based crack detection techniques.

Woike, Mark↗
NASA NTRS2013

Optical Strain and Crack-Detection Measurements on a Rotating Disk

The development of techniques for the in-situ measurement and structural health monitoring of the rotating components in gas turbine engines is of major interest to NASA. As part of this on-going effort, several experiments have been undertaken to develop methods for detecting cracks and measuring strain on rotating turbine engine like disks. Previous methods investigated have included the use of blade tip clearance sensors to detect the presence of cracks by monitoring the change in measured blade tip clearance and analyzing the combined disk-rotor system's vibration response. More recently, an experiment utilizing a novel optical Moiré based concept has been conducted on a subscale turbine engine disk to demonstrate a potential strain measurement and crack detection technique. Moiré patterns result from the overlap of two repetitive patterns with slightly different spacing. When this technique is applied to a rotating disk, it has the potential to allow for the detection of very small changes in spacing and radial growth in a rotating disk due to a flaw such as a crack. This investigation was a continuation of previous efforts undertaken in 2011-2012 to validate this optical concept. The initial demonstration attempted on a subscale turbine engine disk was inconclusive due to the minimal radial growth experienced by the disk during operation. For the present experiment a new subscale Aluminum disk was fabricated and improvements were made to the experimental setup to better demonstrate the technique. A circular reference pattern was laser etched onto a subscale engine disk and the disk was operated at speeds up to 12 000 rpm as a means of optically monitoring the Moiré created by the shift in patterns created by the radial growth due the presence of the simulated crack. Testing was first accomplished on a clean defect free disk as a means of acquiring baseline reference data. A notch was then machined in to the disk to simulate a crack and testing was repeated for the purposes of demonstrating the concept. Displacement data was acquired using external blade tip clearance and shaft displacement sensors as a means of confirming the optical data and for validating other sensor based crack detection techniques.

Woike, Mark↗
DOE OSTI2024

AI-Driven Crack Detection for Remanufacturing Cylinder Heads Using Deep Learning and Engineering-Informed Data Augmentation

Detecting cracks in cylinder heads traditionally relies on manual inspection, which is time-consuming and susceptible to human error. As an alternative, automated object detection utilizing computer vision and machine learning models has been explored. However, these methods often face challenges due to a lack of sufficiently annotated training data, limited image diversity, and the inherently small size of cracks. Addressing these constraints, this paper introduces a novel automated crack-detection method that enhances data availability through a synthetic data generation technique. Unlike general data augmentation practices, our method involves copying cracks from one location to another, guided by both random and informed engineering decisions about likely crack formations due to cyclic thermomechanical loads. The innovative aspect of our approach lies in the integration of domain-specific engineering knowledge into the synthetic generation process, which substantially improves detection accuracy. We evaluate our method’s effectiveness using two metrics: the F2 score, which emphasizes recall to prioritize detecting all potential cracks, and mean average precision (MAP), a standard measure in object detection. Experimental results demonstrate that, without engineering insights, our method increases the F2 score from 0.40 to 0.65, while maintaining a stable MAP. Incorporating detailed engineering knowledge further enhances the F2 score to 0.70 and improves MAP to 0.57, representing increases of 63% and 43%, respectively. These results confirm that our approach not only mitigates the limitations of traditional data augmentation but also significantly advances the reliability and precision of crack detection in industrial settings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2010

Nondestructive Crack Detection in a Fuel System Component

The paper discusses development of various NDE techniques to detect cracks in A40 steel poppets used in a valve of the fuel system of the Space Shuttle Orbiter. The valve assembly experiences a severe high cycle fatigue environment during its operation. Cracks were discovered at the radius of the poppet flange. Experience shows that very small cracks or material anomalies do not cause failure in a single operation event. While the design is being modified to eliminate the issue, NDE has been used to screen the poppets for cracks before every use. Several surface flaw detection techniques were considered and a few NDE techniques were developed to provide NDE screening for the flaw detection. The primary method used was the eddy current testing. In the eddy current technique, the X-Y channel test data from the eddy current instrument was recorded as computer files. A Matlab data review and plotting application was developed to analyze the data files. The Matlab application provides much higher resolution than the eddy current instrument that was used to acquire the data. Other techniques that were used included ultrasonic surface wave and magnetic particle testing. A probability of detection (POD) study was undertaken to determine the 90/95 size for the eddy current technique. This study used specimens with same geometry and material as the poppet. Fatigue cracks were grown in these specimens. Information on results of the NDE techniques and results of the POD study are provided.

Koshti, Ajay↗
DOE OSTI2025

Integrating Crack Detection and Pipe Shape Optimization for Enhanced Sewage System Durability

Crack detection in underground reinforced concrete pipes has been essential in determining the state of stormwater infrastructure. Detection models have been implemented for detecting cracks and other defects in pipes using CCTV footage for stormwater drainage systems. In addition, Finite element models have been used to determine optimum shapes and pipe thickness for different boundary conditions such as header pipes in power plants. The concept of shape optimization emerges as a crucial factor in power plant design and operation, with the potential to maximize performance while minimizing the use of materials. Shape optimization not only enhances efficiency but also contributes to reducing the environmental footprint. This paper discusses the integration of both topics by using the cracks detected in underground pipes as boundary conditions for shape optimization of the pipes. A machine learning model has been developed which uses limited data for training and outlines the location of detected cracks. A shape optimization methodology is proposed in which ANSYS modules are used to analyze fluid flow and then optimize the shape of the pipe. The crack detection model developed has been applied to a crack detected in lab setting and machine learning model used has an accuracy of 98% using a random forest algorithm.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

Deep Learning and Photogrammetric Reconstruction for Automated Crack Detection and Dimensional Measurement in Mining Operations

Surface crack detection and dimensional measurement at active mining sites present significant safety and operational challenges. Manual inspection methods are labor-intensive, spatially incomplete, and expose personnel to hazardous environments, while existing automated approaches have been developed primarily for concrete civil infrastructure and have not been validated on the complex, variable surfaces characteristic of mining environments. This dissertation presents an automated pipeline that integrates deep learning semantic segmentation with Structure-from-Motion photogrammetry to detect surface cracks and measure their aperture, length, and vertical displacement from standard RGB imagery acquired during routine Uncrewed Aerial Vehicle (UAV) survey operations, without requiring additional sensor hardware or manual measurement. The pipeline combines a U-Net architecture with an EfficientNet-B0 encoder, pretrained on the SDNET2018 concrete crack dataset and fine-tuned on a mining-specific dataset spanning laboratory concrete specimens, coal refuse impoundment embankments, and post-blast limestone quarry benches. Photogrammetric reconstruction is performed using COLMAP Structure-from-Motion and Multi-View Stereo, with crack segmentation masks projected into the reconstructed point cloud to enable three-dimensional vertical displacement measurement through local plane fitting and bimodal surface detection. The pipeline was validated across 36 controlled laboratory specimens at three imaging distances and four vertical displacement levels, achieving aperture measurement RMSE of 0.047 cm and R² of 0.954, and vertical displacement RMSE of 0.140 cm and R² of 0.966, against independent caliper measurements. Field application at a coal refuse impoundment in southwestern Pennsylvania detected 71 crack components across the embankment crest, with a dominant longitudinal crack exhibiting aperture values reaching 28 cm and a 95th percentile vertical displacement of 35.53 cm, consistent in magnitude and spatial distribution with simultaneously acquired LiDAR-derived estimates. Application across four post-blast limestone quarry bench datasets in California successfully characterized blast-induced fracture networks at ground sampling distances ranging from 0.59 to 1.23 cm/pixel, with detected crack geometries physically consistent with observable surface conditions at each site. The results demonstrate that deep learning-based crack detection and photogrammetric measurement can be integrated into routine UAV inspection workflows at mining sites, providing repeatable, scalable, and quantitative crack characterization across surface types, crack scales, and displacement magnitudes not previously addressed in the literature. The pipeline requires no dedicated surveying equipment beyond the UAV platforms already deployed at mine sites for survey and monitoring purposes, supporting practical adoption within existing operational workflows.

Crack detection, Dimensional Measurement↗
NASA NTRS2016

Dye Penetrant Crack Detectability in External Corners in Presence of Fillet Radius

NASA uses special dye penetrant nondestructive evaluation process to provide reliable detection of very small cracks. Typically the surface crack lengths sizes are 0.030' and 0.050' for special dye penetrant process. Qualification requires demonstration of crack detection on a set of cracks with average crack size smaller than or equal to the qualification crack size. The demonstration is called point estimate demonstration. A set of corner cracks can be used to determine reliably detectable corner crack using the point estimate demonstration method. However, dye penetrant demonstration on surface cracks can be used to assess reliably detectable corner crack sizes by using similarity in the penetrant process. The paper provides similarity analysis approach for determining the reliably detectable corner crack sizes for given a point estimate demonstrated surface crack size.

Koshti, Ajay M.↗
NASA NTRS2005

Vibration Based Crack Detection in a Rotating Disk: Experimental Results - Part 2

This paper describes the experimental results concerning the detection of a crack in a rotating disk. The goal was to utilize blade tip clearance and shaft vibration measurements to monitor changes in the system's center of mass and/or blade deformation behaviors. The concept of the approach is based on the fact that the development of a disk crack results in a distorted strain field within the component. As a result, a minute deformation in the disk's geometry as well as a change in the system's center of mass occurs. Here, a notch was used to simulate an actual crack. The vibration based experimental results failed to identify the existence of a notch when utilizing the approach described above, even with a rather large, circumferential notch (l.2 in.) located approximately mid-span on the disk (disk radius = 4.63 in. with notch at r = 2.12 in.). This was somewhat expected, since the finite element based results in Part 1 of this study predicted changes in blade tip clearance as well as center of mass shifts due to a notch to be less than 0.001 in. Therefore, the small changes incurred by the notch could not be differentiated from the mechanical and electrical noise of the rotor system. Although the crack detection technique of interest failed to identify the existence ofthe notch, the vibration data produced and captured here will be utilized in upcoming studies that will focus on different data mining techniques concerning damage detection in a disk.

Gyekenyesi, Andrew L.↗
NASA NTRS2013

Crack Detection with Lamb Wave Wavenumber Analysis

In this work, we present our study of Lamb wave crack detection using wavenumber analysis. The aim is to demonstrate the application of wavenumber analysis to 3D Lamb wave data to enable damage detection. The 3D wavefields (including vx, vy and vz components) in time-space domain contain a wealth of information regarding the propagating waves in a damaged plate. For crack detection, three wavenumber analysis techniques are used: (i) two dimensional Fourier transform (2D-FT) which can transform the time-space wavefield into frequency-wavenumber representation while losing the spatial information; (ii) short space 2D-FT which can obtain the frequency-wavenumber spectra at various spatial locations, resulting in a space-frequency-wavenumber representation; (iii) local wavenumber analysis which can provide the distribution of the effective wavenumbers at different locations. All of these concepts are demonstrated through a numerical simulation example of an aluminum plate with a crack. The 3D elastodynamic finite integration technique (EFIT) was used to obtain the 3D wavefields, of which the vz (out-of-plane) wave component is compared with the experimental measurement obtained from a scanning laser Doppler vibrometer (SLDV) for verification purposes. The experimental and simulated results are found to be in close agreement. The application of wavenumber analysis on 3D EFIT simulation data shows the effectiveness of the analysis for crack detection. Keywords: : Lamb wave, crack detection, wavenumber analysis, EFIT modeling

Tian, Zhenhua↗
NASA NTRS2004

Vibration-Based Data Used to Detect Cracks in Rotating Disks

Rotor health monitoring and online damage detection are increasingly gaining the interest of aircraft engine manufacturers. This is primarily due to the fact that there is a necessity for improved safety during operation as well as a need for lower maintenance costs. Applied techniques for the damage detection and health monitoring of rotors are essential for engine safety, reliability, and life prediction. Recently, the United States set the ambitious goal of reducing the fatal accident rate for commercial aviation by 80 percent within 10 years. In turn, NASA, in collaboration with the Federal Aviation Administration, other Federal agencies, universities, and the airline and aircraft industries, responded by developing the Aviation Safety Program. This program provides research and technology products needed to help the aerospace industry achieve their aviation safety goal. The Nondestructive Evaluation (NDE) Group of the Optical Instrumentation Technology Branch at the NASA Glenn Research Center is currently developing propulsion-system-specific technologies to detect damage prior to catastrophe under the propulsion health management task. Currently, the NDE group is assessing the feasibility of utilizing real-time vibration data to detect cracks in turbine disks. The data are obtained from radial blade-tip clearance and shaft-clearance measurements made using capacitive or eddy-current probes. The concept is based on the fact that disk cracks distort the strain field within the component. This, in turn, causes a small deformation in the disk's geometry as well as a possible change in the system's center of mass. The geometric change and the center of mass shift can be indirectly characterized by monitoring the amplitude and phase of the first harmonic (i.e., the 1 component) of the vibration data. Spin pit experiments and full-scale engine tests have been conducted while monitoring for crack growth with this detection methodology. Even so, published data are extremely limited, and the basic foundation of the methodology has not been fully studied. The NDE group is working on developing this foundation on the basis of theoretical modeling as well as experimental data by using the newly constructed subscale spin system shown in the preceding photograph. This, in turn, involved designing an optimal sub-scale disk that was meant to represent a full-scale turbine disk; conducting finite element analyses of undamaged and damaged disks to define the disk's deformation and the resulting shift in center of mass; and creating a rotordynamic model of the complete disk and shaft assembly to confirm operation beyond the first critical concerning the subscale experimental setup. The finite element analysis data, defining the center of mass shift due to disk damage, are shown. As an example, the change in the center of mass for a disk spinning at 8000 rpm with a 0.963-in. notch was 1.3 x 10(exp -4) in. The actual vibration response of an undamaged disk as well as the theoretical response of a cracked disk is shown. Experiments with cracked disks are continuing, and new approaches for analyzing the captured vibration data are being developed to better detect damage in a rotor. In addition, the subscale spin system is being used to test the durability and sensitivity of new NDE sensors that focus on detecting localized damage. This is designed to supplement the global response of the crack-detection methodology described here.

Gyekenyesi, Andrew L.↗
NASA NTRS1985

Reliability Studies for Fatigue-Crack Detection

Reusable test panels available to assess reliability of techniques that use fluorescent penetrant to detect fatigue cracks. Ultrasonic cleaning method developed for removing penetrant from panels prior to reuse.

Christner, B. K.↗
NASA NTRS2010

Crack-Detection Experiments on Simulated Turbine Engine Disks in NASA Glenn Research Center's Rotordynamics Laboratory

The development of new health-monitoring techniques requires the use of theoretical and experimental tools to allow new concepts to be demonstrated and validated prior to use on more complicated and expensive engine hardware. In order to meet this need, significant upgrades were made to NASA Glenn Research Center s Rotordynamics Laboratory and a series of tests were conducted on simulated turbine engine disks as a means of demonstrating potential crack-detection techniques. The Rotordynamics Laboratory consists of a high-precision spin rig that can rotate subscale engine disks at speeds up to 12,000 rpm. The crack-detection experiment involved introducing a notch on a subscale engine disk and measuring its vibration response using externally mounted blade-tip-clearance sensors as the disk was operated at speeds up to 12 000 rpm. Testing was accomplished on both a clean baseline disk and a disk with an artificial crack: a 50.8-mm- (2-in.-) long introduced notch. The disk s vibration responses were compared and evaluated against theoretical models to investigate how successful the technique was in detecting cracks. This paper presents the capabilities of the Rotordynamics Laboratory, the baseline theory and experimental setup for the crack-detection experiments, and the associated results from the latest test campaign.

Woike, Mark R.↗
NASA NTRS2010

Nondestructive Crack Detection in a Fuel System Component

The presentation examines the background and objective of nondestructive crack detection, flow control valve assembly and poppet post flight evaluation, poppet properties. magnetic property characterization of lab data, NDE, eddy current inspection, simulation, eddy current criteria, poppet cycle testing and NDE criteria, and the use of ultrasonic surface wave for crack detection.

Koshti, Ajay↗
NASA NTRS1967

Surface-crack detection by microwave methods

Microwave surface-crack detection system examines metallic surfaces with a noncontacting probe. The change in the microwave signal reflected from the surface under investigation is an indication of the existence of surface flaws. This technique can detect flaws and scratches as small as 100 microinches.

Feinstein, L.↗
DOE OSTI2023

Crack detection in fuel cell electrodes using a spatial filtering technique for overcoming noisy backgrounds

Image processing is a powerful tool that allows for rapid and automated data parsing in settings that occupy large variable spaces and require large data sets. Feature detection on difficultly discerned backgrounds is a subset of image processing that facilitates the extraction of quantitative metrics from otherwise subjective data. Crack detection and quantification is an important capability in polymer electrolyte membrane fuel cell quality control, failure analysis, and optimization. This work presents a technique to perform crack detection and quantification which overcomes challenges faced by commonly used image segmentation techniques. We demonstrate the use of a geometrically filtered noise‐level detection technique to select a binary threshold value from which we then quantify how cracked a sample is. Furthermore, we demonstrate the accuracy of our technique using programmatically generated test images of known crack amounts and their performance on real‐world fuel cell catalyst layer samples.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.

42 ENGINEERING↗