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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Autonomous Deicing System For Airplane Wing

Prototype autonomous deicing system for airplane includes network of electronic and electromechanical modules at various locations in wings and connected to central data-processing unit. Small, integrated solid-state device, using long coils installed under leading edge, exciting small vibrations to detect ice and larger vibrations to knock ice off. In extension of concept, outputs of vibration sensors and other sensors used to detect rivet-line fractures, fatigue cracks, and other potentially dangerous defects.

Hickman, G. A.↗
NASA NTRS1959

Exploratory Investigation of Advanced-Temperature Nickel-Base Alloys

An investigation was conducted to provide an advanced-temperature nickel-base alloy with properties suitable for aircraft turbine blades as well as for possible space vehicle applications. An entire series of alloys that do not require vacuum melting techniques and that generally provide good stress-rupture and impact properties was evolved. The basic-alloy composition of 79 percent nickel, 8 percent molybdenum, 6 percent chromium, 6 percent aluminum, and 1 percent zirconium was modified by a series of element additions such as carbon, titanium, and boron, with the nickel content adjusted to account for the additives. Stress-rupture, impact, and swage tests were made with all the alloys. The strongest composition (basic alloy plus 1.5 percent titanium plus 0.125 percent carbon) displayed 384- and 574-hour stress-rupture lives at 1800 F and 15,000 psi in the as-cast and homogenized conditions, respectively. All the alloys investigated demonstrated good impact resistance. Several could not be broken in a low-capacity Izod impact tester and, on this basis, all compared favorably with several high-strength high-temperature alloys. Swaging cracks were encountered with all the alloys. In several cases, however, these cracks were slight and could be detected only by zyglo examination. Some of these compositions may become amenable to hot working on further development. On the basis of the properties indicated, it appears that several of the alloys evolved, particularly the 1.5 percent titanium plus 0.125 percent carbon basic-alloy modification, could be used for advanced- temperature turbine blades, as well as for possible space vehicle applications.

Freche, John C.↗
NASA NTRS2010

Acoustic Emission Measurement with Fiber Bragg Gratings for Structure Health Monitoring

Structural Health monitoring (SHM) is a way of detecting and assessing damage to large scale structures. Sensors used in SHM for aerospace structures provide real time data on new and propagating damage. One type of sensor that is typically used is an acoustic emission (AE) sensor that detects the acoustic emissions given off from a material cracking or breaking. The use of fiber Bragg grating (FBG) sensors to provide acoustic emission data for damage detection is studied. In this research, FBG sensors are used to detect acoustic emissions of a material during a tensile test. FBG sensors were placed as a strain sensor (oriented parallel to applied force) and as an AE sensor (oriented perpendicular to applied force). A traditional AE transducer was used to collect AE data to compare with the FBG data. Preliminary results show that AE with FBGs can be a viable alternative to traditional AE sensors.

Banks, Curtis E.↗
NASA NTRS2010

Acoustic Emission Measurement with Fiber Bragg Gratings for Structure Health Monitoring

Structural Health monitoring (SHM) is a way of detecting and assessing damage to large scale structures. Sensors used in SHM for aerospace structures provide real time data on new and propagating damage. One type of sensor that is typically used is an acoustic emission (AE) sensor that detects the acoustic emissions given off from a material cracking or breaking. The use of fiber Bragg grating (FBG) sensors to provide acoustic emission data for damage detection is studied. In this research, FBG sensors are used to detect acoustic emissions of a material during a tensile test. FBG sensors were placed as a strain sensor (oriented parallel to applied force) and as an AE sensor (oriented perpendicular to applied force). A traditional AE transducer was used to collect AE data to compare with the FBG data. Preliminary results show that AE with FBGs can be a viable alternative to traditional AE sensors.

Banks, Curtis E.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-17800-DND

Currently, there are two national challenge problems that guide much of the research in durability and damage tolerance at NASA Langley. The first, Airframe Digital Twin, is a concept that combines as-built vehicle components, as-experienced loads and environments, and other vehicle-specific characteristics to enable ultrahigh fidelity modeling of aircraft and spacecraft throughout their service lives. The second, Materials Genome Initiative, is an analog to the Human Genome Project, and is intended to improve the rate at which materials scientists can discover, understand fundamental physics, and improve material systems. This presentation will highlight several research projects ongoing at NASA Langley that are in support of the above challenge problems. Two of those topics will be the subject of detailed discussion. First, investigations of microstructurally-small fatigue cracking (MSFC) in Al-2Cu and Al-4Cu, fabricated in-house, will be presented. Single- and oligo-crystals of Al-Cu specimens were loaded in uniaxial fatigue, while high-resolution in-situ measurements of deformation were made using image correlation (IC) in a scanning-electron microscope (SEM). The Al-Cu specimens were then replicated as crystal plasticity finite element models (CPFEM), where evolution of slip localization near grain boundaries was computed. Comparison among experiment and CPFEM is made. In addition, XRay diffraction measurements of the as-fabricated specimens were carried out, where direct measurements of the embedded copper precipitates were made, and their influence on growing MSFCs were directly observed. The second main topic will illustrate ongoing work in the area of so-called damage-sensing particles. In this work, shape-memory alloys are embedded in an aluminum alloy matrix. Upon the propagation of a fatigue crack, these particles undergo a strain-induced phase transformation which is detected using an acoustic sensor, providing real-time information on propagating cracks. Experiments and simulations regarding the development of this system will also be detailed.

Jacob Hochhalter↗
DOE OSTI2025

Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

Additive manufacturing↗
NASA NTRS2009

A Mode-Shape-Based Fault Detection Methodology for Cantilever Beams

An important goal of NASA's Internal Vehicle Health Management program (IVHM) is to develop and verify methods and technologies for fault detection in critical airframe structures. A particularly promising new technology under development at NASA Langley Research Center is distributed Bragg fiber optic strain sensors. These sensors can be embedded in, for instance, aircraft wings to continuously monitor surface strain during flight. Strain information can then be used in conjunction with well-known vibrational techniques to detect faults due to changes in the wing's physical parameters or to the presence of incipient cracks. To verify the benefits of this technology, the Formal Methods Group at NASA LaRC has proposed the use of formal verification tools such as PVS. The verification process, however, requires knowledge of the physics and mathematics of the vibrational techniques and a clear understanding of the particular fault detection methodology. This report presents a succinct review of the physical principles behind the modeling of vibrating structures such as cantilever beams (the natural model of a wing). It also reviews two different classes of fault detection techniques and proposes a particular detection method for cracks in wings, which is amenable to formal verification. A prototype implementation of these methods using Matlab scripts is also described and is related to the fundamental theoretical concepts.

Tejada, Arturo↗
NASA NTRS1987

Nondestructive evaluation of structural ceramics by photoacoustic microscopy

A photoacoustic microscopy (PAM) digital imaging system was developed and utilized to characterize silicon nitride material at the various stages of the ceramic fabrication process. Correlation studies revealed that photoacoustic microscopy detected failure initiating defects in substantially more specimens than microradiography and ultrasonic techniques. Photoacoustic microscopy detected 10 to 100 micron size surface and subsurface pores and inclusions, respectively, up to 80 microns below the interrogating surface in machined sintered silicon nitride. Microradiography detected 50 micron diameter fracture controlling pores and inclusions. Subsurface holes were detected up to a depth of 570 microns and 1.00 mm in sintered silicon nitride and silicon carbide, respectively. Seeded voids of 20 to 30 micron diameters at the surface and 50 microns below the interrogating surface were detected by photoacoustic microscopy and microradiography with 1 percent X-ray thickness sensitivity. Tight surface cracks of 96 micron length x 48 micron depth were detected by photoacoustic microscopy. PAM volatilized and removed material in the green state which resulted in linear shallow microcracks after sintering. This significantly limits the use of PAM as an in-process NDE technique.

Khandelwal, Pramod K.↗
NASA NTRS2006

A Resonant Synchronous Vibration Based Approach for Rotor Imbalance Detection

This paper presents a methodology of detecting rotor imbalances, such as mass imbalance and crack-induced imbalance, using shaft synchronous vibrations. An iterative scheme is developed to identify parameters from measured synchronous vibration data. A detection system is integrated by using state-of-the-art commercial analysis equipment. A laboratory rotor test rig is used to verify the system integration and algorithm validation. A real engine test has been carried out and the results are reported.

Luo, Huangeng↗
NASA NTRS2011

Damage Characterization in SiC/SiC Composites using Electrical Resistance

SiC/SiC ceramic matrix composites (CMCs) under creep-rupture loading accumulate damage by means of local matrix cracks that typically form near a stress concentration, such as a 90o fiber tow or large matrix pore, and grow over time. Such damage is difficult to detect through conventional techniques. Electrical resistance changes can be correlated with matrix cracking to provide a means of damage detection. Sylramic-iBN fiber-reinforced SiC composites with both melt infiltrated (MI) and chemical vapor infiltrated (CVI) matrix types are compared here. Results for both systems exhibit an increase in resistance prior to fracture, which can be detected either in situ or post-damage.

Smith, Craig E.↗
DOE OSTI2023

Machine Learning Enabled Sensor Fusion for In-Situ Defect Detection in Laser Powder Bed Fusion

Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. The current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques such as X-Ray Computed Tomography (XCT), which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. RTX Technologies Research Center (RTRC) has licensed ORNL’s Peregrine software package – a printer- and camera-agnostic data analytics tool designed specifically for detecting process anomalies using in situ data collected during powder bed printing. The goal of this project was to feed temporally rich, multi-modal sensor data, including visible light, integrated near infrared (NIR), and spatially mapped co-axial melt pool thermal emission data into Peregrine to enable detection of subsurface flaws. XCT data was used as ground truth training data to allow Peregrine’s deep learning algorithms to recognize anomalies in these complex data streams in both test artifacts and industrially relevant geometries. Completion of this program has seen the successful implementation of multi-modal, multi-layer sensor data footprints for training of machine learning models in Peregrine. Flaws detected in XCT data have been successfully detected directly from this in situ data footprint, and initial analyses of the in situ probability-of-detection has been conducted, showing performance levels commensurate with traditional non-destructive evaluation (NDE) methods. The in situ monitoring methodology was then applied to an industrially relevant component that was using post-build NDE, highlighting the utility of the proposed method for hard-to-inspect AM components. As a direct result of this program, two journal manuscripts [1], [2] have been published in Additive Manufacturing, with additional manuscripts planned following program completion.

36 MATERIALS SCIENCE↗
NASA NTRS2002

Evolutionary Design of a Robotic Material Defect Detection System

During the post-flight inspection of SSME engines, several inaccessible regions must be disassembled to inspect for defects such as cracks, scratches, gouges, etc. An improvement to the inspection process would be the design and development of very small robots capable of penetrating these inaccessible regions and detecting the defects. The goal of this research was to utilize an evolutionary design approach for the robotic detection of these types of defects. A simulation and visualization tool was developed prior to receiving the hardware as a development test bed. A small, commercial off-the-shelf (COTS) robot was selected from several candidates as the proof of concept robot. The basic approach to detect the defects was to utilize Cadmium Sulfide (CdS) sensors to detect changes in contrast of an illuminated surface. A neural network, optimally designed utilizing a genetic algorithm, was employed to detect the presence of the defects (cracks). By utilization of the COTS robot and US sensors, the research successfully demonstrated that an evolutionarily designed neural network can detect the presence of surface defects.

Ballard, Gary↗
NASA NTRS1979

Advanced quantitative magnetic nondestructive evaluation methods - Theory and experiment

The paper reviews the scale of fatigue crack phenomena in relation to the size detection capabilities of nondestructive evaluation methods. An assessment of several features of fatigue in relation to the inspection of ball and roller bearings suggested the use of magnetic methods; magnetic domain phenomena including the interaction of domains and inclusions, and the influence of stress and magnetic field on domains are discussed. Experimental results indicate that simplified calculations can be used to predict many features of these results; the data predicted by analytic models which use finite element computer analysis predictions do not agree with respect to certain features. Experimental analyses obtained on rod-type fatigue specimens which show experimental magnetic measurements in relation to the crack opening displacement and volume and crack depth should provide methods for improved crack characterization in relation to fracture mechanics and life prediction.

Barton, J. R.↗
NASA NTRS2007

Triboluminescent Materials for Smart Optical Damage Sensors for Space Applications

Triboluminescence is light that is produced by pressure, friction or mechanical shock. New composite materials are constantly being reengi neered in an effort to make lightweight spacecrafts for various NASA missions. For these materials there is interest in monitoring the con dition of the composite in real time to detect any delamination or cr acking due to damage, fatigue or external forces. Methods of periodic inspection of composite structures for mechanical damage such as ult rasonic testing are rather mature. However, there is a need to develop a new technique of damage detection for composites, which could dete ct cracking or delamination from any desired location within a materi al structure in real time. This could provide a valuable tool in the confident use of composite materials for various space applications. Recently, triboluminnescent materials have been proposed as smart sen sors of structural damage. To sense the damage, these materials can b e epoxy bonded or coated in a polymer matrix or embedded in a composi te host structure. When the damage or fracture takes place in the hos t structure, it will lead to the fracture of triboluminescent crystal s resulting in a light emission. This will warn, in real time, that a structural damage has occurred. The triboluminescent emission of the candidate phosphor has to be sufficiently bright, so that the light signal reaching from the point of fracture to the detector through a fiber optic cable is sufficiently strong to be detected. There are a large number of triboluminescent materials, but few satisfy the above criterion. Authors have synthesized a Eu based organic material know n as Europium tetrakis (dibenzoylmethide) triethylammonium .(EuD(sub 4)TEA), one of the bright triboluminescent materials, which is a pote ntial candidate for application as a damage sensor and could be made into a wireless sensor with the addition of microchip, antenna and el ectronics. Preliminary results on the synthesis and characterization of this material shall be presented.

Aggarwal, Mohan D.↗
NASA NTRS1993

An Application of a New Electromagnetic Sensor to Real-Time Monitoring of Fatigue Crack Growth in Thin Metal Plates

A major part of fracture mechanics is concerned with studying the initiation and propagation of fatigue cracks. This typically requires constant monitoring of crack growth during fatigue cycles which necessitates automation of the whole process. If the rate of crack growth can be determined the experimenter can vary externally controlled parameters such as load level, load cycle frequency and so on. Hence, knowledge of the precise location of the crack tip at any given time is very valuable. One technique currently available for measuring fatigue crack length is the DC potential drop method. The method, however, may be inaccurate if the direction of crack growth deviates considerably from what was assumed initially or the curvature of the crack becomes significant. Another approach is to digitize an optical image of the test specimen surface and then apply a pattern recognition technique to locate the crack tip, but this method is still under development. The present work is an initial study on applying eddy current-type probes to monitoring fatigue crack growth. The performance of two types of electromagnetic probes, a conventional eddy current probe and a newly developed self-nulling probe, was evaluated for the detection characteristics at and near the tips of fatigue cracks. The scan results show that the latter probe provides a very well defined local maximum in its output in the crack tip region suggesting the definite possibility of precisely locating the tip, while the former provides a somewhat ambiguous distribution of the sensor output in the same region. The paper is organized as follows: We start by reviewing the design and performance characteristics of the self-nulling probe and then describe the scan results which demonstrate the basic properties of the self-nulling probe. Next, we provide a brief description of the software developed for tracing a simulated crack and give a brief discussion of the main results of the test. The final section summarizes the major accomplishments of the present work and the elements of the future R&D needs.

Namkung, M.↗
DOE OSTI2025

Deep Learning Super-Resolution X-Ray Computed Tomography Algorithms for Additive Manufacturing

Industrial X-ray computed tomography (XCT) is a nondestructive method for inspection and characterization of additively manufactured (AM) materials and parts. In practice, the resolution of XCT can be limited by factors such as detector binning, restricted field of view for large-scale objects, system blur, motion during scanning, and acquisition settings. These limitations can reduce the detectability of critical flaws such as pores, cracks, and lack of fusion. Super-resolution (SR) techniques offer a promising solution for improving the effective resolution and image quality of XCT reconstructions without the need for expensive hardware upgrades or laborious, time-consuming scans. In particular, deep learning-based SR methods have garnered attention in recent years as powerful tools for reconstructing high-resolution volumes from low-resolution inputs. In this work, a novel deep learning-based SR method is proposed for XCT scans of AM parts, and compared against several existing state-of-the-art (SOTA) methods. The proposed method, Simurgh-SR, is built on the pre-existing Simurgh framework and consists of a 2.5D U-Net trained to map low-quality inputs containing noise and artifacts to high-quality reconstructions characterized by higher flaw contrast, better noise texture, and reduced artifacts. The experimental results demonstrate superior performance of Simurgh-SR in performing 4× SR on real industrial XCT scans of thick 316L components, enhancing the structural similarity score and peak signal-to-noise ratio (>7dB) compared to the LR counterpart while improving the F1-score for flaw detection by more than 2.3× when compared to alternative SOTA SR methods. This improvement enables more accurate and significantly faster characterization of metal AM components. Additionally, Simurgh-SR was trained for both 2X and 4X SR and performs effectively at both levels, enabling the use of a single model for various SR factors.

Rahman, Obaid [ORNL] (ORCID:0000000277810840)↗
NASA NTRS1972

Study of acoustic emission during mechanical tests of large flight weight tank structure

A polyphenylane oxide insulated, flight weight, subscale, aluminum tank was monitored for acoustic emissions during a proof test and during 100 cycles of environmental test simulating space flights. The use of a combination of frequency filtering and appropriate spatial filtering to reduce background noise was found to be sufficient to detect acoustic emission signals of relatively small intensity expected from subcritical crack growth in the structure. Several emission source locations were identified, including the one where a flaw was detected by post-test X-ray inspections. For most source locations, however, post-test inspections did not detect flaws; this was partially attributed to the higher sensitivity of the acoustic emission technique than any other currently available NDT method for detecting flaws.

Nakamura, Y.↗