SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
In this paper we use a photothermal imaging technique to characterize the damage caused to an imperfectly coated gold-coated Kapton sample exposed to successively increased fluences of atomic oxygen in a laboratory atomic source.
Silicon carbide / silicon carbide ceramic matrix composites (SiC/SiC CMCs) are structural ceramics that are well-suited for the extreme environment conditions of aerospace applications largely due to their low weight, creep resistance, damage tolerance, and high specific strength. In CMCs, the initiation and evolution of damage is influenced by a number of factors including characteristics of the constituent landscape (porosity, interfacial properties, and geometric distribution of CMC constituents), thermo-mechanical loading parameters, and environmental conditions. However, much is still unknown about these factors and the interactions between them. In order to accurately predict the lifetimes of these advanced composites, it is critical to understand the evolution of damage and to characterize which early damage mechanisms (and their relationship to key microstructural features) subsequently lead to crack coalescence and macroscopic failure. This research focuses on the use of a multi-modal, experimental approach to examine the relative activity of surface and subsurface damage mechanisms in CMCs. The impact of fabrication choices on the evolution of damage to final failure, and the influence of constituent architecture on early damage mechanisms, are investigated. This approach combines acoustic emission (AE) with microscale deformation tracking via digital image correlation inside a scanning electron microscope (SEM-DIC). The potential application of this combined approach towards characterization of damage in SiC/SiC CMCs under more complex testing conditions is explored.
Detection of damage due to foreign object impact is an important factor in the development of new aerospace vehicles. Acoustic waves generated on impact can be detected using a set of piezoelectric transducers, and the location of impact can be determined by triangulation based on the differences in the arrival time of the waves at each of the sensors. These sensors generate electrical signals in response to mechanical motion resulting from the impact as well as from natural vibrations. Due to electrical noise and mechanical vibration, accurately determining these time differentials can be challenging, and even small measurement inaccuracies can lead to significant errors in the computed damage location. Wavelet transforms are used to analyze the signals at multiple levels of detail, allowing the signals resulting from the impact to be isolated from ambient electromechanical noise. Data extracted from these transformed signals are input to an artificial neural network to aid in identifying the moment of impact from the transformed signals. By distinguishing which of the signal components are resultant from the impact and which are characteristic of noise and normal aerodynamic loads, the time differentials as well as the location of damage can be accurately assessed. The combination of wavelet transformations and neural network processing results in an efficient and accurate approach for passive in-flight detection of foreign object damage.
Standard structural health monitoring techniques face well-known difficulties for comprehensive defect diagnosis in real-world structures that have structural, material, or geometric complexity. This motivates the exploration of machine-learning-based structural health monitoring methods in complex structures. However, creating sufficient training data sets with various defects is an ongoing challenge for data-driven machine (deep) learning algorithms. The ability to transfer the knowledge of a trained neural network from one component to another or to other sections of the same component would drastically reduce the required training data set. Also, it would facilitate computationally inexpensive machine learning based inspection systems. In this work, a machine-learning-based multi-level damage characterization is demonstrated with the ability to transfer trained knowledge within the sparse sensor network. A novel network spatial assistance and an adaptive convolution technique are proposed for efficient knowledge transfer within the deep learning algorithm. Proposed structural health monitoring method is experimentally evaluated on an aluminum plate with artificially induced defects. It was observed that the method improves the performance of knowledge transferred damage characterization by 50% during localization and 24% during severity assessment. Further, experiments using time windows with and without multiple edge reflections are studied. Results reveal that multiply scattered waves contain rich and deterministic defect signatures that can be mined using deep learning neural networks, improving the accuracy of both identification and quantification. In the case of a fixed sensor network, using multiply scattered waves shows 100% prediction accuracy at all levels of damage characterization.
This article describes collaborative research between Shell International Exploration and Production (IE&P) scientists and ISAG personnel to investigate the feasibility of ultrasonic-based characterization of spacecraft tile damage for in-space inspection applications. The approach was proposed by Shell personnel in a Shell-NASA "speed-matching" session in early 2011 after ISAG personnel described challenges inherent in the inspection of MMOD damage deep within spacecraft thermal protection system (TPS) tiles. The approach leveraged Shell's relevant sensor and analytical expertise. The research addressed the difficulties associated with producing 3D models of MMOD damage cavities under the surface of a TPS tile, given that simple image-based sensing is constrained by line of sight through entry holes that have diameters considerably smaller than the underlying damage cavities. Damage cavity characterization is needed as part of a vehicle inspection and risk reduction capability for long-duration, human-flown space missions. It was hoped that cavity characterization could be accomplished through the use of ultrasonic techniques that allow for signal penetration through solid material.
Here, we report the application of high-speed synchrotron X-ray phase contrast imaging (PCI) in real-time damage characterization for glass fiber reinforced composites (GFRCs) subjected to dynamic loading. Dynamic single-edge notched bending (DSENB) experiments on pre-notched S-2 GFRCs were performed on a modified Kolsky compression bar. During loading, the synchrotron X-ray beam penetrated through the composite specimen from the side to detect damage evolution inside the material. Entire dynamic events were recorded by a high-speed camera as image sequences.0° and 90° unidirectional and cross-ply composites were investigated. An optical imaging technique was also employed to capture similar dynamic events in comparison with the radiographic imaging. It is demonstrated that high-speed X-ray PCI had sufficient phase contrast to characterize a crack initiation at a 20-μm spatial resolution within 920 ns and track the crack geometry during propagation, thereby providing reliable data to quantify the dynamic damage resistance of GFRCs. Furthermore, being capable of recognizing microscopic damage-related features at a sub-10-μm resolution, high-speed X-ray PCI provided fundamental material failure mechanisms to reveal the essential of macroscale structural failure of composites. It can also track the damage evolution inside and between individual plies of laminated composites. However, current high-speed X-ray PCI technique only supports in-situ observation and the high timing and spatial resolutions are limited within a field of view of ~2.5 mm in square, preventing its application in the three-dimensional and larger-area damage detection for GFRC structures.
This technical report is in submission of completion of the Level 2 milestone number M2AT 23OR1101022, titled “Be 2 C synthesis, properties, and ion-beam irradiation damage characterization,” for the AT-23OR110102 “Beryllium Carbide as moderator for MSRs—ORNL” WBS number 2.04.11.01 within the larger Advanced Reactor Technologies program at Oak Ridge National Laboratory. This effort is an initial study of the viability of Be 2 C as a future neutron moderator for molten salt reactors and other high-temperature reactors. The research was awarded funding in September 2022, and this Level 2 milestone presents the work that completed by the end of July 2023. This work is a collaboration between Oak Ridge National Laboratory and the University of Michigan. This report discusses the status of this effort and planned work before the final reporting.
Displacement damage effects from protons and neutrons are compared. Differences in the annealing properties were observed which are due to the different microscopic structure of the damaged regions from the two ty pes of particles.
Damage diagnosis in critical components is essential for ensuring the safety and reliability of operations across industries, spanning manufacturing, aerospace, and energy. Traditional acoustic nondestructive testing methods primarily focus on detecting defects through the direct scattering of single-mode incident waves from the damage, which limit their applicability to simple structures and small inspection areas. Our earlier research demonstrated that machine learning algorithms combined with sparse sensor networks can identify critical defect signatures even from multiply scattered, multi-mode acoustic signals, indicating the potential for improved defect inspection in complex, real-world structures. In this work, we demonstrate the successful implementation of this approach in a fixed sensor configuration to rapidly and accurately detect simulated defects in a geometrically complex, real-world structure, a brake rotor hub. Three different types of defects were physically simulated on the surface of the hub, and the collected data were used to train an autoencoder-based deep learning model. Two models were tested, one using single measurements and the other using multiple measurements taking advantage of the spatial distribution of the sensor network. After training, the multi-measurement model achieved 100 % accuracy in identifying, classifying, and locating unseen, unique damages. This work illustrates the potential of the proposed method for a wide range of industrial applications.
Explore the source record for details and available documents.
This work presents a computationally-ecient approach for damage determination that quanti es uncertainty in the provided diagnosis. Given strain sensor data that are polluted with measurement errors, Bayesian inference is used to estimate the location, size, and orientation of damage. This approach uses Bayes' Theorem to combine any prior knowledge an analyst may have about the nature of the damage with information provided implicitly by the strain sensor data to form a posterior probability distribution over possible damage states. The unknown damage parameters are then estimated based on samples drawn numerically from this distribution using a Markov Chain Monte Carlo (MCMC) sampling algorithm. Several modi cations are made to the traditional Bayesian inference approach to provide signi cant computational speedup. First, an ecient surrogate model is constructed using sparse grid interpolation to replace a costly nite element model that must otherwise be evaluated for each sample drawn with MCMC. Next, the standard Bayesian posterior distribution is modi ed using a weighted likelihood formulation, which is shown to improve the convergence of the sampling process. Finally, a robust MCMC algorithm, Delayed Rejection Adaptive Metropolis (DRAM), is adopted to sample the probability distribution more eciently. Numerical examples demonstrate that the proposed framework e ectively provides damage estimates with uncertainty quanti cation and can yield orders of magnitude speedup over standard Bayesian approaches.
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.
Due to their high temperature capabilities, Ceramic Matrix Composite (CMC) components are being developed for use in hot-section aerospace engine applications. Harsh engine environments have led to the development of Environmental Barrier Coatings (EBCs) for silicon-based CMCs to further increase thermal and environmental capabilities. This study aims at understanding the damage mechanisms associated with these materials under simulated operating conditions. A high heat-flux laser testing rig capable of imposing large through-thickness thermal gradients by means of controlled laser beam heating and back-side air cooling is used. Tests are performed on uncoated composites, as well as CMC substrates that have been coated with state-of-the-art ceramic EBC systems. Results show that the use of the EBCs may help increase temperature capability and creep resistance by reducing the effects of stressed oxidation and environmental degradation. Also, the ability of electrical resistance (ER) and acoustic emission (AE) measurements to monitor material condition and damage state during high temperature testing is shown; suggesting their usefulness as a valuable health monitoring technique. Micromechanics models are used to describe the localized stress state of the composite system, which is utilized along with ER modeling concepts to develop an electromechanical model capable of characterizing material behavior.
The development of validated multidisciplinary Integrated Vehicle Health Management (IVHM) tools, technologies, and techniques to enable detection, diagnosis, prognosis, and mitigation in the presence of adverse conditions during flight will provide effective solutions to deal with safety related challenges facing next generation aircraft. The adverse conditions include loss of control caused by environmental factors, actuator and sensor faults or failures, and damage conditions. A major concern in these structures is the growth of undetected damage/cracks due to fatigue and low velocity foreign impact that can reach a critical size during flight, resulting in loss of control of the aircraft. Hence, development of efficient methodologies to determine the presence, location, and severity of damage/cracks in critical structural components is highly important in developing efficient structural health management systems.
The development of validated multidisciplinary Integrated Vehicle Health Management (IVHM) tools, technologies, and techniques to enable detection, diagnosis, prognosis, and mitigation in the presence of adverse conditions during flight will provide effective solutions to deal with safety related challenges facing next generation aircraft. The adverse conditions include loss of control caused by environmental factors, actuator and sensor faults or failures, and damage conditions. A major concern in these structures is the growth of undetected damage (cracks) due to fatigue and low velocity foreign impacts that can reach a critical size during flight, resulting in loss of control of the aircraft. Hence, development of efficient methodologies to determine the presence, location, and severity of damage in critical structural components is highly important in developing efficient structural health management systems.
Not Available
Not provided.