Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “cluster characterization”

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.

At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Resolving local structural motifs across the phase evolution of zinc titanates with computational x-ray absorption spectroscopy

Resolving the local structure motifs that characterize phase evolution as a function of composition is a key challenge in structure characterization of complex materials. Here, in this study, we combine first-principles simulations and x-ray absorption near-edge structures (XANES) analysis to gain insights into the structure evolution revealed by measurements across a combinatorial zinc titanate thin film, which was grown with smoothly varying composition over a wide range of the Ti:Zn ratio. Specifically, we propose a cluster blind-signal-separation (cBSS) method for XANES spectral analysis based on a library of the structures and spectra of representative local motifs. In addition to motifs from zinc titanate crystals, two types of Ti-defect models constructed in this study are key to the understanding of the structure characteristics in the Zn-rich region. The cBSS method makes use of both spectral clustering of the simulated site-XANES spectra library and the BSS procedure to construct high-fidelity spectral basis functions from an experimental spectral sequence. The method provides a rigorous measure of the spectral sensitivity and basis completeness. The results of the XANES analysis are corroborated with other experimental modalities, including x-ray diffraction and spectroscopic ellipsometry, to validate the cBSS method. The calculated motif weights resulting from fitting the XANES spectra with the cBSS basis probe the atomic structure characteristics of both crystalline and amorphous phases as a function of the Ti/Zn composition. The insights of the local structure motif evolution are pivotal to the understanding of the nonmonotonic trend in the optical gap, which may lead to potential applications through tuning the optical properties of zinc titanate. The workflow of the XANES spectral analysis developed in this work can be generalized to construct the structure-property relationship in a broad material space.

36 MATERIALS SCIENCE↗
NASA NTRS1985

Evaluation of Thematic Mapper data for mapping forest, agricultural and soil resources

Color composite TM film products which include TM5, TM4, and a visible band (TM1, TM2, or TM3) are superior to composites which exclude TM4 for discriminating most forest and agricultural cover types and estimating area proportions for inventory and sampling purposes. Clustering a subset of TM data results in a spectral class map which groups diverse forest cover types into spectrally and ecologically similar areas suitable for use as a stratification base in traditional forest inventory practices. Analysis of simulated Thematic Mapper data indicate that the location and number of TM spectral bands are suitable for detecting differences in major soil properties and characterizing soil spectral curve form and magnitude.

Degloria, S.↗
NASA NTRS2024

Growth Rate of Deep Convective System Cloud Shields: Satellite Observations and Km-Scale Radiative Convective Equilibrium Simulations

Deep convection gives rise to large upper level clouds that strongly interact with radiation and are important to the climate energy budget. From an object-oriented perspective, these individual deep cloud systems are characterized by a well depicted cloud shield life cycle, starting with small cloud extents that grow at varying rates before decaying and vanishing. A simple formulation of the growth rate of the cloud shield has been proposed that links together the growth rate on the convective part of the cloud, the mass flux of both the convective and stratiform parts of the cluster and a simple removal sink term (Elsaesser et al., 2022). In this presentation we first show using a suite of satellite observations (infrared from geostationary satellites, GPM radar, etc.) that the functional form of the proposed equation is well suited to quantify the shield growth rate. We then focus on RCE simulations, with deep cloud system objects post processed, to explore the relative role of each term of the growth rate budget. Three different models are used in the same RCEMIP-like configurations. The results show that the budget equation works very well for each model, although the time constants require model-dependent adjustments. We will further show in Vienna the commonalities and the specificities of each model.

Deep convection↗
DOE OSTI2023

Exploratory analysis of machine learning techniques in the Nevada geothermal play fairway analysis

Play fairway analysis (PFA) is commonly used to generate geothermal potential maps and guide exploration studies, with a particular focus on locating and characterizing blind geothermal systems. This study evaluates the application of machine learning techniques to PFA in the Great Basin region of Nevada. Following the evaluation of various techniques, we identified two approaches to PFA that produced promising results, 1) supervised Bayesian probabilistic neural networks to generate geothermal potential maps with confidence intervals, and 2) unsupervised principal component analysis paired with k-means clustering to generate both cluster maps to help identify spatial patterns, as well as new combined feature inputs. We applied these techniques to perform a comparative analysis between two principal sets of geological and geophysical features related to permeability and heat and a set of positive (known geothermal resources) and negative training sites (known drill sites with unsuitable geothermal conditions). We found that these methods constrain previously unrecognized feature controls on geothermal favorability, many of which are spatially organized within the extent of cluster groups and the major structural-hydrologic domains of the study area. Furthermore, we utilized exploratory unsupervised modeling to highlight spatial relationships between input data and predictive output results of our supervised modeling. As a result, we demonstrate how our models compare to the previous Nevada PFA and how the rapid insights these machine learning techniques offer may support future assessments of both known and undiscovered blind geothermal systems in the Great Basin region of Nevada and beyond.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Development of Multimodal Few-Shot Analytics for Electron Micrographs

Recent advances in materials data analytics have provided new avenues for determining process-structure-property (PSP) linkages in a variety of materials. Machine learning techniques including few-shot learning have increased the efficiency of classifying microscopy images for the purposes of material characterization. Attempts at creating a multimodal approach can provide further improvements to current models and help extract more salient features from data. In this vein, raw spectrum data was taken to provide an additional modality to our current pyCHIP classifier. Modifications in segmentation also show potential in improving the accuracy of the pyCHIP classifier. Classifier output was analyzed using network graphs and unsupervised clustering algorithms such as spectral clustering to detect better segmentation methods than the current “chipping” approach. We suggest that the chip selection process can be automated in the future using a combination of these techniques to enable high-throughput analyses.

36 MATERIALS SCIENCE↗
NASA NTRS2014

Global Inventory and Characterization of Pyroclastic Deposits on Mercury: New Insights into Pyroclastic Activity from MESSENGER Orbital Data

We present new observations of pyroclastic deposits on the surface of Mercury from data acquired during the orbital phase of the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) mission. The global analysis of pyroclastic deposits brings the total number of such identified features from 40 to 51. Some 90% of pyroclastic deposits are found within impact craters. The locations of most pyroclastic deposits appear to be unrelated to regional smooth plains deposits, except some deposits cluster around the margins of smooth plains, similar to the relation between many lunar pyroclastic deposits and lunar maria. A survey of the degradation state of the impact craters that host pyroclastic deposits suggests that pyroclastic activity occurred on Mercury over a prolonged interval. Measurements of surface reflectance by MESSENGER indicate that the pyroclastic deposits are spectrally distinct from their surrounding terrain, with higher reflectance values, redder (i.e., steeper) spectral slopes, and a downturn at wavelengths shorter than approximately 400nm (i.e., in the near-ultraviolet region of the spectrum). Three possible causes for these distinctive characteristics include differences in transition metal content, physical properties (e.g., grain size), or degree of space weathering from average surface material on Mercury. The strength of the near-ultraviolet downturn varies among spectra of pyroclastic deposits and is correlated with reflectance at visible wavelengths. We suggest that this interdeposit variability in reflectance spectra is the result of either variable amounts of mixing of the pyroclastic deposits with underlying material or inherent differences in chemical and physical properties among pyroclastic deposits.

Stratigraphic relationships↗
DOE OSTI2023

Light-activated microtubule-based two-dimensional active nematic

We assess the ability of two light responsive kinesin motor clusters to drive dynamics of microtubule-based active nematics: opto-K401, a processive motor, and opto-K365, a non-processive motor. Measurements reveal an order of magnitude improvement in the contrast of nematic flow speeds between maximally- and minimally-illuminated states for opto-K365 motors when compared to opto-K401 construct. For opto-K365 nematics, we characterize both the steady-state flow and defect density as a function of applied light. We also examine the transient behavior as the system switches between steady-states upon changes in light intensities. Although nematic flows reach a steady state within tens of seconds, the defect density exhibits transient behavior for up to 10 minutes, showing a separation between small-scale active flows and system-scale structural states. Furthermore, our work establishes an experimental platform that can exploit spatiotemporally-heterogeneous patterns of activity to generate targeted dynamical states.

42 ENGINEERING↗
DOE OSTI2024

K–Co–Mo–S x chalcogel: high-capacity removal of Pb 2+ and Ag + and the underlying mechanisms

Chalcogenide-based aerogels, known as chalcogels, represent a novel class of nanoparticle-based porous amorphous materials characterized by high surface polarizability and Lewis base properties, exhibiting promising applications in clean energy and separation science. This work presents a K–Co–Mo–S x (KCMS) chalcogel as a highly efficient sorbent for heavy metal ions and details its sorption mechanisms. Its incoherent structure comprises Mo 2 V (S 2 ) 6 and Mo 3 IV S(S 6 ) 2 anion-like clusters with four- and six-coordinated Co–S polyhedra, forming a Co–Mo–S covalent network that hosts K + ions through electrostatic attraction. The interactions of KCMS with heavy metal ions, particularly Pb 2+ and Ag + , reveal that KCMS is exceptionally effective in removing these ions from ppm concentrations down to trace levels (≤5 ppb). KCMS rapidly removes Ag + (≈81.7%) and Pb 2+ (≈99.5%) within five minutes, achieving >99.9% removal within an hour, with a distribution constant K d ≥10 8 mL g -1 . KCMS exhibits an impressive removal capacity of 1378 mg g -1 for Ag + and 1146 mg g -1 for Pb 2+ , establishing it as one of the most effective materials known to date for heavy metal removal. This material is also effective for the removal of Ag + and Pb 2+ along with Hg 2+ , Ni 2+ , Cu 2+ , and Cd 2+ from various water sources even in the presence of highly concentrated and chemically diverse cations, anions, and organic species. Analysis of the post-interacted KCMS by synchrotron X-ray pair distribution function (PDF), X-ray photoelectron spectroscopy (XPS) and energy dispersive X-ray spectroscopy (EDS) revealed that the sorption of Pb 2+ , Ag + , and Hg 2+ mainly occurs by the exchange of K + and Co 2+ . Despite being amorphous, this material exhibits unprecedented ion-exchange mechanisms both for the ionically and covalently bound K + and Co 2+ , respectively. In conclusion, this discovery advances our knowledge of amorphous gels and guides material synthesis principles for the highly selective and efficient removal of heavy metal ions from water.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Measurement of substructure-dependent jet suppression in Pb + Pb collisions at 5.02 TeV with the ATLAS detector

The ATLAS detector at the Large Hadron Collider has been used to measure jet substructure modification and suppression in Pb + Pb collisions at a nucleon–nucleon center-of-mass energy $\sqrt{{^S}_{NN}}$ = 5.02 TeV in comparison with proton–proton (pp) collisions at $\sqrt{{^S}}$ = 5.02 TeV. The Pb + Pb data, collected in 2018, have an integrated luminosity of 1.72 nb -1 , while the pp data, collected in 2017, have an integrated luminosity of 260 pb -1 . Jets used in this analysis are clustered using the anti- k t algorithm with a radius parameter R = 0.4. The jet constituents, defined by both tracking and calorimeter information, are used to determine the angular scale r g of the first hard splitting inside the jet by reclustering them using the Cambridge–Aachen algorithm and employing the soft-drop grooming technique. The nuclear modification factor, R AA , used to characterize jet suppression in Pb + Pb collisions, is presented differentially in r g , jet transverse momentum, and in intervals of collision centrality. The R AA value is observed to depend significantly on jet r g . Jets produced with the largest measured r g are found to be twice as suppressed as those with the smallest r g in central Pb + Pb collisions. The R AA values do not exhibit a strong variation with jet p T in any of the r g intervals. The r g and p T dependence of jet R AA is qualitatively consistent with a picture of jet quenching arising from coherence and provides the most direct evidence in support of this approach.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Effect of Remnant Carbon and Etching of Particles on Pyrolysis Bonded Silicon Carbide (PBSC)

Silicon carbide (SiC) formed through pyrolysis of preceramic polymers loaded with SiC particles has gained significant attention for applications such as coatings, composite matrix modifications, and most importantly additive manufacturing. This work presents combined synchrotron XRD, Raman spectroscopy, scanning electron microscopy, nano-indentation, and Vickers indentation of pyrolysis bonded SiC to shed light on the changes of composition and mechanical properties of these materials. Characterization was performed on samples that were heat treated ranging from the synthesis 850 °C up to 1500 °C. Pre-treatments of the powders prior to pellet synthesis, such as heat treatment and etching using a hydrofluoric acid (HF), were investigated. It is shown that the degradation of mechanical properties when exposed to higher temperatures is due to the burnout of amorphous carbon clusters remnant of the pyrolysis process of the preceramic polymer. Furthermore, prior HF etching and removal of the native oxide layer of the powders showed improved density and hardness values in the final pellets. The average Vickers hardness of the control samples were 4.59 GPa and later 3.74 GPa when exposed to 1500 °C, while the samples synthesized using powders that were etched with HF had an average hardness value of 9.37 GPa and later 6.86 GPa when exposed to 1500 °C.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1982

Particle physics meets cosmology - The search for decaying neutrinos

The fundamental physical implications of the possible detection of massive neutrinos are discussed, with an emphasis on the Grand Unified Theories (GUTs) of matter. The Newtonian and general-relativistic pictures of the fundamental forces are compared, and the reduction of electromagnetic and weak forces to one force in the GUTs is explained. The cosmological consequences of the curved-spacetime gravitation concept are considered. Quarks, leptons, and neutrinos are characterized in a general treatment of elementary quantum mechanics. The universe is described in terms of quantized fields, the noninteractive 'particle' fields and the force fields, and cosmology becomes the study of the interaction of gravitation with the other fields, of the 'freezing out' of successive fields with the expansion and cooling of the universe. While the visible universe is the result of the clustering of the quark and electron fields, the distribution of the large number of quanta in neutrino field, like the mass of the neutrino, are unknown. Cosmological models which attribute anomalies in the observed motions of galaxies and stars to clusters or shells of massive neutrinos are shown to be consistent with a small but nonzero neutrino mass and a universe near the open/closed transition point, but direct detection of the presence of massive neutrinos by the UV emission of their decay is required to verify these hypotheses.

Henry, R. C.↗
DOE OSTI2020

Ensembles of Metastable States Govern Heterogeneous Catalysis on Dynamic Interfaces

Heterogeneous catalysis is at the heart of chemical industry. Being able to tune and design efficient catalysts for processes of interest is of an utmost importance, and for this, the molecular-level understanding of heterogeneous catalysts is the first step, and indeed a prime focus of the modern catalysis research. For a long time, a single most thermodynamically stable structure of the catalytic interface attained in reaction conditions had been envisioned as the reactive phase. However, some catalytic interfaces continue to undergo structural dynamics in the steady state, triggered by high temperatures, pressures, and binding and changing reagents. Among particularly dynamic interfaces are such widely-used catalysts as crystalline and amorphous surfaced supporting (sub-)nano metallic clusters. Recently, it became clear that this dynamic fluxionality causes the supported clusters to populate many distinct structural and stoichiometric states in catalytic conditions. Hence, the catalytic interface should be viewed as an evolving statistical ensemble of many (not one) structures. As such, every member in the ensemble contributes to the properties of the catalyst differently, and in proportion to its probability to be populated. This new notion flips the established paradigm and calls for new theory, modeling approaches, operando measurements, and updated design strategies. The statistical ensemble nature of surface-supported sub-nano cluster catalysts can be exemplified by oxide-supported and adsorbate-covered Pt, Pd, Cu, CuPd clusters, catalytic toward oxidative and non-oxidative dehydrogenation. They have access to a variety of 3D and quasi-2D shapes. The compositions of their thermal ensembles are dependent on the cluster size, leading to size-specific catalytic activities and the famous “every atom counts” phenomenon. The support and adsorbates affect catalyst structures, and state of the reacting species causes the ensemble to change in every reaction intermediate. The most stable member of the ensemble dominates the thermodynamic properties of the corresponding intermediate, whereas the kinetics can be determined by more active but less populated metastable catalyst states, and that suggests that many earlier studies might have overlooked the actual active sites. Both effects depend on the relative time-scales of catalyst restructuring and reaction dynamics. The catalyst may routinely operate off-equilibrium. Ensemble phenomena lead to surprising exceptions from established rules of catalysis, such as scaling relations, and Arrhenius behavior. Catalyst deactivation is also an ensemble property, and its extent of mitigation can be predicted through the new paradigm. These findings were enabled by advances in theory, such as global optimization and subsequent utilization of multiple local minima, and pathways sampling, as well as operando catalyst characterization. The fact that the per-site and per-species resolution is needed for the description and predicting of catalyst properties gives theory the central role in catalysis research, as most experiments provide ensemble-average information and cannot detect the crucial minority species that may be responsible for the catalytic activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

DeFault: DEep‐Learning‐Based FAULT Delineation Using the IBDP Passive Seismic Data at the Decatur CO2 Storage Site

Abstract The carbon capture, utilization, and storage (CCUS) framework is an essential component in reducing greenhouse gas emissions, with its success hinging on the comprehensive knowledge of subsurface geology and geomechanics. Passive seismic event relocation and fault detection offer vital insights into subsurface structures and the ability to monitor fluid migration pathways. Accurate identification and localization of seismic events, however, face significant challenges, including the necessity for high‐quality seismic data and advanced computational methods. To address these challenges, we introduce a novel deep learning method, , specifically designed for passive seismic source relocation and fault delineating for passive seismic monitoring projects. By leveraging data domain‐adaptation, allows us to train a neural network with labeled synthetic data and apply it directly to field data. Using , the passive seismic sources are automatically clustered based on their recording time and spatial locations, and subsequently, faults and fractures are delineated accordingly. We demonstrate the efficacy of on a field case study involving injection related microseismic data from Decatur, Illinois area. Our approach accurately and efficiently relocated passive seismic events, identified faults and could aid in potential damage induced by seismicity. Our results highlight the potential of as a valuable tool for passive seismic monitoring, emphasizing its role in ensuring CCUS project safety. This research bolsters the understanding of subsurface characterization in CCUS, illustrating machine learning’s capacity to refine these methods. Ultimately, our work has significant implications for CCUS technology deployment, an essential strategy in combating climate change. Plain Language Summary In our quest to tackle climate change, we use a strategy known as carbon capture, utilization, and storage (CCUS) to keep greenhouse gases out of the atmosphere. This strategy relies heavily on our ability to understand what's happening deep under the earth's surface. To make sure we store super critical safely, we need to accurately map out the geological structure, especially faults, but this is tough without high‐quality data and complex computer programs. We've developed a new tool called “DeFault,” which uses advanced machine learning to improve how we find and map these underground features. “DeFault” is smart enough to learn from numerically simulated data and then apply what it’s learned to real‐world situations. It groups together seismic activity—tiny tremors and shifts in the earth—based on when and where they happen, which helps us spot where there might be cracks or faults. We tested “DeFault” in Illinois, where CO 2 is injected underground, and it successfully pinpointed where these tremors occurred and mapped out the faults, helping to prevent accidents accurately in the future. Our study shows that “DeFault” will be a powerful ally in making CCUS safer and more effective, especially for the Illinois Basin Decatur Project. Key Points Faults and fractures introduced by carbon storage can be monitored by passive seismicity DeFault algorithm enables an automatic process for accurate and efficient passive seismic event locating and clustering

58 GEOSCIENCES↗
DOE OSTI2025

Quantifying and visualizing the microscopic degrees of freedom of grain boundaries in the Wigner–Seitz cell of the displacement-shift-complete lattice

Here we introduce a grain boundary (GB) translation vector, t ws , to describe and quantify the domain of the microscopic degrees of freedom of GBs. It has long been recognized that for fixed macroscopic degrees of freedom of a GB there exists a large multiplicity of states characterized by different relative grain translations. More recently another degree of freedom, [n], the number of GB atoms, has emerged and is now recognized as an equally important component of GB structural multiplicity. In this work, we show that all GB microstates can be uniquely characterized by their value of t ws , which is located within the Wigner–Seitz (WS) cell of the Displacement-Shift-Complete lattice (DSCL) of the GB. The GB translation vector captures information about both the translation state and the number of GB atoms. We show that the density of GB microstates inside the WS cell of the DSCL is not uniform and can form clusters that correspond to different GB phases. Further, the vectors connecting the centers of the clusters correspond to the Burgers vectors of GB phase junctions, which can be predicted without building the junctions. Using t ws , we quantify GB excess shear and argue that it is defined up to a DSCL vector, which has implications for thermodynamic equilibrium conditions. Additionally, this work generalizes the definition of the number of GB atoms [n] to asymmetric boundaries.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Oxygen Defect Configurations in Single-Phase UO 2.15

Hyperstoichiometric UO 2.15 was characterized by neutron total scattering at high temperature in the single-phase UO 2+x region of the U/O phase diagram. The diffraction data confirmed a single-phase fluorite structure at high temperature. Analysis of the short-range data showed that the same structural model does not fit the pair distribution functions well. Instead, structural models containing specific configurations of oxygen defect clusters best represent the local atomic arrangement. In conclusion, prevalent defect clusters previously proposed were fit to the experimental data, and moderately distorted oxygen cuboctahedra hypothesized by recent molecular dynamics simulations fit the data most accurately.

neutron pair-distribution function analysis↗
DOE OSTI2020

Epitaxial and Strong Support Interactions between Pt and LaFeO 3 Films Stabilize Pt Dispersion

The ability to stabilize very small Pt crystallites in supported-metal catalysts following harsh treatments is an important industrial problem. Herein, we demonstrate that Pt particles can be maintained in the 1- to 2-nm range following multiple oxidation and reduction cycles at 1073 K when the particles are supported on 0.5-nm LaFeO 3 films that have been deposited onto MgAl 2 O 4 using atomic layer deposition (ALD). Characterization by Scanning Transmission Electron Microscopy (STEM) suggest that, when the catalyst is oxidized at 1073 K, the Pt crystallites are oriented with respect to the underlying LaFeO 3 . X-Ray Absorption Spectroscopy (XAS) also shows evidence for changes in the Pt environment. CO-oxidation rates for the reduced catalyst remain unchanged after five redox cycles at 1073 K. Epitaxial growth of Pt clusters and the consequent strong metal-support interaction between Pt and LaFeO 3 are indicated to be prime reasons for the enhanced catalytic performances.

36 MATERIALS SCIENCE↗
DOE OSTI2023

VPS26 Moonlights as an Arrestin-like Adapter for a 7-transmembrane RGS 2 protein in Arabidopsis thaliana

Extracellular signals perceived by 7-transmembrane (7TM)-spanning receptors on the plasma membrane utilize a cytoplasmic adaptor that propagates signaling and initiates a feedback circuit for desensitization by removal of these receptors at the plasma membrane leading to desensitization to that signal. Signal (agonist) binding often evokes phosphorylation at the C-terminal tail of many 7TM G-protein-coupled receptors in animal cells which then recruits a cytoplasmic intermediate adaptor, β-arrestin, that sets in motion clathrin-mediated endocytosis (CME). Some 7TM receptors have agonist-induced phosphorylation and CME that do not involve β-arrestin, therefore it is unclear how these phosphorylated 7TM receptors are internalized. Arrestins, neither α– or β-types, are not encoded in the Arabidopsis genome, yet Arabidopsis cells have a well characterized signal-induced CME of a 7TM protein, Regulator of G Signaling 1 (AtRGS1). We show that a component of the retromer complex, VPS26, moonlights as an arrestin-like adapter. VPS26A and VPS26B form heterodimer instead of homodimer or monomer. flg22, a bacterium-derived signal that alerts plant cells of the presence of a potential pathogen, induces phosphorylation of AtRGS1 within a cluster of serines in its C-terminal tail, dimerization, and its CME. The phosphorylated C-terminal tail peptide binds to VPS26A/B heterodimer with greater affinity than the unphosphorylated peptide. We propose that VPS26 serves as an arrestin-like adaptor in the CME of AtRGS1 dimerization biased for VPS26 signaling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2002

Ultrasonic Characterization of Fatigue Cracks in Composite Materials

Microcracking in composite structures due to combined fatigue and cryogenic loading can cause leakage and failure of the structure and can be difficult to detect in-service. In aerospace systems, these leaks may lead to loss of pressure/propellant, increased risk of explosion and possible cryo-pumping. The success of nondestructive evaluation to detect intra-ply microcracking in unlined pressure vessels fabricated from composite materials is critical to the use of composite structures in future space systems. The work presented herein characterizes measurements of intraply fatigue cracking through the thickness of laminated composite material by means of correlation with ultrasonic resonance. Resonant ultrasound spectroscopy provides measurements which are sensitive to both the microscopic and macroscopic properties of the test article. Elastic moduli, acoustic attenuation, and geometry can all be probed. The approach is based on the premise of half-wavelength resonance. The method injects a broadband ultrasonic wave into the test structure using a swept frequency technique. This method provides dramatically increased energy input into the test article, as compared to conventional pulsed ultrasonics. This relative energy increase improves the ability to measure finer details in the materials characterization, such as microcracking and porosity. As the microcrack density increases, more interactions occur with the higher frequency (small wavelength) components of the signal train causing the spectrum to shift toward lower frequencies. Several methods are under investigation to correlate the degree of microcracking from resonance ultrasound measurements on composite test articles including self organizing neural networks, chemometric techniques used in optical spectroscopy and other clustering algorithms.

Workman, Gary L.↗