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A Potts Model parameter study of particle size, Monte Carlo temperature, and “Particle-Assisted Abnormal Grain Growth”
A Potts Model was proposed to account for temperature and particle heterogeneity-dependent Zener Pinning. This was accomplished by relating the random fluctuations of grain boundary position allowed by the Potts Model switching probability at finite simulation temperature to experimentally observed grain growth stagnation behavior. We assume that these fluctuations arise from random fluctuations in the thermodynamic and kinetic properties of the grain boundaries and/or interfaces as they change with temperature. As an application of this model, the grain growth kinetics of U-10 wt. % Mo nuclear fuels were simulated with the input of microstructural images during different heat treatment processes. Simulated average grain growth behavior is in good agreement with experiments.
3D analysis of abnormal grain growth in calcia doped alumina in the presence of large pores
Abnormal grain growth has been previously found to increase with the inclusion of large pores (∼60 µm in diameter) in 80 ppm calcium doped alumina. However, the two-dimensional analysis could not accurately describe the proximity of the abnormal grains and pores to establish a correlation. This study uses laboratory-based diffraction contrast tomography and x-ray computed tomography to spatially map the pores and abnormal grains in 3D. A preference towards the formation of abnormal grains near large pores is found when compared to all potential sites. This finding supports that the pores may be a nucleation site for complexion transitions that promote abnormal grain growth. The growth of the abnormal grains in a subsequent heat treatment at 1600°C for 16 hrs was also studied. The elongation along the principal axes is similar for both small and large grains, again supporting that abnormal grain growth was facilitated by a complexion transition.
Development and Validation of a Scoring System for Abnormalities in the Gopher Frog (Rana capito)
Headstarting efforts are thought to be critical in supplementing populations of the at-risk Gopher Frog (Rana capito); however, recent efforts have occasionally resulted in juveniles with developmental abnormalities. In response, we developed a scoring system to collect quantitative data on the presence and severity of these developmental abnormalities. Our objective was to describe and validate the abnormality scoring system so that it can be used by all Gopher Frog headstarting facilities. The scoring system covers five primary conditions encompassing commonly observed abnormalities. Two groups of participants with different levels of prior experience working with Gopher Frogs assigned scores to a set of images presented to them for each condition. We used intra-class correlation coefficients (ICC) to test the scoring system for inter- and intra-rater agreement as well as agreement with the benchmark standard (established by the authors). We found high ICC values for inter-rater agreement, intra-rater agreement, and agreement to the benchmark standard indicating either excellent or good reliability for all five conditions and for all raters when grouped together. These findings support the reliability and validity of the proposed developmental abnormality scoring system. Gopher Frog headstarting facilities can implement this scoring system to assist in tracking the frequency and severity of abnormalities observed in future headstarting efforts. We hope that by creating a reliable scoring system for Gopher Frogs, it can provide an overall framework and serve as a valuable resource to evaluate abnormalities across any amphibian species.
Abnormal grain growth in ultrafine grained Ni under high-cycle loading
Abnormal grain growth can occur in polycrystalline materials with only a fraction of grains growing drastically to consume other grains. We report abnormal grain growth in ultrafine grained metal in a rarely explored high-cycle loading regime at ambient temperature. Abnormal grain growth is observed in electroplated Ni microbeams with average initial grain sizes less than 640 nm under a large number of loading cycles (up to 109) with low strain amplitudes (< 0.3%). Such abnormal grain growth occurs predominantly in the family of grains whose <100> orientation is along the tensile/compressive loading direction. Micromechanics analysis suggests that the elastic anisotropy of grains dictates the thermodynamic driving force of abnormal grain growth, such that the lowest strain energy density of the <100> oriented grain family dominates grain growth. This work unveils a unique type of abnormal grain growth that may be harnessed to tailor grain microstructures in materials.
Scalable hierarchical abnormality localization in cyber-physical systems
A cyber-physical system may have monitoring nodes that generate a series of current monitoring node values over time that represent current operation of the system. A hierarchical abnormality localization computer platform accesses a multi-level hierarchy of elements, and elements in a first level of the hierarchy are associated with elements in at least one lower level of the hierarchy and at least some elements may be associated with monitoring nodes. The computer platform may then determine, based on feature vectors and a decision boundary, an abnormality status for a first element in the highest level of the hierarchy. If the abnormality status indicates an abnormality, the computer platform may determine an abnormality status for elements, associated with the first element, in at least one level of the hierarchy lower than the level of the first element. These determinations may be repeated until an abnormality is localized to a monitoring node.
DECAF Cross-device characterization of tokamak disruptions indicated by abnormalities in plasma vertical position and current
Abnormal (deviating from target) variations in the plasma vertical position Z and current I p (such as vertical displacements, transient I p 'spikes' and quenches) constitute common elements of a disruption, a phenomenon that is to be mitigated, or ultimately avoided in future reactor-relevant tokamaks. While those abnormalities are generally recognized cross-shot and cross-device, details in terms of appearance (or not) and order of those abnormalities in disruption event chains is bound with the plasma state at the time of the chain initiation. Detection of those abnormalities is thus indicative not only of the onset of the plasma collapse itself, but also of the disruption driving cause that is promoted at the particular plasma state. Here, occurrence of disruptions, explored via detection of a Ip quench, and analysis of disruption event chains constituted by Ip and Z abnormalities, is reported for in total 7 full years of operation of 3 devices (KSTAR, MAST-U and NSTX-U) using the DECAFTM code expanded tools and capabilities. It is shown that the disruption occurrence depends not only on details of the plasma state, but also on (device-dependent) technical elements of the shot exit scenario. A year-to-year change in main disruption causes and a reduction of the disruptivity rate, bound with device and operation upgrades, are reported. Particular trigger instances of disruption event chains (and the full chains, when applicable) are shown to occupy different parts of the operation space diagrams, in accordance with prior expectation. Plasma elongation is identified as an important factor influencing details of the chains and its role will be further explored.
Detect the Unobservable: Abnormality Detection in mixed Autonomy for Lane Change Maneuver with Following Vehicles’ Trajectories Only
Highly Automated Vehicles (HAVs) and Advanced Driver-Assistance Systems (ADAS) are transforming modern transportation with enhanced mobility, safety, and efficiency. Despite their advantages, cybersecurity vulnerabilities in these systems can lead to abnormal behavior, posing significant risks to surrounding human-driven vehicles (HDVs) in mixed traffic environments. Here, this article addresses the challenge of detecting abnormal lateral movements of HAVs/ADAS vehicles using only trajectory profiles of following HDVs. Specifically, we propose a novel modeling approach that captures both normal and abnormal lateral behaviors through vehicle kinematics, integrated decision-making processes, vehicle control using symbolic regression for lane change vehicles. Additionally, we introduce an abnormality detection framework that relies on observable HDV data, even in occlusion scenarios. The framework evaluates the sensitivity of various car-following models to detect abnormal behaviors, providing insights into the interaction between HAVs/ADAS and HDVs in mixed autonomy systems.
Apollo 6, Anomaly Report No. 6: Abnormal Structural Performance During Launch Phase
Approximately 2 minutes 13 seconds after lift-off of the Apollo 6 mission, abrupt changes of strain, vibration, and acceleration measurements were indicated in the S-IVB, instrument unit, adapter, lunar module, and command and service modules; photographs showed objects coming from the area of the adapter. The adapter, however, continued to sustain the required loads with no impairment of the mission. The investigation was first focused upon the understanding of the coupled vibration modes and characteristics of the launch vehicle and spacecraft.' Extensive test programs were conducted. It was eventually concluded that the adapter failure was not caused by vibration. Extensive study of the airborne photography and other evidence indicated that a large area of the adapter had lost inner facesheet from the honeycomb sandwich panels. Loads and stresses resulting from vibration were determined to be insufficient to initiate such a failure. The investigation was then directed toward determining the range of pressures that could have been trapped in the Apollo 6 adapter sandwich panels, and toward determining the tolerance of the panels to withstand pressure with various degrees of flaws such as adhesive voids and facesheet dents. The degradation effects of moisture and heat exposure on the adhesive strength were also studied and tested. These tests and analyses led to the conclusion that pressure internal to the sandwich panels could have caused the failure, if a large flaw existed. The pressure buildup would have been caused by aerodynamic heating effects on air and moisture trapped in the panel. The probable cause of the failure was found in the original ultrasonic inspection scan record of the affected adapter panel. In the center of the region where the adapter failed, horizontally along the station 709 panel splice, the record contained two thick anomalous lines extending several feet. Without an X-ray record of this region, the significance of this particular scan record cannot be fully understood. However, since all other evidence had indicated that the adhesive had to be weakened in a rather large area to initiate the failure, the investigation was focused intently upon the station 709 splices of other adapters. Sufficient information was developed to verify that deficient assembly techniques have consistently resulted in abnormalities in the structure at this station. These abnormalities were identified in adapters 12, 13, 14, 15, and 16. Before the splice abnormalities were pinpointed, corrective action was taken to reduce pressure "buildup in the honeycomb panels and to reduce heat degrading effects on the adhesive. This was done by drilling vent holes in the inner facesheet and covering the outer facesheet with cork. The adapters having identified abnormalities in the station 709 splice are being repaired, and the contractor is investigating ways of avoiding these -abnormalities in panels yet to be bonded.
Unsupervised Pattern Classifier for Abnormality-Scaling of Vibration Features for Helicopter Gearbox Fault Diagnosis
A new unsupervised pattern classifier is introduced for on-line detection of abnormality in features of vibration that are used for fault diagnosis of helicopter gearboxes. This classifier compares vibration features with their respective normal values and assigns them a value in (0, 1) to reflect their degree of abnormality. Therefore, the salient feature of this classifier is that it does not require feature values associated with faulty cases to identify abnormality. In order to cope with noise and changes in the operating conditions, an adaptation algorithm is incorporated that continually updates the normal values of the features. The proposed classifier is tested using experimental vibration features obtained from an OH-58A main rotor gearbox. The overall performance of this classifier is then evaluated by integrating the abnormality-scaled features for detection of faults. The fault detection results indicate that the performance of this classifier is comparable to the leading unsupervised neural networks: Kohonen's Feature Mapping and Adaptive Resonance Theory (AR72). This is significant considering that the independence of this classifier from fault-related features makes it uniquely suited to abnormality-scaling of vibration features for fault diagnosis.
Local and global decision fusion for cyber-physical system abnormality detection
Monitoring nodes may generate a series of current monitoring node values over time representing current operation of a cyber-physical system. A decision fusion computer platform may receive, from a local status determination module, an indication of whether each node has an initial local status of “normal”/“abnormal” and a local certainty score (with higher values of the local certainty score representing greater likelihood of abnormality). The computer platform may also receive, from a global status determination module, an indication of whether the system has an initial global status of “normal”/“abnormal” and a global certainty score. The computer platform may output, for each node, a fused local status of “normal” or “abnormal,” at least one fused local status being based on the initial global status. The decision fusion computer platform may also output a fused global status of “normal” or “abnormal” based on at least one initial local status.
Eddy covariance towers as sentinels of abnormal radioactive material releases
Ensuring accurate detection and attribution of abnormal releases of radioactive material is critical for protecting human health and safety. Most commonly, such detection is accomplished via active monitoring approaches involving the collection of physical samples. Further, this is labor intensive and limits the temporal and spatial resolution of any detected events to a relatively coarse level. As an alternative first step towards passive monitoring, we developed an approach using eddy flux tower data records to identify signals from a known abnormal release and quantify the extent to which that signal also occurs at other times in the data record. Through two case studies, one of which targeted the Fukushima nuclear disaster and the other targeting an abnormal release event at a radioisotope production facility in Fleurus, Belgium, we tested our approach and identified several potential heretofore unidentified abnormal events that were consistent with atmospheric circulation patterns and/or wind direction from known release sites. Because our approach is relatively simple and is resistant to systematic errors in the observational record, it has broad applicability beyond specific constituents and ecosystem types to identify a wide variety of limited-duration anomalies in flux tower data to ensure human health and industrial safety.
Acoustic sensing and autoencoder approach for abnormal gas detection in a spent nuclear fuel canister mock-up
Currently, spent nuclear fuel (SNF) from commercial nuclear power plants is stored in stainless-steel canisters for interim dry storage. To provide an inert environment, these canisters are backfilled with helium after vacuum drying. However, the helium environment may be contaminated during extended storage because of the material degradation. For example, the heavier fission gas xenon may be released from the fuel rods into the canister cavity should the fuel cladding be breached. Other gases such as air and water vapor may also be present as a result of leakage caused by chloride-induced stress corrosion cracking on the canister walls or by insufficient vacuum drying. Therefore, monitoring the gas composition can provide critical information about the health of SNF canisters. In this study, noninvasive testing was conducted on a 2/3-scaled SNF canister mock-up using acoustic sensing. Ultrasonic transducers were placed on the exterior surface of the canister to probe the gas composition. A dataset was collected by sealing the canister mock-up and introducing up to 1.53% argon or 1.29% air into the helium background gas. Three methods were used to detect changes in the gas composition: the time-of-flight (TOF) method, the differential method, and the autoencoder method. Results showed that the TOF method had sufficient resolution to detect abnormal gas concentrations of less than 1.0%. The differential method demonstrated a periodic in-phase and out-of-phase behavior between the benchmark (i.e., pure helium) and abnormal (i.e., with argon or air) state signals. The variational autoencoder (VAE) and the Wasserstein autoencoder (WAE) were trained on the benchmark data and were applied directly to the abnormal state data. It was found that both the unsupervised VAE and the WAE were able to distinguish the benchmark and abnormal states of the canister mock-up based on the reconstruction error.
Reversible skeletal abnormalities in gamma-glutamyl transpeptidase-deficient mice
Gamma-glutamyl transpeptidase (GGT) is a widely distributed ectopeptidase responsible for the degradation of glutathione in the gamma-glutamyl cycle. This cycle is implicated in the metabolism of cysteine, and absence of GGT causes a severe intracellular decrease in this amino acid. GGT-deficient (GGT-/-) mice have multiple metabolic abnormalities and are dwarf. We show here that this latter phenotype is due to a decreased of the growth plate cartilage total height resulting from a proliferative defect of chondrocytes. In addition, analysis of vertebrae and tibiae of GGT-/- mice revealed a severe osteopenia. Histomorphometric studies showed that this low bone mass phenotype results from an increased osteoclast number and activity as well as from a marked decrease in osteoblast activity. Interestingly, neither osteoblasts, osteoclasts, nor chondrocytes express GGT, suggesting that the observed defects are secondary to other abnormalities. N-acetylcysteine supplementation has been shown to reverse the metabolic abnormalities of the GGT-/- mice and in particular to restore the level of IGF-1 and sex steroids in these mice. Consistent with these previous observations, N-acetylcysteine treatment of GGT-/- mice ameliorates their skeletal abnormalities by normalizing chondrocytes proliferation and osteoblastic function. In contrast, resorbtion parameters are only partially normalized in GGT-/- N-acetylcysteine-treated mice, suggesting that GGT regulates osteoclast biology at least partly independently of these hormones. These results establish the importance of cysteine metabolism for the regulation of bone remodeling and longitudinal growth.
4D Observations of the initiation of abnormal grain growth in commercially pure Ni
Abnormal grain growth (AGG), where a small fraction of grains grow faster than others, is critical to predict because it can significantly impact material properties. However, the mechanism behind AGG remains unclear. In this study, laboratory-based x-ray diffraction contrast tomography (LabDCT) is employed to non-destructively track the 3D microstructural evolution of high-purity nickel during the onset of AGG at 800 °C. The initial microstructure state is used to test hypothesized microstructure predictors of the initiation of AGG. The change in grain size was not related to the initial grain size or normalized integral mean curvature. Additionally, the grain boundary energy distributions of abnormal grains were indistinguishable from those of control groups exhibiting normal grain growth. However, the grains that later became abnormal exhibit large areas of asymmetric tilt boundaries that were previously found to be fast. This 3D microstructure dataset is useful to investigate new hypotheses for the initiation of AGG.
Elucidating Abnormal Grain Growth in Thermomagnetic Processed Materials with Transfer Learning and Reinforcement Learning
The goal of this research program is to establish the mechanism governing local grain boundary motion, which is needed to design and process desirable microstructures for better performance, by identifying the relative contributions of grain boundary (GB) energy and mobility to grain growth. Classical models for grain growth assume that the primary mechanism for reducing the total interfacial energy is area reduction and that GB restructuring is not significant. This assumption implies that grain growth is locally driven by curvature. However, recent experimental observations using new non-destructive 3D x-ray diffraction microscopy techniques (3D-XRM) reveal that classic descriptors (i.e., curvature, number of neighbors, grain size) do not predict real grain growth. Instead, local GB motion appears to be governed by its energy relative to its neighbors such that low-energy boundaries replace those of higher energy. However, simulations that incorporate GB energy anisotropy still fail to reproduce these observations. These discrepancies suggest that the common assumption for grain growth theory must be re-examined to predict and, thus, control microstructure evolution in real polycrystals. A significant challenge to testing this assumption is due to anisotropic GB mobility. Mobility may cause abnormal grain growth or affect the final grain shapes or growth rate but its true contributions are unknown because it is difficult to measure. For example, observations in Fe have found that grains associated with high energy and high mobility boundaries tend to experience abnormal grain growth, whereas abnormal grain growth is associated with low energy and high mobility boundaries in alumina. As mobility and energy both control GB motion, it is challenging to isolate the local driving forces necessary to test the common assumption that the primary mechanism is area reduction. The novelty of this work is the use of machine learning tools to capture GB mobility and energy from 3D-XRM measurements in polycrystals to test the common assumption used in grain growth models. Machine learning can capture high-order correlations in dynamic systems like those found in the evolving GB topology. The PIs have developed a physics-regularized interpretable machine learning microstructure evolution (PRIMME) model that accurately replicates the grain growth behavior of its trained data set.
Experimental investigation of abnormal transverse flow enhancement of α particles in heavy-ion collisions
The mass dependence of the transverse flow for Z = 1–5 fragments from the collisions of 40 Ar + 27 Al, 40 Ar + 48 Ti, and 40 Ar + 58 Ni at 47 MeV/nucleon is investigated experimentally in this article. The transverse flow values are determined using the in-plane components of the fragment transverse momenta, where three conventional methods, i.e., the kinetic flow tensor method, the transverse momentum analysis method, and the azimuthal correlation method, are applied to reconstruct the reaction plane in an event-by-event basis. It is demonstrated from the comparison of the present experimental mass dependent flow measurements and the model simulations using an improved antisymmetrized molecular dynamics model that the experimentally observed abnormal α transverse flow enhancement is closely related to the reaction plane reconstruction procedure in the flow extraction. We further investigate the physical existence of the abnormal α flow behavior using a two-particle azimuthal correlation method, which allows us to provide the relative flow magnitude information with an identification of fragment charge number without the knowledge of the reaction plane differing from the three conventional methods. It is found that the relative flow magnitudes deduced from the two-particle azimuthal correlation functions with an identification of Z, with the correction for the recoil effect imposed by the momentum conservation, show a monotonically increasing trend as a function of fragment charge number, with no exception of the α flow enhancement. These results, in addition to those from the improved antisymmetrized molecular dynamics model simulations, definitely provide experimental evidences for the inexistence of the abnormal α flow behavior in the heavy-ion collisions at the present incident energy region in nature.
Dynamic P-Q Capability and Abnormal Operation Analysis of a Wind Turbine with Doubly-Fed Induction Generator
After several accidents occurred in wind farms in the United States and around the world, the subsynchronous oscillation (SSO) issues in grid-connected wind farms have gained serious attention. Particularly, these issues have caused significant challenges to wind turbines with a doubly fed induction generator (DFIG) because it is connected to the grid via both its stator and rotor paths. Traditionally, a P-Q capability chart is utilized to assure the safe operation boundary for a synchronous generator. But the energy conversion characteristics of a DFIG wind turbine are completely different. A critical factor to affect the reliable operation of a DFIG wind turbine is the rated current and pulse-width modulation (PWM) saturation constraints of its power converters. These constraint conditions can be affected by the wind turbine rotating speed and grid conditions. However, a detailed study of DFIG P-Q capability from these perspectives has not been conducted, which has hindered adequate understanding of many abnormal wind turbine operations reported in the literature and the development of advanced control technologies to overcome the challenges. The proposed study in this paper considers vector control implementation to DFIG power electronic converters in the dq reference frame, and the models and algorithms developed for the P-Q capability study have addressed specific DFIG power converter constraints that are different from those of a traditional synchronous generator. The paper especially focuses on exploring the dynamic natures of DFIG P-Q capability under uncertain and variable conditions to explore the root causes of many abnormal operations of DFIG wind turbines reported in the literature. The proposed study is validated through an electromagnetic transient simulation model of a grid-connected DFIG wind turbine. The proposed study has the potential to lead to the development of new DFIG control technologies that can help overcome the challenges for many abnormal operations of DFIG wind turbines.