Prediction of delamination in glass fibre reinforced composite materials using elasto-plastic modelling

Glass Fibre reinforced composite (GFRC) has been used for numerous structural applications in Aerospace, Chemical, Automotive and Civil infrastructure fields over a hundred of years. Due to this reason, understanding the intricate fracture behaviour of GFRC materials is crucial and essential for designing critical structural components. Voids and micro-cracks are considered as imperfections in Glass Fibre Reinforced composites. Much research has been undertaken on approaches to calculate and evaluate the effects of the imperfections on mechanical properties. However, it is an established fact that the micro-mechanical approach alone is not sufficient to understand a complete damage accumulation process during delamination. The damage mechanism which largely affects the performance of GFRC structures is commonly known as 'delamination'. Since the delamination is invisible, and hard to detect with ordinary non-destructive evaluation methods, therefore it is considered as a hidden killer which can cause catastrophic failure without any prior warnings. Due to this reason, research work on delamination modelling, damage detection and self-healing materials have been the highly placed research topics for more than five decades. Unfortunately there are a number of unresolved problems in delamination damage modelling and prediction, and few grey areas regarding application of Structural Health Monitoring systems to monitor delamination damages. This thesis has proposed to study the insight into the cause of delamination damage and its propagation mechanisms, by analytical modelling and experimental verifications. Within this research project, extension of the work by Tsukrov and Kachanov (2000) – “An innovative Elasto-plastic model” has been undertaken to evaluate, investigate and model the onset and propagation of delamination damages. Mode I, Mode II as well as Mixed Mode I/II delamination damage analysis has been utilised to study the proposed model predictions for GFRC structures for both in-plane and out-of-plane load applications. The proposed model has been validated using the Double Cantilever Beam (DCB), End Notch Flexure configurations (ENF) and Cracked Lap Shear (CLS) experiments on 0/90-glass woven cloth specimens. For the validation process, the procedures stipulated by ASTM standards were employed. It was observed that there were significant discrepancies between calculated fracture energies using standard procedures and the proposed model. Interestingly these observations have revealed some inconsistencies associated with the standard method for strain measurements that majorly controls the fracture energy calculations. This research project has demonstrated and evidently proven the accuracy of the proposed model predictions using the strain measured with embedded Fibre Bragg Grating (FBG) sensors, located inside the sample in proximity of the crack tip. The extended use of FBG strain measurement has created a breakthrough in Structural Health Monitoring (SHM) of composite structures. Non-availability of a suitable damage prediction model is an issue for accurate damage monitoring process. The proposed model has also demonstrated the potential for its integration with Structural Health Monitoring (SHM) systems. Additionally, Thermoplastic Stress Analysis (TSA) has been employed to monitor delamination. The potential for integration of FBG sensors and TSA techniques has been experimentally demonstrated during this project and, it is another breakthrough in SHM field as a result of this research. In addition to analytical model, a detailed Finite Element model was also created on ABAQUS commercial software. The cohesive elements with state variables (SDV) and UMAT codes were used for FEA simulations. Interestingly, the FEA results have shown an excellent correlation with the experimental results. Finally, this thesis has evidently proved the validity of the proposed model and integration of model with SHM system based on FBG sensors and TSA techniques. The outcomes of the thesis have provided a novel and innovative damage prediction model and a breakthrough technology for SHM systems

An analysis of the elastic properties of a porous aluminium oxide film by means of indentation techniques

The elastic modulus of thin films can be directly determined by instrumented indentation when the indenter penetration does not exceed a fraction of the film thickness, depending on the mechanical properties of both film and substrate. When it is not possible, application of models for separating the contribution of the substrate is necessary. In this work, the robustness of several models is analyzed in the case of the elastic modulus determination of a porous aluminium oxide film produced by anodization of an aluminium alloy. Instrumented indentation tests employing a Berkovich indenter were performe data nanometric scale, which allowed a direct determination of the film elastic modulus, whose value was found to be approximately 11 GPa. However, at a micrometric scale the elastic modulus tends toward the value corresponding to the substrate, of approximately 73 GPa. The objective of the present work is to apply different models for testing their consistency over the complete set of indentation data obtained from both classical tests in microindentation and the continuous stiffness measurement mode in nanoindentation. This approach shows the continuity between the two scales of measurement thus allowing a better representation of the elastic modulus variation between two limits corresponding to the substrate and film elastic moduli. Gao's function proved to be the best to represen the elastic modulus variation

Thomas-Fermi Statistical Models of Finite Quark Matter

I introduce and discuss models of finite quark matter using the formalism of the Thomas-Fermi statistical model. Similar to bag models, a vacuum energy term is introduced to model long distance confinement, but the model produces bound states from the residual color Coulomb attraction even in the absence of such a term. I discuss three baryonic applications: an equal mass nonrelativistic model with and without volume pressure, the ultra-relativistic limit confined by volume pressure, and a color-flavor locking massless model. These model may be extended to multi-meson and other mixed hadronic states. Hopefully, it can help lead to a better understanding of the phenomenology of high multi-quark states in preparation for more detailed lattice QCD calculations.Comment: 29 pages, 14 figures; final journal version (Nucl. Phys. A

Resiliency Assessment and Enhancement of Intrinsic Fingerprinting

Intrinsic fingerprinting is a class of digital forensic technology that can detect traces left in digital multimedia data in order to reveal data processing history and determine data integrity. Many existing intrinsic fingerprinting schemes have implicitly assumed favorable operating conditions whose validity may become uncertain in reality. In order to establish intrinsic fingerprinting as a credible approach to digital multimedia authentication, it is important to understand and enhance its resiliency under unfavorable scenarios. This dissertation addresses various resiliency aspects that can appear in a broad range of intrinsic fingerprints. The first aspect concerns intrinsic fingerprints that are designed to identify a particular component in the processing chain. Such fingerprints are potentially subject to changes due to input content variations and/or post-processing, and it is desirable to ensure their identifiability in such situations. Taking an image-based intrinsic fingerprinting technique for source camera model identification as a representative example, our investigations reveal that the fingerprints have a substantial dependency on image content. Such dependency limits the achievable identification accuracy, which is penalized by a mismatch between training and testing image content. To mitigate such a mismatch, we propose schemes to incorporate image content into training image selection and significantly improve the identification performance. We also consider the effect of post-processing against intrinsic fingerprinting, and study source camera identification based on imaging noise extracted from low-bit-rate compressed videos. While such compression reduces the fingerprint quality, we exploit different compression levels within the same video to achieve more efficient and accurate identification. The second aspect of resiliency addresses anti-forensics, namely, adversarial actions that intentionally manipulate intrinsic fingerprints. We investigate the cost-effectiveness of anti-forensic operations that counteract color interpolation identification. Our analysis pinpoints the inherent vulnerabilities of color interpolation identification, and motivates countermeasures and refined anti-forensic strategies. We also study the anti-forensics of an emerging space-time localization technique for digital recordings based on electrical network frequency analysis. Detection schemes against anti-forensic operations are devised under a mathematical framework. For both problems, game-theoretic approaches are employed to characterize the interplay between forensic analysts and adversaries and to derive optimal strategies. The third aspect regards the resilient and robust representation of intrinsic fingerprints for multiple forensic identification tasks. We propose to use the empirical frequency response as a generic type of intrinsic fingerprint that can facilitate the identification of various linear and shift-invariant (LSI) and non-LSI operations

Probing the Low-Energy Electronic Structure of Complex Systems by ARPES

Angle-resolved photoemission spectroscopy (ARPES) is one of the most direct methods of studying the electronic structure of solids. By measuring the kinetic energy and angular distribution of the electrons photoemitted from a sample illuminated with sufficiently high-energy radiation, one can gain information on both the energy and momentum of the electrons propagating inside a material. This is of vital importance in elucidating the connection between electronic, magnetic, and chemical structure of solids, in particular for those complex systems which cannot be appropriately described within the independent-particle picture. The last decade witnessed significant progress in this technique and its applications, thus ushering in a new era in photoelectron spectroscopy; today, ARPES experiments with 2 meV energy resolution and 0.2 degree angular resolution are a reality even for photoemission on solids. In this paper we will review the fundamentals of the technique and present some illustrative experimental results; we will show how ARPES can probe the momentum-dependent electronic structure of solids providing detailed information on band dispersion and Fermi surface, as well as on the strength and nature of those many-body correlations which may profoundly affect the one-electron excitation spectrum and, in turn, determine the macroscopic physical properties.Comment: Lecture notes for the 2003 Exciting Summer School (http://www.fysik4.fysik.uu.se/~thor/school.html). A HIGH-RESOLUTION pdf file is available at http://www.physics.ubc.ca/~damascel/ARPES_Intro.pdf, and related viewgraphs at http://www.physics.ubc.ca/~damascel/Exciting2003.pd

ARPES: A probe of electronic correlations

Angle-resolved photoemission spectroscopy (ARPES) is one of the most direct methods of studying the electronic structure of solids. By measuring the kinetic energy and angular distribution of the electrons photoemitted from a sample illuminated with sufficiently high-energy radiation, one can gain information on both the energy and momentum of the electrons propagating inside a material. This is of vital importance in elucidating the connection between electronic, magnetic, and chemical structure of solids, in particular for those complex systems which cannot be appropriately described within the independent-particle picture. Among the various classes of complex systems, of great interest are the transition metal oxides, which have been at the center stage in condensed matter physics for the last four decades. Following a general introduction to the topic, we will lay the theoretical basis needed to understand the pivotal role of ARPES in the study of such systems. After a brief overview on the state-of-the-art capabilities of the technique, we will review some of the most interesting and relevant case studies of the novel physics revealed by ARPES in 3d-, 4d- and 5d-based oxides.Comment: Chapter to appear in "Strongly Correlated Systems: Experimental Techniques", edited by A. Avella and F. Mancini, Springer Series in Solid-State Sciences (2013). A high-resolution version can be found at: http://www.phas.ubc.ca/~quantmat/ARPES/PUBLICATIONS/Reviews/ARPES_Springer.pdf. arXiv admin note: text overlap with arXiv:cond-mat/0307085, arXiv:cond-mat/020850

Audio Splicing Detection and Localization Based on Acquisition Device Traces

In recent years, the multimedia forensic community has put a great effort in developing solutions to assess the integrity and authenticity of multimedia objects, focusing especially on manipulations applied by means of advanced deep learning techniques. However, in addition to complex forgeries as the deepfakes, very simple yet effective manipulation techniques not involving any use of state-of-the-art editing tools still exist and prove dangerous. This is the case of audio splicing for speech signals, i.e., to concatenate and combine multiple speech segments obtained from different recordings of a person in order to cast a new fake speech. Indeed, by simply adding a few words to an existing speech we can completely alter its meaning. In this work, we address the overlooked problem of detection and localization of audio splicing from different models of acquisition devices. Our goal is to determine whether an audio track under analysis is pristine, or it has been manipulated by splicing one or multiple segments obtained from different device models. Moreover, if a recording is detected as spliced, we identify where the modification has been introduced in the temporal dimension. The proposed method is based on a Convolutional Neural Network (CNN) that extracts model-specific features from the audio recording. After extracting the features, we determine whether there has been a manipulation through a clustering algorithm. Finally, we identify the point where the modification has been introduced through a distance-measuring technique. The proposed method allows to detect and localize multiple splicing points within a recording

Wide-Area Measurement-Based Applications for Power System Monitoring and Dynamic Modeling

Due to the increasingly complex behavior exhibited by large-scale power systems with more uncertain renewables introduced to the grid, wide-area measurement system (WAMS) has been utilized to complement the traditional supervisory control and data acquisition (SCADA) system to improve operators’ situational awareness. By providing wide-area GPS-time-synchronized measurements of grid status at high time-resolution, it is able to reveal power system dynamics which cannot be captured before and has become an essential tool to deal with current and future power grid challenges. According to the time requirements of different power system applications, the applications can be roughly divided into online applications (e.g., data visualization, fast disturbance and oscillation detection, and system response prediction and reduction) and offline applications (e.g., measurement-driven dynamic modeling and validation, post-event analysis, and statistical analysis of historical data). In this dissertation, various wide-area measurement-based applications are presented. Firstly a pioneering WAMS deployed at the distribution level, the frequency monitoring network (FNET/GridEye) is introduced. For conventional large-scale power grid dynamic simulation, two major challenges are 1) accuracy of detailed dynamic models, and 2) computation burden for online dynamic assessment. To overcome the restrictions of the traditional approach, a measurement-based system response prediction tool using a Multivariate AutoRegressive (MAR) model is developed. It is followed by a measurement-based power system dynamic reduction tool using an autoregressive model vi to represent the external system. In addition, phasor measurement unit (PMU) data are employed to perform the generator dynamic model validation study. It utilizes both simulation data and measurement data to explore the potentials and limitations of the proposed approach. As an innovative application of using wide-area power system measurement, digital recordings could be authenticated by comparing the extracted frequency and phase angle from recordings with power system measurement database. It includes four research studies, i.e., oscillator error removal, ENF phenomenology, tampering detection, and frequency localization. Finally, several preliminary data analytics studies including inertia estimation and analysis, fault-induced delayed voltage recovery (FIDVR) detection, and statistical analysis of oscillation database, are presented

AMR Compressed-Domain Analysis for Multimedia Forensics Double Compression Detection

An audio recording must be authentic to be admitted as evidence in a criminal prosecution so that the speech is saved with maximum fidelity and interpretation mistakes are prevented. AMR (adaptive multi-rate) encoder is a worldwide standard for speech compression and for GSM mobile network transmission, including 3G and 4G. In addition, such encoder is an audio file format standard with extension AMR which uses the same compression algorithm. Due to its extensive usage in mobile networks and high availability in modern smartphones, AMR format has been found in audio authenticity cases for forgery searching. Such exams compound the multimedia forensics field which consists of, among other techniques, double compression detection, i. e., to determine if a given AMR file was decompressed and compressed again. AMR double compression detection is a complex engineering problem whose solution is still underway. In general terms, if an AMR file is double compressed, it is not an original one and it was likely doctored. The published works in literature about double compression detection are based on decoded waveform AMR files to extract features. In this paper, a new approach is proposed to AMR double compression detection which, in spite of processing decoded audio, uses its encoded version to extract compressed-domain linear prediction (LP) coefficient-based features. By means of feature statistical analysis, it is possible to show that they can be used to achieve AMR double compression detection in an effective way, so that they can be considered a promising path to solve AMR double compression problem by artificial neural networks

Development and application of synchronized wide-area power grid measurement

Phasor measurement units (PMUs) provide an innovative technology for real-time monitoring of the operational state of entire power systems and significantly improve power grid dynamic observability. This dissertation focuses on development and application of synchronized power grid measurements. The contributions of this dissertation are as followed:First, a novel method for successive approximation register analog to digital converter control in PMUs is developed to compensate for the sampling time error caused by the division remainder between the desirable sampling rate and the oscillator frequency. A variable sampling interval control method is presented by interlacing two integers under a proposed criterion. The frequency of the onboard oscillator is monitored in using the PPS from GPS.Second, the prevalence of GPS signal loss (GSL) on PMUs is first investigated using real PMU data. The correlation between GSL and time, spatial location, solar activity are explored via comprehensive statistical analysis. Furthermore, the impact of GSL on phasor measurement accuracy has been studied via experiments. Several potential solutions to mitigate the impact of GSL on PMUs are discussed and compared.Third, PMU integrated the novel sensors are presented. First, two innovative designs for non-contact PMUs presented. Compared with conventional synchrophasors, non-contact PMUs are more flexible and have lower costs. Moreover, to address nonlinear issues in conventional CT and PT, an optical sensor is used for signal acquisition in PMU. This is the first time the utilization of an optical sensor in PMUs has ever been reported.Fourth, the development of power grid phasor measurement function on an Android based mobile device is developed. The proposed device has the advantages of flexibility, easy installation, lower cost, data visualization and built-in communication channels, compared with conventional PMUs.Fifth, an identification method combining a wavelet-based signature extraction and artificial neural network based machine learning, is presented to identify the location of unsourced measurements. Experiments at multiple geographic scales are performed to validate the effectiveness of the proposed method using ambient frequency measurements. Identification accuracy is presented and the factors that affect identification performance are discussed