Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations

Characterisation of Dynamic Process Systems by Use of Recurrence Texture Analysis

This thesis proposes a method to analyse the dynamic behaviour of process systems using sets of textural features extracted from distance matrices obtained from time series data. Algorithms based on the use of grey level co-occurrence matrices, wavelet transforms, local binary patterns, textons, and the pretrained convolutional neural networks (AlexNet and VGG16) were used to extract features. The method was demonstrated to effectively capture the dynamics of mineral process systems and could outperform competing approaches

Multiscale modelling of delayed hydride cracking

A mechanistic model of delayed hydride cracking (DHC) is crucial to the nuclear industry as a predictive tool for understanding the structural failure of zirconium alloy components that are used to clad fuel pins in water-cooled reactors. Such a model of DHC failure must be both physically accurate and computationally efficient so that it can inform and guide nuclear safety assessments. However, this endeavour has so far proved to be an unsurmountable challenge because of the seemingly intractable multiscale complexity of the DHC phenomenon, which is a manifestation of hydrogen embrittlement that involves the interplay and repetition of three constituent processes: atomic scale diffusion, microscale precipitation and continuum scale fracture. This investigation aims to blueprint a novel multiscale modelling strategy to simulate the early stages of DHC initiation: stress-driven hydrogen diffusion-controlled precipitation of hydrides near loaded flaws in polycrystalline zirconium. Following a careful review of the experimental observations in the literature as well as the standard modelling techniques that are commonplace in nuclear fuel performance codes in the first part of this dissertation, the second and third parts introduce a hybrid multiscale modelling strategy that integrates concepts across a spectrum of length and time scales into one self-consistent framework whilst accounting for the complicated nuances of the zirconium-hydrogen system. In particular, this strategy dissects the DHC mechanism into three interconnected modules: (i) stress analysis, which performs defect micromechanics in hexagonal close-packed zirconium through the application of the mathematical theory of planar elasticity to anisotropic continua; (ii) stress-diffusion analysis, which bridges the classical long-range elastochemical transport with the quantum structure of the hydrogen interstitialcy in the trigonal environment of the tetrahedral site; and (iii) diffusion-precipitation analysis, which translates empirical findings into an optimised algorithm that emulates the thermodynamically favourable spatial assembly of the microscopic hydride needles into macroscopic hydride colonies at prospective nucleation sites. Each module explores several unique mechanistic modelling considerations, including a multipolar expansion of the forces exerted by hydrogen interstitials, a distributed dislocation representation of the hydride platelets, and a stoichiometric hydrogen mass conservation criterion that dictates the lifecycle of hydrides. The investigation proceeds to amalgamate the stress, stress-diffusion and diffusion-precipitation analyses into a unified theory of the mesoscale mechanics that underpin the early stages of DHC failure and a comprehensive simulation of the flaw-tip hydrogen profiles and hydride microstructures. The multiscale theory and simulation are realised within a bespoke software which incorporates computer vision to generate mesoscale micrographs that depict the geometries, morphologies and contours of key metallographic entities: cracks and notches, grains, intergranular and intragranular nucleation sites as well as regions of hydrogen enhancement and complex networks of hydride features. Computer vision mediates the balance between simulation accuracy and simulation efficiency, which is completely novel in the context of DHC research as a paradigm at the intersection of computational science and computer science. Preliminary tests show that the simulation environment of the hybrid model is significantly more accurate and efficient in comparison with the traditional finite element and phase field methodologies. Due to this unprecedented simulation accuracy-efficiency balance, realistic flaw-tip hydrogen profiles and hydride microstructures can be simulated within seconds, which naturally facilitates statistical averaging over ensembles. Such statistical capabilities are highly relevant to nuclear safety assessments and, therefore, a systematic breakdown of the model formulation is presented in the style of a code specification manual so that the bespoke software can be readily adapted within an industrial setting. As the main contribution to DHC research, the proposed multiscale model comprises a state-of-the-art microstructural solver whose unrivalled versatility is demonstrated by showcasing a series of simulated micrographs that are parametrised by flaw acuity, grain size, texture, alloy composition, and histories of thermomechanical cycles. Direct comparisons with experimental micrographs indicate good quantitative agreement and provide some justification to the known qualitative trends. Furthermore, the overall hybrid methodology is proven to scale linearly with the number of hydrides, which is computationally advantageous in its own right because it allows the bespoke software to be extended without compromising its speed. Several possible extensions are outlined which would improve the phenomological accuracy of the multiscale model whilst retaining its efficiency. In its current form, however, this hybrid multiscale model of the early stages of DHC goes far beyond existing methodologies in terms of simulation scope.Open Acces

Atomistic modelling of all dislocations and twins in HCP and BCC Ti

Ti exhibits complex plastic deformation controlled by active dislocation and twinning systems. Understandings on dislocation cores and twin interfaces are currently not complete or quantitative, despite extensive experimental and simulation studies. Here, we determine all the core and twin interface properties in both HCP and BCC Ti using a Deep Potential (DP) and DFT. We determine the core structures, critical resolved shear stresses and mobilities of , , dislocations in HCP and /2 dislocations in BCC Ti. The slip consists of slow core migration on pyramidal-I planes and fast migration on prism-planes, and is kinetically limited by cross-slips among them. This behaviour is consistent with "locking-unlocking" phenomena in TEM and is likely an intrinsic property. Large-scale DFT calculations provide a peek at the screw core and glide behaviour, which is further quantified using DP-Ti. The screw is unstable on pyramidal-II planes. The mixed is nearly sessile on pyramidal-I planes, consistent with observations of long dislocations in this orientation. The edge and mixed are unstable against a pyramidal-to-basal (PB) transition and become sessile at high temperatures, corroborate the difficulties in -axis compression of Ti. Finally, in BCC Ti, the /2 screw has a degenerate core with average glide on {112} planes; the /2 edge and mixed dislocations have non-dissociated cores on {110} planes. This work paints a self-consistent, complete picture on all dislocations in Ti, rationalises previous experimental observations and points to future HRTEM examinations of unusual dislocations such as the mixed and PB transformed cores

Camera Spatial Frequency Response Derived from Pictorial Natural Scenes

Camera system performance is a prominent part of many aspects of imaging science and computer vision. There are many aspects to camera performance that determines how accurately the image represents the scene, including measurements of colour accuracy, tone reproduction, geometric distortions, and image noise evaluation. The research conducted in this thesis focuses on the Modulation Transfer Function (MTF), a widely used camera performance measurement employed to describe resolution and sharpness. Traditionally measured under controlled conditions with characterised test charts, the MTF is a measurement restricted to laboratory settings. The MTF is based on linear system theory, meaning the input to output must follow a straightforward correlation. Established methods for measuring the camera system MTF include the ISO12233:2017 for measuring the edge-based Spatial Frequency Response (e-SFR), a sister measure of the MTF designed for measuring discrete systems. Many modern camera systems incorporate non-linear, highly adaptive image signal processing (ISP) to improve image quality. As a result, system performance becomes scene and processing dependant, adapting to the scene contents captured by the camera. Established test chart based MTF/SFR methods do not describe this adaptive nature; they only provide the response of the camera to a test chart signal. Further, with the increased use of Deep Neural Networks (DNN) for image recognition tasks and autonomous vision systems, there is an increased need for monitoring system performance outside laboratory conditions in real-time, i.e. live-MTF. Such measurements would assist in monitoring the camera systems to ensure they are fully operational for decision critical tasks. This thesis presents research conducted to develop a novel automated methodology that estimates the standard e-SFR directly from pictorial natural scenes. This methodology has the potential to produce scene dependant and real-time camera system performance measurements, opening new possibilities in imaging science and allowing live monitoring/calibration of systems for autonomous computer vision applications. The proposed methodology incorporates many well-established image processes, as well as others developed for specific purposes. It is presented in two parts. Firstly, the Natural Scene derived SFR (NS-SFR) are obtained from isolated captured scene step-edges, after verifying that these edges have the correct profile for implementing into the slanted-edge algorithm. The resulting NS-SFRs are shown to be a function of both camera system performance and scene contents. The second part of the methodology uses a series of derived NS-SFRs to estimate the system e-SFR, as per the ISO12233 standard. This is achieved by applying a sequence of thresholds to segment the most likely data corresponding to the system performance. These thresholds a) group the expected optical performance variation across the imaging circle within radial distance segments, b) obtain the highest performance NS-SFRs per segment and c) select the NS-SFRs with input edge and region of interest (ROI) parameter ranges shown to introduce minimal e-SFR variation. The selected NS-SFRs are averaged per radial segment to estimate system e-SFRs across the field of view. A weighted average of these estimates provides an overall system performance estimation. This methodology is implemented for e-SFR estimation of three characterised camera systems, two near-linear and one highly non-linear. Investigations are conducted using large, diverse image datasets as well as restricting scene content and the number of images used for the estimation. The resulting estimates are comparable to ISO12233 e-SFRs derived from test chart inputs for the near-linear systems. Overall estimate stays within one standard deviation of the equivalent test chart measurement. Results from the highly non-linear system indicate scene and processing dependency, potentially leading to a more representative SFR measure than the current chart-based approaches for such systems. These results suggest that the proposed method is a viable alternative to the ISO technique

Numerical modelling of additive manufacturing process for stainless steel tension testing samples

Nowadays additive manufacturing (AM) technologies including 3D printing grow rapidly and they are expected to replace conventional subtractive manufacturing technologies to some extents. During a selective laser melting (SLM) process as one of popular AM technologies for metals, large amount of heats is required to melt metal powders, and this leads to distortions and/or shrinkages of additively manufactured parts. It is useful to predict the 3D printed parts to control unwanted distortions and shrinkages before their 3D printing. This study develops a two-phase numerical modelling and simulation process of AM process for 17-4PH stainless steel and it considers the importance of post-processing and the need for calibration to achieve a high-quality printing at the end. By using this proposed AM modelling and simulation process, optimal process parameters, material properties, and topology can be obtained to ensure a part 3D printed successfully

Probabilistic Programming for Deep Learning

We propose the idea of deep probabilistic programming, a synthesis of advances for systems at the intersection of probabilistic modeling and deep learning. Such systems enable the development of new probabilistic models and inference algorithms that would otherwise be impossible: enabling unprecedented scales to billions of parameters, distributed and mixed precision environments, and AI accelerators; integration with neural architectures for modeling massive and high-dimensional datasets; and the use of computation graphs for automatic differentiation and arbitrary manipulation of probabilistic programs for flexible inference and model criticism. After describing deep probabilistic programming, we discuss applications in novel variational inference algorithms and deep probabilistic models. First, we introduce the variational Gaussian process (VGP), a Bayesian nonparametric variational family, which adapts its shape to match complex posterior distributions. The VGP generates approximate posterior samples by generating latent inputs and warping them through random non-linear mappings; the distribution over random mappings is learned during inference, enabling the transformed outputs to adapt to varying complexity of the true posterior. Second, we introduce hierarchical implicit models (HIMs). HIMs combine the idea of implicit densities with hierarchical Bayesian modeling, thereby defining models via simulators of data with rich hidden structure

Viscoelastic and Viscoplastic Materials

This book introduces numerous selected advanced topics in viscoelastic and viscoplastic materials. The book effectively blends theoretical, numerical, modeling and experimental aspects of viscoelastic and viscoplastic materials that are usually encountered in many research areas such as chemical, mechanical and petroleum engineering. The book consists of 14 chapters that can serve as an important reference for researchers and engineers working in the field of viscoelastic and viscoplastic materials