The application of speckle interferometry to infra-red astronomy

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Introduction to digital core analysis: 3D reconstruction, numerical flow simulations and pore network modeling

Digital core analysis can replace the use of conventional core samples by decreasing the time required for conducting experiments. Moreover, it gets a better understanding of complex reservoir structures due to its ability to capture pore geometries and fluid behavior at pore scale level. In the current thesis, the whole workflow of digital core analysis from image study to overlook of pore network models had been performed. It started from analysis of color 2D thin section images. It was noticed that they might contain significant amount of information about pore microstructure. In addition, several different approaches of image binarization were considered. Then some 3D micro-CT models (incl. four sandstone and one carbonate samples from the open database of the Imperial College London) had been analyzed and reconstructed. To calculate the absolute permeability, a direct pore-scale modeling approach using the lattice Boltzmann method (LBM) was implemented in PALABOS software. It is worth noting that several parameters influenced on the obtained results, for example, the image resolution and the volume of analyzed microstructure. It was found that while using low quality images the permeability by LBM might be significantly overestimated. In addition, to obtain reliable permeability (i.e. close to experimentally measured), the volume should be greater or equal to representative elementary volume (REV). Furthermore, it was possible to compare LBM permeability for reconstructed cases and original models. Results showed the best consistency for medium permeable sandstone samples. However, it had been noticed that reconstruction was not able to produce exactly the same porous media, since several other properties of reconstructed models, e.g. formation factor and specific surface area, were differed from original inputs. When multiphase flow occurs in porous structures, it is difficult to estimate relative permeabilities directly, since in this case REV should be bigger and LBM calculations are very computationally expensive. That is the reason why the pore network modeling approach was briefly discussed, and several models generated by maximal ball extraction algorithm had been reviewed

Décomposition volumique d'images pour l'étude de la microstructure de la neige

Les avalanches de neige sont des phénomènes naturels complexes dont l'occurrence s'explique principalement par la structure et les propriétés du manteau neigeux. Afin de mieux comprendre les évolutions de ces propriétés au cours du temps, il est important de pouvoir caractériser la microstructure de la neige, notamment en termes de grains et de ponts de glace les reliant. Dans ce contexte, l'objectif de cette thèse est la décomposition d'échantillons de neige en grains individuels à partir d'images 3-D de neige obtenues par microtomographie X. Nous présentons ici deux méthodes de décomposition utilisant des algorithmes de géométrie discrète. Sur la base des résultats de ces segmentations, certains paramètres, comme la surface spécifique et la surface spécifique de contact entre grains sont ensuite estimés sur des échantillons de neiges variées. Ces méthodes de segmentation ouvrent de nouvelles perspectives pour la caractérisation de la microstructure de la neige, de ses propriétés, ainsi que de leur évolution au cours du temps.Snow avalanches are complex natural phenomena whose occurrence is mainly due to the structure and properties of the snowpack. To better understand the evolution of these properties over time, it is important to characterize the microstructure of snow, especially in terms of grains and ice necks that connect them. In this context, the objective of this thesis is the decomposition of snow samples into individual grains from 3-D images of snow obtained by X-ray microtomography. We present two decomposition methods using algorithms of discrete geometry. Based on the results of these segmentations, some parameters such as the specific surface area and the specific contact area between grains are then estimated from samples of several snow types. These segmentation methods offer new outlooks for the characterization of the microstructure of snow, its properties, and its time evolution

Orbital Angular Momentum Waves: Generation, Detection and Emerging Applications

Orbital angular momentum (OAM) has aroused a widespread interest in many fields, especially in telecommunications due to its potential for unleashing new capacity in the severely congested spectrum of commercial communication systems. Beams carrying OAM have a helical phase front and a field strength with a singularity along the axial center, which can be used for information transmission, imaging and particle manipulation. The number of orthogonal OAM modes in a single beam is theoretically infinite and each mode is an element of a complete orthogonal basis that can be employed for multiplexing different signals, thus greatly improving the spectrum efficiency. In this paper, we comprehensively summarize and compare the methods for generation and detection of optical OAM, radio OAM and acoustic OAM. Then, we represent the applications and technical challenges of OAM in communications, including free-space optical communications, optical fiber communications, radio communications and acoustic communications. To complete our survey, we also discuss the state of art of particle manipulation and target imaging with OAM beams

Synthesis of nanostructured metal chalcogenides used for energy conversion and storage

Despite that now most of our energy is still originated from burning of fossil fuels which are non-renewable in short period and may cause serious pollution to the environment; it has become a common sense that human being is facing with more and more serious energy crisis as well as environmental problems including global warming and pollutions. To save our future, now the developing and utilization of green renewable energy, such as solar energy, waste heat recovery, fuel cells, and so on, has been one of the hottest topics all around the world\u27s scientists

Optical Signal Processing For Data Compression In Ultrafast Measurement

Today the world is filled with continuous deluge of digital information which are ever increasing by every fraction of second. Real-time analog information such as images, RF signals needs to be sampled and quantized to represent in digital domain with help of measurement systems for information analysis, further post processing and storage. Photonics offers various advantages in terms of high bandwidth, security, immunity to electromagnetic interference, reduction in frequency dependant loss as compared to conventional electronic measurement systems. However the large bandwidth data needs to be acquired as per Nyquist principle requiring high bandwidth electronic sampler and digitizer. To address this problem, Photonic Time Stretch has been introduced to reduce the need for high speed electronic measurement equipment by significantly slowing down the speed of sampling signal. However, this generates massive data volume. Photonics-assisted methods such as Anamorphic Stretch Transform, Compressed Sensing and Fourier spectrum acquisition sensing have been addressed to achieve data compression while sampling the information. In this thesis, novel photonic implementations of each of these methods have been investigated through numerical and experimental demonstrations. The main contribution of this thesis include (1) Application of photonic implementation of compressed sensing for Optical Coherence Tomography, Fiber Bragg Grating enabled signal sensing and blind spectrum sensing applications (2) Photonic compressed sensing enabled ultra-fast imaging system (3) Fourier spectrum acquisition for RF spectrum sensing with all-optical approach (4) Adaptive non-uniform photonic time stretch methods using anamorphic stretch transform to reduce the the number of samples to be measured

Optical Light Manipulation and Imaging Through Scattering Media

Typical optical systems are designed to be implemented in free space or clean media. However, the presence of optical scattering media scrambles light waves and becomes a problem in light field control, optical imaging, and sensing. To address the problem caused by optical scattering media, we discuss two types of solutions in this thesis. One type of solution is active control, where active modulators are used to modulate the light wave to compensate the wave distortion caused by optical scattering. The other type of solution is computational optics, where physical and mathematical models are built to computationally reconstruct the information from the measured distorted wavefront. In the part of active control, we first demonstrate coherent light focusing through scattering media by transmission matrix inversion. The transmission matrix inversion approach can realize coherent light control through scattering media with higher fidelity compared to conventional transmission matrix approaches. Then, by combining the pre-designed scattering metasurface with wavefront shaping, we demonstrate a beam steering system with large angular and high angular resolution. Next, we present optical-channel-based intensity streaming (OCIS), which uses only intensity information of light fields to realize light control through scattering media. This solution can be used to control spatially incoherent light propagating through scattering media. In the part of computational optics, we first demonstrate the idea of interferometric speckle visibility spectroscopy (ISVS) to measure the information cerebral blood flow. In ISVS, a camera records the speckle frames of diffused light from the human subject interferometrically, and the speckle statistics is used to calculate the speckle decorrelation time and consequently the blood flow index. Then, we compare the two methods of decorrelation time measurements - temporal sampling methods and spatial ensemble methods - and derive unified mathematical expressions for them in terms of measurement accuracy. Based on current technology of camera sensors and single detectors, our results indicate that spatial ensemble methods can have higher decorrelation time measurement accuracy compared to commonly used temporal sampling methods.</p