Shape Generation using Spatially Partitioned Point Clouds

We propose a method to generate 3D shapes using point clouds. Given a point-cloud representation of a 3D shape, our method builds a kd-tree to spatially partition the points. This orders them consistently across all shapes, resulting in reasonably good correspondences across all shapes. We then use PCA analysis to derive a linear shape basis across the spatially partitioned points, and optimize the point ordering by iteratively minimizing the PCA reconstruction error. Even with the spatial sorting, the point clouds are inherently noisy and the resulting distribution over the shape coefficients can be highly multi-modal. We propose to use the expressive power of neural networks to learn a distribution over the shape coefficients in a generative-adversarial framework. Compared to 3D shape generative models trained on voxel-representations, our point-based method is considerably more light-weight and scalable, with little loss of quality. It also outperforms simpler linear factor models such as Probabilistic PCA, both qualitatively and quantitatively, on a number of categories from the ShapeNet dataset. Furthermore, our method can easily incorporate other point attributes such as normal and color information, an additional advantage over voxel-based representations.Comment: To appear at BMVC 201

Biotechnological aspects of sulfate reduction with methane as electron donor

Biological sulfate reduction can be used for the removal and recovery of oxidized sulfur compounds and metals from waste streams. However, the costs of conventional electron donors, like hydrogen and ethanol, limit the application possibilities. Methane from natural gas or biogas would be a more attractive electron donor. Sulfate reduction with methane as electron donor prevails in marine sediments. Recently, several authors succeeded in cultivating the responsible microorganisms in vitro. In addition, the process has been studied in bioreactors. These studies have opened up the possibility to use methane as electron donor for sulfate reduction in wastewater and gas treatment. However, the obtained growth rates of the responsible microorganisms are extremely low, which would be a major limitation for applications. Therefore, further research should focus on novel cultivation technique

Development of reduced polynomial chaos-Kriging metamodel for uncertainty quantification of computational aerodynamics

2018 Summer.Includes bibliographical references.Computational fluid dynamics (CFD) simulations are a critical component of the design and development of aerodynamic bodies. However, as engineers attempt to capture more detailed physics, the computational cost of simulations increases. This limits the ability of engineers to use robust or multidisciplinary design methodologies for practical engineering applications because the computational model is too expensive to evaluate for uncertainty quantification studies and off-design performance analysis. Metamodels (surrogate models) are a closed-form mathematical solution fit to only a few simulation responses which can be used to remedy this situation by estimating off-design performance and stochastic responses of the CFD simulation for far less computational cost. The development of a reduced polynomial chaos-Kriging (RPC-K) metamodel is another step towards eliminating simulation gridlock by capturing the relevant physics of the problem in a cheap-to-evaluate metamodel using fewer CFD simulations. The RPC-K metamodel is superior to existing technologies because its model reduction methodology eliminates the design parameters which contribute little variance to the problem before fitting a high-fidelity metamodel to the remaining data. This metamodel can capture non-linear physics due to its inclusion of both the long-range trend information of a polynomial chaos expansion and local variations in the simulation data through Kriging. In this thesis, the RPC-K metamodel is developed, validated on a convection-diffusion-reaction problem, and applied to the NACA 4412 airfoil and aircraft engine nacelle problems. This research demonstrates the metamodel's effectiveness over existing polynomial chaos and Kriging metamodels for aerodynamics applications because of its ability to fit non-linear fluid flows with far fewer CFD simulations. This research will allow aerospace engineers to more effectively take advantage of detailed CFD simulations in the development of next-generation aerodynamic bodies through the use of the RPC-K metamodel to save computational cost

Multivariate financial econometrics: with applications to volatility modelling, option pricing and asset allocation

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Techniques for the Analysis and Understanding of Cosmic Evolution

abstract: The Cosmic Microwave Background (CMB) has provided precise information on the evolution of the Universe and the current cosmological paradigm. The CMB has not yet provided definitive information on the origin and strength of any primordial magnetic fields or how they affect the presence of magnetic fields observed throughout the cosmos. This work outlines an alternative method to investigating and identifying the presence of cosmic magnetic fields. This method searches for Faraday Rotation (FR) and specifically uses polarized CMB photons as back-light. I find that current generation CMB experiments may be not sensitive enough to detect FR but next generation experiments should be able to make highly significant detections. Identifying FR with the CMB will provide information on the component of magnetic fields along the line of sight of observation. The 21cm emission from the hyperfine splitting of neutral Hydrogen in the early universe is predicted to provide precise information about the formation and evolution of cosmic structure, complementing the wealth of knowledge gained from the CMB. 21cm cosmology is a relatively new field, and precise measurements of the Epoch of Reionization (EoR) have not yet been achieved. In this work I present 2σ upper limits on the power spectrum of 21cm fluctuations (Δ²(k)) probed at the cosmological wave number k from the Donald C. Backer Precision Array for Probing the Epoch of Reionization (PAPER) 64 element deployment. I find upper limits on Δ²(k) in the range 0.3 < k < 0.6 h/Mpc to be (650 mK)², (450 mK)², (390 mK)², (250 mK)², (280mK)², (250 mK)² at redshifts z = 10.87, 9.93, 8.91, 8.37, 8.13 and 7.48 respectively Building on the power spectrum analysis, I identify a major limiting factor in detecting the 21cm power spectrum. This work is concluded by outlining a metric to evaluate the predisposition of redshifted 21cm interferometers to foreground contamination in power spectrum estimation. This will help inform the construction of future arrays and enable high fidelity imaging and cross-correlation analysis with other high redshift cosmic probes like the CMB and other upcoming all sky surveys. I find future arrays with uniform (u,v) coverage and small spectral evolution of their response in the (u,v,f) cube can minimize foreground leakage while pursuing 21cm imaging.Dissertation/ThesisDoctoral Dissertation Physics 201

THE USE OF SELECTIVE ANNEALING FOR SUPERPLASTIC FORMING OF MG AZ31 ALLOY

A recent study on the Post-Formed properties of Superplastically Formed Magnesium AZ31B has shown that the heating time prior to testing has a major effect on the Post Forming properties of the superplastically material. To this point, there has been very little examination into the effect of pre-heating or annealing on superplastic forming (SPF) properties. In this work, the effects of annealing prior to the SPF of Mg AZ31 alloy were examined. Both high temperature SPF tensile and bulge specimens were formed after annealing. Multiple annealing temperatures were examined to produce specimens with grain sizes ranging from 8 andamp;igrave;m to 15 andamp;igrave;m for comparison with traditional SPF results. The results show that the effect of annealing can be suitable for the improvement of thinning and possibly the forming time of superplastically formed Magnesium alloys through the control of the microstructure

Surrogate Model Optimisation for PWR Fuel Management

Pressurised Water Reactor (PWR) fuel management is an operational problem for nuclear operators, requiring solutions on a regular basis throughout the life of the plant. A variety of conflicting factors and changing goals mean that fuel loading pattern design problems are multiobjective and, by design, have many input variables. This causes a combinatorial explosion, known as the ‘curse of dimensionality’, which makes these complex problems difficult to investigate. In this thesis, the method of surrogate model optimisation is adapted to PWR loading pattern generation. Surrogate models are developed based around three approaches: deep learning methods (convolutional neural networks and multi-layer perceptrons), the fission matrix and simulated quantum annealing. The models are used to predict core parameters of reactors in simplified optimisation scenarios for a microcore, a small modular reactor, and a ‘standard’ PWR. The experiments with deep learning models show that competitive results can be obtained for training sets using a much lower number of simulations than direct optimisation. Fission matrix experiments demonstrate the method to predict core parameters for the first time, with interesting preliminary results. Novel experiments using simulated quantum annealing demonstrate the technique is able to generate loading patterns by following heuristic rules and is suitable for application to custom optimisation hardware. The principal contribution of this work is to show that surrogate model optimisation can be used to augment fuel loading pattern optimisation, generating competitive results and providing enormous computational cost reduction and thus permitting more investigation within a given computational budget. These methods can also make use of new computational hardware such as neural chips and quantum annealers. The promising methods developed in this thesis thus provide candidate implementations that can bring the benefits of these innovations to the sphere of nuclear engineering

Biotechnological aspects of anaerobic oxidation of methane coupled to sulfate reduction

Sulfate reduction (SR) can be used for the removal and recovery of metals and oxidized sulfur compounds from waste streams. Sulfate-reducing bacteria reduce oxidized sulfur compounds to sulfide. Subsequently, sulfide can precipitate dissolved metals or can be oxidized to elemental sulfur. Both metal sulfides and elemental sulfur can be reused in various applications. SR with hydrogen or ethanol as electron donor is an established biotechnological process. However, the costs of these electron donors limit the application possibilities. Methane would be a cheaper and more attractive electron donor. SR coupled to the anaerobic oxidation of methane (AOM) occurs in marine sediments. Uncultured archaea, distantly related to methanogens, and bacteria are involved in this process. The in vitro demonstration of SR coupled to AOM gave rise to this research, which aims to develop a biotechnological process in which methane is used as electron donor for SR. Three types of anaerobic granular sludge were screened for the ability to reduce sulfate with methane as electron donor. To do so, incubations were done with 13C-labeled methane. All three sludge types anaerobically oxidized 13C-labeled methane to 13C-labeled carbon dioxide. Moreover, the presence of methane enhanced the SR rate. However, AOM by sludge was not coupled to SR, but coincides with net methanogenesis. The methane-dependent SR was caused by the inhibitory effect of methane on methanogens competing (possibly in syntrophic consortia with acetogenic bacteria) with sulfate reducers for the same endogenous substrate. Therefore, anaerobic granular sludge does not form a suitable inoculum for sulfate-reducing bioreactors fed with methane. Well-mixed ambient-pressure submersed-membrane bioreactors, fed with sulfate and methane, were inoculated with sediment from Eckernförde Bay (Baltic Sea). Initially AOM rates were extremely low (0.004 mmol L-1 day-1), but at 15ºC AOM and SR rates increased over the course of 884 days to 0.60 mmol L-1 day-1 or 1.0 mmol gVSS-1 day-1. The AOM rate doubled approximately every 3.8 months. Molecular analyses revealed that the archaea in the obtained enrichment belonged predominately to the anaerobic methanotroph ANME-2a. Both bacteria and archaea incorporated carbon derived from 13C-labeled methane into their lipids, indicating that both were involved in AOM coupled to SR. To investigate which kind of waste streams can be treated by the methane-oxidizing sulfate-reducing enrichment, the effect of environmental conditions and alternative substrates on AOM and SR was assessed. The optimum pH, salinity and temperature for SR with methane by the enrichment were 7.5, 30‰ and 20°C, respectively. The biomass had a good affinity for sulfate (Km  1.0 mM), a low affinity for methane (Km > 75 kPa) and AOM was completely inhibited by 2.4 (±0.1) mM sulfide. The enrichment utilized sulfate, thiosulfate and sulfite as electron acceptors for methane oxidation, and methane, formate, acetate and hydrogen as electron donors for SR. This study shows that methane can be used as electron donor for sulfate reduction in bioreactors. However, the low growth rate of the responsible microorganisms still forms a major bottleneck for biotechnological applications. <br/