Rheology and Flow Emplacement Processes of the 1954 Lavas, Mount Ngauruhoe

The rheology and emplacement dynamics of basaltic and rhyolitic lava flows have been studied extensively, leading to the development of numerous rheological and flow behaviour models; however, the flow dynamics of more intermediate composition lavas, particularly those emplaced on relatively steep slopes, is less well-constrained. The 1954-55 eruption of Mount Ngauruhoe, a young, composite cone of the Tongariro Volcanic Centre, generated at least 11, well-preserved, spatter-fed, basaltic andesite a'a lavas on the steep, north-west flanks of the cone. The rheological properties and flow dynamics of these lavas are quantified by incorporating morphological, petrographic and geochemical data collected from these flow deposits into a range of existing numerical models, and the results compared with documented eye-witness accounts. The lava flow deposits are typically long, narrow, discrete units characterised by comparable morphological traits on the steep slopes of the cone and varying in dimension, morphology and flow surface features on the shallower slopes. Flow surfaces are typically autobrecciated and display a large-scale, lateral trend in clast size and morphology across flow widths. The 1954 lavas are petrographically and geochemically homogenous with no apparent trends associated with successively emplaced lava flows. The rheological properties of the lavas at the time of initial flow advance are therefore assumed to be comparable for each flow. Lava viscosity was estimated at 102 to 104 Pa s for the temperature range 1150 to 950 C. Yield strength was difficult to quantify but is assumed in this study to be relatively low (~ 25 Pa). Calculated flow velocity, effusion rate and emplacement duration are not well-constrained against documented eye-witness accounts. Mean flow velocity (0.03 to 0.04 m/s) was estimated from eye-witness reports, and used to determine flow emplacement durations between ~ 2 to 48 hours, comparable with documented duration times. Effusion rates could not be definitively quantified but flow deposit morphology and documented accounts indicate that intermittent episodes of high effusion rates over a short duration were associated with the emplacement of the 1954 lavas. Three major controls on the emplacement of the 1954 lava flows have been identified. Effusion rate and duration was the primary control on the development of single unit lava flows, flow channel drainage on steep slopes and in limiting run-out distances from the vent. Low initial viscosity and yield strength promoted high flow velocities on steep slopes and low surface cooling rates. Relatively short flow emplacement duration times precluded significant downflow viscosity and yield strength increases. Slope gradient and topographic obstacles were major controls on flow emplacement processes. Slope gradient was the dominant control on flow velocity, flow width and depth and surface autobrecciation, while morphology, flow path direction and surface folding were constrained by local slope gradient variations and/or topographic obstacles

Trajectory Generation for Noise-Constrained Autonomous Flight Operations

One of the major factors in acceptance of aircraft operating in urban areas is noise. In this work, we build on a framework for trajectory generation in order to account for limits on acousticmetrics at one ormore observer locations. The spatial trajectories are generated using Bzier polynomials and satisfy dynamic, acoustic, and mission constraints. The trajectories also guarantee spatial or temporal separation between vehicles for multi-vehicle operations. A simulation example is provided that demonstrates the reduction in noise levels at a set of measurement locations

Science support for the Earth radiation budget sensor on the Nimbus-7 spacecraft

Experimental data supporting the Earth radiation budget sensor on the Nimbus 7 Satellite is given. The data deals with the empirical relations between radiative flux, cloudiness, and other meteorological parameters; response of a zonal climate ice sheet model to the orbital perturbations during the quaternary ice ages; and a simple parameterization for ice sheet ablation rate

Real-time Predictive Energy Management of Hybrid Electric Heavy Vehicles by Sequential Programming

With the objective of reducing fuel consumption, this paper presents real-time predictive energy management of hybrid electric heavy vehicles. We propose an optimal control strategy that determines the power split between different vehicle power sources and brakes. Based on the model predictive control (MPC) and sequential programming, the optimal trajectories of the vehicle velocity and battery state of charge are found for upcoming horizons with a length of 5-20 km. Then, acceleration and brake pedal positions together with the battery usage are regulated to follow the requested speed and state of charge that is verified using a vehicle plant model. The main contribution of this paper is the development of a sequential linear program for predictive energy management that is faster and simpler than sequential quadratic programming in tested solvers and gives trajectories that are very close to the best trajectories found by nonlinear programming. The performance of the method is also compared to two different sequential quadratic programs

Appearance Preserving Rendering of Out-of-Core Polygon and NURBS Models

In Computer Aided Design (CAD) trimmed NURBS surfaces are widely used due to their flexibility. For rendering and simulation however, piecewise linear representations of these objects are required. A relatively new field in CAD is the analysis of long-term strain tests. After such a test the object is scanned with a 3d laser scanner for further processing on a PC. In all these areas of CAD the number of primitives as well as their complexity has grown constantly in the recent years. This growth is exceeding the increase of processor speed and memory size by far and posing the need for fast out-of-core algorithms. This thesis describes a processing pipeline from the input data in the form of triangular or trimmed NURBS models until the interactive rendering of these models at high visual quality. After discussing the motivation for this work and introducing basic concepts on complex polygon and NURBS models, the second part of this thesis starts with a review of existing simplification and tessellation algorithms. Additionally, an improved stitching algorithm to generate a consistent model after tessellation of a trimmed NURBS model is presented. Since surfaces need to be modified interactively during the design phase, a novel trimmed NURBS rendering algorithm is presented. This algorithm removes the bottleneck of generating and transmitting a new tessellation to the graphics card after each modification of a surface by evaluating and trimming the surface on the GPU. To achieve high visual quality, the appearance of a surface can be preserved using texture mapping. Therefore, a texture mapping algorithm for trimmed NURBS surfaces is presented. To reduce the memory requirements for the textures, the algorithm is modified to generate compressed normal maps to preserve the shading of the original surface. Since texturing is only possible, when a parametric mapping of the surface - requiring additional memory - is available, a new simplification and tessellation error measure is introduced that preserves the appearance of the original surface by controlling the deviation of normal vectors. The preservation of normals and possibly other surface attributes allows interactive visualization for quality control applications (e.g. isophotes and reflection lines). In the last part out-of-core techniques for processing and rendering of gigabyte-sized polygonal and trimmed NURBS models are presented. Then the modifications necessary to support streaming of simplified geometry from a central server are discussed and finally and LOD selection algorithm to support interactive rendering of hard and soft shadows is described

Controls on the evolution of strength and failure style in shallow rock slope failures

Rock fall failure comprises fracturing through zones of intact rock, known as rock bridges, and kinematic release along discontinuity surfaces. Understanding controls on magnitude – frequency relationships of rockfalls, and their associated failure characteristics aids susceptibility analysis and interpretation of pre-failure deformation. For failure to occur, these rock bridges must have been weakened, with this damage accumulation driven by a suite of weathering processes. This thesis aims to explore the spatial and temporal controls on weathering induced strength degradation and its subsequent influence on the mechanics of rockfall detachment. Within this, it examines the role of gravitational ambient stress, as dictated by slope topography and rock mass structure, which recent research suggests influences the efficiency of weathering processes. The project integrates field observations, analogue experiments and numerical modelling over varying spatial scales. Terrestrial laser scanning and gigapixel photography are combined to forensically map rock bridge attributes within rockfall detachment surfaces. The role of slope geometry and rock mass structure in concentrating stress is assessed via conceptual finite element models. Finally, samples are subjected to stress conditions induced by the slope structure and environmental conditions in a series of weathering analogue experiments. Together, these results indicate that weathering significantly reduces intact rock strength with areas of stress concentration purely a mechanical control on rockfall release rather than a temporal control on weakening. Weaker rock is characterised by substantial post-peak strength, which requires multiple stages of brittle fracture before ultimate failure occurs. This in turn influences the stages of failure required through rock bridges before final failure, with this number of rock bridges dependent on rockfall size. Mechanically, failure mode is dependent on rock bridge proportion, distribution and location for individual rockfalls. A conceptual model describes magnitude-frequency characteristics and the observable pattern of pre-failure deformation expected for different stages of weathering

Smart Classifiers and Bayesian Inference for Evaluating River Sensitivity to Natural and Human Disturbances: A Data Science Approach

Excessive rates of channel adjustment and riverine sediment export represent societal challenges; impacts include: degraded water quality and ecological integrity, erosion hazards to infrastructure, and compromised public safety. The nonlinear nature of sediment erosion and deposition within a watershed and the variable patterns in riverine sediment export over a defined timeframe of interest are governed by many interrelated factors, including geology, climate and hydrology, vegetation, and land use. Human disturbances to the landscape and river networks have further altered these patterns of water and sediment routing. An enhanced understanding of river sediment sources and dynamics is important for stakeholders, and will become more critical under a nonstationary climate, as sediment yields are expected to increase in regions of the world that will experience increased frequency, persistence, and intensity of storm events. Practical tools are needed to predict sediment erosion, transport and deposition and to characterize sediment sources within a reasonable measure of uncertainty. Water resource scientists and engineers use multidimensional data sets of varying types and quality to answer management-related questions, and the temporal and spatial resolution of these data are growing exponentially with the advent of automated samplers and in situ sensors (i.e., “big data”). Data-driven statistics and classifiers have great utility for representing system complexity and can often be more readily implemented in an adaptive management context than process-based models. Parametric statistics are often of limited efficacy when applied to data of varying quality, mixed types (continuous, ordinal, nominal), censored or sparse data, or when model residuals do not conform to Gaussian distributions. Data-driven machine-learning algorithms and Bayesian statistics have advantages over Frequentist approaches for data reduction and visualization; they allow for non-normal distribution of residuals and greater robustness to outliers. This research applied machine-learning classifiers and Bayesian statistical techniques to multidimensional data sets to characterize sediment source and flux at basin, catchment, and reach scales. These data-driven tools enabled better understanding of: (1) basin-scale spatial variability in concentration-discharge patterns of instream suspended sediment and nutrients; (2) catchment-scale sourcing of suspended sediments; and (3) reach-scale sediment process domains. The developed tools have broad management application and provide insights into landscape drivers of channel dynamics and riverine solute and sediment export

BRUISE DETECTION IN APPLES USING 3D INFRARED IMAGING AND MACHINE LEARNING TECHNOLOGIES

Bruise detection plays an important role in fruit grading. A bruise detection system capable of finding and removing damaged products on the production lines will distinctly improve the quality of fruits for sale, and consequently improve the fruit economy. This dissertation presents a novel automatic detection system based on surface information obtained from 3D near-infrared imaging technique for bruised apple identification. The proposed 3D bruise detection system is expected to provide better performance in bruise detection than the existing 2D systems. We first propose a mesh denoising filter to reduce noise effect while preserving the geometric features of the meshes. Compared with several existing mesh denoising filters, the proposed filter achieves better performance in reducing noise effect as well as preserving bruised regions in 3D meshes of bruised apples. Next, we investigate two different machine learning techniques for the identification of bruised apples. The first technique is to extract hand-crafted feature from 3D meshes, and train a predictive classifier based on hand-crafted features. It is shown that the predictive model trained on the proposed hand-crafted features outperforms the same models trained on several other local shape descriptors. The second technique is to apply deep learning to learn the feature representation automatically from the mesh data, and then use the deep learning model or a new predictive model for the classification. The optimized deep learning model achieves very high classification accuracy, and it outperforms the performance of the detection system based on the proposed hand-crafted features. At last, we investigate GPU techniques for accelerating the proposed apple bruise detection system. Specifically, the dissertation proposes a GPU framework, implemented in CUDA, for the acceleration of the algorithm that extracts vertex-based local binary patterns. Experimental results show that the proposed GPU program speeds up the process of extracting local binary patterns by 5 times compared to a single-core CPU program

Synchronization of electrically coupled resonate-and-fire neurons

Electrical coupling between neurons is broadly present across brain areas and is typically assumed to synchronize network activity. However, intrinsic properties of the coupled cells can complicate this simple picture. Many cell types with strong electrical coupling have been shown to exhibit resonant properties, and the subthreshold fluctuations arising from resonance are transmitted through electrical synapses in addition to action potentials. Using the theory of weakly coupled oscillators, we explore the effect of both subthreshold and spike-mediated coupling on synchrony in small networks of electrically coupled resonate-and-fire neurons, a hybrid neuron model with linear subthreshold dynamics and discrete post-spike reset. We calculate the phase response curve using an extension of the adjoint method that accounts for the discontinuity in the dynamics. We find that both spikes and resonant subthreshold fluctuations can jointly promote synchronization. The subthreshold contribution is strongest when the voltage exhibits a significant post-spike elevation in voltage, or plateau. Additionally, we show that the geometry of trajectories approaching the spiking threshold causes a "reset-induced shear" effect that can oppose synchrony in the presence of network asymmetry, despite having no effect on the phase-locking of symmetrically coupled pairs

Atomistic Simulation Studies of Thin Film Growth and Plastic Deformation in Metals and Metal/Ceramic Nanostructures

Despite the significant improvements in manufacturing and synthesis processes of metals and ceramics in the past decades, there are still areas in which the procedure is still frequently more of an art or skill rather than a science. Therefore, systematic and combined experimental and computational studies are required to facilitate the development of techniques that offer thorough understanding of the events taking place during manufacturing and synthesis processes. With regard to these issues, it is paramount to address microscale characterizations and atomic scale understanding of the events during fabrication processes. One of the focuses of this study is unraveling fundamental events and mechanisms during thin film deposition of Cu on TiN substrates. It is demonstrated for the first time that at the very early stage of growth, BCC-Cu grows pseudomorphically on the TiN substrate as a very thin continuous film using a sequential molecular dynamics (MD)/time-stamped force-bias Monte Carlo (tfMC) algorithm. The Nishiyama-Wasserman mechanism, however, causes the Cu thin film to change from predominantly BCC-Cu to predominantly FCC-Cu with abundant nanotwins. As another topic, because of the tendency towards miniaturization in the past decades, studying the mechanical behavior of fabricated specimen at microscale or nanoscale via atomistic simulations is beneficial to characterize the deformation mechanisms associated with the observed phenomena in experiments. In that regard, we examined the impact of geometry and nanotwinned structure on the mechanical response and deformation mechanisms of nanoscale cylindrical Cu pillars capped between rigid substrates under tensile loading at a constant strain rate using MD simulation. The last topic in this dissertation is about the generalized stacking fault energy profile, which is a crucial component of alloy design since it is vital to models of metal plasticity. Models for thermal vibrations must take into account the stacking fault free energy profile; however, existing techniques can only determine how intrinsic stacking faults vary with temperature. We demonstrate how the PAFI linear scaling method, which completely takes into account anharmonic thermal vibrations that can be used to determine the complete stacking fault free energy profile