The present work presents a framework for multiscale modeling of multimaterial flows using surrogate modeling techniques in the particular context of shocks interacting with clusters of particles. The work builds a framework for bridging scales in shock-particle interaction by using ensembles of resolved mesoscale computations of shocked particle laden flows. The information from mesoscale models is “lifted” by constructing metamodels of the closure terms - the thesis analyzes several issues pertaining to surrogate-based multiscale modeling frameworks. First, to create surrogate models, the effectiveness of several metamodeling techniques, viz. the Polynomial Stochastic Collocation method, Adaptive Stochastic Collocation method, a Radial Basis Function Neural Network, a Kriging Method and a Dynamic Kriging Method is evaluated. The rate of convergence of the error when used to reconstruct hypersurfaces of known functions is studied. For sufficiently large number of training points, Stochastic Collocation methods generally converge faster than the other metamodeling techniques, while the DKG method converges faster when the number of input points is less than 100 in a two-dimensional parameter space. Because the input points correspond to computationally expensive micro/meso-scale computations, the DKG is favored for bridging scales in a multi-scale solver. After this, closure laws for drag are constructed in the form of surrogate models derived from real-time resolved mesoscale computations of shock-particle interactions. The mesoscale computations are performed to calculate the drag force on a cluster of particles for different values of Mach Number and particle volume fraction. Two Kriging-based methods, viz. the Dynamic Kriging Method (DKG) and the Modified Bayesian Kriging Method (MBKG) are evaluated for their ability to construct surrogate models with sparse data; i.e. using the least number of mesoscale simulations. It is shown that unlike the DKG method, the MBKG method converges monotonically even with noisy input data and is therefore more suitable for surrogate model construction from numerical experiments. In macroscale models for shock-particle interactions, Subgrid Particle Reynolds’ Stress Equivalent (SPARSE) terms arise because of velocity fluctuations due to fluid-particle interaction in the subgrid/meso scales. Mesoscale computations are performed to calculate the SPARSE terms and the kinetic energy of the fluctuations for different values of Mach Number and particle volume fraction. Closure laws for SPARSE terms are constructed using the MBKG method. It is found that the directions normal and parallel to those of shock propagation are the principal directions of the SPARSE tensor. It is also found that the kinetic energy of the fluctuations is independent of the particle volume fraction and is 12-15% of the incoming shock kinetic energy for higher Mach Numbers. Finally, the thesis addresses the cost of performing large ensembles of resolved mesoscale computations for constructing surrogates. Variable fidelity techniques are used to construct an initial surrogate from ensembles of coarse-grid, relative inexpensive computations, while the use of resolved high-fidelity simulations is limited to the correction of initial surrogate. Different variable-fidelity techniques, viz the Space Mapping Method, RBFs and the MBKG methods are evaluated based on their ability to correct the initial surrogate. It is found that the MBKG method uses the least number of resolved mesoscale computations to correct the low-fidelity metamodel. Instead of using 56 high-fidelity computations for obtaining a surrogate, the MBKG method constructs surrogates from only 15 resolved computations, resulting in drastic reduction of computational cost.

Native palms, such as the Sabal palmetto, play an important role in maintaining the ecological balance in Florida. As a side-effect of modern globalization, new phytopathogens like Texas Phoenix Palm Decline have been introduced into forest systems that threaten native palms. This presents new challenges for forestry managers and geographers. Advances in remote sensing has assisted the practice of forestry by providing spatial metrics regarding the type, quantity, location, and the state of heath for trees for many years. This study provides spatial details regarding the general palm decline in Florida by taking advantage of the new developments in deep learning constructs coupled with high resolution WorldView-2 multispectral/temporal satellite imagery and LiDAR point cloud data. A novel approach using TensorFlow deep learning classification, multiband spatial statistics and indices, data reduction, and step-wise refinement masking yielded a significant improvement over Random Forest classification in a comparison analysis. The results from the TensorFlow deep learning were then used to develop an Empirical Bayesian Kriging continuous raster as an informative map regarding palm decline zones using Normalized Difference Vegetation Index Change. The significance from this research showed a large portion of the study area exhibiting palm decline and provides a new methodology for deploying TensorFlow learning for multispectral satellite imagery.

No / Sulfate causes various health issues for human if on average daily intake of sulfate is more than 500 mg from drinking-water, air, and food. Moreover, the presence of sulfate in rainwater causes acid rains which has harmful effects on animals and plants. Food is the major source of sulfate intake; however, in areas of South-Punjab, Pakistan, the drinking-water containing high levels of sulfate may constitute the principal source of intake. The spatial behavior of sulfate in groundwater is recorded for South-Punjab province, Pakistan. The spatial dependence of the response variable (sulfate) is modeled by using various variograms models that are estimated by maximum likelihood method, restricted maximum likelihood method, ordinary least squares, and weighted least squares. The parameters of estimated variogram models are utilized in ordinary kriging, universal kriging, Bayesian kriging with constant trend, and varying trend and the above methods are used for interpolation of sulfate concentration. The K-fold cross validation is used to measure the performances of variogram models and interpolation methods. Bayesian kriging with a constant trend produces minimum root mean square prediction error than other interpolation methods. Concentration of sulfate in drinking water within the study area is increasing to the Northern part, and health risks are really high due to poor quality of water.

The implementation and application of Geographic Information Systems (GIS) and spatial analyses have become standard practice in many archaeological projects. In this study, we demonstrate how GIS can play a crucial role in the study of taphonomy, i.e., understanding the processes that underpinned the creation of archaeological deposits, in this case the distribution of artifacts across an archeological site. The Stelida Naxos Archeological Project (SNAP) is focused on the exploration of a Paleolithic-Mesolithic stone tool quarry site located on the island of Naxos, Greece. An extensive pedestrian survey was conducted during the 2013 and 2014 archeological field seasons. An abundance of lithic material was collected across the surface, with some diagnostic pieces dating to more than 250 Kya. Spatial statistical analysis (Empirical Bayesian Kriging) was conducted on the survey data to generate predictive distribution maps for the site. This study then determined the contextual integrity of the surface artifact distributions through a study of geomorphic processes. A digital surface model (DSM) of the site was produced using Unmanned Aerial Vehicle (UAV) aerial photography and Structure from Motion (SfM) terrain modeling. The DSM employed to develop a Revised Universal Soil Loss Equation (RUSLE) model and hydrological flow models. The model results provide important insights into the site geomorphological processes and allow categorization of the diagnostic surface material locations based on their contextual integrity. The GIS analysis demonstrates that the surface artifact distribution has been significantly altered by post-depositional geomorphic processes, resulting in an overall low contextual integrity of surface artifacts. Conversely, the study identified a few areas with high contextual integrity, loci that represent prime locations for excavation. The results from this study will not only be used to inform and guide further development of the archeological project (as well as representing significant new data in its own right), but also contributes to current debates in survey archaeology, and in mapping and prospection more generally. This project demonstrates the benefit of using spatial analysis as a tool for planning of pedestrian surveys and for predictive mapping of artifact distributions prior to archaeological excavations. / Thesis / Master of Science (MSc)

Submitted by (ana.araujo@ufrpe.br) on 2016-05-20T15:59:30Z No. of bitstreams: 1 Antonio Lopes Pesoa.pdf: 686455 bytes, checksum: 89c64469a23f829cb322108723a916c9 (MD5) / Made available in DSpace on 2016-05-20T15:59:30Z (GMT). No. of bitstreams: 1 Antonio Lopes Pesoa.pdf: 686455 bytes, checksum: 89c64469a23f829cb322108723a916c9 (MD5) Previous issue date: 2006-02-24 / This study analyzed the existing relationship among measurements of soil apparent electrical conductivity in an alluvial valley in the Agreste region of Pernambuco State and its spatial variability in the subsurface. The soil apparent electric conductivity was investigated through an electromagnetic induction EM 38 equipment. The readings have been carried out both in the vertical and horizontal modes. The measurements have been analyzed through the classic descriptive statistics as well as geostatistics and bayesian approach. The statistical analyses had shown that the data of apparent electric conductivity had adjusted to a normal distribution, presenting a high space variability for the horizontal mode and an average space variability for the way of vertical operation. In order to allow the use of the geostatistical methodology, the experimental semivariogram was constructed, and fitted to a theoretical model. Considering the spatial dependence mapping of the salinized areas have been performed. The best theoretical models for the vertical mode and for the horizontal operation were the gaussian model and the exponential model, through the crossed validation and using the Akaike’s Information Criterion .The bayesian approach focused the spatial predictionrelating the method of the maximum likelihood with the functions of prioris distributions for each parameter, considering the uncertainty associated to each one of these distributions. It was verified that the adjusted semivariograms had not presented significant differences in the validation of the geostatistics methodology and in the bayesian approach. / Esta dissertação analisou a relação existente entre medidas de condutividade elétrica aparente de um solo aluvial da região Agreste do Estado de Pernambuco,e a sua variabilidade espacial na camada subsuperficial. A condutividade elétrica aparente do solo foi investigada através de equipamento de indução eletromagnética EM 38. As leituras efetuadas com o EM 38 foram tanto no modo vertical como no modo horizontal. As medidas obtidas em campo foram analisadas através da estatística descritiva clássica, bem como através das metodologias geoestatística e abordagem bayesiana. As análises estatísticas mostraram que os dados de condutividade elétrica aparente se ajustaram a uma distribuição normal a apresentaram uma alta variabilidade espacial para o modo de operação horizontal e uma média variabilidade espacial para o modo de operação vertical. Através da metodologia geoestatística foi construído o semivariograma experimental que, posteriormente, foi ajustado a um modelo teórico. O melhor ajuste de modelo teórico foi obtido, tanto para o modo de operação vertical como para o modo de operação horizontal, para o modelo gaussiano e para o modelo exponencial, efetuada através da validação cruzada edo Critério de Informação de Akaike. A partir da dependência espacial, foi elaborado o mapeamento das áreas salinizadas. A abordagem bayesiana focalizou a predição espacial, relacionando o método da máxima verossimilhança com as funções de distribuições prioris de cada parâmetro, considerando o grau de incerteza associado a cada uma dessas distribuições. Verificou-se que os semivariogramas ajustados não apresentaram diferenças significativas na validação da metodologia geoestatística e na abordagem bayesiana.