The Ensemble Kalman Filter (EnKF) is a popular data assimilation method for state-parameter estimation. Following a sequential assimilation strategy, it breaks the problem into alternating cycles of forecast and analysis steps. In the forecast step, the dynamical model is used to integrate a stochastic sample approximating the state analysis distribution (called analysis ensemble) to obtain a forecast ensemble. In the analysis step, the forecast ensemble is updated with the incoming observation using a Kalman-like correction, which is then used for the next forecast step. In realistic large-scale applications, EnKFs are implemented with limited ensembles, and often poorly known model errors statistics, leading to a crude approximation of the forecast covariance. This strongly limits the filter performance. Recently, a new EnKF was proposed in [1] following a one-step-ahead smoothing strategy (EnKF-OSA), which involves an OSA smoothing of the state between two successive analysis. At each time step, EnKF-OSA exploits the observation twice. The incoming observation is first used to smooth the ensemble at the previous time step. The resulting smoothed ensemble is then integrated forward to compute a "pseudo forecast" ensemble, which is again updated with the same observation. The idea of constraining the state with future observations is to add more information in the estimation process in order to mitigate for the sub-optimal character of EnKF-like methods. The second EnKF-OSA "forecast" is computed from the smoothed ensemble and should therefore provide an improved background. In this work, we propose a deterministic variant of the EnKF-OSA, based on the Singular Evolutive Interpolated Ensemble Kalman (SEIK) filter. The motivation behind this is to avoid the observations perturbations of the EnKF in order to improve the scheme's behavior when assimilating big data sets with small ensembles. The new SEIK-OSA scheme is implemented and its efficiency is demonstrated by performing assimilation experiments with the highly nonlinear Lorenz model and a realistic setting of the Advanced Circulation (ADCIRC) model configured for storm surge forecasting in the Gulf of Mexico during Hurricane Ike.

This thesis compares the simple Autoregressive (AR) model against the k- Nearest Neighbor (k-NN) model to make a point forecast of five energy commodity prices. Those commodities are natural gas, heating oil, gasoline, ethanol, and crude oil. The data for the commodities are monthly and, for each commodity, two-thirds of the data are used for an in-sample forecast, and the remaining one-third of the data are used to perform an out-of-sample forecast. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) are used to compare the two forecasts. The results showed that one method is superior by one measure but inferior by another. Although the differences of the two models are minimal, it is up to a decision maker as to which model to choose. The Diebold-Mariano (DM) test was performed to test the relative accuracy of the models. For all five commodities, the results failed to reject the null hypothesis indicating that both models are equally accurate.

This thesis discusses development of a future visioning system model that can be adopted to create new product concepts for consumer electronics companies operating in a highly competitive business environment. The research work investigates consumer electronic product companies and their market environment to identify problematic issues and indicates that a proactive new product strategy which opens new markets through developing concept-led products is a strategic priority, thus the concept development stage in new product development process is in need of improvement. An evaluation of existing concept development tools for the purpose of proactive product strategy is presented and concludes that future visioning procedure is the most appropriate tool. To develop a future visioning system model as a concept development tool, the theoretical future visioning system models are analysed and mapped to extract essential structure and contents of future visioning procedure. The consequent future visioning system model is then revised according to the findings and suggestions from the field research work which investigated four major consumer electronics product companies in practice. The findings also validates the necessity of adopting a proactive product strategy and evaluates acceptability of the future visioning system model for practical use. The final future visioning system model is defined after the opinions of the design managers are considered and applied. The major suggestions from the research findings are: (1) Executing proactive product strategy can be a valuable strategic tool (2) A new process is necessary for the companies to create one-step-ahead product (3) Future visioning system is recommended as an advanced approach that creates new product concept. (4) Future visioning system model should consist of eight stages: project initiation, environmental scanning, future visioning, generating product concepts, scenario planning, concept testing, concept visualisation, and finalized concepts. (5) Product concepts can be generated from future vision by applying backcasting. (6) Scenario planning should be used in the future visioning system model as a concept testing tool providing objective validating criteria. (7) Executing a future visioning system model creates new roles for the designer such as information integrator, process moderator, and futurist.

Predicting and characterizing the state of the ocean is needed for various scientific, industrial, social, management, and recreational activities. Despite the tremendous progress in ocean modeling and simulation capabilities, the ocean models still suffer from different sources of uncertainties. To obtain accurate ocean state predictions, data assimilation (DA) is widely used to constrain the ocean model outputs with available observations. Ensemble Kalman filtering (EnKF) is a sequential DA approach that represents the distribution of the system state through an ensemble of ocean state samples. Different factors may limit the performance of an EnKF in realistic ocean applications, particularly the use of small ensembles and poorly known model error statistics, and also to a lesser extent the strongly nonlinear variations and abrupt regime changes, and unsatisfied underlying assumptions such as the commonly used white observation noise assumption. The objective of this PhD thesis is to develop, implement and test efficient ensemble filtering schemes to enhance the performances of EnKFs in such challenging settings. We resort to the one-step-ahead (OSA) smoothing formulation of the Bayesian filtering problem to introduce EnKFs involving a new update step with future observations (smoothing) between two successive analyses, thereby conditioning the ensemble sampling with more information. We show that this approach enhances the EnKFs performances by providing improved ensemble background statistics, and showcase its performance with realistic ocean DA and forecasting applications, namely a storm surge EnKF forecasting system and the Red Sea ensemble DA and forecasting system. We then derive new EnKF-based schemes accounting for time-correlated observation errors for efficient DA into the class of large dimensional DA problems where observation errors statistics are correlated in time, and further propose a new approach for online estimation of the parameters of the observation error time-correlations model concurrently with the state. We also exploit the OSA-smoothing formulation to propose a new joint EnKF with OSA-smoothing which mitigates for the reported inconsistencies in the joint EnKF update for efficient DA into one-way-coupled systems.

Scanning Laser Epitaxy (SLE) is a new layer-by-layer additive manufacturing process being developed in the Direct Digital Manufacturing Laboratory at Georgia Tech. SLE allows for the fabrication of three-dimensional objects with specified microstructure through the controlled melting and re-solidification of a metal powder placed atop a base substrate. This dissertation discusses the work done to date on assessing the feasibility of using SLE to both repair single crystal (SX) turbine airfoils and manufacture functionally graded turbine components. Current processes such as selective laser melting (SLM) are not able to create structures with defined microstructure and often have issues with warping of underlying layers due to the high temperature gradients present when scanning a high power laser beam. Additionally, other methods of repair and buildup have typically been plagued by crack formation, equiaxed grains, stray grains, and grain multiplication that can occur when dendrite arms are separated from their main dendrites due to remelting. In this work, it is shown that the SLE process is capable of creating fully dense, crack-free equiaxed, directionally-solidified, and SX structures. The SLE process, though, is found to be currently constrained by the cumbersome method of choosing proper parameters and a relative lack of repeatability. Therefore, it is hypothesized that a real-time feedback control scheme based upon a robust offline model will be necessary both to create specified defect-free microstructures and to improve the repeatability of the process enough to allow for multi-layer growth. The proposed control schemes are based upon temperature data feedback provided at high frame rate by a thermal imaging camera. This data is used in both PID and model reference adaptive control (MRAC) schemes and drives the melt pool temperature during processing towards a reference melt pool temperature that has been found to give a desired microstructure in the robust offline model of the process. The real-time control schemes will enable the ground breaking capabilities of the SLE process to create engine-ready net shape turbine components from raw powder material.