The thesis introduces an approach for obtaining higher level decision support information using electromagnetic transient (EMT) simulation programs. In this approach, a suite of higher level driver programs (decision support tools) control the simulator to gain important information about the system being simulated. These tools conduct a sequence of simulation runs, in each of which the study parameters are carefully selected based on the observations of the earlier runs in the sequence. In this research two such tools have been developed in conjunction with the PSCAD/EMTDC electromagnetic transient simulation program. The first tool is an improved optimization algorithm, which is used for automatic optimization of the system parameters to achieve a desired performance. This algorithm improves the capabilities of the previously reported method of optimization-enabled electromagnetic transient simulation by using an enhanced gradient-based optimization algorithm with constraint handling techniques. In addition to allow handling of design problems with more than one objective the thesis proposes to augment the optimization tool with the technique of Pareto optimality. A sequence of optimization runs are conducted to obtain the Pareto frontier, which quantifies the tradeoffs between the design objectives. The frontier can be used by the designer for decision making process. The second tool developed in this research helps the designer to study the effects of uncertainties in a design. By using a similar multiple-run approach this sensitivity analysis tool provides surrogate models of the system, which are simple mathematical functions that represent different aspects of the system performance. These models allow the designer to analyze the effects of uncertainties on system performance without having to conduct any further time-consuming EMT simulations. In this research it has been also proposed to add probabilistic analysis capabilities to the developed sensitivity analysis tool. Since probabilistic analysis of a system using conventional techniques (e.g. Monte-Carlo simulations) normally requires a large number of EMT simulation runs, using surrogate models instead of the actual simulation runs yields significant savings in terms of shortened simulation time. A number of examples have been used throughout the thesis to demonstrate the application and usefulness of the proposed tools.

As a result of concern about the effects of CO2 emssions on the global warming, there is increasing pressure to reduce such emissions from power generation systems. The use of biomass co-firing with coal in conventional pulverised fuel power plants has provided the most immediate route to introduce a class of fuel that is regarded as both sustainable and carbon neutral as it produces less net CO2 emissions. In the future it is anticipated that increased levels of biomass will be required to use in such systems to accomplish the desired CO2 emissions targets. The use of biomass, however, is believed to result in severe fireside corrosion of superheater and reheater tubing and cause unexpected early failures of tubes, which can lead to significant economic penalties. Moreover, future pulverised fuel power systems will need to use much higher steam temeptures and pressures to increase the boiler efficiency. Higher operating temperatures and pressures will also increase the risk of fireside corrosion damage to the boiler tubing and lead to shorter component life. Predicting the remaining service life of superheater and reheater tubes in coal-biomass fired power plants is therefore an important aspect of managing such power plants. The path to this type of failure of heat exchangers involves five processes: combustion, deposition, fireside corrosion, steam-side oxidation, and creep. Various models or partial models each of these processes are available from existing research, but to fully understand the impact of new fuel mixtures (i.e. biomass and coal) and changing operating conditions on such failures, an integrated model of all of these processes is required. This work has produced an integrated set of models and so predicted the remaining service life of superheater/reheater tubes based on the three frameworks which have been developed by analysing those models used in depicting the five processes: one was conceptual and the other two were based on mathematical model. In addition, the outputs of the integrated mathematical models were compared with the laboratory generated data from Cranfield University as well as historical data from Central Electricity Research Laboratories. Furthermore, alternative models for each process were applied in the model and the results were compared with other models results as well as with the experimental data. Based on these comparisons and the availability of models constants the best models were chosen in the integrated model. Finally, a sensitivity analysis was performed to assess the effect of different model input values on the residual life superheater and reheater tubing. Mid-wall metal temperature of tubes was found to be the most important factor affecting the remaining service life of boiler tubing. Tubing wall thickness and outer diameter were another critical input in the model. Significant differences were observed between the residual life of thin-walled and thick-walled tubes.

This thesis improved a farm-level stochastic model with Monte Carlo simulation to estimate the impact of health performance and market conditions on dairy farm economics. The main objective of this model was to estimate the costs of seven common clinical dairy diseases (mastitis, lameness, metritis, retained placenta, left displaced abomasum, ketosis, and milk fever) in the U.S. An online survey was conducted to estimate veterinary fees, treatment costs, and producer labor data. The total disease costs were higher in multiparous cows than in primiparous cows. Left displaced abomasum had the greatest costs in all parities ($404.74 in primiparous cows and $555.79 in multiparous cows). Milk loss, treatment costs, and culling costs were the largest three cost categories for all diseases. A secondary objective of this model was to evaluate the dairy cow’s value, the optimal culling decision, and the cost of days open with flexible model inputs. Dairy cow value under 2013 market conditions was lower than previous studies due to the high slaughter and feed price and low replacement price. The first optimal replacement moment appeared in the middle of the first parity. Furthermore, the cost of days open was considerably influenced by the market conditions.

Dedicated energy crops have the potential to supply a sustainable biomass feedstock to support the bioenergy industry. However, a major constraint for promoting energy crops has been the availability of land for establishing energy crops. In this study, we developed a spatially-explicit model to identify suitable and economically feasible sites for establishing energy crops based on biomass price, production costs and site-specific biomass productivity. Results from our study provided an objective evaluation of factors that influence the amount and spatial distribution of land suitable for establishing energy crops. In addition, our model had the ability to capture variation across the feasible areas because of changing biomass market and policy conditions. By performing a sensitivity analysis with different market and policy scenarios, we were able to identify the most effective and favorable scenarios that could maximize the available land for producing energy crops.

No description available.

<p>To understand the underlying mechanisms of cardiac arrhythmias, computational models are used to study heart rhythm dynamics. The parameters of these models carry inherent uncertainty. Therefore, to interpret the results of these models, uncertainty quantification (UQ) and sensitivity analysis (SA) are important. Polynomial chaos (PC) is a computationally efficient method for UQ and SA in which a model output Y, dependent on some independent uncertain parameters represented by a random vector &xi;, is approximated as a spectral expansion in multidimensional orthogonal polynomials in &xi;. The expansion can then be used to characterize the uncertainty in Y.</p><p>PC methods were applied to UQ and SA of the dynamics of a two-dimensional return-map model of cardiac action potential duration (APD) restitution in a paced single cell. Uncertainty was considered in four parameters of the model: three time constants and the pacing stimulus strength. The basic cycle length (BCL) (the period between stimuli) was treated as the control parameter. Model dynamics was characterized with bifurcation analysis, which determines the APD and stability of fixed points of the model at a range of BCLs, and the BCLs at which bifurcations occur. These quantities can be plotted in a bifurcation diagram, which summarizes the dynamics of the model. PC UQ and SA were performed for these quantities. UQ results were summarized in a novel probabilistic bifurcation diagram that visualizes the APD and stability of fixed points as uncertain quantities.</p><p>Classical PC methods assume that model outputs exist and reasonably smooth over the full domain of &xi;. Because models of heart rhythm often exhibit bifurcations and discontinuities, their outputs may not obey the existence and smoothness assumptions on the full domain, but only on some subdomains which may be irregularly shaped. On these subdomains, the random variables representing the parameters may no longer be independent. PC methods therefore must be modified for analysis of these discontinuous quantities. The Rosenblatt transformation maps the variables on the subdomain onto a rectangular domain; the transformed variables are independent and uniformly distributed. A new numerical estimation of the Rosenblatt transformation was developed that improves accuracy and computational efficiency compared to existing kernel density estimation methods. PC representations of the outputs in the transformed variables were then constructed. Coefficients of the PC expansions were estimated using Bayesian inference methods. For discontinuous model outputs, SA was performed using a sampling-based variance-reduction method, with the PC estimation used as an efficient proxy for the full model.</p><p>To evaluate the accuracy of the PC methods, PC UQ and SA results were compared to large-sample Monte Carlo UQ and SA results. PC UQ and SA of the fixed point APDs, and of the probability that a stable fixed point existed at each BCL, was very close to MC UQ results for those quantities. However, PC UQ and SA of the bifurcation BCLs was less accurate compared to MC results.</p><p>The computational time required for PC and Monte Carlo methods was also compared. PC analysis (including Rosenblatt transformation and Bayesian inference) required less than 10 total hours of computational time, of which approximately 30 minutes was devoted to model evaluations, compared to approximately 65 hours required for Monte Carlo sampling of the model outputs at 1 &times; 10<super>6</super> &xi; points.</p><p>PC methods provide a useful framework for efficient UQ and SA of the bifurcation diagram of a model of cardiac APD dynamics. Model outputs with bifurcations and discontinuities can be analyzed using modified PC methods. The methods applied and developed in this study may be extended to other models of heart rhythm dynamics. These methods have potential for use for uncertainty and sensitivity analysis in many applications of these models, including simulation studies of heart rate variability, cardiac pathologies, and interventions.</p> / Dissertation

The vaccine cold chain is an integral part of the process of storing and distributing vaccines prior to administration. A key component of this cold chain for developing countries is the primary vaccine storage warehouse. As the starting point for the distribution of vaccines throughout the country, these buildings have a significant amount of refrigerated space and therefore consume large amounts of energy. Therefore, this thesis focuses on analyzing the relative importance of parameters for the design of an energy efficient primary vaccine storage warehouse with the end goal of achieving Net-Zero Energy operation. A total of 31 architectural design parameters, such as roof insulation U-Value and external wall thermal mass, along with 14 building control parameters, including evaporator coil defrost termination and thermostat set points, are examined. The analysis is conducted across five locations in the developing world with significant variations in climate conditions: Buenos Aires, Argentina; Tunis, Tunisia; Asuncion, Paraguay; Mombasa, Kenya; and Bangkok, Thailand. Variations in the parameters are examined through the implementation of a Monte Carlo-based global uncertainty and sensitivity analysis to a case study building layout. A regression-based sensitivity analysis is used to analyze both the main effects of each parameter as well as the interactions between parameter pairs. The results of this research indicate that for all climates examined, the building control parameters have a larger relative importance than the architectural design parameters in determining the warehouse energy consumption. This is due to the dominance of the most influential building control parameter examined, the Chilled Storage evaporator fan control strategy. The importance of building control parameters across all climates examined emphasizes the need for an integrated design method to ensure the delivery of an energy efficient primary vaccine warehouse.

Derivation of an electromagnetic model, regarding the wave propagation in a very long (10 km or more) High Voltage Direct Current (HVDC) power cable, is the central part of this thesis. With an existing “perfect” electromagnetic model there are potentially a wide range of applications.The electromagnetic model is focused on frequencies between 0 and 100 kHz since higher frequencies essentially will be attenuated. An exact dispersion relation is formulated and the propagation constant is computed numerically. The dominating mode is the first Transversal Magnetic (TM) mode of order zero, denoted TM01, which is also referred to as the quasi-TEM mode. A comparison is made with the second propagating TM mode of order zero denoted TM02. The electromagnetic model is verified against real time data from Time Domain Reflection (TDR) measurements on a HVDC power cable. A mismatch calibration procedure is performed due to matching difficulties between the TDR measurement equipment and the power cable regarding the single-mode transmission line model.An example of power cable length measurements is addressed, which reveals that with a “perfect” model the length of an 80 km long power cable could be estimated to an accuracy of a few centimeters. With the present model the accuracy can be estimated to approximately 100 m.In order to understand the low-frequency wave propagation characteristics, an exact asymptotic analysis is performed. It is shown that the behavior of the propagation constant is governed by a square root of the complex frequency in the lowfrequency domain. This thesis also focuses on an analysis regarding the sensitivity of the propagation constant with respect to some of the electric parameters in the model. Variables of interest when performing the parameter sensitivity study are the real relative permittivityand the conductivity.

Uncertainty analysis of a system response is an important part of engineering probabilistic analysis. Uncertainty analysis includes: (a) to evaluate moments of the response; (b) to evaluate reliability analysis of the system; (c) to assess the complete probability distribution of the response; (d) to conduct the parametric sensitivity analysis of the output. The actual model of system response is usually a high-dimensional function of input variables. Although Monte Carlo simulation is a quite general approach for this purpose, it may require an inordinate amount of resources to achieve an acceptable level of accuracy. Development of a computationally efficient method, hence, is of great importance. First of all, the study proposed a moment method for uncertainty quantification of structural systems. However, a key departure is the use of fractional moment of response function, as opposed to integer moment used so far in literature. The advantage of using fractional moment over integer moment was illustrated from the relation of one fractional moment with a couple of integer moments. With a small number of samples to compute the fractional moments, a system output distribution was estimated with the principle of maximum entropy (MaxEnt) in conjunction with the constraints specified in terms of fractional moments. Compared to the classical MaxEnt, a novel feature of the proposed method is that fractional exponent of the MaxEnt distribution is determined through the entropy maximization process, instead of assigned by an analyst in prior. To further minimize the computational cost of the simulation-based entropy method, a multiplicative dimensional reduction method (M-DRM) was proposed to compute the fractional (integer) moments of a generic function with multiple input variables. The M-DRM can accurately approximate a high-dimensional function as the product of a series low-dimensional functions. Together with the principle of maximum entropy, a novel computational approach was proposed to assess the complete probability distribution of a system output. Accuracy and efficiency of the proposed method for structural reliability analysis were verified by crude Monte Carlo simulation of several examples. Application of M-DRM was further extended to the variance-based global sensitivity analysis of a system. Compared to the local sensitivity analysis, the variance-based sensitivity index can provide significance information about an input random variable. Since each component variance is defined as a conditional expectation with respect to the system model function, the separable nature of the M-DRM approximation can simplify the high-dimension integrations in sensitivity analysis. Several examples were presented to illustrate the numerical accuracy and efficiency of the proposed method in comparison to the Monte Carlo simulation method. The last contribution of the proposed study is the development of a computationally efficient method for polynomial chaos expansion (PCE) of a system's response. This PCE model can be later used uncertainty analysis. However, evaluation of coefficients of a PCE meta-model is computational demanding task due to the involved high-dimensional integrations. With the proposed M-DRM, the involved computational cost can be remarkably reduced compared to the classical methods in literature (simulation method or tensor Gauss quadrature method). Accuracy and efficiency of the proposed method for polynomial chaos expansion were verified by considering several practical examples.

Global sensitivity analysis has a key role to play in the design and parameterization of functional-structural plant growth models (FSPM) which combine the description of plant structural development (organogenesis and geometry) and functional growth (biomass accumulation and allocation). Models of this type generally describe many interacting processes, count a large number of parameters, and their computational cost can be important. The general objective of this thesis is to develop a proper methodology for the sensitivity analysis of functional structural plant models and to investigate how sensitivity analysis can help for the design and parameterization of such models as well as providing insights for the understanding of underlying biological processes. Our contribution can be summarized in two parts: from the methodology point of view, we first improved the performance of the existing Sobol's method to compute sensitivity indices in terms of computational efficiency, with a better control of the estimation error for Monte Carlo simulation, and we also designed a proper strategy of analysis for complex biophysical systems; from the application point of view, we implemented our strategy for 3 FSPMs with different levels of complexity, and analyzed the results from different perspectives (model parameterization, model diagnosis).