Data collection is an integral part of hydrologic investigations; yet, hydrologic data collection is costly, particularly in subsurface environments. Consequently, it is critical to target data collection efforts toward prospective data sets that will best address the questions at hand, in the context of the study. Experimental and monitoring network designs that have been carefully planned with a specific objective in mind are likely to yield information-rich data that can address critical questions of concern. Conversely, data collection undertaken without careful planning may yield datasets that contain little information relevant to the questions of concern. This dissertation research develops and presents approaches that can be used to support careful planning of hydrologic experiments and monitoring networks. Specifically, three general types of problems are considered. Under the first problem type, the objective of the hydrologic investigation is to discriminate among rival conceptual models, or among rival predictive groupings. A Bayesian methodology is presented that can be used to rank prospective datasets during the planning phases of a hydrologic investigation. Under the second problem type, the objective is to quantify the impact of existing data on reductions in parameter uncertainty. An inverse modeling approach is presented to quantify the impact of existing data on parameter uncertainty when the hydrogeologic conceptual model is uncertain. The third and final problem type focuses on data collection in a water resource management context, with the specific goal to maximize profits without imposing adverse environmental impacts. A risk-based decision support framework is developed using detailed hydrologic simulation to evaluate probabilistic constraints. This enables direct calculation of the profit gains associated with prospective reductions in system parameter uncertainty, and the possible environmental impacts of unknown bias in the system parameters.

The subject of this dissertation is the calculation and visualization of intraoperative measurement uncertainty in computer-assisted surgical procedures. Error is the difference between the observed or measured value and the true value (called ground-truth) of a quantity. Uncertainty is the unknown difference between the measured and true values, and exists in the absence of knowledge of ground truth. If one has an algorithm for computing the ground truth then one can get an accurate estimate of error. However, in computer-assisted surgery, the ground truth is often unknown. The introduction of error to surgical procedures is inevitable: it cannot be avoided by simply taking very careful measurements, providing more accurate algorithms, or by improving instrument calibration. One can only reduce errors as much as reasonably possible, calculate a reliable estimate of the uncertainty, and provide a meaningful way to convey this uncertainty information to clinicians. In this dissertation, I demonstrate that the visualization of registration uncertainty improves surgical navigation and that real-time computation of intraoperative measurement uncertainty is possible. In an extensive user study of surgeons and surgical residents, I compare methods of visualizing intraoperative uncertainty and determine that there are several methods of effectively conveying uncertainty in surgical navigation. / Thesis (Ph.D, Computing) -- Queen's University, 2010-01-25 16:33:26.755

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A methodology has been developed to quantify the simulation uncertainty of a computational model calibrated against test data. All test data used in the study undergoes an experimental uncertainty analysis. The modeling software ROCETS is used and its structure is explained. The way the model was calibrated is presented. Next, a general simulation uncertainty analysis methodology is shown that is valid for calibrated models. Finally the ROCETS calibrated model and its simulation uncertainty are calculated using the general methodology and compared to a second set of comparison test data. The simulation uncertainty analysis methodology developed and implemented can be used for any modeling with a calibrated model. The methodology works well for a process of incremental testing and recalibration of the model whenever new test data is available.

In the screening and concept selection stages of gas storage projects, many estimates are required to value competing projects and development concepts. These estimates are important because they influence which projects are selected and which concept proceeds into detailed engineering. In most cases, there is uncertainty in all of the estimates. As a result, operators are faced with the complex problem of determining the optimal design. A systematic uncertainty analysis can help operators solve this problem and make better decisions. Ideally, the uncertainty analysis is comprehensive and includes all uncertain variables, and simultaneously accounts for reservoir behavior, facility options, and economic objectives. This thesis proposes and demonstrates a workflow and an integrated optimization model for uncertainty analysis in gas storage. The optimization model is fast-solving and eliminates most constraints on the scope of the uncertainty analysis. Using this or similar workflows and models should facilitate analysis and communication of results within the project team and with other stakeholders. / text

Aircraft operators demand an ever increasing availability of their fleets with constant reduction of their operational costs. With the age of many fleets measured in decades, the options to face these challenges are limited. Integrated Vehicle Health Management (IVHM) uses data gathered through sensors in the aircraft to assess the condition of components to detect and isolate faults or even estimate their Remaining Useful Life (RUL). This information can then be used to improve the planning of maintenance operations and even logistics and operational planning, resulting in shorter maintenance stops and lower cost. Retrofitting health monitoring technology onto legacy aircraft has the capability to deliver what operators and maintainers demand, but working on aging platforms presents numerous challenges. This thesis presents a novel methodology to select the combination of diagnostic and prognostic tools for legacy aircraft that best suits the stakeholders’ needs based on economic return and financial risk. The methodology is comprised of different steps in which a series of quantitative analyses are carried out to reach an objective solution. Beginning with the identification of which components could bring higher reduction of maintenance cost and time if monitored, the methodology also provides a method to define the requirements for diagnostic and prognostic tools capable of monitoring these components. It then continues to analyse how combining these tools affects the economic return and financial risk. Each possible combination is analysed to identify which of them should be retrofitted. Whilst computer models of maintenance operations can be used to analyse the effect of retrofitting IVHM technology on a legacy fleet, the number of possible combinations of diagnostic and prognostic tools is too big for this approach to be practicable. Nevertheless, computer models can go beyond the economic analysis performed thus far and simulations are used as part of the methodology to get an insight of other effects or retrofitting the chosen toolset.

Storm sewer systems play a very important role in urban areas. The design of a storm sewer system should be based on an appropriate level of preventing flooding. This thesis focuses on issues relevant to decision-making in storm sewer network design considering flood risk. Uncertainty analysis is often required in an integrated approach to a comprehensive assessment of flood risk. The first part of this thesis discusses the understanding and representation of uncertainty in general setting. It also develops methods for propagating uncertainty through a model under different situations when uncertainties are represented by various mathematical languages. The decision-making process for storm sewer network design considering flood risk is explored in this thesis. The pipe sizes and slopes of the network are determined for the design. Due to the uncertain character of the flood risk, the decision made is not unique but depends on the decision maker’s attitude towards risk. A flood risk based storm sewer network design method incorporating a multiple-objective optimization and a “choice” process is developed with different design criteria. The storm sewer network design considering flood risk can also be formed as a single-objective optimization provided that the decision criterion is given a priori. A framework for this approach with a single objective optimization is developed. The GA is adapted as the optimizer. The flood risk is evaluated with different methods either under several design storms or using sampling method. A method for generating samples represented by correlated variables is introduced. It is adapted from a literature method providing that the marginal distributions of variables as well as the correlations between them are known. The group method is developed aiming to facilitate the generation of correlated samples of large sizes. The method is successfully applied to the generation of rainfall event samples and the samples are used for storm sewer network design where the flood risk is evaluated with rainfall event samples.

Sustainable production and consumption have become more important internationally, which has led to the transformation of market structures and competitive situations into the direction of servitisation. This means that manufacturing companies are forced to compete through the supply of services as opposed to products. Particularly the suppliers of long-life products such as submarines and airplanes no longer simply sell these products but provide their capability or availability. Companies such as Rolls-Royce Engines achieve 60% of their revenue through selling a service rather than the engine itself. For a manufacturing company, the shift towards being a service provider means that they usually have to bid for service contracts, sometimes competitively. In the context of competitive bidding, the decision makers face various uncertainties that influence their decision. Ignoring these uncertainties or their influences can result in problems such as the generation of too little profit or even a loss or the exposure to financial risks. Raising the decision maker’s awareness of the uncertainties in the form of e.g. a decision matrix, expressing the trade-off between the probability of winning the contract and the probability of making a profit, aims at integrating these factors in the decision process. The outcome is to enable the bidding company to make a more informed decision. This was the focus of the research presented in this thesis. The aim of this research was to support the pricing decision by defining a process for modelling the influencing uncertainties and including them in a decision matrix depicting the trade-off between the probability of winning the contract and the probability of making a profit. Three empirical studies are described and the associated decision process and influencing uncertainties are discussed. Based on these studies, a conceptual framework was defined which depicts the influencing factors on a pricing decision at the bidding stage and the uncertainties within these. The framework was validated with a case study in contract bidding where the uncertainties were modelled and included in a decision matrix depicting the probability of winning the contract and the probability of making a profit. The main contributions of this research are the identification of the uncertainties influencing a pricing decision, the depiction of these in a conceptual framework, a method for ascertaining how to model these uncertainties and assessing the use of such an approach via an industrial case study.

<p>The International Atomic Energy Agency (IAEA) and the World Health Organisation (WHO) operate the IAEA/WHO TLD postal dose audit programme. The purpose of the programme is to verify the beam calibration in radiotherapy centres in developing countries and to check the Secondary Standards Dosimetry Laboratories (SSDLs). Thermoluminescence dosimeters (TLDs) are used as transfer dosimeters and the evaluation of these are done at the IAEA Dosimetry Laboratory. In the present work the uncertainties in the process of dose determination from TLD readings have been evaluated.</p><p>The analysis comprises the TLD reading reproducibility, uncertainties in the calibration coefficient, and uncertainties in factors correcting for fading of TL signal, influence of TLD holder, energy response and dose response non-linearity. The individual uncertainties were combined to estimate the total uncertainty in the evaluated dose from TLD readings. Experimental data from 2001-2005 were used in the analysis.</p><p>The total uncertainty has been estimated to be 1.2 % for irradiations with 60Co -rays and 1.6 % for irradiations with high-energy X-rays. Results from irradiations by the Bureau International des Poids et Mesures (BIPM), Primary Standard Dosimetry Laboratories (PSDLs), Secondary Standard Dosimetry Laboratories (SSDLs) and reference centres compare favourably with the estimated uncertainties.</p><p>The largest uncertainty components are in the energy correction factor (for high-energy X-rays) with a value of 1.1 % and in the dose response non-linearity correction factor with a value of 0.9 %.</p><p>It has been shown that the acceptance limits of 5 % for TLD results of hospitals and 3.5 % for SSDLs are justified when related to the uncertainties in the dose calculations and the uncertainty in the determination of absorbed dose to water at the centre, as discussed in IAEA TRS-398 (IAEA, 2000), provided that it is followed.</p>

This project presents the development and design of a comprehensive interface coupled with a geodatabase (ArcGISwat 2003), for the Soil and Water Assessment Tool (SWAT). SWAT is a hydrologically distributed, lumped parameter model that runs on a continuous time step. The quantity and extensive detail of the spatial and hydrologic data, involved in the input and output, both make SWAT highly complex. A new interface, that will manage the input/output (I/O) process, is being developed using the Geodatabase object model and concepts from hydrological data models such as ArcHydro. It also incorporates uncertainty analysis on the process of modeling. This interface aims to further direct communication and integration with other hydrologic models, consequently increasing efficiency and diminishing modeling time. A case study is presented in order to demonstrate a common watershed-modeling task, which utilizes SWAT and ArcGIS-SWAT2003.