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.

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.

Transport parameter estimation and contaminant source identification are critical steps in the development of a physically based groundwater contaminant transport model. Due to the irreversibility of the dispersion process, the calibration of a transport model of interest is inherently ill-posed, and very sensitive to the simplification employed in the development of the lumped models. In this research, a methodology for the calibration of physically based computationally intensive transport models was developed and applied to a case study, the Reich Farm Superfund site in Toms River, New Jersey. Using HydroGeoSphere, a physically based transient three-dimensional computationally intensive groundwater flow model with spatially and temporally varying recharge was developed. Due to the convergence issue of implementing saturation versus permeability curve (van Genuchten equation) for the large scale models with coarse discretization, a novel flux-based method was innovated to determined solutions for the unsaturated zone for soil-water-retention models. The parameters for the flow system were determined separately from the parameters for the contaminant transport model. The contaminant transport and source parameters were estimated using both approximately 15 years of TCE concentration data from continuous well records and data over a period of approximately 30 years from traditional monitoring wells, and compared using optimization with two heuristic search algorithms (DDS and MicroGA) and a gradient based multi-start PEST. The contaminant transport model calibration results indicate that overall, multi-start PEST performs best in terms of the final best objective function values with equal number of function evaluations. Multi-start PEST also was employed to identify contaminant transport and source parameters under different scenarios including spatially and temporally varying recharge and averaged recharge. For the detailed, transient flow model with spatially and temporally varying recharge, the estimated transverse dispersivity coefficients were estimated to be significantly less than that reported in the literature for the more traditional approach that uses steady-state flow with averaged, less physically based recharge values. In the end, based on the Latin Hypercube sampling, a methodology for comprehensive uncertainty analysis, which accounts for multiple parameter sets and the associated correlations, was developed and applied to the case study.

Transport parameter estimation and contaminant source identification are critical steps in the development of a physically based groundwater contaminant transport model. Due to the irreversibility of the dispersion process, the calibration of a transport model of interest is inherently ill-posed, and very sensitive to the simplification employed in the development of the lumped models. In this research, a methodology for the calibration of physically based computationally intensive transport models was developed and applied to a case study, the Reich Farm Superfund site in Toms River, New Jersey. Using HydroGeoSphere, a physically based transient three-dimensional computationally intensive groundwater flow model with spatially and temporally varying recharge was developed. Due to the convergence issue of implementing saturation versus permeability curve (van Genuchten equation) for the large scale models with coarse discretization, a novel flux-based method was innovated to determined solutions for the unsaturated zone for soil-water-retention models. The parameters for the flow system were determined separately from the parameters for the contaminant transport model. The contaminant transport and source parameters were estimated using both approximately 15 years of TCE concentration data from continuous well records and data over a period of approximately 30 years from traditional monitoring wells, and compared using optimization with two heuristic search algorithms (DDS and MicroGA) and a gradient based multi-start PEST. The contaminant transport model calibration results indicate that overall, multi-start PEST performs best in terms of the final best objective function values with equal number of function evaluations. Multi-start PEST also was employed to identify contaminant transport and source parameters under different scenarios including spatially and temporally varying recharge and averaged recharge. For the detailed, transient flow model with spatially and temporally varying recharge, the estimated transverse dispersivity coefficients were estimated to be significantly less than that reported in the literature for the more traditional approach that uses steady-state flow with averaged, less physically based recharge values. In the end, based on the Latin Hypercube sampling, a methodology for comprehensive uncertainty analysis, which accounts for multiple parameter sets and the associated correlations, was developed and applied to the case study.

This study focuses on developing and evaluating efficient and effective parameter calibration and uncertainty methods for hydrologic modeling. Five single objective optimization algorithms and six multi-objective optimization algorithms were tested for automatic parameter calibration of the SWAT model. A new multi-objective optimization method (Multi-objective Particle Swarm and Optimization & Genetic Algorithms) that combines the strengths of different optimization algorithms was proposed. Based on the evaluation of the performances of different algorithms on three test cases, the new method consistently performed better than or close to the other algorithms. In order to save efforts of running the computationally intensive SWAT model, support vector machine (SVM) was used as a surrogate to approximate the behavior of SWAT. It was illustrated that combining SVM with Particle Swarm and Optimization can save efforts for parameter calibration of SWAT. Further, SVM was used as a surrogate to implement parameter uncertainty analysis fo SWAT. The results show that SVM helped save more than 50% of runs of the computationally intensive SWAT model The effect of model structure on the uncertainty estimation of streamflow simulation was examined through applying SWAT and Neural Network models. The 95% uncertainty intervals estimated by SWAT only include 20% of the observed data, while Neural Networks include more than 70%. This indicates the model structure is an important source of uncertainty of hydrologic modeling and needs to be evaluated carefully. Further exploitation of the effect of different treatments of the uncertainties of model structures on hydrologic modeling was conducted through applying four types of Bayesian Neural Networks. By considering uncertainty associated with model structure, the Bayesian Neural Networks can provide more reasonable quantification of the uncertainty of streamflow simulation. This study stresses the need for improving understanding and quantifying methods of different uncertainty sources for effective estimation of uncertainty of hydrologic simulation.

Due to limitation of economics and legislation, the power system is not allowed serious accident on modern social. In order to enhance system reliability, many types of special protection systems (SPS) have been implemented by utilities around the world. One of the main concerns in the design of an SPS is whether the designed system can achieve the reliability requirement. Currently, the literature that discusses the SPS reliability issue is scarce. In this thesis, a comparison of several techniques suitable for performing reliability assessment of SPS is presented. Discussed reliability models include using reliability block diagram, fault tree analysis, Markov modeling and Monte Carlo simulations. In order to understand the uncertainty effects of input data on the calculated system reliability, Monte Carlo Sampling method is utilized in this study to take the input parameters uncertainty into account in the system modeling. To deal with the problem of not being able to reach the reliability requirement after uncertainty analysis, a sensitivity analysis is proposed to analyze the importance of the components involved in the system. Sensitivity analysis can be used to identity the most effective component in the enhancement the SPS reliability. A Taipower SPS is used in this thesis to explain the proposed reliability assessment methods.

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.

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.