Wind energy is one of the fastest growing sources of renewable energy in the world. There has been a lot of research, development, and investment in wind energy in recent years. Offshore sites offer stronger winds and low turbulence, along with fewer noise and visual impacts. Establishing large turbines at deepwater sites offers promising opportunities for generating high power output while utilizing the favorable environmental conditions. Researchers at Sandia National Laboratories (SNL) have developed a very large wind turbine model with a 13.2 MW rating that has 100-meter long blades; this turbine is designated as the SNL100 13.2 MW wind turbine. With a hub height of 146 meters and a rotor diameter of 205 meters, such a large turbine is best suited for offshore sites. Developing a wind turbine model for an offshore site requires that a platform model be developed first. Of the various kinds of floating platforms, a moored semi-submersible platform supporting the wind turbine, which offers stability by virtue of the intercepted water-plane area, is an appropriate choice. The goal of this study is to develop a semi-submersible platform model to support the 13.2 MW wind turbine, while keeping loads and deflections within safe limits. The platform is developed based on work completed as part of the Offshore Code Comparison Collaboration Continuation (OC4) Phase II project, which involved a 5 MW wind turbine supported by a semi-submersible platform. The present study focuses on three important topics: (i) development of the combined offshore wind turbine system model with the 13.2 MW wind turbine, a floating semi-submersible platform, and a mooring system; (ii) the entire procedure involved in modeling and analyzing first-order hydrodynamics using two codes, MultiSurf and WAMIT; and (iii) assembling of the integrated aero-hydro-servo-elastic model considering hydrodynamics in order to verify the steady-state and stochastic response of the integrated wind turbine system. / text

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<p>Pharmacometric modelling is an increasingly used method for analysing the outcome from clinical trials in drug development. The model building process is complex and involves testing, evaluating and diagnosing a range of plausible models aiming to make an adequate inference from the observed data and predictions for future studies and therapy. </p><p>The aim of this thesis was to advance the approaches used in pharmacometrics by introducing improved models and methods for application in essential parts of model building procedure: (i) structural model development, (ii) stochastic model development and (iii) model diagnostics. </p><p>As a contribution to the structural model development, a novel flexible structural model for drug absorption, a transit compartment model, was introduced and evaluated. This model is capable of describing various drug absorption profiles and yet simple enough to be estimable from data available from a typical trial. As a contribution to the stochastic model development, three novel methods for parameter distribution estimation were developed and evaluated; a default NONMEM nonparametric method, an extended grid method and a semiparametric method with estimated shape parameters. All these methods are useful in circumstances when standard assumptions of parameter distributions in the population do not hold. The new methods provide less biased parameter estimates, better description of variability and better simulation properties of the model. As a contribution to model diagnostics, the most commonly used diagnostics were evaluated for their usefulness. In particular, diagnostics based on individual parameter estimates were systematically investigated and circumstances which are likely to misguide modelers towards making erroneous decisions in model development, relating to choice of structural, covariate and stochastic model components were identified. </p><p>In conclusion, novel approaches, insights and models have been provided to the pharmacometrics community. </p><p>Implementation of these advances to make model building more efficient and robust has been facilitated by development of diagnostic tools and automated routines.</p>

Model checking is used to aid in the design and verification of complex concurrent systems. An abstracted finite state model of a system and a set of mathematically based correctness properties based on the design specifications are defined. The model checker then performs an exhaustive state space search of the model, checking that the correctness properties hold at each step. This thesis describes how the SPIN model checker has been used to find and correct problems in the software design of a distributed marine vessel control system currently under development at a control systems specialist in New Zealand. The system under development is a mission critical control system used on large marine vessels. Hence, the requirement to study its architecture and verify the implementation of the system. The model checking work reported here focused on analysing the implementation of the Time-Triggered Controller-Area-Network (TTCAN) protocol, as this is used as the backbone for communications between devices and thus is a crucial part of their control system. A model of the ISO TTCAN protocol has been created using the SPIN model checker. This was based on work previously done by Leen and Heffernan modelling the protocol with the UPPAAL model checker [Leen and Heffernan 2002a]. In the process of building the ISO TTCAN model, a set of general techniques were developed for model checking TTCAN-like protocols. The techniques developed include modelling the progression of time efficiently in SPIN, TTCAN message transmission, TTCAN error handling, and CAN bus arbitration. These techniques then form the basis of a set of models developed to check the sponsoring organisation’s implementation of TTCAN as well as the fault tolerance schemes added to the system. Descriptions of the models and properties developed to check the correctness of the TTCAN implementation are given, and verification results are presented and discussed. This application of model checking to an industrial design problem has been successful in identifying a number of potential issues early in the design phase. In cases where problems are identified, the sequences of events leading to the problems are described, and potential solutions are suggested and modelled to check their effect of the system.

Condition monitoring business has been of interest to Sulzer since the 1990s when the Sulzer Diagnostic System (SUDIS) was developed. However, since the invention of SUDIS, Sulzer has had limited commercial success with condition monitoring products and services. Several recent investigations at Sulzer have explored possibilities for new machine monitoring business, with the most recent being the condition monitoring equipment survey of Nyitray. This report leads on from the work of Nyitray to evaluate the attractiveness of current machine monitoring markets and the strength of current and concept control and monitoring business models. Sales and customer support services (CSS) staff in all business segments and areas were surveyed for information regarding customer demand for machine monitoring solutions. The results of the survey lacked a unified view on customer needs, indicating that customer needs vary significantly with industry and region. Results also indicated that Sulzer sales and CSS staff currently have very little contact with customers regarding machine monitoring issues, which was expected since Sulzer currently has very limited machine monitoring offers. Overall customer interest in machine monitoring for cost saving purposes is high. Moreover, some customers expect equipment manufactures like Sulzer to support their equipment with machine monitoring offers. Business model environmental factors for each Sulzer Pumps focus market were identified allowing the most attractive markets for machine monitoring business to be selected. A survey of Sulzer Pumps business segment heads also contributed to the market selection process. This evaluation concluded that the water and power generation industries had favourable markets for machine monitoring business, mainly because Sulzer has a good competitive position in these markets. Unfortunately pumps in the electricity generation industry are relatively reliable compared to other machinery such as electrical generators. Hence, opportunities identified in the electricity generation industry seemed to be more appropriate for Sulzer Turbo Services than Sulzer Pumps. However, cross-divisional collaboration of Sulzer Pumps and Sulzer Turbo services would allow Sulzer to offer solutions for entire drivetrains. Other opportunities suitable for Sulzer Pumps were identified in the district heating and water industries, with energy monitoring being a common theme. An evaluation of the oil pipeline industry yielded that there is significant market demand for machine and pipeline monitoring. However, Sulzer currently does not have the experience or resources to provide the demanded monitoring services independently. Hence searching for key partners or acquisition targets was acknowledged as an essential activity for Sulzer Pumps to enter this market. Another means of market entry would be to develop novel technology or integrate emerging technologies (e.g. online viscosity sensors) into new pipeline monitoring solutions, i.e. to create a novel value proposition. Subsequent feedback from Sulzer alliance managers concluded that oil pipeline customer acquisition may be difficult since many pipeline companies already have monitoring solutions which they are satisfied with. Hence the oil pipeline market is not recommended for new machine monitoring business ventures. ABS pump control and monitoring solutions are currently the only machine monitoring solutions offered by Sulzer Pumps. In this study the business model behind these solutions was analysed to evaluate its strength and identify areas for improvement. Although the ABS control and monitoring business model is profitable, sales figures are below their potential. Recommendations to improve the business models effectiveness mainly focused on improving channels through which Sulzer connects with its customers. These included improving Sulzer digital marketing material, improving product selection tools, increasing complementary advertising and quotation contents to connect Sulzer control and monitoring products with pump equipment products, and most importantly increasing the amount of sales and CSS staff training.

Youth offending is an increasingly major problem in many countries and cultures. Several theories imply that a subset of young people display delinquent behaviour at a young age and go on to have an extensive and serious criminal career. Recently, there has been interest in the literature in identifying these young people early on and carrying out interventions in order to deter them from a criminal career. Many studies have examined the development and usefulness of actuarial measures of risk of future violence or recidivism in adult offenders. However, the same attention has not been paid to the youth offender population. The present study gathered data from the population (N = 4307) of all young persons in New Zealand whose antisocial behaviour resulted in a Youth Justice intake from the Department of Child, Youth, and Family (CYF) in 2002. Information was obtained about this population from the CYF database, CYRAS, and from the Police National Intelligence Application database for a stratified random sample (N = 500). Three models were developed using Hierarchical Cox regression to predict recidivism, and they each used a different definition of recidivism. The performance of the models was assessed using ROC analysis and they were found to predict recidivism with a moderately good level of accuracy. A validation sample (N = 500), different from the sample on which the models were developed, was used to further assess the performance of the models by showing that they were able to generalize to a new data set and continue to perform at an adequate level. An actuarial model, like the one developed in the present study, could be used to help make decisions about which young people within the Youth Justice System require intervention in order to reduce the likelihood of subsequent reoffending.

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