Fitting conditional and simultaneous autoregressive spatial models in hglm

We present a new version (> 2.0) of the hglm package for fitting hierarchical generalized linear models (HGLMs) with spatially correlated random effects. CAR() and SAR() families for conditional and simultaneous autoregressive random effects were implemented. Eigen decomposition of the matrix describing the spatial structure (e.g., the neighborhood matrix) was used to transform the CAR/SAR random effects into an independent, but eteroscedastic, Gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR and SAR models. This gives a computationally efficient algorithm for moderately sized problems.

Design and assessment of a large commercial Photovoltaic System in Barbados

One of the main aims of this thesis is to design an optimized commercial Photovoltaic (PV) system in Barbados from several variables such as racking type, module type and inverter type based on practicality, technical performance as well as financial returns to the client. Detailed simulations are done in PVSYST and financial models are used to compare different systems and their viability. Once the preeminent system is determined from a financial and performance perspective a detailed design is done using PVSYST and AutoCAD to design the most optimal PV system for the customer. In doing so, suitable engineering drawings are generated which are detailed enough for construction of the system. Detailed cost with quotes from relevant manufacturers, suppliers and estimators become instrumental in determining Balance of System Costs in addition to total project cost. The final simulated system is suggested with a PV capacity of 425kW and an inverter output of 300kW resulting in an array oversizing of 1.42. The PV system has a weighted Performance Ratio of 77 %, a specific yield of 1467 kWh/kWp and a projected annual production of 624 MWh/yr. This system is estimated to offset approximately 28 % of Carlton’s electrical load annually. Over the course of 20 years the PV system is projected to produce electricity at a cost of $0.201USD/kWh which is significantly lower than the $0.35 USD/kWh paid to the utility at the time of writing this thesis. Due to the high cost of electricity on the island, an attractive Feed-In-Tariff is not necessary to warrant the installation of a commercial System which over a lifetime which produces electricity at less than 60% of the cost to the user purchasing electricity from the utility. A simple payback period of 5.4 years, a return on investment of 17 % without incentives, in addition to an estimated diversion of 6840 barrels of oil or 2168 tonnes of CO2 further provides compelling justification for the installation of a commercial Photovoltaic System not only on Carlton A-1 Supermarket, but also island wide as well as regionally where most electricity supplies are from imported fossil fuels.

Experimental and computational investigation of the roll forming process

One of the first questions to consider when designing a new roll forming line is the number of forming steps required to produce a profile. The number depends on material properties, the cross-section geometry and tolerance requirements, but the tool designer also wants to minimize the number of forming steps in order to reduce the investment costs for the customer. There are several computer aided engineering systems on the market that can assist the tool designing process. These include more or less simple formulas to predict deformation during forming as well as the number of forming steps. In recent years it has also become possible to use finite element analysis for the design of roll forming processes. The objective of the work presented in this thesis was to answer the following question: How should the roll forming process be designed for complex geometries and/or high strength steels? The work approach included both literature studies as well as experimental and modelling work. The experimental part gave direct insight into the process and was also used to develop and validate models of the process. Starting with simple geometries and standard steels the work progressed to more complex profiles of variable depth and width, made of high strength steels. The results obtained are published in seven papers appended to this thesis. In the first study (see paper 1) a finite element model for investigating the roll forming of a U-profile was built. It was used to investigate the effect on longitudinal peak membrane strain and deformation length when yield strength increases, see paper 2 and 3. The simulations showed that the peak strain decreases whereas the deformation length increases when the yield strength increases. The studies described in paper 4 and 5 measured roll load, roll torque, springback and strain history during the U-profile forming process. The measurement results were used to validate the finite element model in paper 1. The results presented in paper 6 shows that the formability of stainless steel (e.g. AISI 301), that in the cold rolled condition has a large martensite fraction, can be substantially increased by heating the bending zone. The heated area will then become austenitic and ductile before the roll forming. Thanks to the phenomenon of strain induced martensite formation, the steel will regain the martensite content and its strength during the subsequent plastic straining. Finally, a new tooling concept for profiles with variable cross-sections is presented in paper 7. The overall conclusions of the present work are that today, it is possible to successfully develop profiles of complex geometries (3D roll forming) in high strength steels and that finite element simulation can be a useful tool in the design of the roll forming process.

Modeling and forecasting long-term natural gas (NG) consumption in Iran, using particle swarm optimization (PSO)

The gradual changes in the world development have brought energy issues back into high profile. An ongoing challenge for countries around the world is to balance the development gains against its effects on the environment. The energy management is the key factor of any sustainable development program. All the aspects of development in agriculture, power generation, social welfare and industry in Iran are crucially related to the energy and its revenue. Forecasting end-use natural gas consumption is an important Factor for efficient system operation and a basis for planning decisions. In this thesis, particle swarm optimization (PSO) used to forecast long run natural gas consumption in Iran. Gas consumption data in Iran for the previous 34 years is used to predict the consumption for the coming years. Four linear and nonlinear models proposed and six factors such as Gross Domestic Product (GDP), Population, National Income (NI), Temperature, Consumer Price Index (CPI) and yearly Natural Gas (NG) demand investigated.