Over one million simulations were conducted using the Hec-Ras4b (US Army Corps of Engineers, 2004) model to evaluate the sensitivity of model predictions to field data accuracy, density and estimation techniques and provide guidance towards balancing human resource allocation with model accuracy. Notable differences were identified in model accuracy if a project is concerned with river processes occurring within the limits of the bankfull channel versus floodplain regions. Increased cross section discretization, bankfull channel detail and main channel roughness were of greatest field survey and measurement importance when processes relevant to the bankfull channel are of concern (i.e. geomorphic processes or sediment transport). Conversely, where flood conditions are of highest consideration, estimates of floodplain roughness dominate the accuracy of the results of computed water surface elevations. Results for this case study also demonstrate that higher orders of total station field surveys provide little additional accuracy in final predicted water surface elevations, relative to proper estimates of in-channel and floodplain roughness. As long as drift in field surveys has been accounted for during or subsequent to total station surveys, survey techniques such as hangers can be readily employed with very little increase in final model prediction error, while improving field data acquisition efficiency.

Over one million simulations were conducted using the Hec-Ras4b (US Army Corps of Engineers, 2004) model to evaluate the sensitivity of model predictions to field data accuracy, density and estimation techniques and provide guidance towards balancing human resource allocation with model accuracy. Notable differences were identified in model accuracy if a project is concerned with river processes occurring within the limits of the bankfull channel versus floodplain regions. Increased cross section discretization, bankfull channel detail and main channel roughness were of greatest field survey and measurement importance when processes relevant to the bankfull channel are of concern (i.e. geomorphic processes or sediment transport). Conversely, where flood conditions are of highest consideration, estimates of floodplain roughness dominate the accuracy of the results of computed water surface elevations. Results for this case study also demonstrate that higher orders of total station field surveys provide little additional accuracy in final predicted water surface elevations, relative to proper estimates of in-channel and floodplain roughness. As long as drift in field surveys has been accounted for during or subsequent to total station surveys, survey techniques such as hangers can be readily employed with very little increase in final model prediction error, while improving field data acquisition efficiency.

The Biomass Integrated Gasification Application Systems (BIGAS) consortium is a research group whose focus is on developing modern biomass gasification technology for New Zealand's wood industry. This thesis is undertaken under objective four of the BIGAS consortium, whose goal is to develop modelling tools for aiding in the design of pilot-scale gasification plant and for assessing the economic feasibility of gasification energy plant. This thesis presents a chemical equilibrium-based gasification model and an economic feasibility assessment of gasification energy plant. Chemical equilibrium is proven to accurately predict product gas composition for large scale, greater than one megawatt thermal, updraft gasification. However, chemical equilibrium does not perform as well for small scale, 100 to 150 kilowatt thermal, Fast Internally Circulating Fluidised Bed (FICFB) gasification. Chemical equilibrium provides a number of insights on how altering gasification parameters will affect the composition of the product gas and will provide a useful tool in the design of pilot-scale plant. The economic model gives a basis for judging the optimal process and the overall appeal of integrating biomass gasification-based heat and power plants into New Zealand's MDF industry. The model is what Gerrard (2000) defines as a 'study estimate' model which has a probable range of accuracy of ±20% to ±30%. The modelling results show that gasification-gas engine plants are economically appealing when sized to meet the internal electricity demands of an MDF plant. However, biomass gasification combined cycle plants (BIGCC) and gasificationgas turbine plants are proven to be uneconomic in the New Zealand context.

Evaluation of the environmental impacts of Intelligent Transport Systems and transport infrastructure management schemes relies heavily on the development of accurate and reliable environmental emissions models. Existing state-of-the-art models estimate pollutants based on a typical urban driving cycle using an aggregate modelling approach where a 'characteristic' vehicle is used to represent dissimilar vehicle populations. While this approach has been accepted by transport planners for strategic level studies, it can be argued that modelling individual vehicle emissions based on vehicle dynamics would result in more reliable evaluations of operational-level project impacts. The primary objective of this thesis is to develop vehicle emissions and fuel consumption models under hot stabilised settings and various traffic conditions using Australian fleet data collected from laboratory tests. The models use second-by-second vehicle real-time data to predict fuel consumption (FC) and pollutant emissions (HC, CO, NOx) at different levels of speed, acceleration, air-to-fuel ratio and torque. The data required for model development, calibration and validation was collated from laboratory tests conducted by the Second National In-Service Emissions (NISE 2) project. A total of 27 vehicles (including small, medium and large passenger vehicles; four-wheel drive (small and large); and light commercial vehicles were used in model development. The laboratory data, which comprised more than 48,500 second-by-second observations, was then pre-processed and randomly assigned to calibration and validation data sets for model development. The thesis then adopted a rigorous approach to develop and evaluate a large number of neural network architectures to determine the most suitable modelling framework. First, a pilot test was conducted to test different model development scenarios and establish some guidelines on the general framework for model development. The results were used to determine some of the crucial neural network parameters (eg learning rule or optimisation technique and most appropriate architecture) for use in subsequent modelling. Selected models were then further refined using test data from individual and aggregate vehicle types. This resulted in further refinement of modelling inputs where, for example, sensitivity analysis showed that speed and acceleration were the two most crucial inputs and that including other input parameters did not improve the accuracy of the results. The performance of selected neural network models was then compared to a number of sophisticated and complex statistical techniques based on multiple and non-linear regression models. The results generally showed that ANN models are effective and suitable for modelling emissions and that they perform as well or even better than the complex regression models tested in this study. Another general finding across all vehicles and for all models (neural and statistical) is that predictions are more accurate for fuel consumption and CO emissions than for other vehicular emissions. The models were also found to under-predict the emissions values at the peaks of graphs, but were generally consistent in their outputs across all other driving conditions. In this study, it was also found that one of the main advantages of the neural network approach over regression is the ease of developing one model to accurately predict multiple outputs. This is in contrast to the regression modelling approach, where it was found that accurate results matching neural network performance can only be achieved using one distinct model for predicting each output. This would clearly undermine the statistical approach because a large number of models would then need to be developed for a road network where second-by-second data is available for hundreds of vehicle types. Hence, the benefits of using neural networks immediately become clear and more appealing. This thesis also identified a number of issues for future research directions. To increase the accuracy and overall quality of the models, future research needs to include further classifications of vehicle types and other pertinent variables such as manufacture year, odometer reading and making use of a larger sample of modern vehicles representing current vehicle fleet compositions. There is also scope to improve the testing procedures by including road grade and air condition use, which are important factors that impact on vehicle performance and emissions. Future research can also benefit from testing other drive cycles and cross validation of models across different driving cycles. Model performance can also be enhanced by collecting instantaneous data using instrumented vehicles where emissions can be collected under real-life conditions rather than from controlled laboratory environments. Finally, the real benefit from development of these models is the ability to interface them to micro-simulation models where instantaneous speed and acceleration data can be provided to the emissions model on a second-by-second basis. The neural network emissions model would then be used to evaluate the impacts of ITS and other traffic management strategies with the aim of identifying the best environment-friendly traffic management approaches. This thesis has successfully achieved its objectives by demonstrating the feasibility of using neural networks for modelling vehicle emissions. The thesis further demonstrated the superior quality and advantages of the neural network approach over the more established statistical regression methods. Finally, the models developed in this study will allow researcher and practitioners alike to develop a better understanding and appreciation of the environmental impacts resulting from transport schemes aimed at reducing traffic congestion and enhancing environmental quality.

Evaluation of the environmental impacts of Intelligent Transport Systems and transport infrastructure management schemes relies heavily on the development of accurate and reliable environmental emissions models. Existing state-of-the-art models estimate pollutants based on a typical urban driving cycle using an aggregate modelling approach where a 'characteristic' vehicle is used to represent dissimilar vehicle populations. While this approach has been accepted by transport planners for strategic level studies, it can be argued that modelling individual vehicle emissions based on vehicle dynamics would result in more reliable evaluations of operational-level project impacts. The primary objective of this thesis is to develop vehicle emissions and fuel consumption models under hot stabilised settings and various traffic conditions using Australian fleet data collected from laboratory tests. The models use second-by-second vehicle real-time data to predict fuel consumption (FC) and pollutant emissions (HC, CO, NOx) at different levels of speed, acceleration, air-to-fuel ratio and torque. The data required for model development, calibration and validation was collated from laboratory tests conducted by the Second National In-Service Emissions (NISE 2) project. A total of 27 vehicles (including small, medium and large passenger vehicles; four-wheel drive (small and large); and light commercial vehicles were used in model development. The laboratory data, which comprised more than 48,500 second-by-second observations, was then pre-processed and randomly assigned to calibration and validation data sets for model development. The thesis then adopted a rigorous approach to develop and evaluate a large number of neural network architectures to determine the most suitable modelling framework. First, a pilot test was conducted to test different model development scenarios and establish some guidelines on the general framework for model development. The results were used to determine some of the crucial neural network parameters (eg learning rule or optimisation technique and most appropriate architecture) for use in subsequent modelling. Selected models were then further refined using test data from individual and aggregate vehicle types. This resulted in further refinement of modelling inputs where, for example, sensitivity analysis showed that speed and acceleration were the two most crucial inputs and that including other input parameters did not improve the accuracy of the results. The performance of selected neural network models was then compared to a number of sophisticated and complex statistical techniques based on multiple and non-linear regression models. The results generally showed that ANN models are effective and suitable for modelling emissions and that they perform as well or even better than the complex regression models tested in this study. Another general finding across all vehicles and for all models (neural and statistical) is that predictions are more accurate for fuel consumption and CO emissions than for other vehicular emissions. The models were also found to under-predict the emissions values at the peaks of graphs, but were generally consistent in their outputs across all other driving conditions. In this study, it was also found that one of the main advantages of the neural network approach over regression is the ease of developing one model to accurately predict multiple outputs. This is in contrast to the regression modelling approach, where it was found that accurate results matching neural network performance can only be achieved using one distinct model for predicting each output. This would clearly undermine the statistical approach because a large number of models would then need to be developed for a road network where second-by-second data is available for hundreds of vehicle types. Hence, the benefits of using neural networks immediately become clear and more appealing. This thesis also identified a number of issues for future research directions. To increase the accuracy and overall quality of the models, future research needs to include further classifications of vehicle types and other pertinent variables such as manufacture year, odometer reading and making use of a larger sample of modern vehicles representing current vehicle fleet compositions. There is also scope to improve the testing procedures by including road grade and air condition use, which are important factors that impact on vehicle performance and emissions. Future research can also benefit from testing other drive cycles and cross validation of models across different driving cycles. Model performance can also be enhanced by collecting instantaneous data using instrumented vehicles where emissions can be collected under real-life conditions rather than from controlled laboratory environments. Finally, the real benefit from development of these models is the ability to interface them to micro-simulation models where instantaneous speed and acceleration data can be provided to the emissions model on a second-by-second basis. The neural network emissions model would then be used to evaluate the impacts of ITS and other traffic management strategies with the aim of identifying the best environment-friendly traffic management approaches. This thesis has successfully achieved its objectives by demonstrating the feasibility of using neural networks for modelling vehicle emissions. The thesis further demonstrated the superior quality and advantages of the neural network approach over the more established statistical regression methods. Finally, the models developed in this study will allow researcher and practitioners alike to develop a better understanding and appreciation of the environmental impacts resulting from transport schemes aimed at reducing traffic congestion and enhancing environmental quality.

Made available in DSpace on 2016-08-29T15:33:10Z (GMT). No. of bitstreams: 1 tese_3334_.pdf: 6590453 bytes, checksum: 5654758329fe7af83bec07339d8a3ffb (MD5) Previous issue date: 2009-12-16 / Business process modelling basically comprises an activity whose main goal is to provide a formalization of business processes in an organization or a set of cooperating organizations (Recker, et al., 2006) (van der Aalst, et al., 2003). By modelling an organizations business processes, it is possible to capture how the organization coordinates the work and resources with the aim of achieving its goals and strategies (Sharp, et al., 2001). Since business processes and goals are intrinsically interdependent, establishing an alignment between the process and the goal domains arises as a natural approach. This thesis reports on a real-life exploratory case study in which we investigated the relationship between the elements of the enterprise (modeled in the ARIS framework) and the goals (modeled in the Tropos framework and modeling language) which are attained by these elements. The case study has been conducted in the Rheumatology Department of a University Hospital in Brazil. In the course of the case study, we have identified the need of splitting this effort into three phases: the elicitation phase (in which goal models and business process models are captured from the organizational domain), the harmonization phase (in which the goal domain is structured for alignment according to the business processes structures that will support it) and the alignment phase (in which the relationships between the goal domain and the elements of the organizational domain are established). In order to investigate the relation between goals and enterprise elements, we propose an ontological account for both architectural domains. We recognize the importance in considering the business process as the means for implementing an enterprises strategy, but we do not exclude the remaining enterprise elements. Furthermore, we are concerned with both the identification of the relationships and with a classification for their nature.

Yes / A multi-element constitutive model for a lactide-based shape memory polymer has been developed that represents loading to large tensile deformations, stress relaxation and stress recovery at 60, 65 and 70°C. The model consists of parallel Maxwell arms each comprising neo-Hookean and Eyring elements. Guiu-Pratt analysis of the stress relaxation curves yields Eyring parameters. When these parameters are used to define the Eyring process in a single Maxwell arm, the resulting model yields at too low a stress, but gives good predictions for longer times. Stress dip tests show a very stiff response on unloading by a small strain decrement. This would create an unrealistically high stress on loading to large strain if it were modelled by an elastic element. Instead it is modelled by an Eyring process operating via a flow rule that introduces strain hardening after yield. When this process is incorporated into a second parallel Maxwell arm, there results a model that fully represents both stress relaxation and stress dip tests at 60°C. At higher temperatures a third arm is required for valid predictions.

No description available.

This thesis presents the results of calculating model atmospheres for the accretion column of a magnetic white dwarf. A basic stellar atmosphere calculation is refined to model the specific conditions at the base of an accretion column. Calculated spectra for a variety of different input conditions are shown. The calculated spectra are fitted with black body spectra in order to ascertain the errors associated with black body fitting of observed spectra. Simulated lightcurves are calculated using these model atmosphere spectra. The resultant lightcurves are folded through the EXOSAT (European X-ray Observatory Satellite) detectors and used to fit lightcurves from the magnetic polar system AM Herculis. Following the assumption that a thin accretion disc around a supermassive black hole is the central power source for active galactic nuclei (AGN's) a large grid of model atmospheres is calculated. This grid is then used to calculate the spectrum from such a disc.

Model Driven software development has been considered to be a further software construction technology following object-oriented software development methods and with the potential to bring new breakthroughs in the research of software development. With deepening research, a growing number of Model Driven software development methods have been proposed. The model is now widely used in all aspects of software development. One key element determining progress in Model Driven software development research is how to better express and describe the models required for various software components. From a study of current Model Driven development technologies and methods, Domain-Specific Modelling is suggested in the thesis as a Model Driven method to better realise the potential of Model-Driven Software Development. Domain-specific modelling methods can be successfully applied to actual software development projects, which need a flexible and easy to extend, meta-modelling language to provide support. There is a particular requirement for modelling languages based on domain-specific modelling methods in Meta-modelling as most general modelling languages are not suitable. The thesis focuses on implementation of domain-specific modelling methods. The "domain" is stressed as a keystone of software design and development and this is what most differentiates the approach from general software development process and methods. Concerning the design of meta-modelling languages, the meta-modelling language based on XML is defined including its abstract syntax, concrete syntax and semantics. It can support description and construction of the domain meta-model and the domain application model. It can effectively realise visual descriptions, domain objects descriptions, relationships descriptions and rules relationships of domain model. In the area of supporting tools, a meta-meta model is given. The meta-meta model provides a group of general basic component meta-model elements together with the relationships between elements for the construction of the domain meta-model. It can support multi-view, multi-level description of the domain model. Developers or domain experts can complete the design and construction of the domain-specific meta-model and the domain application model in the integrated modelling environment. The thesis has laid the foundation necessary for research in descriptive languages through further study in key technologies of meta-modelling languages based on Model Driven development.