This thesis advances a proposition that designers of exemplary industrial buildings adopt design heuristics based on evolving conceptual prototypes. The detail design priorities and formal expressions may vary, but the essential structure of their design solution search patterns should show a high degree of commonality. This commonality is based on the balance between rational and intuitive strategies for design decision making. Therefore it is the central objective of this thesis to demonstrate that a pattern between these two cognitive approaches to design thinking occurs across the cases investigated. The research was organised as a comparative study of the design process used by a selection of designers to design industrial buildings which subsequently received design awards for the quality of their design. Through a series of six case studies using semi-structured interviews and follow-up repertory grid analysis, data was obtained which allowed the various design methods to be recorded, analysed and compared. A control sample of designers of non award winning industrial buildings was also conducted as a comparison to validate the classification of designers on the basis of design quality on the grounds of design awards. The thesis will propose that each designer resorted to a constantly evolving genenc 'prototype' for the design of industrial buildings which helps reduce the search area for a design solution. Therefore relatively little time is required to analyse the problem at the beginning of the process before a suitable solution is formulated. The prototype allows the designers to quickly identify aspects of the design that might prove problematic and hence require specific attention. The strategy is then one of rational analysis to solve specific problems rather than an extensive rationally based development of the whole design. Hence the supporting role of rationally based techniques during the design process. Finally, the familiarity that comes from using a design method based on a constantly evolving prototype provides the designer with greater 'outcome certainty', in that they have an increased likelihood of achieving a successful design as the potential of the prototype is known to the designer at the commencement of the project

Nucleation is a phenomenon of broad scientific interest and technological importance. It refers to the very early stages of the formation of a new phase, which can be solid, gaseous, and liquid, in a metastable parent phase. Most nucleation occurs heterogeneously unless the metastable parent phase from which the nuclei form is perfectly homogeneous and isolated from any catalyzing medium. This thesis project deals with heterogeneous nucleation on convex and concave spherical surfaces. In brief, the main achievements of the project are • An innovative analytical thermodynamic approach has been invented, which enabled rigorous thermodynamic formation of the energy barrier to nucleation, the critical radius and the shape factor, for nucleation on both convex and on concave surfaces. The rigorous thermodynamic analyses conducted have revealed a number of features for heterogeneous nucleation on convex and concave spherical surfaces as opposed to heterogeneous nucleation on a flat substrate surface. These are described in detail in Chapters 2 and 4. • Nucleation is the easiest on a concave spherical surface while it is the most difficult on a convex spherical surface assuming the contact angle and the critical embryo radius are the same. Nucleation on a flat substrate surface falls in between. This is determined by their shape factors. • The ratio R = 20r*, where R is the radius of the spherical substrate and r* is the critical embryo radius, (always define the symbols when they are first used. No one knows what they mean. R could be gas constant) can be regarded as a sufficiently accurate boundary that distinguishes between spherical and flat substrates for heterogeneous nucleation. • The investigation of the growth of crystal nuclei on a convex spherical surface has revealed that no growth barrier exists to the growth of a nucleus on a convex spherical substrate surface regardless of R < r* or R > r*. All nuclei formed on a convex spherical substrate surface are thus transformation nuclei. Turnbull’s transformation nucleus model or the recently developed free growth model does not apply to the growth of a spherical-cap nucleus on a convex spherical surface. • For heterogeneous nucleation in undercooled liquid metals, the cap thickness varies in a very narrow range by just a few angstroms and is typically about a few atomic layers thick according to Turnbull’s nucleation rate equation. The variations in the cap thickness are generally limited to less than 1Å when the contact angle Ɵ is varied in the range from 0° to 45°. It is anticipated that these findings will help to better understand the classical models for heterogeneous nucleation and provide new insights into the control of heterogeneous nucleation where convex or concave spherical substrate surfaces are involved.

Thesis (Ph. D.)--University of Texas at Austin, 2005. / Vita. Includes bibliographical references.

In recent years, hardware-in-the-loop (HIL) simulation has assumed a prominent role in the vehicle development process. A physical part, which may be a prototype at any stage of development, is tested, while the rest of the vehicle is represented by a mathematical model. Vehicle models used with hardware-in-the-loop must be capable of simulating an event in less time than it takes the event to occur in reality. Fast simulation necessitates a model that is represented by very efficient simulation code. This thesis presents a procedure for automatically generating this simulation code, given a description of the vehicle as input. For this work, a symbolic formulation procedure based on linear graph theory and the principle of orthogonality is used to generate governing equations for vehicle systems; this procedure forms the basis of the DynaFlexPro software package. In order to generate simulation code for vehicle dynamics studies, the DynaFlexPro component model template was extended to include rules for calculating intermediate variables and rules for calling external functions. These changes enabled the development of a tire component model, known as DynaFlexPro/Tire, that adds critically important (and computationally efficient) blocks to the overall vehicle simulation code. The combination of DynaFlexPro and DynaFlexPro/Tire allows analysts to construct a model for any vehicle topology and gives analysts great freedom to define how tire forces and moments will be calculated. Simulation code describing the vehicle model is automatically generated using symbolic computing techniques. The accuracy of the approach was validated by comparing results for DynaFlexPro vehicle models to results for equivalent models developed in a well-established tool for vehicle dynamics simulation (MSC.ADAMS). Two different vehicle models were constructed using DynaFlexPro and DynaFlex- Pro/Tire: a generic 4-wheeled vehicle with independent suspension and an articulated forestry skidder. Both models had an open-loop topology. When appropriate modeling variables were selected, each model was described by a minimal set of ordinary differential equations (ODEs) and the simulation code generated by DynaFlexPro was capable of being used for hardware-in-the-loop applications; the braking and handling behavior of the example models was simulated faster than real-time on a desktop PC with a 3.2 GHz Pentium 4 processor and 1 GB of RAM. For the same vehicle models, a different choice of modeling variables resulted in a mixed set of differential and algebraic equations (DAEs); in that case, HIL-capable simulation code could not be consistently generated. The approach works well for vehicle models described by ODEs, but more research is needed into the treatment of DAEs for real-time simulation of vehicle dynamics.

Multiobjective optimization is a branch of mathematical programming for modelling problems with multiple conflicting objectives. Multiobjective optimization problems can be solved using Pareto optimization techniques including evolutionary multiobjective optimization algorithms. Many real world applications involve multiple objective functions and can be addressed within a multiobjective optimization framework. Multiobjective optimization methods allow exploration of the attainable values of the objective functions and trade-offs between objective functions without soliciting preference information from the decision maker(s) before potential solutions are presented. In order to be sufficiently representative of the possibilities and trade-offs, the results of multiobjective optimization may be too numerous or complex in shape for decision makers to reasonably consider. Previous approaches to this problem have aimed to reduce the solution set to a smaller representative set. The methodology developed and evaluated in this thesis employs hierarchical cluster analysis to organize the solutions from multiobjective optimiation into a tree structure based on their objective function values. Unlike previous approaches none of the solutions are removed from consideration before being presented to the decision makers. A hierarchical cluster structure is desirable since it presents a nested organization of the plans which can be used in decision making as shown in an example decision. The resulting dendrogram is a tree of clusters that can be used to see the attainable trade-offs on the Pareto front. As well, it can be used to interactively reduce the set of solutions under consideration or consider several subsets of solutions that lie in different regions of the Pareto front. A land use change problem in an urban fringe area in Southern Ontario, Canada is used as motivation and as an example application to evaluate the proposed methodology. Relevant literature in planning support systems is reviewed in order to focus the methodology on the application. The multiobjective optimization problem for this application was formulated and analyzed by Roberts (2003); the optimization algorithm used to generate the approximation of the optimal solutions is the Non-dominated Sorting Genetic Algorithm II, NSGA-II, developed by Deb et al. (2002). Future work will link the resulting objective function-based tree to map visualizations of the landscape under consideration. Decision makers will be able to use the tree structure to explore different potential land use plans based on their performance on the objective functions representing the quality of those plans for natural and human uses. This approach is applicable to multiobjective problems with more than three objective functions and discrete decision variables or hierarchically clustered Pareto optimal sets. The suitability for reuse with other datasets or other applications is discussed as well as the potential for inclusion in a decision support system (DSS).

The goal of this research is to develop a model-based tracking framework with biomedical imaging applications. This is an interdisciplinary area of research with interests in machine vision, image processing, and biology. This thesis presents methods of image modeling, tracking, and data association applied to problems in multi-cellular image analysis, especially hematopoietic stem cell (HSC) images at the current stage. The focus of this research is on the development of a robust image analysis interface capable of detecting, locating, and tracking individual hematopoietic stem cells (HSCs), which proliferate and differentiate to different blood cell types continuously during their lifetime, and are of substantial interest in gene therapy, cancer, and stem-cell research. Such a system can be potentially employed in the future to track different groups of HSCs extracted from bone marrow and recognize the best candidates based on some biomedical-biological criteria. Selected candidates can further be used for bone marrow transplantation (BMT) which is a medical procedure for the treatment of various incurable diseases such as leukemia, lymphomas, aplastic anemia, immune deficiency disorders, multiple myeloma and some solid tumors. Tracking HSCs over time is a localization-based tracking problem which is one of the most challenging tracking problems to be solved. The proposed cell tracking system consists of three inter-related stages: i) Cell detection/localization, ii) The association of detected cells, iii) Background estimation/subtraction. that will be discussed in detail.

System design, parameter design, and tolerance design are the three stages of product or process development advocated by Genichi Taguchi. Parameter design, or robust parameter design (RPD), is the method to determine nominal parameter values of controllable variables such that the quality characteristics can meet the specifications and the variability transmitted from uncontrollable or noise variables is minimized for the process or product. Tolerance design is used to determine the best limits for the parameters to meet the variation and economical requirements of the design. In this thesis, response surface methodology (RSM) and nonlinear programming methods are adopted to integrate the parameter and tolerance design. The joint optimization method that conducts parameter design and tolerance design simultaneously is more effective than the traditional sequential process. While Taguchi proposed the crossed array design, the combined array design approach is more flexible and efficient since it combines controllable factors, internal noise factors, and external noise factors in a single array design. A combined array design and the dual response surface method can provide detailed information of the process through process mean and process variance obtained from the response model. Among a variety of cuboidal designs and spherical designs, standard or modified central composite designs (CCD) or face-centered cube (FCC) designs are ideal for fitting second-order response surface models, which are widely applied in manufacturing processes. Box-Behnken design (BBD), mixed resolution design (MRD), and small composite design (SCD) are also discussed as alternatives. After modeling the system, nonlinear programming can be used to solve the constrained optimization problem. Dual RSM, mean square error (MSE) loss criterion, generalized linear model, and desirability function approach can be selected to work with quality loss function and production cost function to formulate the object function for optimization. This research also extends robust design and RSM from single response to the study of multiple responses. It was shown that the RSM is superior to Taguchi approach and is a natural fit for robust design problems. Based on our study, we can conclude that dual RSM can work very well with ordinary least squares method or generalized linear model (GLM) to solve robust parameter design problems. In addition, desirability function approach is a good selection for multiple-response parameter design problems. It was confirmed that considering the internal noise factors (standard deviations of the control factors) will improve the regression model and have a more appropriate optimal solution. In addition, simulating the internal noise factors as control variables in the combined array design is an attractive alternative to the traditional method that models the internal noise factors as part of the noise variables. The purpose of this research is to develop the framework for robust design and the strategies for RSM. The practical objective is to obtain the optimal parameters and tolerances of the design variables in a system with single or multiple quality characteristics, and thereby achieve the goal of improving the quality of products and processes in a cost effective manner. It was demonstrated that the proposed methodology is appropriate for solving complex design problems in industry applications.

In recent years, hardware-in-the-loop (HIL) simulation has assumed a prominent role in the vehicle development process. A physical part, which may be a prototype at any stage of development, is tested, while the rest of the vehicle is represented by a mathematical model. Vehicle models used with hardware-in-the-loop must be capable of simulating an event in less time than it takes the event to occur in reality. Fast simulation necessitates a model that is represented by very efficient simulation code. This thesis presents a procedure for automatically generating this simulation code, given a description of the vehicle as input. For this work, a symbolic formulation procedure based on linear graph theory and the principle of orthogonality is used to generate governing equations for vehicle systems; this procedure forms the basis of the DynaFlexPro software package. In order to generate simulation code for vehicle dynamics studies, the DynaFlexPro component model template was extended to include rules for calculating intermediate variables and rules for calling external functions. These changes enabled the development of a tire component model, known as DynaFlexPro/Tire, that adds critically important (and computationally efficient) blocks to the overall vehicle simulation code. The combination of DynaFlexPro and DynaFlexPro/Tire allows analysts to construct a model for any vehicle topology and gives analysts great freedom to define how tire forces and moments will be calculated. Simulation code describing the vehicle model is automatically generated using symbolic computing techniques. The accuracy of the approach was validated by comparing results for DynaFlexPro vehicle models to results for equivalent models developed in a well-established tool for vehicle dynamics simulation (MSC.ADAMS). Two different vehicle models were constructed using DynaFlexPro and DynaFlex- Pro/Tire: a generic 4-wheeled vehicle with independent suspension and an articulated forestry skidder. Both models had an open-loop topology. When appropriate modeling variables were selected, each model was described by a minimal set of ordinary differential equations (ODEs) and the simulation code generated by DynaFlexPro was capable of being used for hardware-in-the-loop applications; the braking and handling behavior of the example models was simulated faster than real-time on a desktop PC with a 3.2 GHz Pentium 4 processor and 1 GB of RAM. For the same vehicle models, a different choice of modeling variables resulted in a mixed set of differential and algebraic equations (DAEs); in that case, HIL-capable simulation code could not be consistently generated. The approach works well for vehicle models described by ODEs, but more research is needed into the treatment of DAEs for real-time simulation of vehicle dynamics.

Multiobjective optimization is a branch of mathematical programming for modelling problems with multiple conflicting objectives. Multiobjective optimization problems can be solved using Pareto optimization techniques including evolutionary multiobjective optimization algorithms. Many real world applications involve multiple objective functions and can be addressed within a multiobjective optimization framework. Multiobjective optimization methods allow exploration of the attainable values of the objective functions and trade-offs between objective functions without soliciting preference information from the decision maker(s) before potential solutions are presented. In order to be sufficiently representative of the possibilities and trade-offs, the results of multiobjective optimization may be too numerous or complex in shape for decision makers to reasonably consider. Previous approaches to this problem have aimed to reduce the solution set to a smaller representative set. The methodology developed and evaluated in this thesis employs hierarchical cluster analysis to organize the solutions from multiobjective optimiation into a tree structure based on their objective function values. Unlike previous approaches none of the solutions are removed from consideration before being presented to the decision makers. A hierarchical cluster structure is desirable since it presents a nested organization of the plans which can be used in decision making as shown in an example decision. The resulting dendrogram is a tree of clusters that can be used to see the attainable trade-offs on the Pareto front. As well, it can be used to interactively reduce the set of solutions under consideration or consider several subsets of solutions that lie in different regions of the Pareto front. A land use change problem in an urban fringe area in Southern Ontario, Canada is used as motivation and as an example application to evaluate the proposed methodology. Relevant literature in planning support systems is reviewed in order to focus the methodology on the application. The multiobjective optimization problem for this application was formulated and analyzed by Roberts (2003); the optimization algorithm used to generate the approximation of the optimal solutions is the Non-dominated Sorting Genetic Algorithm II, NSGA-II, developed by Deb et al. (2002). Future work will link the resulting objective function-based tree to map visualizations of the landscape under consideration. Decision makers will be able to use the tree structure to explore different potential land use plans based on their performance on the objective functions representing the quality of those plans for natural and human uses. This approach is applicable to multiobjective problems with more than three objective functions and discrete decision variables or hierarchically clustered Pareto optimal sets. The suitability for reuse with other datasets or other applications is discussed as well as the potential for inclusion in a decision support system (DSS).

The goal of this research is to develop a model-based tracking framework with biomedical imaging applications. This is an interdisciplinary area of research with interests in machine vision, image processing, and biology. This thesis presents methods of image modeling, tracking, and data association applied to problems in multi-cellular image analysis, especially hematopoietic stem cell (HSC) images at the current stage. The focus of this research is on the development of a robust image analysis interface capable of detecting, locating, and tracking individual hematopoietic stem cells (HSCs), which proliferate and differentiate to different blood cell types continuously during their lifetime, and are of substantial interest in gene therapy, cancer, and stem-cell research. Such a system can be potentially employed in the future to track different groups of HSCs extracted from bone marrow and recognize the best candidates based on some biomedical-biological criteria. Selected candidates can further be used for bone marrow transplantation (BMT) which is a medical procedure for the treatment of various incurable diseases such as leukemia, lymphomas, aplastic anemia, immune deficiency disorders, multiple myeloma and some solid tumors. Tracking HSCs over time is a localization-based tracking problem which is one of the most challenging tracking problems to be solved. The proposed cell tracking system consists of three inter-related stages: i) Cell detection/localization, ii) The association of detected cells, iii) Background estimation/subtraction. that will be discussed in detail.