Data centers are computing infrastructure facilities used by industries with large data processing needs and the rapid increase in power density of high performance computing equipment has caused many thermal issues in these facilities. Systems-level thermal management requires modeling and analysis of complex fluid flow and heat transfer processes across several decades of length scales. Conventional computational fluid dynamics and heat transfer techniques for such systems are severely limited as a design tool because their large model sizes render parameter sensitivity studies and optimization impractically slow. The traditional proper orthogonal decomposition (POD) methodology has been reformulated to construct physics-based models of turbulent flows and forced convection. Orthogonal complement POD subspaces were developed to parametrize inhomogeneous boundary conditions and greatly extend the use of the existing POD methodology beyond prototypical flows with fixed parameters. A flux matching procedure was devised to overcome the limitations of Galerkin projection methods for the Reynolds-averaged Navier-Stokes equations and greatly improve the computational efficiency of the approximate solutions. An implicit coupling procedure was developed to link the temperature and velocity fields and further extend the low-dimensional modeling methodology to conjugate forced convection heat transfer. The overall reduced-order modeling framework was able to reduce numerical models containing 105 degrees of freedom (DOF) down to less than 20 DOF, while still retaining greater that 90% accuracy over the domain. Rigorous a posteriori error bounds were formulated by using the POD subspace to partition the error contributions and dual residual methods were used to show that the flux matching procedure is a computationally superior approach for low-dimensional modeling of steady turbulent convection. To efficiently model large-scale systems, individual reduced-order models were coupled using flow network modeling as the component interconnection procedure. The development of handshaking procedures between low-dimensional component models lays the foundation to quickly analyze and optimize the modular systems encountered in electronics thermal management. This modularized approach can also serve as skeletal structure to allow the efficient integration of highly-specialized models across disciplines and significantly advance simulation-based design.

Traditional methods in engineering design involve producing solutions at a single level. However, in complex engineering design problems, such as concurrent product and materials design, various levels of model complexity are considered. A design process in which design problems are defined and analyzed at various levels of design complexity is referred to as multilevel design. One example of multilevel design is the design of a material, product, assembly, and system. Dividing a design problem into multiple levels increases the possibility for introducing and propagating uncertainty. Design solutions that perform predictably in the presence of uncertainty are robust designs. Robust design concepts that were originally developed for designs at a single level can be applied to a multilevel design process. The Inductive Design Exploration Method (IDEM) is an existing design method used to produce robust multilevel design solutions. In this thesis, the strategy presented in IDEM is incorporated into design templates in order to extend its overall usefulness. Design templates are generic, reusable, modules that provide the theoretical and computational framework for solving design problems. Information collected, stored, and analyzed from design templates is leveraged for a variety of design problems. In this thesis, the possibilities of a template-based approach to multilevel design are explored. Two example problems, which employ the developed multilevel robust design template, are considered. Multilevel design templates are created for the design of a cantilever beam and its associated material and the design of a blast resistant panel. The design templates developed for example problems can be extended to facilitate a generic, modular, template-based approach to multilevel robust design.

Materials have traditionally been selected for the design of a product; however, advances in the understanding of material processing along with simulation and computation techniques are now making it possible to systematically design materials by tailoring the properties of the material to achieve the desired product performance. Material design offers the potential to increase design freedom and enable improved product performance; however, this increase in design freedom brings with it significant complexity in predictive models used for design, as well as many new design variables to consider. Material selection, on the other hand, is a well-established method for identifying the best materials for a product and does not require the complex models needed for material design. But material selection inherently limits the design of products by only considering existing materials. To balance increasing design costs with potentially improved product performance, designers must have a method for assessing the value of material design in the context of product design. In this thesis, the Design Space Expansion Strategy (DSES) and the Value of Design Space Expansion (VDSE) metric are proposed for supporting a designer s decision between material selection and material design in the context of product design. The strategy consists of formulating and solving two compromise Decision Support Problems (cDSP). The first cDSP is formulated and solved using a selected baseline material. The second cDSP is formulated and solved in an expanded material design space defined by material property variables in addition to other system variables. The two design solutions are then compared using the VDSE metric to quantify the value of expanding the material design space. This strategy is demonstrated in this thesis with an example of blast resistant panel design and is validated by application of the validation square, a framework for the validating design methods.

Serum PSA (Prostate Specific Antigen) level is used for prediction of prostatic carcinoma, but it suffers from weak sensitivity and specificity. We applied logistic regression, artificial neural networks, decision tree, and genetic algorithm to prostate cancer prediction problem to design a model for Turkish population. A hybrid model of logistic regression and decision tree has been designed. The model could prevent 33 biopsies (4.4% of our patients who have PSA level between 0 and 10) from our data set without a loss from sensitivity. The prepared online decision support tool and a questionnaire were published on a website. Fifty urologists have completed the questionnaire. Cronbach&rsquo / s alpha was 0.770. On a five graded Likert scale, the mean score of &ldquo / attitude to computer use in healthcare&rdquo / (ACH) was 4.2. The mean of eight responses related to the online tool (Attitude to Decision Support Tool / ADST), was 3.7. ADST was correlated with ACH (r=0.351, p=0.013). Physicians who have positive attitude to computer use in healthcare tend to use the tool (r=0.459, p=0.001). The first factor influencing the opinions of the urologists was the attitude of the user to computer use in healthcare, the other factor was the attitude of the user to the decision support tool itself. To increase the acceptance, education and training of physicians in the use of information technologies in healthcare, informing users about the logic of the decision support tool, and redesigning the system according to user feedback may be helpful.

In medicine, reliable data are available only in a few areas and necessary information on prognostic implications is generally missing. In spite of the fact that a great amount of money has been invested to ease the process, an effective solution has yet to be found. Unfortunately, existing data collection approaches in medicine seem inadequate to provide accurate and high quality data, which is a prerequisite for building a robust and effective DDSS. In this thesis, many different medical reporting methodologies and systems which have been used up to now are evaluated / their strengths and deficiencies are revealed to shed light on how to set up an ideal medical reporting type. This thesis presents a new medical reporting method, namely &ldquo / Structured, Interactive, Standardized and Decision Supporting Method&rdquo / (SISDS) that encompasses most of the favorable features of the existing medical reporting methods while removing most of their deficiencies such as inefficiency and cognitive overload as well as introducing and promising new advantages. The method enables professionals to produce multilingual medical reports much more efficiently than the existing approaches in a novel way by allowing free-text-like data entry in a structured form. The proposed method in this study is proved to be more effective in many perspectives, such as facilitating the complete and the accurate data collection process and providing opportunities to build DDSS without tedious pre-processing and data preparation steps, mainly helping health care professionals practice better medicine.

A key challenge facing designers creating innovative products is concept generation. Conceptual design is more effective when the design space is broadened by using an integrated design of product and material concepts approach. Conceptual design can also be accelerated by including problem solving and solution triggering tools in its structure. In this approach, structured analogy is used to transfer underlying principles from a solution suitable in one domain (i.e., product or mechanical domain) to an analogous solution in another domain (i.e., material domain). The nature of design analogy does not require as full of an exploration of the target domain as would otherwise be necessary; affording the possibility of a more rapid development. The addition of problem solving and solution triggering tools to a design method also decreases the design time and/or improves the quality of the final solution. This approach is formulated through a combination of the Theory of Inventive Problem Solving (TRIZ) proposed by Altshuller, and the systematic approach of Pahl and Beitz, for products that are jointly considered at the product and material level. These types of problems are ones where customer performance requirements are fulfilled through both the designed product and the designed material. The systematic approach of Pahl and Beitz is used as the base method through which TRIZ is used as a means of transferring abstract information about the design problem between the domains with the aim of accelerating conceptual design. This also allows for multi-domain design tools such as Su-Field-Model integration with design repositories for the transfer of information at different levels of abstraction; expanding the design space and effectively directing the designer. The explanation of this approach is presented through a simple example of a spring design improvement and validated through concept generation of a reactive material containment system.

In this thesis, a decision model for examining prescribed risk management practices in engineering design is presented. The decision model explicitly considers the effects that design decisions under uncertainty have on the overall utility of the design process. These effects are important to consider because, according to Utility Theory, the designer should make decisions such that the expected utility is maximized. However, a significant portion of the literature neglects the costs of the design process, and focuses only on the quality of the design artifact, or at best includes its manufacture when determining the utility of an alternative. When designers neglect the costs of the design process, they cannot make tradeoffs between the costs of the design process and the quality of the artifact. As compared to previous work in this area, the decision model presented includes the effects of temporally degrading product utility on design decisions. The decision model is used to investigate the impacts of degrading product utilities in products that launch later as a result of the duration of design actions performed. In this thesis, the decision model is leveraged to investigate two key trends in engineering design resulting from increasing temporally-based costs. To support the conclusions in this thesis, quantitative evaluations of the decision model are investigated for two case studies. The conclusions are additionally supported through evaluations of the decision model in boundary plots that visualize prescribed behavior for designers over varying model parameters.

This thesis offers a decision support framework to establish the economic feasibility associated with considering the installation of a greywater system. Because of the potential dangers and lack of widespread knowledge of greywater systems, the study begins by providing an explanation of current greywater technology to include the history of the technology, an explanation of greywater as opposed to reclaimed water, the potential risks of greywater use, and the necessary components of a greywater system. This decision support framework can be used with any scale of greywater system to be installed within any scale of facility. The example of an typical Atlanta, Georgia, USA multifamily rental development is used within the study to explain the framework by showing a working model. The need for water conservation in Georgia is shown and how greywater use dovetails with the need to lower overall usage. The legality of greywater use in Georgia along with the specific legal uses is also shown. The findings are then made State of Georgia and use specific to a multifamily development. The decision support framework provided is a viable tool. The sample framework in chapter 5 shows that the implementation of a greywater unit in the sampled facility would save 5,060,739.6 gallons of potable water per year with a 10.49 year payback cycle.

Thesis (Ph. D.)--University of California, San Diego, 2002. / Vita. Includes bibliographical references.

Thesis (doctoral)--Universität Kaiserslautern, 2002. / Includes bibliographical references (p. 219-234) and index.