Gas turbine engines are conceptually designed using performance maps that describe the compressor’s effect on the cycle. During the traditional design process, the cycle designer selects a compressor design point based on criteria to meet cycle design point requirements, and performance maps are found or created for off-design analysis that meet this design point selection. Although the maps always have a pedigree to an existing compressor design, oftentimes these maps are scaled to account for design or technology changes. Scaling practices disconnect the maps from the geometry and flow associated with the reference compressor, or the design parameters which are needed for compressor preliminary design. A goal in gas turbine engine research is to bridge this disconnect in order to produce acceptable performance maps that are coupled with compressor design parameters. A new compressor conceptual design and performance prediction method has been developed which will couple performance maps to conceptual design parameters. This method will adapt and combine the key elements of compressor conceptual design with multiple-meanline analysis, allowing for a map of optimal performance that is attached to reasonable design parameters to be defined for cycle design. This method is prompted by the development of multi-fidelity (zooming) analysis capabilities, which allow compressor analysis to be incorporated into cycle analysis. Integrating compressor conceptual design and map generation into cycle analysis will allow for more realistic decisions to be made sooner, which will reduce the time and cost used for design iterations.

Design Space Exploration is the task of identifying optimal implementation architectures for an application. On the front-end, it involves multi-objective optimization through a large space of options, and lends itself to a multitude of algorithmic approaches. On the back-end, it relies extensively on common capabilities such as model refinement, simulation and assessment of parameters like performance and cost. These characteristics present an opportunity to create an infrastructure that enables multiple approaches to be deployed using generic back-end services. In this work, we describe such a framework, developed using the System-on-Chip Environment, and we demonstrate the benefits and feasibility of deploying a variety of design space exploration approaches built on top of this basic infrastructure. / text

Thesis (M.Arch.) - Carleton University, 2005. / Includes bibliographical references (p. 130-132). Also available in electronic format on the Internet.

Undergraduate honors paper--Mount Holyoke College, 2008. Dept. of Art. / CD-ROM contains images of illusionary three-dimensional spaces. Includes bibliographical references (leaves [13]).

Thesis (M.Arch.) - Carleton University, 2007. / Includes bibliographical references (p. 81-84). Also available in electronic format on the Internet.

Thesis (M. Arch.)--Carleton University, 2000. / Includes bibliographical references (p. 82-83). Also available in electronic format on the Internet.

<p> Designing self-regulating machines that can see and comprehend various real world objects around it are the main purpose of the AI domain. Recently, there has been marked advancements in the field of deep learning to create state-of-the-art DNNs for various CV applications. It is challenging to deploy these DNNs into resource-constrained micro-controller units as often they are quite memory intensive. Design Space Exploration is a technique which makes CNN/DNN memory efficient and more flexible to be deployed into resource-constrained hardware. MobileNet is small DNN architecture which was designed for embedded and mobile vision, but still researchers faced many challenges in deploying this model into resource limited real-time processors.</p><p> This thesis, proposes three new DNN architectures, which are developed using the Design Space Exploration technique. The state-of-the art MobileNet baseline architecture is used as foundation to propose these DNN architectures in this study. They are enhanced versions of the baseline MobileNet architecture. DSE techniques like data augmentation, architecture tuning, and architecture modification have been done to improve the baseline architecture. First, the Thin MobileNet architecture is proposed which uses more intricate block modules as compared to the baseline MobileNet. It is a compact, efficient and flexible architecture with good model accuracy. To get a more compact models, the KilobyteNet and the Ultra-thin MobileNet DNN architecture is proposed. Interesting techniques like channel depth alteration and hyperparameter tuning are introduced along-with some of the techniques used for designing the Thin MobileNet. All the models are trained and validated from scratch on the CIFAR-10 dataset. The experimental results (training and testing) can be visualized using the live accuracy and logloss graphs provided by the Liveloss package. The Ultra-thin MobileNet model is more balanced in terms of the model accuracy and model size out of the three and hence it is deployed into the NXP i.MX RT1060 embedded hardware unit for image classification application.</p>

Indiana University-Purdue University Indianapolis (IUPUI) / Designing self-regulating machines that can see and comprehend various real world objects around it are the main purpose of the AI domain. Recently, there has been marked advancements in the field of deep learning to create state-of-the-art DNNs for various CV applications. It is challenging to deploy these DNNs into resource-constrained micro-controller units as often they are quite memory intensive. Design Space Exploration is a technique which makes CNN/DNN memory efficient and more flexible to be deployed into resource-constrained hardware. MobileNet is small DNN architecture which was designed for embedded and mobile vision, but still researchers faced many challenges in deploying this model into resource limited real-time processors. This thesis, proposes three new DNN architectures, which are developed using the Design Space Exploration technique. The state-of-the art MobileNet baseline architecture is used as foundation to propose these DNN architectures in this study. They are enhanced versions of the baseline MobileNet architecture. DSE techniques like data augmentation, architecture tuning, and architecture modification have been done to improve the baseline architecture. First, the Thin MobileNet architecture is proposed which uses more intricate block modules as compared to the baseline MobileNet. It is a compact, efficient and flexible architecture with good model accuracy. To get a more compact models, the KilobyteNet and the Ultra-thin MobileNet DNN architecture is proposed. Interesting techniques like channel depth alteration and hyperparameter tuning are introduced along-with some of the techniques used for designing the Thin MobileNet. All the models are trained and validated from scratch on the CIFAR-10 dataset. The experimental results (training and testing) can be visualized using the live accuracy and logloss graphs provided by the Liveloss package. The Ultra-thin MobileNet model is more balanced in terms of the model accuracy and model size out of the three and hence it is deployed into the NXP i.MX RT1060 embedded hardware unit for image classification application.

Many companies use product families in order to offer product variants that appeal to different market segments while minimizing costs. Because the market demand is generally not uniform for all possible product variants, during the design phase a decision must be made as to which variants will be offered and how many. This thesis presents a new approach to solving this problem. The product is defined in terms of performance parameters. The market demand is captured in a preference model and applied to these parameters in order to represent the total potential market. The number and placement of the product variants are optimized in order to maximize percentage of the potential market that they span. This method is applied to a family of mountain bikes and a family of flow-regulating disks used in industrial applications. These examples show that usage of this method can result in a significant increase in potential market and a significant reduction in production costs.

Formulation space exploration is a new strategy for multiobjective optimization that facilitates both divergent searching and convergent optimization during the early stages of design. The formulation space is the union of all variable and design objective spaces identified by the designer as being valid and pragmatic problem formulations. By extending a computational search into the formulation space, the solution to an optimization problem is no longer predefined by any single problem formulation, as it is with traditional optimization methods. Instead, a designer is free to change, modify, and update design objectives, variables, and constraints and explore design alternatives without requiring a concrete understanding of the design problem a priori. To facilitate this process, a new vector/matrix-based definition for multiobjective optimization problems is introduced, which is dynamic in nature and easily modified. Additionally, a set of exploration metrics is developed to help guide designers while exploring the formulation space. Finally, several examples are presented to illustrate the use of this new, dynamic approach to multiobjective optimization.