The agile creation of 3D CAD platforms (3D CAD models that can be configured to obtain a family of Products) has become an important factor for increasing competitiveness of organizations that create discrete products. Design Automation (DA) is a powerful tool that can be used for speeding up and optimizing the design process of those 3D CAD platforms. Nonetheless, for effectively applying DA on the development of 3D CAD platforms it is desirable to count on tools that address the three fundamental hurdles that are also obstructing the wide adoption of DA in practice. These hurdles are the lack of identification of DA opportunities, the absence of performance metrics, and the absence of methods for continuous improvement. This dissertation contributes a set of methods and tools to incrementally improve the process for creating 3D CAD platforms towards increased automation. The tools proposed include the development of a Metrics Framework (MF) for assessing the capabilities of an organization for creating 3D CAD platforms; a method for increasing the organizational capabilities for creating 3D CAD platforms, and a method for identifying optimal improvement efforts for creating 3D CAD platforms in a multi-objective scenario

The global prominence of energy-efficient retrofit in the context of aging properties has garnered noteworthy attention. This surge in interest can be attributed to several advantages, encompassing economically viable carbon dioxide (CO₂) emissions reduction, diminished energy expenditures, and improved indoor air quality. Passive retrofits, such as thermal insulation and fenestration improvement, and active retrofits, such as heating setpoint temperature optimization, offer great potential for CO₂ reduction and energy savings. The central objective of this study is ascertaining the feasibility of attaining a 40% reduction in CO₂ emissions with the lowest cost and with constraints on heating setpoints temperature by finding optimal design parameters encompassing thermal insulation (including both single and double-layer), fenestration, and heating setpoint temperatures. This inquiry is substantiated through a case study of the Leblanc residence on the University of Ottawa campus. In pursuit of this objective, a thermal model of the Leblanc building was developed via EnergyPlus and subsequently subjected to a validation process following ASHRAE Guideline 14. After validation, an array of discrete optimization scenarios was executed using the NSGA-II model, facilitated by the JEPLUS+EA software. This approach aimed to identify the most suitable parameters for achieving optimal CO₂ reduction and cost outcomes. Notably, the results showcased 20 solutions, each boasting a reduction of 40% or more in CO₂ emissions and heating setpoint temperature higher than 18 °C. While the choice to prioritize either cost or CO₂ reduction remains at the user's discretion, four solutions have been discerned as the most effective. Furthermore, the findings suggest that implementing these optimal solutions can significantly decrease CO₂ emissions, ranging between 41.79% and 46.36%. The associated costs were also determined to fall within $36,262 to $57,934.

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In this dissertation, applications of thermodynamics at the macroscopic and quantum levels of description are developed. Within the macroscopic level, an upper-level Sustainability Assessment Framework (SAF) is proposed for evaluating the sustainable and resilient synthesis/design and operation of sets of small renewable and non-renewable energy production technologies coupled to power production transmission and distribution networks via microgrids. The upper-level SAF is developed in accord with the four pillars of sustainability, i.e., economic, environmental, technical and social. A superstructure of energy producers with a fixed transmission network initially available is synthesized based on the day with the highest energy demand of the year, resulting in an optimum synthesis, design, and off-design network configuration. The optimization is developed in a quasi-stationary manner with an hourly basis, including partial-load behavior for the producers. Since sustainability indices are typically not expressed in the same units, multicriteria decision making methods are employed to obtain a composite sustainability index. Within the quantum level of description, steepest-entropy-ascent quantum thermodynamics (SEA-QT) is used to model the phenomenon of decoherence. The two smallest microscopic composite systems encountered in Nature are studied. The first of these is composed of two two-level-type particles, while the second one is composed of a two-level-type particle and an electromagnetic field. Starting from a non-equilibrium state of the composite and for each of the two different composite systems, the time evolution of the state of the composite as well as that of the reduced and locally-perceived states of the constituents are traced along their relaxation towards stable equilibrium at constant system energy. The modeling shows how the initial entanglement and coherence between constituents are reduced during the relaxation towards a state of stable equilibrium. When the constituents are non-interacting, the initial coherence is lost once stable equilibrium is reached. When they are interacting, the coherence in the final stable equilibrium state is only that due to the interaction. For the atom-photon field composite system, decoherence is compared with data obtained experimentally by the CQED group at Paris. The SEA-QT method applied in this dissertation provides an alternative and comprehensive explanation to that obtained with the "open system" approach of Quantum Thermodynamics (QT) and its associated quantum master equations of the Kossakowski-Lindblad-Gorini-Sudarshan type. / Ph. D.

This research involves the development of a comprehensive decision support system for energy planning through the increased use of renewable energy sources, while still considering the role of existing electricity generating facilities. This dissertation focuses on energy planning at the regional level, with the Greater Southern Appalachian Mountain region chosen for analysis due to the dependence on coal as the largest source of generation and the availability of wind and solar resources within the region. The first stage of this planning utilizes a geographic information system (GIS) for the discovery of renewable energy sources. This GIS model analyzes not just the availability of wind and solar power based on resource strength, but also considers the geographic, topographic, regulatory, and other constraints that limit the use of these resources. The model determines potential wind and solar sites within the region based on these input constraints, and finally the model calculates the cost and generation characteristics for each site. The results of the GIS model are then input into the second section of the model framework which utilizes a multi-objective linear programming (MOLP) model to determine the optimal mix of new renewable energy sources and existing fossil fuel facilities. In addition to the potential wind and solar resources discovered in the GIS, the MOLP model considers the implementation of solid wood waste biomass for co-fire at coal plants. The model consists of two competing objectives, the minimization of annual generation cost and the minimization of annual greenhouse gas emissions, subject to constraints on electricity demand and capital investment, amongst others. The model uses the MiniMax function in order to find solutions that consider both of the objective functions. The third major section of this dissertation analyzes three potential public policies — renewable portfolio standard, carbon tax, and renewable energy production tax credit — that have been used to foster increased renewable energy usage. These policies require minor modifications to the MOLP model for implementation. The results of these policy cases are then analyzed to determine the impact that these policies have on generation cost and pollution emissions within the region. / Ph. D.

Sustainability is increasingly becoming a significant part of strategic asset management worldwide. Road agencies are providing guidelines to assess the relative sustainability of road projects. Unfortunately, environmental features of a road project are still considered as stand-alone evaluations, an added value. Very little has been done to integrate environmental impacts as a part of pavement management systems and other decision support tools to choose between different strategies. In this way, being awarded with a "green" certificate for a specific road project could result in the belief that recognition would correspond to the optimal strategy. Furthermore, a road project awarded with a "green" rating during the construction phase does not mean that the project results "green" if a life cycle approach is considered. Indeed, the most environmental friendly strategies may not be the ones with the highest performance. Using "greener" materials or performing recycle-related practices may lead to a lower performance over the life cycle and therefore produce an increase in maintenance needed, which could in turn result into more congestion due to work zones and higher total emissions. Therefore, construction and maintenance strategies should be analyzed according to three main parameters: cost, performance or effectiveness, and environmental impacts. The cost analysis part takes into account outflows over the service life of the pavement according to the well-known Life Cycle Cost Analysis methodology. The cheapest maintenance technique over the analysis period was expounded and sensitivity analyses to involved factors were conducted. Performance assessment was developed according to experimental on site data gathered and analyzed over several years to develop deterioration pavement models. Effectiveness of maintenance treatments is further provided and compared to the volume of traffic. In addition, environmental impacts related to maintenance and rehabilitation strategies were analyzed. Emissions were computed over the life cycle of the pavement from the manufacture of raw materials for the initial construction, placement, and maintenance phase. Finally, an optimization procedure was developed for including environmental impacts into a Pavement Management System. A methodology to set a multi-attribute approach system, computing costs, performance, and eco-efficiency over the life cycle of the pavement, is therefore proposed. / Ph. D.

With the proliferation of fairly powerful mobile devices and ubiquitous wireless technology, traditional mobile ad hoc networks (MANETs) now migrate into a new era of service-oriented MANETs wherein a node can provide and receive service from other nodes it encounters and interacts with. This dissertation research concerns trust management and its applications for service-oriented MANETs to answer the challenges of MANET environments, including no centralized authority, dynamically changing topology, limited bandwidth and battery power, limited observations, unreliable communication, and the presence of malicious nodes who act to break the system functionality as well as selfish nodes who act to maximize their own gain. We propose a context-aware trust management model called CATrust for service-oriented ad hoc networks. The novelty of our design lies in the use of logit regression to dynamically estimate trustworthiness of a service provider based on its service behavior patterns in a context environment, treating channel conditions, node status, service payoff, and social disposition as 'context' information. We develop a recommendation filtering mechanism to effectively screen out false recommendations even in extremely hostile environments in which the majority recommenders are malicious. We demonstrate desirable convergence, accuracy, and resiliency properties of CATrust. We also demonstrate that CATrust outperforms contemporary peer-to-peer and Internet of Things trust models in terms of service trust prediction accuracy against collusion recommendation attacks. We validate the design of trust-based service management based on CATrust with a node-to-service composition and binding MANET application and a node-to-task assignment MANET application with multi-objective optimization (MOO) requirements. For either application, we propose a trust-based algorithm to effectively filter out malicious nodes exhibiting various attack behaviors by penalizing them with trust loss, which ultimately leads to high user satisfaction. Our trust-based algorithm is efficient with polynomial runtime complexity while achieving a close-to-optimal solution. We demonstrate that our trust-based algorithm built on CATrust outperforms a non-trust-based counterpart using blacklisting techniques and trust-based counterparts built on contemporary peer-to-peer trust protocols. We also develop a dynamic table-lookup method to apply the best trust model parameter settings upon detection of rapid MANET environment changes to maximize MOO performance. / Ph. D.