京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24264号 / 情博第808号 / 新制||情||136(附属図書館) / 京都大学大学院情報学研究科数理工学専攻 / (主査)教授 山下 信雄, 准教授 福田 秀美, 教授 太田 快人 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

The complete design process for a new nuclear power plant concept is costly, long, complicated, and the work is generally split between several specialized groups. These design groups separately do their best to design the portion of the reactor that falls in their expertise according to the design criteria before passing the design to the subsequent design group. Ultimately, the work of each design group is combined, with significant iteration between groups striving to facilitate the integration of each of the heavily interdependent systems. Such complex interaction between experts leads to three significant problems: (1) the issues associated with knowledge management, (2) the lack of design optimization, and (3) the failure to discover the hidden interdependencies between different design parameters that may exist. Some prior work has been accomplished in both developing common frame of reference (CFR) support systems to aid in the design process and applying optimization to nuclear system design.The purpose of this work is to use multi-objective optimization to address the second and third problems above on a small subset of reactor design scenarios. Multi-objective optimization generates several design optima in the form of a Pareto front, which portrays the optimal trade-off between design objectives. As a major part of this work, a system design optimization tool is created, namely the Optimization and Preference Tool for the Improvement of Nuclear Systems (OPTIONS). The OPTIONS tool is initially applied to several individual nuclear systems: the power conversion system (PCS) of the Integral, Inherently Safe Light Water Reactor (I²S-LWR), the Kalina cycle being proposed as the PCS for a LWR, the PERCS (or Passive Endothermic Reaction Cooling System), and the core loop of the Zion plant. Initial sensitivity analysis work and the application of the Non-dominated Sorting Particle Swarm Optimization (NSPSO) method provides a Pareto front of design optima for the PCS of the I²S-LWR, while bringing to light some hidden pressure interdependencies for generating steam using a flash drum. A desire to try many new PCS configurations leads to the development of an original multi-objective optimization method, namely the Mixed-Integer Non-dominated Sorting Genetic Algorithm (MI-NSGA). With this method, the OPTIONS tool provides a novel and improved Pareto front with additional optimal PCS configurations. Then, the simpler NSGA method is used to optimize the Kalina cycle, the PERCS, and the Zion core loop, providing each problem with improved designs and important objective trade-off information. Finally, the OPTIONS tool uses the MI-NSGA method to optimize the integration of three systems (Zion core loop, PERCS, and Rankine cycle PCS) while increasing efficiency, decreasing costs, and improving performance. In addition, the tool is outfitted to receive user preference input to improve the convergence of the optimization to a Pareto front.

Large-Scale Digital Twins Notification System (LSDTNS) monitors a Digital Twin (DT) cluster for a predefined critical state, and once it detects such a state, it sends a Notification Event (NE) to a predefined recipient. Additionally, the time from producing the DT's Complex Event (CE) to sending an alarm has to be less than a predefined deadline. However, addressing scalability and multi-objectives, such as deployment cost, resource utilization, and meeting the deadline, on top of process scheduling, presents a complex challenge. Therefore, this thesis presents a complex methodology consisting of three contributions that address system scalability, multi-objectivity and scheduling of CE processes using Reinforcement Learning (RL). The first contribution proposes the IoT Notification System Architecture based on a micro-service-based notification methodology that allows for running and seamlessly switching between various CE reasoning algorithms. Our proposed IoT Notification System architecture addresses the scalability issue in state-of-the-art CE Recognition systems. The second contribution proposes a novel methodology for multi-objective optimization for cloud provisioning (MOOP). MOOP is the first work dealing with multi-optimization objectives for microservice notification applications, where the notification load is variable and depends on the results of previous microservices subtasks. MOOP provides a multi-objective mathematical cloud resource deployment model and demonstrates effectiveness through the case study. Finally, the thesis presents a Scheduler for large-scale Critical Notification applications based on a Deep Reinforcement Learning (SCN-DRL) scheduling approach for LSDTNS using RL. SCN-DRL is the first work dealing with multi-objective optimization for critical microservice notification applications using RL. During the performance evaluation, SCN-DRL demonstrates better performance than state-of-the-art heuristics. SCN-DRL shows steady performance when the notification workload increases from 10% to 90%. In addition, SCN-DRL, tested with three neural networks, shows that it is resilient to sudden container resources drop by 10%. Such resilience to resource container failures is an important attribute of a distributed system.

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.