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Development of Parallel Genetic Algorithm and Application to Small Modular Fast Reactor Design Optimization : 다목적 유전자 연산법을 이용한 소형 조립식 고속로 설계 최적화
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Kune Yull Suh | - |
dc.contributor.author | 진양의 | - |
dc.date.accessioned | 2017-10-31T07:35:47Z | - |
dc.date.available | 2017-10-31T07:35:47Z | - |
dc.date.issued | 2017-08 | - |
dc.identifier.other | 000000145244 | - |
dc.identifier.uri | https://hdl.handle.net/10371/137375 | - |
dc.description | 학위논문 (석사)-- 서울대학교 대학원 공과대학 에너지시스템공학부, 2017. 8. Kune Yull Suh. | - |
dc.description.abstract | In multi-objective nuclear reactor design problem, instead of implementing a single-objective optimization scalarized from the multi-objective problem, for example, by assigning each objective an importance, it is beneficial to provide a trade-off-surface to the decision maker for further consideration. However, the relatively expensive calculation in the nuclear reactor design prevents the true Pareto front to be established. Instead, the pseudo-trade-off surface is usually provided. Thus, when a preferred solution has been decided, the decision maker comes to face the question that whether this solution is the non-dominated solution. The Genetic Algorithm with the valuable phenotype archival rule developed in this work abnegates the logic that higher quality individuals should have the priority to be selected. The new rule addresses more about of the balanced accomplishment of the objectives rather than pitch into the elitism. This Optimized Logic Genetic Algorithm has demonstrated its efficiency and robustness in assisting the designer to obtain the better flexibility by providing the diverse potential solutions that can dominate or are similar to the interested solution on the pseudo-trade-off surface. | - |
dc.description.tableofcontents | 1. Introduction 1
1.1. Background 1 1.2. Objective of the Study 2 1.3. Reference Reactor and the Interested Pseudo-Local-Optimum 4 1.4. Optimization Problem Description 7 2. Genetic Algorithm and the Operators 10 2.1. The Simple Genetic Algorithm 10 2.2. Chromosome Encoding Method 12 2.3. Selection Operator 13 2.4. Crossover Operator 16 2.5. Mutation Operator 19 3. Optimized Logic Genetic Algorithm 21 3.1. Non-dominated Sorting Genetic Algorithm with Valuable Phenotype Archive 21 3.2. Development of Parallel Computing Framework 24 3.3. Tasks Dispatcher Module 24 3.4. Slave Module 26 3.5. Genetic Algorithm Module 28 4. Neutronics Submodule 31 4.1. Simulation Setup 31 4.2. Layout of BORIS Core 32 4.3. Material Composition 32 4.3.1. U-Pu-MA-N 32 4.3.2. Lead 33 4.3.3. HT9 33 4.3.4. Parametric Survey and Design Domain 35 5. Thermal-Hydraulics Submodule 39 5.1. Governing Equations 40 5.1.1. Energy conservation and mass conservation equations 40 5.1.2. Loop momentum conservation equation 41 5.1.3. Heat transfer equations 44 5.1.4. Coolant property correlations 46 5.2. Model Validation 46 5.2.1. Case description 46 5.2.2. Validation Results 47 5.3. Parametric Survey 50 6. Optimization Implementation and Results 56 7. Conclusion 62 REFERENCES 63 | - |
dc.format | application/pdf | - |
dc.format.extent | 2436678 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | Genetic Algorithm | - |
dc.subject | Multiobjective Optimization | - |
dc.subject | Nondominated Sorting | - |
dc.subject | Valuable Phenotype | - |
dc.subject | Small Modular Fast Reactor | - |
dc.subject.ddc | 622.33 | - |
dc.title | Development of Parallel Genetic Algorithm and Application to Small Modular Fast Reactor Design Optimization | - |
dc.title.alternative | 다목적 유전자 연산법을 이용한 소형 조립식 고속로 설계 최적화 | - |
dc.type | Thesis | - |
dc.description.degree | Master | - |
dc.contributor.affiliation | 공과대학 에너지시스템공학부 | - |
dc.date.awarded | 2017-08 | - |
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