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Hazardous Chemical Dispersion Analysis and Surrogate Model Optimization using Artificial Neural Network : 유해화학물질 확산 분석 및 인공신경망을 사용한 대리 모델 최적화

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dc.contributor.advisor한종훈-
dc.contributor.author양시엽-
dc.date.accessioned2017-10-27T16:46:53Z-
dc.date.available2017-10-27T16:46:53Z-
dc.date.issued2017-08-
dc.identifier.other000000144944-
dc.identifier.urihttps://hdl.handle.net/10371/136862-
dc.description학위논문 (박사)-- 서울대학교 대학원 공과대학 화학생물공학부, 2017. 8. 한종훈.-
dc.description.abstractCFD simulations can be used to estimate the accident consequences by hazardous chemical releases. Both strength and weakness of this method lies on the complexity of the CFD simulation. In other words, CFD can predict chemical dispersion in the complex geometry like urban area, but it takes much computational resources.
Comparison between actual dispersion and CFD simulation is done to show usefulness of the CFD simulation. Anhydrous hydrogen fluoride dispersion field test is simulated and it is within the dispersion model criteria. Actual hydrogen fluoride accidental release in 2012 is also simulated and its human and environmental damage prediction is similar to actual accident consequences.
Case study of the toxic gas dispersion in the urban area varying source and meteorological condition is conducted, and numerical method is tested for the worst case scenario. Time variant CFL stepping method and multi-threading reduce the calculation time. Also ANN is able to classify the safe and danger based on the input condition.
CFD simulation can be applied in the detector allocation problem in the process unit. Simulation inputs are determined by computer experiment procedure and other sets of data detection time is estimated with ANN. Then based on these simulated and estimated data MILP is solved and showed better result than using simulation data alone.		-
dc.description.tableofcontentsCHAPTER 1 : Introduction 1
1.1. Research motivation 1
1.2. Description of CFD model in this thesis 2
1.3. Outline of the thesis 3
Chapter 2: Comparison between CFD model and actual dispersion 4
2.1. Chapter outline 4
2.2. Validation of a CFD model against a field test 4
2.2.1 Validation objective 4
2.2.2. Validation results in literature 5
2.2.3. The Goldfish hydrogen fluoride spill field test 6
2.2.4. Field test validation result 8
2.3. Comparison between a CFD model and an actual accident 14
2.3.1. Accident description 14
2.3.2. The hydrogen fluoride gas leak simulation setting 15
2.3.3. FLACS simulation settings 17
2.3.4. Hydrogen fluoride gas leak simulation result 21
2.3.5. Comparison between the simulation and reported damage 24
2.4. Chapter conclusion 35
Chapter 3: Chlorine dispersion CFD model in an urban area 36
3.1. Chapter outline 36
3.2. Background 37
3.2.1. CFL number 37
3.2.2. Chlorine toxicity calculation 38
3.3. Method 39
3.3.1. Data acquisition 39
3.3.2. CFD simulation setting 43
3.3.3. Time step increase method 45
3.3.4. Classification method 46
3.4. Result and discussion 48
3.4.1. Case study result 48
3.4.2. Time step change result 51
3.4.3. Classification result 56
3.5. Chapter conclusion 60
Chapter 4: Detector allocation optimization using CFD 61
4.1. Background 61
4.1.1. Detector allocation problem 61
4.1.2. Surrogate model with CFD 62
4.2. Method 63
4.2.1. CFD Setting 66
4.2.3. ANN Regression 69
4.2.3. Sensor Allocation 71
4.3. Results 74
4.3.1. ANN results 74
4.3.2. Sensor Allocation Result 77
4.3.3. Discussion 79
4.4. Chapter conclusion 83
Chapter 5: Conclusion 84
5.1. Concluding remarks 84
5.2. Future works 84
Nomenclature 85
Literature cited 87
Abstract in Korean (요 약) 97		-
dc.formatapplication/pdf-
dc.format.extent4204071 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectComputational Fluid Dynamics-
dc.subjectChemical Safety-
dc.subjectArtificial Neural Network-
dc.subjectMixed Integer Linear Programming-
dc.subjectSurrogate Model-
dc.subject.ddc660.6-
dc.titleHazardous Chemical Dispersion Analysis and Surrogate Model Optimization using Artificial Neural Network-
dc.title.alternative유해화학물질 확산 분석 및 인공신경망을 사용한 대리 모델 최적화-
dc.typeThesis-
dc.contributor.AlternativeAuthorSeeyub Yang-
dc.description.degreeDoctor-
dc.contributor.affiliation공과대학 화학생물공학부-
dc.date.awarded2017-08-
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