검색 상세

BIM-Informed Decision-Support Framework for Evaluating PCM- Integrated Glazing in Korean Residential Buildings

한국 주거용 건축물의 PCM 통합 글레이징 성능평가를 위한 BIM 기반 의사결정지 원 프레임워크

초록/요약

The building envelope strongly influences residential energy demand and indoor comfort, particularly in apartment buildings exposed to both winter heating demand and summer overheating risk. Phase change material, PCM, integrated glazing has been proposed as an advanced façade technology because it can add latent thermal storage to transparent envelope components. However, its performance depends on building context, climate, glazing configuration, PCM activation, and modelling approach. This study developed and applied a BIM-informed pre-implementation evaluation framework to assess PCM-integrated glazing in a representative Korean residential apartment. The framework integrated BIM-informed model preparation, glazing-property generation, EnergyPlus simulation, diagnostic case comparison, bounded latent- capacity assessment, and multi-criteria classification. Four simulation case types were used: a baseline case, non-PCM envelope reference cases, PCM thermal-mass diagnostic cases, and complete PCM-integrated glazing candidate cases. Three PCM deployment strategies were evaluated. The results showed that the complete PCM-integrated glazing candidates reduced annual cooling demand and peak cooling load, but increased annual heating demand, peak heating load, and uncomfortable hours. Peak cooling-load reductions ranged from 0.24% to 1.65%, while peak heating-load increases ranged from 0.76% to 2.82%. Combined annual heating-and-cooling demand increased by 1.79% in D_S1, 0.40% in D_S2, and 2.31% in D_S3. D_S2 achieved the most balanced response and was classified as conditionally satisfied, while D_S1 and D_S3 were classified as not satisfied. The findings indicate that PCM-integrated glazing should not be assessed solely through cooling reduction or theoretical latent-storage capacity. Larger PCM deployment did not necessarily produce better whole-building performance. The proposed BIM-informed framework supports early-stage evaluation of emerging smart façade technologies by linking digital modelling, simulation-based performance assessment, and transparent multi-criteria classification. Keywords: Phase Change Materials; PCM-Integrated Glazing; Building Energy Modelling; Heating and Cooling Demand; Multi-Criteria Classification.

more

목차

Chapter 1. Introduction 1
1.1 Background 1
1.2 Problem Statement 3
1.3 Research Aim, Objectives and Questions 5
1.3.1 Research Aim 5
1.3.2 Research Objectives 5
1.3.3 Research Questions 6
1.4 Significance of the Study 6
1.5 Definitions of Key Terms 7
Chapter 2. Literature Review 9
2.1 Introduction 9
2.2 Korean Residential Envelope Performance Challenges 11
2.3 PCM-Integrated Glazing as an Envelope Technology 13
2.4 Pre-Implementation Evaluation and Decision Support 17
2.5 Synthesis, Research Gap and Conceptual Basis of the Thesis Framework 20
Chapter 3. Methodology 23
3.1 General Framework Overview 23
3.2 Stage 1: BIM Model Development 26
3.2.1 BIM-Grounded Workflow 26
3.2.2 Thermal Zoning Strategy 26
3.2.3 Export Pipeline and Quality Assurance 29
3.3 Stage 2: Glazing Validation and Baseline Setup 31
3.3.1 NFRC 100-2010 Boundary Conditions in LBNL WINDOW 7.8 32
3.3.2 Glazing Specification and Spectral Data Source 34
3.3.3 Whole-Window Property Calculation: Frame Correction. 35
3.3.4 PCM Glazing Configuration and Equivalent-Window Modelling 37
3.3.5 Validation Matrix: Centre-of-Glass and Whole-Window Properties 38
3.4 Stage 3: EnergyPlus Scenario Simulation 41
3.4.1 Simulation Engine and Solver Settings 41
3.4.2 PCM InternalMass Representation 42
3.4.3 HVAC Demand and Boundary Conditions 44
3.4.4 Control-Case Matrix 44
3.4.5 PCM Deployment Strategies 46
3.5 Stage 4: Bounded Latent-Capacity Assessment 47
3.5.1 Purpose and Scope 47
3.5.2 Utilization and Effective-Cycle Assumptions 48
3.5.3 Peak-Period Latent Heat-Rate Capacity 54
3.5.4 Annual Latent-Storage Capacity 56
3.5.5 Interpretation Limits 59
3.6 Stage 5: Multi-Criteria Classification Framework 59
3.6.1 Evidence Role in the Classification Framework 60
3.6.2 Classification Criteria 61
3.6.3 Criterion-Level Classification Rules 62
3.6.4 Final Classification Rule 64
3.6.5 Methodological Limits 65
Chapter 4- Results 66
4.1 Chapter Overview 66
4.2 Direct Simulation Results from Stage 3 67
4.2.1 Baseline Case Performance 67
4.2.2 Annual Energy Demand across the Simulation Matrix 68
4.2.3 Peak-Load and Thermal-Comfort Response 70
4.2.4 Summary of Direct Simulation Results 74
4.3 Stage 4 Results for Bounded Latent-Capacity Indicators 75
4.3.1 Storage-Capacity Scale 75
4.3.2 Peak-Period and Annual Latent-Capacity Indicators 75
4.3.3 Capacity-to-Peak Context Ratio 77
4.4 Stage 5 Multi-Criteria Classification Results 78
4.4.1 Seasonal Peak-Load Trade-Off 79
4.4.2 Annual Heating-and-Cooling Demand Change 80
4.4.3 Thermal-Comfort Response 82
4.4.4 Seasonal Energy Trade-Off 83
4.4.5 Final Classification Outcomes 85
Chapter 5. Discussion 86
Chapter 6. Conclusion 89
References 91

more
반출 Meta View 목록