24 research outputs found
A Study on Optimal Array Configuration of Tidal Stream Turbine Farm based on Actuator Disc Modeling
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Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : 곡과λν 건μ€ν경곡νλΆ, 2019. 2. ν©μ§ν.μ‘°λ₯μλμ§λ μ§μκ°λ₯ν λ체μλμ§ μ€μμλ μμΈ‘κ°λ₯ν νΉμ±μΌλ‘ μΈν΄ κ°κ΄λ°κ³ μλ€. μ‘°λ₯λ°μ λ¨μ§λ λͺ©ν μ λ ₯λ ν보λ₯Ό μν΄ λ°±λ μ΄μμ ν°λΉμΌλ‘ ꡬμ±νκΈ° λλ¬Έμ λ°°μ΄μ μ΅μ νκ° νμμ μ΄λ€. λΉλ‘ μ€μ 쑰건μμμ μ΅μ λ°°μ΄μ λ°λ₯μ§ν λ° μ νλ κ°μ© ꡬμ λ±μ ν° μν₯μ λ°μΌλ, μ‘°λ₯μλμ§ λΆμ‘΄λ μ°μ κ³Όμ μμ μ¬μ©κ°λ₯ν μ ννλ μ΅μ λ°°μ΄μ κ΄ν μ°κ΅¬κ° νμν μ€μ μ΄λ€. λν μ€μ λ°°μ΄μ μ€κ³ν λλ μ‘°λ₯λ°μ λ¨μ§μ νΉμ±μ κ²½μ¬λ κΈ°λ° μ΅μ νκ° λΆκ°νΌνλ°, κ²½μ¬λ κΈ°λ° μ΅μ νμ ν΄λ μ΄κΈ°λ°°μ΄μ‘°κ±΄μ λ°λΌ κ΅μ§ μ΅μ ν΄λ‘ μλ ΄ν κ°λ₯μ±μ΄ λκΈ° λλ¬Έμ μ μ μ΅μ ν΄μ κ°κΉμ΄ μ΄κΈ°μ‘°κ±΄μ μ¬μ©ν νμκ° μλ€.
λ°λΌμ λ³Έ μ°κ΅¬λ μ¬λ¬κ°μ§ μ ν쑰건μ λνμ¬ 2μ°¨μ μ‘°λ₯λ°μ λ¨μ§ λ°°μ΄μ μ ννλ μ΅μ ν΄λ₯Ό μ μνκ³ μ νλ€. λͺ©μ ν¨μμΈ μ΄ μλμ§ μΆμΆλμ ꡬμνλ νΈλ―ΈλΆ λ°©μ μμ 2μ°¨μ μ μμν μ²μλ°©μ μμ μ¬μ©νμ¬ μ‘°λ₯ν°λΉμ μν μ‘°λ₯νλ¦μ λΉμ νμ μΈ λ³νλ₯Ό λ°μν μ μλλ‘ νμλ€. μ²μλ°©μ μ μλ²μ μ΅μ νλ₯Ό 컀νλ§νλ νλ‘κ·Έλ¨μΌλ‘λ νμ΄μ¬ κΈ°λ° μ€νμμ€ μννΈμ¨μ΄μΈ OpenTidalFarmμ μ¬μ©νμλ€. μ‘°λ₯ν°λΉμ μ‘μΈμμ΄ν° λμ€ν¬λ‘μ¨ λͺ¨λΈλ§ νμκ³ , λ€μν μ νμ°κ΅¬μμ μ μν νλ₯μ μ μ λ³νλ₯Ό κ°μ₯ λΉμ·νκ² λͺ¨μνλ λ―Έμ κ³μλ€μ μ‘°ν©μ μ°Ύμ μ‘°λ₯ λͺ¨λΈλ§μ μ¬μ©νμλ€. λ€λ³μ ν¨μμ κ²½μ¬λ κΈ°λ° μ΅μ νλ μ μ μ΅λκ°μ΄ μλ μ΄κΈ°μ‘°κ±΄μ λ°λΌ λ€λ₯Έ κ΅μ§ κ·Ήλκ°μΌλ‘ μλ ΄νκΈ° λλ¬Έμ μ΄μν λ μ‘°λ₯ν΄νμ λ€μν μ΄κΈ°λ°°μ΄μ‘°κ±΄μ μ μ©ν΄ μ¬λ¬ κ΅μ§ μ΅μ ν΄ μ€ κ°μ₯ μΆλ ₯λμ΄ λμ λ°°μ΄μ ννλ₯Ό μ°Ύμλ€.
μμΉλͺ¨μ κ²°κ³Ό κ°λ₯νλ€λ©΄ κ°μ©μμμ ν μ 체μ κ±Έμ³ λ±κ°κ²©μΌλ‘ λ°°μΉλ μ ν 보 ννμ λ°°μ΄μ΄ μ΅μ ννλ‘ λνλ¬λ€. νμ§λ§ ν°λΉκ° μ΅μκ°κ²© 쑰건과 κ°μ©μμ ν λ±μ μ ν쑰건λ€μ μν΄ μμ κ°μ λ°°μ΄μ΄ λΆκ°λ₯νλ€λ©΄ 곑λ₯ μ κ°λ 보 ννλ, λ λμκ° V ν 보 ννμ λ°°μ΄μ΄ μ΅μ ννλ‘ λνλ¬λ€. μ΄λ ν°λΉ λ°°μ΄μ΄ μ‘°λ₯λ₯Ό νλ°©ν₯μΌλ‘ κ°λ‘λ§λ ννκ° ν¨μ¨μ μ΄λΌλ κ²μ μλ―Ένλ©°, μ°μΆκ°λ₯ν μλμ§λ₯Ό κ³μ°ν κ²°κ³Ό κ°μ κ°μμ ν°λΉμ μλ‘ μν₯μ μ£Όμ§ μλλ‘ λ
립μ μΌλ‘ λ°°μΉνμ λ μ»μ μ μλ μλμ§λ³΄λ€ μ΅λ 30% μ΄μ ν¨μ¨μ μ΄λ€. λν κ°μ κ°μμ ν°λΉμ μ΅μ λ°°μ΄μ΄ μ‘°λ ₯λ°μ λ¨μ§ μ€κ³μ μ ν쑰건 (ν°λΉκ°μ μ΅μ κ°κ²©, κ°μ© ꡬμμ ν μ ν λ±)μ μν΄ μλμ§ μΆλ ₯λμ΄ μ΅λ 50%κΉμ§ μ°¨μ΄κ° λ°μν μ μμμ νμΈνμλ€.
μ΄λ¬ν κ²°κ³Όλ ν¨μ¨μ μΈ μ‘°λ ₯λ°μ λ¨μ§λ₯Ό μ€κ³νκΈ° μν΄μλ λ¨μν κ° μ ν쑰건μμμ μ΅μ λ°°μ΄μ μ°Ύλ λ¬Έμ λΏλ§ μλλΌ μ μ ν μ ν쑰건μ μ€κ³νλ λ¬Έμ μ μ€μμ±μ μ μνλ€. λ³Έ μ°κ΅¬μμ μ μν λ°©λ²λ‘ μ μμ μμμμ μ΅μ ν΄μ κ°κΉμ΄ ν°λΉλ°°μ΄μ μ°ΎκΈ° μν ν©λ¦¬μ μΈ μ κ·Όλ°©μμ΄λ©°, ν°λΉμ΄ μ€μΉλ μ±λμ λμνμ νΉμ±μ κ΄ν μ§κ΄μ μ 곡νλ€κ³ νκ°λλ€.Tidal current energy is a sustainable and predictable renewable energy resource. In tidal farm design, optimization of array configuration is essential as tidal farm is constituted of hundreds of turbines. This study aimed to suggest a generalized optimal array configuration for idealized tidal straits. Due to the strong nonlinear interaction between tidal device and tidal flow, optimizing two-dimensional array position should base on PDE-constrained gradient-based optimization algorithm. PDE is given as two-dimensional nonlinear steady shallow water equation in order to reflect the movement of tidal flow and reduce computational cost. Total power output is the target functional to be maximized, and OpenTidalFarm is used as a tool for coupling PDE solver and optimization algorithm. Turbine was parameterized as an actuator disc. Optimization was undertaken with various situations, such as varying number of deployed turbines N, minimum distance constraint , spanwise farm site constraint and initial conditions. It was found that optimization result is highly dependent on and , and also sensitive to initial condition. Thus, it is recommended to use proper initial condition which resembles optimal array configurations. Analysis on the various case of optimized result (over 100 cases) suggests that linear barrage with uniform spacing can be considered as acceptable optimal array. Furthermore, if linear barrage shape is impossible due to the optimization constraints (such as and ), it was observed that connecting all turbines as a curved or V shaped barrage performs better than splitting the array into two parts. This study highlights the necessity of designing proper site constraints and distance constraints, showing that the performance of optimal array for identical N can vary up to 50% with different constraints. It is expected that this optimal shape of array can be implemented for array design in tidal energy resource assessment or for recommended initial condition in gradient-based optimization.ABSTRACT i
Table of Contents iii
List of Figures vi
List of Tables viii
Nomenclature ix
Chapter 1. Introduction 1
1.1 Tidal current energy and its assessment 1
1.2 Tidal farm optimization 4
1.3 Necessity of defining generalized configuration of decent tidal array 5
1.4 Aim of the thesis 6
1.4.1 Modeling tidal turbine in 2D Shallow Water using Actuator Disc modeling 7
1.4.2 Finding optimal array configuration via PDE-constrained gradient-based optimization 7
1.4.3 Analyzing the relationship between optimization constraints and optimization results. 8
Chapter 2. Theoretical background 9
2.1 Linear Momentum Actuator Disc Theory (LMADT) 9
2.1.3 Betzs unbounded flow 11
2.3.2 Garrett and Cumminss rigid lid flow 14
2.3.3 Houlsby et al.s free-surface flow 18
2.3.4 Limitations of LMADT 24
2.2 Shallow Water Equation (SWE) 25
2.3 PDE-constrained Gradient-based Optimization using Adjoint method 27
2.3.1 Gradient-based Optimization 28
Chapter 3. Methodology 30
3.1 Specifications of OpenTidalFarm 30
3.1.1 Design Parameters 30
3.1.2 PDE constraint 31
3.1.3 Turbine Parameterisation 31
3.1.4 Target functional 32
3.1.5 Box and inequality constraints 33
3.1.6 Gradient-based optimization with adjoint approach 34
3.2 Realistic Actuator Disc Modeling in 2D SWE 35
3.2.1 Actuator disc modeling in 2D SWE and 3D flow model 36
3.2.2 Wake properties behind tidal turbine 37
3.2.3 Simulation settings for tuning K and 38
3.2.4 Simulation results 44
3.3 Pilot Test 1: effect of initial condition and number of turbines on optimization result 46
3.3.1 Simulation settings 46
3.3.2 Simulation results 49
3.3.3 Conclusions of pilot test 1 49
3.4 Pilot Test 2: effect of minimum distance constraint and spanwise length of the farm site on optimization result 50
3.4.1 Simulation settings 50
3.4.2 Simulation results 52
3.4.3 Conclusions of pilot test 2 55
3.5 Main test: maximum number of turbines which can be considered as a barrage type configuration 56
3.5.1 Simulation settings 56
Chapter 4. Results and Discussions 60
4.1 Analysis on optimal array configuration and tidal farm output 60
4.2 Extracted Power and Nmax 64
4.3 Limitations of the Modeling 65
Chapter 5. Conclusions 68
APPENDIXS 71
Appendix A. 71
Appendix B. 73
Appendix C. 78
REFERENCES 86
κ΅λ¬Έμ΄λ‘ 91Maste
μ¬νλ΄μ¬λͺ λ Ήμ λμ μ§νμ κ΄ν μ°κ΅¬
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Όλ¬Έ(μμ¬)--μμΈλνκ΅ νμ λνμ :νμ νκ³Ό νμ νμ 곡,2003.Maste
κ³μ μ κ΄ λ³ν λ° μΈκ³΅ μ‘°λͺ μ²λ¦¬κ° μ¨μ€ μ¬λ°° μ½μ₯(Artemisia princeps)μ μμ₯ λ° νλΌλ³΄λ Έμ΄λ μμ°μ λ―ΈμΉλ μν₯
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Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : λμ
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κ³Όνλν μμνκ³Ό, 2019. 2. μμ μ΅.Artemisia princeps (Ganghwa wormwood) is a medicinal plant with two major flavonoids, eupatilin and jaceosidin, which have gastritis and peptic ulcers treatment properties. Until now, the plants are cultivated in the fields which provides one cultivation period, and produces unstable flavonoid contents by environmental changes. The objective of this study was to analyze the effects of seasonal light variation and artificial light treatments on growth and flavonoid production of A. princeps grown under greenhouse conditions for year-round production. From April 2016 to April 2017, nine sets of the plants were cultivated and harvested under natural seasonal light conditions in greenhouses. From September 2016 to January 2017, four additional artificial light treatments were applied for two sets of the plants: supplemental light, night interruption, low light, and low light with night interruption. The plants grown under natural light condition in greenhouses were used as a control. After harvest, plant growth was measured, and the contents of eupatilin and jaceosidin were determined. The plants had the highest biomass when the accumulated radiation and duration were highest. Plant growth and flavonoid production were significantly associated with accumulated radiation and light duration. Supplemental light and night interruption treatments resulted in significantly higher biomass and flavonoid production. For consistent biomass and flavonoid production of A. princeps, night interruption treatment is suggested in greenhouse cultivation during low irradiation and short days (less than 13 h).Artemisia princeps(κ°νμ½μ₯)μ μκΆ€μ λ° μκ²½λ ¨ μΉλ£μ νΉμ±μ κ°μ§κ³ μλ νλΌλ³΄λ
Έμ΄λ μ ννΈλ¦°κ³Ό μμΈμ€μλμ ν¨μ νκ³ μλ€. μΌλ°μ μΌλ‘ λ
Έμ§μμ μ°μ€ 1ν μ¬λ°°λλ©° νκ²½ λ³νμ λ°λΌ νλΌλ³΄λ
Έμ΄λ ν¨λμ΄ λΆμμ νλ€. λ³Έ μ°κ΅¬μ λͺ©μ μ κ°νμ½μ₯μ μ°μ€ μμ μ μΈ μ¨μ€ μ¬λ°°λ₯Ό μν΄ κ³μ μ μΈ κ΄ μ‘°κ±΄ λ³νμ μΈκ³΅ μ‘°λͺ
μ²λ¦¬μ λν μλ¬Όμ μμ₯κ³Ό νλΌλ³΄μ΄λ
Έμ΄λ ν¨λ λ³νλ₯Ό λΆμνλ κ²μ΄λ€. μ°μ€ μ΄ 9νμ μ¬λ°° μκΈ°λ‘ μ¨μ€μ κ³μ μ μΈ κ΄ μ‘°κ±΄ νμμ μ¬λ°°νμλ€. κ΄ μ‘°κ±΄μ λ³νκ° ν¬κ² λνλλ 겨μΈμ² 2νμ μκΈ° μ€μλ μΈκ³΅ μ‘°λͺ
μ²λ¦¬ μΆκ°λ‘ 보κ΄, μΌν, μ κ΄ λ° μΌν 쑰건μμ μ¬λ°°νμλ€. μν ν, μλ¬Όμ μμ₯μ μΈ‘μ νκ³ , μ ννΈλ¦°κ³Ό μμΈμ€μλμ ν¨λμ λΆμνμλ€. λμ μΌμ¬λκ³Ό λμ μΌμ₯μ΄ λμμλ‘ μλ¬Όμ μμ₯μ΄ μ’μμΌλ©°, μμ₯κ³Ό νλΌλ³΄λ
Έμ΄λ ν¨λμ λμ μΌμ¬λκ³Ό λμ μΌμ₯κ³Ό μ μν κ΄κ³λ₯Ό 보μλ€. λ³΄κ΄ λ° μΌν μ²λ¦¬λ‘ μΈν΄ μμ₯κ³Ό νλΌλ³΄λ
Έμ΄λ ν¨λμ΄ νμ ν λκ² λνλ¬λ€. μΌμ₯μ΄ 13μκ° μ΄νμΈ κ²¨μΈμ² μ μ΄ μμ₯κ³Ό νλΌλ³΄λ
Έμ΄λ ν¨λ ν₯μμ μν΄ μ¨μ€ μ¬λ°° μ μΌν μ²λ¦¬λ‘ μμ° μ¦λμ μ°μ€ μμ μ μΈ μμ°μ΄ κ°λ₯νλ€κ³ νλ¨λλ€.ABSTRACT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
CONTENTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
LIST OF TABLES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
LIST OF FIGURES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
INTRODUCTION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1
LITERATURE REVIEW. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4
Pharmaceutical uses of A. princeps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4
Flavonoids and environment conditions. . . . . . . . . . . . . . . . . . . . . . . . . . 4
Medicinal plant cultivation in controlled environments. . . . . . . . . . . . . . .5
MATERIALS AND METHODS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6
RESULTS AND DISCUSSION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
CONCLUSIONS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
LITERATURE CITED. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
ABSTRACT IN KOREAN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .36Maste
A Study on the Categorical Spectrum and Function of Bound Noun in Korean
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Όλ¬Έ(μμ¬) -- μμΈλνκ΅λνμ : μΈλ¬Έλν κ΅μ΄κ΅λ¬Ένκ³Ό, 2022. 8. λ¬Έμμ.λ³Έ μ°κ΅¬λ μμ‘΄λͺ
μ¬μ λ¨μ΄ λΆλ₯λ‘μμ μμ±κ³Ό μμ‘΄λͺ
μ¬μ λΆν¬μ λ°λ₯Έ κΈ°λ₯μ λ°νλ κ²μ λͺ©μ μΌλ‘ νλ€.
2μ₯μμλ λ¨μ΄ λΆλ₯λ‘μμ μμ‘΄λͺ
μ¬μ μμ±μ λν΄ λ
Όμνμλ€. λ¨Όμ νλ νκ΅μ΄ μμ‘΄λͺ
μ¬μ λͺ©λ‘μ μμΈν λ°νκ³ , μμ‘΄λͺ
μ¬μ μΈμ λ²μ£ΌμΈ μ΄λ―Έ, μ‘°μ¬, 보쑰μ©μΈ, μ λ―Έμ¬μμ κ΄κ³λ₯Ό μ΄ν΄λ³΄μλ€. λ€μμΌλ‘λ μ ν΅μ μΈ νμ¬ λΆλ₯ κΈ°μ€μ λ°λ₯Έ μμ‘΄λͺ
μ¬μ μμ±μ λ°νκ³ μ΄λ₯Ό μΌλ°μ μΈ λͺ
μ¬μ μμ±κ³Ό λΉκ΅Β·λμ‘°νμλ€. μμ‘΄λͺ
μ¬λ λͺ
μ¬μ μμ±μ κ°μ§κ³ μμ§λ§, κ·Έ μ μ½μ΄ μ¬ν λΆλ₯μΈλ°, μμ‘΄λͺ
μ¬μ κ·Έλ¬ν μΈ‘λ©΄μ μμ‘΄μ±κ³Ό μ‘°μ¬ λ° μ±λΆ μ μ½, μμ μ΄ μ μ½, λ΄μ©μ΄μ κΈ°λ₯μ΄μμ λΉκ΅λ₯Ό ν΅ν΄ μμ보μλ€. λν, μμ‘΄λͺ
μ¬λ κ²°ν© μ μ½μ λ°λΌ νΉμ΄ν λΆν¬λ₯Ό 보μ΄λλ°, μμ‘΄λͺ
μ¬μ λΆν¬λ₯Ό μ‘°μ¬λ₯Ό μ€μ¬μΌλ‘ λΆλ₯νκ³ μμ‘΄λͺ
μ¬μ κΈ°λ₯μ λν΄ κ°λ΅νκ² μ΄νΌμλ€.
3μ₯μμλ 2μ₯μ λΆλ₯μ μκ±°νμ¬ μ‘°μ¬ κ²°ν©μ μ μ½μ΄ μ μ μμ‘΄λͺ
μ¬(보νΈμ μμ‘΄λͺ
μ¬)μ κΈ°λ₯κ³Ό λΆν¬λ₯Ό μ΄νΌμλ€. 보νΈμ μμ‘΄λͺ
μ¬λ μ£Όλ‘ λμ© λ° μ§μ κΈ°λ₯μ μννλ€. μμ‘΄λͺ
μ¬μ λ°λΌ νΉμ ν λμ© λ° μ§μμ κΈ°λ₯μ΄ νμ λλ κ²λ μκ³ μ¬λ¬ λμμ λμ© λ° μ§μνλ κ²μΌλ‘ κΈ°λ₯μ΄ νμ₯λλ κ²λ μλ€. ννΈ μ‘°μ¬ κ²°ν©μ μ μ½μ΄ μ μ μμ‘΄λͺ
μ¬λΌκ³ νλλΌλ μ ννλ κ΄νμ΄μ μ’
λ₯λ μμ κ²°ν© λ±μ μ μ½μ 보μ΄κΈ°λ νλλ° μ΄λ¬ν μμ‘΄λͺ
μ¬μ λΆν¬λ₯Ό μ‘°μ¬ν¨μΌλ‘μ¨ λ³΄νΈμ μμ‘΄λͺ
μ¬μ ν΅μ¬μ νΉμ§μ μ΄ν΄λ³΄μλ€.
4μ₯μμλ μ‘°μ¬ κ²°ν©μ μ μ½μ΄ μ¬ν μμ‘΄λͺ
μ¬(μ νμ μμ‘΄λͺ
μ¬)μ κΈ°λ₯κ³Ό λΆν¬λ₯Ό μ΄νΌμλ€. μ νμ μμ‘΄λͺ
μ¬λ€μ λ¬Έμ₯μμ ꡬμ±μ μ΄λ£¨μ΄ λ¬Έλ²μ κΈ°λ₯μ μννλλ° μ΄μ κ΄λ ¨λ λ¬Έλ²μ κΈ°λ₯μΌλ‘λ μ°κ²°, μν, μ, μ§μ λ° λμ©μ΄ μλ€. κ° κΈ°λ₯μ κ°λ΅ν μ 리νκ³ κΈ°λ₯λ³ μμ‘΄λͺ
μ¬ κ΅¬μ±μ λΆν¬λ₯Ό μ‘°μ¬ν¨μΌλ‘μ¨ μ νμ μμ‘΄λͺ
μ¬μ ν΅μ¬μ νΉμ§μ μ μνμλ€. λν κ°μ μλ―Έλ₯Ό νννλ μμ‘΄λͺ
μ¬λΌ νμ¬λ μΈλΆμ μΈ νΉμ§μλ μ°¨μ΄κ° μμλλ° λΉκ΅μ λμ‘°λ₯Ό ν΅ν΄ μ΄λ¬ν μ°¨μ΄λ₯Ό λ°νμλ€.
5μ₯μ κ²°λ‘ λΆλΆμΌλ‘ μ΄μμ λ΄μ©μ μμ½ λ° μ 리νκ³ , λ―Έμ§ν μ μ λ°νλ©° λ
Όμλ₯Ό λ§λ¬΄λ¦¬νμλ€.This study aims to identify the attributes of bound nouns as a class of words and their functions according to distribution.
In Chapter 2, the study discusses the attributes of Bound nouns as word classes. First, the modern Korean bound morphemes are listed in detail, and the relation with the adjacent categories, such as ending, particle, auxiliary verb, and suffix, are observed. Next, the attributes of bound morphemes by the traditional criteria of parts of speech are addressed, followed by comparison and contrast with those of general nouns. Bound nouns have the attributes of nouns, but are unique in that they have many restrictions, which are discussed through boundness, particle and component restrictions, verb restrictions, and comparison with content words and function words. Further, bound nouns have interesting distribution depending on the conjugation restrictions, which is addressed in this paper by classifying the distribution of bound nouns by particle and briefly observing the functions of bound nouns.
In Chapter 3, based on the distribution of bound nouns, the paper addresses the functions and distribution of bound nouns with least limitations with particle conjugation. It also addresses the functions of substitute and reference that the universal bound nouns serve. Meanwhile, even though dependent nouns have fewer restrictions in particle conjugation, there are restrictions in conjugation depending on the types of the preceding abdominals and tense. By studying the distribution of such bound nouns, the chapter exposits the syntactic characteristics of universal bound nouns.
Chapter 4 discusses the functions and distribution of bound morphemes with more restrictions in particle conjugation. Restrictive bound nouns serve a grammatical function by forming constructions in sentences. Related grammatical functions are reference, substitute, conjunction, modality, and aspect. The chapter suggests the syntactic characteristics of the restrictive bound morphemes by briefly organizing each function and studying the distribution of the nouns by function.
Chapter 5, as a conclusion, summarizes and organizes the above discussion and considers limitations.1. μλ‘ 1
1.1. μ°κ΅¬ λͺ©μ 1
1.2. μ ν μ°κ΅¬ 3
1.3. μ°κ΅¬ λμ 5
1.4. λ
Όμμ κ΅¬μ± 6
2. λ¨μ΄ λΆλ₯λ‘μμ μμ‘΄λͺ
μ¬μ μμ± 8
2.1. μμ‘΄λͺ
μ¬μ λͺ©λ‘κ³Ό μΈμ λ²μ£Ό 8
2.2. νμ¬ λΆλ₯ κΈ°μ€μ λ°λ₯Έ μμ‘΄λͺ
μ¬μ μμ± 23
2.3. λͺ
μ¬μ λΉκ΅Β·λμ‘°ν μμ‘΄λͺ
μ¬μ μμ± 33
2.3.1. λͺ
μ¬μ μμ± 33
2.3.2. μμ‘΄μ± 36
2.3.3. μ‘°μ¬ μ μ½ λ° μ±λΆ μ μ½ 39
2.3.4. μμ μ΄ μ μ½ 41
2.3.5. λ΄μ©μ΄μ κΈ°λ₯μ΄μ μ€κ° 43
2.4. λΆν¬μ μ
κ°ν μμ‘΄λͺ
μ¬μ λΆλ₯ 50
2.5. μμ‘΄λͺ
μ¬μ κΈ°λ₯ 54
3. 보νΈμ μμ‘΄λͺ
μ¬μ λΆν¬μ κΈ°λ₯ 61
3.1. βκ²βλ₯ μμ‘΄λͺ
μ¬μ λΆν¬μ κΈ°λ₯ 62
3.2. βλ±βλ₯ μμ‘΄λͺ
μ¬μ λΆν¬μ κΈ°λ₯ 65
3.3. βλμ βλ₯ μμ‘΄λͺ
μ¬μ λΆν¬μ κΈ°λ₯ 67
3.4. βμͺ½βλ₯ μμ‘΄λͺ
μ¬μ λΆν¬μ κΈ°λ₯ 69
3.5. β격βλ₯ μμ‘΄λͺ
μ¬μ λΆν¬μ κΈ°λ₯ 72
4. μ νμ μμ‘΄λͺ
μ¬μ λΆν¬μ κΈ°λ₯ 74
4.1. μ°κ²°μ μμ‘΄λͺ
μ¬ 74
4.1.1. μ°κ²°μ μμ‘΄λͺ
μ¬μ λΆλ₯ 74
4.1.2. μ°κ²°μ μμ‘΄λͺ
μ¬μ λΆν¬μ κΈ°λ₯ 78
4.1.2.1. λμ΄ 78
4.1.2.2. λμ‘° 79
4.1.2.3. μ ν 80
4.1.2.4. λ°°κ²½ 81
4.1.2.5. μ ν 83
4.1.2.6. μμΈ 85
4.1.2.7. λͺ©μ 86
4.2. μμ μμ‘΄λͺ
μ¬ 87
4.2.1. μμ μμ‘΄λͺ
μ¬μ λΆλ₯ 87
4.2.2. μμ μμ‘΄λͺ
μ¬μ λΆν¬μ κΈ°λ₯ 89
4.2.2.1. μ§νμ 89
4.2.2.2. μμ μ 92
4.3. μνμ μμ‘΄λͺ
μ¬ 94
4.3.1. μνμ μμ‘΄λͺ
μ¬μ λΆλ₯ 94
4.3.2. μνμ μμ‘΄λͺ
μ¬μ λΆν¬μ κΈ°λ₯ 98
4.3.2.1. μΈμ μν 98
4.3.2.2. λΉμ μν 102
4.3.2.3. λμ μν 104
4.3.2.4. κ°μ μν 106
4.4. μ§μ λ° λμ©μ μμ‘΄λͺ
μ¬ 108
4.5. κΈ°ν μμ‘΄λͺ
μ¬ 109
5. κ²°λ‘ 114
μ°Έκ³ λ¬Έν 122
λΆλ‘ 130
Abstract 134μ