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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蔡宛珊(Christina W. Tsai) | |
| dc.contributor.author | Yen-Ting Lin | en |
| dc.contributor.author | 林彥廷 | zh_TW |
| dc.date.accessioned | 2021-06-16T05:22:06Z | - |
| dc.date.available | 2016-08-21 | |
| dc.date.copyright | 2014-08-21 | |
| dc.date.issued | 2014 | |
| dc.date.submitted | 2014-08-15 | |
| dc.identifier.citation | [1] Birsan, M.-V., Molnar, P., Burlando, P., and Pfaundler, M. (2005). 'Streamflow trends in Switzerland.' Journal of Hydrology, 314(1), 312-329.
[2] Chow, V. T., Maidment, D. R., and Mays, L. W. (1988). Applied hydrology, McGraw-Hill, New York. [3] Frei, C., Davies, H. C., Gurtz, J., and Schar, C. (2000). 'Climate dynamics and extreme precipitation and flood events in Central Europe.' Integrated Assessment, 1(4), 281-300. [4] Frei, C., and Schar, C. (2001). 'Detection probability of trends in rare events: Theory and application to heavy precipitation in the Alpine region.' Journal of Climate, 14(7). [5] Hanson, F. B. (2007). Applied stochastic processes and control for jump-diffusions: modeling, analysis, and computation, Siam. [6] Heemink, A. (1990). 'Stochastic modelling of dispersion in shallow water.' Stochastic hydrology and hydraulics, 4(2), 161-174. [7] Hunter, J., Craig, P., and Phillips, H. (1993). 'On the use of random walk models with spatially variable diffusivity.' Journal of Computational Physics, 106(2), 366-376. [8] Man, C. (2007). Stochastic modeling of suspended sediment transport in regular and extreme flow environments, ProQuest. [9] Man, C., and Tsai, C. W. (2007). 'Stochastic partial differential equation-based model for suspended sediment transport in surface water flows.' Journal of engineering mechanics, 133(4), 422-430. [10] Milstein, G. N., Schoenmakers, J. G., and Spokoiny, V. (2004). 'Transition density estimation for stochastic differential equations via forward-reverse representations.' Bernoulli, 10(2), 281-312. [11] Molnar, P. (2001). 'Climate change, flooding in arid environments, and erosion rates.' Geology, 29(12), 1071-1074. [12] Muste, M., Yu, K., Fujita, I., and Ettema, R. (2009). 'Two-phase flow insights into open-channel flows with suspended particles of different densities.' Environmental fluid mechanics, 9(2), 161-186. [13] Oh, J. (2011). Stochastic particle tracking modeling for sediment transport in open channel flows, State University of New York at Buffalo. [14] Oh, J., and Tsai, C. W. 'Estimation of particle concentrations using the stochastic particle tracking method in open channel flows.' Proc., World Environmental and Water Resources Congress, Providence, Rhode Island. [15] Oh, J., and Tsai, C. W. (2010). 'A stochastic jump diffusion particle‐tracking model (SJD‐PTM) for sediment transport in open channel flows.' Water Resources Research, 46(10). [16] Pope, S. (1994). 'Lagrangian PDF methods for turbulent flows.' Annual review of fluid mechanics, 26(1), 23-63. [17] Rijn, L. C. v. (1984). 'Sediment transport, Part II: Suspended load transport.' Journal of Hydraulic Engineering, 110(11), 1613-1641. [18] Ross, S. M. (2007). Introduction to probability models, Academic Press, Boston. [19] Sawford, B., and Borgas, M. (1994). 'On the continuity of stochastic models for the Lagrangian velocity in turbulence.' Physica D: Nonlinear Phenomena, 76(1), 297-311. [20] Schumer, R., Meerschaert, M. M., and Baeumer, B. (2009). 'Fractional advection‐dispersion equations for modeling transport at the Earth surface.' Journal of Geophysical Research: Earth Surface (2003–2012), 114(F4). [21] Sharma, S., and Kavvas, M. (2005). 'Modeling noncohesive suspended sediment transport in stream channels using an ensemble-averaged conservation equation.' Journal of hydraulic engineering, 131(5), 380-389. [22] Spivakovskaya, D., Heemink, A., Milstein, G., and Schoenmakers, J. (2005). 'Simulation of the transport of particles in coastal waters using forward and reverse time diffusion.' Advances in water resources, 28(9), 927-938. [23] Spivakovskaya, D., Heemink, A., and Schoenmakers, J. (2007). 'Two-particle models for the estimation of the mean and standard deviation of concentrations in coastal waters.' Stochastic Environmental Research and Risk Assessment, 21(3), 235-251. [24] Tsai, C. W., Man, C., and Oh, J. (2014). 'Stochastic particle based models for suspended particle movement in surface flows.' International Journal of Sediment Research, 29(2), 195-207. [25] Vollmer, S., and Kleinhans, M. G. (2007). 'Predicting incipient motion, including the effect of turbulent pressure fluctuations in the bed.' Water Resources Research, 43(5). [26] Walpole, R. E. (2007). Probability & statistics for engineers & scientists, Pearson Prentice Hall, Upper Saddle River, NJ. [27] Wu, F.-C., and Chou, Y.-J. (2003). 'Rolling and lifting probabilities for sediment entrainment.' Journal of Hydraulic Engineering, 129(2), 110-119. [28] Zwiers, F. W., and Kharin, V. V. (1998). 'Changes in the extremes of the climate simulated by CCC GCM2 under CO2 doubling.' Journal of Climate, 11(9). | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/56290 | - |
| dc.description.abstract | It is important to develop a forecast model to predict the trajectory of sediment particles when extreme flow events occur. In extreme flow environments, the stochastic jump diffusion particle tracking model (SJD-PTM) can be used to model the movement of sediment particles in response to extreme events. This proposed SJD-PTM can be separated into three main parts — a drift motion, a turbulence term and a jump term due to random occurrences of extreme flow events. The study is intended to modify the jump term, which models the abrupt changes of particle position in the extreme flow environments. Firstly, considering the probabilistic occurrences of extreme events, both the magnitude and occurrences of extreme flow events can be simulated by the extreme value type I distribution (EVI) and the Poisson process, respectively. The evidence shows that the proposed model can more explicitly describe the uncertainty of particle movement by taking into considerations both the random arrival process of extreme flows and the variability of extreme flow magnitude. Secondly, the frequency of extreme flow occurrences might change due to many uncertain factors such as climate change. The study also attempts to use the concept of the logistic regression and the parameter of odds ratio, namely the trend magnitude to investigate the frequency change of extreme flow event occurrences and its impact on sediment particle movement. With the SJD-PTM, the ensemble mean and variance of particle trajectory can be quantified via simulations. The results show that by taking into the effect of the trend magnitude, the particle position and its uncertainty may undergo a significant increase. Such findings will have many important implications to the environmental and hydraulic engineering design and planning. For instance, when the frequency of the occurrence of flow events with higher extremity increases, particles can travel further and faster downstream. And more likely flow events with higher extremity can induce a higher degree of entrainment and particle resuspension, and consequently more significant bed and bank erosion. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T05:22:06Z (GMT). No. of bitstreams: 1 ntu-103-R01521317-1.pdf: 1653562 bytes, checksum: f8d7385b69b82cbb8415f8bedb98e2be (MD5) Previous issue date: 2014 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
中文摘要 i ABSTRACT ii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES ix Chapter 1 Introduction 1 1.1 Problem Statement 4 1.2 Research Hypotheses 5 1.3 Objectives of Study 7 1.4 Overview of Thesis 7 Chapter 2 Literature Review 8 2.1 Particle Tracking Models 9 2.1.1 Hunter et al. (1993) 9 2.1.2 Pope (1994) 11 2.1.3 Spivakovskaya et al. (2005) 12 2.1.4 Spivakovskaya et al. (2007) 13 2.2 Summary 14 Chapter 3 Stochastic Theories 16 3.1 Brownian Motion 17 3.2 Wiener Process 18 3.3 Poisson Process 19 3.4 Exponential Distribution 20 3.5 Summary 21 Chapter 4 Stochastic Particle Tracking Model using Extreme Value Generator in Jump Term 22 4.1 Introduction 23 4.2 Stochastic Jump Diffusion Particle Tracking Model 24 4.2.1 Governing Equation 24 4.2.2 Determination of Jump Terms 26 4.2.3 Extreme Value Distribution 29 4.2.4 Poisson Jump Processes 30 4.3 Applications in Open Channel Flows 33 4.3.1 Hydraulic Parameters 34 4.4 Simulation Results 37 4.4.1 Example 1: Two-Dimensional 37 4.4.2 Example 2: Open Channel Flow 40 4.4.3 Example 3: Forecast Model 43 4.5 Summary and Conclusions 47 Chapter 5 Trend Analysis of Large Flow Perturbations and Applications to Stochastic Particle Tracking Model 49 5.1 Introduction 50 5.2 Logistic Trend model 52 5.3 Analysis of Trends of Large Flow Perturbations 54 5.3.1 Case 1: Large Timescales Flow Events 54 5.3.2 Case 2: Small Timescales Flow Events 57 5.3.3 Conclusions 58 5.4 Poisson Jump Processes 59 5.5 Applications in Stochastic Jump Particle Tracking Model 62 5.6 Simulation Results 63 5.6.1 Case 1: Large Timescales Flow Events 63 5.6.2 Case 2: Small Timescales Flow Events 66 5.7 Discussions 68 5.8 Summary and Conclusions 69 Chapter 6 Summary and Recommendations 71 6.1 Summary and Conclusions 72 6.2 Recommendations for Future Research 73 REFERENCES 75 APPENDIX 78 | |
| dc.language.iso | en | |
| dc.title | 以隨機微分方程法探討極端事件下泥砂運動機制 | zh_TW |
| dc.title | Stochastic Particle Tracking Modeling for Sediment Transport in Extreme Flow Environments | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 102-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 吳富春,余化龍,賴悅仁 | |
| dc.subject.keyword | 隨機微分方程,序率模式,顆粒軌跡模型,泥砂運動,極端流動事件,氣候變遷, | zh_TW |
| dc.subject.keyword | stochastic differential equations,stochastic method,particle tracking model,sediment transport,extreme flow events,climate change, | en |
| dc.relation.page | 82 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2014-08-15 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
| 顯示於系所單位： | 土木工程學系 | |
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