DSpace 集合:https://scholars.lib.ntu.edu.tw/handle/123456789/376262024-04-05T00:08:48Z2024-04-05T00:08:48ZDesilting efficiency improvement using a dredged channel in a reservoirLee, Fong ZuoChen, Siang YingLai, Jihn SungYIH-CHI TANHWA-LUNG YUhttps://scholars.lib.ntu.edu.tw/handle/123456789/6417162024-04-01T10:00:35Z2023-01-01T00:00:00Z標題: Desilting efficiency improvement using a dredged channel in a reservoir
作者: Lee, Fong Zuo; Chen, Siang Ying; Lai, Jihn Sung; YIH-CHI TAN; HWA-LUNG YU
摘要: The core problem of reservoir sediment management lies in using desilting facilities to effectively remove or reduce sediment entering the reservoir, reduce reservoir silting, and ensure a stable water supply. The feasible strategies are mainly divided into hydraulic sand removal and mechanical dredging. However, the benefits of hydraulic sand removal can be increased by utilizing mechanical dredging, which can achieve complementary effects. Therefore, this study considers the bottom dredged channel formed by mechanical dredging, including different dredged channel lengths and reservoir water level scenarios. It intends to concentrate and increase the turbid water body that reaches the desilting tunnel entrance in front of the dam, thereby increasing sediment desilting efficiency. In this study, the Two-dimensional layer-averaged model (TDLAM) was used as a simulation tool to investigate the inflowing turbid water in the case of reservoirs with a bottom dredged channel. According to the simulation results of the TDLAM model, this study preliminarily found that the more extended the bottom dredged channel, the greater the sediment desilting efficiency. However, the change in water level will not significantly affect the sediment desilting efficiency.2023-01-01T00:00:00ZA new 2D ESPH bedload sediment transport model for rapidly varied flows over mobile bedsChang, Kao HuaWu, Yu TingWang, Chia HoTSANG-JUNG CHANGhttps://scholars.lib.ntu.edu.tw/handle/123456789/6417142024-04-01T09:53:12Z2024-05-01T00:00:00Z標題: A new 2D ESPH bedload sediment transport model for rapidly varied flows over mobile beds
作者: Chang, Kao Hua; Wu, Yu Ting; Wang, Chia Ho; TSANG-JUNG CHANG
摘要: A 2D Eulerian meshless bedload sediment transport model is developed using smoothed particle hydrodynamics (SPH) to simulate rapidly varied flows over mobile beds. In the developed model, we adopt a weakly coupled numerical approach to explicitly solve the governing equations, including 2D shallow water equations for fluid flow motion and the Exner equation for bed sediment movement at the same time step. A defined virtual bedload velocity in a weakly coupled approach is involved in the calculation of time step sizes. However, nonphysical virtual bedload velocities, which often occur in cases with nearly flat beds, require extremely small time step sizes. A formulation of the lower and upper bounds of information propagation speeds for an HLLC approximate Riemann solver is thus proposed to remedy the problem. Four case studies involving dam break flows in prismatic and erodible channels, knickpoint migration, dam erosion due to overtopping flow and dam-break flow in an erodible channel with an abrupt expansion are adopted to validate the developed model. In addition, to compare the performances of different particle interaction configurations under Cartesian uniform particle arrangements, four and eight interacting particles are obtained by changing the smoothing length. Against the measured results, the case of eight interacting particles shows more accurate predictions of erosion peaks along the measured cross-sections because the lateral flows are significant. The good agreement between the simulated and measured results shows that the developed 2D Eulerian SPH bedload sediment transport model with eight interacting particles is well suited for simulating complicated flow-induced bed erosion.2024-05-01T00:00:00ZUncertainty-Based Capacity Factors of Operational Wind Turbines Using the Generalized Likelihood Uncertainty Estimation (GLUE) MethodTSANG-JUNG CHANGChang, Kao HuaYu, Hsiang LinChen, Chung YiSHU-YUAN PANhttps://scholars.lib.ntu.edu.tw/handle/123456789/6417122024-04-01T09:44:57Z2024-01-01T00:00:00Z標題: Uncertainty-Based Capacity Factors of Operational Wind Turbines Using the Generalized Likelihood Uncertainty Estimation (GLUE) Method
作者: TSANG-JUNG CHANG; Chang, Kao Hua; Yu, Hsiang Lin; Chen, Chung Yi; SHU-YUAN PAN
摘要: This study proposes a novel framework that couples the general likelihood uncertainty estimation (GLUE) method with a deterministic forecasting approach to conduct a new uncertainty analysis approach for assessing the energy production of operational wind turbines installed in the Jhongtun wind farm at Penghu (an island in the middle of Taiwan Strait). The 10-year measured data of wind speeds and energy output collected on these wind turbines is divided into two 5-year data sets for the present analysis framework of execution and validation to demonstrate the predictability of the GLUE method. The present study considers 15 scenario testing cases with various time periods, i.e., twelve months, the strong-wind (October-March) regime, the weak-wind (April-September) regime, and one year, for the framework to investigate the applicability of the GLUE method on long-term wind energy forecasting. In the execution framework, the 5-year measured data is used by the GLUE method to access the uncertainties involved in the deterministic approach (i.e., the shape and scale parameters of the Weibull wind speed distribution (WWSD), the performance curve, and the capacity factor) with two confidence intervals of 50% and 90%. The framework is then validated by the measured capacity factors in the last 5-year data and compared with the results of the uncertainty analysis approach by the Monte Carlo (MC) approach to discover the applicability of the new uncertainty analysis approach. From the simulated results, it is found that the proposed uncertainty analysis approach provides predictions of confidence intervals that match the measured data better than the MC-based uncertainty analysis approach. Specifically, the proposed approach can match the measured capacity factors in all the simulated scenarios. Conversely, the MC-based approach is found to create narrow confidence intervals that cannot completely capture the measured capacity factors, particularly for the strong-wind, weak-wind, and one-year scenarios. Therefore, this novel uncertainty analysis approach is proven to be useful in predicting the uncertainties of wind energy production.2024-01-01T00:00:00ZOptimizing complementary operation of mega cascade reservoirs for boosting hydropower sustainabilityZhu, YuxinZhou, YanlaiXu, Chong YuFI-JOHN CHANGhttps://scholars.lib.ntu.edu.tw/handle/123456789/6417072024-04-01T08:58:17Z2024-04-01T00:00:00Z標題: Optimizing complementary operation of mega cascade reservoirs for boosting hydropower sustainability
作者: Zhu, Yuxin; Zhou, Yanlai; Xu, Chong Yu; FI-JOHN CHANG
摘要: Hydropower generation and flood prevention of mega cascade reservoirs have far-reaching influences on the synergies of hydropower output, water utilization, and carbon dioxide (CO2) emission reduction. However, synergetic optimization is challenging, especially true under dynamic flood forecast conditions. This study proposed a dynamic optimization framework of complementary operation of cascade reservoirs driven by hydropower generation and flood prevention for boosting synergies. A multi-objective optimization model integrating dynamic non-dominated sorting genetic algorithm-III with support vector machine was developed to simultaneously maximize reservoir hydropower generation and minimize the peak flow for a flood control station. Seven mega cascade reservoirs of the upper Yangtze River basin constituted the case study, and the standard operation policy formed the benchmark. The results suggested that the proposed method could efficiently promote synergistic benefits with improvement rates of 8.5%, 6.5%, and 8.4% in hydropower output, floodwater utilization efficiency, and CO2 emission reduction, respectively. This study not only offers science technical support for the complementary operation of mega cascade reservoirs to promote synergies between hydropower generation and flood prevention but also suggests policymakers with favorable strategies delineating both potential risks and benefits regarding the synergetic optimization of complementary operation in the interest of sustainable hydropower development.2024-04-01T00:00:00Z