Assessment of Effective Monitoring Sites in a Reservoir Watershed by Support Vector Machine Coupled with Multi-Objective Genetic Algorithm for Sediment Flux Prediction during Typhoons

Water Resources Management - Effectively assessing crucial monitoring sites with suspended sediment concentration (SSC) is a vital challenge for achieving accurate prediction of sediment flux on...     

一种新型全控式自平衡爬楼车的设计与研究

为了解决全控式爬楼车工作时振动和冲击较大的问题,论文提出了一种全控式自平衡爬楼车的设计。本设计在爬楼的行星轮中间增加旋转的充气轮,增加了爬楼车的爬升和缓冲能力,通过模型的受力分析,验证了该设计的可行性;根据单摆平衡原理增加了自平衡控制,通过等比缩放模型的理论计算,验证了该控制的有效性,爬楼车重心更加平稳,减少了振动。对本设计的实物进行了测试,证明了理论推导的准确性。     

Improving the Long Lead-Time Inundation Forecasts Using Effective Typhoon Characteristics

In this paper, a new type of inundation forecasting model with the effective typhoon characteristics is proposed by integrating support vector machine (SVM) with multi-objective genetic algorithm (MOGA). Firstly, a comparison of the proposed model and an existing model based on back-propagation network (BPN) is made to highlight the improvement in forecasting performance. Next, the proposed model is compared with the SVM-based model without typhoon characteristics to investigate the influence of typhoon characteristics on inundation forecasting. Effective typhoon characteristics for improving forecasting performance are identified as well. An application to Chiayi City, Taiwan, is conducted to demonstrate the superiority of the proposed model. The results confirm that the proposed model with the effective typhoon characteristics does improve the forecasting performance and the improvement increases with increasing lead-time, especially for long lead-time forecasting. The proposed model is capable of optimizing the input to decrease the negative impact when increasing forecast lead time. In conclusion, effective typhoon characteristics are recommended as key inputs for inundation forecasting during typhoons.     

Real-time Neural-network-based Ensemble Typhoon Flood Forecasting Model with Self-organizing Map Cluster Analysis: A Case Study on the Wu River Basin in Taiwan

Accurate hourly real-time flood forecasting is necessary for early flood warning systems, especially during typhoon periods. Artificial intelligence methods have been increasingly used for real-time flood forecasting. This study developed a real-time flood forecasting model by using back-propagation networks (BPNs) with a self-organizing map (SOM) to create ensemble forecasts. Random weights and biases were set for the BPNs to learn the characteristics of a catchment system. An unsupervised SOM network with a classification function was then used to cluster representative BPN weights and biases; clusters of BPNs with high accuracy were selected to act as experts for the ensemble models to forecast flow rates. The model was applied to flood events in the Wu River Basin of Taiwan. Most observed values were within the forecasting intervals of the BPN clusters in the calibration and validation phases, indicating that the models had acceptable accuracy. For the large flood events of typhoons Saola in the calibration phase and Soulik in the validation phase, the mean average error of the ensemble mean model for the cluster A was 143.1 and 327.4 m³/s, respectively; these values were lower than those for the best individual model within the cluster (194.3 and 917.9 m³/s). The ensemble model thus outperformed the individual models and can accurately forecast flood values and intervals. Therefore, the model can be used to accurately forecast floods.

     

Large-eddy simulation of turbulent heat convection in a spanwise rotating channel flow

In this paper, we investigate the effects of the Coriolis force in a heated plane channel flow subjected to spanwise rotation using the method of large-eddy simulation. We present both the general and simplified transport equations for the resolved turbulent stresses, which are essential for understanding the unique pattern of turbulent kinetic energy production in a rotating system. Numerical simulations are performed using primarily two dynamic subgrid-scale stress models and one dynamic subgrid-scale heat flux model; namely, the conventional dynamic model (DM) and a novel dynamic nonlinear model (DNM) for closure of the filtered momentum equation, and an advanced dynamic full linear tensor thermal diffusivity model (DFLTDM) for closure of the filtered thermal energy equation. The turbulent flow field studied herein is characterized by a Reynolds number Re_τ = 150 and various rotation numbers Ro_τ ranging from 0 to 7.5. In order to validate the LES approach, turbulent statistics obtained from the simulations are thoroughly compared with the available experimental results and direct numerical simulation (DNS) data. A detailed comparative study has been conducted in order to evaluate the performance of the DM and DNM in terms of their prediction of characteristic features of the velocity and temperature fields and their capability of reflecting both forward and backward scatter of kinetic energy between the filtered and subgrid scales.     

Turbulent Dispersion of a Passive Scalar in a Staggered Array of Cubes

In this article, we report a numerical and experimental study of turbulent dispersion of a passive scalar released from a continuous ground-level point source in a staggered array of 16 × 16 cubical obstacles. Experimental measurements of the flow and dispersion were obtained in a boundary-layer water channel using laser-induced fluorescence (for concentration) and laser Doppler velocimetry (for velocity). Numerical simulations of the flow and scalar fields for this experimental configuration were performed using two in-house computer codes based on the Reynolds-averaged Navier-Stokes (RANS) method. Results of a detailed comparison between water-channel measurements and model predictions of the mean flow, turbulence kinetic energy, mean concentration, and concentration variance are presented. An advanced model for the concentration variance dissipation rate is validated in the new context of plume dispersion within and above a staggered array of cubical obstacles.     

GSML网格编程语言的一种实现方法

随着网格计算技术的不断发展，部署网格应用的环境越来越多，而网格编程语言还停留在C，Java这样的程序设计语言上，GSML是在中科院计算所织女星网格项目中设计的高级编程语言和用户界面语言，它采用了一种标记语言实现编程技术，旨在降低网格编程的难度，提高开发和部署网格应用的效率，介绍了对该语言的一种实现方法，讨论了GSML实现过程中需要解决的关键技术问题，以及网格社区等涉及GSML实现的相关内容。     

基于光相位锁定的载波可变光的毫米波产生技术

提出和实现了一种新型适用于光/无线混合接入的光子载微波产生方案.该方案利用直接调制分布反馈式半导体激光器可以产生宽谱信号的特点,将较低频率正弦信号直接调制半导体激光器,在半导体激光器中进行频谱展宽,然后把宽谱信号注入到多个半导体激光器中进行相位锁定从而作为相干锁定光源.用上述光源在不同波长加载不同信号进行传输,在接收端使用不同通带特性的光学滤波器对信号进行处理,可以选择接收基带信号、光子微波时钟信号、上行光源或不同载波频率的光子微波调制信号.作为验证,分别完成了2.5 Gbps基带信号、20 GHz和40 GHz光副载波调制信号的产生与接收.因为受限于试验条件,只进行了原理验证,但该系统理论上可集成波导设计,无需高频调制器件,并可生成更高载频光子微波信号,有利于未来的光/无线混合接入和超密集波分复用系统.     

Sn对Ge-Se-Te玻璃结构与光学特性的影响

采用熔融淬冷法制备了系列Ge₃Se₅Te₂Sn_x(x=0、0.4、0.8、1.2、1.6、2.0, mol%)硫系玻璃。采用X射线衍射(XRD)图谱、差示扫描量热曲线(DSC)、可见/近红外光谱、傅里叶红外(FTIR)光谱、显微拉曼光谱等手段对玻璃的物化性能及结构进行表征, 研究发现Sn的引入导致Ge-Se-Te玻璃系统物化性能的变化: 玻璃的转变温度T_g降低、红外截止波长发生红移, 并有效地降低了杂质吸收峰对样品红外透过率的影响。利用Philips网络约束理论计算的玻璃平均配位数及拉曼光谱的变化, 讨论了引入Sn对Ge-Se-Te玻璃的影响。     

AN EFFICIENT SOLUTION SCHEME FOR APPLYING THE INTEGRAL-TYPE DYNAMIC LOCALIZATION SUBGRID-SCALE MODEL IN TURBULENCE WITH HOMOGENEOUS DIRECTIONS

The conventional integral-type dynamic localization subgrid-scale stress model is a Fredholm integral equation of the second kind. This model is mathematically consistent, but it has not been widely used in the large-eddy simulation community due to the relatively high computational cost of solving the Fredholm integral equation using an iterative scheme. In this article, a direct solution scheme based on a discrete Gaussian filter is developed to solve the integral system for turbulent flow with homogeneous dimensions. The proposed direct solution scheme is computationally efficient and makes the integral-type dynamic localization subgrid-scale stress model affordable. Turbulent Couette flows with Reynolds numbers of 2,600 and 4,762 are used in numerical simulations to validate the proposed approach.