Application of probabilistic neural network to design breakwater armor blocks

This study presents a probabilistic neural network (PNN) technique for predicting the stability number of armor blocks of breakwaters. The PNN is prepared using the experimental data of Van der Meer. The predicted stability numbers of the PNN are compared with those of previous studies, i.e. by an empirical formula and a previous neural network model. The agreement index between the measured and predicted stability numbers by PNN are better than those by the previous studies. The PNN offers a way to interpret the network's structure in the form of a probability density function and it is easy to implement. Therefore, it can be an effective tool for designers of rubble mound breakwaters.     

Shape optimization of two-dimensional cavitators in supercavitating flows, using NSGA II algorithm

The reduction of energy consumption of high speed submersible bodies is an important challenge in hydrodynamic researches. In this paper, shape optimization of two-dimensional cavitators in supercavitating flows is studied. A two dimensional supercavitation potential flow passes a symmetric two dimensional cavitator, which is placed perpendicular to the flow in a channel of infinite length and immediately a cavity is formed behind the cavitator. This is because of the generation of a gas or vapor cavity between the body and the surrounding liquid due to the change in a high speed flow direction passing the cavitator. Drag force acting on this supercavitating body dictates the thrust requirements for the propulsion system, to maintain a required cavity at the operating speed. Therefore, any reduction in the drag force, by modifying the shape of the cavitator, will lead to decrease this force. This study concentrates on the optimization of two dimensional cavitators in order to decrease drag coefficient for a specified after body length and velocity in a potential flow. To achieve this goal a multi-objective optimization problem is defined to optimize cavitator shapes in supercavitating flow. The so-called NSGA II (Non-dominated Sorting Genetic Algorithm) algorithm is used as an optimization method. Design parameters and constraints are obtained according to supercavitating flow characteristics and cavitator modeling and objective functions are generated using Linear Regression Method. The obtained results are compared with other classic optimization methods, like the weighted sum method, for validation.     

Analysis of threshold and incipient conditions for sediment movement

Prediction of threshold conditions and incipient motion is the essential issue for the study of sediment transport. This work compares existing empirical threshold curves proposed for Shields diagram, a method based on the concept of probability of sediment movement, and an empirical method based on movability number. These methods are used to predict the incipient motion conditions for experimental runs taken from various studies. Most of the experimental data, used in this work, have not been used before in derivation of alternative formulations for Shields diagram and other methods. The empirical threshold curves based on the Shields entrainment function was the least successful at predicting the measured incipient motion conditions, while the use of the movability number gives good predictions of critical shear velocity compared with experimental data.     

广州市雷暴日数变化特征分析

通过对广州基准站(站号59287,纬度23.00°N,经度113.13°E,海拔高度10.4m)1951-2007年的雷暴观测资料进行对比统计分析,总结出广州市雷暴日数气候变化规律和特征,得到了一些对防雷减灾工作有积极意义的分析结果.     

集合卡尔曼滤波在流域水文模型流量预报中的应用

针对流域降雨入渗过程,引入集合卡尔曼滤波(EnKF)理论,视整个边坡流域为一个随机动态系统,将边坡流域流量观测值作为系统的输出,用集合卡尔曼滤波模型来描述系统的状态;结合流域流量计算方法,实现水文模型参数的随机动态估计,在有效获得待估参数的同时还给出估计值的不确定性.通过数值算例表明,集合卡尔曼滤波可以有效地对含噪声的量测数据进行处理,能够跟踪水文模型的动态变化.相对于常用最优化算法,集合卡尔曼滤波同时给出反演结果和先验知识的后验分布,显示出更好的实时性和可靠性.     

基于POM模式与blending同化法建立中国近海潮汐模型

利用POM海洋数值模式建立了中国近海（2°N-41°N,99°E～132°E）分辨率为5′×5′的潮汐模型,模式采用blending同化法同化了由10年TOPEX／Poseidon测高数据反演的潮汐参数与沿岸52个验潮站观测。精度分析表明建立的潮汐模型的8分潮RSS为12．5cm。     

基于CCSM3气候模式的同化模拟试验

基于美国NCAR及其他科学家合作发展的共同气候系统模式CCSM3,利用nudging方法开展了把15 m到465 m的次表层海温同化到该模式的研究。1980—2000年的同化试验结果表明,经过同化得到的模拟结果与实际较为一致,较好的再现了中低纬太平洋海洋和大气的平均特征和随时间演变的规律,但仍存在如海表温度偏高、降水偏强等问题。尤其是在大洋的东边界,陆地地形比较陡峭的地区,通常出现较大的偏差。     

不同倍性大黄鱼核仁数目银染观察

用银染法对不同倍性(单倍体、正常二倍体、正常三倍体、雌核发育二倍体、雌核发育鱼子代三倍体)大黄鱼胚胎、仔鱼或成鱼的不同组织进行细胞核仁数目观察计数。结果表明,大黄鱼各种倍性个体细胞核仁数目与染色体倍性之间有良好的对应关系,且核仁数随个体倍性的增加而增加;非诱导雌核发育的大黄鱼细胞核仁众数与染色体组数相等(二倍体为2,三倍体为3),而人工诱导雌核发育大黄鱼及其子代细胞核仁众数却比其相同倍性的非雌核发育鱼少1(雌核发育二倍体为1,雌核发育三倍体为2)。雌核发育鱼核仁数减少是一个非常有趣的现象,值得进一步研究。大黄鱼不同发育阶段(胚胎、仔鱼、成鱼)以及同一个体不同组织(鳃、肾、鳍)的细胞核仁数目(众数)相同。因此,借助核仁银染法可以快速、可靠、简便地进行染色体倍性鉴定。而雌核发育鱼核仁数减少的机理值得进一步研究。     

基于扩散滤波的多尺度三维变分研究

将扩散方程引入三维变分分析,揭示了传统3D-VAR不能有效提取多尺度观测信息的根本原因,即观测导致的目标函数梯度在空间的不连续分布.将扩散滤波融入基于梯度的最优化算法,发展了基于扩散滤波的多尺度3D-VAR.海表面温度数据同化试验结果表明,新方法可从长波至短波有效地提取多尺度的观测信息.     

高度计波高数据同化对印度洋海域海浪模式预报影响研究

为提高海浪模式预报的精度,改善初始场是途径之一。研制了基于最优插值(OI)方法的海浪数据同化并行程序模块,并将其植入第三代海浪模式WAVEWATCH IIITM,建立了印度洋海域海浪同化预报方法,使用卫星高度计波高数据进行了同化预报试验。OI模块的并行设计使得植入同化模块的海浪模式仍能以并行方式运行。文中5°S以北印度洋海域为目标区域,嵌套在WAVE-WATCH IIITM的全球网格中,使得目标区域开边界条件得到较好解决。同化数据使用Jason-2高度计测量有效波高(SWH)沿轨数据。海浪同化预报模式由大气模式WRF(Weather Research andForecasting)输出的1小时一次的海面10 m风场驱动。将同化的模式结果(SWH)、无同化的模式结果(SWH)分别与高度计沿轨数据(SWH)进行比较,表明同化改善模式预报初始场的效果是明显的。以同化初始场出发进行海浪预报试验,结果表明,高度计波高数据同化在一定程度上可改进海浪短期预报的精度。