Effect of wetted surface area on friction,pressure, wave and total drag of a kayak     

Beatriz B. Gomes  Leandro Machado  Nuno V. Ramos  Filipe A. V. Conceição  Ross H. Sanders  Mário A. P. Vaz 《Sports biomechanics / International Society of Biomechanics in Sports》2018,17(4):453-461

Using theoretical principles, the components of drag (friction D _F, pressure D _PR and wave D _W) of a single-seat kayak were analysed. The purpose was to examine the effect of changes in wetted surface area due to changes in kayaker’s weight and the relative contribution of D _F, D _PR and D _W to the total passive drag as function of velocity. The total passive drag values were based on experimental data collected in a single-seat kayak. Three different kayaker simulated weights were tested – 65, 75 and 85 kg. D _F was the drag component that contributed the greatest percentage (between 60 and 68% at 5.56 m/s the top velocity tested) to the total passive drag for all the velocities tested and simulated weights. D _W was the most affected by the increase in kayaker’s simulated weight, mainly when comparing 65/75 to 85 kg. Results support the importance of a kayak design selection that minimises the kayak’s drag for the individual weight of the kayaker. Also, the results suggest that the path for better hydrodynamic kayak performance should seek changes that can reduce D _F, D _PR and D _W with D _F offering the most potential to reduce passive drag.  相似文献   

基于奇异值分解的电网潮流转移模式研究     

邓慧琼王璜 《福建工程学院学报》2017,(1):39-43

针对电网潮流转移场景下支路传输功率和电网节点注入功率之间的关联作用关系,提出了一种分析节点注入功率模式的方法。根据支路传输功率和电网节点注入功率之间的关联作用关系矩阵,利用矩阵奇异值分解,将节点注入功率分解为各种模式;在此基础上,进一步分析节点注入功率的各种模式的表现形式并进行算例分析。算例分析初步表明,在节点注入功率模式的各种表现形式中,比较常见的是以部分奇异值为主导的作用模式,在实际分析和处理中,把握这样的主导模式,将有利于进一步的分析和预防潮流转移引起的不利后果。  相似文献   

资产收益率解析   总被引：1，自引：0，他引：1  

王春香 《辽宁师范大学学报(社会科学版)》2005,28(3):25-27

资产收益率是综合反映公司业绩的重要指标。将资产收益率分解出多个因子,并与同行公司比较各因子的水平,可以发现改善业绩的途径。这里尝试对资产收益率做进一步的探讨,建立了资产收益率与产品寿命周期、价值链等现代战略思想之间的逻辑关系．并设计了由资产收益率及其因子得出公司战略导向和手段的基本思路。  相似文献   

一种用于电类实验测量数据判决的改进KNN算法北大核心     

申赞伟  周军盈  张士文  殳国华  张峰 《实验技术与管理》2022,(1):18-23

电类实验教学过程中人工评判学生所测数据工作烦琐,影响了教学质量和效率。该文提出了改进的K近邻(K-nearest neighbors,KNN)分类算法,即基于均值漂移、安全间隔和核主成分分析(KPCA)的M-KPCA-KNN(KNN based on margin and KPCA)算法,以判断学生测量数据正确与否和错误原因。首先利用KPCA对高维实验数据进行降维,然后利用均值漂移向量找到不同类别数据的最密集位置,并在不同类别数据的边界设置安全间隔,最后,将与待测样本距离最近的k个数据设置权重,计算每个类别的权重和,权重和最大的类别为待测样本的类别。与现有的KNN算法相比,M-KPCA-KNN算法不仅提高了分类正确率,而且降低了时间复杂度。  相似文献