压入式局部通风工作面风流分布数值模拟研究

笔者应用三维k -ε紊流模型描述压入式局部通风工作面风流的流动过程 ,并采用控制容积法导出了描述流体流动方程的离散化方程式 ;用计算流体力学的方法求得了三维k -ε紊流模型的数值解。采用SIMPLE(压力耦合方程式的半阴解法 )算法解算流场 ;TDMA(三对角线算法 )和Gauss Seidel法结合通过线顺法求解离散方程。利用弱松弛法防止非线性方程组迭代求解过程中的发散现象 ,获得了掘进工作面空间的速度分布 ;并将模拟解算结果与在具有实际尺寸的拱形巷道模型中测得的实验结果进行了对比。数值计算结果与模型实验测定结果非常一致 ,验证了笔者的数学模型和数值模拟方法的正确性。     

Fish recruitment prediction, using robust supervised classification methods

Improving our ability to predict recruitment is a key element in fisheries management. However, the interactions between population dynamics and different environmental factors are complex and often non-linear, making it difficult to produce robust predictions. ‘Machine-learning’ techniques (in particular, supervised classification methods) have been proposed as useful tools, to overcome such difficulties. In this study, a methodology is proposed to build a robust classifier for fish recruitment prediction with sparse and noisy data. The methodology consists of 4 steps: (1) a semi-automated recruitment discretization method; (2) supervised discretization of predictors; (3) multivariate and non-redundant predictors selection; (4) learning a probabilistic classifier. In terms of fisheries management, the classifier estimated performance has important consequences and, to be useful, the manager needs to know the risk that is being taken when using this number. Probabilistic classifiers such as ‘naive Bayes’, have the advantage that, in addition to the predictions, estimate also the probability of each possible outcome. Anchovy (Engraulis encrasicolus) and hake (Merluccius merluccius) recruitments are used as application examples. ‘Two-intervals’ recruitment discretization accomplishes 70% accuracies and Brier scores of around 0.10, for both anchovy and hake recruitment. In comparison, ‘three-intervals’ recruitment discretization accomplishes 50% accuracies; and Brier scores of around 0.25 for anchovy and 0.30 for hake recruitment. These statistics are the result of validating not only the classifier, but also the previous steps, as a whole methodology.