FE-SEA方法在平台支持船噪声预报中的应用

平台支持船由于作业需要通常配备有动力定位系统,其在侧推工况下舱室噪声超标较为严重。针对这个问题采用计算流体力学(CFD)方法,得到侧推螺旋桨作用在导管上的脉动压力,并将时域计算结果转换成噪声计算的激励条件。采用有限元(FE)与统计能量分析(SEA)混合方法建立船体中频段FE-SEA耦合模型并建立船体高频段SEA模型,对某65 m AHTS船侧推工况下全频段(63 Hz~8000 Hz)舱室噪声进行预报,分析该船噪声分布规律及主要影响因素。并建立起全船的SEA模型,在中频段对比SEA与FE-SEA两种方法得到的舱室声压级频谱曲线,验证了使用混合模型的必要性。     

Optimization of flow in additively manufactured porous columns with graded permeability

Chemical engineering systems often involve a functional porous medium, such as in catalyzed reactive flows, fluid purifiers, and chromatographic separations. Ideally, the flow rates throughout the porous medium are uniform, and all portions of the medium contribute efficiently to its function. The permeability is a property of a porous medium that depends on pore geometry and relates flow rate to pressure drop. Additive manufacturing techniques raise the possibilities that permeability can be arbitrarily specified in three dimensions, and that a broader range of permeabilities can be achieved than by traditional manufacturing methods. Using numerical optimization methods, we show that designs with spatially varying permeability can achieve greater flow uniformity than designs with uniform permeability. We consider geometries involving hemispherical regions that distribute flow, as in many glass chromatography columns. By several measures, significant improvements in flow uniformity can be obtained by modifying permeability only near the inlet and outlet.     

Rheokinematics for product development—Formulation screening in rotational rheometers

Product formulations for industrial processes are typically developed at laboratory scale. However, the mixing conditions are not easily mimicked in the laboratory. A rotational device is proposed in this study as a fast laboratory-scale formulation development, which enables mimicking the mixing conditions in the industrial process. The geometrical configurations of the rotational device are from rheometry devices (plate-plate and cone-plate). The main advantages of this method are the small amounts of raw materials and shorter testing times. This methodology is applied to an industrial case study, the reaction injection molding (RIM) process. The mixing length scales evolution in the rotational rheometer were matched to those in RIM machines. The main novelty of this study is the introduction of a protocol that bridges the processing conditions at laboratory using small amounts of raw materials to high throughput continuous flow reactors.     

放射性气载流出物取样代表性分析

为获取核设施放射性气载流出物单点连续监测取样位置,提出基于随机轨道模型(DRW)的气-固多相湍流耦合方法求解代表性取样区域。借助k-epsilon湍流模型模拟连续相,并引入离散颗粒模型(DPM)模拟离散相,建立基于DRW模型的排风管道内流道气-固多相湍流耦合计算模型,计算了核设施气载流出物在管道内流道流场分布规律,分析了内流道流体气旋角、气流速度、示踪气体浓度、气溶胶粒子浓度与管道高度间的关联关系。分析结果表明,随着截面高度的增加,气旋角、气流速度变异系数(COV)、示踪气体浓度COV及示踪气体浓度最大值与平均值的偏差逐渐降低并趋于稳定,气溶胶粒子浓度COV在截面6与截面8满足取样代表性要求;基于计算流体动力学方法可快速地确定出代表性取样位置,为气载流出物单点取样现场试验提供了理论参考依据。     

On the application of remote sensing towards the estimation of cultivated land lost to urbanization

In this research work, a 40-km² SPOT-5 High-Resolution Imagery (HRI) of the Warsak locality in district Peshawar, Pakistan, was utilized to approximate the quantity of cultivated land lost to urbanization, due to the construction of new homes and buildings. The imagery from a period of 2005 to 2015 for wheat crop was taken, specifically during the months of March and June when the crop is rich green and golden ripe respectively. eCognition ® program’s Object-Oriented Classification Method (OOCM) was employed for recognition of land versus buildings. Nearest Neighbour (NN), Support Vector Machine (SVM), Decision Trees (DT) and Random Forests (RF) were utilized for the classification process. The results demonstrated that the urbanized area had increased by approximately 28 per cent in the area considered. Moreover, the efficacy of the proposed method is depicted by an accuracy of 97.9 per cent and a Kappa Statistics of 0.975 for the SVM classifier.     

基于时频域的卷积神经网络运动想象脑电信号识别方法

针对目前运动想象脑电（EEG）信号识别率较低的问题，考虑到脑电信号蕴含着丰富的时频信息，提出一种基于时频域的卷积神经网络（CNN）运动想象脑电信号识别方法。首先，利用短时傅里叶变换（STFT）对脑电信号的相关频带进行预处理，并将多个电极的时频图组合构造出一种二维时频图；然后，针对二维时频图的时频特性，通过一维卷积的方法设计了一种新颖的CNN结构；最后，通过支持向量机（SVM）对CNN提取的特征进行分类。基于BCI数据集的实验结果表明，所提方法的平均识别率为86.5%，优于其他传统运动想象脑电信号识别方法；同时将该方法应用在智能轮椅上，验证了其有效性。     

Airborne spread of expiratory droplet nuclei between the occupants of indoor environments: A review

This article reviews past studies of airborne transmission between occupants in indoor environments, focusing on the spread of expiratory droplet nuclei from mouth/nose to mouth/nose for non‐specific diseases. Special attention is paid to summarizing what is known about the influential factors, the inappropriate simplifications of the thermofluid boundary conditions of thermal manikins, the challenges facing the available experimental techniques, and the limitations of available evaluation methods. Secondary issues are highlighted, and some new ways to improve our understanding of airborne transmission indoors are provided. The characteristics of airborne spread of expiratory droplet nuclei between occupants, which are influenced correlatively by both environmental and personal factors, were widely revealed under steady‐state conditions. Owing to the different boundary conditions used, some inconsistent findings on specific influential factors have been published. The available instrumentation was too slow to provide accurate concentration profiles for time‐dependent evaluations of events with obvious time characteristics, while computational fluid dynamics (CFD) studies were mainly performed in the framework of inherently steady Reynolds‐averaged Navier‐Stokes modeling. Future research needs in 3 areas are identified: the importance of the direction of indoor airflow patterns, the dynamics of airborne transmission, and the application of CFD simulations.     

叶片实度对建筑增强型垂直轴风力机气动性能的影响

采用数值模拟的方法对建筑环境中具有不同实度的建筑增强型垂直轴风力机进行模拟。对NACA0021翼型作为叶片截面的三叶片原始直线翼垂直轴风力机周围流场进行数值模拟并与实验值进行比较,且进行了网格无关性验证,验证了计算模型的合理。结果表明:随实度的增加,建筑增强型风力机气动性能与自启动性能得到大幅改善,风力机平均力矩增大,风能利用率最大提升至4.47倍,但其载荷波动也非常剧烈,对结构安全性与可靠性提出更高的要求。实度过小则无法有效地捕获风能;实度过大则叶片之间的干扰增加,最大风能利用率增幅可降低至3.11倍。