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1.
Investigation on the miniaturized parallel multichannel-based devices packed with glass beads to improve the mass exchange execution is the critical focal point of the current study. One of the essential parameters to specify the miniaturized devices' flow distribution is the residence time distribution (RTD). In the present context, the RTDs of a liquid tracer were investigated for the air-water multiphase flows (concurrent) across the multichannel-based miniaturized devices (comprising of 11 similar dimensional parallel channels). The devices were variable in height and packed with glass beads. The conductivity estimations generated the RTD curves and were addressed by the axial dispersion model (ADM). The fluid-flow rates differed within the range of 5–23 ml min−1. The axial dispersion coefficients and the rate of the specific energy dispersion were investigated. The effects of pressure difference and geometry on the hydrodynamic attributes and mixing properties were well-illustrated, and the new correlations were suggested.  相似文献   
2.
A body-centered cubic equiatomic TiZrTaNbAl multi-principal element alloy (MPEA) with elemental fluctuations was investigated to further understand the relationship between the microstructure and hydrogen distribution. In this study, a composition dependence of the hydrogen distribution was observed in the TiZrTaNbAl MPEA. An inhomogeneous electron density distribution of the MPEA was revealed by advanced differential phase-contrast scanning electron microscopy (DPC-STEM) for the first time. The results showed that the electron density has a significant effect on the hydrogen distribution in TiZrTaNbAl MPEAs. This work provides new insight into the design of materials with high hydrogen storage capacity and high hydrogen embrittlement resistance.  相似文献   
3.
5G蜂窝网络发展迅猛,其覆盖面积将逐渐增大,因此使用5G蜂窝网络进行定位是有研究潜力的研究方向。本文提出一种新的深度学习技术来实现高效、高精度和低占用的定位,以代替传统指纹定位过程中繁重的指纹库生成以及距离计算。该方法建立了一个特殊的卷积神经网络,并根据5G天线信号的接收信号强度指示、相位和到达角等特征量,选择合适的输入数据格式构造样本组建训练集,对该卷积神经网络进行训练。训练得到的卷积神经网络可以替代指纹定位中的庞大指纹库,非常有利于直接在5G移动设备端实现定位。虽然卷积神经网络在训练过程中需要大量时间,但在训练完毕后直接进行分类定位的速度非常快,可以保障定位实现的实时性。本文所实现的卷积神经网络权重与偏置所占内存不到0.5 MB,且能够在实际应用环境中以95%的定位准确率以及0.1 m的平均定位精度实现高精度定位。  相似文献   
4.
燕麦为西藏自治区典型牧草之一,由于种植区地域辽阔,灌溉试验结果受限,西藏燕麦主要种植区的灌溉定额尚不明确。本文在西藏燕麦主要种植区内选取28个典型站点进行资料收集,遵循农业气候相似原则进行区域划分,基于水量平衡法揭示了西藏燕麦主要种植区灌溉定额的空间分布特征,并根据统计学原理分析了其影响因素。研究表明:燕麦主要种植区的灌溉定额呈由西藏中部至东部呈现先递增后递减的趋势,50%水文年下的燕麦灌溉定额在56~265 mm之间变化。降雨量是影响研究区内燕麦灌溉定额的主要因素(R2为0.515),ET0次之(R2为0.152);其它气象因素中,日照时数对研究区燕麦灌溉定额影响较大(R2为0.462),且呈正相关关系;相对湿度对燕麦灌溉定额影响较小。西藏燕麦主要种植区的灌溉定额及其空间分布可为西藏自治区灌溉用水管理提供支撑。  相似文献   
5.
6.
To evaluate the separate impacts on human health and establish effective control strategies, it is crucial to estimate the contribution of outdoor infiltration and indoor emission to indoor PM2.5 in buildings. This study used an algorithm to automatically estimate the long-term time-resolved indoor PM2.5 of outdoor and indoor origin in real apartments with natural ventilation. The inputs for the algorithm were only the time-resolved indoor/outdoor PM2.5 concentrations and occupants’ window actions, which were easily obtained from the low-cost sensors. This study first applied the algorithm in an apartment in Tianjin, China. The indoor/outdoor contribution to the gross indoor exposure and time-resolved infiltration factor were automatically estimated using the algorithm. The influence of outdoor PM2.5 data source and algorithm parameters on the estimated results was analyzed. The algorithm was then applied in four other apartments located in Chongqing, Shenyang, Xi'an, and Urumqi to further demonstrate its feasibility. The results provided indirect evidence, such as the plausible explanations for seasonal and spatial variation, to partially support the success of the algorithm used in real apartments. Through the analysis, this study also identified several further development directions to facilitate the practical applications of the algorithm, such as robust long-term outdoor PM2.5 monitoring using low-cost light-scattering sensors.  相似文献   
7.
Utilizing inner-crystal piezoelectric polarization charges to control carrier transport across a metal-semiconductor or semiconductor–semiconductor interface, piezotronic effect has great potential applications in smart micro/nano-electromechanical system (MEMS/NEMS), human-machine interfacing, and nanorobotics. However, current research on piezotronics has mainly focused on systems with only one or rather limited interfaces. Here, the statistical piezotronic effect is reported in ZnO bulk composited of nanoplatelets, of which the strain/stress-induced piezo-potential at the crystals’ interfaces can effectively gate the electrical transport of ZnO bulk. It is a statistical phenomenon of piezotronic modification of large numbers of interfaces, and the crystal orientation of inner ZnO nanoplatelets strongly influence the transport property of ZnO bulk. With optimum preferred orientation of ZnO nanoplatelets, the bulk exhibits an increased conductivity with decreasing stress at a high pressure range of 200–400 MPa, which has not been observed previously in bulk. A maximum sensitivity of 1.149 µS m−1 MPa−1 and a corresponding gauge factor of 467–589 have been achieved. As a statistical phenomenon of many piezotronic interfaces modulation, the proposed statistical piezotronic effect extends the connotation of piezotronics and promotes its practical applications in intelligent sensing.  相似文献   
8.
对Inconel 690传热管材进行钨极气体保护焊(GTAW)对接焊,采用拉伸试验机、压扁试验机和光学显微镜测试和分析传热管焊接接头,同时利用ANSYS软件开展焊接接头在设计工况失压时的一次应力强度校核。研究结果表明:焊缝中心为树枝胞状晶,熔合线附近为粗大柱状晶。室温时接头的平均抗拉强度为619 MPa,平均屈服强度为292 MPa,350℃时接头平均抗拉强度为475 MPa,平均屈服强度为206 MPa,拉伸接头断裂从熔合区开始贯穿整个焊缝组织,呈塑性断裂。压扁试验和反向压扁试验结果表明管接头完好。通过ANSYS分析可知,设计工况下传热管接头350℃许用应力强度150 MPa限值可满足其一次应力强度要求,且裕量较大。  相似文献   
9.
为缓解我国水、能源和粮食资源紧张问题,促进资源可持续利用,构建水-能源-粮食系统,利用耦合协调度模型对我国的30个省(自治区、直辖市)进行测算,并利用空间杜宾模型分析主要影响因素。结果表明:2003—2017年,我国能源、粮食评价[JP]指数高于水资源评价指数,系统综合评价指数逐年递增;大部分省份耦合协调度处于初级协调水平且呈现逐年上升的态势,个别省份耦合协调度濒临失调;耦合协调度空间自相关性较强,虽有明显波动,但是呈现逐年加强的态势;影响耦合协调度的主要因素有从业人口数、固定资产投资额、人均生产总值、人口总数、[JP]文盲人口占比、工业污染排放、城镇化。  相似文献   
10.
Prediction of mode I fracture toughness (KIC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression (LMR) and gene expression programming (GEP) methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and elastic modulus (E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets. Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of KIC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) were utilized herein. In the case of testing datasets, the values of R2, RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156, respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R2 value and lower errors.  相似文献   
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