Effects of the film thickness and poling electric field on photovoltaic performances of (Pb,La)(Zr,Ti)O3 ferroelectric thin film-based devices

The ferroelectric photovoltaic (FPV) effect obtained in inorganic perovskite ferroelectric materials has received much attention because of its large potential in preparing FPV devices with superior stability, high open-circuit voltage (V_oc) and large short-circuit current density (J_sc). In order to obtain suitable thickness for the ferroelectric thin film as light absorption layer, in which, the sunlight can be fully absorbed and the photo-generated electrons and holes are recombined as few as possible, we prepare Pb_0.93La_0.07(Zr_0.6Ti_0.4)_0.9825O₃ (PLZT) ferroelectric thin films with different layer numbers by the sol-gel method and based on these thin films, obtain FPV devices with FTO/PLZT/Au structure. By measuring photovoltaic properties, it is found that the device with 4 layer-PLZT thin film (~300 nm thickness) exhibits the largest V_oc and J_sc and the photovoltaic effect obviously depends on the value and direction of the poling electric field. When the device is applied a negative poling electric field, both the V_oc and J_sc are significantly higher than those of the device applied the positive poling electric field, due to the depolarization field resulting from the remnant polarization in the same direction with the built-in electric field induced by the Schottky barrier, and the higher the negative poling electric field, the larger the V_oc and J_sc. At a -333 kV/cm poling electric field, the FPV device exhibits the most superior photovoltaic properties with a V_oc of as high as 0.73 V and J_sc of as large as 2.11 μA/cm². This work opens a new way for developing ferroelectric photovoltaic devices with good properties.     

Optimal design of nonlinear model predictive controller based on new modified multitracker optimization algorithm

The controller design for the robotic manipulator faces different challenges such as the system's nonlinearities and the uncertainties of the parameters. Furthermore, the tracking of different linear and nonlinear trajectories represents a vital role by the manipulator. This paper suggests an optimal design for the nonlinear model predictive control (NLMPC) based on a new improved intelligent technique and it is named modified multitracker optimization algorithm (MMTOA). The proposed modification of the MTOA is carried out based on opposition-based learning (OBL) and quasi OBL approaches. This modification improves the exploration behavior of the MTOA to prevent it from becoming trapped in a local optimum. The proposed method is applied on the robotic manipulator to track different linear and nonlinear trajectories. The NLMPC parameters are tuned by the MMTOA rather than the trial and error method of the designer. The proposed NLMPC based on MMTOA is compared with the original MTOA, genetic algorithm, and cuckoo search algorithm in literature. The superiority and effectiveness of the proposed controller are confirmed to track different linear and nonlinear trajectories. Furthermore, the robustness of the proposed method is emphasized against the uncertainties of the parameters.     

Mathematical modeling of growth of Salmonella spp. and spoilage microorganisms in raw oysters

The main objective of this study was to develop the primary and secondary models to describe the growth kinetics of Salmonella as well as background microorganisms in raw, shucked oysters. Samples, inoculated with a cocktail of two Salmonella serotypes, S. Typhimurium (CICC22956) and S. Enteritidis (CICC21482), were incubated at 4, 8, 12, 16, 20, 25, 30, 33, 37, 40, and 43 °C. Growth of Salmonella was observed at all temperatures, except at 4 °C. The background microorganisms grew at all temperatures. All growth curves clearly exhibited lag, exponential and stationary phases, and were analyzed using the Huang growth model. Three secondary models (Ratkowsky square-root, Huang square-root, and Cardinal parameter models) were compared for evaluating the effect of temperature on bacterial growth rates. Data analysis was performed using IPMP 2013, a free predictive microbiology software tool developed by the USDA ARS.The Cardinal parameters model underestimated the specific rates of the microorganisms at low temperatures. The Huang square-root model was more suitable than the Ratkowsky square-root model for describing the effect of temperature on growth of Salmonella, while the Ratkowsky square-root model, on the other hand, was more suitable for background microorganisms. For both Salmonella and background microorganisms, the logarithms of the lag phase were expressed as linear functions of the logarithms of specific growth rates. The results of this study can be used by the food retailers and regulatory agencies to estimate the microbial shelf-life of raw, shucked oysters.     

Evaluation of hydrodynamic behavior of the perforated gas distributor of industrial gas phase polymerization reactor using CFD-PBM coupled model

A 2D computational fluid dynamics (Eulerian–Eulerian) multiphase flow model coupled with a population balance model (CFD-PBM) was implemented to investigate the fluidization structure in terms of entrance region in an industrial-scale gas phase fluidized bed reactor. The simulation results were compared with the industrial data, and good agreement was observed. Two cases including perforated distributor and complete sparger were applied to examine the flow structure through the bed. The parametric sensitivity analysis of time step, number of node, drag coefficient, and specularity coefficient was carried out. It was found that the results were more sensitive to the drag model. The results showed that the entrance configuration has significant effect on the flow structure. While the dead zones are created in both corners of the distributors, the perforated distributor generates more startup bubbles, heterogeneous flow field, and better gas–solid interaction above the entrance region due to jet formation.     

云杉八齿小蠹Ips typographus L.虚拟模型构建方法研究

本文以云杉八齿小蠹Ips typographus Linnaeus为例,经标本选取,观察虫体整体形态,绘制整体结构草图,电子显微镜观察局部、得到局部数字图像,分别建立虫体每一部分的高精度细节。把模型每部分拼装在一起组成整体模型,构建出了云杉八齿小蠹三维虚拟昆虫数字化模型。     

Ultrasonic tomographic velocimeter for visualization of axial flow fields in pipes

An ultrasonic tomographic velocimeter to provide quantitative images of axial flow fields in pipes is developed and presented in this work. To detect the flow in various directions and positions, a novel transducer configuration strategy is proposed. All-in-one transducers are mounted in two sectional planes of the pipe. In each plane, N transducers are equally spaced along the circumference. Overlapped propagation paths are introduced by the configuration strategy, and the influence of the vortex flow can be eliminated theoretically by averaging the line velocities of the overlapped paths. To achieve a fast detection speed, the projection data is collected via an electrical scan in a fan-beam mode. After rearrangement and interpolation of the projection data, the parallel beam filtered back projection (FBP) algorithm is implemented to reconstruct the axial flow field. Numerical simulations with the theoretical velocity profiles were performed. The compensation method for the vortex flow is proved to be effective and necessary, and the number of transducers required for reconstruction of common flow profiles was estimated. Accordingly, an ultrasonic tomographic velocimeter consisting of 2×12 transducers was fabricated. Experiments were conducted in the straight pipe and downstream of a single bend pipe and compared with the computational fluid dynamics (CFD) simulation results. As demonstrated, the ultrasonic tomographic velocimeter was capable of visualizing both symmetric and asymmetric axial flow fields with high reliability.     

基于梯度提升决策树的车轮轮缘厚度磨耗预测

轮对在列车走行过程中起着导向、承受以及传递载荷的作用，其踏面及轮缘磨耗对地铁列车运行安全性和钢轨的寿命都将产生重要影响。根据地铁列车车轮磨耗机理，分析车轮尺寸数据特点，针对轮缘厚度这一型面参数，基于梯度提升决策树算法构建轮缘厚度磨耗预测模型。在该模型的基础上，任意选取某轮对数据进行验证分析，结果表明：基于梯度提升决策树的轮对磨耗预测模型具有较好的预测精度，可预测出1~6个月的轮缘厚度变化趋势范围，预测时间范围较长，可为地铁维保部门对轮对的维修方式由状态修转为预防修提供指导性建议。     

亲子乐园空调送风方式的数值模拟研究

大型综合室内亲子乐园属于高大空间,设有游乐设施和游戏的特殊性使得对空间的舒适性要求一致,但是送风气流遇阻严重,室内存在较多气流死角,影响室内空气质量和儿童健康。因此其空调设计不仅需要考虑温度、风速的空间均匀度,还要考虑各点的空气龄和PMV-PPD指标。以天津某亲子乐园为研究对象,利用scSTREAM软件对适用于该房间的辐射供冷加新风、置换通风、混合通风三种空调方式的送风效果进行数值模拟分析,从流场的均匀性、人员的热舒适性等方面对模拟结果进行探讨,研究结果表明辐射供冷加新风方式的空间均匀性和PMV指标最佳,混合通风方式的空气龄最小。     

Upscaled DEM-CFD model for vibrated fluidized bed based on particle-scale similarities

Simulation based on discrete element method (DEM) coupled with computational fluid dynamics (CFD), coupled DEM-CFD, is a powerful tool for investigating the details of dense particle–fluid interaction problems such as in fluidized beds and pneumatic conveyers. The addition of a mechanical vibration to a system can drastically alter the particle and fluid flows; however, their detailed mechanisms are not well understood. In this study, a DEM-CFD model based on a non-inertial frame of reference is developed to achieve a better understanding of the influence of vibration in a vibrated fluidized bed. Because the high computational cost of DEM-CFD calculations is still a major problem, an upscaled coarse-graining model is also employed. To realize similar behaviors with enlarged model particles, non-dimensional parameters at the particle scale were deduced from the governing equations. The suitability and limitations of the proposed model were examined for a density segregation problem of a binary system. To reduce the computational costs, we show that the ratio between the bed width and model particle size can be reduced to a minimum value of 100; to obtain similar segregation behaviors, the ratio between the bed height and model particle size is considered unchanged.     

An automated patient monitoring using discrete‐time wireless sensor networks

In recent years, Internet of Things (IoT) devices are used for remote health monitoring. For remotely monitoring a patient, only the health information at different time points are not sufficient; predicted values of biomarkers (for some future time points) are also important. In this article, we propose a powerful statistical model for an efficient dynamic patient monitoring using wireless sensor nodes through Bayesian Learning (BL). We consider the setting where a set of correlated biomarkers are measured from a patient through wireless sensors, but the sensors only report the ordinal outcomes (say, good, fair, high, or very high) to the sink based on some prefixed thresholds. The challenge is to use the ordinal outcomes for monitoring and predicting the health status of the patient under consideration. We propose a linear mixed model where interbiomarker correlations and intrabiomarker dependence are modeled simultaneously. The estimated and the predicted values of the biomarkers are transferred over the internet so that health care providers and the family members of the patient can remotely monitor the patient. Extensive simulation studies are performed to assess practical usefulness of our proposed joint model, and the performance of the proposed joint model is compared to that of some other traditional models used in the literature.