An Improved Torque Sensorless Speed Control Method for Electric Assisted Bicycle With Consideration of Coordinate Conversion

In this paper, we propose an improved torque sensorless speed control method for electric assisted bicycle, this method considers the coordinate conversion. A low-pass filter is designed in disturbance observer to estimate and compensate the variable disturbance during cycling. A DC motor provides assisted power driving, the assistance method is based on the real-time wheel angular velocity and coordinate system transformation. The effect of observer is proved, and the proposed method guarantees stability under disturbances. It is also compared to the existing methods and their performances are illustrated through simulations. The proposed method improves the performance both in rapidity and stability.      

Identification of Host Cellular Protein Substrates of SARS-COV-2 Main Protease

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease-19 (COVID-19) being associated with severe pneumonia. Like with other viruses, the interaction of SARS-CoV-2 with host cell proteins is necessary for successful replication, and cleavage of cellular targets by the viral protease also may contribute to the pathogenesis, but knowledge about the human proteins that are processed by the main protease (3CLpro) of SARS-CoV-2 is still limited. We tested the prediction potentials of two different in silico methods for the identification of SARS-CoV-2 3CLpro cleavage sites in human proteins. Short stretches of homologous host-pathogen protein sequences (SSHHPS) that are present in SARS-CoV-2 polyprotein and human proteins were identified using BLAST analysis, and the NetCorona 1.0 webserver was used to successfully predict cleavage sites, although this method was primarily developed for SARS-CoV. Human C-terminal-binding protein 1 (CTBP1) was found to be cleaved in vitro by SARS-CoV-2 3CLpro, the existence of the cleavage site was proved experimentally by using a His₆-MBP-mEYFP recombinant substrate containing the predicted target sequence. Our results highlight both potentials and limitations of the tested algorithms. The identification of candidate host substrates of 3CLpro may help better develop an understanding of the molecular mechanisms behind the replication and pathogenesis of SARS-CoV-2.     

Design structure matrix (DSM) method application to issue of modeling and analyzing the fault tree of a wind energy asset

The perpetual energy production of a wind farm could be accomplished (under proper weather conditions) if no failures occurred. But even the best possible design, manufacturing, and maintenance of a system cannot eliminate the failure possibility. In order to understand and minimize the system failures, the most crucial components of the wind turbines, which are prone to failures, should be identified. Moreover, it is essential to determine and classify the criticality of the system failures according to the impact of these failure events on wind turbine safety. The present study is processing the failure data from a wind farm and uses the Fault Tree Analysis as a baseline for applying the Design Structure Matrix technique to reveal the failure and risk interactions between wind turbine subsystems. Based on the analysis performed and by introducing new importance measures, the “readiness to fail” of a subsystem in conjunction with the “failure riskiness” can determine the “failure criticality.” The value of the failure criticality can define the frame within which interventions could be done. The arising interventions could be applied either to the whole system or could be focused in specified pairs of wind turbine subsystems. In conclusion, the method analyzed in the present research can be effectively applied by the wind turbine manufacturers and the wind farm operators as an operation framework, which can lead to a limited (as possible) design‐out maintenance cost, failures' minimization, and safety maximization for the whole wind turbine system.     

Automated classification of simulated wind field patterns from multiphysics ensemble forecasts

In this study, we have proposed an automated classification approach to identify meaningful patterns in wind field data. Utilizing an extensive simulated wind database, we have demonstrated that the proposed approach can identify low‐level jets, near‐uniform profiles, and other patterns in a reliable manner. We have studied the dependence of these wind profile patterns on locations (eg, offshore vs onshore), seasons, and diurnal cycles. Furthermore, we have found that the probability distributions of some of the patterns depend on the underlying planetary boundary layer schemes in a significant way. The future potential of the proposed approach in wind resource assessment and, more generally, in mesoscale model parameterization improvement is touched upon in this paper.     

A review on China's wind power accommodation in the background of “Internet+” strategy

The consumption of fossil fuel has resulted in global warming, environmental pollution, and many other crucial problems. Replacing fossil fuel with renewable energy has become an important issue over the recent decades. As a renewable clean energy, wind power is a relatively well‐developed and promising energy method for current technology development in China. Under the background of growing demand for electricity and enhancing awareness for environmental, the “Internet+ wind power” concept has emerged based on both the wind power's characteristics that renewable and non‐polluting, and the rapid development of the Internet in China. Through querying an amount of literature and information, this paper reveals the resource endowment and policy environment about wind power and energy Internet at first. Then, the PEST‐SWOT strategy analysis model is used to analyze the internalities (strengths and weaknesses) and the externalities (opportunities and threats) of “Internet+ wind power”. According to these results, the paper puts forward some measures (development and utilization, business mode) for wind power accommodation. Then some policy recommendations have been proposed. The government should provide favorable conditions for wind power grid with the “Internet+” technology innovation.     

海洋工程和船舶焊接变形预测方法研究

目前,影响海洋工程和船舶焊接结构质量的重要影响因素之一就是焊接变形,其产生的根本原因就是在焊接过程中因结构体受热不均、热梯度、残余应力等。基于此,本文对焊接变形预测的理论发展进程及其相关理论内涵进行详细阐述,并对基于神经网络的新型焊接变形预测理论的研究发展简单概述。     

Optimization of three-dimensional boiling enhancement structure at evaporation surface for high power light emitting diode

A theoretical model of phase change heat sink was established in terms of thermal resistance network. The influence of different parameters on the thermal resistance was analyzed and the crucial impact factors were determined. Subsequently, the forming methods including ploughing–extrusion and stamping method of boiling enhancement structure at evaporation surface were investigated, upon which three-dimensional microgroove structure was fabricated to improve the efficiency of evaporation. Moreover, the crucial parameters related to the fabrication of miniaturized phase change heat sink were optimized. The heat transfer performance of the heat sink was tested. Results show that the developed phase change heat sink has excellent heat transfer performance and is suitable for high power LED applications.     

热轧除鳞系统节能方案

针对热轧除鳞系统普遍存在的能源浪费严重的问题,设计了基于预测模型的变频节能方案,通过板坯位置预测、除鳞时间预测、除鳞水量预测和除鳞泵转速控制,合理利用轧制间歇时间使水泵电机变频降速,模拟计算结果表明,可使除鳞电耗降低23.57%。     

风速时间序列混沌判定方法比较研究

混沌识别是对非线性时间序列进行混沌预测的前提。针对时间序列风速确定性与随机性相结合的复杂非线性特征,研究了不同的混沌识别方法,并对风速时间序列进行混沌特征识别。应用随机噪声、周期运动及经典混沌系统的时间序列对所选方法进行可靠性验证。对美国国家风能研究中心M2测风塔实测时间序列风速数据进行非线性混沌特征识别。结果表明:风速时间序列具有明显的混沌特征;各风速时间序列表现出不同程度的混沌特征;各混沌识别方法对风速时间序列混沌特征的表达形式不同,互为补充,相互验证。     

A PV installation framework concerning electricity variable rates

This paper assesses building integrated photovoltaic (BIPV) installation parameters based on the profit generated by a photovoltaic system. It takes into consideration a home building case study and it investigates its monthly energy demand based on a specific location and a typical occupancy. The capability of a photovoltaic (PV) system to generate more profit occurs when solar intensity is maximum while the electric energy price is at its highest rate. The paper traces a framework that encompasses different aspects such as energy demand, energy price, and solar intensity. This framework identifies profit alternatives according to different installation parameters. A tool that predicts a PV installation hourly electric energy production is developed. The profit generated is simulated for home buildings located in Beirut (Lebanon) and Xihua (China), both at 33.8° latitude north. The paper highlights a new approach for BIPV installations, taking into account weather conditions, energy demand, and electric energy utility rates.