基于ABAQUS的显式动力学分析方法研究

显式动力学(The Explicit Dynamic)是针对隐式求解器的一个补充,其分析方法对于求解广泛、各类非线性结构力学问题是一个非常有效的工具,显式方法中的单个增量步取决于模型的最高固有频率,与持续时间、载荷类型无关。ABAQUS/Explicit主要用于碰撞、接触以及失效分析。探讨了ABAQUS显式动力学的基本理论、求解的问题,对显示动力学分析方法中的求解算法、稳定时间极限和能量平衡问题进行研究,给出了显示动力学分析的一般性分析方法。通过案例验证了该方法的可行性,应用该方法可有效解决实际工业生产中的碰撞问题、复杂的接触问题以及复杂的后屈曲问题,可为显式动力学分析方法应用于工程实践奠定理论与方法基础。     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

Decay Rate Kinetics of Ozone Gas in Rice Grains

The present study was carried out to evaluate the effects of ozone on rice grains for the following three conditions: saturation time, decay rate, and half-life of ozone. Experiments were performed in different bed thicknesses (5 and 10 cm) and moisture content (11.4 and 14.2% wb) at atmospheric conditions. The lowest saturation time of ozone was 119 min, with the concentration of 516 ppm for rice grains ozonated at 5-cm bed thickness with 11.4% (wb) moisture content. The decay rate kinetics of ozone obtained were consistent with a first-order model. Regarding the half-life of ozone, the lowest value obtained was 6.78 min for rice grains ozonated at 10-cm bed thickness with 14.2% (wb) moisture content.     

Estimation of power density in IMPATT using different materials

ABSTRACT

The RF output power dissipated per unit area is calculated using Runge-Kutta method for the high-moderate-moderate-high (n⁺-n-p-p⁺) doping profile of double drift region (DDR)-based impact avalanche transit time (IMPATT) diode by taking different substrate at Ka band. Those substrates are silicon, gallium arsenide, germanium, wurtzite gallium nitride, indium phosphide and 4H-silicon carbide. A comparative study regarding power dissipation ability by the IMPATT using different material is being presented thereby modelling the DDR IMPATT diode in a one-dimensional structure. The IMPATT based on 4H-SiC element has highest power density in the order of 10¹⁰ Wm^?2 and the Si-based counterpart has lowest power density of order 10⁶ Wm^?2 throughout the Ka band. So, 4H-SiC-based IMPATT should be preferable over others for the power density preference based application. This result will be helpful to estimate the power density of the IMPATT for any doping profile and to select the proper element for the optimum design of the IMPATT as far as power density is concerned in the Ka band. Also, we have focused on variation of power density with different junction temperatures and modelled the heat sink with analysis of thermal resistances.     

CMOS缓冲器的时延估算模型

随着集成电路生产工艺的进展，互连线在集成电路设计中的影响越来越大。为了减小互连线的影响，通常在芯片互连中插入缓冲器，但这样做会增加时延。因此，为了精确地对系统进行时延估计，必须对缓冲器的时延进行估算。基于Sakurai的器件模型，提出了一种新的缓冲器时延估算模型。     

Evaluation of Monkman-Grant parameters for type 316LN and modified 9Cr-Mo stainless steels

The Monkman-Grant (M-G) and its modified parameters were evaluated for type 316LN and modified 9Cr-Mo stainless steels prepared with minor element variations. Several sets of creep data for the two alloy systems were obtained by constant-load creep tests in 550-650°C temperature range. The M-G parameters,m, m’,C, andC’ were proposed and discussed for the two alloy systems. Them value of the M-G relation was 0.90 in type 316LN steel and 0.84 in modified 9Cr-Mo steel. Them’, value of the modified relation was 0.94 in type 316LN steel and 0.89 in 9Cr-Mo steel. Although creep fracture modes and creep properties between type 316LN and modified 9Cr-Mo steels showed a basic difference, the M-G and its modified relations demonstrated linearity quite well. Them’ of modified relation almost overlapped regardless of the creep testing conditions and chemical variations in the two alloy systems, and the parameterm’ was closer to unity than that of the M-G relation.     

双时基频率校准电路

提供精确的时间基准是精密仪器和高精度设备计时和工作的关键技术之一。文中采用两套晶振电路，通过分频产生高频和低频两个频率信号，采用编码的方法对两个频率信号在每一个时钟周期内进行动态校核，只有两个晶振的频率在允许的精度范围内，或同时增大（缩小）相同的倍数，校核后频率信号才是正确的，且能给系统提供精确的时间基准。该方法能有效防止系统的时钟周期因意外的原因伸长或缩短。整个电路可以作为精密仪器或高精度设备的一个模块电路。     

A versatile data acquisition system for time resolved X-ray scattering using gas proportional detectors with delay line readout

A data acquisition system for time resolved X-ray scattering experiments using linear, quadrant or area gas proportional detectors with delay line readout based on commercially available hardware (National Instruments) is described. The system can easily be configured for recording data from point detectors (e.g. photomultipliers and photodiodes) and/or ancillary data only. Applications involving measurements with two different types of time to digital converters illustrate the features and performances of the system.     

A time series bootstrap procedure for interpolation intervals

A sieve bootstrap procedure for constructing interpolation intervals for a general class of linear processes is proposed. This sieve bootstrap provides consistent estimators of the conditional distribution of the missing values, given the observed data. A Monte Carlo experiment is used to show the finite sample properties of the sieve bootstrap and finally, the performance of the proposed method is illustrated with a real data example.     

Cycle detection using a stack

We present an algorithm for detecting periodicity in sequences produced by repeated application of a given function. Our algorithm uses logarithmic memory with high probability, runs in linear time, and is guaranteed to stop within the second loop through the cycle. We also present a partitioning technique that offers a time/memory tradeoff. Our algorithm is especially well suited for sequences where the cycle length is typically small compared to the length of the acyclic prefix.