复相陶瓷导电性与其粒径级配的关系研究

通过对无序渗流模型的修正，建立了具有不同特征尺寸的两相无序体系的导电模型，推导了相关关系式，将其结果与相复合导电陶瓷的实验结果进行了比较，结论相当一致。由此揭示了陶瓷生料的两相粒径分布对复合导电陶瓷导电性的影响。     

再论变化电磁场中带电超微粒子的精确量子论

利用创新的SU(1 ,1 )Lie代数理论及SO(3)Lie代数理论 ,得到变化电磁场中带电超微粒子的精确量子论 ,由此进一步明确微观领域中的时空概念     

NUMERICAL SIMULATION OF PARTICLE SEPARATION IN AN OIL-SAND SEPARATOR

ＮＯＴＡＴＩＯＮＳＡ　Ａｒｅａｏｆｉｎｌｅｔ ,ｍ2ａ　Ｃｅｎｔｒｉｆｕｇａｌａｃｃｅｌｅｒａｔｉｏｎ ,ｍ2 /ｓｉ　Ｇｒｉｄｏｆａｘｉａｌｄｉｒｅｃｔｉｏｎｍｌ 　ＭａｘｉｍｕｍｇｒｉｄｎｕｍｂｅｒｏｆｒａｄｉａｌｄｉｒｅｃｔｉｏｎＱ　Ｆｌｕｉｄｍａｓｓｒａｔｅ ,ｍ3/ｓＲｃ 　Ｍａｘｉｍｕｍｒａｄｉｕｓｏｆｃｙｃｌｏｎｅ ,ｍＲｅ　Ｒｅｙｎｏｌｄｓｎｕｍｂｅｒｒ　Ｄｉｍｅｎｓｉｏｎｌｅｓｓｒａｄｉａｌｄｉｓｔａｎｃｅｔ　Ｄｉｍｅｎｓｉｏｎｌｅｓｓｔｉｍｅｕ　Ｄｉｍｅｎｓｉｏｎｌｅｓｓａｘｉａｌｖｅｌｏｃ…     

钻井岩屑粒径分布规律的研究

钻井振动筛是固控系统的重要分离设备,其主要任务是清除钻井液中直径大于74μm的固相颗粒。对辽河油田7口井深0～3500m的钻井岩屑进行了实地取样,分析了岩样颗粒直径大于74μm的粒径分布特征。分析结果表明,钻井岩屑具有明显的片状特征,颗粒的粒径分布主要与地层岩性和破岩工具有关,随着井深的变化,其岩样粒径分布呈现出幂分布、2分布和瑞利分布等3组典型规律。给出了岩屑颗粒的概率直径;建议在筛面颗粒运动特性研究、筛网工作载荷的计算和激振方式的选择中,应考虑岩屑颗粒片状特性的影响。     

MCS接枝共聚物的结构和形态

将甲基丙烯酸甲酯（ＭＭＡ）、苯乙烯（Ｓ）在氯化聚乙烯（ＣＰＥ）存在下进行悬浮接枝共聚，获得ＭＣＳ树脂。本文着重考察了ＭＣＳ树脂的化学结构及胶粒形态。结果表明，确实发生了接枝共聚。ＣＰＥ用量、ＲＳＨ含量、引发剂浓度、反应温度、溶胀时间及聚合转化率对ＭＣＳ树脂的接枝率、接枝效率均有影响。ＭＣＳ树脂是接枝共聚物、ＭＭＡ／Ｓ共聚物与ＭＭＡ均聚物的混合物，橡胶相ＣＰＥ在ＭＣＳ树脂中的分散情况为“海岛结构”，     

Binary particle swarm optimization with multiple evolutionary strategies

This paper introduces a novel variation of binary particle swarm optimization(BPSO) algorithm and a further extension to improve its performance.Firstly,mimicking the behaviors of some creatures group,multiple evolutionary strategies BPSO(MBPSO) is introduced which takes different evolutionary strategies for various particles according to their performances.Then,on the basis of MBPSO,a new strategy is discussed to improve the performance of the MBPSO(M2BPSO) which adopts the concept of the mutation operator aiming to overcome the premature convergence and slow convergent speed during the later stages of the optimization.The proposed two algorithms are tested on seven benchmark functions and their results are compared with those obtained by other methods.Experimental results show that our methods outperform the other algorithms.     

基于Vega粒子系统的自由大气分子运动虚拟仿真

在军事训练中,三维仿真的战场环境发挥了极为重要的作用。本文将对战场环境中的自由大气进行虚拟仿真。首先分析自由大气中运动分子的性质,详细介绍在模拟不规则自然现象中运用最多的粒子系统技术,最后在Vega中运用粒子系统对大气分子的运动进行模拟仿真。仿真结果能基本模拟出大气分子的运动特性,且可以通过接口编程访问粒子的属性,从而能在无人机等航迹规划的危险天气现象的规避中加以运用。     

LPNet: A DNN based latency prediction technique for application mapping in Network-on-Chip design

Analytical models used for latency estimation of Network-on-Chip (NoC) are not producing reliable accuracy. This makes these analytical models difficult to use in optimization of design space exploration. In this paper, we propose a learning based model using deep neural network (DNN) for latency predictions. Input features for DNN model are collected from analytical model as well as from Booksim simulator. Then this DNN model has been adopted in mapping optimization loop for predicting the best mapping of given application and NoC parameters combination. Our simulations show that using the proposed DNN model, prediction error is less than 12% for both synthetic and application specific traffic. More than 108 times speedup could be achieved using DPSO with DNN model compared to DPSO using Booksim simulator.     

Turbulent Details Simulation for SPH Fluids via Vorticity Refinement

A major issue in smoothed particle hydrodynamics (SPH) approaches is the numerical dissipation during the projection process, especially under coarse discretizations. High‐frequency details, such as turbulence and vortices, are smoothed out, leading to unrealistic results. To address this issue, we introduce a vorticity refinement (VR) solver for SPH fluids with negligible computational overhead. In this method, the numerical dissipation of the vorticity field is recovered by the difference between the theoretical and the actual vorticity, so as to enhance turbulence details. Instead of solving the Biot‐Savart integrals, a stream function, which is easier and more efficient to solve, is used to relate the vorticity field to the velocity field. We obtain turbulence effects of different intensity levels by changing an adjustable parameter. Since the vorticity field is enhanced according to the curl field, our method can not only amplify existing vortices, but also capture additional turbulence. Our VR solver is straightforward to implement and can be easily integrated into existing SPH methods.     

A new time invariant fuzzy time series forecasting method based on particle swarm optimization

In the analysis of time invariant fuzzy time series, fuzzy logic group relationships tables have been generally preferred for determination of fuzzy logic relationships. The reason of this is that it is not need to perform complex matrix operations when these tables are used. On the other hand, when fuzzy logic group relationships tables are exploited, membership values of fuzzy sets are ignored. Thus, in defiance of fuzzy set theory, fuzzy sets’ elements with the highest membership value are only considered. This situation causes information loss and decrease in the explanation power of the model. To deal with these problems, a novel time invariant fuzzy time series forecasting approach is proposed in this study. In the proposed method, membership values in the fuzzy relationship matrix are computed by using particle swarm optimization technique. The method suggested in this study is the first method proposed in the literature in which particle swarm optimization algorithm is used to determine fuzzy relations. In addition, in order to increase forecasting accuracy and make the proposed approach more systematic, the fuzzy c-means clustering method is used for fuzzification of time series in the proposed method. The proposed method is applied to well-known time series to show the forecasting performance of the method. These time series are also analyzed by using some other forecasting methods available in the literature. Then, the results obtained from the proposed method are compared to those produced by the other methods. It is observed that the proposed method gives the most accurate forecasts.