Using the high pressure methane isotherm for determination of pore size distribution of carbon adsorbents

A method of determining pore size distribution, PSD, of carbon adsorbents based on the high pressure methane isotherm is presented. A generic software package, and an IBM compatible PC, have been used to search for a PSD in the form of a histogram. The method relies on a known local isotherm, in this case, assuming a simplified model of infinite slit shaped carbon pores.Three carbons, having very different pore structures: BPL, PX-21, and PVDC, were analyzed using the new method and the results compared with those obtained from subcritical Ar, and N₂ isotherms. The analysis from the high pressure methane isotherm gave results which are different than those from the low pressure low temperature isotherms but not significantly enough to be unrealistic.     

Plasma lens experiment at the final focus test beam

The proposed plasma lens experiment at the Final focus Test Beam (FFTB) facility of the Stanford Linear Accelerator Center has been approved by the adminstration. The experiment would allow the examination of plasma focusing devices for particle beams in the parameter regime of interest to future high-energy colliders. It is expected to lead to compact plasma lens designs capable of focusing the beam to unprecedented small spot sizes.     

AN EXPERIMENTAL INVESTIGATION OF THE GROWTH OF SMALL CORNER FATIGUE CRACKS IN ALUMINIUM 6061-T651

Results of an experimental investigation of the fatigue growth of small corner cracks emanating from small flaws are presented. Growth-arrest behaviour was observed, and increases in crack length during growth periods were of the order of the transverse grain size. For the test material, the corner crack front intersects, on average, only three–six grains in the small crack regime monitored, so only a small number of constrained, interior grains is encountered. It is suggested that the presence of partially constrained surface grains may contribute to the 'anomalous' growth behaviour which has been observed by a number of investigators.
The crack growth histories of the test data presented exhibit considerable scatter. It is shown that a Student's t -test can be used to estimate confidence intervals in order to provide a measure of the observed scatter. The variation in confidence intervals in the transition from small to long fatigue crack growth is discussed.     

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.     

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.     

A new hybrid approach for dynamic continuous optimization problems

A new hybrid approach for dynamic optimization problems with continuous search spaces is presented. The proposed approach hybridizes efficient features of the particle swarm optimization in tracking dynamic changes with a new evolutionary procedure. In the proposed dynamic hybrid PSO (DHPSO) algorithm, the swarm size is varied in a self-regulatory manner. Inspired from the microbial life, the particles can reproduce infants and the old ones die. The infants are especially reproduced by high potential particles and located near the local optimum points, using the quadratic interpolation method. The algorithm is adapted to perform in continuous search spaces, utilizing continuous movement of the particles and using Euclidian norm to define the neighborhood in the reproduction procedure. The performance of the new proposed approach is tested against various benchmark problems and compared with those of some other heuristic optimization algorithms. In this regard, different types of dynamic environments including periodic, linear and random changes are taken with different performance metrics such as real-time error, offline performance and offline error. The results indicate a desirable better efficiency of the new algorithm over the existing ones.     

An enhanced,acoustic emission-based,single-fiber-composite test

In the single-fiber-composite (SFC) test, a fiber imbedded in a matrix is loaded in tension, resulting in a fragmentation of the fiber. In the conventional version of this test, the final fiber fragmentation length distribution is used with a micro-mechanical model to determine the average fiber/matrix interfacial shear stress. In the enhanced version of this test, one also determines the applied stress at each fiber fracture, and from this, one can evaluate the strength of the fiber at short gage lengths. In our measurement system, we utilize an acoustic emission (AE) technique to detect the fiber fractures and to locate the fiber breaks and so determine both the fiber failure stresses as well as the fiber fragmentation lengths while the test is in progress. Critical to the success of this test is a broadband AE system that utilizes point-like AE sensors, procedures for evaluatingin situ, the wavespeed of the first wave arrival and signal processing techniques for determining the arrival time of this signal as precisely as possible for a broad range of wave shapes. Here we describe the application of such an enhanced SFC test procedure to investigate the failure of a Nicalon fiber in an epoxy matrix.     

一种新的修正恒模算法

阐述了恒模算法(CMA)、修正恒模算法(MCMA)和判决引导(DD)算法的基本原理。针对CMA和MCMA收敛速度慢,固定步长条件下收敛速度和剩余误差之间存在矛盾的缺陷,在分析CMA误差特性的基础上,利用DD算法误差函数的模值和判决器输出构造新的误差函数,提出了一种新的变步长MCMA。新算法在加快收敛速度的同时保持小的剩余误差。仿真结果表明,新算法比CMA和MCMA收敛速度快,均衡输出剩余码间干扰(ISI)小而且能克服相位偏移,具有很好的实用性。     

基于群智能混合算法的物流配送路径研究

针对物流车辆路径优化问题,考虑到基本蚁群算法有收敛速度慢、易陷入局部最优的缺点,采用了一种双种群蚁群算法,在蚁群的基础上引入差分进化(DE)和粒子群算法(PSO)。通过在PSOAS种群和DEAS种群之间建立一种信息交流机制,使信息能够在两个种群中传递,以免某一方因错误的信息判断而陷入局部最优点。通过matlab仿真实验测试,表明该群智能混合算法可以较好地解决TSP的问题。     

Destabilising persistent coal froth using silicone oil

Persistent froth is becoming more and more common in coal and mineral flotation plants and presents safety and operational challenges. No effective method has been developed to destabilise persistent froth. As a new initiative, this study examined the structural difference between persistent foam and coal froth, based on which a solution was developed to maximumly destabilise coal froth. Destabilisation test, oscillatory rheology measurement and scanning electron microscopy (SEM) analysis indicated that the coal froth was more stable than the foam due to the formation of thin capillaries and tightly arranged coal particles on bubble surfaces. Although 107 µm silicone oil droplet could completely destabilise the foam at 2 mmol/L concentration, it only destabilised less than 50% coal froth even at 6 mmol/L concentration. To maximumly destabilise the coal froth formed by −38 and −20 µm particles, 24 and 18 µm silicone oil droplets were required to pass through the thin capillaries and enter the bubble films, respectively. However, smaller silicone oil droplets could not bridge the bubble films to destabilise coal froth and therefore a critical droplet size occurred depending on the size of particles stabilising the froth.