Neural computing models for prediction of permeability coefficient of coarse-grained soils

Correlations are very significant from the earliest days; in some cases, it is essential as it is difficult to measure the amount directly, and in other cases it is desirable to ascertain the results with other tests through correlations. Soft computing techniques are now being used as alternate statistical tool, and new techniques such as artificial neural networks, fuzzy inference systems, genetic algorithms, and their hybrids were employed for developing the predictive models to estimate the needed parameters, in the recent years. Determination of permeability coefficient (k) of soils is very important for the definition of hydraulic conductivity and is difficult, expensive, time-consuming, and involves destructive tests. In this paper, use of some soft computing techniques such as ANNs (MLP, RBF, etc.) and ANFIS (adaptive neuro-fuzzy inference system) for prediction of permeability of coarse-grained soils was described and compared. As a result of this paper, it was obtained that the all constructed soft computing models exhibited high performance for predicting k. In order to predict the permeability coefficient, ANN models having three inputs, one output were applied successfully and exhibited reliable predictions. However, all four different algorithms of ANN have almost the same prediction capability, and accuracy of MLP was relatively higher than RBF models. The ANFIS model for prediction of permeability coefficient revealed the most reliable prediction when compared with the ANN models, and the use of soft computing techniques will provide new approaches and methodologies in prediction of some parameters in soil mechanics.     

A method for real power transfer allocation using multivariable regression analysis

A multivariable regression (MVR) approach is proposed to identify the real power transfer between generators and loads. Based on solved load flow results, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then, the results of MNE method and load flow information are utilized to determine suitable regression coefficients using MVR model to estimate the power transfer. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of the MVR output compared to that of the MNE method. The error of the estimate of MVR method ranges from 0.001 4 to 0.007 9. Furthermore, when compared to MNE method, MVR method computes generator contribution to loads within 26.40 ms whereas the MNE method takes 360 ms for the calculation of same real power transfer allocation. Therefore, MVR method is more suitable for real time power transfer allocation.     

The effect of heat treatment on high temperature mechanical properties of microalloyed medium carbon steel

In the present work, high temperature tensile properties and abrasive wear performance of a microalloyed medium carbon steel has been examined. Tensile and abrasive wear testing were carried out on as-received and heat treated specimens. The research has shown that microalloyed medium carbon steel was susceptible to dynamic strain ageing due to interaction of mobile dislocations and solid atoms, such as carbon and/or nitrogen. The interaction between dislocations and solid atoms at 200–300 °C changes the work hardening rate and contributes to dynamic strain ageing. These interactions also increased abrasive wear resistance of the microalloyed medium carbon steel at 300 °C. Therefore, the inference can be drawn that dynamic strain ageing caused an improvement on abrasion resistance.     

Corrosion behavior of carbon steel rock bolt in simulated Yucca Mountain ground waters

Medium carbon steel (AISI 1040) was one of the candidate materials for rock bolts to reinforce the borehole liners and emplacement drifts of the high-level nuclear waste repository in Yucca Mountain. The corrosion performance of this structural steel was investigated by techniques such as linear polarization, electrochemical impedance spectroscopy (EIS), and laboratory immersion tests in simulated ground waters. The corrosion rates of the steel were measured for the temperatures in the range from 25 °C to 85 °C, for the ionic concentrations of 1 time (1×), 10 times (10×), and a hundred times (100×) ground water concentration. The steel corroded uniformly at penetration rates of 35 to 200 μm/year in the deaerated waters, and at 200 to 1000 μm/year in the aerated waters. Increasing temperatures in the deaerated waters increased the corrosion rate of the steel. However, increasing ionic concentrations influenced the corrosion rate only slightly. In the aerated 1×and 10×waters, increasing temperatures increased the rates of the steel significantly. In the aerated 100×waters, the corrosion rate increased from 25 °C to 45 °C and decreased at higher temperatures (65 °C and up) due to the formation of oxide/hydroxide films and salt scales on the surface of the steel specimen. The steel suffered pitting corrosion in the both deaerated and aerated hot ground water environments after anodic polarization. This article is based on a presentation made in the symposium “Effect of Processing on Materials Properties for Nuclear Waste Disposition,” November 10–11, 2003, at the TMS Fall meeting in Chicago, Illinois, under the joint auspices of the. TMS Corrosion and Environmental Effects and Nuclear Materials Committees.     

A knowledge-based decision support system for the management of parts and tools in FMS

Flexible manufacturing systems (FMS) are very complex systems with large part, tool, and information flows. The aim of this work is to develop a knowledge-based decision support system (KBDSS) for short-term scheduling in FMS strongly influenced by the tool management concept to provide a significant operational control tool for a wide range of machining cells, where a high level of flexibility is demanded, with benefits of more efficient cell utilization, greater tool flow control, and a dependable way of rapidly adjusting short-term production requirements. Development of a knowledge-based system to support the decision making process is justified by the inability of decision makers to diagnose efficiently many of the malfunctions that arise at machine, cell, and entire system levels during manufacturing. In this context, this paper proposes three knowledge-based models to ease the decision making process: an expert production scheduling system, a knowledge-based tool management decision support systems, and a tool management fault diagnosis system. The entire system has been created in a hierarchical manner and comprises more than 400 rules. The expert system (ES) was implemented in a commercial expert system shell, Knowledge Engineering System (KES) Production System (PS).     

A Conjecture on Wiener Indices in Combinatorial Chemistry

Drugs and other chemical compounds are often modeled as polygonal shapes, where each vertex represents an atom of the molecule, and covalent bonds between atoms are represented by edges between the corresponding vertices. This polygonal shape derived from a chemical compound is often called its molecular graph, and can be a path, a tree, or in general a graph. An indicator defined over this molecular graph, the Wiener index, has been shown to be strongly correlated to various chemical properties of the compound. The Wiener index conjecture for trees states that for any integer n (except for a finite set), one can find a tree with Wiener index n. This conjecture has been open for quite some time, and many authors have presented incremental progress on this problem. In this paper we present further progress towards proving this conjecture—through the design of efficient algorithms, we show that enumerating all possible trees to verify this conjecture (as done by all the previous approaches) is not necessary, but instead searching in a small special family of trees suffices, thus achieving the first polynomial (in n) time algorithm to verify the conjecture up to integer n. More precisely, we (i) present an infinite family of trees and prove various properties of these trees, (ii) show that a large number of integers, up to at least 10⁸ (compared with the previous best 10⁴) are representable as Wiener indices of trees in this succinct family, (iii) provide several efficient algorithms for computing trees with given Wiener indices, and (iv) implement our algorithms and experimentally show that their performance is asymptotically much better than their theoretical worst-case upper bound.     

Hypergraph Models and Algorithms for Data-Pattern-Based Clustering

In traditional approaches for clustering market basket type data, relations among transactions are modeled according to the items occurring in these transactions. However, an individual item might induce different relations in different contexts. Since such contexts might be captured by interesting patterns in the overall data, we represent each transaction as a set of patterns through modifying the conventional pattern semantics. By clustering the patterns in the dataset, we infer a clustering of the transactions represented this way. For this, we propose a novel hypergraph model to represent the relations among the patterns. Instead of a local measure that depends only on common items among patterns, we propose a global measure that is based on the cooccurences of these patterns in the overall data. The success of existing hypergraph partitioning based algorithms in other domains depends on sparsity of the hypergraph and explicit objective metrics. For this, we propose a two-phase clustering approach for the above hypergraph, which is expected to be dense. In the first phase, the vertices of the hypergraph are merged in a multilevel algorithm to obtain large number of high quality clusters. Here, we propose new quality metrics for merging decisions in hypergraph clustering specifically for this domain. In order to enable the use of existing metrics in the second phase, we introduce a vertex-to-cluster affinity concept to devise a method for constructing a sparse hypergraph based on the obtained clustering. The experiments we have performed show the effectiveness of the proposed framework.     

Power Flattening in ARIES-RS Fusion Breeder Reactor Using Mixed Fuels

This study presents the possibility of the power flattening in the ARIES-RS breeder reactor using mixed (Th,U)C or (Th,U)N fuels. Two different types of mixing, namely, homogeneous mixing (HM) and linear mixing (LM) were used to investigate the uniformity of fission power distribution through the fuel zone. In HM, fraction of uranium content were kept constant in all rows of the fuel zone whereas, in LM the fraction of the uranium were linearly increased from the first to last fuel row in the fuel zone. Neutron transport calculations were performed with the aid of the SCALE4.3 system by solving the Boltzmann transport equation with the XSDRNPM code in 238 neutron groups and a S₈–P₃ approximation. Flat fission power distribution was maintained successfully for the blanket using linearly mixed fuels. However, the fission density profile was not uniform in the blanket with homogeneously mixed fuels. It decreased exponentially form the 1st to 10th fuel row.     

Recent developments in catalyst synthesis using DBD plasma for reforming applications

The catalyst has a significant role in gas processing applications such as reforming technologies for H₂ and syngas production. The stable catalyst is requisite for any industrial catalysis application to make it commercially viable. Several methods are employed to synthesize the catalysts. However, there is still a challenge to achieve a controlled morphology and pure catalyst which majorly influences the catalytic activity in reforming applications. The conventional methods are expansive, and the removal of the impurities are major challenges. Nevertheless, it is not straightforward to achieve the desired structure and stability. Therefore, significant interest has been developed on the advanced techniques to take control of the physicochemical properties of the catalyst through non-thermal plasma (NTP) techniques. In this review, the systematic evolution of the catalyst synthesis using NTP technique is elucidated. The emerging DBD plasma to synthesized and effective surface treatment is reviewed. DBD plasma synthesized catalyst performance in reforming application for H₂ and syngas production is summarised. Furthermore, the status of DBD plasma for catalyst synthesis and proposed future avenues to design environmentally suitable and cost-effective synthesis techniques are discussed.     

Evaluation of damage to light structures erected on a fill material rich in expansive soil

The paper reports a study of the cause of defects in light structures and the toppling of a wall constructed on a fill material rich in Ankara clay. Laboratory tests were carried out on vertical and horizontal samples from boreholes and a trial pit was excavated near the damaged structures. The results showed that in the vicinity of the toppled wall, swelling pressures in the horizontal direction were greater than those measured in the vertical direction. The swelling properties of the fill material were higher than those of original Ankara clay as determined previously by other investigators, suggesting that breakdown of the cementing bonds and a change in the fabric are the main factors affecting the swelling pressure of disturbed and compacted expansive soils. The calculations to predict uplift showed a good agreement with the observations in the damaged structures. It is concluded that swelling was the main cause of the damage to the light structures at the study site and resulted from the highly expansive nature of the fill material, poor drainage, the semi-arid climate, poor construction methods and ineffective precautions. Some recommendations for minimizing the effects of swelling at the study site are briefly outlined.