Oxidation behavior of copper at high temperatures under two different modes of direct-current applications

Oxidation kinetics of copper in the temperature range of 973–1173 K atP _{O 2}=21.27 kPa exhibit enhancement and deceleration in the rates with changing polarity compared to normal oxidation under interrupted mode of directcurrent application. These conditions are achieved by connecting the oxidizing copper covered with an initially formed thin oxide film to the positive and negative terminal of a dc source, respectively. However, the influence of direction of the current is found to be opposite under uninterrupted mode of impressed current flow in the same temperature range. The effect of short-circuiting the metal to the outer oxide/air interface on the reaction kinetics is also reported. The rate of oxide-scale growth under normal condition, and two different modes of current applications as well as with shorting circuitry attachment conform to the parabolic growth law. The results pertaining to the two different modes of impressed current have been discussed considering both the phenomena of electrolysis of the oxide electrolyte and the polarization at the two phase boundaries. The enhancement and the reduction in rates under uninterrupted impressed current conditions are explained on the basis of increased and decreased average defect concentrations, respectively, within the oxide layer. The acceleration and deceleration in the rates under interrupted mode of current flow have been explained in the light of sustenance of a steeper and flatter electrochemical-potential gradient of defects, respectively, across the growing-oxide layer. The possible different responses of the metal/oxide and oxide/air interfaces to the impressed current brought into play by two different modes of current application, have enabled to display a better insight on the mechanistic aspects of scale growth under the influence of an externally applied current.     

Synergistic influence of alloy grain size and Si content on the oxidation behavior of 9Cr−1Mo steel

The synergistic influence of prior-austenite grain size and silicon content of 9Cr–1Mo steel on the resistance to scale spallation has been studied in air at 773 K (for 500 hr) and 973 K (12 hr). Two steels, irrespective of their grain size and Si content, did not show spallation during oxidation at 773 K. Spallation occurred at 973 K, and fine-grain steels exhibited less spallation resistance than coarse-grain ones (in low-as well as high-Si steels). Among the four possible combinations of grain size ans Si content, the steel with low Si and fine grains showed least resistance to spallation, while the steel with high Si and coarse grains showed the best resistance. Spallation was found to initiate in the areas adjoining the oxide ridges formed at the alloy grain boundaries. Oxide scales at the ridges and within the grains were analyzed by scanning electron microscopy (SEM/EDX) and secondary-ion mass spectrometry (SIMS). These analyses suggest depletion of silicon from the areas adjoining grain boundaries, resulting in thicker scaling that triggers spallation in such areas. For similar grain-size materials, the necessary thickness for spallation was attained earlier with low-Si steel rather than in high-Si steel.     

Influence of a mixed Al2O3+Cr2O3 superficial coating on the non-isothermal oxidation behavior of Fe and Fe-Cr alloys

The non-isothermal oxidation behavior of electrolytic-grade iron and Fe-Cr alloys in dry air has been studied using linear heating rates of 6 K/min, 10 K/ min, and 15 K/min up to a final temperature of 1273–1473 K. Some of the iron and iron-chromium alloy samples were given a surface treatment by dipping them in an aqueous solution containing both Cr and Al ions before their oxidation studies. This pretreatment has resulted in improved oxidation resistance and scale adherence as depicted by no scale rupture even after a second thermal cycle. Mass changes were recorded gravimetrically, and scales have been characterized by SEM, EPMA, and x-ray diffraction analyses.     

SWGMM: a semi-wrapped Gaussian mixture model for clustering of circular–linear data

Finite mixture models are widely used to perform model-based clustering of multivariate data sets. Most of the existing mixture models work with linear data; whereas, real-life applications may involve multivariate data having both circular and linear characteristics. No existing mixture models can accommodate such correlated circular–linear data. In this paper, we consider designing a mixture model for multivariate data having one circular variable. In order to construct a circular–linear joint distribution with proper inclusion of correlation terms, we use the semi-wrapped Gaussian distribution. Further, we construct a mixture model (termed SWGMM) of such joint distributions. This mixture model is capable of approximating the distribution of multi-modal circular–linear data. An unsupervised learning of the mixture parameters is proposed based on expectation maximization method. Clustering is performed using maximum a posteriori criterion. To evaluate the performance of SWGMM, we choose the task of color image segmentation in LCH space. We present comprehensive results and compare SWGMM with existing methods. Our study reveals that the proposed mixture model outperforms the other methods in most cases.     

A Two-Stage Approach for Network Monitoring

A goal of network tomography is to infer the status (e.g. delay) of congested links internal to a network, through end-to-end measurements at boundary nodes (end-hosts) via insertion of probe signals. Because (a) probing constitutes traffic overhead, and (b) in any typical scenario, the number of congested links is a small fraction of the total number in the network, a desirable design objective is to identify those (few) congested links using a minimum number of probes. In this paper, we make a contribution to solving this problem, by proposing a new two-stage approach for this problem. First, we develop a binary observation model linking end-to-end observations with individual link statuses and derive necessary and sufficient conditions for whether at least one link in the network is congested. Stage I of the proposed method shows that achieving 1-identifiability with a minimum number of probes is equivalent to the familiar minimum set covering problem that can be efficiently solved via a greedy heuristic. A sequential algorithm is described, leading to a significantly lowered computational complexity vis-a-vis a batch algorithm. Next, a binary splitting algorithm originally developed in group testing is used to identify the location of the congested links. The proposed scheme is evaluated by simulations in OPNET and experiments on the PlanetLab testbed to validate the advantages of our 2-stage approach vis-a-vis a conventional (batch) algorithm.     

On preconditioning for a parallel solution of the Richards equation

In this paper, we present a class of preconditioning methods for a parallel solution of the three-dimensional Richards equation. The preconditioning methods Jacobi scaling, block-Jacobi, incomplete lower–upper, incomplete Cholesky and algebraic multigrid were applied in combination with a parallel conjugate gradient solver and tested for robustness and convergence using two model scenarios. The first scenario was an infiltration into initially dry, sandy soil discretised in 500,000 nodes. The second scenario comprised spatially distributed soil properties using 275,706 numerical nodes and atmospheric boundary conditions. Computational results showed a high efficiency of the nonlinear parallel solution procedure for both scenarios using up to 64 processors. Using 32 processors for the first scenario reduced the wall clock time to slightly more than 1% of the single processor run. For scenario 2 the use of 64 processors reduces the wall clock time to slightly more than 20% of the 8 processors wall clock time. The difference in the efficiency of the various preconditioning methods is moderate but not negligible. The use of the multigrid preconditioning algorithm is recommended, since on average it performed best for both scenarios.     

Automatic Construction and Verification of Isotopy Invariants

We extend our previous study of the automatic construction of isomorphic classification theorems for algebraic domains by considering the isotopy equivalence relation. Isotopism is an important generalisation of isomorphism, and is studied by mathematicians in domains such as loop theory. This extension was not straightforward, and we had to solve two major technical problems, namely, generating and verifying isotopy invariants. Concentrating on the domain of loop theory, we have developed three novel techniques for generating isotopic invariants, by using the notion of universal identities and by using constructions based on subblocks. In addition, given the complexity of the theorems that verify that a conjunction of the invariants form an isotopy class, we have developed ways of simplifying the problem of proving these theorems. Our techniques employ an interplay of computer algebra, model generation, theorem proving, and satisfiability-solving methods. To demonstrate the power of the approach, we generate isotopic classification theorems for loops of size 6 and 7, which extend the previously known enumeration results. This work was previously beyond the capabilities of automated reasoning techniques. The author’s work was supported by EPSRC MathFIT grant GR/S31099.