High-Tc dc-SQUID magnetometer and its application in eddy current non-destructive evaluation

We have fabricated and tested a high-T_c dc-SQUID magnetometer patterned on a YBCO thin film deposited on a SrTiO₃ bicrystal substrate. The measurement of noise spectrum at 77 K showed that the magnetometer has a white noise of 333 fT/Hz^1/2 without superconducting shield, corresponding to a flux sensitivity of 14.5 μΦ₀/Hz^1/2. We constructed a test system for eddy current non-destructive evaluation of conducting aluminum plates by using the magnetometer. Flaws over 10 mm below the surface can be clearly identified. By mapping the magnetic field distribution, we can locate the flaw position. The signals caused by flaw were expanded due to the use of double-D type driving coil with a wider central slit. The experimental results confirmed that this test system could operate in a magnetic unshielded environment.     

Application of autoregressive spectral analysis to missing data problems

Time series solutions for spectral analysis in missing data problems use reconstruction of the missing data, or a maximum likelihood approach that analyzes only the available measured data. Maximum likelihood estimation yields the most accurate spectra. An approximate maximum likelihood algorithm is presented that uses only previous observations falling in a finite interval to compute the likelihood, instead of all previous observations. The resulting nonlinear estimation algorithm requires no user-provided initial solution, is suited for order selection, and can give very accurate spectra even if less than 10% of the data remains.     

Finite sample properties of ARMA order selection

The cost of order selection is defined as the loss in model quality due to selection. It is the difference between the quality of the best of all available candidate models that have been estimated from a finite sample of N observations and the quality of the model that is actually selected. The order selection criterion itself has an influence on the cost because of the penalty factor for each additionally selected parameter. Also, the number of competitive candidate models for the selection is important. The number of candidates is, of itself, small for the nested and hierarchical autoregressive/moving average (ARMA) models. However, intentionally reducing the number of selection candidates can be beneficial in combined ARMA(p,q) models, where two separate model orders are involved: the AR order p and the MA order q. The selection cost can be diminished by creating a nested sequence of ARMA(r,r-1) models. Moreover, not evaluating every combination (p,q) of the orders considerably reduces the required computation time. The disadvantage may be that the true ARMA(p,q) model is no longer among the nested candidate models. However, in finite samples, this disadvantage is largely compensated for by the reduction in the cost of order selection by considering fewer candidates. Thus, the quality of the selected model remains acceptable with only hierarchically nested ARMA(r,r-1) models as candidates.     

Performance of simple response method for the establishment and adjustment of calibration intervals

Managing calibration intervals in order to fulfill requirements of quality-oriented organizations is discussed in this paper. In particular, the simple response method is considered. According to this technique, the interval between successive calibrations is adjusted adaptively on the basis of the outcome of the last calibration process. An original analysis is presented about the properties of such calibration interval handling methods. Finally, directions are given on how to tune its parameters optimally, in order to meet desired levels of long-term measurement reliability.     

Implementation of a mechanics-based system for estimating the strength of timber

The most accurate way of determining the strength of lumber requires destructive testing. An intelligent mechanics-based lumber-grading system was developed to provide a better estimation of the strength of a board nondestructively. This system processed X-ray-extracted geometric features (of 1080 boards that eventually underwent destructive strength testing) by using finite element methods to generate associated stress fields. The stress fields were then fed to a feature-extracting-processor, which produced 26 strength predicting features. The best strength predicting features were determined from the coefficient of determination (correlation r/sup 2/) between the features and actual strengths of the boards. The coefficients of determination of each feature (or combination of features), with the actual strength of the board, were calculated and compared. A coefficient of determination of 0.4158 was achieved by using a longitudinal (along the local grain angle) maximum stress concentration (MSC) feature to predict the estimated strength of lumber.     

Properties of fine-grained graphites based on two-component fillers

The effect of the nature of two-component fillers on the physical and mechanical properties of finegrained graphites was studied using highly oriented needle coke and nearly isotropic synthetic graphite as components of fillers. Each filler was prepared in the form of coarse (-120 µm) and fine (-10 µm) powders. Different mixtures of these components were used in producing graphite samples, which were then characterized by density, thermal expansion, compressive strength, and modulus of elasticity measurements. The results demonstrate that the thermal expansion, compressive strength, and modulus of elasticity of the graphites thus prepared depend on the microtexture, size, and shape of the filler particles.Translated from Neorganicheskie Materialy, Vol. 40, No. 12, 2004, pp. 1448–1454.Original Russian Text Copyright © 2004 by Samoilov.     

Preparation of Nickel, Iron, and Cobalt Nanopowders via Reduction of Salts with Sodium Dissolved in Liquid Ammonia

Nickel, iron, and cobalt powders are prepared via reduction of salts with sodium dissolved in liquid ammonia. The low temperature of the process and the ability of liquid ammonia to stabilize dangling chemical bonds notably reduce the growth rate of metal particles and prevent their aggregation. The magnetic properties of the nanopowders are investigated.     

Composition, Structure, and Thermal Expansion of Ca3Fe4S3O6 and CaFeSO

The iron calcium oxysulfides Ca₃Fe₄S₃O₆ and CaFeSO are synthesized by reacting mixtures of CaO and FeS in an inert atmosphere. Their elemental compositions and lattice parameters are determined by x-ray microanalysis and x-ray diffraction. From the temperature-dependent lattice parameters of the oxysulfides (measured up to 1020 K), their thermal expansion coefficients are evaluated.     

Fixed mesh front‐tracking methodology for finite element simulations

A direct front‐tracking method using an Eulerian–Lagrangian formulation is developed in two space dimensions. The front‐tracking method is general in that it can track any type of interface once its local velocity is specified or has been determined by calculation. The method uses marker points to describe the interface position and tracks the interface evolution on a fixed finite‐element mesh, including growth, contraction, splitting and merging. Interfacial conditions are applied directly at the interface position. The method is applied to three scenarios that involve different interface conditions and are based on energy and mass diffusion. The three calculations are for the dendritic solidification of a pure substance, the cellular growth of an alloy, and the Ostwald ripening of silica particles in silicon. Numerical results show that very complicated interface morphologies and topological changes can be simulated properly and efficiently. Copyright © 2004 John Wiley & Sons, Ltd.