Thermal barrier coating application of zircon sand

Naturally occurring zircon sand was plasma spray coated on steel substrates previously coated with NiCrAlY bond coat. The coatings were characterized for their microstructure, chemical composition, thermal shock resistance, and the nature of structural phases present, The as-sprayed coatings consisted of t-ZrO2 (major phase), m-ZrO2, ZrSiO4 (minor phases), and amorphous SiO2. These coatings, when annealed at 1200 degrees C/1.44 x 10(4) s yielded a ZrSiO4 phase as a result of the reaction between ZrO2 and SiO2, Dramatic changes occurred in the characteristics of the coatings when a mixture of zircon sand and Y2O3 was plasma spray coated and annealed at 1400 degrees C/1.44 x 10(4) s, The t-ZrO2 phase was completely stabilized, and these coatings were found to have considerable potential for thermal barrier applications.

On evaluating erosion by sand particles in polythene system without and with ceramic particles

The effect of the inclusion of ceramic particles in polythene material on the response to erosion due to impingement by sand particles at three angles is investigated. It is seen that erosion resistance varies with ceramic inclusions. The work also considers the limitations posed by the system in adopting weight change measurements as a measure to follow erosive wear owing to the softer nature of the matrix material. Consequently, the investigation looks at two other experimental parameter, that can readily be measured to quantify erosion. Of the two approaches. the advantages of following wear through measuring linear dimension of the resulting crater is stressed in this work. The study also highlights the problems associated in assessing the depth of the crater as a parameter to express the extent of erosion owing to the phenomenon of material flow suggested and schematically illustrated in the work. Corroborative evidence for this flow behaviour through scanning electron microscopic studies is presented. (C) 2002 Elsevier Science Ltd. All rights reserved.

Volume Change behaviour in Calcitic Soil influenced with Sulphuric Acid using Artificial Neural Networks

This paper elucidates the methodology of applying artificial neural network model (ANNM) to predict the percent swell of calcitic soil in sulphuric acid solutions, a complex phenomenon involving many parameters. Swell data required for modelling is experimentally obtained using conventional oedometer tests under nominal surcharge. The phases in ANN include optimal design of architecture, operation and training of architecture. The designed optimal neural model (3-5-1) is a fully connected three layer feed forward network with symmetric sigmoid activation function and trained by the back propagation algorithm to minimize a quadratic error criterion.The used model requires parameters such as duration of interaction, calcite mineral content and acid concentration for prediction of swell. The observed strong correlation coefficient (R2 = 0.9979) between the values determined by the experiment and predicted using the developed model demonstrates that the network can provide answers to complex problems in geotechnical engineering.

Stress-Strain Prediction of Jointed Rocks using Artificial Neural Networks

The applicability of Artificial Neural Networks for predicting the stress-strain response of jointed rocks at varied confining pressures, strength properties and joint properties (frequency, orientation and strength of joints) has been studied in the present paper. The database is formed from the triaxial compression tests on different jointed rocks with different confining pressures and different joint properties reported by various researchers. This input data covers a wide range of rock strengths, varying from very soft to very hard. The network was trained using a 3 layered network with feed forward back propagation algorithm. About 85% of the data was used for training and remaining15% for testing the predicting capabilities of the network. Results from the analyses were very encouraging and demonstrated that the neural network approach is efficient in capturing the complex stress-strain behaviour of jointed rocks. A single neural network is demonstrated to be capable of predicting the stress-strain response of different rocks, whose intact strength vary from 11.32 MPa to 123 MPa and spacing of joints vary from 10 cm to 100 cm for confining pressures ranging from 0 to 13.8 MPa.

End-bearing capacity of driven piles in sand using the stress characteristics method: analysis and implementation

The method of stress characteristics has been employed to compute the end-bearing capacity of driven piles. The dependency of the soil internal friction angle on the stress level has been incorporated to achieve more realistic predictions for the end-bearing capacity of piles. The validity of the assumption of the superposition principle while using the bearing capacity equation based on soil plasticity concepts, when applied to deep foundations, has been examined. Fourteen pile case histories were compiled with cone penetration tests (CPT) performed in the vicinity of different pile locations. The end-bearing capacity of the piles was computed using different methods, namely, static analysis, effective stress approach, direct CPT, and the proposed approach. The comparison between predictions made by different methods and measured records shows that the stress-level-based method of stress characteristics compares better with experimental data. Finally, the end-bearing capacity of driven piles in sand was expressed in terms of a general expression with the addition of a new factor that accounts for different factors contributing to the bearing capacity. The influence of the soil nonassociative flow rule has also been included to achieve more realistic results.

Three-Dimensional Site Characterization Model Using Artificial Neural Networks

This research is designed to develop a new technique for site characterization in a three-dimensional domain. Site characterization is a fundamental task in geotechnical engineering practice, as well as a very challenging process, with the ultimate goal of estimating soil properties based on limited tests at any half-space subsurface point in a site.In this research, the sandy site at the Texas A&M University's National Geotechnical Experimentation Site is selected as an example to develop the new technique for site characterization, which is based on Artificial Neural Networks (ANN) technology. In this study, a sequential approach is used to demonstrate the applicability of ANN to site characterization. To verify its robustness, the proposed new technique is compared with other commonly used approaches for site characterization. In addition, an artificial site is created, wherein soil property values at any half-space point are assumed, and thus the predicted values can compare directly with their corresponding actual values, as a means of validation. Since the three-dimensional model has the capability of estimating the soil property at any location in a site, it could have many potential applications, especially in such case, wherein the soil properties within a zone are of interest rather than at a single point. Examples of soil properties of zonal interest include soil type classification and liquefaction potential evaluation. In this regard, the present study also addresses this type of applications based on a site located in Taiwan, which experienced liquefaction during the 1999 Chi-Chi, Taiwan, Earthquake.

Metal mould reactions during casting of titanium in zircon sand moulds

Metal-mold reaction during Ti casting in zircon sand molds has been studied using scanning electron microscope, energy and wave length dispersive analysis of X-rays, X-ray diffraction, microhardness measurements, and chemical analysis. Experimental results suggest that oxides from the mold are not fully leached out by liquid Ti, but oxygen is preferentially transferred to liquid Ti, leaving behind metallic constituents in the mold as lower oxides or intermetallics of Ti. The electron microprobe analysis has revealed the depth profile of contaminants from the mold into the cast Ti metal. The elements Si, Zr and O were found to have diffused to a considerable distance within the Ti metals. A possible mechanism has now been evolved in regard to the reactions that occur during casting of Ti in zircon sand molds.