Relationship between Fracture. Toughness and Phase Assemblage in Mg-PSZ

The phase content, size, and distribution have a marked effect on the thermomechanical properties of a 9 mol% MgO-ZrO₂ (Mg-PSZ) alloy. The work undertaken in this study is the first investigation to simultaneously measure and characterize the quantitative bulk phase contents and correlate these parameters to the fracture toughness and R -curve behavior of a variously aged Mg-PSZ alloy. Quantitative bulk phase characterization entailed a systematic evaluation using neutron diffraction. In addition, microscopy techniques were used to determine the phase distribution and assemblage. The results indicate that the maximum toughness is attained, for a constant tetragonal precipitate size, when the anion-ordered δ-phase (Mg₂Zr₅O₁₂) replaces a major portion of the cubic matrix phase after and 1100°C aging treatment. The shape of the R -curve and the stress required to transform the tetragonal precipitates are also significantly influenced by the δ-pnase content.     

Microcavity Formation in Alumina Using Ti Templates I: Formation Conditions

It has been found that fully enclosed microcavities can be engineered within Al₂O₃ ceramics using 125 μm diameter Ti wires as templates. A high-energy milling pretreatment of the alumina causes diffusion of the Ti into the surrounding alumina leaving all of the Kirkendall porosity consolidated into a central cavity. Control experiments using unmilled alumina confirm the necessity of the milling procedure and experiments with different milling media have excluded incidental doping by milling induced contamination as a primary driver of cavity formation. The internal microcavities produced here may lead to new applications in small scale instrumentation and implantable therapeutic devices.     

Elastic constants of polycrystalline Ti3AlC2 and Ti3SiC2 measured using coherent inelastic neutron scattering

The magnitude of the single‐crystal elastic constant c₄₄ in the MAX phase Ti₃SiC₂ is under debate. In this paper, estimates for the magnitude of c₄₄ for MAX phases Ti₃AlC₂ and Ti₃SiC₂ are determined from a partially oriented polycrystalline sample via coherent inelastic neutron scattering. The largely quasi‐isotropic nature of these M_n+1AX_n phase elastic constants as previously predicted by density functional theory calculations is confirmed experimentally for Ti₃AlC₂ to be c₄₄=115.3 ± 30.7 GPa. In contrast, Ti₃SiC₂ is confirmed to be shear stiff with c₄₄=402.7 ± 78.3 GPa supporting results obtained by earlier elastic neutron diffraction experiments.     

Neural networks for estimation of discharge capacity of triangular labyrinth side-weir located on a straight channel

Side-weirs are flow diversion devices widely used in irrigation, land drainage, and urban sewage systems. It is essential to correctly predict the discharge coefficient for hydraulic engineers involved in the technical and economical design of side-weirs. In this study, the discharge capacity of triangular labyrinth side-weirs is estimated by using artificial neural networks (ANN). Two thousand five hundred laboratory test results are used for determining discharge coefficient of triangular labyrinth side-weirs. The performance of the ANN model is compared with multi nonlinear regression models. Root mean square errors (RMSE), mean absolute errors (MAE) and correlation coefficient (R) statistics are used as comparing criteria for the evaluation of the models’ performances. Based on the comparisons, it was found that the neural computing technique could be employed successfully in modelling discharge coefficient from the available experimental data. There were good agreements between the measured values and the values obtained using the ANN model. It was found that the ANN model with RMSE of 0.0674 in validation stage is superior in estimation of discharge coefficient than the multiple nonlinear and linear regression models with RMSE of 0.1019 and 0.1507, respectively.     

A New Optimization Approach for the Least-Cost Design of Water Distribution Networks: Improved Crow Search Algorithm

Due to large number of decision variables and several hydraulic constraints, optimal design of water distribution networks (WDNs) is considered as one of the most complex optimization problems. This paper introduces and applies a new optimization approach, improved crow search algorithm (ICSA), based on the improvement of original crow search algorithm (CSA) by adding an operator parameter. Both approaches (i.e., CSA and ICSA) were applied to two case studies (i.e., Two-Reservoir and Khorramshahr City networks) by linking the hydraulic simulator (e.g., EPANET 2.0). The proposed ICSA saved the total construction cost by 2.16% and 1.79% for the Two-Reservoir and Khorramshahr City networks compared to the original CSA based on optimal network design, respectively. Results revealed that the proposed ICSA provided outstanding design for the both WDNs compared to previous studies and original CSA.

     

Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques

This paper investigates the ability of four artificial intelligence techniques, including artificial neural network (ANN), radial basis neural network (RBNN), adaptive neuro-fuzzy inference system (ANFIS) with grid partitioning, and ANFIS with fuzzy c-means clustering, to predict the peak and residual conditions of actively confined concrete. A large experimental test database that consists of 377 axial compression test results of actively confined concrete specimens was assembled from the published literature, and it was used to train, test, and validate the four models proposed in this paper using the mentioned artificial intelligence techniques. The results show that all of the neural network and ANFIS models fit well with the experimental results, and they outperform the conventional models. Among the artificial intelligence models investigated, RBNN model is found to be the most accurate to predict the peak and residual conditions of actively confined concrete. The predictions of each proposed model are subsequently used to study the interdependence of critical parameters and their influence on the behavior of actively confined concrete.

     

Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff

This study investigated the effects of upstream stations’ flow records on the performance of artificial neural network (ANN) models for predicting daily watershed runoff. As a comparison, a multiple linear regression (MLR) analysis was also examined using various statistical indices. Five streamflow measuring stations on the Cahaba River, Alabama, were selected as case studies. Two different ANN models, multi layer feed forward neural network using Levenberg–Marquardt learning algorithm (LMFF) and radial basis function (RBF), were introduced in this paper. These models were then used to forecast one day ahead streamflows. The correlation analysis was applied for determining the architecture of each ANN model in terms of input variables. Several statistical criteria (RMSE, MAE and coefficient of correlation) were used to check the model accuracy in comparison with the observed data by means of K-fold cross validation method. Additionally, residual analysis was applied for the model results. The comparison results revealed that using upstream records could significantly increase the accuracy of ANN and MLR models in predicting daily stream flows (by around 30%). The comparison of the prediction accuracy of both ANN models (LMFF and RBF) and linear regression method indicated that the ANN approaches were more accurate than the MLR in predicting streamflow dynamics. The LMFF model was able to improve the average of root mean square error (RMSE_ave) and average of mean absolute percentage error (MAPE_ave) values of the multiple linear regression forecasts by about 18% and 21%, respectively. In spite of the fact that the RBF model acted better for predicting the highest range of flow rate (flood events, RMSE_ave/RBF = 26.8 m³/s vs. RMSE_ave/LMFF = 40.2 m³/s), in general, the results suggested that the LMFF method was somehow superior to the RBF method in predicting watershed runoff (RMSE/LMFF = 18.8 m³/s vs. RMSE/RBF = 19.2 m³/s). Eventually, statistical differences between measured and predicted medians were evaluated using Mann-Whitney test, and differences in variances were evaluated using the Levene's test.     

Constructing neural network sediment estimation models using a data-driven algorithm

Artificial neural network (ANN) models are designed for suspended sediment estimation using statistical pre-processing of the data. Statistical properties such as cross-, auto- and partial auto-correlation of the data series are used for identifying a unique input vector to the ANN that best represents the sediment estimation process for a basin. The methodology is evaluated using the flow and sediment data from the stations Quebrada Blanca and Rio Valenciano in USA. The result of the study indicates that the statistical pre-processing of the data could significantly reduce the effort and computational time required in developing an ANN model. Three ANN training algorithms are also compared with each other for the selected input vector.     

Hydrogen activation of LaNi5: Implications of fracture mechanisms on hydriding properties

The fracture mechanisms of two different LaNi₅ alloys (A and B) have been investigated during the first few hydrogen loading/unloading cycles that constitute activation of this material. The principal difference between the two formulations was the presence of Nideficient planar inclusions, oriented perpendicular to the crystallographicc-axis, in alloy A. Both types were found to fracture at high loadings of hydrogen (hydrogen-to-metal ratios of ca. 1) during the activation cycle, as a result of the differential expansion between the microdomains of the -and -hydride phases. However, alloy A also fractured at an earlier stage in the activation cycle, at very low hydrogen-to-metal ratios (0.06), by delamination at the planar inclusion boundaries. This had the effect of reducing the hysteresis during the first activation cycle, most probably due to faster kinetics. The implication is that the hydrogen storage properties of the alloy can be tailored by microstructural design.     

A study in the mechanical milling of alumina powder

The mechanical milling of alumina in order to reduce grain sizes to ≤100 nm has been proposed as a means of reducing sintering temperatures and improving pressureless sintered density, particularly as a means of allowing co-firing with metallic components for biomedical implants. There is a persistent problem with contamination from the milling media, usually hardened steel which can be only partially alleviated by acid leaching. We have explored the use of alternative milling media with a view to reducing the levels of contamination. Alumina powders were milled with hardened steel, tungsten carbide, alumina and zirconia milling media under identical conditions of ball mass:powder mass ratio 10:1 and target milling times of 32 h. All of the milling media were found to cause unacceptable levels of contamination. Zirconia media gave the lowest contamination (3–4%) and in some circumstances, the addition of a small amount of zirconia may lead to increased toughness without loss of bio-compatibility.