Microstructure and mechanical behavior of Cr-Cr2Hfin situ intermetallic composites

A detailed investigation of the effects of microstructural changes on the mechanical behavior of twoin situ intermetallic composites with Cr and Cr₂Hf phases in the Cr-Hf system was performed. The nominal compositions (at. pct) of the alloys were Cr-5.6Hf (hypoeutectic) and Cr-13Hf (eutectic). The study included evaluations of strength, ductility, and fracture toughness as a function of temperature and creep behavior. Two microstructures in each alloy were obtained by heat treatments at 1250 ‡C (fine microstructure) and 1500 ‡C (coarse microstructure). A decrease in elastic strength (stress at the onset of inelastic response in the load-deflection curve) with the coarsening of the microstructures was noted for both alloys below 1000 ‡C. The Cr-13Hf alloy retained strength to a higher test temperature, relative to Cr-5.6Hf alloy, under both microstructural conditions. The alloys showed no evidence of ductility at room temperature. However, in the coarse microstructure of the Cr-5.6Hf alloy, the primary Cr exhibited ductility at and above 200 ‡C; ductility in primary Cr could be seen only at and above 1000 ‡C for the fine microstructure. In other words, the temperature at which ductility was first observed decreased from about 1000 ‡C to about 200 ‡C due to high-temperature heat treatment in this alloy. Both microstructures of Cr-5.6Hf alloy showed a significant increase in fracture toughness with increasing test temperature. However, the increases in fracture toughness with temperature for the Cr-13Hf alloy microstructures were relatively small. Both alloys showed about four orders of magnitude reduction in steady-state creep rates relative to pure Cr at 1200 ‡C. The results are analyzed in the light of deformation characteristics and fracture micromechanisms. The effects of microstructural factors, such as the size and continuity of phases, solubility levels of Hf as well as interstitial elements in Cr, on the observed mechanical behavior are discussed. Formerly Research Scientist, Materials and Processes, UES, Inc.     

Localized collapse of shells of revolution using a local-global strategy

The mode of collapse in many metallic shells is, oftentimes, one of localized collapse, wherein severe plastic deformation and high displacement gradients are localized within some region. The combined material and geometric non-linearity emanating from this local region is the predominant cause of instability in shells of revolution. In such a situation a local-global strategy is very useful for an efficient analysis. This strategy consists of employing two dimensional degenerated isoparametric shell elements with non-linear capability in a well defined 'local' zone where all the non-linearities are expected to be localized and which also contains some form of local deviation from axisymmetry such as a circumferential imperfection, cutouts, cracks, etc. Linear elastic ring type elements are employed in the remaining axisymmetric zone and the two zones are linked through a transformation between the degrees of freedom involved. The solution of the non-linear problem is achieved by appropriate condensation procedures to reduce the number of active degrees of freedom and the load incrementation is achieved by the well known 'arc length' iterative procedure. Numerical exampies are presented to demonstrate that this strategy is very efficient and accurate for problems with well defined non-linear local zones. The capability of this strategy for treating problems with local material discontinuities such as cracks, cutouts, etc. as an efficient alternative to a complete two dimensional discretization is pointed out.     

Application of dynamic recrystallization model for the prediction of microstructure during hot working

During hot working operation, the work-piece deforms to the shape of the die geometry at the imposed deformation rates and temperatures. Deformation processing maps, obtained based on the concepts of Dynamic Materials Modeling, can be used to identify optimum deformation conditions. Dynamic Recrystallization (DRX) is shown to be the operating softening mechanism at these optimum deformation conditions, and results in predictable microstructures. The model proposed for explaining the microstructural evolution during DRX is extended to predict the resulting microstructure based on the information about the deformation loads and work-piece temperatures. The model predictions are validated on Al and Cu. This model can be applied for on-line process control, provided the metal forming equipment is appropriately instrumented.     

Probabilistic analysis of a continuous review perishable inventory system with Markovian demand,Erlangian life and non-instantaneous lead time

Summary This article obtains an explicit cost expression for a continuous review (S, s) ordering policy inventory system of perishable items, in the stationary case. The cost expression is closely related to the stationary distribution of the stochastic processL(t), representing the inventory at any timet. The stochastic behaviour of the process {L(t),t-0} is characterised by identifying an imbedded MRP. The demands of the system are governed by a Poisson process. The items in the inventory have a useful life time characterised by an Erlangian distribution. The lead time is a random variable with specified probability distribution. It is assumed that the demands that arrive when the inventory is zero are lost.

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Zusammenfassung Der vorliegende Artikel behandelt einen expliziten Kostenausdruck für eine stetige Überprüfung der (S, s) Bestellpolitik eines Lagerhaltungssystems verderblicher Güter im stationären Fall. Der Kostenausdruck ist eng verbunden mit der stationären Verteilung des stochastischen ProzessesL(t), der den Lagerbestand zu jeder beliebigen Zeitt darstellt. Das stochastische Verhalten des Prozesses {L(t),t-0} wird characterisiert durch die Identifikation eines eingebetteten MRP. Die Nachfragewerte des Systems werden durch einen Poisson-Prozeß bestimmt. Die gelagerten Güter haben eine nutzbare Lebensdauer, die durch eine Erlang-Verteilung beschrieben wird. Die Durchlaufzeit ist eine Zufallsvariable mit spezifierter Wahrscheinlichkeitsverteilung. Es wird zudem unterstellt, daß nicht befriedigte Nachfrage bei leerem Lager verloren geht.

     

Understanding the Role of Grain Boundaries on Charge-Carrier and Ion Transport in Cs2AgBiBr6 Thin Films

Halide double perovskites have gained significant attention, owing to their composition of low-toxicity elements, stability in air, and recent demonstrations of long charge-carrier lifetimes that can exceed 1 µs. In particular, Cs₂AgBiBr₆ is the subject of many investigations in photovoltaic devices. However, the efficiencies of solar cells based on this double perovskite are still far from the theoretical efficiency limit of the material. Here, the role of grain size on the optoelectronic properties of Cs₂AgBiBr₆ thin films is investigated. It is shown through cathodoluminescence measurements that grain boundaries are the dominant nonradiative recombination sites. It also demonstrates through field-effect transistor and temperature-dependent transient current measurements that grain boundaries act as the main channels for ion transport. Interestingly, a positive correlation between carrier mobility and temperature is found, which resembles the hopping mechanism often seen in organic semiconductors. These findings explain the discrepancy between the long diffusion lengths >1 µm found in Cs₂AgBiBr₆ single crystals versus the limited performance achieved in their thin film counterparts. This work shows that mitigating the impact of grain boundaries will be critical for these double perovskite thin films to reach the performance achievable based on their intrinsic single-crystal properties.     

Assessing Teacher’s Performance Evaluation and Prediction Model Using Cloud Computing Over Multi-dimensional Dataset

The main objective is to create a secured classifier for datasets based on clustering algorithm. K-means algorithm is one of the efficient techniques for mining large databases based on cloud computing platform to store large database with least cost. Cloud computing allows users to outsource their data. For multi-dimensional data the clustering technique is implemented which performs clustering of related elements without advance knowledge. The K-nearest neighbor classification is analyzed by using dataset under different conditions of parameters. In view of the above, the development of data management with a cloud computing is gaining more attention towards multi-dimensional datasets. It is a challenging task to obtain secured data in evolution of data mining technique based on cloud computing employed using classifier techniques. Quality of education depends largely on teacher’s ability, performance, knowledge, assessment and prediction on the basis of data mining techniques and clustering. These approaches permit the educational institution to decide and evaluate the classification rule to determine and recruit the best teacher based on knowledge by using cloud database which is a challenging task. The proposed technique provides secured cloud computing details regarding teacher’s recruitment, privacy of user’s input query, selecting the best teacher and hides the access patterns on cloud. The proposed idea is computed by extracting the data and proves that it provides better accuracy for selecting the best teachers and also improves the speed and constancy of recruitment application. The teacher’s recruitment is used in evaluating the ranks based on performance so that, the institution takes a better decision for recruitment.

     

Clustering of heavy particles in vortical flows: a selective review

Heavy particles in a turbulent flow tend to leave regions of high vorticity and cluster into regions of high strain. The consequences of such clustering have been studied in a variety of situations over the past few decades, and this problem has seen several review papers already. Our objectives in this paper are three-fold. (i) We introduce the reader to the basic ideas, and explain why the problem is interesting. (ii) Using an N-vortex system we present an interesting case where particles are attracted to the vicinity of vortices. A new scaling for the critical Stokes number of attraction is obtained. (iii) We review a number of papers, which are related to cloud physics in this context.     

Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals

Automated biomedical signal processing becomes an essential process to determine the indicators of diseased states. At the same time, latest developments of artificial intelligence (AI) techniques have the ability to manage and analyzing massive amounts of biomedical datasets results in clinical decisions and real time applications. They can be employed for medical imaging; however, the 1D biomedical signal recognition process is still needing to be improved. Electrocardiogram (ECG) is one of the widely used 1-dimensional biomedical signals, which is used to diagnose cardiovascular diseases. Computer assisted diagnostic models find it difficult to automatically classify the 1D ECG signals owing to time-varying dynamics and diverse profiles of ECG signals. To resolve these issues, this study designs automated deep learning based 1D biomedical ECG signal recognition for cardiovascular disease diagnosis (DLECG-CVD) model. The DLECG-CVD model involves different stages of operations such as pre-processing, feature extraction, hyperparameter tuning, and classification. At the initial stage, data pre-processing takes place to convert the ECG report to valuable data and transform it into a compatible format for further processing. In addition, deep belief network (DBN) model is applied to derive a set of feature vectors. Besides, improved swallow swarm optimization (ISSO) algorithm is used for the hyperparameter tuning of the DBN model. Lastly, extreme gradient boosting (XGBoost) classifier is employed to allocate proper class labels to the test ECG signals. In order to verify the improved diagnostic performance of the DLECG-CVD model, a set of simulations is carried out on the benchmark PTB-XL dataset. A detailed comparative study highlighted the betterment of the DLECG-CVD model interms of accuracy, sensitivity, specificity, kappa, Mathew correlation coefficient, and Hamming loss.     

A comprehensive analysis on the state‐of‐the‐art developments in reflectarray,transmitarray, and transmit‐reflectarray antennas

Contemplating the advancements in communication technology, the analysis of the features of reflectarray, transmitarray, and transmit‐reflectarray becomes essential for future adaptability. This article presents a thorough review of such high‐gain antennas, presenting some of the most relevant solutions published by the scientific society in the field of antennas and wave propagation. Several examples of unit cells for array implementation and complete array designs discussed in various literatures are analyzed. The analysis is focused in identifying the unit cell layouts, such as those developed using microstrip patches, frequency selective surfaces, or metamaterials. The analysis is extended to the ways of improving bandwidth, for example, true time delay elements, phase delay lines, meander lines, and so on, and the various methods used to enable reconfiguration, for example, p‐i‐n diodes, varactor diodes, or microelectromechanical systems. In addition, some antennas, which produce bidirectional beams simultaneously, are also discussed. Finally, all the models are compared against each other in order to highlight their benefits and limitations, summarizing their main characteristics, such as the frequency of operation, bandwidth, phase range, gain, aperture efficiency, sidelobe levels, cross polarization levels, and maximum beam‐steering range.