Influence of microstructural inhomogeneities on the fracture toughness of modified 9Cr–1Mo steel at 298–823 K

Quasistatic fracture behaviour of two heats of modified 9Cr–1Mo steel for steam generator applications have been assessed at 298, 653 and 823 K. J–R curves were established and the elastic–plastic fracture toughness values at 0.2 mm crack extension (J_0.2) were determined. The fracture mechanisms were entirely different for the two steels at 298 K, with brittle fracture controlled by cleavage crack initiation in one and ductile fracture in the other by void nucleation and growth. At 653 and 823 K, fracture in both materials was by ductile crack growth. The difference in behaviour between the two steels at 298 K was attributed to the differences in microstructure, distribution and density of inclusions as well as phosphorous contents.     

Fabrication and characterisation of bulk micromachined ZnO energy transducer with interdigitated electrodes

The proposed research work deals with the design, fabrication and characterisation of a ZnO cantilever energy transducer on Si(c) without the use of SOI wafers, thereby, reducing the cost of fabrication. The energy transducer is operated in the longitudinal mode through the interdigitated electrodes. This is for the first time, we have attempted to fabricate a cantilever transducer with interdigitated electrodes on Si(c) in our lab. The design frequency has been chosen in the range of 700–1000 Hz for a typical tire pressure monitoring system application in mind. The experimentally obtained frequency is 876.25 Hz and d₃₃ was calculated as 3.9 pC/N from the measurements. The experimental results are further validated by simulation and the feasibility of its application as energy harvester is demonstrated. The fabrication process is being optimised to fabricate devices with higher piezoelectric coefficients.     

Direct three dimensional tomography of flames using maximization of entropy technique

Presently, there are numerous applications for non-destructive techniques like emission tomography, laser based methods and particle image velocimetry that are used to study flame characteristics. Reconstruction of the flame intensity field using emission tomography has the advantage over other technologies that it gives accurate results but at the same time requires relatively inexpensive equipment, and therefore, has numerous industrial applications. In the present paper, a new algorithm performing Direct-3D reconstruction using the maximization of entropy (MENT) methodology has been introduced. Through detailed studies using a mathematical object, it has been shown that the Direct-3D algorithm shows significantly reduced errors as compared to 2D slice-by-slice reconstruction algorithms. Secondly, the major features of the proposed algorithm, for e.g., effect of orientation, effect of number of views, and robustness have been discussed. Finally, a few qualitative results from actual flames have been presented using a candle and a gas fired burner, and the results match well with the actual flame geometry and intensity distribution.     

Investigation of Ambipolar Conduction and RF Stability Performance in Novel Germanium Source Dual Halo Dual Dielectric Triple Material Surrounding Gate TFET

Silicon - In this study, we present an ambipolar conduction and RF stability performance for a Germanium Source Dual Halo Dual Dielectric Triple Material Surrounding Gate Tunnel FET...     

BAGEL: A non-ignorable missing value estimation method for mixed attribute datasets

Surveys are mainly conducted to obtain valuable information on some criteria from a specified population. But, the survey results often become biased due to non-response of the subjects under study for highly significant attributes. Such non-ignorable missingness need to be treated and the actual values should be retrieved. Many methods have already been proposed for handling missing values in either discrete or continuous attributes. But, there exists a large gap in handling non-ignorable missing values in datasets with mixed attributes. With the intent of addressing this gap, this paper proposes a methodology called as Bayesian Genetic Algorithm (BAGEL) with hybridized Bayesian and Genetic Algorithm principles. In BAGEL, the initial population is generated using Bayesian model and fitness values of the chromosomes are evaluated using Bayesian principles. BAGEL is implemented in real datasets for imputing both discrete and continuous missing values and the imputation accuracy is observed. The experimental results show the superior performance of BAGEL than other standard imputation techniques. Statistical tests conducted to validate the experimental results also prove that BAGEL outperforms at all missing rates from 5% to 50%.     

Emission constrained power system planning: a pinch analysis based study of Indian electricity sector

In the light of rising electricity demands and a need to curb carbon dioxide emissions, this article investigates the problem of power system planning with emission targeting. A pinch analysis based approach is utilised here. The key aspect of this study is investigating the parameters that decide the priority of one type of power plant over another. For this, a quantity called prioritised cost, a trade off between cost incurred and emission from a new power plant is identified. In addition to cost and emission factor of a power plant, a third parameter, the present state of the system, also plays a significant role in deciding a power plant’s prioritised cost. The analysis done proves that new power plants can be added to the system in the order of their prioritised cost. This methodology is applied to Indian power sector as a case study. Two different problems, involving minimisation of investment and annualised cost, are considered. It is observed that renewables are slightly more favoured when the objective is to minimise overall cost and not just the capital investment. In both cases, the energy mix is still dominated by coal-based power generation. The share of renewables was seen to increase with more stringent emission targets when the objective was to minimise overall cost.     

Environmentally sustainable rice husk ash reinforced cardanol based polybenzoxazine bio-composites for insulation applications

Polymer Bulletin - Tri-substituted cardanol based benzoxazine with functionalized rice husk ash (CBz/FRHA) bio-composites were developed using renewable resource materials, and...     

A multilevel hybrid anomaly detection scheme for industrial wireless sensor networks

Real-time sensing plays an important role in ensuring the reliability of industrial wireless sensor networks (IWSNs). Sensor nodes in IWSNs have inherent limitations that give rise to different anomalies in the network. These anomalies can lead to disastrous and harmful situations or even serious system failures. This article presents a formulation to the design of an anomaly detection scheme for detecting the anomalous node along with the type of anomaly. The proposed scheme is divided into two major parts. First, spatiotemporal correlation within a cluster is obtained for the normal and anomalous behavior of sensor nodes. Second, the multilevel hybrid classifier is used by combining the sequential minimal optimization support vector machine (SMO-SVM) as a binary classifier with optimally pruned extreme learning machine (OP-ELM) as a multiclass classifier for detection of an anomalous node and type of anomalies, respectively. Mahalanobis distance-based lightweight K-Medoid clustering is used to build a new set of training datasets that represents the original training dataset, by significantly reducing the training time of a multilevel hybrid classifier. Results are analyzed using standard WSN datasets. The proposed model shows high accuracy, i.e., 94.79% and detection rate, i.e., 94.6% with a reduced false positive rate as compared to existing hybrid methods.     

Phosphorus- and silicon-containing amino curing agent for epoxy resin

Iranian Polymer Journal - Current research work focuses on the synthesis of phosphorus- and silicon-containing amine curing agent (PSA) for epoxy resins. PSA was synthesized using phenyl phosphonic...     

A simulation as a service cloud middleware

Many seemingly simple questions that individual users face in their daily lives may actually require substantial number of computing resources to identify the right answers. For example, a user may want to determine the right thermostat settings for different rooms of a house based on a tolerance range such that the energy consumption and costs can be maximally reduced while still offering comfortable temperatures in the house. Such answers can be determined through simulations. However, some simulation models as in this example are stochastic, which require the execution of a large number of simulation tasks and aggregation of results to ascertain if the outcomes lie within specified confidence intervals. Some other simulation models, such as the study of traffic conditions using simulations may need multiple instances to be executed for a number of different parameters. Cloud computing has opened up new avenues for individuals and organizations with limited resources to obtain answers to problems that hitherto required expensive and computationally-intensive resources. This paper presents SIMaaS, which is a cloud-based Simulation-as-a-Service to address these challenges. We demonstrate how lightweight solutions using Linux containers (e.g., Docker) are better suited to support such services instead of heavyweight hypervisor-based solutions, which are shown to incur substantial overhead in provisioning virtual machines on-demand. Empirical results validating our claims are presented in the context of two case studies.