Time Dependent Ambient Oxidation of AA6061-T6 Alloy at the Temperature of 580°C

Protection of Metals and Physical Chemistry of Surfaces - The AA6061-T6 alloy is used in many industrial applications such as hydraulic pistons, aerospace and marine vehicle fittings, including the...     

A geometrically nonlinear size-dependent hypothesis for porous functionally graded micro-plate

The static bending behavior of porous functionally graded (PFG) micro-plate under the geometrically nonlinear analysis is studied in this article. A small-scale nonlinear solution is established using the Von-Kármán hypothesis and the modified couple stress theory (MCST). To obtain the deflection of the plate, the Reddy higher-order plate theory coupled with isogeometric analysis (IGA) is utilized. The distribution of porosities is assumed to be even and uneven across the plate’s thickness and the effective material properties of porous functionally graded micro-plate are calculated using the refined rule-of-mixture hypothesis. The influence of power index, porosity parameter and material length scale parameter on the nonlinear behaviors of static bending of porous FGM micro-plates are also investigated using several numerical examples.

     

A data-driven digital-twin prognostics method for proton exchange membrane fuel cell remaining useful life prediction

Prognostics and health management of proton exchange membrane fuel cell (PEMFC) systems have driven increasing research attention in recent years as the durability of PEMFC stack remains as a technical barrier for its large-scale commercialization. To monitor the health state during PEMFC operation, digital twin (DT), as a smart manufacturing technique, is applied in this paper to establish an ensemble remaining useful life prediction system. A data-driven DT is constructed to integrate the physical knowledge of the system and a deep transfer learning model based on stacked denoising autoencoder is used to update the DT with online measurement. A case study with experimental PEMFC degradation data is presented where the proposed data-driven DT prognostics method has applied and reached a high prediction accuracy. Furthermore, the predicted results are proved to be less affected even with limited measurement data.     

Efficient recursive least squares parameter estimation algorithm for accurate nanoCMOS variable gain amplifier performances

ABSTRACT

With the aggressive scaling of integrated circuit technology, parametric estimation is a critical task for designers who looked for solutions to the challenges of some Nanoscale CMOS parameters. This paper presented the prediction of primary parameters of CMOS transistor for 16 nm to 10 nm process nodes using both of Bisquare Weights (BW) method and a novel recursive least squares (RLS) parameter estimation algorithm. The proposed RLS algorithm consists of the minimisation of a quadratic criterion relating to the prediction error in order to attain the best estimated parameters of the developed mathematical model. The obtained results thanks to the proposed RLS algorithm were better than those reached using the BW method. Comparisons between Predictive Technology Model (PTM) data and parameters estimated with RLS algorithm were made to check the validity and the consistency of the proposed algorithm. These predicted primary parameters were helpful to estimate and to optimise the performances of the Variable Gain Amplifier (VGA) which was a basic circuit element with a key role in the design of new upcoming receivers.     

Intrinsic growth rate: a new approach to evaluate the effects of temperature, photoperiod and phosphorus-nitrogen concentrations on duckweed growth under controlled eutrophication

A comprehensive model based on a new approach was developed to simulate the duckweed growth under controlled conditions. Contrary to other approaches which use the specific growth rate, this approach uses the intrinsic growth rate which permits to differentiate the effect of duckweed mat density from that of temperature, photoperiod and phosphorus-nitrogen concentrations. The model was calibrated using data from laboratory experiments carried out during the present study and validated using other data from two literature sources. In both cases, the results demonstrated that the model was capable of predicting duckweed growth with a reliability of 95%.     

Effect of the compatibilizer,on the engineering properties of TPV based on Hypalon® and PP prepared by dynamic vulcanization

Elastomeric Chlorosulfonated polyethylene (Hypalon^®) and thermoplastic Polypropylene (PP) based thermoplastic vulcanizates (TPVs) were prepared in presence of different doses of compatibilizer, maleic anhydride grafted PP (PP‐g‐MA) by employing dynamic vulcanization technique. The effect of incorporation in different proportions of compatibilizer on mechanical, spectral, morphological, thermal, and rheological properties of such TPVs was studied and the same were compared to that of virgin PP and amongst themselves. The mechanical analysis of the prepared TPVs exhibited significant improvements in stress at 25% modulus, ultimate tensile strength (UTS), and hardness values. FTIR studies revealed that a chemical interaction had taken place between Hypalon^® and functionalized compatibilizer during the process of dynamic vulcanization which led to an enhancement of interfacial adhesion between them. The two‐phase morphologies were clearly observed by scanning electron microscopic studies. The T_g values of Hypalon^® was modified in the TPVs as exhibited by differential scanning calorimetric studies. TGA studies indicated the increase in thermal stability of all TPVs with respect to the elastomeric Hypalon^®. Rheological properties showed that the compatibilizer reduces the melt viscosity of TPVs and thus facilitates the processibility of such TPVs. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40312.     

Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial neural network

Vibration-based damage detection methods have become widely used because of their advantages over traditional methods. This paper presents a new approach to identify the crack depth in steel beam structures based on vibration analysis using the Finite Element Method (FEM) and Artificial Neural Network (ANN) combined with Butterfly Optimization Algorithm (BOA). ANN is quite successful in such identification issues, but it has some limitations, such as reduction of error after system training is complete, which means the output does not provide optimal results. This paper improves ANN training after introducing BOA as a hybrid model (BOA-ANN). Natural frequencies are used as input parameters and crack depth as output. The data are collected from improved FEM using simulation tools (ABAQUS) based on different crack depths and locations as the first stage. Next, data are collected from experimental analysis of cracked beams based on different crack depths and locations to test the reliability of the presented technique. The proposed approach, compared to other methods, can predict crack depth with improved accuracy.     

On the Strategic Impact of the Degenerative Transfer of Xanthates on Synthetic Planning

The present brief account relates synthetic endeavours revolving around a novel chemistry of xanthates (dithiocarbonates) being developed in our group. The degenerative reversible transfer of xanthates has a unique ability to store reactive radicals in a dormant form, and thereby to enhance their lifetime in a concentrated medium, while at the same time, regulating their absolute and relative concentrations. It allows intermolecular radical additions even to unactivated alkenes, and can promote otherwise sluggish radical processes, such as 5-endo, 6-exo, 6-endo, 7-endo, and 8-endo cyclisations, as well as ring closures on (hetero)aromatic structures. This chemistry has proved remarkably versatile and powerful, both for solving synthetic problems and for the industrial production of block copolymers through the RAFT/MADIX technology.     

Prevalence,characterization, and genetic diversity of Listeria monocytogenes isolated from foods of animal origin in North East India

Listeria monocytogenes is a pathogenic microorganism infects man mostly through food. A total of 1615 samples of foods of animal origin and water were collected from retail meat shops of North-Eastern India and processed. Sixty-three (3.9%) samples were positive for L. monocytogenes. Animal origin foods showing the highest prevalence was chevon (9.8%) followed by beef (8.9%), chicken (8.5%), pork (2.8%) and milk (1.8%). The prevalence rate in water from retail meat shops was 10%. Recovered L. monocytogenes were distributed into 3 serogroups, of which 74.6% fit in to 1/2a, 3a serogroup, 17.5% to 1/2b, 3b and 7.9 % to 4b, 4d, 4e serogroups. Thirty-five isolates out of 63 possessed all the tested four virulence genes. RAPD- and ERIC -PCR based analyses jointly revealed a discriminative genetic profile for the L. monocytogenes. On the whole, the occurrence of L. monocytogenes in foods of animal origin of North Eastern India displays public health hazard.