Vortex characteristics due to nozzle clogging in water caster mould: modelling and validation

In continuous slab casting, clogging in the submerged entry nozzle (SEN) ports leads to flow asymmetry and vortex formation in the mould. Knowledge of vortexing and its influence on product quality is fundamental for defect-free production. In this study, the interconnected effects of nozzle clogging and SEN submergence depth, variation on flow asymmetry and vortex characteristics in a 0.4 scale water caster have been characterised by CFD investigation and validated with experimental results from the authors’ previous work. Mean flow velocities at the sub-meniscus and near the port exit predicted by the computational model are compared with the time-averaged values of the impeller probe velocity measurements and found to be in reasonable agreement. Three different clogging conditions (0, 33 and 66% in the left port of the SEN) for SEN submergence depth of 60?mm are studied and the 66% clogging produced vortices having largest diameter, which is consistent with the experimental observations. The effects of SEN submergence depth on flow asymmetry and vortexing are investigated with three different conditions – 40, 60 and 80?mm. It is found that the shallow SEN submergence depth (40?mm) produces vortices of largest diameter and the flow is most stable for a SEN submergence depth of 60?mm among the three cases. Vortex bending towards the SEN as noticed in the experimental observations is also observed in the computational study. This work illustrates the possibility of capturing features of vortexing using validated CFD model.     

Mechanical Characterization and Water Ageing Behavior Studies of Grewia Serrulata Bast Fiber Reinforced Thermoset Composites

Natural fiber-reinforced polyester composites were prepared using bast fibers as the reinforcement which were extracted from Grewia serrulata trees. Chemical treatments such as alkali pretreatment followed by permanganate treatment, acetylation and silane treatment were exclusively applied to modify the fiber surface. Hand lay-up technique with compression molding was adapted for the fabrication of axially oriented fiber reinforced composites. Tensile, flexural and impact strength properties of the specimens prepared were evaluated following the standard procedures. The SEM images of the fractured surfaces shows improvement in compatibility between the chemically treated fibers and the matrix. It was found that the tensile and flexural strengths of chemically treated fiber-reinforced polyester composites have significantly improved when compared to untreated fiber-reinforced polyester specimens. The properties of 1200 hrs water aged specimens were found deteriorated considerably due to diffusion of water into the composite material system. The chemical treatment of fibers prior to fabrication of composites improves the resistance to water absorption tendencies.     

Synthesis,growth and characterization of π conjugated organic nonlinear optical chalcone derivative

A new potentially useful nonlinear optical organic material, 1-(5-chlorothiophen-2-yl)-3-(2,3-dimethoxyphenyl)prop-2-en-1-one, has been synthesized and grown as a high-quality single crystal by the slow evaporation technique. The grown crystals were characterized by FT-IR, NMR, thermal analysis, and UV–visible spectroscopy. The material is thermally stabile up to 111 °C. The mechanical property of the grown crystals was studied using Vickers microhardness tester and the load dependence hardness was observed. The third order nonlinear optical properties of the material such as real and imaginary part of χ⁽³⁾, nonlinear absorption coefficient and nonlinear refractive index were determined using nanosecond laser pulses at 532 nm wavelength by employing Z-scan technique. The nonlinear refractive index is found to be of the order of 10⁻¹¹ cm² W⁻¹. The magnitude of third order susceptibility is of the order of 10⁻¹³ esu. The observed increase in the third order nonlinearity in these molecules clearly indicates the electronic origin. The compounds exhibit good optical limiting at 532 nm. The best optical limiting behavior of this molecule is due to the substituted strong electron donor.     

Service life prediction of organic coatings: electrochemical impedance spectroscopy vs actual service life

Electrochemical impedance spectroscopy (EIS) is used to derive an expression for predicting the service life of organic coating in a C4-type environment (industrial and costal areas with moderate salinity) as defined in ISO 12944 standard for paints and varnishes—corrosion protection of steel structures by protective paint systems. Three coating systems with a record of 2, 5, and 10 years of durability were selected for the study. The selection was also based on proven composition and dry film thickness (DFT) of the coatings as per ISO 12944. Electrochemical impedance measurements of the paint-coated panels were carried out by exposing the coated mild steel panels without scribe in different corrosive environments such as immersion in NaCl solution, neutral salt spray, etc. Neutral salt spray exposure was found to be the most severe corrosive environment among all the three coating systems. In most of the cases, EIS gave early indication of coating failure when compared to visual defects such as blistering and over-film corrosion.     

Investigating the role of key ingredients on cathodic delamination resistance of high-build pigmented epoxy coatings

Organic coatings applied on cathodically protected metallic structures must have good resistance to cathodic delamination or disbonding (CD). Both environmental conditions and coating composition influence the CD resistance. In the present study, the effect of types of epoxy resin, curing agents and their mixing ratio on cathodic delamination rate was studied in a high-build pigmented coating. Furthermore, the influence of platey fillers on CD resistance was also studied. In order to bring out correlations, if any, between adhesion and CD resistance, pull-off adhesion strengths (both dry and wet) of these coatings were also measured. Fairly good correlation was found between residual (wet) pull-off adhesion strength and CD resistance. When tested at 60 and 90°C, all the coatings under investigation showed chalking. Among the coatings under investigation, the one based on Bisphenol F epoxy and modified cycloaliphatic amine adduct exhibited excellent CD resistance.     

Synthesis of poly(4, 4′-biphenylene sulfonyl succinamide)-polysulfone blend membranes for removal of toxic metal ions from water

A novel polymer poly(4,4′-biphenylene sulfonyl succinamide) (PBSS) was synthesized via polycondensation reaction. Succinyl chloride and 4-aminophenyl sulfone were used as reactive monomers and anhydrous AlCl₃ was used as a catalyst. Both polysulfone (PSf) and PBSS were dissolved in N-methyl-2-pyrrolidone (NMP) at different compositions to obtain a homogeneous solution to fabricate PSf-PBSS blend membranes. The structure of PBSS was characterized by ATR-IR and ¹H-NMR spectroscopy. Thermal properties of PBSS were analyzed by TGA-DTA. Mechanical properties and morphology of blend membranes were analyzed by universal testing machines and field emission scanning electron microscope, respectively. The hydrophilicity of blend membranes with respect to the concentration of PBSS was studied by contact angle and water uptake studies. Upon blending, the hydrophilicity of PSf-PBSS membranes drastically increased due to the presence of large number of amide and sulfonyl groups in the matrix. The blend membranes exhibited significant increase in water flux from 100 L m⁻² h to 650 L m⁻² h⁻¹, and rejection of 100% for Pb(II) and 80% for both Cd(II) and As(III) toxic heavy metal ions. The hydrophilic nature of  CO NH and inter and intramolecular hydrogen bonding among PBSS polymer chains dispersed within rigid PSf matrix imparts softness, amide and sulfonyl groups enhance interconnected porosity and hydrophilicity of blend membranes. Hence, PBSS may serve as a low-cost novel polymeric additive for water purification and separation membrane applications. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 48254.     

Periodic operation of a bioreactor with input multiplicities

The performance of a continuous bioreactor under periodic inlet substrate concentration is theoretically analyzed for productivity improvement which under conventional steady-state operation shows input multiplicities in the feed substrate concentration. Two values of feed substrate concentration give identical productivity under conventional steady-state operation. A periodic rectangular pulse is assumed for the feed substrate concentration. It is shown by simulation that under concentration forcing the average productivity is significantly different for these two substrate concentrations. The larger value of feed substrate concentration gives improved average productivity and shows a resonance with the period of oscillation. The effect of periodic operation on the average productivity under the input multiplicities in dilution rate is also analysed.     

A data-based framework for fault detection and diagnostics of non-linear systems with partial state measurement

A novel framework based on the use of dynamic neural networks for data-based process monitoring, fault detection and diagnostics of non-linear systems with partial state measurement is presented in this paper. The proposed framework considers the presence of three kinds of states in a generic system model: states that can easily be measured in real time and in-situ, states that are difficult to measure online but can be measured offline to generate training data, and states that cannot be measured at all. The motivation for such a categorization of state variables comes from a wide class of problems in the manufacturing and chemical industries, wherein certain states are not measurable without expensive equipments or offline analysis while some other states may not be accessible at all. The framework makes use of a recurrent neural network for modeling the hidden dynamics of the system from available measurements and uses this model along with a non-linear observer to augment the information provided by the measured variables. The performance of the proposed method is verified on a synthetic problem as well as a benchmark simulation problem.     

A Bayesian machine learning method for sensor selection and fusion with application to on-board fault diagnostics

In applications like feature-level sensor fusion, the problem of selecting an optimal number of sensors can lead to reduced maintenance costs and the creation of compact online databases for future use. This problem of sensor selection can be reduced to the problem of selecting an optimal set of groups of features during model selection. This is a more complex problem than the problem of feature selection, which has been recognized as a key aspect of statistical model identification. This work proposes a new algorithm based on the use of a Bayesian framework for the purpose of selecting groups of features during regression and classification. The hierarchical Bayesian formulation introduces grouping for the parameters of a generalized linear model and the model hyper-parameters are estimated using an empirical Bayes procedure. A novel aspect of the algorithm is its ability to simultaneously perform feature selection within groups to reduce over-fitting of the data. Further, the parameters obtained from this algorithm can be used to obtain a rank order among the selected sensors. The performance of the algorithm is first tested on a synthetic regression example. Finally, it is applied to the problem of fault detection in diesel engines (30,000 data records from 43 sensors, 8 classes) and used to compare the misclassification rates with a varying number of sensors.